research paper in financial management

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ORIGINAL RESEARCH article

Financial management behavior among young adults: the role of need for cognitive closure in a three-wave moderated mediation model.

• 1 Department of Social and Organizational Psychology, Universidad Nacional de Educación a Distancia, Madrid, Spain
• 2 Department of Business Economics and Accounting, Universidad Nacional de Educación a Distancia, Madrid, Spain
• 3 Department of Psychology, University of Bologna, Bologna, Italy

This three-wave study aims to explore whether the impact of investment literacy on the financial management behavior is mediated by investment advice use and moderated by the need for cognitive closure. A total number of 272 financially independent adults, under 40 years, completed questionnaires at three different times with 3-month intervals. The results reveal that employees with more investment advice use and characterized by high need for cognitive closure show a higher level of financial management behavior, in relation to both the urgency (seizing) of getting knowledge and the permanence (freezing) of such knowledge. The present study contributes to better understand how and when investment literacy drives well-informed and responsible financial behavior. According to these results, interventions to improve financial behavior should focus on the combination of investment advice use and metacognitive strategies used by individuals to make financial decisions.

Introduction

Why are some people more efficient in their financial behaviors than others? Financial management is a complex set of behaviors and decisions that can change as a function of the importance and difficulty of implementing the behavior, as well as of people’s capabilities, skills, and opportunities to perform such behaviors. The undesirable short-, mid-, and long-term consequences of inadequate financial management behavior not only affect individuals, but also their household, and ultimately could produce a wide range of unwanted events on the entire society ( Fenton et al., 2016 ). For instance, inadequate financial behaviors can lead to temporary or chronic debts, inability to pay utility bills or filing for bankruptcy and such behaviors result from economic factors together with psychological ones.

Financial literacy has been defined as “the ability and confidence to use one’s own financial knowledge to make financial decisions” ( Huston, 2010 , p. 307). This concept not only concerns individual investors but also professional ones working in companies that manage money. It is in fact important not only to establish a long-term financial plan but also to know, and to have, financial alternatives in which to invest money or to save it. Financial planning is a very important knowledge and skill considering that individuals live longer and have to save for their old age, when they are no longer working.

Recent studies investigated the impact of financial literacy on various financial behaviors, like loans, mortgages, or retirement planning. The fact that financial literacy is rather low, even across well developed countries, is a critical factor toward well-informed financial decision making and behaviors. Hence, financial behavior management is a topic of interest to economists, social workers and policy makers as well.

However, a large-scale analysis of recent data indicated that financial education interventions explain only 0.1% of the variance in financial behaviors. In contrast, financial literacy has a stronger effect on financial behavior when the former is measured rather than manipulated ( Fernandes et al., 2014 ). However, Fernandes et al. (2014) study shows also that financial literacy has less impact on financial behavior when psychological and social variables, often omitted in previous research, are considered. Therefore, this study aims to fill this gap by taking a psychosocial approach and including cognitive, motivational and social factors in the relationship between financial literacy and financial behavior.

Huston (2010) distinguishes two concepts often considered as synonymous: financial literacy and financial knowledge. A successful measure of financial literacy should allow to identify which outcomes are most impacted by a lack of financial knowledge and skill, and, consequently, allow educators to provide knowledge achieve a desired outcome ( Huston, 2010 ).

In addition, as most of the studies have used samples of students, that is, adolescents or people who are still in their early youth, and not yet financially independent, in this study, we will analyze the financial management behavior of young adults who have their own economic income. Economic independence is in fact a key indicator of transition to adulthood ( Lee and Mortimer, 2009 ).

Based on Huston (2010) theoretical model, this work aims to explore predictors, mediators, and moderators of financial management behavior when people have independent economic resources to save for the future. Specifically, in the present study, we argue that it is necessary to consider the mediating role of investment advice use in the relation between investment literacy and financial management behavior among young adults. As Huston (2010 , p. 307) stated, “financial literacy is a component of human capital that can be used in financial activities” to increase behaviors that enhance financial wellbeing. Hence, financial knowledge would be translated in behaviors by using available resources “directly related to successfully navigating personal finances” ( Huston, 2010 , p. 307), as professional investment advisory services. In addition, we propose that need for cognitive closure (hereafter, NCC), an individual dispositional characteristic, moderates the relations between investment advice use and financial management behavior. The moderated mediation analysis that includes both processes will allow us to better understand the variables that facilitate or hinder young adults’ financial management behavior.

In summary, this study makes three main theoretical and methodological contributions. First, we investigate if the strong direct relationship between financial literacy and financial behaviors is valid when considering two psycho-social variables that consider conditions and types of individuals showing the financial behaviors. Second, we consider younger adulthood, which is a period of individuals’ life-cycle in which many important financial choices start to be made, like buying commodities, a house or setting up a family ( Webley et al., 2002 ). Three, considering what reported by Fernandes et al. (2014) , we investigate if the consistent association between financial literacy and financial behavior observed in many cross-sectional studies is observed also when such independent and dependent variables are measured in different moments.

Financial Management Behavior

Financial management behavior is the acquisition, allocation, and use of financial resources oriented toward some goal. Empirical evidence supports that, if families achieve effective financial management, both their economic well-being and their financial satisfaction improve at the long term ( Consumer Financial Protection Bureau, 2015 ). However, financial management behavior is complex and difficult to implement. The supervision of money and expenditure, which includes frugal and careful spending of money, is a useful protection against risky financial practices.

Moreover, financial management behavior may vary between younger and older people. Although the repeated experience and practice of financial activities influence people’s skills to manage their finances, empirical evidence seems to support that young people practice fewer basic financial tasks, such as budgeting or regularly planning their long-term savings ( Jorgensen and Savla, 2010 ). Because of this evidence, it is of interest to analyze the antecedents of young adults’ financial management behavior.

Investment Literacy

Investment literacy implies, firstly, an accumulation of knowledge about personal concepts and financial products, obtained by means of education or direct experience. Secondly, it includes a series of abilities and self-confidence to effectively apply the knowledge to the management of one’s own finances. Different empirical works have shown the consistent relations between the specific financial knowledge, the probability of saving, the effectiveness of investment strategies, and saving behaviors in general ( Jorgensen and Savla, 2010 ). Hence, considering we measured our variables at three points in time, we propose that:

Hypothesis 1: Investment literacy at time 1 (hereafter T1) will be positively related to financial management behavior at time 3 (hereafter T3).

Investment Advice Use

The use of financial consultants has been proposed as a useful support to financial decisions and as a substitute of financial knowledge and capacity for individuals and family with lower resources. However, Collins (2012) shows that financial literacy, and search and use of professional advice, are not only distinct and complementary processes, but also positively related, because results show that individuals with higher incomes, better educated and with more financial literacy are the most likely to search and use financial advice. Individuals that are less knowledgeable tend to overestimate their abilities and are unable to recognize their limited financial competences ( Kruger and Dunning, 1999 ). However, other studies show that the use of financial consultants seems to have a direct influence in guiding individuals and families toward more profitable investments ( Joo and Grable, 2004 ). In the light of this evidence, we argue that individuals financially competent, aware of the complexities of the economic field, may search for, understand and then implement the advices provided by financial consultants and, consequently, show good financial management behaviors. Accordingly, we propose that:

Hypothesis 2: Investment advice use at time 2 (hereafter T2) will mediate the relationship between investment literacy at T1 and financial management behavior at T3.

Need for Cognitive Closure

Although some empirical studies have addressed the influence of personality on earning and saving, most of them have focused on psychological biases, self-control problems, procrastination ( Rahimi et al., 2016 ), future time perspective and risk tolerance ( Pak and Mahmood, 2015 ). However, other studies have called attention to the influence of relatively stable individual differences in information processing and complex decision making, such as the NCC ( Webster and Kruglanski, 1994 ).

Need for cognitive closure refers to the individual necessity of arriving to a clear and definitive opinion, or answer to a problem, and particularly any opinion or answer rather than experiencing confusion, ambiguity or inconsistency ( Webster and Kruglanski, 1994 ). Empirical research reports significant differences between people with high and low NCC; such differences concern the amount of information they can process, the intensity of that information, the rules employed in decision-making processes, and the self-confidence on the decisions that they reached ( De Dreu et al., 1999 ; Szumowska and Kossowska, 2017 ). Due to this characteristic, people with low NCC are more available to consider complex information that is difficult to process, such as financial information. They are also concerned about the loss of information and more oriented toward the accuracy of the response than to the speed with which it is reached. As a consequence, these people tend to consider more information and decide more slowly, to be more open minded and more creative. In contrast, people with high NCC are more likely to focus on information they can process easily, to reject the more complex or even incomplete one ( Livi et al., 2015 ), and less likely to consider new evidence and update their investments when changes in market uncertainty appear ( Disatnik and Steinhart, 2015 ).

Need for cognitive closure has been described as characterized by two different tendencies: the tendency of the urgency to achieve knowledge ( Seizing ) and the tendency to retain permanently that knowledge ( Freezing ) ( Roets et al., 2006 ). People with high NCC have a pressing desire to achieve closure and to retain it permanently. Thus, these people tend to limit the quantity of information to be processed in order to facilitate decision-making and then to retain and perpetuate the information on which they have based this judgment.

This pattern of information processing has been shown in a broad array of situations related to information processing and decision-making ( Dolinski et al., 2016 ), such as consumer purchasing choices, attitudes about complex technological products, suppliers’ purchasing decisions to manage business supply chains, or helping behavior, among others. Due to the fact that financial management behavior includes processing of complex information and the anticipation of needs with a high degree of uncertainty, we argue that individuals with high NCC will consider a limited amount of information provided by the financial consultant, and particularly information that solve their immediate needs; will revise or modify such information with some reluctance, and all this will result in a less efficient financial management behavior. In contrast, we expect that low NCC remain open to information provided by the consultant and, through the elaboration, integration and revision of such information, they will be more consistent and efficient in the management of their financial behavior. Accordingly, in the present study, we propose that:

Hypothesis 3: The relationship between investment literacy at T1 and financial management behavior at T3, mediated by investment advice use at T2, will be moderated by both NCC dimensions (seizing and freezing) at T1. Specifically, we expect the relationship between investment advice use (T2) and financial management behavior (T3) to be weaker for individuals with high levels of both NCC dimensions (T1) than for individuals with low levels of both NCC dimensions (T1).

Materials and Methods

Ethics statement.

The Institutional Ethics Committee of the first and second authors’ university (National Distance Education University, UNED) approved this research on May 4th, 2016.

Participants and Procedure

This study, with a three-wave design, was carried out with a sample of young, non-student, Spanish adults, who completed the questionnaires at three different moments (T1, T2, and T3), with an interval of 3 months between each one. Following Taris and Kompier (2016) suggestions, and due to the limited longitudinal studies available on these factors, the real time lag between these factors is unknown; considering literature and the processes under examination, we retain the 3 months as an appropriate period to explore such relations. Also, because the time-lag design contributes to control and counteract the common method variance ( Podsakoff et al., 2003 ). The T1 measurement was carried out in January–February. Participation in the study was voluntarily, and potential participants were informed about the anonymity, and all subjects gave their informed consent for inclusion before they participated in the study. The only inclusion criteria in the study were being younger than 40 years of age and having a paid job (being full time or part time active workers). A total 500 people were invited to participate at T1, but we only obtained 390 responses (78% response rate), and 304 responses at T2. At T3, the sample was reduced to 272 respondents, who are included in this study. The mean age of the participants at T1 was 26.3 years ( SD = 4.9), and at T3 mean age was 26.8 years. Men made up 40.4% of the sample. Average job seniority was 9.9 years ( SD = 6.6). In terms of educational level, 57% of the sample had received a university or similar level of education, 29% finished the Secondary School, and 11% had received only basic education. Professionally, 63.2% of participants were employees, 22.8% were middle managers, and full-time workers accounted for 91.9% of the sample, and the rest were employed part-time.

Instruments

Financial management behavior was assessed with the Financial Practices Scale ( Loibl et al., 2006 ), consisting of seven items that measure the probability of the participants’ adopting positive practices of financial management behaviors. The Likert-type response scale ranged from 1 ( unlikely ) to 5 ( very likely ). Examples of some items are: “Pay your bills on time every month”; “Start saving for emergencies”; “Develop a written plan for expenses”; “Have more organized records of payments.” The authors recommend adding the scores to create a global measure of financial management behavior. Reliability was α = 0.78 in the present study.

Investment literacy was appraised with the Financial Knowledge Scale , of Joo and Grable (2004) . This 10-item scale was designed to assess investors’ financial literacy. Higher scores indicate more knowledge. The original dichotomic response scale was transformed into a Likert-type response scale ranging between 1 ( strongly disagree ) and 5 ( strongly agree ). Examples of some items are: “Both employee and employer contribute to Social Security”; “Over a 20-year period, one is more likely to win than to lose money in the stock market”; “Interest paid on a credit card is deducted from taxes” (reversed score). Reliability was α = 0.81 in the present study.

Investment advice use was assessed using the Investment Advice Use Scale of Li et al. (2002) which contains eight items. The original four-point response scale, which ranges between 1 ( strongly disagree ) and 4 ( strongly agree ), was adapted to a five-point Likert-type format, with an intermediate rating for indifference ( neither disagree nor agree ). Examples of items are: “I prefer to consult with a specialist when I take financial decisions”; “I would be willing to pay for the advice of a financial expert”; “I feel qualified to make my own investment decisions without advisors” (reversed score). Reliability was α = 0.77 in the present study.

Need for cognitive closure was assessed with the Need for Cognitive Closure Scale , in its translated version ( Mannetti et al., 2002 ), adapted to Spanish by Ramelli (2011) . This scale has two factors: Seizing (predisposition to seek an immediate response when faced with uncertainty) and Freezing (predisposition to retain closure and avoid considering new information that might question it). The scale has 14 items that are rated with scores ranging between 1 ( strongly disagree ) and 5 ( strongly agree ). Reliability of the Spanish version was adequate, both in the original study (with α = 0.78; Ramelli, 2011 ), and in the present study (with α = 0.78). Examples of seizing (urgency) items are: “In case of uncertainty, I prefer to decide immediately, whatever it may be”; “When I have several potentially valid alternatives, I decide in favor of one quickly and without hesitation”; “After finding the solution to a problem, I think it is a waste of time to take other possible solutions into account.” Item examples of the freezing (permanence) dimension are: “I feel very uncomfortable when things are not in their proper place”; “I feel uncomfortable when I do not get a fast answer to a problem I face.” The NCC scale was subjected to Confirmatory Factor Analysis with Amos 24.0. The generalized least squares procedure was used. This two-factor CFA fitted the data reasonably well (χ 2 = 139.199, p < 0.000; df = 71, CMIN/df = 1.96; GFI = 0.93; AGFI = 0.90, RMSEA = 0.06).

All the factor loadings for the items exceed the 0.40 and both factor correlated as expected (0.72). Some covariances among error have been allowed due to the similarity of the item content, but in any case, between items included under the same factor. Factor loadings, and the Spanish formulation of items, are displayed in Table 1 .

TABLE 1. Need of Cognitive Closure Scale ( Ramelli, 2011 ) and factor loadings.

Analytic Strategy

In order to test the study hypotheses, we performed a linear regression analysis. Before testing the hypothesized moderated mediation model, the indirect and moderating effects were first tested separately with the PROCESS macros for SPSS 24 ( Hayes, 2013 ). With bootstrap procedures of 5,000 samples at a 95% confidence level, the confidence intervals that do not contain 0 indicate that the indirect effect is significant. We did not include any control variables in the following analyses.

Descriptive statistics and Pearson correlations between the study variables are provided in Table 2 . Investment literacy was positively and significantly associated both with investment advice use ( r = 0.19) and with financial management behavior ( r = 0.31), whereas investment advice use and financial management behavior showed the strongest correlation ( r = 0.41). The relation between freezing and financial management behavior reached statistical significance ( r = 0.16). NCC dimensions showed a positive relationship with each other ( r = 0.44).

TABLE 2. Descriptive statistics and correlation matrix.

Table 3 shows the results obtained when testing the first hypothesis. The linear regression analysis shows the total effect ( b = 0.17, p < 0.000) of investment literacy on financial management behavior [ R 2 = 0.22, F (2,269) = 37.54, p < 0.001].

TABLE 3. Regression results of testing the mediation of investment advice use (T2) in the relationships between investment literacy (T1) and financial management behavior (T3) (hypotheses 1 and 2).

Regarding the mediation of investment advice use in the relationship between investment literacy and financial management behavior, a significant and positive association between investment literacy and investment advice use ( b = 0.20, p < 0.000) was observed. Furthermore, a statistically significant direct effect of investment literacy on financial management behavior ( b = 0.16, p < 0.001) was found, as well as a statistical significant effect of investment advice use on financial management behavior ( b = 0.23, p < 0.001). Hence, there is a significant indirect effect of investment literacy on financial management behavior through investment advice use ( b = 0.05). Finally, we tested the significance of this mediation effect through the bootstrapping procedure, which showed that the confidence interval for the indirect effect does not contain zero [0.01, 0.09], supporting the significance of the mediation effect. These results provide reasonable confirmation of hypothesis 2.

Finally, we tested hypothesis 3 following the procedures recommended by Hayes (2013) , as shown in Table 4 .

TABLE 4. Results of testing the moderation of NCC (T1) on the investment advice use (T2) – financial management behavior relationship (T3) (hypothesis 3).

Firstly, Table 4 shows a negative direct effect between NCC – seizing and financial behavior ( b = -0.32, p < 0.05), which suggests that the higher the tendency to seek an immediate solution to solve an uncertainty, the lower the management of financial behavior. Secondly, upon testing hypothesis 3 regarding the moderating effect of seizing on the relationship between investment literacy and financial management behavior, mediated by investment advice use, we found a statistically significant positive interaction effect ( b = 0.12, p < 0.01). Thirdly, regarding the moderating effect of freezing on the relationship between investment literacy and financial management behavior, mediated by investment advice use, we also found a statistically significant positive interaction effect ( b = 0.12, p < 0.01). The index of moderated mediation for the seizing dimension was 0.024 ( SE = 0.013), while the 95% confidence interval with bootstrapping of 5,000 samples did not contain zero (Boot CI [0.003, 0.059]), and for the freezing dimension, the index was 0.023 ( SE = 0.013, Boot CI [0.002, 0.059]).

Hence, the data support hypothesis 3. The indirect conditional effects of investment literacy on financial management behaviors at the two levels of the moderators are displayed in Table 5 , where the effect of investment literacy on financial management behavior was strong at the high level of NCC (seizing and freezing), and it was correspondingly weak when NCC was low. The two effects are statistically significant although in the opposite direction that was expected.

TABLE 5. Results of testing moderated mediation of NCC dimensions in the relationship between investment literacy (T1) and financial management behavior (T3).

Figures 1 , 2 depict the moderation effect of both NCC dimensions. What they show is not consistent with our expectations: individuals reporting higher investment advice use also showed a greater level of financial management behavior if they were characterized by high NCC-seizing at T1 (see Figure 1 ).

FIGURE 1. Moderation of NCC-Seizing (T1) on the investment advice use (T2) – financial management behavior (T3) relationship.

FIGURE 2. Moderation of NCC-Freezing (T1) on the investment advice use (T2) – financial management behavior (T3) relationship.

Also, contrary to our expectations, respondents reporting higher investment advice use at T2 showed a greater level of financial management behavior at T3 if they were characterized by high NCC freezing at T1 (see Figure 2 ).

Taken together, this result implies that investment advice use (T2) mediates more strongly the relationship between investment literacy (T1) and financial management behavior (T3) for young adults characterized by moderate to high levels of NCC (T1) than in adults with lower levels of NCC (T1). These results are depicted in Figure 3 .

FIGURE 3. Results of the moderated mediation analysis. NCC, need for cognitive closure; [95% CI]; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. Values in italics: correspond to the Freezing dimension.

The present work supports the hypothesis that investment literacy may affect subsequent financial management behavior in young, financially independent, adults. These findings corroborate the key assumption of a long research tradition that links financial literacy with the improvement of financial management behavior. In addition, the present investigation suggests that efficacious financial management should not be conceived as only a mere consequence of knowledge and confidence to use it, but rather as the outcome of the joint influence of cognitive aspects and social influences that affect individuals. In fact, in the present work, the impact of investment literacy on financial management behavior is explained by the use of investment advices provided, in a social communication exchange, by a financially expert advisor. Therefore, the present study has focused on facets predominantly studied in current economic psychology ( Webley et al., 2002 ).

Following the growing number of works suggesting that personality traits affect financial behavior beyond the influence of people’s knowledge and external factors ( Norvilitis et al., 2006 ; Warmoth et al., 2016 ), this work shows that NCC plays a moderating role in the relation between investment literacy and financial management behavior, mediated by investment advice use. Thus, our evidence shows how the personal tendencies of seizing and freezing influence predictors of financial management behavior. On this regard, results show a two side picture. From one side, as we expected, seizing is negatively related to financial behavior; which suggests that individuals with higher tendency to reach quickly a knowledge, a solution to some financial problem, the lower the rate of financial practices. On the other side, contrary to our expectations, individuals that look for financial advice and with high NCC, both for seizing a solution and for freezing it, probably accept quickly the suggestion from the advisor and start to implement it consistently and repeatedly, thus improving their financial performance, in comparison to individuals with lower NCC that may take longer to implement the advice provided by the financial advisor.

This work presents a new viewpoint of how to improve financial behavior among youth and, therefore, can contribute to increasing the efficacy of early interventions to develop responsible financial behavior ( Gariepy et al., 2017 ). Firstly, confirming previous studies (e.g., Calcagno and Monticone, 2011; Collins, 2012 ), it seems that to benefit of financial advice it is, at least, useful (if not, necessary), to have a good level of financial literacy. Thus, educational, social and political systems should consider how to create opportunities for young adolescents to experience and practice financial competences. Secondly, in this same line, intervention strategies should be oriented toward increasing the coherence between knowledge, expert advice, and financial management behaviors to practice the specific behaviors of saving and investment during young adulthood. Translating this into concrete practices, early assessment of people’s tendencies of Seizing and Freezing could help to recognize these early propensities and their potential bias in the processing of financial information. For example, special attention should be paid during adolescence to these psychological traits to help people develop strategies that compensate these tendencies and reduce their potential negative impact on processes of making complex decisions which may require more time for the analysis and processing of more complex information ( Gerlach, 2017 ). Following these recommendations, parents and educators can develop training programs specifically designed to offset those biases.

Thirdly, while the relationship between investment advice use and financial management behavior is not questionable, the present findings indicate that the quality and quantity of the effects are influenced by employees’ NCC tendencies. According to the present findings, financial advisors might rely upon a complementary tool to increase the efficacy of their interventions. In particular, by monitoring the level of NCC of investors, they may provide some customized services. This would support the idea that not all the products or services fit all the customers, but rather that professionals should fine tune their work in relation to investors’ need to remain open or to close and fix the financial suggestions that are provided. If high NCC individuals might be efficient in implementing easily and quickly the advices provided to them, it is also necessary to remind them of the need to continue to search regularly the advices, to update, and modify financial choices that might become outdated and no more matching the financial situation of the market. In comparison, they must present much wider and more complex financial solutions to low NCC investors, to satisfy their need for extended information processing and thus, facilitate their passage to the actual and concrete financial behavior.

This study presents some limitations that should be considered. Firstly, even though we have considered some cognitive, social, and personality variables in accordance with Huston (2010) model, many other variables could have been considered and should be considered in future research. When referring to long-term economic planning, young workers’ expectations about occupational security, career development, promotion, and progress might also influence their financial management behavior ( Ekici and Koydemir, 2016 ).

Secondly, in this study we measured financial management behavior by tapping participants’ perceptions of their behavior; future studies should include real daily behaviors (e.g., checking one’s bank account, making a monthly budget, controlling credit card expenditures), for example, using research procedures like day reconstruction methods or experience sampling.

Thirdly, in this study we used a 3 months’ lag time between each wave and the following. This lag time allowed anyway to detect a significant relationship between financial literacy and use of financial advice, and between this latter and financial behavior. However, time between waves might be extended to investigate how long is the effect of financial literacy on investment advice, and especially how long such advices may affect financial performance. Fourthly, another limitation is that investment literacy was included only at a first point in time, precluding the possibility of establishing the reverse causation between behavior and knowledge. A research design including the same three variables in each wave, will allow to investigate if, for instance, it is an underperforming financial situation to stimulate the search of financial advices.

Fifthly, in this study, we did not deal with attitudes toward financial professionals, such as customers’ trust and anxiety when consulting them ( Grable et al., 2015 ). In future studies, one might directly ask participants what they think and feel about their financial advisors and incorporate this information as a moderating variable.

Finally, financial literacy studies in general showed another limitation that is due to the well-known association between lower literacy with poor health, low income, and other undesirable outcomes but, as with the present findings on financial management behavior, there is not enough evidence to support any causal direction ( Ma, 2016 ). To date, little is known about the causes and correlates of wrong financial decisions during the life course ( Budowski et al., 2016 ). This kind of knowledge needs to be improved, despite the difficulty of obtaining information from the participants regarding their wealth, financial literacy, and consumer behaviors, and this study does not escape to similar challenges and gaps in data ( Manske et al., 2016 ).

However, this investigation can provide some suggestions to guide future research. First, although we did not examine the impact of gender on financial literacy and financial behavior, it seems that gender differences are related to the quality of financial decisions, even though women’s levels of financial literacy and economic income have improved regarding past decades ( Heilman and Kusev, 2017 ). Therefore, investigating the relationship between gender and NCC could help educators in general, and financial advisors, to design intervention strategies to help women to achieve efficacious financial management ( Rudzinska-Wojciechowska, 2017 ).

Second, research seems to indicate that NCC and risk intolerance are associated. Specifically, risk intolerance is a widely studied variable in the financial setting, but the antecedents of intolerance of risk and ambiguity are still unclear. Therefore, a possible link with NCC could be analyzed, as has been shown in an experimental study ( Vermeir and van Kenhove, 2005 ).

Third, research indicates that executive functions such as impulse control, attention regulation or mental flexibility could be linked to NCC ( Dolinski et al., 2016 ) and to performance in complex tasks and financial well-being. However, recent studies related to the executive functions show that they develop throughout adolescence. Accordingly, early intervention with youth could contribute to improving these cognitive functions, with their consequent influence on NCC and subsequent benefit for the management of complex behaviors, like finances ( Barnhoorn et al., 2016 ; Urquijo et al., 2016 ).

Lastly, NCC and its correlates of ambiguity intolerance and risk aversion have always been analyzed from an individual perspective. However, recent works propose the possible influence of social comparison in decision making in general and, specifically, in risk-taking behavior ( Wang et al., 2016 ). In this sense, it would be interesting to analyze in future works the influence of the social gains of decisions and their possible interaction with the decision-makers’ NCC.

Financial literacy and decision making should be further explored to better understand how health and well-being are influenced by them during the life course. This research could help societies and policy makers to reduce the considerable economic and public health challenge that posed fast population aging, associated with low financial knowledge and overconfident decision making ( Khan et al., 2016 ). Ultimately, such data will guide interventions to improve literacy and promote independence, wealth, health, and well-being among people from young adulthood to old age.

Author Contributions

GT, MH-S, and SZ designed the research, analyzed the data, and wrote and revised the manuscript. GT collected the data.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : financial management behavior, investment literacy, investment advice use, need for cognitive closure, retirement, retirement planning

Citation: Topa G, Hernández-Solís M and Zappalà S (2018) Financial Management Behavior Among Young Adults: The Role of Need for Cognitive Closure in a Three-Wave Moderated Mediation Model. Front. Psychol. 9:2419. doi: 10.3389/fpsyg.2018.02419

Received: 26 July 2018; Accepted: 16 November 2018; Published: 30 November 2018.

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Copyright © 2018 Topa, Hernández-Solís and Zappalà. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gabriela Topa, [email protected]

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Financial Management Research Paper Topics

Financial management research paper topics have emerged as an essential part of contemporary education in business and economics. As financial management continues to evolve with global economic changes, the need for research and analysis in this area grows. This article provides a comprehensive guide for students who study management and are assigned to write research papers on various aspects of financial management. From understanding the diverse topics to learning how to write an impactful research paper, this page offers valuable insights. Additionally, it introduces iResearchNet’s writing services, specifically tailored to assist students in achieving academic excellence. The content is structured to guide students through topic selection, writing, and leveraging professional services to meet their academic goals. Whether a novice or an advanced student of financial management, this resource offers a multifaceted perspective on the vast and dynamic field of financial management research.

100 Financial Management Research Paper Topics

The field of financial management offers a vast array of research paper topics. This complex discipline touches every aspect of business operations, influencing strategic planning, decision-making, and organizational growth. Below, you will find a comprehensive list of financial management research paper topics, divided into 10 categories. Each category offers 10 unique topics that cater to various interests within financial management. These topics have been carefully selected to reflect the richness and diversity of the subject.

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• The Role of Budgeting in Financial Planning
• Strategic Financial Management in SMEs
• The Impact of Working Capital Management on Profitability
• Ethical Considerations in Financial Planning
• Risk Management in Financial Planning
• Cost Control Techniques in Manufacturing
• Financial Decision-making Processes in Non-profit Organizations
• The Impact of Inflation on Financial Planning
• International Financial Planning Strategies
• The Relationship between Corporate Governance and Financial Planning

Investment Analysis and Portfolio Management

• The Efficient Market Hypothesis: A Critical Analysis
• The Role of Behavioral Finance in Investment Decisions
• Modern Portfolio Theory and Its Limitations
• Risk and Return Analysis in Emerging Markets
• Socially Responsible Investment Strategies
• The Impact of Political Instability on Investment Decisions
• Real Estate Investment Trusts (REITs): An In-depth Study
• Impact of Technology on Portfolio Management
• Mutual Funds vs. ETFs: A Comparative Study
• The Role of Artificial Intelligence in Investment Management

Corporate Finance

• Capital Structure Decisions in Startups
• The Role of Dividends in Corporate Financial Management
• Mergers and Acquisitions: Strategic Financial Analysis
• Corporate Financing in Developing Economies
• An Analysis of Venture Capital Financing
• The Impact of Corporate Social Responsibility on Financial Performance
• The Role of Financial Management in Business Turnaround Strategies
• Debt Financing vs. Equity Financing: A Comparative Analysis
• Corporate Financial Risk Management Strategies
• Financing Innovation: Challenges and Opportunities

International Financial Management

• Exchange Rate Dynamics and International Financial Decisions
• The Role of International Financial Institutions in Economic Development
• Cross-border Mergers and Acquisitions
• Globalization and Its Impact on Financial Management
• International Tax Planning Strategies
• Challenges in Managing International Financial Risk
• Currency Risk Management in Multinational Corporations
• International Capital Budgeting Decisions
• The Impact of Cultural Differences on International Financial Management
• Foreign Direct Investment Strategies and Financial Management

Financial Markets and Institutions

• The Role of Central Banks in Financial Stability
• The Evolution of Microfinance Institutions
• The Impact of Regulation on Banking Operations
• An Analysis of Stock Market Efficiency
• Financial Derivatives and Risk Management
• The Role of Technology in Financial Services
• A Study of Financial Crises and Regulatory Responses
• Peer-to-Peer Lending Platforms: A New Paradigm
• The Role of Credit Rating Agencies in Financial Markets
• The Future of Cryptocurrency in the Financial Landscape

Personal Finance Management

• Financial Literacy and Personal Investment Decisions
• The Role of Technology in Personal Finance Management
• Retirement Planning Strategies
• Impact of Consumer Behavior on Personal Financial Decisions
• Personal Finance Management in the Gig Economy
• A Study of Personal Bankruptcy Trends
• Credit Card Management Strategies for Individuals
• The Effect of Education on Personal Financial Management
• The Role of Financial Counseling in Personal Finance
• Estate Planning: A Comprehensive Analysis

Risk Management

• Enterprise Risk Management: A Strategic Approach
• The Role of Insurance in Financial Risk Management
• Financial Innovations in Risk Management
• A Study of Credit Risk Management in Banks
• Risk Management Strategies in Supply Chain Finance
• Cyber Risk Management in Financial Institutions
• The Impact of Climate Change on Financial Risks
• A Study of Operational Risk Management in the Healthcare Sector
• Behavioral Aspects of Risk Management
• Crisis Management and Financial Stability

Financial Technology (FinTech)

• The Rise of Blockchain Technology in Finance
• The Impact of FinTech on Traditional Banking
• Regulatory Challenges in the Age of FinTech
• Financial Inclusion through FinTech Innovation
• Artificial Intelligence in Financial Services
• The Future of Cryptocurrencies: Opportunities and Risks
• A Study of Peer-to-Peer Lending Platforms
• FinTech and Consumer Privacy: Ethical Considerations
• Mobile Banking: An Evolutionary Study
• The Role of Big Data Analytics in Financial Decision Making

Ethics and Sustainability in Finance

• Ethical Investing: Trends and Challenges
• Corporate Social Responsibility Reporting in Finance
• Sustainable Finance in Emerging Economies
• Environmental, Social, and Governance (ESG) Criteria in Investment
• The Impact of Business Ethics on Financial Performance
• The Role of Sustainability in Corporate Financial Strategy
• Green Bonds and Financing Sustainable Development
• Social Impact Investing: Opportunities and Challenges
• A Study of Gender Equality in Financial Institutions
• Financial Strategies for Achieving Sustainable Development Goals

Accounting and Finance

• Forensic Accounting: Techniques and Case Studies
• The Role of Management Accounting in Financial Decision-making
• International Financial Reporting Standards (IFRS) Adoption
• The Impact of Taxation on Financial Management
• Accounting Information Systems: An In-depth Analysis
• The Role of Auditing in Corporate Governance
• Accounting Ethics: A Study of Professional Conduct
• Environmental Accounting and Sustainable Development
• The Effect of Automation on Accounting Practices
• A Comparative Study of GAAP and IFRS

The extensive list above offers a broad spectrum of financial management research paper topics. They cater to different academic levels and areas of interest, providing a wealth of opportunities for students to explore the multi-dimensional world of financial management. The selection of these topics can lead to exciting discoveries and insights, pushing the boundaries of existing knowledge in the field. Whether it’s understanding the intricate dynamics of global finance or delving into the ethical considerations in investment decisions, these topics serve as starting points for thought-provoking research that can shape future practices in financial management. By choosing a topic from this comprehensive list, students embark on a journey of intellectual exploration that can contribute to both academic success and the broader understanding of financial management in the modern world.

Financial Management and the Range of Research Paper Topics

Financial management is a multifaceted discipline that stands at the intersection of economics, business administration, and finance. It governs the planning, organizing, directing, and controlling of financial activities within an organization or individual framework. In an ever-changing global economy, the importance of financial management cannot be overstated. It empowers organizations and individuals to make informed decisions, manage risks, and achieve financial stability and growth. This article delves into the vast domain of financial management and explores the wide array of research paper topics it offers.

A. Definition and Core Concepts of Financial Management

Financial management refers to the efficient and effective management of money to achieve specific objectives. It involves processes and tasks such as budgeting, forecasting, investment analysis, risk management, and financial reporting. The primary goals are to maximize shareholder value, ensure liquidity, and maintain solvency.

• Budgeting and Forecasting : These processes involve planning and estimating future financial needs and outcomes. They guide decision-making and help in aligning resources with organizational goals.
• Investment Analysis : This includes evaluating investment opportunities and determining the most profitable and sustainable investments.
• Risk Management : This aspect focuses on identifying, evaluating, and mitigating financial risks, including market risk, credit risk, and operational risk.
• Financial Reporting : This entails the preparation and presentation of financial statements that accurately reflect the financial position of an organization.

B. Importance of Financial Management

• Ensuring Financial Stability : Effective financial management helps in maintaining the financial health of an organization or individual by ensuring a balance between income and expenditure.
• Optimizing Resources : It enables the optimal utilization of resources by aligning them with short-term and long-term goals.
• Strategic Planning : Financial management plays a key role in strategic planning by providing insights into financial capabilities and constraints.
• Enhancing Profitability : By making informed investment and operational decisions, financial management enhances the profitability of an organization.

C. Modern Trends and Challenges in Financial Management

The evolution of technology, globalization, regulatory changes, and societal expectations have shaped modern financial management. Some noteworthy trends and challenges include:

• Financial Technology (FinTech) : The integration of technology into financial services has revolutionized banking, investing, and risk management.
• Globalization : The interconnectedness of global markets presents both opportunities and challenges in managing international financial operations.
• Sustainability and Ethics : The growing focus on environmental, social, and governance (ESG) criteria has led to ethical investing and sustainable finance.
• Regulatory Compliance : Navigating the complex regulatory landscape is a challenge that requires constant adaptation and vigilance.

D. Range of Research Paper Topics in Financial Management

The vastness of financial management offers a rich source of research paper topics. From exploring the intricacies of investment analysis to understanding the ethical dimensions of finance, the possibilities are endless. The following are some broad categories:

• Corporate Finance : Topics related to capital structure, mergers and acquisitions, dividend policies, and more.
• Investment and Portfolio Management : Including research on investment strategies, portfolio optimization, risk and return analysis, etc.
• International Financial Management : This encompasses studies on exchange rate dynamics, global financial strategies, cross-border investments, etc.
• Risk Management : Topics include various risk management techniques, insurance, financial innovations in risk management, etc.
• Personal Finance Management : This field covers financial planning for individuals, retirement strategies, credit management, etc.
• Financial Technology : Blockchain, cryptocurrencies, mobile banking, and more fall under this innovative domain.
• Ethics and Sustainability in Finance : Research in this area may focus on ethical investing, corporate social responsibility, green financing, etc.

Financial management is an expansive and dynamic field that intertwines various elements of finance, economics, and business administration. Its importance in today’s world is immense, given the complexities of the global financial system. The array of research paper topics that this subject offers is indicative of its diversity and depth.

From traditional concepts like budgeting and investment analysis to modern phenomena like FinTech and sustainability, the world of financial management continues to evolve. It invites scholars, practitioners, and students to explore, question, and contribute to its understanding.

The range of research paper topics in financial management offers avenues for academic inquiry and practical application. Whether it’s investigating the effects of globalization on financial strategies or exploring personal finance management in the gig economy, there’s a topic to spark curiosity and inspire research. These research endeavors not only enrich academic literature but also play a crucial role in shaping the future of financial management. In a rapidly changing world, continuous exploration and learning in this field are essential to remain relevant, innovative, and responsible.

How to Choose Financial Management Research Paper Topics

Choosing the right research paper topic in the field of financial management is a critical step in the research process. The chosen topic can shape the direction, depth, and impact of the research. Given the wide array of subfields within financial management, selecting a suitable topic can be both exciting and challenging. Here’s a comprehensive guide to assist you in choosing the ideal financial management research paper topic.

1. Understand Your Interest and Strengths

• Assess Your Interests : Consider what aspects of financial management intrigue you the most. Your enthusiasm for a subject can greatly enhance the research process.
• Identify Your Strengths : Understanding where your skills and knowledge lie can guide you towards a topic that you can explore competently.
• Connect with Real-world Issues : Relating your interests to current industry challenges or trends can make your research more relevant and engaging.

2. Consider the Scope and Depth

• Define the Scope : A clear understanding of the scope helps in keeping the research focused. Too broad a topic can make the research vague, while too narrow may limit your exploration.
• Determine the Depth : Decide how deep you want to delve into the topic. Some subjects may require extensive quantitative analysis, while others may be more theoretical.

3. Examine Academic and Industry Relevance

• Align with Academic Requirements : Ensure that the topic aligns with your course objectives and academic requirements.
• Analyze Industry Needs : Consider how your research could contribute to the industry or address specific financial management challenges.

4. Evaluate Available Resources and Data

• Assess Data Accessibility : Ensure that you can access the necessary data and information to conduct your research.
• Consider Resource Limitations : Be mindful of the time, financial, and technological resources that you’ll need to complete your research.

5. Review Existing Literature

• Analyze Previous Research : Review related literature to identify gaps, controversies, or emerging trends that you can explore.
• Avoid Duplication : Ensure that your chosen topic is unique and not merely a repetition of existing studies.

6. Consult with Experts and Peers

• Seek Guidance from Faculty : Consulting with faculty or mentors can provide valuable insights and direction.
• Collaborate with Peers : Discussions with classmates or colleagues can help in refining ideas and getting diverse perspectives.

7. Consider Ethical Implications

• Evaluate Ethical Considerations : Ensure that your research complies with ethical guidelines, especially if it involves human subjects or sensitive data.
• Reflect on Social Impact : Consider how your research might influence society, policy, or industry standards.

8. Test the Feasibility

• Conduct a Preliminary Study : A small-scale preliminary study or analysis can help in gauging the feasibility of the research.
• Set Realistic Goals : Ensure that your research objectives are achievable within the constraints of time, resources, and expertise.

9. Align with Career Goals

• Consider Future Applications : Think about how this research might align with your career goals or professional development.
• Build on Past Experience : Leveraging your past experiences or projects can lend depth and continuity to your research.

10. Stay Flexible and Adaptable

• Be Open to Change : Research is often an iterative process; being flexible allows for adaptation as new insights or challenges emerge.
• Maintain a Balanced Perspective : While focusing on your chosen topic, keep an open mind to interrelated areas that might enrich your research.

Choosing the right financial management research paper topic is a nuanced process that requires careful consideration of various factors. From understanding personal interests and academic needs to evaluating resources, ethics, and feasibility, each aspect plays a significant role in shaping the research journey.

By following this comprehensive guide, students can navigate the complexities of selecting a suitable research paper topic in financial management. The ultimate goal is to find a topic that resonates with one’s interests, aligns with academic and professional objectives, and contributes meaningfully to the field of financial management. Whether delving into the dynamics of risk management or exploring the impact of FinTech innovations, the chosen topic should be a catalyst for inquiry, creativity, and growth.

How to Write a Financial Management Research Paper

Writing a research paper on financial management is a rigorous process that demands a thorough understanding of financial concepts, analytical skills, and adherence to academic standards. From selecting the right topic to presenting the final piece, each step must be methodically planned and executed. This section provides a comprehensive guide to help students craft an impactful financial management research paper.

1. Understand the Assignment

• Read the Guidelines : Begin by understanding the specific requirements of the assignment, including formatting, length, deadlines, and the expected structure.
• Clarify Doubts : If any aspect of the assignment is unclear, seek clarification from your instructor or mentor to avoid mistakes.

2. Choose a Strong Topic

• Identify Your Interest : Select a topic that interests you, aligns with your strengths, and meets academic and industry relevance. Refer to the previous section for detailed guidelines on choosing a topic.

3. Conduct Extensive Research

• Explore Varied Sources : Use academic journals, textbooks, online databases, and industry reports to gather diverse perspectives and evidence.
• Evaluate the Credibility : Ensure that the sources are credible, relevant, and up-to-date.
• Organize Your Findings : Maintain well-organized notes, including source citations, to facilitate smooth writing later.

4. Develop a Thesis Statement

• Define Your Focus : Craft a clear and concise thesis statement that outlines the central argument or purpose of your research.
• Align with the Evidence : Ensure that your thesis is well-supported by the evidence you have gathered.

5. Create an Outline

• Structure Your Paper : Plan the structure of your paper, including the introduction, body, and conclusion.
• Organize Ideas : Arrange your ideas, arguments, and evidence logically within the outline.

6. Write a Compelling Introduction

• Introduce the Topic : Provide background information and context to the reader.
• Present the Thesis : Clearly state your thesis to guide the reader through your research.
• Engage the Reader : Use compelling language to create interest in your study.

7. Develop the Body of the Paper

• Present Your Arguments : Use clear and concise paragraphs to present each main idea or argument.
• Support with Evidence : Include relevant data, charts, graphs, or quotations to support your claims.
• Use Subheadings : Subheadings can help in organizing the content and making it more reader-friendly.

8. Include Financial Analysis

• Apply Financial Models : Use relevant financial models, theories, or frameworks that pertain to your topic.
• Perform Quantitative Analysis : Utilize statistical tools, if necessary, to analyze financial data.
• Interpret the Results : Ensure that you not only present the numbers but also interpret what they mean in the context of your research.

9. Write a Thoughtful Conclusion

• Summarize Key Points : Recap the main arguments and findings of your paper.
• Restate the Thesis : Reiterate your thesis in light of the evidence presented.
• Provide Insights : Offer insights, implications, or recommendations based on your research.

10. Revise and Edit

• Review for Clarity : Read through the paper to ensure that the ideas flow logically and the arguments are well-articulated.
• Check for Errors : Look for grammatical, spelling, and formatting errors.
• Seek Feedback : Consider getting feedback from peers, tutors, or mentors to enhance the quality of your paper.

11. Follow Formatting Guidelines

• Adhere to Citation Style : Follow the required citation style (APA, MLA, etc.) consistently throughout the paper.
• Include a Bibliography : List all the references used in the research in a properly formatted bibliography.
• Add Appendices if Needed : Include any supplementary material like data sets or additional calculations in the appendices.

Writing a financial management research paper is a complex task that demands meticulous planning, diligent research, critical analysis, and clear communication. By adhering to these detailed guidelines, students can craft a research paper that not only meets academic standards but also contributes to the understanding of intricate financial management concepts.

Whether exploring investment strategies, corporate finance, or financial risk management, a well-crafted research paper showcases one’s analytical capabilities, comprehension of financial principles, and the ability to apply theoretical knowledge to real-world scenarios. It is an invaluable exercise in intellectual exploration and professional development within the realm of financial management.

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Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis

• Open access
• Published: 20 January 2024
• Volume 4 , article number  23 , ( 2024 )

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• Salman Bahoo 1 ,
• Marco Cucculelli   ORCID: orcid.org/0000-0003-0035-9454 2 ,
• Xhoana Goga 2 &
• Jasmine Mondolo 2  

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Over the past two decades, artificial intelligence (AI) has experienced rapid development and is being used in a wide range of sectors and activities, including finance. In the meantime, a growing and heterogeneous strand of literature has explored the use of AI in finance. The aim of this study is to provide a comprehensive overview of the existing research on this topic and to identify which research directions need further investigation. Accordingly, using the tools of bibliometric analysis and content analysis, we examined a large number of articles published between 1992 and March 2021. We find that the literature on this topic has expanded considerably since the beginning of the XXI century, covering a variety of countries and different AI applications in finance, amongst which Predictive/forecasting systems, Classification/detection/early warning systems and Big data Analytics/Data mining /Text mining stand out. Furthermore, we show that the selected articles fall into ten main research streams, in which AI is applied to the stock market, trading models, volatility forecasting, portfolio management, performance, risk and default evaluation, cryptocurrencies, derivatives, credit risk in banks, investor sentiment analysis and foreign exchange management, respectively. Future research should seek to address the partially unanswered research questions and improve our understanding of the impact of recent disruptive technological developments on finance.

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Introduction

The first two decades of the twenty-first century have experienced an unprecedented way of technological progress, which has been driven by advances in the development of cutting-edge digital technologies and applications in Artificial Intelligence (AI). Artificial intelligence is a field of computer science that creates intelligent machines capable of performing cognitive tasks, such as reasoning, learning, taking action and speech recognition, which have been traditionally regarded as human tasks (Frankenfield 2021 ). AI comprises a broad and rapidly growing number of technologies and fields, and is often regarded as a general-purpose technology, namely a technology that becomes pervasive, improves over time and generates complementary innovation (Bresnahan and Trajtenberg 1995 ). As a result, it is not surprising that there is no consensus on the way AI is defined (Van Roy et al. 2020 ). An exhaustive definition has been recently proposed by Acemoglu and Restrepo ( 2020 , p.1), who assert that Artificial Intelligence is “(…) the study and development of intelligent (machine) agents, which are machines, software or algorithms that act intelligently by recognising and responding to their environment.” Even though it is often difficult to draw precise boundaries, this promising and rapidly evolving field mainly comprises machine learning, deep learning, NLP (natural language processing) platforms, predictive APIs (application programming interface), image recognition and speech recognition (Martinelli et al. 2021 ).

The term “Artificial intelligence” was first coined by John McCarthy in 1956 during a conference at Dartmouth College to describe “thinking machines” (Buchanan 2019 ). However, until 2000, the lack of storage capability and low computing power prevented any progress in the field. Accordingly, governments and investors lost their interest and AI fell short of financial support and funding in 1974–1980 and again in 1987–1993. These periods of funding shortage are also known as “AI winters Footnote 1 ”.

However, the most significant development and spread of AI-related technologies is much more recent, and has been prompted by the availability of large unstructured databases, the explosion of computing power, and the rise in venture capital intended to support innovative, technological projects (Ernst et al. 2018 ). One of the most distinctive The term AI winter first appeared in 1characteristics of AI technologies is that, unlike industrial robots, which need to receive specific instructions, generally provided by a software, before they perform any action, can learn for themselves how to map information about the environment, such as visual and tactile data from a robot’s sensors, into instructions sent to the robot’s actuators (Raj and Seamans 2019 ). Additionally, as remarked by Ernst et al. ( 2018 ), whilst industrial robots mostly perform manual tasks, AI technologies are able to carry out activities that, until some years ago, were still regarded as typically human, i.e. what Ernst and co-authors label as “mental tasks”.

The adoption of AI is likely to have remarkable implications for the subjects adopting them and, more in general, for the economy and the society. In particular, it is expected to contribute to the growth of the global GDP, which, according to a study conducted by Pricewater-house-Coopers (PwC) and published in 2017, is likely to increase by up to 14% by 2030. Moreover, companies adopting AI technologies sometimes report better performance (Van Roy et al. 2020 ). Concerning the geographic dimension of this field, North America and China are the leading investors and are expected to benefit the most from AI-driven economic returns. Europe and emerging markets in Asia and South America will follow, with moderate profits owing to fewer and later investments (PwC 2017 ). AI is going to affect labour markets as well. The demand for high-skilled employees is expected to increase, whilst the demand for low-skilled jobs is likely to shrink because of automation; the resulting higher unemployment rate, however, is going to be offset by the new job opportunities offered by AI (Ernst et al. 2018 ; Acemoglu and Restrepo 2020 ).

AI solutions have been introduced in every major sector of the economy; a sector that is witnessing a profound transformation led by the ongoing technological revolution is the financial one. Financial institutions, which rely heavily on Big Data and process automation, are indeed in a “unique position to lead the adoption of AI” (PwC 2020 ), which generates several benefits: for instance, it encourages automation of manufacturing processes which in turn enhances efficiency and productivity. Next, since machines are immune to human errors and psychological factors, it ensures accurate and unbiased predictive analytics and trading strategies. AI also fosters business model innovation and radically changes customer relationships by promoting customised digital finance, which, together with the automation of processes, results in better service efficiency and cost-saving (Cucculelli and Recanatini 2022 ). Furthermore, AI is likely to have substantial implications for financial conduct and prudential supervisors, and it also has the potential to help supervisors identify potential violations and help regulators better anticipate the impact of changes in regulation (Wall 2018 ). Additionally, complex AI/machine learning algorithms allow Fintech lenders to make fast (almost instantaneous) credit decisions, with benefits for both the lenders and the consumers (Jagtiani and John 2018 ). Intelligent devices in Finance are used in a number of areas and activities, including fraud detection, algorithmic trading and high-frequency trading, portfolio management, credit decisions based on credit scoring or credit approval models, bankruptcy prediction, risk management, behavioural analyses through sentiment analysis and regulatory compliance.

In recent years, the adoption of AI technologies in a broad range of financial applications has received increasing attention by scholars; however, the extant literature, which is reviewed in the next section, is quite broad and heterogeneous in terms of research questions, country and industry under scrutiny, level of analysis and method, making it difficult to draw robust conclusions and to understand which research areas require further investigation. In the light of these considerations, we conduct an extensive review of the research on the use of AI in Finance thorough which we aim to provide a comprehensive account of the current state of the art and, importantly, to identify a number of research questions that are still (partly) unanswered. This survey may serve as a useful roadmap for researchers who are not experts of this topic and could find it challenging to navigate the extensive and composite research on this subject. In particular, it may represent a useful starting point for future empirical contributions, as it provides an account of the state of the art and of the issues that deserve further investigation. In doing so, this study complements some previous systematic reviews on the topic, such as the ones recently conducted by Hentzen et al. ( 2022b ) and (Biju et al. 2020 ), which differ from our work in the following main respects: Hentzen and co-authors’ study focuses on customer-facing financial services, whilst the valuable contribution of Biju et al. poses particular attention to relevant technical aspects and the assessment of the effectiveness and the predictive capability of machine learning, AI and deep learning mechanisms within the financial sphere; in doing so, it covers an important issue which, however, is out of the scope of our work.

From our review, it emerges that, from the beginning of the XXI century, the literature on this topic has significantly expanded, and has covered a broad variety of countries, as well as several AI applications in finance, amongst which Predictive/forecasting systems, Classification /detection/early warning systems and Big data Analytics/Data mining /Text mining stand out. Additionally, we show that the selected articles can be grouped into ten main research streams, in which AI is applied to the stock market, trading models, volatility forecasting, portfolio management, performance, risk & default evaluation, cryptocurrencies, derivatives, credit risks in banks, investor sentiment analysis and foreign exchange management, respectively.

The balance of this paper is organised as follows: Sect. “ Methodology ” shortly presents the methodology. Sect. “ A detailed account of the literature on AI in Finance ” illustrates the main results of the bibliometric analysis and the content analysis. Sect. “ Issues that deserve further investigation ” draws upon the research streams described in the previous section to pinpoint several potential research avenues. Sect. “ Conclusions ” concludes. Finally, Appendix 1 clarifies some AI-related terms and definitions that appear several times throughout the paper, whilst Appendix 2 provides more information on some of the articles under scrutiny.

Methodology

To conduct a sound review of the literature on the selected topic, we resort to two well-known and extensively used approaches, namely bibliometric analysis and content analysis. Bibliometric analysis is a popular and rigorous method for exploring and analysing large volumes of scientific data which allows us to unpack the evolutionary nuances of a specific field whilst shedding light on the emerging areas in that field (Donthu et al. 2021 ). In this study, we perform bibliometric analysis using HistCite, a popular software package developed to support researchers in elaborating and visualising the results of literature searches in the Web of Science platform. Specifically, we employ HistCite to recover the annual number of publications, the number of forward citations (which we use to identify the most influential journals and articles) and the network of co-citations, namely, all the citations received and given by journals belonging to a certain field, which help us identify the major research streams described in Sect. “ Identification of the major research streams ”. After that, to delve into the contents of the most pertinent studies on AI in finance, we resort to traditional content analysis, a research method that provides a systematic and objective means to make valid inferences from verbal, visual, or written data which, in turn, permit to describe and quantify specific phenomena (Downe-Wambolt 1992 ).

In order to identify the sample of studies on which bibliometric and content analysis were performed, we proceeded as follows. First, we searched for pertinent articles published in English be-tween 1950 and March 2021. Specifically, we scrutinised the “Finance”, “Economics”, “Business Finance” and “Business” sections of the “Web of Science” (WoS) database using the keyword “finance” together with an array of keywords concerning Artificial Intelligence (i.e. “Finance” AND (“Artificial Intelligence” OR “Machine Learning” OR “Deep Learning” OR “Neural Networks*” OR “Natural Language Processing*” OR “Algorithmic Trading*” OR “Artificial Neural Network” OR “Robot*” OR “Automation” OR “Text Mining” OR “Data Mining” OR “Soft Computing” OR “Fuzzy Logic Analysis” OR “Biometrics*” OR “Geotagging” OR “Wearable*” OR “IoT” OR “Internet of Thing*” OR “digitalization” OR “Artificial Neutral Networks” OR “Big Data” OR “Industry 4.0″ OR “Smart products*” OR Cloud Computing” OR “Digital Technologies*”). In doing so, we ended up with 1,218 articles. Next, two researchers independently analysed the title, abstract and content of these papers and kept only those that address the topic under scrutiny in a non-marginal and non-trivial way. This second step reduced the number of eligible papers to 892, which were used to perform the first part of the bibliometric analysis. Finally, we delved into the contents of the previously selected articles and identified 110 contributions which specifically address the adoption and implications in Finance of AI tools focussing on the economic dimension of the topic, and which are employed in the second part of the bibliometric analysis and in the content analysis.

A detailed account of the literature on AI in Finance

In this section, we explore the patterns and trends in the literature on AI in Finance in order to obtain a compact but exhaustive account of the state of the art. Specifically, we identify some relevant bibliographic characteristics using the tools of bibliometric analysis. After that, focussing on a sub-sample of papers, we conduct a preliminary assessment of the selected studies through a content analysis and detect the main AI applications in Finance. Finally, we identify and briefly describe ten major research streams.

Main results of the bibliometric analysis

First, using HistCite and considering the sample of 892 studies, we computed, for each year, the number of publications related to the topic “AI in Finance”. The corresponding publication trend is shown in Fig.  1 , which plots both the annual absolute number of sampled papers (bar graph in blue) and the ratio between the latter and the annual overall amount of publications (indexed in Scopus) in the finance area (line graph in orange). We also compute relative numbers to see if the trend emerging from the selected studies is not significantly attributable to a “common trend” (i.e. to the fact that, in the meantime, also the total number of publications in the financial area has significantly increased). It can be noted that both graphs exhibit a strong upward trend from 2015 onwards; during the most recent years, the pace of growth and the degree of pervasiveness of AI adoption in the financial sphere have indeed remarkably strengthened, and have become the subject of a rapidly growing number of research articles.

Publication Trend, 1992–2021

After that, focussing on the more pertinent (110) articles, we checked the journals in which these studies were published. Table 1 presents the top-ten list of journals reported in the Academic Journal Guide-ABS List 2020 and ranked on the basis of the total global citation score (TGCS), which captures the number of times an article is cited by other articles that deal with the same topic and are indexed in the WoS database. For each journal, we also report the total number of studies published in that journal. We can notice that the most influential journals in terms of TGCS are the Journal of Finance (with a TGCS equal to 1283) and the Journal of Banking and Finance (with a TGCS of 1253), whilst the journals containing the highest number of articles on the topic are Quantitative Finance (68 articles) and Intelligent Systems in Accounting, Finance and Management (43).

Finally, Fig.  2 provides a visual representation of the citation-based relationships amongst papers starting from the most-cited papers, which we obtained using the Java application CiteSpace.

Source: authors’ elaboration of data from Web of Science; visualisation produced using CiteSpace

Citation Mapping and identification of the research streams.

Preliminary results of the content analysis

In this paragraph, we shortly illustrate some relevant characteristics of our sub-sample made up of 110 studies, including country and industry coverage, method and underpinning theoretical background. Table 2 comprises the list of countries under scrutiny, and, for each of them, a list of papers that perform their analysis on that country. We can see that our sample exhibits significant geographical heterogeneity, as it covers 74 countries across all continents; however, the most investigated areas are three, that is Europe, the US and China. These results corroborate the fact that the above-mentioned regions are the leaders of the AI-driven financial industry, as suggested by PwC ( 2017 ). The United States, in particular, are considered the “early adopters” of AI and are likely to benefit the most from this source of competitive advantage. More lately, emerging countries in Southeast Asia and the Middle East have received growing interest. Finally, a smaller number of papers address underdeveloped regions in Africa and various economies in South America.

The most investigated sectors are reported in Table  3 . We can notice that, although it primarily deals with banking and financial services, the extant research has addressed the topic in a vast array of industries. This confirms that the application potential of AI is very broad, and that any industry may benefit from it.

Through our analysis, we also detected the key theories and frameworks applied by researchers in the prior literature. As shown in Table  4 , 73 (out of 110) papers explicitly refer to some theoretical framework. Specifically, ten of them (14%) resort to computational learning theory; this theory, which is an extension of statistical learning, provides researchers with a theoretical guide for finding the most suitable learning model for a given problem, and is regarded as one of the most important and most used theories in the field. Specific theories concerning types of neural networks and learning methods are used too, such as the fuzzy set theory, which is mentioned in 8% of the sample, and to a lesser extent, the Naive Bayes theorem, the theory of neural networks, the theory of genetic programming and the TOPSIS analytical framework. Finance theories (e.g. Arbitrage Pricing Theory; Black and Scholes 1973 ) are jointly employed with portfolio management theories (e.g. modern portfolio theory), and the two of them account together for 21% (15) of the total number of papers. Finally, bankruptcy theories support business failure forecasts, whilst other theoretical underpinnings concern mathematical and probability concepts.

The content analysis also provides information on the main types of companies under scrutiny. Table 5 indicates that 30 articles (out of 110) focus on large companies listed on stock exchanges, whilst only 16 studies cover small and medium enterprises. Similarly, trading and digital platforms are examined in 16 papers that deal with derivatives and cryptocurrencies.

Furthermore, Table  6 summarises the key methods applied in the literature, which are divided by category (note that all the papers employ more than one method). Looking at the table, we see that machine learning and artificial neural networks are the most popular ones (they are employed in 41 and 51 articles, respectively). The majority of the papers resort to different approaches to compare their results with those obtained through autoregressive and regression models or conventional statistics, which are used as the benchmark; therefore, there may be some overlaps. Nevertheless, we notice that support vector machine and random forest are the most widespread machine learning methods. On the other hand, the use of artificial neural networks (ANNs) is highly fragmented. Backpropagation, Recurrent, and Feed-Forward NNs are considered basic neural nets and are commonly employed. Advanced NNs, such as Higher-Order Neural network (HONN) and Long Short-Term Memory Networks (LSTM), are more performing than their standard version but also much more complicated to apply. These methods are usually compared to autoregressive models and regressions, such as ARMA, ARIMA, and GARCH. Finally, we observe that almost all the sampled papers are quantitative, whilst only three of them are qualitative and four of them consist in literature reviews.

A taxonomy of AI applications in Finance

After scrutinising some relevant features of the papers, we make a step forward and outline a taxonomy of AI applications used in Finance and tackled by previous literature. The main uses of AI in Finance and the papers that address each of them are summarised in Table  7 .

Many research papers (39 out of 110) employ AI as a predictive instrument for forecasting stock prices, performance and volatility. In 23 papers, AI is employed in classification problems and warning systems to detect credit risk and frauds, as well as to monitor firm or bank performance. The former use of AI permits to classify firms into two categories based on qualitative and quantitative data; for example, we may have distressed or non-distressed, viable–nonviable, bankrupt–non-bankrupt, or financially healthy–not healthy, good–bad, and fraud–not fraud. Warning systems follow a similar principle: after analysing customers’ financial behaviour and classifying potential fraud issues in bank accounts, alert models signal to the bank unusual transactions. Additionally, we see that 14 articles employ text mining and data mining language recognition, i.e. natural language processing, as well as sentiment analysis. This may be the starting point of AI-driven behavioural analysis in Finance. Amongst others, trading models and algorithmic trading are further popular aspects of AI widely analysed in the literature. Moreover, interest in Robo-advisory is growing in the asset investment field. Finally, less studied AI applications concern the modelling capability of algorithms and traditional machine learning and neural networks.

Identification of the major research streams

Drawing upon the co-citation analysis mentioned in Sect. " Methodology ", we detected ten main research streams: (1) AI and the stock market; (2) AI and Trading Models; (3) AI and Volatility Forecasting; (4) AI and Portfolio Management; (5) AI and Performance, Risk, and Default Valuation; (6) AI and Bitcoin, Cryptocurrencies; (7) AI and Derivatives; (8) AI and Credit Risk in Banks; (9) AI and Investor Sentiments Analysis; (10) AI and Foreign Exchange Management. Some research streams can be further divided into sub-streams as they deal with various aspects of the same main topic. In this section, we provide a compact account for each of the aforementioned research streams. More detailed information on some of the papers fuelling them is provided in Appendix 2.

Stream 01: AI and the stock market

The stream “AI and the Stock Market” comprises two sub-streams, namely algorithmic trading and stock market, and AI and stock price prediction. The first sub-stream deals with the impact of algorithmic trading (AT) on financial markets. In this regard, Herdershott et al. ( 2011 ) argue that AT increases market liquidity by reducing spreads, adverse selection, and trade-related price discovery. This results in a lowered cost of equity for listed firms in the medium–long term, especially in emerging markets (Litzenberger et al. 2012 ). As opposed to human traders, algorithmic trading adjusts faster to information and generates higher profits around news announcements thanks to better market timing ability and rapid executions (Frino et al. 2017 ). Even though high-frequency trading (a subset of algorithmic trading) has sometimes increased volatility related to news or fundamentals, and transmitted it within and across industries, AT has overall reduced return volatility variance and improved market efficiency (Kelejian and Mukerji 2016 ; Litzenberger et al. 2012 ).

The second sub-stream investigates the use of neural networks and traditional methods to forecast stock prices and asset performance. ANNs are preferred to linear models because they capture the non-linear relationships between stock returns and fundamentals and are more sensitive to changes in variables relationships (Kanas 2001 ; Qi 1999 ). Dixon et al. ( 2017 ) argue that deep neural networks have strong predictive power, with an accuracy rate equal to 68%. Also, Zhang et al. ( 2021 ) propose a model, the Long Short-Term Memory Networks (LSTM), that outperforms all classical ANNs in terms of prediction accuracy and rational time cost, especially when various proxies of online investor attention (such as the internet search volume) are considered.

Stream 02: AI and trading models

From the review of the literature represented by this stream, it emerges that neural networks and machine learning algorithms are used to build intelligent automated trading systems. To give some examples, Creamer and Freund ( 2010 ) create a machine learning-based model that analyses stock price series and then selects the best-performing assets by suggesting a short or long position. The model is also equipped with a risk management overlayer preventing the transaction when the trading strategy is not profitable. Similarly, Creamer ( 2012 ) uses the above-mentioned logic in high-frequency trading futures: the model selects the most profitable and less risky futures by sending a long or short recommendation. To construct an efficient trading model, Trippi and DeSieno ( 1992 ) combine several neural networks into a single decision rule system that outperforms the single neural networks; Kercheval and Zhang ( 2015 ) use a supervised learning method (i.e. multi-class SVM) that automatically predicts mid-price movements in high-frequency limit order books by classifying them in low-stationary-up; these predictions are embedded in trading strategies and yield positive payoffs with controlled risk.

Stream 03: AI and volatility forecasting

The third stream deals with AI and the forecasting of volatility. The volatility index (VIX) from Chicago Board Options Exchange (CBOE) is a measure of market sentiment and expectations. Forecasting volatility is not a simple task because of its very persistent nature (Fernandes et al. 2014 ). According to Fernandes and co-authors, the VIX is negatively related to the SandP500 index return and positively related to its volume. The heterogeneous autoregressive (HAR) model yields the best predictive results as opposed to classical neural networks (Fernandes et al. 2014 ; Vortelinos 2017 ). Modern neural networks, such as LSTM and NARX (nonlinear autoregressive exogenous network), also qualify as valid alternatives (Bucci 2020 ). Another promising class of neural networks is the higher-order neural network (HONN) used to forecast the 21-day-ahead realised volatility of FTSE100 futures. Thanks to its ability to capture higher-order correlations within the dataset, HONN shows remarkable performance in terms of statistical accuracy and trading efficiency over multi-layer perceptron (MLP) and the recurrent neural network (RNN) (Sermpinis et al. 2013 ).

Stream 04: AI and portfolio management

This research stream analyses the use of AI in portfolio selection. As an illustration, Soleymani and Vasighi ( 2020 ) consider a clustering approach paired with VaR analysis to improve asset allocation: they group the least risky and more profitable stocks and allocate them in the portfolio. More elaborate asset allocation designs incorporate a bankruptcy detection model and an advanced utility performance system: before adding the stock to the portfolio, the sophisticated neural network estimates the default probability of the company and asset’s contribution to the optimal portfolio (Loukeris and Eleftheriadis 2015 ). Index-tracking powered by deep learning technology minimises tracking error and generates positive performance (Kim and Kim 2020 ). The asymmetric copula method for returns dependence estimates further promotes the portfolio optimization process (Zhao et al. 2018 ). To sum up, all papers show that AI-based prediction models improve the portfolio selection process by accurately forecasting stock returns (Zhao et al. 2018 ).

Stream 05: AI and performance, risk, default valuation

This research stream comprises three sub-streams, namely AI and Corporate Performance, Risk and Default Valuation; AI and Real Estate Investment Performance, Risk, and Default Valuation; AI and Banks Performance, Risk and Default Valuation.

The first sub-stream examines corporate financial conditions to predict financially distressed companies (Altman et al. 1994 ). As an illustration, Jones et al. ( 2017 ) and Gepp et al. ( 2010 ) determine the probability of corporate default. Sabău Popa et al. ( 2021 ) predict business performance based on a composite financial index. The findings of the aforementioned papers confirm that AI-powered classifiers are extremely accurate and easy to interpret, hence, superior to classic linear models. A quite interesting paper surveys the relationship between face masculinity traits in CEOs and firm riskiness through image processing (Kamiya et al. 2018 ). The results reveal that firms lead by masculine-faced CEO have higher risk and leverage ratios and are more frequent acquirers in MandA operations.

The second sub-stream focuses on mortgage and loan default prediction (Feldman and Gross 2005 ; Episcopos, Pericli, and Hu, 1998 ). For instance, Chen et al. ( 2013 ) evaluate real estate investment returns by forecasting the REIT index; they show that the industrial production index, the lending rate, the dividend yield and the stock index influence real estate investments. All the forecasting techniques adopted (i.e. supervised machine learning and ANNs) outperform linear models in terms of efficiency and precision.

The third sub-stream deals with banks’ performance. In contradiction with past research, a text mining study argues that the most important risk factors in banking are non-financial, i.e. regulation, strategy and management operation. However, the findings from text analysis are limited to what is disclosed in the papers (Wei et al. 2019 ). A highly performing NN-based study on the Malaysian and Islamic banking sector asserts that negative cost structure, cultural aspects and regulatory barriers (i.e. low competition) lead to inefficient banks compared to the U.S., which, on the contrary, are more resilient, healthier and well regulated (Wanke et al. 2016a, b, c, d; Papadimitriou et al. 2020 ).

Stream 06: AI and cryptocurrencies

Although algorithms and AI advisors are gaining ground, human traders still dominate the cryptocurrency market (Petukhina et al. 2021 ). For this reason, substantial arbitrage opportunities are available in the Bitcoin market, especially for USD–CNY and EUR–CNY currency pairs (Pichl and Kaizoji 2017 ). Concerning daily realised volatility, the HAR model delivers good results. Likewise, the feed-forward neural network effectively approximates the daily logarithmic returns of BTCUSD and the shape of their distribution (Pichl and Kaizoji 2017 ).

Additionally, the Hierarchical Risk Parity (HRP) approach, an asset allocation method based on machine learning, represents a powerful risk management tool able to manage the high volatility characterising Bitcoin prices, thereby helping cryptocurrency investors (Burggraf 2021 ).

Stream 07: AI and derivatives

ANNs and machine learning models are accurate predictors in pricing financial derivatives. Jang and Lee ( 2019 ) propose a machine learning model that outperforms traditional American option pricing models: the generative Bayesian NN; Culkin and Das ( 2017 ) use a feed-forward deep NN to reproduce Black and Scholes’ option pricing formula with a high accuracy rate. Similarly, Chen and Wan ( 2021 ) suggest a deep NN for American option and deltas pricing in high dimensions. Funahashi ( 2020 ), on the contrary, rejects deep learning for option pricing due to the instability of the prices, and introduces a new hybrid method that combines ANNs and asymptotic expansion (AE). This model does not directly predict the option price but measures instead, the difference between the target (i.e. derivative price) and its approximation. As a result, the ANN becomes faster, more accurate and “lighter” in terms of layers and training data volume. This innovative method mimics a human learning process when one learns about a new object by recognising its differences from a similar and familiar item (Funahashi 2020 ).

Stream 08: AI and credit risk in banks

The research stream labelled “AI and Credit Risk in Banks” Footnote 2 includes the following sub-streams: AI and Bank Credit Risk; AI and Consumer Credit Risk and Default; AI and Financial Fraud detection/ Early Warning System; AI and Credit Scoring Models.

The first sub-stream addresses bank failure prediction. Machine learning and ANNs significantly outperform statistical approaches, although they lack transparency (Le and Viviani 2018 ). To overcome this limitation, Durango‐Gutiérrez et al. ( 2021 ) combine traditional methods (i.e. logistic regression) with AI (i.e. Multiple layer perceptron -MLP), thus gaining valuable insights on explanatory variables. With the scope of preventing further global financial crises, the banking industry relies on financial decision support systems (FDSSs), which are strongly improved by AI-based models (Abedin et al. 2019 ).

The second sub-stream compares classic and advanced consumer credit risk models. Supervised learning tools, such as SVM, random forest, and advanced decision trees architectures, are powerful predictors of credit card delinquency: some of them can predict credit events up to 12 months in advance (Lahmiri 2016 ; Khandani et al. 2010 ; Butaru et al. 2016 ). Jagric et al. ( 2011 ) propose a learning vector quantization (LVQ) NN that better deals with categorical variables, achieving an excellent classification rate (i.e. default, non-default). Such methods overcome logit-based approaches and result in cost savings ranging from 6% up to 25% of total losses (Khadani et al. 2010 ).

The third group discusses the role of AI in early warning systems. On a retail level, advanced random forests accurately detect credit card fraud based on customer financial behaviour and spending pattern, and then flag it for investigation (Kumar et al. 2019 ). Similarly, Coats and Fant ( 1993 ) build a NN alert model for distressed firms that outperforms linear techniques. On a macroeconomic level, systemic risk monitoring models enhanced by AI technologies, i.e. k-nearest neighbours and sophisticated NNs, support macroprudential strategies and send alerts in case of global unusual financial activities (Holopainen, and Sarlin 2017 ; Huang and Guo 2021 ). However, these methods are still work-in-progress.

The last group studies intelligent credit scoring models, with machine learning systems, Adaboost and random forest delivering the best forecasts for credit rating changes. These models are robust to outliers, missing values and overfitting, and require minimal data intervention (Jones et al. 2015 ). As an illustration, combining data mining and machine learning, Xu et al. ( 2019 ) build a highly sophisticated model that selects the most important predictors and eliminates noisy variables, before performing the task.

Stream 09: AI and investor sentiment analysis

Investor sentiment has become increasingly important in stock prediction. For this purpose, sentiment analysis extracts investor sentiment from social media platforms (e.g. StockTwits, Yahoo-finance, eastmoney.com) through natural language processing and data mining techniques, and classifies it into negative or positive (Yin et al. 2020 ). The resulting sentiment is regarded either as a risk factor in asset pricing models, an input to forecast asset price direction, or an intraday stock index return (Houlihan and Creamer 2021 ; Renault 2017 ). In this respect, Yin et al. ( 2020 ) find that investor sentiment has a positive correlation with stock liquidity, especially in slowing markets; additionally, sensitivity to liquidity conditions tends to be higher for firms with larger size and a higher book-to-market ratio, and especially those operating in weakly regulated markets. As for predictions, daily news usually predicts stock returns for few days, whereas weekly news predicts returns for longer period, from one month to one quarter. This generates a return effect on stock prices, as much of the delayed response to news occurs around major events in company life, specifically earnings announcement, thus making investor sentiment a very important variable in assessing the impact of AI in financial markets. (Heston and Sinha 2017 ).

Stream 10: AI and foreign exchange management

The last stream addresses AI and the management of foreign exchange. Cost-effective trading or hedging activities in this market require accurate exchange rate forecasts (Galeshchuk and Mukherjee 2017 ). In this regard, the HONN model significantly outperforms traditional neural networks (i.e. multi-layer perceptron, recurrent NNs, Psi sigma-models) in forecasting and trading the EUR/USD currency pair using ECB daily fixing series as input data (Dunis et al. 2010 ). On the contrary, Galeshchuk and Mukherjee ( 2017 ) consider these methods as unable to predict the direction of change in the forex rates and, therefore, ineffective at supporting profitable trading. For this reason, they apply a deep NN (Convolution NNs) to forecast three main exchange rates (i.e. EUR/USD, GBP/USD, and JPY/USD). The model performs remarkably better than time series models (e.g. ARIMA: Autoregressive integrated moving average) and machine learning classifiers. To sum up, from this research stream it emerges that AI-based models, such as NARX and the above-mentioned techniques, achieve better prediction performance than statistical or time series models, as remarked by Amelot et al. ( 2021 ).

Issues that deserve further investigation

As shown in Sect. " A detailed account of the literature on AI in Finance ", the literature on Artificial Intelligence in Finance is vast and rapidly growing as technological progress advances. There are, however, some aspects of this subject that are unexplored yet or that require further investigation. In this section, we further scrutinise, through content analysis, the papers published between 2015 and 2021 (as we want to focus on the most recent research directions) in order to define a potential research agenda. Hence, for each of the ten research streams presented in Sect. " Identification of the major research streams ", we report a number of research questions that were put forward over time and are still at least partly unaddressed. The complete list of research questions is enclosed in Table  8 .

AI and the stock market

This research stream focuses on algorithmic trading (AT) and stock price prediction. Future research in the field could analyse more deeply alternative AI-based market predictors (e.g. clustering algorithms and similar learning methods) and draw up a regime clustering algorithm in order to get a clearer view of the potential applications and benefits of clustering methodologies (Law, and Shawe-Taylor 2017 ). In this regard, Litzenberger et al. ( 2012 ) and Booth et al. ( 2015 ) recommend broadening the study to market cycles and regulation policies that may affect AI models’ performance in stock prediction and algorithmic trading, respectively. Footnote 3 Furthermore, forecasting models should be evaluated with deeper order book information, which may lead to a higher prediction accuracy of stock prices (Tashiro et al. 2019 ).

AI and trading models

This research stream builds on the application of AI in trading models. Robo advisors are the evolution of basic trading models: they are easily accessible, cost-effective, profitable for investors and, unlike human traders, immune to behavioural biases. Robo advisory, however, is a recent phenomenon and needs further performance evaluations, especially in periods of financial distress, such as the post-COVID-19 one (Tao et al. 2021 ), or in the case of the so-called “Black swan” events. Conversely, trading models based on spatial neural networks (an advanced ANN) outperform all statistical techniques in modelling limit order books and suggest an extensive interpretation of the joint distribution of the best bid and best ask. Given the versatility of such a method, forthcoming research should resort to it with the aim of understanding whether neural networks with more order book information (i.e. order flow history) lead to better trading performance (Sirignano 2018 ).

AI and volatility forecasting

As previously mentioned, volatility forecasting is a challenging task. Although recent studies report solid results in the field (see Sermpinis et al. 2013 ; Vortelinos 2017 ), future work could deploy more elaborated recurrent NNs by modifying the activation function of the processing units composing the ANNs, or by adding hidden layers and then evaluate their performance (Bucci 2020 ). Since univariate time series are commonly used for realised volatility prediction, it would be interesting to also inquire about the performance of multivariate time series.

AI and portfolio management

This research stream examines the use of AI in portfolio selection strategies. Past studies have developed AI models that are capable of replicating the performance of stock indexes (known as index tracking strategy) and constructing efficient portfolios with no human intervention. In this regard, Kim and Kim ( 2020 ) suggest focussing on optimising AI algorithms to boost index-tracking performance. Soleymani and Vasighi ( 2020 ) recognise the importance of clustering algorithms in portfolio management and propose a clustering approach powered by a membership function, also known as fuzzy clustering, to further improve the selection of less risky and most profitable assets. For this reason, analysis of asset volatility through deep learning should be embedded in portfolio selection models (Chen and Ge 2021 ).

AI and performance, risk, default valuation

Bankruptcy and performance prediction models rely on binary classifiers that only provide two outcomes, e.g. risky–not risky, default–not default, good–bad performance. These methods may be restrictive as sometimes there is not a clear distinction between the two categories (Jones et al. 2017 ). Therefore, prospective research might focus on multiple outcome domains and extend the research area to other contexts, such as bond default prediction, corporate mergers, reconstructions, takeovers, and credit rating changes (Jones et al. 2017 ). Corporate credit ratings and social media data should be included as independent predictors in credit risk forecasts to evaluate their impact on the accuracy of risk-predicting models (Uddin et al. 2020 ). Moreover, it is worth evaluating the benefits of a combined human–machine approach, where analysts contribute to variables’ selection alongside data mining techniques (Jones et al. 2017 ). Forthcoming studies should also address black box and over-fitting biases (Sariev and Germano 2020 ), as well as provide solutions for the manipulation and transformation of missing input data relevant to the model (Jones et al. 2017 ).

AI and cryptocurrencies

The use of AI in the cryptocurrency market is in its infancy, and so are the policies regulating it. As the digital currency industry has become increasingly important in the financial world, future research should study the impact of regulations and blockchain progress on the performance of AI techniques applied in this field (Petukhina et al., 2021 ). Cryptocurrencies, and especially Bitcoins, are extensively used in financial portfolios. Hence, new AI approaches should be developed in order to optimise cryptocurrency portfolios (Burggraf 2021 ).

AI and derivatives

This research stream examines derivative pricing models based on AI. A valuable research area that should be further explored concerns the incorporation of text-based input data, such as tweets, blogs, and comments, for option price prediction (Jang and Lee 2019 ). Since derivative pricing is an utterly complicated task, Chen and Wan ( 2021 ) suggest studying advanced AI designs that minimise computational costs. Funahashi ( 2020 ) recognises a typical human learning process (i.e. recognition by differences) and applies it to the model, significantly simplifying the pricing problem. In the light of these considerations, prospective research may also investigate other human learning and reasoning paths that can improve AI reasoning skills.

AI and credit risk in banks

Bank default prediction models often rely solely on accounting information from banks’ financial statements. To enhance default forecast, future work should consider market data as well (Le and Viviani 2018 ). Credit risk includes bank account fraud and financial systemic risk. Fraud detection based on AI needs further experiments in terms of training speed and classification accuracy (Kumar et al. 2019 ). Early warning models, on the other hand, should be more sensitive to systemic risk. For this reason, subsequent studies ought to provide a common platform for modelling systemic risk and visualisation techniques enabling interaction with both model parameters and visual interfaces (Holopainen and Sarlin 2017 ).

AI and investor sentiment analysis

Sentiment analysis builds on text-based data from social networks and news to identify investor sentiment and use it as a predictor of asset prices. Forthcoming research may analyse the effect of investor sentiment on specific sectors (Houlihan and Creamer 2021 ), as well as the impact of diverse types of news on financial markets (Heston and Sinha 2017 ). This is important for understanding how markets process information. In this respect, Xu and Zhao ( 2022 ) propose a deeper analysis of how social networks’ sentiment affects individual stock returns. They also believe that the activity of financial influencers, such as financial analysts or investment advisors, potentially affects market returns and needs to be considered in financial forecasts or portfolio management.

AI and foreign exchange management

This research stream investigates the application of AI models to the Forex market. Deep networks, in particular, efficiently predict the direction of change in forex rates thanks to their ability to “learn” abstract features (i.e. moving averages) through hidden layers. Future work should study whether these abstract features can be inferred from the model and used as valid input data to simplify the deep network structure (Galeshchuk and Mukherjee 2017 ). Moreover, the performance of foreign exchange trading models should be assessed in financial distressed times. Further research may also compare the predictive performance of advanced times series models, such as genetic algorithms and hybrid NNs, for forex trading purposes (Amelot et al. 2021 ).

Conclusions

Despite its recent advent, Artificial Intelligence has revolutionised the entire financial system, thanks to advanced computer science and Big Data Analytics and the increasing outflow of data generated by consumers, investors, business, and governments’ activities. Therefore, it is not surprising that a growing strand of literature has examined the uses, benefits and potential of AI applications in Finance. This paper aims to provide an accurate account of the state of the art, and, in doing so, it would represent a useful guide for readers interested in this topic and, above all, the starting point for future research. To this purpose, we collected a large number of articles published in journals indexed in Web of Science (WoS), and then resorted to both bibliometric analysis and content analysis. In particular, we inspected several features of the papers under study, identified the main AI applications in Finance and highlighted ten major research streams. From this extensive review, it emerges that AI can be regarded as an excellent market predictor and contributes to market stability by minimising information asymmetry and volatility; this results in profitable investing systems and accurate performance evaluations. Additionally, in the risk management area, AI aids with bankruptcy and credit risk prediction in both corporate and financial institutions; fraud detection and early warning models monitor the whole financial system and raise expectations for future artificial market surveillance. This suggests that global financial crises or unexpected financial turmoil will be likely to be anticipated and prevented.

All in all, judging from the rapid widespread of AI applications in the financial sphere and across a large variety of countries, and, more in general, based on the growth rate exhibited by technological progress over time, we expect that the use of AI tools will further expand, both geographically, across sectors and across financial areas. Hence, firms that still struggle with coping with the latest wave of technological change should be aware of that, and try to overcome this burden in order to reap the potential benefits associated with the adoption of AI and remain competitive. In the light of these considerations, policymakers should motivate companies, especially those that have not adopted yet, or have just begun to introduce AI applications, to catch up, for instance by providing funding or training courses aimed to strengthen the complex skills required by employees dealing with these sophisticated systems and languages.

This study presents some limitations. For instance, it tackles a significant range of interrelated topics (in particular, the main financial areas affected by AI which have been the main object of past research), and then presents a concise description for each of them; other studies may decide to focus on only one or a couple of subjects and provide a more in-depth account of the chosen one(s). Also, we are aware that technological change has been progressing at an unprecedented fast and growing pace; even though we considered a significantly long time-frame and a relevant amount of studies have been released in the first two decades of the XXI century, we are aware that further advancements have been made from 2021 (the last year included in the time frame used to the select our sample); for instance, in the last few years, AI experts, policymakers, and also a growing number of scholars have been debating the potential and risks of AI-related devices, such as chatGBT and the broader and more elusive “metaverse” (see for instance Mondal et al. 2023 and Calzada 2023 , for an overview). Hence, future contributions may advance our understanding of the implications of these latest developments for finance and other important fields, such as education and health.

Data availability

Full data are available from authors upon request.

The term AI winter first appeared in 1984 as the topic of a public debate at the annual meeting of the American Association of Artificial Intelligence (AAAI). It referred to hype generated by over promises from developers, unrealistically high expectations from end users, and extensive media promotion.

Since credit risk in the banking industry remarkably differs from credit risk in firms, the two of them are treated separately.

As this issue has not been addressed in the latest papers, we include these two papers although their year of publication lies outside the established range period.

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Bahoo, S., Cucculelli, M., Goga, X. et al. Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis. SN Bus Econ 4 , 23 (2024). https://doi.org/10.1007/s43546-023-00618-x

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