phd machine learning in finance

Machine Learning - CMU

Phd program in machine learning.

Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.

Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.

The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning.  For questions and concerns, please   contact us .

The PhD program is a full-time in-person committment and is not offered on-line or part-time.

PhD Requirements

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

Financial Support

Application Information

For applicants applying in Fall 2023 for a start date of August 2024 in the Machine Learning PhD program, GRE Scores are REQUIRED. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.

Proof of English Language Proficiency If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo.  We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored. We do not issue waivers for non-native speakers of English.   In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university.  We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States.  No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Submit valid, recent scores:   If as described above you are required to submit proof of English proficiency, your TOEFL, IELTS or Duolingo test scores will be considered valid as follows: If you have not received a bachelor’s degree in the U.S., you will need to submit an English proficiency score no older than two years. (scores from exams taken before Sept. 1, 2021, will not be accepted.) If you are currently working on or have received a bachelor's and/or a master's degree in the U.S., you may submit an expired test score up to five years old. (scores from exams taken before Sept. 1, 2018, will not be accepted.)

Graduate Online Application

• Early Application Deadline – November 29, 2023 (3:00 p.m. EST)
• Final Application Deadline - December 13, 2023 (3:00 p.m. EST)

Machine Learning for Finance

Perform advanced financial analysis with algorithms and statistical techniques.

At a Glance

Upcoming dates.

Students may register up to 7 days after the course start.

Get in Touch

Use data-driven analysis to identify relevant financial trends.

The University of Chicago’s eight-week Machine Learning for Finance course will teach you to collect, organize, and use data to perform advanced financial analysis with algorithms and statistical techniques and tools.

Designed For

Designed for financial professionals who want to develop a career in the present-day financial industry or in an organization’s finance department.

Machine Learning for Finance Objectives

Organizations are constantly trying to streamline processes, cut costs, and drive profitability. Data has become a key driver in producing better financial analytics, providing leaders with the insights they need to make strategic decisions. 

After completing this course on Machine Learning in Finance, you will be able to:

• Apply basic concepts of statistics to finance, including the random walk model.
• Understand what Exploratory Data Analysis is and how to perform it with Python and Pandas.
• Engineer new functions using existing data.

What Insights Are Necessary for Financial Reporting?

Learn how organizations incorporate data-driven analysis to identify financial trends.

Financial Analytics Curriculum

Understand how to use data to perform advanced financial analysis with algorithms and statistical techniques and tools in order to make strategic financial decisions in your organization.

Overview of this course on Machine Learning in Finance

• Review statistics and probability and apply basic concepts of statistics to finance.
• Understand what linear regression is, when to use it, and how to apply linear regression metrics to a model. 
• Make models more rigorous by adding train/test split and cross-validation.
• Backtest a model and understand why backtesting is important.
• Use simulation to solve a portfolio allocation problem.
• Converse at a high level about several advanced topics in financial machine learning.

Machine Learning for Finance course structure

• Eight weeks in length.
• Weekly, self-paced interactive learning modules and assignments are time-sensitive and should be completed by the set deadlines.
• Synchronous sessions and live question-and-answer sessions.
• Mentors will provide continuous support and encourage a dynamic and positive learning environment.

Weekly course schedule

Understand how to use data to perform advanced financial analysis with algorithms and statistical techniques and tools in order to make strategic financial decisions in your organization. 

Gain an introduction to Python, which covers variables, functions, control structures, loops, and Pandas, and learn about probability and statistics, including statistics for finance.

Learn about exploratory data analysis including the univariate and bivariate models, scatterplots, histograms, and boxplots, as well as regression and regression metrics.

Understand how to conduct train-test splits, cross-validation, and overfitting and regularization; learn about feature engineering and selection, more specifically in terms of transforming independent and dependent variables; before delving into its application to finance, such as returns and interest rates.

Define ARIMA modeling, and learn about stationarity for time series models, metrics and tests; and use the statsmodels package in Python to build an ARIMA model. 

Discover the different types of testing regimes, such as backtesting a time series-ARIMA model; simple rolling pseudo-out-of-sample backtesting; cross-validation backtesting; and back-testing for linear regression, and learn to monitor and troubleshoot models in production.

Understand logistic regression and metrics, such as accuracy, precision and recall, and confusion matrix. Learn about ensemble methods such as bootstrap aggregation, random forests, and boosting, as well as clustering.

Define “risk” in finance such as no variance in returns; risky and risk-free assets including expected returns and variance. Learn about resampling and efficient portfolios, such as utility functions, and use Monte Carlo simulation for out-of-sample testing. 

Become familiar with cloud computing and industry leaders such as Amazon Web Services, Google, and Microsoft. Understand deep learning and neural nets, including back propagation and keras. And, learn about bayesian inference with a focus on beyond frequentist statistics and PyMC3.

Meet Your Instructor

Our course instructor has extensive experience in data analytics and fintech, as well as many years of experience in the world of finance, which she is ready to share with you. 

Lara Kattan, MPP

Data Science Educator and Curriculum Writer

Lara Kattan teaches for the University of Chicago Booth School of Business and data science learning platforms. Previously, she was a consultant at McKinsey and EY. She has an MPP with a concentration in econometrics from UChicago.

Career Outlook

Traditional financial reporting like profit and loss statements, balance sheets, cash flows, and variance analysis are no longer enough. Today’s businesses need data-based financial analysis to gain deeper insights that will allow them to connect business operations to long-term value, model scenarios in real time, and allocate resources efficiently. The increasing demand for advanced finance functions such as connecting operational KPIs to financial metrics, along with technological advancements in cloud-based services, has led to the financial analytics market’s current valuation of  6.32 billion . Experts anticipate it will nearly double in size by 2026, with a projected value of 11.02 billion.

The  average annual base pay  for a financial analyst in the US

The  anticipated size  of the financial analytics market by 2026

The projected CAGR  of the industry from 2021 to 2026

Potential job titles in Financial Analytics

• Asset/Wealth Manager
• Commercial Banker
• Finance Manager
• Financial Advisor
• Financial Analyst
• Investment Banker

Offered by The University of Chicago's Professional Education

Of Interest

• Professional Certificate in Strategic Financial Management

Read, understand, analyze, and use financial statements and introduce risk management in your...

Necessary Skills to Work with Data

Blueprints for Thriving Companies

• Artificial Intelligence and Data Science for Leaders

Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

• Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
• Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
• Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
• Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
• Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
• Foster economic development in the state of Georgia.
• Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

• Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
• Area electives (5 courses, 15 hours).
• Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
• Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
• Doctoral minor (2 courses, 6 hours).
• Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
• PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

• ISYE 6412 , Theoretical Statistics
• ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
• MATH 7251 High Dimension Probability
• MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

• CS 7545 Machine Learning Theory and Methods
• CS 7616 , Pattern Recognition
• CSE 6740 / ISYE 6740 , Computational Data Analysis
• ECE 6254 , Statistical Machine Learning
• ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

• ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
• ISYE 6661 , Linear Optimization
• ISYE 6663 , Nonlinear Optimization
• ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

• AE 6505 , Kalman Filtering
• BMED 6700 , Biostatistics
• ECE 6558 , Stochastic Systems
• ECE 6601 , Random Processes
• ECE 6605 , Information Theory
• ISYE 6404 , Nonparametric Data Analysis
• ISYE 6413 , Design and Analysis of Experiments
• ISYE 6414 , Regression Analysis
• ISYE 6416 , Computational Statistics
• ISYE 6420 , Bayesian Statistics
• ISYE 6761 , Stochastic Processes I
• ISYE 6762 , Stochastic Processes II
• ISYE 7400 , Adv Design-Experiments
• ISYE 7401 , Adv Statistical Modeling
• ISYE 7405 , Multivariate Data Analysis
• ISYE 8803 , Statistical and Probabilistic Methods for Data Science
• ISYE 8813 , Special Topics in Data Science
• MATH 6263 , Testing Statistical Hypotheses
• MATH 6266 , Statistical Linear Modeling
• MATH 6267 , Multivariate Statistical Analysis
• MATH 7244 , Stochastic Processes and Stochastic Calculus I
• MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

• CS 7280 , Network Science
• CS 7510 , Graph Algorithms
• CS 7520 , Approximation Algorithms
• CS 7530 , Randomized Algorithms
• CS 7535 , Markov Chain Monte Carlo Algorithms
• CS 7540 , Spectral Algorithms
• CS 8803 , Continuous Algorithms
• ECE 6283 , Harmonic Analysis and Signal Processing
• ECE 6555 , Linear Estimation
• ISYE 7682 , Convexity
• MATH 6112 , Advanced Linear Algebra
• MATH 6221 , Advanced Classical Probability Theory
• MATH 6241 , Probability I
• MATH 6580 , Introduction to Hilbert Space
• MATH 7338 , Functional Analysis
• MATH 7586 , Tensor Analysis
• MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

• AE 6373 , Advanced Design Methods
• AE 8803 , Machine Learning for Control Systems
• AE 8803 , Nonlinear Stochastic Optimal Control
• BMED 6780 , Medical Image Processing
• BMED 8813 BHI, Biomedical and Health Informatics
• BMED 8813 MHI, mHealth Informatics
• BMED 8813 MLB, Machine Learning in Biomedicine
• BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
• CS 6440 , Introduction to Health Informatics
• CS 6465 , Computational Journalism
• CS 6471 , Computational Social Science
• CS 6474 , Social Computing
• CS 6475 , Computational Photography
• CS 6476 , Computer Vision
• CS 6601 , Artificial Intelligence
• CS 7450 , Information Visualization
• CS 7476 , Advanced Computer Vision
• CS 7630 , Autonomous Robots
• CS 7632 , Game AI
• CS 7636 , Computational Perception
• CS 7643 , Deep Learning
• CS 7646 , Machine Learning for Trading
• CS 7650 , Natural Language Processing
• CSE 6141 , Massive Graph Analysis
• CSE 6240 , Web Search and Text Mining
• CSE 6242 , Data and Visual Analytics
• CSE 6301 , Algorithms in Bioinformatics and Computational Biology
• ECE 4580 , Computational Computer Vision
• ECE 6255 , Digital Processing of Speech Signals
• ECE 6258 , Digital Image Processing
• ECE 6260 , Data Compression and Modeling
• ECE 6273 , Methods of Pattern Recognition with Application to Voice
• ECE 6550 , Linear Systems and Controls
• ECE 8813 , Network Security
• ISYE 6421 , Biostatistics
• ISYE 6810 , Systems Monitoring and Prognosis
• ISYE 7201 , Production Systems
• ISYE 7204 , Info Prod & Ser Sys
• ISYE 7203 , Logistics Systems
• ISYE 8813 , Supply Chain Inventory Theory
• HS 6000 , Healthcare Delivery
• MATH 6759 , Stochastic Processes in Finance
• MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

• CS 6515 , Introduction to Graduate Algorithms
• CS 6550 , Design and Analysis of Algorithms
• CSE 6140 , Computational Science and Engineering Algorithms
• CSE 6643 , Numerical Linear Algebra
• CSE 6644 , Iterative Methods for Systems of Equations
• CSE 6710 , Numerical Methods I
• CSE 6711 , Numerical Methods II
• ISYE 6644 , Simulation
• ISYE 6645 , Monte Carlo Methods
• ISYE 6662 , Discrete Optimization
• ISYE 6664 , Stochastic Optimization
• ISYE 6679 , Computational methods for optimization
• ISYE 7686 , Advanced Combinatorial Optimization
• ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

• CS 6421 , Temporal, Spatial, and Active Databases
• CS 6430 , Parallel and Distributed Databases
• CS 6290 , High-Performance Computer Architecture
• CSE 6220 , High Performance Computing
• CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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Machine Learning for Quantitative Finance

This project sits at the intersection of machine learning and quantitative finance, with a focus on advancing areas such as derivatives pricing and portfolio optimization. The research may extend established studies like option pricing [1, 2] and index tracking [3,4] or explore some new topics including agent-based market simulation.

The successful candidate will harness advanced techniques including Large Language Models (LLMs), AI Agents, Neural Differential Equations, Self-Supervised Learning (SSL), Neural Implicit Representations (NIR), and Meta Learning, with an emphasis on creating accurate, robust, and trustworthy models. The goal is to develop accountable machine learning tools that can be adopted by the finance industry, and promoting open-source research in quantitative finance.

Supervisor: Yongxin Yang -  [email protected]

[1] Y. Yang and T. Hospedales, "Mixture of Normalizing Flows for European Option Pricing", UAI 2023

[2] Y. Yang and T. Hospedales, "On Calibration of Mathematical Finance Models by Hypernetworks", ECML/PKDD 2023

[3] Y. Yang and T. Hospedales, "Partial Index Tracking: A Meta-Learning Approach", CoLLAs 2023

[4] Y. Yang and T. Hospedales, "An Evaluation of Self-Supervised Learning for Portfolio Diversification", ICANN 2023

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Machine Learning and Reinforcement Learning in Finance

Format: Specialization – 4 months

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.

Key Takeaways

• Compare ML for Finance with ML in Technology (image and speech recognition, robotics, etc.)  
• Describe linear regression and classification models and methods of their evaluation
• Explain how Reinforcement Learning is used for stock trading  
• Become familiar with popular approaches to modeling market frictions and feedback effects for option trading.

Who Should Attend

The specialization is designed for three categories of students:

• Practitioners working at financial institutions such as banks, asset management firms or hedge funds
• Individuals interested in applications of ML for personal day trading
• Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance.

Course Outline

• Course 1: Guided Tour of Machine Learning in Finance
• Course 2: Fundamentals of Machine Learning in Finance
• Course 3: Reinforcement Learning in Finance
• Course 4: Overview of Advanced Methods of Reinforcement Learning in Finance

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• Quantitative Finance Specialization
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• Management Science and Analytics (Ph.D.)

The Quantitative Finance specialization in the Ph.D. in Management Science and Analytics program is excellent preparation for either academic careers or for students who want to apply the theoretical, analytical, and quantitative rigor of management science to careers in finance.

Dissertation research in this area may include a wide range of topics such as risk modeling, financial time series analysis, and investment analysis.

Required courses for the Quantitative Finance specialization (three credits per course):

• MSC 621—Corporate Finance
• MSC 623—Investments
• MSC 631—Theory of Finance I
• MSC 633—Theory of Finance II
• MSF 545/MSC 613—Structured Fixed Income Portfolios
• MSF 546/MSC 614—Quantitative Investment Strategies

View the curriculum for the Ph.D. in Management Science (MSC) program and MSC course descriptions .

Career Opportunities

Industry and Research

The specialization in Quantitative Finance prepares students for a wide range of careers in finance, particularly in areas such as investment and commercial banking, trading, and risk management. This background also opens career opportunities across industries in business functions focused on finance, financial modeling, economics, and risk compliance.

Chicago’s position as a global center for finance and fintech, as well as the home to the world’s largest markets in financial derivatives, make it a prime location for internships, networking, and job opportunities for Stuart students in quantitative finance.

Our graduates are ready to step into roles such as:

• Senior quantitative analyst or quantitative analytics manager-economic modeling
• Quantitative developer, senior quantitative modeler, or quantitative risk modeler
• Research data scientist, senior quantitative researcher, or quantitative researcher-asset management
• Portfolio risk analyst, senior quantitative risk analyst, or exotic rates quantitative analyst
• Equity derivatives quantitative strategist or quantitative portfolio strategist
• Senior quantitative markets analyst or machine learning analyst

Students interested in academic careers are supported by strong mentoring relationships with our faculty, opportunities to co-author papers published in prestigious scholarly journals, and help in securing adjunct positions to develop their teaching skills.

As a result, our graduates have launched teaching and research careers as finance faculty members at colleges and universities in the United States and around the world, such as:

• Carnegie Mellon University
• Beijing Normal University
• Lewis University
• Brooklyn College - City University of New York
• Benedictine University
• Northeastern Illinois University
• East China Normal University
• Saint Michael’s College (Vermont)

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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

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A Guide to Machine Learning in Finance

Discover how machine learning in finance can be used to improve your financial decision-making and the types of machine learning finance jobs available.

Is machine learning the key to efficient financial operations? Machine learning applications can be used for everything from risk assessment to asset management, using data for critical insights and streamlining various processes while optimizing results.

Using machine learning in financial applications is an evolving practice utilized in various ways throughout the industry. The diverse applications of machine learning in finance have also opened up many new machine learning finance jobs. But first, it helps to understand machine learning in finance and how it can be used to build a career.

What exactly is machine learning in finance?

Machine learning belongs under the umbrella of artificial intelligence (AI). It deals with designing and developing algorithms that can learn from and make predictions based on data. Machine learning models provide the technology to automate cognitive tasks. Various financial tasks utilize machine learning technology, including credit scoring, investment monitoring and recommendations, fraud detection, and algorithmic trading. 

Machine learning can help finance companies make better pricing, risk, and customer behaviour decisions. The technology can build models that improve understanding large data sets and uncover patterns that facilitate new business systems and processes.

Why finance companies are turning to machine learning and AI

Machine learning and AI can speed up processes and improve how raw data is used. In turn, it can save time and money while improving business outcomes. Finance companies are turning to machine learning and AI more and more to automate repetitive tasks, provide better customer service and experiences, and gain an edge over their competitors.

Streamlining and process automation

Working in finance, the ability to streamline and automate various processes using machine learning has many benefits. Finance companies use these technologies to automate tasks such as paperwork, calculations, data monitoring, and claims processing. This can free up employees to focus on more value-added activities.

Customer engagement and personalization: The Internet of Things

Customer engagement is another critical area where machine learning and AI can be used. Internet of Things (IoT) devices can generate considerable data useful for understanding customer behaviour and preferences [ 1 ]. The data can then be used to create personalized marketing campaigns or to improve customer service. Overall, better customer service and improved customer experiences typically lead to more sales and higher customer satisfaction rates.

Big data analysis for a competitive edge

Big data analysis has become essential for understanding customer behaviour and trends. Machine learning and AI can help you make sense of large data sets, identify patterns, and make predictions. This can help to gain a competitive edge by making better and faster decisions over your competitors.

15 applications of machine learning in finance

Machine learning can be used to help create accurate predictive models that reduce error and risk. New machine learning applications and opportunities are always emerging in the financial sector. Below are some established ways this exciting technology is used in finance.

Data input, monitoring, and update process automation: Automating repetitive and time-consuming tasks.

Security portfolio management (Robo-advisors): Creating and managing investment portfolios

Algorithmic trading: Identifying patterns and developing trading strategies with speed and accuracy.

High-Frequency Trading (HFT): Identifying trading opportunities and executing trades at high speeds.

Fraud detection: Detecting fraudulent activities like money laundering and insider trading.

Loan, underwriting, and credit scoring: Assessing loan applications and the creditworthiness of borrowers.

Risk management: Identifying risks and developing risk management strategies.

Chatbots: Creating chatbots that provide customer support or financial advice.

Document and unstructured data analysis: Extracting information from documents, including contracts and financial reports.

Trade settlements: Automating the trade settlement process.

RegTech: Responding to changes in the regulatory landscape.

Customer experience: Improving the customer experience through personalization and recommendations.

Customer acquisition and onboarding: Automating the process of customer acquisition and onboarding.

Asset valuation and management: Value and manage assets, including stocks and bonds.

Stock market forecasting: Predicting future movements in the stock market.

Machine learning use cases in banking

Many of the biggest banks in Canada—like RBC Royal Bank, TD Bank, and Scotiabank—use machine learning in various ways to improve their operations. Machine learning detects and prevents fraud, better targets marketing efforts, and streames back-office processes. 

In addition, they use machine learning to develop new financial products and services, such as predictive analytics tools. This can help their customers to make better investment decisions.

Careers in machine learning in the finance sector

Machine learning is relatively new in finance and other industries, and there is already a high demand for qualified employees. Machine learning jobs fall under employment categories like computer programming, software development, and financial analysis. The finance sector brings in the second largest income out of all sectors of the Canadian economy [ 2 ]; leaders in this sector recognize the importance and potential of machine learning to improve operations. 

Businesses in the finance sector increasingly rely on data-driven decision-making. As the field of machine learning evolves, there will be new opportunities for those with machine learning expertise to apply their skills in the finance sector.

Jobs titles in machine learning in finance (with salaries)

With machine learning skills and experience, there are many opportunities in finance. Banks, hedge funds, and other financial services firms seek machine learning talent; there is significant demand for machine learning professionals in finance with very competitive pay in Canada.

Quantitative research analyst: $62,079 per year [ 3 ]

Machine learning engineer: $111,763 per year [ 4 ]

Machine learning modeller: $105,190 per year [ 5 ]

Data scientist in finance: $98,569 per year [ 6 ]

Criteria for applying for machine learning jobs

Various types of machine learning jobs are available, each requiring different qualifications and skills. For example, a machine learning engineer will need strong engineering and programming skills, while a machine learning scientist will need strong mathematical and statistical skills. Some of the common criteria for applying to machine learning jobs include:

A four-year degree in computer science, mathematics, or a related field, and, in some cases, a graduate degree in a related area

Proficiency in using programming languages, including Python, R, and Java

Up-to-date certifications and proficiency in relevant software

Experience with statistical analysis and machine learning algorithms

Ability to effectively communicate results of data analysis to non-technical audiences

Ability to work with large data sets

Get started in machine learning with an online course.

As you begin your career in machine learning, you’ll need to familiarize yourself with the foundational concepts, including models, regression, neural networks, training data sets, and computing resources. If you're not already familiar with the basics of machine learning, it's essential to spend some time getting up to speed.

You can find several online resources devoted to teaching the basics of machine learning on Coursera, including courses from leading universities such as Stanford and MIT. For example, Stanford offers a machine learning course . The course enables you to learn and practice the fundamentals of machine learning. It might be a good starting point on your journey, as it has been for nearly five million other learners.

Article sources

Springer US. “ How Artificial Intelligence Will Change the Future of Marketing , https://link.springer.com/article/10.1007/s11747-019-00696-0." Accessed March 5, 2024.

Fin.ML. “ News , https://fin-ml.ca/." Accessed March 5, 2024.

Glassdoor. “ Quantitative Research Analyst Salaries in Canada, https://www.glassdoor.ca/Salaries/quantitative-research-analyst-salary-SRCH_KO0,29.htm?clickSource=careerNav.” Accessed March 5, 2024.

Glassdoor. “ Machine Learning Engineer Salaries in Canada, https://www.glassdoor.ca/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm?clickSource=careerNav.” Accessed March 5, 2024.

Glassdoor. “ Machine Learning Modeller Salaries in Canada https://www.glassdoor.ca/Salaries/machine-learning-modeler-salary-SRCH_KO0,24.htm?clickSource=searchBtn.” Accessed March 5, 2024.

Glassdoor. “ Data Scientist In Finance Salaries in Canada , https://www.glassdoor.ca/Salaries/canada-data-scientist-in-finance-salary-SRCH_IL.0,6_IN3_KO7,32.htm?clickSource=searchBtn.” Accessed March 5, 2024.

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Machine Learning Applications in Finance Research

• First Online: 09 March 2021

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• Hyeik Kim 6  

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Machine learning applications in finance research have conspicuously surged in recent years. This chapter overviews machine learning methods and studies their usages in the finance literature. Previous research finds machine learning particularly helpful when estimating the risk premium in asset pricing, assisting market participants in making financial-economic decisions, and making unstructured data accessible for analysis. The mismeasurement problem is discussed as a challenge machine learning faces. Although machine learning in finance research is still preliminary, its pool of potential predictors is far more expansive than those of conventional econometrics, allowing flexibility to push the frontier of finance research.

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For instance, in asset pricing, the goal is to understand the pricing kernel which essentially is understanding how asset prices will move in the future.

Unstructured data are the ones that cannot simply be transformed into an excel sheet. It can be text reports, videos, images, or audio.

Usually, we call it a “good” prediction when the sum of squared residuals or the mean absolute value of residuals are minimized out-of-sample.

Fama and French [ 14 ] initially proposed a three factor model, and then [ 23 ] came up with a four factor, Fama and French [ 15 ] to five, and now we have six factor model from Barillas and Shanken [ 3 ].

This idea became famous from Milton Friedman’s article at the New York Times magazine in 1970. In the article, he says that a corporate executive is the employee of the owners of a (public) company and has a direct responsibility to his employers.

A number of papers have found that human decisions are oftentimes biased. E.g., racial biases (Munnell et al. [ 34 ], Quintanar [ 36 ], Samorani et al. [ 38 ], Kleinberg et al. [ 26 ]).

Abis S (2017) Man vs. machine: Quantitative and discretionary equity management. Unpublished working paper. Columbia Business School

Google Scholar  

Aubry M, Kräussl R, Manso G, Spaenjers C (2019) Machine learning, human experts, and the valuation of real assets. HEC Paris Research Paper No. FIN-2019-1332

Barillas F, Shanken J (2018) Comparing asset pricing models. J Fin 73(2):715–754

Article   Google Scholar  

Bellstam G, Bhagat S, Cookson JA (2019) A text-based analysis of corporate innovation. Available at SSRN 2803232

Berle AA, Means GC (1932) The modern corporation and private property. New Brunswick, NJ

Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8

Boudoukh J, Feldman R, Kogan S, Richardson M (2019) Information, trading, and volatility: Evidence from firm-specific news. Rev Fin Stud 32(3):992–1033

Catalini C, Foster C, Nanda R (2018) Machine intelligence vs. human judgement in new venture finance. Tech. rep., Mimeo

Chen H, De P, Hu YJ, Hwang BH (2014) Wisdom of crowds: The value of stock opinions transmitted through social media. Rev Fin Stud 27(5):1367–1403

Chen L, Pelger M, Zhu J (2020) Internet appendix for deep learning in asset pricing. Available at SSRN 3600206

Chinco A, Clark-Joseph AD, Ye M (2019) Sparse signals in the cross-section of returns. J Fin 74(1):449–492

Cowgill B (2018) Bias and productivity in humans and algorithms: Theory and evidence from resume screening. Columbia Business School, Columbia University, vol 29

Erel I, Stern LH, Tan C, Weisbach MS (2018) Selecting directors using machine learning. Tech. rep, National Bureau of Economic Research

Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Fin Econ

Fama EF, French KR (2015) A five-factor asset pricing model. J Fin Econ 116(1):1–22

Fama EF, MacBeth JD (1973) Risk, return, and equilibrium: Empirical tests. J Polit Econ 81(3):607–636

Feng G, Giglio S, Xiu D (2020) Taming the factor zoo: A test of new factors. J Fin 75(3):1327–1370

Gathergood J, Mahoney N, Stewart N, Weber J (2019) How do individuals repay their debt? the balance-matching heuristic. Am Econ Rev 109(3):844–75

Gu S, Kelly B, Xiu D (2020) Empirical asset pricing via machine learning. Rev Fin Stud 33(5):2223–2273

Hansen S, McMahon M, Prat A (2018) Transparency and deliberation within the FOMC: a computational linguistics approach. Q J Econ 133(2):801–870

Hobson JL, Mayew WJ, Venkatachalam M (2012) Analyzing speech to detect financial misreporting. J Account Res 50(2):349–392

Hoffman M, Kahn LB, Li D (2018) Discretion in hiring. Q J Econ 133(2):765–800

Hou K, Xue C, Zhang L (2015) Digesting anomalies: An investment approach. Rev Fin Stud 28(3):650–705

Hu A, Ma S (2020) Human interactions and financial investment: A video-based approach. Available at SSRN

Kleinberg J, Lakkaraju H, Leskovec J, Ludwig J, Mullainathan S (2018) Human decisions and machine predictions. Q J Econ 133(1):237–293. Oxford University Press

Kleinberg J, Ludwig J, Mullainathan S, Obermeyer Z (2015) Prediction policy problems. Am Econ Rev 105(5):491–95

Kozak S, Nagel S, Santosh S (2020) Shrinking the cross-section. J Fin Econ 135(2):271–292

Li K, Mai F, Shen R, Yan X (2019) Measuring corporate culture using machine learning. Available at SSRN 3256608

Liu CH, Nowak AD, Smith PS (2020) Asymmetric or incomplete information about asset values? Rev Fin Stud 33(7):2898–2936

Lopez de Prado M (2018) Advances in financial machine learning. Wiley

Mayew WJ, Venkatachalam M (2012) The power of voice: Managerial affective states and future firm performance. J Fin 67(1):1–43

Meehl PE (1954) Clinical versus statistical prediction: A theoretical analysis and a review of the evidence

Mullainathan S, Obermeyer Z (2017) Does machine learning automate moral hazard and error? Am Econ Rev 107(5):476–80

Munnell AH, Tootell GM, Browne LE, McEneaney J (1996) Mortgage lending in Boston: interpreting HMDA data. Am Econ Rev 25–53. JSTOR

Obaid K, Pukthuanthong K (2019) A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news. Available at SSRN 3297930

Quintanar SM (2017) Man vs. machine: an investigation of speeding ticket disparities based on gender and race. J Appl Econ 20(1):1–28. Taylor & Francis

Rossi AG (2018) Predicting stock market returns with machine learning. Tech. rep., Working paper

Samorani M, Harris S, Blount LG, Lu H, Santoro MA (2020) Overbooked and overlooked: machine learning and racial bias in medical appointment scheduling. Available at SSRN 3467047

Varian HR (2014) Big data: New tricks for econometrics. J Econ Perspect 28(2):3–28

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Kim, H. (2021). Machine Learning Applications in Finance Research. In: Choi, P.M.S., Huang, S.H. (eds) Fintech with Artificial Intelligence, Big Data, and Blockchain. Blockchain Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-33-6137-9_9

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Machine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition

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This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance.

Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

• ISBN-10 3030410676
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“This volume aims to present a broad yet technical treatment of (ML) algorithms used by financial practitioners and scholars alike. The book fills a large void. This encourages reproducibility as well as learning by doing, which is highly appreciated.” (Guillaume Coqueret, Quantitative Finance, October 15, 2020)

From the Back Cover

About the author.

Paul Bilokon, Ph.D., is CEO and Founder of Thalesians Ltd. Paul has made contributions to mathematical logic, domain theory, and stochastic filtering theory, and, with Abbas Edalat, has published a prestigious LICS paper. He is a member of the British Computer Society, the Institution of Engineering and the European Complex Systems Society.

Matthew Dixon, FRM, Ph.D., is an Assistant Professor of Applied Math at the Illinois Institute of Technology and an Affiliate of the Stuart School of Business. He has published over 20 peer reviewed publications on machine learning and quant finance and has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert. He is Deputy Editor of the Journal of Machine Learning in Financ e , Associate Editor of the AIMS Journal on Dynamics and Games, and is a member of the Advisory Board of the CFA Quantitative Investing Group.

Igor Halperin, Ph.D., is a Research Professor in Financial Engineering at NYU, and an AI Research associate at Fidelity Investments. Igor has published more than 50 scientific articles in machine learning, quantitative finance and theoretic physics. Prior to joining the financial industry, he held postdoctoral positions in theoretical physics at the Technion and the University of British Columbia.

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• Publisher ‏ : ‎ Springer; 1st ed. 2020 edition (July 2, 2020)
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About the authors

Paul Bilokon

CEO and Founder of Thalesians Ltd. Previously served as Director and Head of global credit and core e-trading quants at Deutsche Bank, the teams that he helped set up with Jason Batt and Martin Zinkin. Having also worked at Morgan Stanley (in Andrew Hausler's and Nicholas Zinn's prime brokerage risk), Lehman Brothers (in Anne Sanciaume's FX research and Ronan Dowling's FX quants), Nomura (in FX e-trading, under Martin Zinkin, Abid Zaidi, and Mark Gardner), and Citigroup (first in FX quants, then in electronic credit and rates trading), Paul pioneered electronic trading in credit with Rob Smith and William Osborn at Citigroup.

Paul has graduated from Christ Church, University of Oxford, with a distinction and Best Overall Performance prize. At Oxford he wrote a distinguished project, Bayesian methods for solving estimation and forecasting problems in the high-frequency trading environment, supervised by Daniel Jones. He also graduated twice from Imperial College London. His MSci thesis Visualising the Invisible: Detecting Objects in Quantum Noise Limited Images, supervised by Duncan Fyfe Gillies and Marin van Heel, won him the university's Donald Davis Prize and the British Computing Society SET Award for Student Making Best Use of IT.

Paul's lectures at Imperial College London in machine learning for MSc students in mathematics and finance and his course consistently achieves top rankings among the students.

Paul has made contributions to mathematical logic, domain theory, and stochastic filtering theory, and, with Abbas Edalat, has published a prestigious LICS paper. Paul has co-authored several books: Machine Learning and Big Data with kdb+/q (with Jan Novotny, Aris Galiotos, and Frédéric Délèze, published by Wiley), and Machine Learning in Finance: From Theory to Practice (with Matthew F. Dixon and Igor Halperin, published by Springer). He is currently working on Python, Data Science, and Machine Learning (to be published by World Scientific).

Dr Bilokon is a Member of British Computer Society and Institution of Engineering and Technology.

Paul is a frequent speaker at premier conferences such as Global Derivatives/QuantMinds, WBS QuanTech, AI, and Quantitative Finance conferences, alphascope, LICS, and Domains.

Matthew F Dixon

Matthew Dixon, FRM, Ph.D., is an Associate Professor of Applied Math at the Illinois Institute of Technology and an Affiliate Associate Professor of the Stuart School of Business. He has published over 40 peer reviewed publications on machine learning and quant finance and has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert. He is Risk's 2022 Buy-side Quant of the Year (joint with Igor Halperin). He is also Associate Editor of the AIMS Journal on Dynamics and Games, and is a member of the Advisory Board of the CFA Quantitative Investing Group.

Igor Halperin

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Enhancing real …

Enhancing real estate investment trust return forecasts via machine learning

The article at a glance.

Thies Lindenthal and Kahshin Leow cover the enhancing of real estate investment trust return forecasts by the use of machine learning.

Category: Finance and accounting Insight

by Thies Lindenthal and Kahshin Leow , Department of Land Economy, University of Cambridge

Attempts to predict stock market returns are as old as the stock markets themselves. However, for today’s efficient markets, one would expect predictability to be nearly non-existent. But the reality is more dynamic, driven by an arms race of data availability, empirical innovation and market efficiency.

Enter the brave new world of machine learning (ML), which has ignited another wave of academic research on asset return predictability. ML algorithms are especially adept at handling the complexities of financial data, notably non-linearities and interactions between predictors. The seminal paper by Gu et al (2020) showcased this strength. They found that ML algorithms, such as random forests and neural networks, have an edge over traditional linear models in predicting next month’s return for US stocks. They achieved a modest, yet positive, out-of-sample R2 of 0.3–0.5%.

For bonds, the predictability is an order of magnitude higher. Bianchi et al. (2021) report out-of-sample R2 values as large as 5% for bonds. Additionally, Leippold et al. (2022) indicated that in less-efficient Chinese stock market, the predictive power of ML models reaches out-of-sample R2 values of up to 3%. Notably, these predictions remain economically significant even after accounting for transaction costs.

Kahshin Leow and I contribute to this growing literature with an analysis of Real Estate Investment Trusts (REITs). REITs are interesting to study since their returns depend, ultimately, on the performance of the properties they own. If these assets produce predictable returns then one might also see predictability at the fund level. Using stock market information (CRSP) and a selection of macroeconomic variables, we conduct a horse race in which various empirical models compete to predict the returns of all REITs traded on US exchanges from 1990 to 2022. We find that the predictability of REIT returns sits between that of general stocks and bonds, as our models achieve out-of-sample R2 ranging between 0.5-3%, depending on the selected time frame (Table 1).

Table 1: Monthly out-of-sample REIT-level prediction performance (percentage R2oos)

Notes: This table reports monthly Roos for the entire panel of REITs and stocks using OLS with all variables (OLS), OLS using only size and book-to-market (OLS-2), OLS using only size, book-to-market, and momentum (OLS-3), least absolute shrinkage and selection operator (LASSO), elastic net (ENet), principal component regression (PCR), random forest (RF), gradient boosted regression trees (GBRT), extremely randomised trees (ERT), and neural networks with one to five layers (NN1–NN5). In addition, we add the corresponding numbers for the US stock market as analysed in Gu et al. (2020). All the numbers are expressed as a percentage.

Although the predictive power of these models may appear modest at first glance, their impact on portfolio optimisation is sizeable. Allen et al (2019) emphasised the importance of even slight predictive advantages. In our study, portfolio returns improved significantly when REITs were selected based on predicted performance, both in long-only and long-short portfolios (Figure 1).

Figure 1: Cumulative return of ML portfolios (value weighted)

Notes: This figure shows the cumulative returns of the best performing machine learning portfolios. The portfolios are based on a long-only strategy of holding REITs in the top 30% quantile, and the benchmark portfolio is the weighted index of all REITs in the sample period.

This predictability also enhances the optimisation of mean-variance portfolios, in line with Markowitz’s (1953) demand that “we must have procedures for finding reasonable μi and σij. These procedures should combine statistical techniques and the judgment of practical men”. Our results showed that portfolios where we update stock mean expectations based on ML predictions outperformed naive 1/n portfolios (Table 2).

Are these results large enough to trade on it? We are not sure yet. What we are confident of, however, is that REITs returns are more predictable than previously thought, especially in times of high heterogeneity of REIT returns as, for instance, seen in 2020/21.

Table 2: Performance of machine learning portfolios using mean-variance optimisation

Notes: This table reports the out-of-sample performance measures for the best performing machine learning models using mean-variance optimisation. The naive strategy involves holding a portfolio weight of 1/N in each of the N REITs. In Panel A, the mean-variance portfolios are constrained to long-only positions to allow for an apples-to-apples comparison to the naive 1/N portfolio. In Panel B, the mean-variance portfolios are permitted to take long-short positions. “Avg” : average realised monthly return(%). “Std”: the standard deviation of realised monthly returns(%). “S.R.”: annualised Sharpe ratio. “T-stat”: t-statistic of realised monthly returns. “Skew”: skewness. “Kurt”: kurtosis. “MaxDD”: the portfolio maximum drawdown (%). “Max 1M Loss”: the most extreme negative realised monthly return(%). “Corr”: correlation of realised monthly returns against the naive 1/N portfolio returns.

Article references

Allen, D., Lizieri, C. and Satchell, S. (2019) “In defense of portfolio optimization: what if we can forecast?” Financial Analysts Journal , 75(3): 20-38

Bianchi, D., Buchner, M. and Tamoni, A. (2021) “Bond risk premiums with machine learning.” Review of Financial Studies , 34(2): 1046-1089

Gu, S., Kelly, B. and Xiu, D. (2020) “Empirical asset pricing via machine learning.” Review of Financial Studies , 33(5): 2223-2273

Leippold, M., Wang, Q. and Zhou, W. (2022) “Machine learning in the Chinese stock market.” Journal of Financial Economics , 145(2): 64-82

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• Emerging Markets Institute

Regulation Comes to AI

By chase young, cornell ’24 (contributions by gabriel mallare, cella kamarga, yixuan wu).

EU Parliament Buildings (CC BY-SA 2.0 stevecadman)

Advances in generative AI have captivated businesses and the general public. Tools like  OpenAI’s GPT-4 generate realistic images and create text that mimics that of a human. While many executives have wondered about the best way to apply generative AI to their business, there is now a new wrinkle: regulation. On March 13, the European Parliament passed the Artificial Intelligence Act, which contains rules and regulations for generative AI and other uses of machine learning. The bill represents the most comprehensive effort to regulate artificial intelligence to date, but it will not be the last. India, Mexico, and other emerging markets are considering their own AI regulations.

The Artificial Intelligence Act

The European Union’s bill bans several AI practices including systems that purposely manipulate others; use biometric data to deduce personal information; scrape facial images from the internet; or infer emotions in the workplace. For these practices, fines of 35 million euros or 7% of worldwide turnover are possible. The bill also designates several “high-risk” applications of AI in areas like critical infrastructure, education, employment, and law enforcement. These systems must have risk management and documentation systems, and if they are in violation of the regulations, companies can face fines of 15 million euros or 3% of worldwide turnover. While most companies will have 36 months to become compliant, executives with exposure to the EU should start thinking about their AI or machine learning use cases.

Current Regulation in Emerging Markets

Countries in emerging markets from the BRICS (Brazil, Russia, India, China, and South Africa) to Mexico are actively considering AI regulation. Brazil is evaluating a bill (2338/2023) modeled after the EU bill, banning AI practices including manipulation and social scoring ( risco excessivo ) and designating other AI practices as high-risk (alto risco). Russia announced an AI ethics code . India recently issued guidance on AI, including that platforms should not “permit any bias or discrimination” and that “under-tested/unreliable artificial intelligence models” must have “explicit permission of the government.” India is expected to follow the guidance with formal legislation. China issued “interim measures” for artificial intelligence, delegating responsibility to industry regulators, but it is working on formal legislation that would involve a permit system. Mexico is considering a bill that creates an autonomous council of citizens and technology experts to make decisions relating to AI ethics. As nations continue to prioritize AI in their legislative agendas, businesses should start preparing their strategy to address AI regulation.

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A business may already be using AI in the form of machine learning. However, the EU now labels some specific machine learning uses such as biometrics, education, employment, critical infrastructure, and creditworthiness as “high-risk.” As the EU legislation is likely to influence legislation elsewhere, businesses should audit how they use machine learning and whether their uses would be considered “high-risk.”

Will businesses do more than regulations require?

Businesses can take accountability and commit to ethical AI principles such as promoting transparency, security, and privacy. Notable companies from Google to Walmart have put forth pledges on how they will use AI. A pledge can help reassure the public on how a company is incorporating AI.

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The Emerging Markets Institute will continue to investigate this area, particularly focusing on the degree of consumer protection in different emerging markets and to what degree businesses will be required to align with government interests.

About the Authors

Chase Young, ’24, Cornell Jeb E. Brooks School of Public Policy. Before doing research for the Emerging Markets Institute , Young interned in the global finance and business management program at JPMorgan Chase and was a research intern for the World Bank’s data development group. Young’s research focuses on the implications of new technologies in emerging markets.

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Family, friends, MSU Army ROTC dedicate Cadet Colby Marlow memorial tree

Contact: Allison Matthews

STARKVILLE, Miss.—Close family and friends gathered Thursday [April 18] with Mississippi State’s Army ROTC Bulldog Battalion to remember student Colby L. Marlow , described as someone who “set an example to not just soldiers, but to everyone.”

A memorial tree was dedicated in honor of Marlow, a Carrollton native who was killed in a 2021 automobile accident during his senior year at MSU. The tree stands along George Perry St. near the Sanderson Center, an area ROTC cadets run by frequently during morning “PT” sessions.

Nick Murphy, MSU Army ROTC cadet and senior criminology major from Winona, gave remarks in remembrance of his friend who encouraged him in his own path to joining the military.

“I hope this tree stands as a reminder of the lasting impact that Colby has left on us. Let its roots symbolize the depth of our connection to him; its branches, the reach of his influence; and its leaves, the memories that we will forever cherish in our hearts. May this tree stand tall as a testament to the friendship that Colby shared with everyone … I am extremely grateful that I had the privilege of knowing Colby,” Murphy said.

Professor of Military Science Lt. Col. Jason Posey said Marlow was a “shining example of dedication and leadership” and his “journey was defined by exceptional achievements and unwavering commitment to service.”

“His passion for service extended beyond his military duties. He was actively involved in several organizations, multiple honor societies, the Student Veteran Association and the Ranger Challenge Team. His dedication to excellence and his unwavering commitment to country and community inspired all who had the privilege of knowing him,” Posey said.

Also giving remarks was Marlow’s twin brother Cade Marlow, a first lieutenant whose path to the Mississippi Army National Guard and MSU Army ROTC mirrored his brother’s.

“Despite the short years of life that Colby had on earth, he strived to make the most of every day and live a life of example for everyone. He was a man of integrity, courage and patriotism. As I reflect on the two and a half years since Colby’s passing, I am extremely humbled and proud to see the impact that he made,” said Cade Marlow.

In addition to the memorial tree on campus, Colby Marlow’s legacy is being honored through an endowed scholarship.

“The scholarship will enable Colby’s memory to live on through cadets for years to come. I cannot express how proud I am to be an alumnus of Mississippi State University and its Army ROTC program,” Marlow said.

Learn more about the Cadet Colby Marlow Scholarship at https://www.armyrotc.msstate.edu/scholarships/cadet-colby-marlow .

Click here to view more photos from the tree dedication ceremony.

Mississippi State University is taking care of what matters. Learn more at www.msstate.edu .

Thursday, April 18, 2024 - 3:29 pm

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