dissertations on algorithmic trading

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Algorithmic trading strategies

Robert L Kissell , Fordham University

This dissertation presents the necessary techniques and framework to enable investors to make appropriate algorithmic trading decisions given the trading goals and investment objectives of the fund. These techniques can utilized by financial institutions, banks, hedge funds, sell-side broker-dealers, and corporations to improve implementation execution decisions, reduce trading costs, manage trading risk, and increase portfolio returns. The dissertation will introduce necessary mathematical models (namely, market impact and timing risk) to evaluate and compare alternative execution strategies, determine appropriate algorithms and algorithmic parameters, and construct more efficient portfolios. A multi-period trade schedule optimization technique is presented to determine "optimal" execution strategies for baskets of stocks in an amount of time that can be useful for investors (namely, minutes compared to hours or more). The dissertation aims to provide transparency and structure to a currently undisciplined field. This dissertation provides several key contributions to finance. They are: a market impact modeling technique, an efficient multi-period trade schedule optimization formulation, a real-time risk management technique, and an algorithmic decision-making framework. It also provides insight into improving portfolio performance through proper treatment of market impact and trading risk in the portfolio construction phase of the investment cycle.

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Kissell, Robert L, "Algorithmic trading strategies" (2006). ETD Collection for Fordham University . AAI3216918. https://research.library.fordham.edu/dissertations/AAI3216918

Since August 24, 2007

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Machine Learning in Algorithmic Trading leading to Reinforced Deep Kalman Filters

• Ferreira_Tadeu_Augusto_202011_PhD_thesis.pdf

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University of Illinois Urbana-Champaign

A thesis on algorithmic trading

Yu, yingjie.

https://hdl.handle.net/2142/78679 Copy

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• Algorithmic trading
• Dynamic programming

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Literature Review on Algorithmic Trading in Financial Markets

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Showing result 1 - 5 of 6 swedish dissertations containing the words algorithmic trading .

1. Efficient Trading in the Short-term Electricity Markets for Integration of Renewable Energy Sources : Multistage Stochastic and Agent-based Modeling Approaches for Continuous Intraday Electricity Market

Author : Priyanka Shinde ; Mikael Amelin ; Lennart Söder ; Nikolaos Paterakis ; KTH ; [] Keywords : TEKNIK OCH TEKNOLOGIER ; ENGINEERING AND TECHNOLOGY ; TEKNIK OCH TEKNOLOGIER ; ENGINEERING AND TECHNOLOGY ; Continuous intraday electricity market ; Virtual power plant ; Trading strategy ; Stochastic dual dynamic program ; Agent-based modeling ; Adaptive learning ; Renewable energy sources ; Cross-border intraday trading ; Flow-based market coupling ; Balancing market ; Imbalance settlement cost ; Randomized progressive hedging ; Multistage stochastic programming ; Energy storage ; Intraday prices ; Intraday price analysis ; Time series analysis ; Kontinuerliga intradagmarknaden ; Virtuellt kraftverk ; Handelsstrategi ; Stokastisk dual dynamisk programmering ; Agentbaserad modellering ; Adaptivt l¨arande ; F¨ornybara energik¨allor ; Gr¨ans¨overskridande intradaghandel ; Fl¨odesbaserad marknadskoppling ; Balansmarknaden ; Balansavr¨akningskostnader ; Randomiserad progressive hedging ; Stokastisk multiskedesprogrammering ; Energilagring ; Intradagpriser ; Intradagprisanalys ; Tidsserieanalys ; Electrical Engineering ; Elektro- och systemteknik ;

Abstract : This thesis investigates the role of different short-term electricity market design aspects that can facilitate better coordination of resources within the power system. The work also emphasizes on better cross-border integration of the short-term markets to improve the market liquidity, competition, social welfare, and flexibility in the system, which is essential for facilitating the integration of renewable sources. READ MORE

2. Reactive Concurrent Data Structures and Algorithms for Synchronization

Author : Phuong Ha ; Chalmers tekniska högskola ; [] Keywords : NATURVETENSKAP ; NATURAL SCIENCES ; non-blocking ; reactive ; multi-word atomic primitives ; online algorithms ; shared memory ; concurrent data structures ; distributed data structures ; randomization ; online financial problems ; synchronization ; spin-locks ;

Abstract : Parallelism plays a significant role in high-performance computing systems, from large clusters of computers to chip-multithreading (CMT) processors. Performance of the parallel systems comes not only from concurrently runningmore processing hardware but also from utilizing the hardware efficiently. READ MORE

3. Gated Bayesian Networks

Author : Marcus Bendtsen ; Jose M. Peña ; Nahid Shahmehri ; Helge Langseth ; Linköpings universitet ; [] Keywords : NATURVETENSKAP ; NATURAL SCIENCES ; NATURVETENSKAP ; NATURAL SCIENCES ;

Abstract : Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. READ MORE

4. Generative models of limit order books

Author : Hanna Hultin ; Henrik Hult ; Erik Lindström ; KTH ; [] Keywords : NATURVETENSKAP ; NATURAL SCIENCES ; Matematik ; Mathematics ;

Abstract : In this thesis generative models in machine learning are developed with the overall aim to improve methods for algorithmic trading on high-frequency electronic exchanges based on limit order books. The thesis consists of two papers. READ MORE

5. Approximation and Calibration of Stochastic Processes in Finance

Author : Jonas Kiessling ; Anders Szepessy ; Jonathan Goodman ; KTH ; [] Keywords : NATURVETENSKAP ; NATURAL SCIENCES ; MATHEMATICS ; MATEMATIK ;

Abstract : This thesis is a study of approximation and calibration of stochastic processes with applications in finance. It consists of an introduction and four research papers. The introduction is as an overview of the role of mathematics incertain areas of finance. READ MORE

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A thesis on algorithmic trading

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So I decided to write my undergrad thesis on algo trading. I am however struggling to zero in on a specific topic that would be interesting for me to research while also not being overly complicated as algo trading often gets. I have a background in machine learning, so I'm comfortable with theory, but nothing too advanced, beyond the basic algorithm structures (SVM, random forest, etc.)

All ideas I have are either too broad or would require a service like Refinitiv or Bloomberg , which I don't have access to because of the lockdown, which is why I am struggling.

I'd be extremely happy to hear any ideas or suggestions you might have. Every thought is welcome. Thanks in advance!

Well, what part of technical analysis interests you the most? I'm not an expert on the subject, but wouldn't algorithmic trading just be programs that implement technical analysis strategies? Pick your favorite momentum strategy and write a Python script that implements it, and boom. "Algorithmic trading."

If you are talking about algo trading in terms of market making and order matching instead of an algorithm built by a quant fund to pick stocks, maybe you could think about how dark pools affect drastic momentum swings in stock prices. Just a idea (don't know if you can do a whole thesis on it) but what you think?

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Soft Material and Algorithmic Innovations for the Improvement of Existing and the Development of Future Wearable Devices

 Wearable electronic devices are growing in popularity because of their potential to portably interface with the human body and provide continuous user-specific sensing and feedback in new and unregulated environments. However, the algorithmic shortcomings and the rigid, bulky, or tethered form factors of wearable devices often cause them to be difficult to use and adapt poorly to various body types and environments, which limits their adoption. To unlock the full potential of wearable devices to be easy-to-use, portable, and accurate across users and environments, both material and algorithmic innovations are needed to develop wearable devices that are smarter and softer. To develop smarter wearable devices, studies have investigated novel integration strategies, sensor fusion algorithms, and the application of machine learning to improve wearable device usability and the robustness of on-body sensing methods. To develop softer wearable devices, studies have investigated new electrically, magnetically, and thermally responsive soft material architectures to enable new approaches toward sensing and actuation. In this dissertation, I consider these approaches and commonly utilized components for wearable devices such as inertial and magnetic sensors, magnetic materials, and optical sensors, and investigate algorithmic and material approaches to improve the usability, form factor, and adaptability of technology for wearable devices. This dissertation focuses on the introduction and investigation of (1) an auto-establishing kinematic framework for a network of wireless inertial sensors, (2) a magnetically responsive elastomer thin film that has two configurations of deformation in response to the state of an electropermanent magnet, and (3) a soft deformable magnetic ring system that can be used to measure normal and shear displacement on rigid and compliant substrates. Together, this work explores algorithmic and soft material innovations to take steps toward improving wearable device technology and further encourage their widespread adoption. 

Degree Type

• Dissertation
• Mechanical Engineering

Degree Name

• Doctor of Philosophy (PhD)

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