Finance and Financial Management Commons^™

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Investigation Into A Practical Application Of Reinforcement Learning For The Stock Market, Philip Traxler, Sadik Aman, Will Rogers, Allyn Okun

SMU Data Science Review

A major problem of the financial industry is the ability to adapt their trading strategies at the same rate the market evolves. This paper proposes a solution using existing Reinforcement Learning libraries to help find new strategies at a practical scale. Using a wide domain of ticker symbols, an algorithm is trained in an environment that better represents reality. The supplied decision-making algorithm is tested using recorded data from the U.S stock market from 2000 through 2022. The results of this research show that existing techniques are statistically better than making decisions at random. With this result, this research shows …

Bayesian Optimization With Switching Cost: Regret Analysis And Lookahead Variants, Peng Liu, Haowei Wang, Wei Qiyu

Research Collection Lee Kong Chian School Of Business

Bayesian Optimization (BO) has recently received increasing attention due to its efficiency in optimizing expensive-to-evaluate functions. For some practical problems, it is essential to consider the path-dependent switching cost between consecutive sampling locations given a total traveling budget. For example, when using a drone to locate cracks in a building wall or search for lost survivors in the wild, the search path needs to be efficiently planned given the limited battery power of the drone. Tackling such problems requires a careful cost-benefit analysis of candidate locations and balancing exploration and exploitation. In this work, we formulate such a problem as …

Three Essays On Climate Finance And Machine Learning In Financial Studies, Huan Kuang

Doctoral Dissertations

This dissertation focuses on climate finance and explore how to incorporate machine learning techniques into financial research. In the first chapter, we focus on climate innovation. Through a novel design to link climate risk and the U.S. firm patents related to climate change mitigation technologies (CCMTs), we find that CCMT innovations generate significant economic value. These innovations are effective in mitigating firms’ carbon risk. We also find that adoption of a new patent classification scheme has promoted more CCMT innovations in the United States. However, we find mixed evidence on firms’ carbon risk and their CCMT innovation activities. Our work …

Essays In Empirical Asset Pricing, Landon James Ross

Arts & Sciences Electronic Theses and Dissertations

This dissertation examines several empirical questions regarding the determiniation of asset prices. The first chapter studies the effect of firm characteristics’ interactions on the cross-section of expected returns via a modified Fama-Macbeth regression suitable for estima- tion problems involving thousands of firm characteristics. The second chapter estimates eco- nomically significant risks from legally required risk disclosures in public companies annual filings via a novel regression specification designed for the estimation of firm characteristics that are both aligned with expected returns and semantically meaningful. The third chapter examines the aggregate financial consequences of firms’ cash holdings for shareholders.

Stock Market Manipulation Detection Using Continuous Wavelet Transform & Machine Learning Classification, Sarah Youssef

Theses and Dissertations

Stock market manipulation detection is important for both investors and regulators. Being able to detect stock manipulation and preventing it gives investors the confidence in the market fairness and integrity. It also helps maintaining liquidity of the stocks and market efficiency. Implementing data mining algorithms in manipulation detection is a relatively recent technique but in the past few years there has been an increasing interest in it's applications in this domain. The benefit of monitoring manipulative trade behavior is that it can be implemented on live feed of stock data, which saves a lot of time in detecting stock price …

An Intelligent Market Cycle Detection System, Michael Azer

Theses and Dissertations

The detection of stock market cycles has attracted the attention of finance scholars and market practitioners. Accurately identifying the direction of a market can significantly increase the returns of investors. Despite this importance, conventional methodologies in the literature have predominantly attempted to evaluate the effect of subsets of factors as precedents to stock market cycles and with little agreement on what constitutes critical factors. There seems to be a lack in the literature for a comprehensive study that examines a multitude of factors at the same time on the S&P500 as the laboratory. Factors are categorized into: political events, economic …

Essays In Corporate Finance And Machine Learning, Manish Jha

Arts & Sciences Electronic Theses and Dissertations

My dissertation focuses on two broad questions. First, why do shareholder’s preferences vary, and how various agents persuade them? And second, how public perceptions about the financial sector and regulations affect economic outcomes? While my research plan contributes to the two distinct fields of literature, a unifying theme of my research is the use of innovative machine learning techniques to overcome the empirical challenges that would typically prevent measuring these sentiments objectively.

In my Chapter 1, I use a supervised machine learning model on mutual fund family’s proxy voting choices to estimate their preferences. I find that hedge fund activists …

Black-Scholes And Neural Networks, Gabriel Adams

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Neural networks have been proven to be universal approximators. We use neural networks to investigate the relationship between the quality of input data and the quality of outputted predictions from a neural network. We show that neural networks perform better on option pricing data with quality data and perform worse with lower quality data.

Scaled Pca: A New Approach To Dimension Reduction, Dashan Huang, Fuwei Jiang, Guoshi Tong, Guofu Zhou

Research Collection Lee Kong Chian School Of Business

The notion that bond risk premium varies with business cycles is challenged once real time macro data are used. In this paper, we argue that the macro factors extracted by using the standard PCA are not the most relevant for forecasting bond risk premium, because the PCA factors are designed to explain the most variation of macro data instead of the variation of bond risk premium. With the latter objective in mind, we propose a scaled PCA (sPCA) approach, which incorporates the information in bond risk premium in the factor extraction procedure. The real time macro sPCA factors have much …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Predicting Intraday Financial Market Dynamics Using Takens' Vectors; Incorporating Causality Testing And Machine Learning Techniques, Abubakar-Sadiq Bouda Abdulai

Electronic Theses and Dissertations

Traditional approaches to predicting financial market dynamics tend to be linear and stationary, whereas financial time series data is increasingly nonlinear and non-stationary. Lately, advances in dynamical systems theory have enabled the extraction of complex dynamics from time series data. These developments include theory of time delay embedding and phase space reconstruction of dynamical systems from a scalar time series. In this thesis, a time delay embedding approach for predicting intraday stock or stock index movement is developed. The approach combines methods of nonlinear time series analysis with those of causality testing, theory of dynamical systems and machine learning (artificial …