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 …

Representation Learning In Finance, Ajim Uddin

Dissertations

Finance studies often employ heterogeneous datasets from different sources with different structures and frequencies. Some data are noisy, sparse, and unbalanced with missing values; some are unstructured, containing text or networks. Traditional techniques often struggle to combine and effectively extract information from these datasets. This work explores representation learning as a proven machine learning technique in learning informative embedding from complex, noisy, and dynamic financial data. This dissertation proposes novel factorization algorithms and network modeling techniques to learn the local and global representation of data in two specific financial applications: analysts’ earnings forecasts and asset pricing.

Financial analysts’ earnings forecast …

A Machine Learning Approach To Stochastic Optimal Control, Pablo Ever Avalos

Open Access Theses & Dissertations

Merton's portfolio optimization problem is a well-renowned problem in financial mathematics which seeks to optimize the investment decision for an investor. In the simplest situation, the market consists of a risk-less asset (i.e. a bond) that pays back a relatively low interest rate, and a risky asset (i.e. a stock) that follows a geometric Brownian motion. The optimal allocation strategy of the investor's wealth is found by optimizing the expected utility along the stochastic evolution of the market. This thesis focuses on several different applications of this optimization problem. We look at pre-constructed analytical solutions and showcase the results. We …

Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii

Undergraduate Honors Theses

Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 portfolios. …

Strategies Business Leaders Use To Mitigate Online Credit Card Fraud, Clarissa Rosario-Tavarez

Walden Dissertations and Doctoral Studies

Online credit card fraud targeting banks, customers, and businesses costs millions of U.S. dollars annually. Online business leaders face challenges securing and regulating the online payment processing environment. Grounded in the situational crime prevention theory, the purpose of this qualitative multiple case study was to explore strategies online business leaders use to mitigate the loss of revenue caused by online credit card fraud. The participants comprised five online business leaders of an organization in the Southwest of the United States, who implemented strategies that successfully mitigated revenue losses due to online credit card fraud. The data were collected from semistructured …

Stock Markets Performance During A Pandemic: How Contagious Is Covid-19?, Yara Abushahba

Theses and Dissertations

Background and Motivation: The coronavirus (“COVID-19”) pandemic, the subsequent policies and lockdowns have unarguably led to an unprecedented fluid circumstance worldwide. The panic and fluctuations in the stock markets were unparalleled. It is inarguable that real-time availability of news and social media platforms like Twitter played a vital role in driving the investors’ sentiment during such global shock.

Purpose:The purpose of this thesis is to study how the investor sentiment in relation to COVID-19 pandemic influenced stock markets globally and how stock markets globally are integrated and contagious. We analyze COVID-19 sentiment through the Twitter posts and investigate its …

Consumers Perspectives On Using Biometric Technology With Mobile Banking, Rodney Alston Clark

Walden Dissertations and Doctoral Studies

The need for applying biometric technology in mobile banking is increasing due to emerging security issues, and many banks’ chief executive officers have integrated biometric solutions into their mobile application protocols to address these evolving security risks. This quantitative study was performed to evaluate how the opinions and beliefs of banking customers in the Mid-Atlantic region of the United States might influence their adoption of mobile banking applications that included biometric technology. The research question was designed to explore how performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), perceived credibility (PC), and task-technology fit (TTF) affected …

Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring, Yan Wang

Doctor of Data Science and Analytics Dissertations

In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) …

The Adoption Of Cryptocurrency Technology Into The Us Banking Infrastructure, Trevor Melito

Senior Theses

This thesis examines the possibility of using Blockchain technology to permanently change the payment structure of the US banking system. First, I examine the current technology that dominates the banking sector. I introduce the most frequently used payments methods including Automatic Clearing House transfers and wire transfers, both domestically and internationally. In addition, I highlight the major players controlling these transactions. Under the current system, frictions between senders and receivers cause billions of dollars in losses each year.

Next, I examine Blockchain’s roots along with some similar cryptocurrency technology, namely Distributed Ledger Technology and Smart Contracts. The transparency, security, and …

Preparing For The Future: The Effects Of Financial Literacy On Financial Planning For Young Professionals, Tanay Singh

Senior Theses

Purpose – Many people between the age of 20 and 34 have not considered planning financially for the future in any significant capacity and in doing so, they limit their potential savings. The purpose of this study is to examine what financial expectations are for people in the early stages of their career and determine if improving financial literacy and revealing financial realities helps to produce more accurate or realistic expectations. Ultimately, the goal is to better prepare participants in the study for the working world and increased responsibilities outside of the college/university environment by getting them to start thinking …

Factors Affecting Electronic Banking Adoption In Barbados, Jacqueline Delores Bend

Walden Dissertations and Doctoral Studies

The low rate of customers' adoption of electronic banking services affects retail banks' profitability. The operating cost for a financial transaction performed by bank tellers averages US$1.07 compared to US$0.01 using electronic banking channels. It is paramount for retail banking leaders to understand the factors influencing customer adoption of electronic banking to sustain competitive advantage. Grounded in the technology acceptance model framework, the purpose of this quantitative correlational study was to examine the relationship between perceived usefulness (PU), perceived ease of use (PEOU), and customer adoption of electronic banking in Barbados. The validated technology acceptance model survey instrument was used …

Enterprise Resource Planning Implementation In Higher Education: Cost Containment Strategies, Tysha K. Tolefree

Walden Dissertations and Doctoral Studies

Lack of effective cost containment strategies to support enterprise resource planning (ERP) system implementations within higher education institutions (HEIs) result in budget overruns 50% of the time. Grounded in Gartner’s IT cost containment techniques, the purpose of this qualitative multiple case study was to explore strategies HEI project directors use to support a successful ERP implementation on time and within budget. The participants comprised 5 project directors and managers from HEIs and another public organization in the state of Washington. Data were collected from semistructured interviews, archival data, and organization documents. Thematic analysis was used to analyze the data, and …

Deep Reinforcement Learning Pairs Trading, Andrew Brim

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

This research applies a deep reinforcement learning technique, Deep Q-network, to a stock market pairs trading strategy for profit. Artificial intelligent methods have long since been applied to optimize trading strategies. This work trains and tests a DQN to trade co-integrated stock market prices, in a pairs trading strategy. The results demonstrate the DQN is able to consistently produce positive returns when executing a pairs trading strategy.

Incorporating Data Governance Frameworks In The Financial Industry, Tarlochan Singh Randhawa

Walden Dissertations and Doctoral Studies

Data governance frameworks are critical to reducing operational costs and risks in the financial industry. Corporate data managers face challenges when implementing data governance frameworks. The purpose of this multiple case study was to explore the strategies that successful corporate data managers in some banks in the United States used to implement data governance frameworks to reduce operational costs and risks. The participants were 7 corporate data managers from 3 banks in North Carolina and New York. Servant leadership theory provided the conceptual framework for the study. Methodological triangulation involved assessment of nonconfidential bank documentation on the data governance framework, …

Adapting Financial Technology Standards To Blockchain Platforms, Gabriel Bello

Theses and Dissertations

Traditional payment systems have standards designed to keep transaction data secure, but blockchain systems are not in scope for such security standards. We compare the Payment Application Data Security Standard’s (PA-DSS) applicability towards transaction-supported blockchain platforms to test the standard’s applicability. By highlighting the differences in implementation on traditional and decentralized transaction platforms, we critique and adapt the standards to fit the decentralized model. In two case studies, we analyze the QTUM and Ethereum blockchain platforms’ industry compliance, as their payment platforms support transactions equivalent to that of applications governed by the PA-DSS. We determine QTUM’s and Ethereum’s capabilities to …

Regression Tree Construction For Reinforcement Learning Problems With A General Action Space, Anthony S. Bush Jr

Electronic Theses and Dissertations

Part of the implementation of Reinforcement Learning is constructing a regression of values against states and actions and using that regression model to optimize over actions for a given state. One such common regression technique is that of a decision tree; or in the case of continuous input, a regression tree. In such a case, we fix the states and optimize over actions; however, standard regression trees do not easily optimize over a subset of the input variables\cite{Card1993}. The technique we propose in this thesis is a hybrid of regression trees and kernel regression. First, a regression tree splits over …

Procure-To-Pay Software In The Digital Age: An Exploration And Analysis Of Efficiency Gains And Cybersecurity Risks In Modern Procurement Systems, Drew Lane

MPA/MPP/MPFM Capstone Projects

Procure-to-Pay (P2P) softwares are an integral part of the payment and procurement processing functions at large-scale governmental institutions. These softwares house all of the financial functions related to procurement, accounts payable, and often human resources, helping to facilitate and automate the process from initiation of a payment or purchase, to the actual disbursal of funds. Often, these softwares contain budgeting and financial reporting tools as part of the offering. As such an integral part of the financial process, these softwares obviously come at an immense cost from a set of reputable vendors. In the case of government, these vendors mainly …

Some Applications Of Higher-Order Hidden Markov Models In The Exotic Commodity Markets, Heng Xiong

Electronic Thesis and Dissertation Repository

The liberalisation of regional and global commodity markets over the last several decades resulted in certain commodity price behaviours that require new modelling and estimation approaches. Such new approaches have important implications to the valuation and utilisation of commodity derivatives. Derivatives are becoming increasingly crucial for market participants in hedging their exposure to volatile price swings and in managing risks associated with derivative trading. The modelling of commodity-based variables is an integral part of risk management and optimal-investment strategies for commodity-linked portfolios. The characteristics of commodity price evolution cannot be captured sufficiently by one-state driven models even with the inclusion …

Equity Trading Evaluation Strategies In Switzerland After The European Mifid Ii, Linn Kristina Karstadt

Walden Dissertations and Doctoral Studies

Swiss bank traders are affected by technological and regulatory challenges, which may affect their broker voting process and may result in a change of trading and evaluation behavior in 2018. Compounded challenges exist when broker evaluation strategies are not effective or Markets in Financial Instruments Directive (MiFID) II compliant. This qualitative, single case study, built on efficient capital market hypothesis and innovative disruption theory, was focused on effective broker evaluation strategies after MiFID II in Switzerland. The sample consisted of 4 buy-side traders, who shared their unique perspectives. Methodological triangulation was achieved through semistructured interviews, a review of the institution's …

Digital Strategies Senior Bank Executives In Mauritius Use To Improve Customer Service, Sailesh Sewpaul

Walden Dissertations and Doctoral Studies

Customers' use of digital banking has reshaped traditional banking, and senior level bank executives must know how to leverage this innovation to improve customer service to increase profitability. Using the technology acceptance model as the conceptual framework, the purpose of this multiple case study was to explore effective digital banking strategies that senior level executives used to improve customer service to increase profitability. The target population for this study included senior-level executives from 3 banks in Mauritius possessing successful development and implementation experience in digital banking strategies to improve customer service. Data were collected through semistructured interviews and organizational documents, …

Multi-Step Tokenization Of Automated Clearing House Payment Transactions, Privin Alexander

USF Tampa Graduate Theses and Dissertations

Since its beginnings in 1974, the Automated Clearing House (ACH) network has grown into one of the largest, safest, and most efficient payment systems in the world. An ACH transaction is an electronic funds transfer between bank accounts using a batch processing system.

Currently, the ACH Network moves almost $43 trillion and 25 billion electronic financial transactions each year. With the increasing movement toward an electronic, interconnected and mobile infrastructure, it is critical that electronic payments work safely and efficiently for all users. ACH transactions carry sensitive data, such as a consumer's name, account number, tax identification number, account holder …

Offshore Outsourcing Of The United States Semiconductor Manufacturing: Management Approaches And Strategies, Oscar Mostofi

Walden Dissertations and Doctoral Studies

The United States manufacturing employment decreased 33% from 1985 to 2014. During the same period, the United States semiconductor manufacturing, accounting for 1.7% of the total of the United States manufacturing workforce, lost 35% of its employees. The decline in semiconductor manufacturing jobs began in 1985 when semiconductor firms began offshoring product manufacturing overseas because of low cost of qualified labor force and facilities. This qualitative case study explored the analytical approaches and strategies business leaders of semiconductor firms that offshore manufacturing use in making informed strategic outsourcing and offshoring decisions conducive to sustainability and profitability of operations. The location …

Optimal Monitoring And Mitigation Of Systemic Risk In Lending Networks, Zhang Li

Open Access Dissertations

This thesis proposes optimal policies to manage systemic risk in financial networks. Given a one-period borrower-lender network in which all debts are due at the same time and have the same seniority, we address the problem of allocating a fixed amount of cash among the nodes to minimize the weighted sum of unpaid liabilities. Assuming all the loan amounts and cash flows are fixed and that there are no bankruptcy costs, we show that this problem is equivalent to a linear program. We develop a duality-based distributed algorithm to solve it which is useful for applications where it is desirable …

An Analysis Of The Relationship Between Security Information Technology Enhancements And Computer Security Breaches And Incidents, Linda Betz

CCE Theses and Dissertations

Financial services institutions maintain large amounts of data that include both intellectual property and personally identifiable information for employees and customers. Due to the potential damage to individuals, government regulators hold institutions accountable for ensuring that personal data are protected and require reporting of data security breaches. No company wants a data breach, but finding a security incident or breach early in the attack cycle may decrease the damage or data loss a company experiences. In multiple high profile data breaches reported in major news stories over the past few years, there is a pattern of the adversary being inside …

Automatically Defined Templates For Improved Prediction Of Non-Stationary, Nonlinear Time Series In Genetic Programming, David Moskowitz

CCE Theses and Dissertations

Soft methods of artificial intelligence are often used in the prediction of non-deterministic time series that cannot be modeled using standard econometric methods. These series, such as occur in finance, often undergo changes to their underlying data generation process resulting in inaccurate approximations or requiring additional human judgment and input in the process, hindering the potential for automated solutions.

Genetic programming (GP) is a class of nature-inspired algorithms that aims to evolve a population of computer programs to solve a target problem. GP has been applied to time series prediction in finance and other domains. However, most GP-based approaches to …

Establishing Mobile Financial Services In Ethiopia, James R. Kanagwa

Walden Dissertations and Doctoral Studies

Mobile phone service is increasing among low income populations; however, with over 1 billion mobile service users worldwide, many people still lack banking services. Banks do not reach out to the poor because of the high operational costs involved. Scholars and industry practitioners have indicated that mobile phones could be an alternative channel for delivering financial services to the less advantaged and unbanked, without requiring a traditional bank with a branch network. The purpose of this bounded case study was to explore the strategies bank managers used to implement the new mobile banking service to the Ethiopian community. The new …

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 …

Effects Of Investor Sentiment Using Social Media On Corporate Financial Distress, Tarek Hoteit

Walden Dissertations and Doctoral Studies

The mainstream quantitative models in the finance literature have been ineffective in detecting possible bankruptcies during the 2007 to 2009 financial crisis. Coinciding with the same period, various researchers suggested that sentiments in social media can predict future events. The purpose of the study was to examine the relationship between investor sentiment within the social media and the financial distress of firms Grounded on the social amplification of risk framework that shows the media as an amplified channel for risk events, the central hypothesis of the study was that investor sentiments in the social media could predict t he level …

Three Essays On Opinion Mining Of Social Media Texts, Shuyuan Deng

Theses and Dissertations

This dissertation research is a collection of three essays on opinion mining of social media texts. I explore different theoretical and methodological perspectives in this inquiry. The first essay focuses on improving lexicon-based sentiment classification. I propose a method to automatically generate a sentiment lexicon that incorporates knowledge from both the language domain and the content domain. This method learns word associations from a large unannotated corpus. These associations are used to identify new sentiment words. Using a Twitter data set containing 743,069 tweets related to the stock market, I show that the sentiment lexicons generated using the proposed method …

Indefinite Knapsack Separable Quadratic Programming: Methods And Applications, Jaehwan Jeong

Doctoral Dissertations

Quadratic programming (QP) has received significant consideration due to an extensive list of applications. Although polynomial time algorithms for the convex case have been developed, the solution of large scale QPs is challenging due to the computer memory and speed limitations. Moreover, if the QP is nonconvex or includes integer variables, the problem is NP-hard. Therefore, no known algorithm can solve such QPs efficiently. Alternatively, row-aggregation and diagonalization techniques have been developed to solve QP by a sub-problem, knapsack separable QP (KSQP), which has a separable objective function and is constrained by a single knapsack linear constraint and box constraints. …