Finance and Financial Management Commons^™

An Ai Approach To Measuring Financial Risk, Lining Yu, Wolfgang Karl Hardle, Lukas Borke, Thijs Benschop

Sim Kee Boon Institute for Financial Economics

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here, we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (λ" role="presentation" style="box-sizing: border-box; display: inline; font-style: normal; font-weight: normal; line-height: normal; font-size: 18px; text-indent: 0px; text-align: left; text-transform: none; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">λλ) of a linear quantile lasso regression. The FRM is calculated by taking the average …

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.

Optimal Design And Ownership Structures Of Innovative Retail Payment Systems, Zhiling Guo, Dan Ma

Research Collection School Of Computing and Information Systems

In response to the Fintech trend, an ongoing debate in the banking industry is how to design the new-generation interbank retail payment and settlement system. We propose a two-stage analytical model that takes into account the value-risk tradeoff in the new payment system design, as well as banks’ participation incentives and adoption timing decisions. We find that, as the system base value increases, banks tend to synchronize their investment and adoption decisions. When the system base value is low and banks are heterogeneous, bank association ownership maximizes social welfare. When both the system base value and bank heterogeneity are moderate, …

Quantum Consensus, Jorden Seet, Paul Griffin

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel consensus mechanism utilizing the quantum properties of qubits. This move from classical computing to quantum computing is shown to theoretically enhance the scalability and speed of distributed consensus as well as improve security and be a potential solution for the problem of blockchain interoperability. Using this method may circumvent the common problem known as the Blockchain Trilemma, enhancing scalability and speed without sacrificing de-centralization or byzantine fault tolerance. Consensus speed and scalability is shown by removing the need for multicast responses and exploiting quantum properties to ensure that only a single multicast is …

Automated Theme Search In Ico Whitepapers, Chuanjie Fu, Andrew Koh, Paul Griffin

Research Collection School Of Computing and Information Systems

The authors explore how topic modeling can be used to automate the categorization of initial coin offerings (ICOs) into different topics (e.g., finance, media, information, professional services, health and social, natural resources) based solely on the content within the whitepapers. This tool has been developed by fitting a latent Dirichlet allocation (LDA) model to the text extracted from the ICO whitepapers. After evaluating the automated categorization of whitepapers using statistical and human judgment methods, it is determined that there is enough evidence to conclude that the LDA model appropriately categorizes the ICO whitepapers. The results from a two-population proportion test …

Investing In The New Blue Economy: The Changing Role Of International Development Organizations In Catalyzing Private Sector Investment In Support Of Regional Strategic Action Programmes For The Sustainable Development Of Coasts And Oceans, Ryan Whisnant, Veerle Vandeweerd

Journal of Ocean and Coastal Economics

Over the last 20 years, governments sharing common coastal and ocean ecosystems have developed and agreed on concrete regional action programs to stop and, in some cases, reverse a trend of deteriorating coastal and ocean resources. Implementation of these action programs requires significant investments by the public and private sectors alike, with the potential for substantial economic growth and enhanced social well-being. For this to happen, new institutional arrangements, technologies, and financial vehicles and asset classes are needed to mainstream innovative “blue economy” projects that have the potential to transition economies and communities to more sustainable development paths.

This paper …

Systemic Risk In Financial Networks, Tathagata Banerjee

McKelvey School of Engineering Theses & Dissertations

In this dissertation, I have used the network model based approach to study systemic risk in financial networks. In particular, I have worked on generalized extensions of the Eisenberg--Noe [2001] framework to account for realistic financial situations viz. pricing of corporate debt while accounting for network effects, asset liquidation mechanisms during fire sales, dynamic clearing and impact of contingent payments such as insurance and credit default swaps. First, I present formulas for the valuation of debt and equity of firms in a financial network under comonotonic endowments. I demonstrate that the comonotonic setting provides a lower bound to the price …

Collusion Attacks And Fair Time-Locked Deposits For Fast-Payment Transactions In Bitcoin, Xingjie Yu, Shiwen Michael Thang, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

In Bitcoin network, the distributed storage of multiple copies of the block chain opens up possibilities for doublespending, i.e., a payer issues two separate transactions to two different payees transferring the same coins. While Bitcoin has inherent security mechanism to prevent double-spending attacks, it requires a certain amount of time to detect the doublespending attacks after the transaction has been initiated. Therefore, it is impractical to protect the payees from suffering in double-spending attacks in fast payment scenarios where the time between the exchange of currency and goods or services is shorten to few seconds. Although we cannot prevent double-spending …

Kelly Fraction Estimation For Multiple Correlated Bets, William Chin

International Conference on Gambling & Risk Taking

It is well-known that expected portfolio growth is maximized by maximizing

expected logarithmic utility. This investment criterion is known as Kelly betting.

It has many optimality properties but is considered to be risky. Blackjack

teams and other advantage gamblers practice a fraction of the Kelly optimal to

decrease risk. Some hedge fund managers are thought to practice according to

Kelly principles. We use a continuous multivariate Geometric Brownian motion

model and present an interval estimate for the historical fraction for a portfolio

of correlated bets, possibly including a risk-free asset. Historical data comes

from a range of sources and the …

Portfolio Optimization Methods: The Mean-Variance Approach And The Bayesian Approach, Hoang Nguyen

Honors Theses

This thesis is a discussion on the mean-variance approach to portfolio optimization and an introduction of the Bayesian approach, which is designed to solve certain limitations of the classical mean-variance analysis. The primary goal of portfolio optimization is to achieve the maximum return from investment given a certain level of risk. The mean-variance approach, introduced by Harry Markowitz, sought to solve this optimization problem by analyzing the means and variances of a certain collection of stocks. However, due to its simplicity, the mean-variance approach is subject to various limitations. In this paper, we seek to solve some of these limitations …

Predictive Distributions Via Filtered Historical Simulation For Financial Risk Management, Tyson Clark

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

Filtered historical simulation with an underlying GARCH process can be used as a valuable tool in VaR analysis, as it derives risk estimates that are sensitive to the distributional properties of the historical data of the produced predictive density. I examine the applications to risk analysis that filtered historical simulation can provide, as well as an interpretation of the predictive density as a poor man’s Bayesian posterior distribution. The predictive density allows us to make associated probabilistic statements regarding the results for VaR analysis, giving greater measurement of risk and the ability to maintain the optimal level of risk per …

A Stochastic Control Model For Electricity Producers, Charles William Beer

Theses and Dissertations

Modern electricity pricing models include a strong reversion to a long run mean and a

number of non-local operators to encapsulate the discontinuous price behavior observed in

such markets. However, incorporating non-local processes into a stochastic control problem

presents significant analytical challenges. The motivation for this work is to solve the problem

of optimal control of the burn rate for a coal-powered electricity plant. We first construct a

pricing model that is a good general representative of the class of models currently used for

electricity pricing as well as a model for the supply of fuel to the plant. Under …

Essays On Time Series And Machine Learning Techniques For Risk Management, Michael Kotarinos

USF Tampa Graduate Theses and Dissertations

The Capital Asset Pricing Model combined with the Sharpe ratio is a standard method for choosing assets for selection in a portfolio. However, this method has many structural issues and was designed for a time when high dimensional computing was in its infancy. An alternative to these methods using a mix of Multi-Level Time Series Clustering, the MACBETH algorithm and traditional time series techniques was constructed that minimized data loss and allow for customized portfolio construction for investors with different risk profiles and specialized investment needs. It was shown that these methods are adaptable to cloud computing environments and allow …

A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.

Jennifer L. Priestley

Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.

Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh

Research Collection School Of Computing and Information Systems

The rise of Initial Coin Offerings (ICO) in recent times and their potential for investment opportunities have investors spending a lot of time researching ICOs or having to follow the crowd. This paper aims to explore four broad factors of ICOs: identity, credibility, investor sentiment, and price movement to develop a framework that is useful in determining ICO quality. Research is shown using data sources including public forums, chat groups, web sites, white papers as well as smart contract details. Finally, a system, based on the framework, is proposed that can be used to detect and regulate ICO activities and …

Risk Pooling, Supply Chain Hierarchy, And Analysts' Forecasts, Nan Hu, Jian-Yu Ke, Ling Liu, Yue Zhang

Research Collection School Of Computing and Information Systems

We investigate whether a firm's risk pooling affects its analysts' forecasts, specifically in terms of forecast accuracy and their use of public vs. private information, and how risk pooling interacts with a firm's position in the supply chain to affect analysts' forecasts. We use a social network analysis method to operationalize risk pooling and supply chain hierarchy, and find that risk pooling significantly reduces analysts' forecast errors and increases (decreases) their use of public (private) information. We also find that the positive (negative) relationships between risk pooling and analyst forecast accuracy and analysts' use of public (private) information are more …

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 …

How Ceo Wealth Affects The Riskiness Of A Firm, Sonik Mandal, Charlie Swartz, Sanjib Guha, Carl B. Mcgowan Jr.

Finance Faculty Publications

The objective of this paper is to analyze the relationship between the ownership level of managers and the risk averse behavior of the firm. We measure the ownership level of the managers by the ratio of their ownership of the company relative to their total wealth for a sample of 69 individuals from the Forbes 400 list of the wealthiest individuals in the world for the period from 2001-11 using an unbalanced panel data analysis. The dependent variable is the Altman Z-score of each firm and we further test these relationships using financial leverage. The independent variables are delta and …

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 …

Stock Returns And Investor Sentiment: Textual Analysis And Social Media, Zachary Mcgurk, Adam Nowak, Joshua C. Hall

Economics Faculty Working Papers Series

The behavioral finance literature has found that investor sentiment has predictive ability for equity returns. This differs from standard finance theory, which provides no role for investor sentiment. We examine the relationship between investor sentiment and stock returns by employing textual analysis on social media posts. We find that our investor sentiment measure has a positive and significant effect on abnormal stock returns. These findings are consistent across a number of different models and specifications, providing further evidence against non-behavioral theories.

Strategies For Small Energy Consulting Business Survivability, Scholastica Nwabueze

Walden Dissertations and Doctoral Studies

Small businesses are critical to economic development. Small businesses create job opportunities and training and innovative products and services, but they encounter significant challenges and many fail in the first 7 years due to lack of sustainable strategies. The purpose of this single case study was to explore strategies owners used to sustain small energy consulting businesses for longer than 7 years. The population in this study consisted of 5 senior executives of a small energy consulting firm in the Washington, DC, metropolitan area. The conceptual framework for the study was the transformational leadership theory that deals with vision and …

Effective Revenue Diversification Strategies In Nonprofit Organizations, Jennifer R. Niswonger

Walden Dissertations and Doctoral Studies

Nonprofit organization leaders increasingly encounter social burdens and financial difficulties, jeopardizing ongoing success and organizational sustainability. The purpose of this single-case study was to explore revenue diversification strategies used by 3 leaders of a small nonprofit organization in the mid-Atlantic region of the United States through the conceptual lens of modern portfolio theory. Data were collected via in-depth semistructured interviews, and member checking was used to facilitate accuracy, consistency, and integrity. Methodological triangulation included a document review and analysis of financial statements, tax returns, strategy objectives, the organizational website, social media, and nonprofit data reports. Data from documents and interviews …

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, …

Success Factors For Power Project Development Businesses In Sub-Saharan Africa, Kodjo Galevissi Afidegnon

Walden Dissertations and Doctoral Studies

Despite the financing gap in the sub-Saharan Africa power sector, private investors struggle to capitalize on the opportunity because of the high failure rate of power project development companies. Using the conceptual framework of the behavioral finance theory, this multiple case study was conducted to explore the strategies used by executives of 4 companies in sub-Saharan Africa who successfully developed power projects within the last 5 years. Data were collected from semistructured interviews and a review of government and institutions' websites. Yin's 5-phased cycle for analyzing case studies provided the guidelines for data analysis. Three themes emerged from data analysis: …

Estimation Of Multivariate Asset Models With Jumps, Angela Loregian, Laura Ballotta, Gianluca Gianluca Fusai, Marcos Fabricio Perez

Business Faculty Publications

We propose a consistent and computationally efficient two-step methodology for the estimation of multidimensional non-Gaussian asset models built using Levy processes. The proposed framework allows for dependence between assets and different tail behaviors and jump structures for each asset. Our procedure can be applied to portfolios with a large number of assets as it is immune to estimation dimensionality problems. Simulations show good finite sample properties and significant efficiency gains. This method is especially relevant for risk management purposes such as, for example, the computation of portfolio Value at Risk and intra-horizon Value at Risk, as we show in detail …

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 …

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 …

Strategies To Sustain A Real Estate Small Business In Postnatural-Disaster Area, Vianka Miranda

Walden Dissertations and Doctoral Studies

Many small real estate business leaders lack effective strategies to resume and sustain operations in a postnatural-disaster environment. This multiple case study investigated strategies that 3 small real estate business leaders in southeastern Louisiana used to resume and sustain operations in the aftermath of a natural disaster. The chaos theory and stakeholder theory were the conceptual frameworks for this study. Data were collected from semistructured interviews, and reviews of business continuity plan documents and member checking. The themes that emerged from data analysis were leaders' strategies relating to business planning and innovation, stakeholder engagement, operations management, and disaster responsiveness. Implications …

Climate Risks And Market Efficiency, Harrison Hong, Frank Weikai Li, Jiangmin Xu

Research Collection Lee Kong Chian School Of Business

Climate science finds that the trend towards higher global temperatures exacerbates the risks of droughts. We investigate whether the prices of food stocks efficiently discount these risks. Using data from thirty-one countries with publicly-traded food companies, we rank these countries each year based on their long-term trends toward droughts using the Palmer Drought Severity Index. A poor trend ranking for a country forecasts relatively poor profit growth for food companies in that country. It also forecasts relatively poor food stock returns in that country. This return predictability is consistent with food stock prices underreacting to climate change risks.