Finance and Financial Management Commons^™

An Analysis Of Drivers Of The Federal Funds Rate, Stephen Johnson, Neha Dixit, Martin Selzer Ph.D.

SMU Data Science Review

The Federal Funds Rate (FFR) is a tool used by the Federal Reserve to set monetary policy on borrowing costs for consumers and businesses. The Fed’s primary motivation with the FFR is to control macroeconomic factors such as inflation and unemployment. Over time, policy stances for the Fed have varied in response to events such as the Great Recession, and more recently the COVID-19 pandemic. In light of the Fed’s actions following these events, there is intensified debate over which macroeconomic factors should be prioritized, and what magnitude of change is sufficient to warrant action. Additionally, when action is taken …

Pooling And Winsorizing Machine Learning Forecasts To Predict Stock Returns With High-Dimensional Data, Erik Mekelburg, Jack Strauss

Finance: Faculty Scholarship

We evaluate US market return predictability using a novel data set of several hundred ag- gregated firm-level characteristics. We apply LASSO, Elastic Net, Random Forest, Neural Net, Extreme Gradient Boosting, and Light Gradient Boosting Machine methods and find these models experience large prediction errors that lead to forecast failures. However, winsorizing and pooling machine learning model forecasts provides consistent out-of-sample predictability. To assess robustness, we apply machine learning methods to high-dimensional data for Canada, China, Germany and the UK as well as the Goyal-Welch data. All machine learning models we consider, except for the ensemble pooled methods, fail to significantly …

Notes And Thoughts On Retrieving Historical Members Of The S&P 500 From Wrds, Lip Hwe Tee

Research Collection Library

Retrieving all historical members or constituents of the S&P 500 can be done using Python coding on the CRSP dataset through an API connection with WRDS (Wharton Research Data Services (WRDS), n.d. -f; WRDS, n.d. -e).

Machine Learning As Arbitrage: Can Economics Help Explain Ai?, Huahao Lu, Matthew Spiegel, Hong Zhang

Sim Kee Boon Institute for Financial Economics

Machine learning algorithms have shown to be remarkably successful tools for predicting asset returns. However, the underlying economic mechanisms behind their performance remain unclear. This paper proposes a model-based dynamic arbitrage trading strategy that combines economic and statistical nonstationarity to demystify this black box. In predicting stock returns based on 153 firm characteristics (anomalies), our strategy ranks anomalies similarly to neural networks in the cross-section. Overall, it accounts for approximately 87.9 bps monthly alphas of the high-minus-low portfolios selected by neural networks in the time series. When unpublished anomalies and microcap stocks are excluded from trading, this strategy can fully …

Essays On Corporate Finance, Su Hee Yun

Dissertations and Theses Collection (Open Access)

Chapter 1. The impact of ESG disasters on Green and Brown firms

I investigate the effect of a firm’s prior ESG reputation on the market impact of ESG incidents. I find that firms with a better ESG reputation, i.e., higher ESG ratings, experience less negative stock-market reactions and analysts' forecast revisions compared to firms with a poorer ESG reputation. But managers of Greener firms, when producing earnings guidance, do not forecast a lower impact of these incidents on future earnings. Similarly, actual decreases in future earnings following these incidents are not significantly different between Green and Brown firms. Altogether, the …

The Impact Of Instrumental Attribution In Ai-Enabled Monitoring On Counterproductive Work Behavior, Qiang Zhang

Dissertations and Theses Collection (Open Access)

AI-enabled monitoring tools are theoretically expected to suppress unethical employee behavior. However, in practice, employees may perceive such monitoring as being driven by leaders' instrumental motives, primarily focused on personal performance evaluation and self-interest. This perception can foster feelings of job insecurity and moral disengagement, ultimately leading to counterproductive work behavior (CWB), which includes unethical employee behavior and turnover. These outcomes may undermine the intended effectiveness of AI-enabled monitoring tools. This study aims to explore the impact of Instrumental Attribution in AIenabled Monitoring (IAAIM) on CWB, specifically focusing on unethical employee behavior and turnover, through both theoretical and empirical lenses. …

Research On The Influence Mechanism Of Value Co-Creation On Enterprise Performance: Empirical Evidence From China's Motorsport Culture Industry, Xiaodong Weng

Dissertations and Theses Collection (Open Access)

We are in an era of digital economy and enterprise change management. The application of digital technologies such as Artificial Intelligence (AI), Blockchain, Cloud Computing, and Big Data (collectively known as ABCD technologies) has increased the competition in the value chain and value network between customers and enterprises. Users can now intervene in product design, transaction, and feedback before, during, and after the process, breaking through the limitations of time and space. They leverage their low-cost advantage to draw attention, shifting from a traditional, unidirectional supply logic to a value co-creation logic. This influences both enterprises and customers to produce …

Advancing Sustainable Investing: A Deep Learning Model For Multi-Source Stock Prediction, Hongxuan Yu, Tingting Zhang, Murat Kizildag

Journal of Global Business Insights

The burgeoning role of the stock market within the national economy elevates the importance of precise stock price analysis and prediction, a field that has garnered substantial interest in academic research. Stock price fluctuations, influenced by many factors, including company fundamentals, market sentiment, capital flows, industry news, and macroeconomic policies, present a highly dynamic and complex challenge for predictive modeling. Addressing this challenge, our study introduces an innovative method that capitalizes on the synthesis of news text and stock price data for forecasting market movements. We employ GloVe embeddings to capture semantic nuances from news text and integrate them with …

Millennials’ Perception Towards Adaption And Intention Of M-Banking: Experience From A Developing Country, Noman Hasan, Md. Shahed Mahmud, Abdul Gaffar Khan, Reshma Pervin Lima, Shiblu Miah

Journal of Global Business Insights

In recent years, m-banking has been developed rapidly around the world. The research aims to measure millennials’ perception towards adaption and intention of m-banking from a developing country’s perspective. A PLS-SEM modeling approach was performed to test the hypothetical model. The empirical results reveal that perceived ease of use, perceived security and privacy, and perceived cost significantly affect the millennials’ attitude to adopt m-banking. In contrast, perceived usefulness and perceived self-efficacy have an insignificant effect. Furthermore, attitude towards adopting m-banking significantly impacts adoption and intention among millennials. Practical and theoretical implications have been identified based on the study results.

Cash Holding And Corporate Governance On Company Financial Performance: Case Study Of Construction Infrastructure Project Supporting Industries On The Indonesian Stock Exchange, Mariana Ing Malelak, Zeplin Jiwa Husada Tarigan, Sautma Ronni Basana, Ferry Jie

Research outputs 2022 to 2026

The Indonesian government currently focuses on high infrastructure development to prepare for connectivity between one city and another. The massive construction of the new government capital is also called the archipelago's capital. This condition impacts companies supporting construction projects, which are growing rapidly. This growth is supported by corporate governance and the cash-holding industry, which supports infrastructure projects for the company's financial performance. This research aims to determine the role of cash holding and corporate governance (board skill, independent director, managerial ownership) on the financial performance of infrastructure-supporting manufacturing companies in Indonesia. The sample used in this research was 83 …

Improving Volatility Forecasting: A Study Through Hybrid Deep Learning Methods With Wgan, Adel Hassan A. Gadhi, Shelton Peiris, David E. Allen

Research outputs 2022 to 2026

This paper examines the predictive ability of volatility in time series and investigates the effect of tradition learning methods blending with the Wasserstein generative adversarial network with gradient penalty (WGAN-GP). Using Brent crude oil returns price volatility and environmental temperature for the city of Sydney in Australia, we have shown that the corresponding forecasts have improved when combined with WGAN-GP models (i.e., ANN-(WGAN-GP), LSTM-ANN-(WGAN-GP) and BLSTM-ANN (WGAN-GP)). As a result, we conclude that incorporating with WGAN-GP will’ significantly improve the capabilities of volatility forecasting in standard econometric models and deep learning techniques.

Tail Risk Network Analysis Of Asian Banks, Thach N. Pham, Robert Powell, Deepa Bannigidadmath

Research outputs 2022 to 2026

This study aims to investigate the tail risk dependence of individual banks in Asian emerging markets. Using value at risk and conditional value at risk to measure tail risk and employing the least absolute shrinkage and selection operator regression to build the network, this study analysed interconnectedness at three levels: system-wide, country level and individual bank level. This study yields three key findings. First, banks in Asian emerging markets have a notably high tail risk network, particularly during more extreme market conditions. Second, the smaller and more interconnected banks are the most systemically important in the region, rather than the …

Low/No-Code And Traditional Code Integration In Digital Banking, Kim Siang Yeo, Alan @ Ali Madjelisi Megargel

Research Collection School Of Computing and Information Systems

This paper seeks to combine the merits of Low/No-Code Programming (LNCP) with Traditional Programming (TP) systems to achieve true “agility” when creating banking infrastructure. While it is easy to fall prey to Shiny Object Syndrome in today’s dynamic and fast-paced banking technology world, it is not easy to pick out the right technology for today and tomorrow’s financial industry. Instead, LNCPs allow us to hedge all bets by equally lowering the technical entry barriers for each technology. The added integration of TP, when needed, also rounds out the faults related to sole LNCP use and provides any bank with a …

The Downstream Impact Of Upstream Tariffs: Evidence From Investment Decisions In Supply Chains, Thorsten Martin, Clemens A. Otto

Research Collection Lee Kong Chian School Of Business

We study how US manufacturing firms' investment responds to tariff reductions in supplier industries. Our estimates, based on tariff reductions following multinational trade agreements, suggest that a hypothetical 10% reduction of all upstream tariffs would increase downstream investment by 4% to 6%. This estimate is not explained by decreasing uncertainty and stems from tariff reductions for homogeneous and low-R\&D inputs, consistent with the investment response resulting from cost reductions rather than superior foreign technology embodied in imported inputs. Evidence from an instrumental variable estimation using the sudden increase in Chinese import penetration suggests that import competition also increases downstream investment.

Climate Challenges: Central Banks In The Hot Seat – Rethinking Monetary Policy And Educational Activity, Łukasz Kurowski

Journal of Banking and Financial Economics

Global warming poses many challenges for all economic entities. The two main challenges facing all countries are climate change mitigation and adaptation. Central banks also face difficult tasks in this context. The difficulty stems from the impact of climate change on all sectors of the economy. Central banking has to deal with the new challenges created by climate change for monetary policy, macroprudential policy, but also climate education. The aim of the article is to verify to what extent climate change is considered in the central bank’s main objective – monetary policy. Therefore, the article examines the frequency with which …

[Abstract For] Using Excel’S “Lambda” Function To Compute Modified Duration, Dollar Duration, And Bond Convexity, Tom Arnold, Joseph Farizo, Andrew C. Szakmary, Nancy Tran

Finance Faculty Publications

We create a modified duration function that is more accessible than Excel’s current Macaulay duration function (=DURATION) which requires several details about the bond.

The dollar duration and convexity functions are not currently available in Excel’s default functions. When these functions are copied to a second Excel file, the functions automatically become available as functions within the second file without the need for recreating the functions.

We provide instructions on how to implement the =LAMBDA function to more than just the creation of the bond application functions.

Defining Value And Measuring Roi For Expatriate International Assignments In Firms That Internationalize As Born-Global Companies, Dale Collins

Electronic Theses and Dissertations

The expatriate international assignment, a time-tested tool for global organizations, is known to be expensive and fraught with risks; defining its value has proven difficult for multinational firms in the twentieth century that internationalized using multi-stage theory. Recently, born-global internationalization theory has gained momentum among emerging firms, notably technology-based firms employing expatriate assignments. This study asks, how do born-global firms define value or benefit when determining return on investment (ROI) for expatriate international assignments? Multi stage theory posits firms first establish domestic markets creating well-developed cultures and sophisticated policies designed to mitigate risk and contain costs prior to internationalizing. Born-global …

Paycheck-To-Paycheck: How Public Service Loan Forgiveness Inhibits Generational Wealth Attainment For Black Women, Ashley Patrice-Rose Sherman

Electronic Theses and Dissertations

Black women who attend college in the United States are more likely to borrow to cover the cost of attendance and carry the largest amount of student loan debt than any other racial and gender group. Federal student loan borrowers are encouraged to work in public service to take advantage of the Public Service Loan Forgiveness Program (PSLF), an income-driven repayment program established by the College Cost Reduction and Access Act (CCRAA) of 2007. PSLF offers total debt relief after ten years of uninterrupted full monthly payments based on an income-driven repayment plan. The significance of this qualitative study will …

Are Short-Selling Restrictions Effective?, Yashar H. Barardehi, Andrew Bird, Stephen A. Karolyi, Thomas G. Ruchti

Business Faculty Articles and Research

Despite strong theoretical predictions based on disagreement, limited empirical evidence links short-selling restrictions to higher prices. We test this relationship using quasi-experimental methods based on rule 201, a threshold-based policy that restricts aggressive short selling when intraday returns cross −10%. When comparing stocks on either side of the threshold in the same hour of trading, we find that the restriction leads to 8% lower short-sale volume and 35 basis points higher daily returns. These price effects do not reverse after the restriction is lifted.

Crypto Currency Exchange And Mining Excel Simulations, Tom Arnold, Joseph Farizo, Jonathan M. Godbey

Finance Faculty Publications

The mathematics underlying blockchain-based cryptocurrencies is beyond the scope of most undergraduate finance programs. However, students should understand the intuition behind blockchain so that they might better understand how to apply this technology to future cases. In this paper, we develop a mathematically simple digital signature example and a mathematically simple proof-of-work simulation for classroom use.