Finance and Financial Management Commons^™

Bitseat: Reimagining The Financing For Airliners Using Nonfungible Tokens (Blockchain Technology), Edwin S. Ongola

Journal of Aviation Technology and Engineering

This essay describes how blockchain technology, particularly nonfungible tokens, can be used to raise funding for airliners. The essay begins with a brief overview on the costs, categories, and acquisition methods of airliners. After that, the essay introduces concepts on blockchain technology, tokens, and smart contracts. The essay then touches on how nonfungible tokens can be used to facilitate fractional ownership of airliners. From there, the essay discusses Bitseat, a conceptual nonfungible token for fractional ownership of airliners, covering its overall design, appeal, marketplace alternatives, and challenges. Finally, in the discussion, the essay summarizes the overall concept and outlines its …

Ai-Driven Predictive Analysis Of Airline Profitability Under Macroeconomic And Sustainable Aviation Fuels, Shivika Singh

Discovery Day - Daytona Beach

This study presents an AI-enhanced predictive model to assess airline profitability under the combined influence of macroeconomic trends and the adoption of sustainable aviation fuel (SAF). By including economic indicators such as GDP growth, inflation rates, and fuel price volatility with airline operational data, including ticket prices and fuel costs, the model simulates profitability across multiple carriers. Scenario-based analyses, encompassing optimistic, moderate, and pessimistic projections, illustrate different financial sensitivities between low-cost and legacy airlines.

What We Know About Accounting Ratios: Methodological Considerations, Wojciech Kuryłek, Oskar Kowalewski

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: This paper provides a comprehensive literature review of the methodological aspects of financial ratio analysis, consolidating dispersed knowledge on the computation, statistical properties, and appropriate usage of accounting ratios.

Design/Methodology/Approach: The study adopts a narrative literature review methodology, systematically surveying published research on financial ratio distributions, normality testing, data transformations, outlier handling, the proportionality assumption, dimensionality reduction techniques, compositional data analysis, and recommended ratio sets for corporate financial research.

Findings: Financial ratios predominantly deviate from normal distributions, exhibiting skewness, excess kurtosis, and sensitivity to outliers. Transformation techniques such as logarithmic, square root, and Box‑Cox methods yield mixed results in …

Cdt-1d Cnn Integration With Simpson-Sobolev Regularization For High-Frequency Options Trading: With Fem-Based Heston Option Pricing, Daniel M. Margolis, Johannes Tausch, Arthur K. Selender

Mathematics Theses and Dissertations

This dissertation presents a computational framework for high-frequency options trading that combines Cross-Data-Type 1-D Convolutional Neural Networks (CDT-1D CNN) with Simpson-Sobolev regularization for directional prediction, and finite element methods (FEM) for realistic option pricing during backtesting. The core innovation lies in developing a mathematically rigorous regularization approach that maintains the adaptability of modern deep learning while enabling accurate evaluation through stochastic volatility models. The primary contribution is the Simpson-Sobolev regularization scheme, which extends traditional Sobolev regularization by incorporating Simpson’s rule for numerical integration. This approach achieves higher-order accuracy in approximating the Sobolev norms that control function smoothness. Simpson’s rule attains …

Measuring Stock Market Inefficiency Using A Multilayer Composite Efficiency Index: A Case Of The Egyptian Exchange, Patrick K. Owido, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

Financial markets play a critical role in resource allocation. Their performance depends on the decisions of millions of independent investors constantly reacting to one another. Their informational efficiency remains a subject of debate across economic systems. When informational efficiency is present at the weak form, historical price information should not consistently predict future returns. Several empirical tests of this hypothesis often focus on the behavior of aggregate market indices, and use individual efficiency proxies such as autocorrelation, GARCH-type volatility, or entropy-based measures to measure efficiency. This has often yielded mixed results, particularly in emerging markets. Here we show that testing …

Modeling Bitcoin Dynamics Using Differential Equations, Boone M. Fleenor

Departmental Honors & Graduate Capstone Projects

In this thesis, we develop and analyze two nonlinear systems of ordinary differential equations to model Bitcoin price dynamics. Analytical techniques are used to obtain exact or approximate solutions where possible. Then, numerical simulations using a fourth-order Runge–Kutta method are employed to explore system behavior beyond analytically tractable regimes. Finally, model outputs are compared to historical Bitcoin price data using normalized and resampled time series. These results suggest that deterministic models can provide meaningful insight into the structural behavior of Bitcoin markets, while highlighting the need for stochastic or time-dependent extensions for more realistic modeling.

Older Adults And Emerging Technology Fraud In The Ai Deepfake Era, Thiago Neves

Research Days

Artificial intelligence has accelerated faster than society's ability to adapt, leaving older adults extremely vulnerable to AI-generated fraud. Americans over age 60 lost $4.9 billion to scams in 2024, 43% more than the previous year. In this research, I investigate how digital illiteracy, combined with AI-generated deepfakes, creates this crisis. Older adults struggle with three principal vulnerabilities: distinguishing legitimate sites from scams, judging whether online information is truthful, and understanding how algorithms use their data. AI weaponizes these gaps through voice clones, synthetic video calls, and personalized phishing emails that avoid the trust cues seniors tend to rely on. I …

Moneyup: A Predictive Financial Management System For College Students, Isaiah J. Adams, Malaya E. Wilburd, Dan V. Le, Joshua P. Golden

ATU Scholars Symposium

College students often lack accessible tools that combine real-time financial tracking, mobile accessibility, predictive analytics, and secure system design, leaving many without structured insight into their spending behavior. MoneyUP is a full-stack financial management platform developed to address these challenges through a secure, data-driven budgeting system deployed as both a web application and a cross-platform Flutter mobile application. The system integrates the Plaid API in its Sandbox environment to synchronize simulated banking data for secure testing without exposing live financial credentials. Transaction data is processed and stored using Supabase with a relational PostgreSQL database structured to enforce normalization, referential integrity, …

Digital Transformation And Market Microstructure: Analyzing The Impact Of Algorithmic Trading On National Stock Exchange Of India Price Discovery Mechanisms Through Complex Systems Theory., Mukesh Bhaskar Ahirrao, Harshal Anil Salunkhe, Vishal Sunil Rana

Northeast Journal of Complex Systems (NEJCS)

Abstract

This research examines the evolution of market microstructure at the National Stock Exchange of India (NSE) from 2020 to 2024, a period characterized by substantial growth in algorithmic trading from 35% to 44% of total trading volume. Using market microstructure data and analytical techniques grounded in complex systems perspectives, the study documents temporal patterns in price discovery, liquidity, volatility, and market efficiency associated with this digital transformation.

The analysis reveals several notable changes in market characteristics. Transaction costs improved significantly, with bid-ask spreads declining by 23.4% and market depth increasing by 18.1%. Price adjustment half-life decreased by 50%, indicating …

A Multi-Layer Complex Adaptive System Framework For Ai-Driven Robo-Advisory Services, Jesty Mariam Philip, Mohit Boralkar, Alwin Joseph

Northeast Journal of Complex Systems (NEJCS)

The rapid integration of Artificial Intelligence (AI) into investment advisory services has changed financial decision-making, giving rise to adaptive robo-advisory systems capable of real-time analysis, personal recommendations, and autonomous portfolio optimization. Existing research evaluates these systems primarily through technological performance or investor adoption, overlooking the complex feedback-driven interactions that emerge when AI analytics, data environments, and human behavior operate together. This study addresses this gap by conceptualizing AI-enabled robo-advisors as a multi-layered Complex Adaptive System comprising historical data, real-time data, AI analytics, investor perception, and decision-making layers. A simulation model grounded in machine learning dynamics, behavioral finance, and complexity theory …

Budgeting Apps, Financial Literacy, And Financial Control, Max Masabo, Abdullah Mohammad Mahi

Presentations - 2026

Our paper shows the real hidden connections between budgeting app usage and financial wellbeing of U.S Households

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …

Artificial Intelligence–Enabled Revenue Cycle Management And Financial Performance In Healthcare Organizations, K’Reesa Webster

Theses, Dissertations and Capstones

The purpose of this review was to examine how artificial intelligence–enabled revenue cycle management (AI-enabled RCM) systems have been associated with financial performance outcomes in healthcare organizations. A literature review following a systematic process consistent with PRISMA 2020 guidelines was conducted to identify quantitative studies published between 2015 and 2026. Eligible studies were required to report at least one financial outcome related to claim denial rate, days in accounts receivable, or operating margin. Twenty-seven studies met all inclusion criteria. Findings across these studies indicated that AI-enabled RCM systems have been associated with lower denial rates, shorter accounts receivable timelines, and …

Entropic Foundation Of Finance And Physics: Securities Price Dynamics And Quantum Theory, Mohammad Abedi

Electronic Theses & Dissertations (2024 - present)

In many scientific and financial contexts, we must reason and make predictions under conditions of incomplete information. This dissertation develops Entropic Dynamics (ED) as a unified framework for deriving dynamical laws directly from principles of inference. Within this approach, probability distributions represent states of knowledge, and their evolution is determined through entropy maximization subject to relevant constraints. This leads to a novel concept of entropic time and a formulation of dynamics as an inferential process. In this talk, I will present how ED provides a common foundation across multiple domains. In physics, quantum dynamics for particles and scalar fields in …

Machine Learning: Thematic Feature Grouping, And The Magnificent Seven: A Forecasting Analysis, Mirarmia Jalali, Mohammad Najand, Andrew Cohen

Finance Faculty Publications

This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, …

Natural Resource Rents And Energy Poverty Nexus In Next Eleven Economies, Muhammad Salah Uddin, Ayub Ali, Zobayer Ahmed, Md Nasir Uddin Sikdar, Ahsan Habib, Abdullah Elah Al-Mahde

Finance Faculty Publications

Energy poverty (EP) remains a persistent global challenge with important implications for economic development, public health, and social welfare. While natural resources, particularly oil and gas, are often viewed as key sources of energy access, their effectiveness in mitigating EP remains underexplored in emerging economies. This study examines the relationship between natural resource rents (NRR), specifically oil rents (OR) and natural gas rents (NGR), and energy poverty (EP) in the Next Eleven (N-11) countries from 2000 to 2020. The primary objective is to assess how NRR influences EP at various levels of poverty using the Method of Moments Quantile Regression …

Financial Literacy And Inclusion Of Philippine Jeepney And Tricycle Drivers, Bryan N. Bernabe, Jyro B. Triviño

Leadership and Strategy Faculty Publications

The study investigated how the different elements of financial literacy influence the financial inclusion of jeepney and tricycle drivers in Caloocan, Metro Manila. Pearson correlation analysis revealed a positive correlation between financial inclusion and attitude, behavior, knowledge, and skills. Additionally, analysis of variance highlighted that education and age play significant roles in enhancing financial literacy. The linear regression findings also supported the idea that income acts as a positive moderator, augmenting the impact of financial literacy on financial inclusion. The study attempted to disaggregate its financial literacy components to understand their impact on financial inclusion, but its interrelationships also require …

Lost In The Language: Data Breaches And The Strategic Fog Of Risk Disclosures, Ling Tuo, Shipeng Han

Accounting Faculty Publications

This study examines whether firms strategically adjust the readability of Item 1A (“Risk Factors”) disclosures following data breaches. Using U.S. firm-year observations from 2006 to 2023, we find that data breaches are associated with a significant decline in Item 1A readability. This decline is not accompanied by a meaningful increase in informational content; instead, post-breach disclosures exhibit higher syntactic complexity, more positive tone, and lower textual similarity to prior and industry peers' filings, consistent with strategic obfuscation rather than transparent reporting. The readability decline is amplified among firms facing higher litigation risk but attenuated among firms with stronger reputations for …

The Effects Of Financing Green And Brown Sectors: What Do Theories And Evidence Say?, Hao Liang, Maria Teresa Punzi

Research Collection Lee Kong Chian School Of Business

This paper critically examines the economic and welfare implications of financing green and brown sectors. Drawing on a comprehensive review of recent theoretical and empirical literature, we highlight that while conventional green finance—allocating capital toward environmentally friendly (“green”) sectors and away from carbon-intensive (“brown”) sectors—can promote decarbonization, it may also produce unintended externalities. In particular, it can inadvertently incentivize higher emissions from brown firms and contribute to economic disruption. Using a dynamic stochastic general equilibrium (DSGE) model, we demonstrate that lowering the cost of capital for green sectors leads to only modest reductions in emissions, whereas raising it for brown …

Policy Uncertainty Reduces Green Innovation, Mengyu Wang, Jeffrey Wurgler, Hong Zhang

Research Collection Lee Kong Chian School Of Business

Policy uncertainty can undermine the power of government subsidies to stimulate environmentally friendly research and development. We show that Chinese firms’ green R&D falls as the uncertainty of environmental subsidies rises: Exogenous, weather-driven air pollution variability induces subsidies to fluctuate, and firms in areas with high weather-driven subsidy variability undertake less green R&D and hire fewer technical employees, controlling for the average level of subsidies. Heavy emitters and environmental technology firms are more affected. The results also illustrate how policy uncertainty can arise when policymakers are influenced by conditions that are salient but with causes that are difficult to disentangle.

A New Functional Setting For Term Structure Modeling Using The Health-Jarrow-Morton Framework, Michael Pokojovy, Ebenezer Nkum, Thomas M. Fullerton Jr.

Mathematics & Statistics Faculty Publications

The well-known Heath–Jarrow–Morton (HJM) framework provides a universal and efficacious instrument for modeling the stochastic evolution of an entire yield curve by explaining the interest rate dynamics in continuous time under no-arbitrage conditions. Existing implementations involve exponentially weighted function spaces as theoretical settings for the former stochastic evolution. While the choice of weight can have a drastic effect on model calibration and subsequent forecasting, it cannot be estimated from market data and does not allow for any objective interpretation. The proposed approach does not have this shortcoming as it adopts a suitably designed unweighted function space. The HJM equation is …

Feedback Strategies In The Market With Uncertainties, Mustapha Nyenye Issah

Graduate Theses and Dissertations (2019 - present)

This paper explores how established firms use strategic advertising to deter new competitors in uncertain markets. Specifically, it models a situation where market demand evolves unpredictably - captured by the CKLS stochastic process, and the incumbent firm may be either strong or weak, a fact hidden from potential entrants. For a company already in the market, advertising is not just about driving immediate sales, it is a strategic tool to project an image of strength and deter potential new competitors. On the other side, a business thinking about entering that market faces a high-stakes, irreversible decision. It will typically hold …

Digital Communications Between Firms And Investors: Impact Of Explanatory Responses On Investor Engagement In Online Financial Q&A, Runyu Wang, Zili Zhang, Keng Siau, Ziqiong Zhang

Research Collection School Of Computing and Information Systems

The emerging trend of digital communications between firms and investors through online question-and-answer (Q&A) platforms is recognized as a vital strategy for managing investor relations, contributing to enhanced market efficiency and information transparency through increased information exchange. Potential investors can seek responses from firm managers to address their information needs, thereby mitigating market uncertainties. To provide foundational insights, we conduct a survey of investors to assess their awareness, usage, and perceptions of firm-investor Q&A platforms. In the subsequent empirical study, we specifically focus on the substance of managers’ responses, which are primarily aimed at clarifying firm events or information. In …

Is Complexity Virtuous?, Ryan Elmore, Jack Strauss

Business Information and Analytics: Faculty Scholarship

(Kelly et al., 2024) show that increasing complexity in linear models, with potentially thousands of predictors, is "virtuous". Their work contradicts the dogma of model selection, including the Principles of Parsimony and Occam's Razor. They find that when the number of predictors far exceeds the number of observations, the bias-variance trade-off breaks down, the variance declines, and the Sharpe ratio increases. In the context of ridge regression, we find that very high complexity coupled with large penalty terms (excessive shrinkage) generate forecasts that converge to a rolling window of past returns. For example, we show the past twelve-month moving average …

Global Standards And Local Ambitions Across Green Taxonomies: Climate Change Mitigation From The European Union To South Africa, Theodor Florian Cojoianu, Andreas G. F. Hoepner, Ifigeneia Paliampelou, Anh Vu, Dariusz Wojcik

Research Collection College of Integrative Studies

Country-level green finance taxonomy standards have emerged to provide clarity on environmentally-sustainable economic activities to attract investment, protect financial services consumers, and counteract greenwashing. This paper adopts the Global Production and Financial Network (GPFN) approach and analyses factors affecting the climate change mitigation ambition level of the South African Green Finance Taxonomy (RSA GFT) in comparison with the EU taxonomy, which served as a model for South Africa's (RSA) regulators. It accounts for (i) the interplay between EU’s and RSA’s production and financial networks, and (ii) RSA’s willingness to attract European funding for sustainable development. We find that EU private …

Leveraging Machine Learning And Causal Inference For Loan Default Prediction, Luca Guida

Doctoral Dissertations and Master's Theses

This research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors …

Managing Rumors On Electronic Interaction Platforms: How Management Responses Affect Investor Reaction, Runyu Wang, Zili Zhang, Keng Siau, Ziqiong Zhang

Research Collection School Of Computing and Information Systems

This study investigates how listed firms respond to investors’ rumor-related inquiries and examines the impact of these responses on investor reactions, as indicated by subsequent daily abnormal stock returns (ARs). Using a unique dataset of question-and-answer (Q&A) interactions from China’s major e-interaction platforms, established by the stock exchanges, our study provides insights into regulated firm-investor communications in a structured Q&A setting. Unlike informal social media channels, these platforms enable official responses from firm representatives, typically board secretaries, under direct regulatory oversight. By analyzing rumor-related Q&A pairs with regression models and several robustness checks, we find that firms can benefit from …

On The Origin Of Green Finance Policies, Theodor Florian Cojoianu, D. French, Andrea G. F. Hoepner, Sheenan L., Anh Vu

Research Collection College of Integrative Studies

Despite the rising number of green finance policies, the socioeconomic determinants shaping them remain largely unexamined. Drawing from the literature analysing the relationship between regulation, market development and institutional economics, we contend that green finance policy adoption is driven by both market-based and institutional factors. Using a survival analysis approach to understand the levers influencing green finance policy adoption across 188 countries from 2000 to 2019, we find that exposure to the fossil fuel industry predominantly drives the initial issuance of green finance policies. The positive effect of fossil fuel commercial financing on the adoption of green finance policies exists …

From Disruption To Integration: Cryptocurrency Prices, Financial Fluctuations, And Macroeconomy, Zhengyang Chen

Faculty Publications

This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrency's deep financial system …

Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma

Research Collection School Of Computing and Information Systems

Potential risk signals reflected in supply chain and equity connections between enterprises and social connections between investors are becoming crucial to identifying enterprise risks in addition to basic financial indicators. Traditional risk management systems face challenges in adapting to these complexities, highlighting the need for a proactive paradigm shift in risk management. Leveraging graph models such as social networks and knowledge graphs offers a promising approach to identifying and managing potential associated risks effectively. To bridge existing research gaps, a novel risk identification framework driven by social-knowledge graphs has been proposed, integrating graph deep learning and reinforcement learning techniques guided …