Finance Commons^™

Impact Of Fiscal Policy On Financial Inclusion And Development In Nigeria, Okwanya Innocent, Taiwo A. Olusegun, Aimua E. Peace

CBN Journal of Applied Statistics (JAS)

This paper examines the effect of fiscal policy on financial inclusion and development in Nigeria. The study employs the Autoregressive Distributed Lag (ARDL) model and impulse response function (IRF) to determine the extent and response of financial inclusion and development to fiscal policy changes in Nigeria. The study derives a financial inclusion index from three core indicators: access, usage and quality of financial services, while financial development is measured as the ratio of money supply to GDP (M2/GDP). The results show that government expenditure has a significant positive effect on financial inclusion and development, while tax revenue exerts a negative …

Does Generative Ai Facilitate Investor Trading? Evidence From Chatgpt Outages, Qiang Cheng, Pengkai Lin, Yue Zhao

Research Collection School Of Accountancy

In this paper, we use ChatGPT outages to investigate whether investors rely on generative artificial intelligence (GAI) to perform trading-related tasks and the associated impact on stock price informativeness. We first document a significant decline in stock trading volume during ChatGPT outages and find that the effect is stronger for firms with corporate news released immediately before or during the outages. We further document similar declines in the short-run price impact, return variance, and bid-ask spreads, consistent with a reduction in informed trading during the outage periods. Lastly, we use trading volume changes during outages to construct a firm-level measure …

Financial Time Series Fusion, Completion, And Prediction With Deep Neural Networks, Dan Zhou

Dissertations

Time-series analysis is essential for a wide range of financial applications, including but not limited to bond valuation, firm earnings forecasts, firm fundamentals predictions, and firm characteristics imputations. Given its considerable value, the financial community has shown a strong interest in refining and advancing time-series analysis techniques. The study in this dissertation contributes to this field by employing advanced machine learning approaches, specifically graph neural networks, deep neural networks, and matrix/tensor methods. The primary objectives are twofold: first, to reveal complex correlations within financial time series to improve prediction accuracy, and second, to enhance the process of integrating and imputing …

U.S. International Climate Finance: An Analysis Of Historical Shortfalls And A Proposal For More Equitable Distribution, Maria-Cristina Kealey

Master's Projects and Capstones

Least-developed countries experienced 69% of deaths from climate disasters over the past 50 years despite comprising only 13% of the world’s population. Low-income and climate vulnerable nations around the world are suffering disproportionately as wealthy, high-emitting countries, such as the U.S., profit from the climate crisis. This research provides a comprehensive overview of past U.S. contributions to international climate finance efforts, assesses the climate finance deficit globally and specifically for developing countries, and proposes a more equitable share of U.S. funding from a quantitative and restorative climate justice approach. The primary analyses included quantifying the U.S. share of global greenhouse …

The Mathematics Of Financial Portfolio Optimization Incorporating Environmental, Social, And Governance Score Information, Ian Driskill

Master's Theses

We numerically investigate the effects that Environmental, Social, and Governance (ESG) scores have on portfolio optimization with Modern Portfolio Theory assumptions and how ESG scores correlate with the market returns of a rated company's stock. Additionally, we review and analyze a research paper published in the Journal of Financial Economics regarding ESG investing titled “Responsible investing: The ESG-efficient frontier” by Pedersen, Fitzgibbons, and Lukasz. Our overall goal is provide insight for socially responsible inclined investors, to help them understand what ESG scores tell us and how those scores may effect their overall investment returns."

Historical Perspectives In Volatility Forecasting Methods With Machine Learning, Zhiang Qiu, Clemens Kownatzki, Fabien Scalzo, Eun Sang Cha

Seaver College Research And Scholarly Achievement Symposium

Volatility forecasting in the financial market plays a pivotal role across a spectrum of disciplines, such as risk management, option pricing, and market making. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works suggest advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive benchmark of current statistical and learning-based methods for such purposes is lacking. Thus, this paper aims to provide a comprehensive survey of the historical evolution of volatility forecasting with a comparative benchmark of key landmark models. We …

Anthropomorphism And Human-Robot Interaction, Rae Yule Kim

Department of Economics Faculty Scholarship and Creative Works

Exploring how human appreciation for and interactions with robots are influenced by anthropomorphic features.

Univariate Extreme Value Analysis Of Quantitative Investment Management, George Agbenyega Zumanu

Graduate Research Theses & Dissertations

The evolution of product development within the variable annuity (VA) business have sparked interest in quantitative investment management, particularly as most VA issuers have integrated volatility-controlled funds into their annuity portfolios. Despite the existence of empirical research on statistical analysis of extreme values in conventional investments, there has been a notable gap in research focus towards risk modeling in volatility-controlled funds.

This study contributes by analyzing and modeling the extreme values of investments in volatility-controlled funds, comparing them to conventional equity funds. The financial returns of S&P Dow Jones Indices (SPDJI) indices - SPXTR, risk control SPXT18UT, and managed risk …

Is The Declining Birthrate Really An Issue For The Economy?, Harsh Ramesh Pednekar, Theodore Lee, Darrion Chin

Introduction to Research Methods RSCH 202

This study aims to explore the complex implications of declining birth rates on the economy, focusing on GDP per capita as a crucial metric, and aims to uncover both potential opportunities and challenges stemming from this demographic transformation using regression analysis. Using a quantitative methodology and secondary data from OECD.stat, World Population Review, and World Bank, the study explores the relationship between declining birth rates and economic impacts. GDP per capita serves as an essential dependent variable, and it accounts for control variables such as labour force participation, literacy, and education levels, child dependence ratio, and physical capital. Past studies …

Bitcoin And South African Stock Market Returns During Covid-19 Pandemic: A Test Of The Safe-Haven Hypothesis, Akaninyene U. Udom, Sopuru W. Nnamani

CBN Journal of Applied Statistics (JAS)

This paper tests the safe-haven property of Bitcoin for South African stocks using Full and Diagonal BEKK-GARCH models. The study uses the Johannesburg stock exchange Top40 index, and bitcoin returns data before COVID-19 (August 2018 to December 2019) and during COVID-19 (January 2020 to June 2021). The results show that bitcoin cannot be considered as safe-haven for stocks in South Africa since it is weakly correlated with stock and had a high volatility during the Pandemic. Therefore, the safe-haven hypothesis of bitcoin on South African stocks is not true for the period under study. The policy implication is that bitcoin …

Modelling The Naira Exchange Rate Dependence Using Static And Time-Varying Copula, Kabir Katata

CBN Journal of Applied Statistics (JAS)

This paper examines the dependence structure of different currencies versus the Nigerian Naira using constant and time-varying copula. Daily Naira/USD, Naira/Yuan, Naira/Pound, and Naira/Euro exchange rates from 23 December 2011 to 12 May 2020 were utilised. We fitted eight constant and time-varying copula families using the exchange rate standardised residuals. The study finds that the Naira exchange rate may be estimated with student t-copula, Symmetrized Joe-Clayton (SJC), or Rotated Gumbel copula models and Autoregressive (AR)– Glosten Jagannathan RunkleGeneralized Autoregressive Conditional Heteroscedastic (GJR-GARCH) (1,1) models with skewed t residuals for margins. The Naira exchange rate returns is timevarying, tail-dependent, and asymmetric. …

Financial Inclusion And Poverty Reduction In Nigeria: The Role Of Microfinance Institutions, Okwudili W. Ugwuoke, Oliver E. Ogbonna, Aye-Agele Freeman

CBN Journal of Applied Statistics (JAS)

This study investigates the role of microfinance institutions as a vehicle for driving financial inclusion and alleviating poverty in Nigeria using the EFinA 2018 household survey data. The probit model, propensity score matching, and average treatment effect methods are applied for the analyses. The study finds that financial inclusion driven by access to, and usage of products/services provided by microfinance institutions reduces poverty. The study recommends among others the need for increased access to microfinance products/services and an integrated poverty reduction policies that identifies microfinance institutions as a critical enabler

Deconstructing The Software Factory: A Practical Application Of Interorganizational Network Analysis, Zachary O. Ryan, Mark Reith, Clay Koschnick

Faculty Publications

Over the past 5 years, the number of DoD software organizations that employ nontraditional organizational structures has increased. These organizations, commonly referred to as software factories, often employ the network-based organizational structures found within high-technology industries. This article details ways in which network analysis techniques can be used to create a big picture view of these nontraditional organizations. Drawing on methodologies employed by network researchers, the authors develop and present an interorganizational analysis process that highlights a program's social and economic structures. Following the case history approach, they demonstrate the applicability of this approach by analyzing an emergent DoD software …

Food Price And Inflation Adjustments In Nigeria: Evidence From Threshold Cointegration, Usman A. Bello, Aliyu R. Sanusi

CBN Journal of Applied Statistics (JAS)

This paper investigates two stages of transmission through which inflation stabilization policy affects headline inflation by estimating a threshold cointegration model. The paper finds that asymmetric monetary shocks passing-through food prices exerts inflationary pressure on consumer prices, and is characterized by deep asymmetric movement, large degree of stickiness in adjusting downward, while exhibiting upward momentum in correcting food price fall. In addition, asymmetries were found in consumer price adjustment as food price shock transmits substantial inflationary pressure to consumer prices, while disinflationary effect originating from a fall in food price is inconsequential. The coefficients of adjustment were found to be …

Dynamic Effect Of Exchange Rate Gap Shocks On Stock Market Deepening: Evidence From Nigeria, Kareem Abidemi Arikewuyo

CBN Journal of Applied Statistics (JAS)

The study examines the exchange rate gap shock–stock market deepening nexus in Nigeria using the structural VAR-X (SVAR-X) technique for the period 1986Q1 to 2018Q4. Findings reveal that exchange rate gap shock has a negative but statistically not significant effect on stock market deepening in Nigeria. It was also found that exchange rate passed–through interest rate from second to thirteen quarter, and further through financial openness whose effect, like exchange rate gap, was negative. This implies that exchange rate gap is significantly and negatively related to interest rate and financial openness in Nigeria. It is therefore recommended that the monetary …

Modelling The Impact Of Government Expenditure On Economic Growth In Nigeria: The Moderating Effects Of Oil And Non-Oil Revenue, Victor U. Ijirshar, Gaius M. Asombo, Florence D. Bundepuun, Ashifa Tersugh, Ayila Ngutsav

CBN Journal of Applied Statistics (JAS)

This study examines the relationship between government expenditure and economic growth and assesses the moderating effects of oil revenue and non-oil revenue in Nigeria from 1981 to 2021. The study uncovered short-term asymmetry in the government expenditure-economic growth nexus while the long-term relationship was symmetric. The study found that government expenditure is a significant determinant of economic growth in Nigeria and that oil and non-oil revenue influences the nexus between government expenditure and economic growth in Nigeria positively. The study recommends efficient management of oil revenue, directing investments during high revenue periods and ensuring fiscal sustainability. Government should also establish …

Improving The Efficiency Of Exponential Ratio-Type Estimator For Population Median: A Calibration Weight Adjustment Approach, Mathew J. Iseh, Kufre J. Bassey

CBN Journal of Applied Statistics (JAS)

This paper modifies the Bahl and Tuteja exponential ratio-type estimator for population median under simple random and stratified sampling schemes using calibration weight adjustment technique with supplementary information to vary the stratum weights. The bias and mean square error of the modified estimator were obtained up to the second-order approximation, which satisfies the necessary conditions for efficiency. The findings show that the new estimator surpasses existing estimators in efficiency gain. This suggests the appropriateness of calibration weight modification in boosting the efficiency of a population parameter estimator under stratified random sampling especially where the population parameter of the auxiliary variable …

An Empirical Evaluation Of Neural Process Meta-Learners For Financial Forecasting, Kevin G. Patel

Master's Theses

Challenges of financial forecasting, such as a dearth of independent samples and non- stationary underlying process, limit the relevance of conventional machine learning towards financial forecasting. Meta-learning approaches alleviate some of these is- sues by allowing the model to generalize across unrelated or loosely related tasks with few observations per task. The neural process family achieves this by con- ditioning forecasts based on a supplied context set at test time. Despite promise, meta-learning approaches remain underutilized in finance. To our knowledge, ours is the first application of neural processes to realized volatility (RV) forecasting and financial forecasting in general.

We …

Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines And Bidirectional Encoders From Transformers, Ian L. Grisham

Electronic Theses and Dissertations

The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model’s ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not …

Consumer Reaction To The Use Of Artificial Intelligence Chatbot On Distribution Of General Insurance In Singapore, Lai Hing Tan

Dissertations and Theses Collection (Open Access)

As technology rapidly permeates all aspects of our lives, it is not unusual to question and even challenge the rationale on why certain industries are slower to adapt to the new digital age. Insurance is a business that is under scrutiny given its traditional ways of selling and legacy challenges. Why is technology investment in insurance companies lagging others? One emerging technological disruption is artificial intelligence (AI). It is the science of designing and building intelligent systems that can complete tasks traditionally performed by humans. AI is expected to fundamentally transform today’s marketplace, for businesses and consumers alike. However, because …

Bridging The Chasm Between Fundamental, Momentum, And Quantitative Investing, Allen Hoskins, Jeff Reed, Robert Slater

SMU Data Science Review

A chasm exists between the active public equity investment management industry's fundamental, momentum, and quantitative styles. In this study, the researchers explore ways to bridge this gap by leveraging domain knowledge, fundamental analysis, momentum, crowdsourcing, and data science methods. This research also seeks to test the developed tools and strategies during the volatile time period of 2020 and 2021.

Application Of Sentiment Analysis And Machine Learning Techniques To Predict Daily Cryptocurrency Price Returns, Edward Wu

CMC Senior Theses

This paper examines the effects of social media sentiment relating to Bitcoin on the daily price returns of Bitcoin and other popular cryptocurrencies by utilizing sentiment analysis and machine learning techniques to predict daily price returns. Many investors think that social media sentiment affects cryptocurrency prices. However, the results of this paper find that social media sentiment relating to Bitcoin does not add significant predictive value to forecasting daily price returns for each of the six cryptocurrencies used for analysis and that machine learning models that do not assume linearity between the current day price return and previous daily price …

Reproducibility In Management Science, David Moore, Miloš Fišar, Ben Greiner, Christoph Huber, Elena Katok, Ali I. Ozkes, Management Science Reproducibility Collaboration

Finance Faculty Works

With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample …

Exploring Information Leakage In Historical Stock Market Data, Edison Hua

Dissertations and Theses

Information leakage is a major concern for traders who want to execute large orders without affecting the market price. In this paper, we explore the sources and effects of information leakage in historical stock market data using various methods and metrics. We first define information leakage as a pattern caused by a trader that would otherwise not occur without the trader’s activity. Using historical data, the direct impact of a potential large trade cannot be measured, but we consider a minimal impact large trade to be one that minimizes changes to the established trading data. We then analyze how information …

Enhanced Maximum Likelihood Models For Underreported Variables: Extending To Multiple Claims Dimension, Shalaka Sudhanshu Sarpotdar

Graduate Research Theses & Dissertations

This thesis builds upon the foundations laid out in Xia et al. [2023], which explored the utilizationof Maximum Likelihood approach to model misrepresentation data in Generalized Linear Models (GLM) ratemaking models. We introduce the concept of “underreported variables”, a form of insurance misrepresentation where insured individuals provide inaccurate information about risk factors that influence insurance eligibility, premiums, and insured amounts. Unlike fraudulent misrepresentation, underreported variables arise from a lack of awareness regarding the insured’s mental and physical health conditions, rather than fraudulent intent. The study rigorously tests the proposed model using health insurance data and extends its applicability to other …

Socio-Economic Comparative Analysis Of Front-Of-The-Meter And Behind-The-Meter Microgrids For Panamnik, Olivia C. Amann Mcshea

Cal Poly Humboldt theses and projects

In Northern California, the Karuk Tribe is feeling the effects of climate change on inadequate energy infrastructures leading to unreliable power supply. Improving energy reliability in a way that also increases energy sovereignty is necessary. Renewable energy microgrids have emerged as a pathway forward.

Modeling ownership structures and cash flows for different microgrid configurations can support the Tribe’s implementation of a microgrid in Orleans, CA that maximizes community benefits. This thesis considers a front-of-the-meter (FTM) and behind-the-meter (BTM) configuration, both approximately 2 MW solar PV and 3 MW/12 MWh battery energy storage. Cash flows including capital costs, operations and maintenance …

Market Risk Factors And Stock Returns In The Nigerian Bourse, Omorose A. Ogiemudia, Osagie Osifo, Igbinovia L. Eghosa

CBN Journal of Applied Statistics (JAS)

This study examines the link between market risk and equity return in Nigeria between 1980 to 2019. It employs the vector error correction model (VECM) to determine the short run dynamics and long run effect of market risk factors on stock return. The findings revealed that a dynamic relationship exists between market risk factors and stock returns in Nigeria. Also, exchange rate risk and oil price risks have significant influence on stock return, while inflation and interest rate risk, and political instability risks have a non-significant impact on stock return. Finally, a unidirectional relationship was detected between interest rate, oil …

Savings-Investment Gap In Sub-Saharan Africa: Does The Interaction Of Financial Sector Development And Migrant Remittances Matter?, Wasiu Adekunle, Oluwatosin Adeniyi, Joshua Afolabi, Musibau Babatunde, Edward Omiwale

CBN Journal of Applied Statistics (JAS)

This study analyses the interactive effects of migrant remittances and financial development on savings-investment gap for a panel of 18 Sub-Saharan Africa (SSA) countries from 1990-2017. Results from a panel ARDL model show that migrant remittances reduce savings-investment gap in the long run. The gap is further reduced when the individual effect of financial development, and the interactive effects of migrant remittances and financial development are taken into consideration. Further analysis reveals evidence of widening effects of rising real GDP growth and bank deposits over a long-term horizon, while higher private sector credit widened the savings-investment gap only in the …

Impact Of Exchange Rate On Trade Flow In Nigeria, Victor U. Ijirshar, Isa J. Okpe, Jerome T. Andohol

CBN Journal of Applied Statistics (JAS)

This study examines the impact of exchange rate on trade flow in Nigeria from 1986 to 2021. The study utilises linear and nonlinear autoregressive distributed lag (ARDL and NARDL) models to test the J-Curve hypothesis and the Marshall-Lerner condition in Nigeria. The study found symmetric effects of exchange rate on trade balance, exports, and imports. The findings also show that real exchange rate depreciation has a strong negative influence on trade balance and exports in the short run but positive in the long run, exhibiting the shape typology of the J-curve. Furthermore, the study reveals evidence of the Marshall-Lerner condition …

War And Money In Ngram Viewer, Robert H. Mcfadden, William Zywiak, Ronald P. Bobroff, Gao Niu

Finance Department Faculty Journal Articles

The second and fourth authors have been inviting Intro to Applied Analytics and Statistics 1 students to use the Ngram Database to explore historical topics of their choosing. This is the first article derived from this exercise. The first author examined the historical relationship between war and money from 1775 to 2005 in the American English corpus. This is followed by an examination of the 3-gram “cost of war” in the American English and British English corpora. Specific to the analyses presented here several military and economic events are discussed. More specifically, both economies and wars are somewhat unpredictable, with …