Numerical Analysis and Scientific Computing Commons^™

Improving Public Transport Through Machine Learning Influence Flow Analysis (Mifa): Southern England Bus Case Study, Benjamin Lee, Wolfgang Garn, Masoud Fakhimi, Nick F. Ryman-Tubb

Research Collection School Of Accountancy

Public transport (PT) is crucial for enhancing the quality of life and enabling sustainable urban development. As part of the UK Transport Investment Strategy, increasing PT usage is critical to achieving efficient and sustainable mobility. This paper introduces Machine Learning Influence Flow Analysis (MIFA), a novel framework for identifying the key influencers of PT usage. Using survey data from bus passengers in Southern England, we evaluate machine learning models. Subsequently, MIFA uncovers that easy payments, e-ticketing, and mobile applications can substantially improve the PT service. MIFA’s implementation demonstrates that strength and importance lead to specific insights into how service characteristics …

A Data-Driven Framework For Optimal Retail Store Location, Ming Hui Tan

Dissertations and Theses Collection (Open Access)

This study develops a data-driven framework for optimal retail store location planning that integrates road network analysis, mobility data and optimization techniques. By addressing the limitations of traditional approaches that rely on outdated census data and manual site selection, this research offers a scalable and adaptable solution for retail expansion in diverse urban environments. Chapters 1 and 2 establish the foundational context and theoretical underpinnings of this research. Chapter 1 introduces the research problem and motivation, highlighting the limitations of existing approaches and defining three key research objectives: automating candidate site identification, improving footfall estimation, and developing a scalable multi-site …

Enhancing Multi-Step Stock Price Forecasting With Social Media Sentiment And Engagement Metrics, Damilare Olaniyan

Electronic Theses and Dissertations

This thesis investigates whether social media sentiment can improve the accuracy of stock price prediction beyond traditional historical data. While financial markets have long relied on structured numerical indicators, the growing influence of public discourse on platforms like Twitter has introduced new opportunities for extracting market-relevant signals from unstructured text. The study focuses on four major technology firms and combines sentiment features derived from Twitter with historical stock prices in a hybrid machine learning framework. Engagement-weighted sentiment, linguistic complexity, and polarity intensity were extracted using natural language processing techniques and incorporated into classification and regression models. Results show that including …

Uncovering Merchants’ Willingness To Wait In On-Demand Food Delivery Markets, Jian Liang, Ya Zhao, Hai Wang, Zuopeng Xiao, Jintao Ke

Research Collection School Of Computing and Information Systems

While traditional on-demand food delivery services help restaurants reach more customers and enable doorstep deliveries, they also come with drawbacks, such as high commission fees and limited control over the delivery process. White-label food delivery services have emerged as an alternative, ready-to-use platform for restaurants to arrange delivery for customer orders received through their applications or websites, without the constraints imposed by traditional on-demand food delivery platforms or the need to develop an in-house delivery operation. Although several studies have investigated consumer behavior when using traditional on-demand food delivery services, there is limited research on merchants’ behavior when adopting white-label …

Does Ceo Agreeableness Personality Mitigate Real Earnings Management?, Shan Liu, Xingying Wu, Nan Hu

Research Collection School Of Computing and Information Systems

Despite efforts to mitigate aggressive financial reporting, earnings management remains challenging to parties interested in inhibiting its dysfunctional effects. Using linguistic algorithms to assess CEO agreeableness personality from their unscripted texts in conference calls, we find that it is a determinant that mitigates a firm's real earnings management. Furthermore, such an effect is more pronounced when firms confront intensive market competition and financial distress and have weaker managerial entrenchment or when CEOs face stronger internal governance. Our findings persist even after we utilize several alternative real earnings management metrics and control other confounding personalities in prior earnings management studies. The …

Stock Market Volatility In The United Kingdom: Simulating Post-Covid-19 Recovery, Bala A. Dahiru, Mohammed Shuaibu, Najibullah Hassanov

CBN Journal of Applied Statistics (JAS)

This paper investigates the time it would take for the FTSE-100 index to reach its post-COVID-19 peak. The paper utilises an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model that accounts for leverage effect and asymmetries. The preferred models amongst competing variants was the Autoregressive Moving Average (ARMA)-EGARCH(2,1) specification and was used to predict daily FTSE-100 data from 5th January 2000 to 21st June 2024. The empirical exercise showed that the COVID-19-induced financial crisis negatively affected the United Kingdom’s stock market performance. The results show that the FTSE100 index could reach its post-pandemic peak around 27th August, 2024 (two months after …

Factors Affecting Online Consumers' Cultural Presence And Cultural Immersion Experiences In Live Streaming Shopping, Lifu Li, Kyeong Kang, Yafei Feng, Anqi Zhao

Research Collection School Of Computing and Information Systems

This paper conducts research on the interaction between ethnic minority group (EMG) live streamers and online consumers, aiming to present how EMG live streamers’ appearance and interaction factors affect online consumers’ cultural experience during live streaming shopping. Based on the social presence theory and the immersion theory, this paper explores how EMG live streamers impact online consumers’ cultural experience, and it designs personal EMG knowledge as a moderating variable under specific social and cultural backgrounds. Through analysing 325 online questionnaires based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the study results support that both …

Reinforcement Learning With Maskable Stock Representation For Portfolio Management In Customizable Stock Pools, Wentao Zhang, Yilei Zhao, Shuo Sun, Jie Ying, Yonggang Xie, Zitao Song, Xinrun Wang, Bo An

Research Collection School Of Computing and Information Systems

Portfolio management (PM) is a fundamental financial trading task, which explores the optimal periodical reallocation of capitals into different stocks to pursue long-term profits. Reinforcement learning (RL) has recently shown its potential to train profitable agents for PM through interacting with financial markets. However, existing work mostly focuses on fixed stock pools, which is inconsistent with investors’ practical demand. Specifically, the target stock pool of different investors varies dramatically due to their discrepancy on market states and individual investors may temporally adjust stocks they desire to trade (e.g., adding one popular stocks), which lead to customizable stock pools (CSPs). Existing …

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path, to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and …

Examining Sustainable Overseas Investment Information-Sharing Model For Automobile Enterprises: A Multi-Modal Weight Network Approach, Yuan Cheng, Xiaofang Chen, Changbo Lin, Sheqing Ma, Jie Feng

Research Collection School Of Accountancy

In an era of globalization, automotive companies are increasingly looking to make overseas investments to expand their production capacity and explore foreign markets. However, the outcomes of such investments are often influenced by a myriad of factors, including policy changes, social dynamics, and market conditions. To address the need for a comprehensive overseas investment information-sharing model, this research proposes an innovative approach based on a multi-modal weight network. This model aims to provide users with a global perspective on overseas investment opportunities, encompassing policy insights, and market dynamics. It integrates data from various sources, offering multi-dimensional information on investment regions, …

Earnhft: Efficient Hierarchical Reinforcement Learning For High Frequency Trading, Molei Qin, Shuo Sun, Wentao Zhang, Haochong Xia, Xinrun Wang, Bo An

Research Collection School Of Computing and Information Systems

High-frequency trading (HFT) is using computer algorithms to make trading decisions in short time scales (e.g., second-level), which is widely used in the Cryptocurrency (Crypto) market, (e.g., Bitcoin). Reinforcement learning (RL) in financial research has shown stellar performance on many quantitative trading tasks. However, most methods focus on low-frequency trading, e.g., day-level, which cannot be directly applied to HFT because of two challenges. First, RL for HFT involves dealing with extremely long trajectories (e.g., 2.4 million steps per month), which is hard to optimize and evaluate. Second, the dramatic price fluctuations and market trend changes of Crypto make existing algorithms …

Market-Gan: Adding Control To Financial Market Data Generation With Semantic Context, Haochong Xia, Shuo Sun, Xinrun Wang, Bo An

Research Collection School Of Computing and Information Systems

Financial simulators play an important role in enhancing forecasting accuracy, managing risks, and fostering strategic financial decision-making. Despite the development of financial market simulation methodologies, existing frameworks often struggle with adapting to specialized simulation context. We pinpoint the challenges as i) current financial datasets do not contain context labels; ii) current techniques are not designed to generate financial data with context as control, which demands greater precision compared to other modalities; iii) the inherent difficulties in generating context-aligned, high-fidelity data given the non-stationary, noisy nature of financial data. To address these challenges, our contributions are: i) we proposed the Contextual …

External Debt Pass-Through To Inflation In Nigeria, Emmanuel A. Asue, James V. Ikyaator

CBN Journal of Applied Statistics (JAS)

This study examines external debt pass-through to inflation in Nigeria using annual data from 1981 to 2020 based on structural vector autoregressive (SVAR) model. The results reveal that an increase in external debt service leads to a significant depreciation of the exchange rate, which leads to a contemporaneous increase in inflation, while the direct response of inflation to external debt is statistically not significant. The impulse response confirms these results. The forecast error variance decomposition depicts that future values of official exchange rate depend on external debt, inflation and external debt service. The study recommends that the Nigerian government should …

Forecasting Stock Indices With The Covid-19 Infection Rate As An Exogenous Variable, Mohammad Saha A. Patwary

School of Computing and Informatics

Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. …

Mastering Stock Markets With Efficient Mixture Of Diversified Trading Experts, Shuo Sun, Xinrun Wang, Wanqi Xue, Xiaoxuan Lou, Bo An

Research Collection School Of Computing and Information Systems

Quantitative stock investment is a fundamental financial task that highly relies on accurate prediction of market status and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, the performance of existing DL methods is unstable with sensitivity to network initialization and hyperparameter selection. One major limitation of existing works is that investment decisions are made based on one individual neural network predictor with high uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle this limitation, we propose AlphaMix, a novel three-stage …

Evolve Path Tracer: Early Detection Of Malicious Addresses In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

With the boom of cryptocurrency and its concomitant financial risk concerns, detecting fraudulent behaviors and associated malicious addresses has been drawing significant research effort. Most existing studies, however, rely on the full history features or full-fledged address transaction networks, both of which are unavailable in the problem of early malicious address detection and therefore failing them for the task. To detect fraudulent behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically, in addition to the general address features, we propose …

Beyond Anthropomorphism: Unraveling The True Priorities Of Chatbot Usage In Smes, Tamas Makany, Sungjong Roh, Kotaro Hara, Jie Min Hua, Felicia Si Ying Goh, Wilson Yang Jie Teh

Research Collection Lee Kong Chian School Of Business

This study examined business communication practices with chatbots among various Small and Medium Enterprise (SME) stakeholders in Singapore, including business owners/employees, customers, and developers. Through qualitative interviews and chatbot transcript analysis, we investigated two research questions: (1) How do the expectations of SME stakeholders compare to the conversational design of SME chatbots? and (2) What are the business reasons for SMEs to add human-like features to their chatbots? Our findings revealed that functionality is more crucial than anthropomorphic characteristics, such as personality and name. Stakeholders preferred chatbots that explicitly identified themselves as machines to set appropriate expectations. Customers prioritized efficiency, …

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 …

Covid-19 In Casinos: Analysis Of Covid-19 Contamination And Spread With Economic Impact Assessment, Anastasia (Stasi) D. Baran, Jason D. Fiege

International Conference on Gambling & Risk Taking

Abstract:

The COVID-19 pandemic caused tremendous disruption for casinos, with the virus causing various lengths of shutdowns, capacity restrictions, and social distancing strategies such as machine removals or section closures. Although most of the world has now eased off these measures, it is important to review lessons learned to understand, and better prepare for similar circumstances in the future. We present Monte Carlo slot floor simulation software customized to simulate players spreading COVID-19 on the slot floor. We simulate the amount of touch surface contamination; the number of potential surface contact exposure events per day, and a proximity exposures statistic …

The Locals Casino As A Social Network – Can An Interconnected Community Of Players Detect Differences In Hold?, Jason D. Fiege, Anastasia (Stasi) D. Baran

International Conference on Gambling & Risk Taking

Abstract

It is difficult for individual players to detect differences in theoretical hold between slot machines without playing an unrealistically large number of games. This difficulty occurs because the fractional loss incurred by a player converges only slowly to the theoretical hold in the presence of volatility designed into slot pay tables. Nevertheless, many operators believe that players can detect changes in hold or differences compared to competition, especially in a locals casino market, and therefore resist increasing holds. Instead of investigating whether individual players can detect differences in hold, we ask whether a population of casino regulars who share …

Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Anchorage: Visual Analysis Of Satisfaction In Customer Service Videos Via Anchor Events, Kam Kwai Wong, Xingbo Wang, Yong Wang, Jianben He, Rong Zhang, Huamin Qu

Research Collection School Of Computing and Information Systems

Delivering customer services through video communications has brought new opportunities to analyze customer satisfaction for quality management. However, due to the lack of reliable self-reported responses, service providers are troubled by the inadequate estimation of customer services and the tedious investigation into multimodal video recordings. We introduce , a visual analytics system to evaluate customer satisfaction by summarizing multimodal behavioral features in customer service videos and revealing abnormal operations in the service process. We leverage the semantically meaningful operations to introduce structured event understanding into videos which help service providers quickly navigate to events of their interest. supports a comprehensive …

Understanding Sentiment Through Context, Richard M.Crowley, M.H. Franco Wong

Research Collection School Of Accountancy

We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is driven by many different contexts and that positive and negative sentiment are driven by different contexts. We then construct context-level sentiment measures and examine whether sentiment works as expected at the context-level across four prediction problems. Our results demonstrate that document-level sentiment exhibits significant noise in prediction and suggest that document-level aggregation of sentiment leads to missed empirical nuances. The contexts driving sentiment results vary …

Dynamic Return Relationships In The Market For Cryptocurrency: A Var Approach, Julian Gouffray

James Madison Undergraduate Research Journal (JMURJ)

This paper examines how the Bitcoin-altcoin return relationship has evolved in periods between 2015 and 2020. To understand this relation, we observe data on the cryptocurrency Bitcoin and prominent altcoins Ethereum, Litecoin, Ripple, Stellar, and Monero, which collectively represent over 90% of the market throughout the observed period. We employ a vector autoregressive model (VAR) to produce forecast error variance decompositions, orthogonal impulse response functions, and Granger-causality tests. We find evidence that Bitcoin return variation has increasingly explained altcoin returns and that market inefficiency increased between 2017 and 2020, as shown by increased Granger causality between Bitcoin and altcoins. These …

Effect Of Monetary Policy Rate On Market Interest Rates In Nigeria: A Threshold And Nardl Approach, Oluwafemi E. Awopegba, Joseph O. Afolabi, Lydia T. Adeoye, Godwin O. Akpokodje

CBN Journal of Applied Statistics (JAS)

This study examines the effect of monetary policy rate (MPR) on market interest rates in Nigeria. For parsimony, we develop two indexes called the short-term interest rate (SINT) and Lending interest rate (LINT) to represent deposit and lending rates respectively. The nonlinear autoregressive distributed lag (NARDL) and threshold regression models are adopted. The study uses monthly data from 2002:M1 to 2019:M12. The results of the threshold regression model indicate that the degree of the effect of MPR on SINT and LINT above the estimated threshold of 11 and 13 percent respectively is greater and significant than if MPR were to …

Social Dimension Of Inclusive Growth In Ecowas: Implication For Poverty Reduction, Toriola K. Anu, Goerge O. Emmanuel, Ajayi O. Felix

CBN Journal of Applied Statistics (JAS)

This study investigates the implication of the social dimension of inclusive growth on poverty reduction in Economic Community of West African States (ECOWAS) countries. It specifically examines how social indices of inclusive growth comprising of income inequality, education, and health outcomes affect poverty reduction. The study uses a panel dataset of the six (6) lower-middle income countries in ECOWAS which was analysed via panel Difference Generalised Method of Moment (D-GMM). The results show that GDP per capita exerts significant negative effect on poverty while inequality, education and health outcomes do not show significant effect on poverty. Although, the estimates of …

Effect Of Fdi Inflows On Employment Generation In Selected Ecowas Countries: Heterogeneous Panel Analysis, Timothy A. Aderemi, Olawunmi Omitogun, Bukonla G. Osisanwo

CBN Journal of Applied Statistics (JAS)

The aim of this study is to examine the effect of FDI on employment in ECOWAS sub region between 1990 and 2019. The study utilizes a panel autoregressive distributed lag model to analyse the short run and long run relationship between FDI and employment across ECOWAS sub region. In the short run, the impact of FDI on employment is negative and statistically not significant. Meanwhile, in the long run FDI has a positive and statistically significant impact on employment rate. This implies that FDI has the capacity to generate employment in countries in ECOWAS sub region. Therefore, this study recommends …

Impact Of Covid-19 Pandemic On The Nigeria Stock Market: A Sectoral Stock Prices Analysis, Peter A. Adekunle, Yakubu A. Bello, Udochukwu G. Nwachukwu

CBN Journal of Applied Statistics (JAS)

This study examines the impact of the COVID-19 pandemic on sectoral stock prices in Nigeria stock market using daily data covering from February 28, 2020 to June 26, 2020. Applying the autoregressive distributed lag (ARDL) bounds test, the study finds that COVID-19 pandemic had adverse impact on the stock market indices in the short run. Furthermore, the study documents negative response of sectoral stock prices to the pandemic while the stock prices of the banking sub-sector are the worst hit. Compared to the consumer goods, and industrial subsector indices, the speed of adjustment to long run equilibrium is faster for …