Financial Theory Research Articles

Financial Management Practices and Financial Sustainability in Mission Hospitals, Kiambu County, Kenya

Purpose: The study aimed to examine the role of financial management practices in the financial sustainability of mission hospitals in Kiambu County, Kenya. It seeks to understand how financial planning, funding practices, working capital management, and health sector regulations impact the economic stability and operational viability of these vital healthcare institutions. Methodology: The study utilized a descriptive cross-sectional research design to explore the financial management practices and their impact on the financial sustainability of mission hospitals in Kiambu County, Kenya. Data collected on various financial indicators and management practices were analyzed using the Statistical Package for the Social Sciences (SPSS) version 22. Descriptive statistics such as mean and standard deviation were computed to summarize the financial management practices observed in the hospitals. The findings were presented using tables to display the summary statistics and charts and graphs to illustrate trends and comparisons across different financial indicators. Additionally, inferential analyses, including Pearson correlation and multiple linear regression, were conducted to assess relationships between variables and determine the predictors influencing the financial sustainability of the hospitals. Findings: The study achieved an 89.6% response rate, revealing a predominantly male (53.4%) and mature workforce, with most respondents aged 31–40 and over 51. Significant positive relationships were found between financial sustainability and financial planning and control practices (β1 = 0.241, p = 0.003), financing and funding practices (β2 = 0.231, p = 0.002), and working capital management practices (β3 = 0.332, p = 0.000). Financial management practices explained 39.2% of the variation in financial sustainability. Health sector regulations significantly moderated the relationship between financial planning and control practices and financial sustainability (β4a = -0.215, p = 0.035), but had no significant moderating effect on financing or working capital management practices. Unique Contribution to Theory, Practice and Policy: The study findings align with several financial theories, providing valuable insights into financial management practices in mission hospitals. They support the Pecking Order Theory by highlighting the prioritization of internal financing to maintain autonomy and minimize information asymmetry. Additionally, the findings contribute to the Cash Conversion Cycle Theory, emphasizing the role of efficient working capital management in enhancing liquidity and reducing the need for external financing. Moreover, the research supports contingency theory by underscoring the influence of financial management practices on financial sustainability and advocating for tailored financial strategies in mission hospitals. The study also contributes to the theory of budgeting by stressing the importance of systematic budgeting in financial decision-making for resource allocation and financial stability. These insights offer valuable guidance for both policymakers and practitioners in mission hospitals, informing policy decisions and providing practical recommendations to enhance financial management practices and achieve greater financial sustainability in these healthcare institutions.

Multi-feature fusion stock prediction based on knowledge graph

PurposeStock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.Design/methodology/approachThis study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.FindingsExperimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.Originality/valueThe study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

A Hundred Years of Misunderstanding (?): Magna Carta and Power of the Purse

Magna Carta Libertatum was interpreted by social scientists firstly as the initial step forward transition to representative democracy; secondly, within the context of limiting the taxation authority of the king, it was considered, by public finance theorists, especially in developing countries such as Turkey, as the first document regarding the principle of "no taxation without representation" as a reflection of the power of the purse. On the other hand; in many studies until 1960s; it has been put forward that, what were limited by the Carta were not taxes, that the limiting body was "not representative", and that taxes in England have always been based on the consent factor since the 10th century (therefore, the Carta did not introduce a new practice for taxation). Furthermore, in open sources it is stated that, a word denoting tax [task or taske(n)] was used during the signation period of Carta. Surprisingly, this word does not appear anywhere in the Carta. Unfortunately, or deliberately, these studies were totally ignored in studies written after 1960s in public finance and budgeting fields of the literature in developed and developing countries. In conclusion; it is a debatable pre-assumption that Carta is the first document which limits the taxation authority, and it is an unproven assumption that it is the first written document on the power of the purse.

Economic Impact of Major Sporting Events on Local Economies

Purpose: This study sought to explore the economic impact of major sporting events on local economies. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the economic impact of major sporting events on local economies. Preliminary empirical review revealed that while such events often yielded significant short-term economic benefits, including increased tourism revenue and job creation, their long-term sustainability remained uncertain due to common challenges like cost overruns and underutilized infrastructure. Factors such as event scale, location, and management influenced the extent of economic impact, with effective planning and stakeholder engagement playing crucial roles. Legacy planning and sustainable development strategies were deemed essential for maximizing economic benefits beyond the event itself, with a focus on inclusivity and social equity. The study emphasized the importance of ongoing research and evaluation to inform future decision-making processes regarding event hosting and resource allocation. Unique Contribution to Theory, Practice and Policy: The Urban Economics theory, Tourism Economics theory and Public Finance theory may be used to anchor future studies on the economic impact of major sporting events on local economies. Researchers emphasized the importance of conducting comprehensive economic impact assessments prior to hosting events, facilitating informed decision-making. Legacy planning and sustainable development strategies were advocated to ensure that event-related investments contributed to long-term economic development. Stakeholder engagement and collaboration were prioritized to distribute economic benefits equitably. Diversifying revenue streams and leveraging ancillary economic opportunities were recommended to maximize economic benefits. Additionally, promoting destination branding and tourism development was highlighted as a key strategy for attracting investment and business opportunities. Policymakers were advised to adopt a holistic approach to event planning, integrating economic, social, cultural, and environmental considerations to host successful and sustainable events.

Does digital transformation matter for trade credit provision? Evidence from China

This study demonstrated that digital transformation can promote the supply of trade credit in China. The results are robust to different measures of trade credit provision and endogeneity. The impact of digital transformation on trade credit provision is more pronounced among firms in regions with lower marketization and worse information environment. This study investigates this potential channel and finds that digital transformation increases trade credit provision by improving firms' financing availability. Overall, the results are consistent with the financial theory of trade credit.

MIT and the Origins of the Modern Theory of Asset Pricing

This article argues that it was no coincidence that one of the places in which the modern theory of finance was developed was the Massachusetts Institute of Technology. Not only had it attracted Paul Samuelson, who had a long-standing interest in the problem, but its academic culture as a science-focused engineering school that fostered a competitive atmosphere and close relations between its economics department and business school was conducive to the development of such theories.

Evaluating the influence of Green Growth, Foreign Direct Investment and Financial Development towards Sustainable Environment: Evidence by Green Finance Theory

Evaluating the influence of Green Growth, Foreign Direct Investment and Financial Development towards Sustainable Environment: Evidence by Green Finance Theory

Literature Study of Stock Investment Risk Analysis for Beginner Investors

Awareness of investment has been owned by all parties, including young people who unfortunately still do not understand the risks of investment. This can certainly have a negative impact that is far from the expectations or objectives of the investment itself. This article was compiled using a literature review based on the Google Scholar database for the period 2014-2024, with one of the objectives to study the risks often experienced by novice investors. The results of the literature review show that investment decisions whose decision making is ideally rational, but often become irrational and this condition is very relevant to various theories such as the theory of Efficient Market Hypothesis (EMH), the theory of Behavioral Finance, the Theory of Planned Behavior.

Behavioral economics and the nature of neoclassical paradigm

AbstractPsychological observations are by now well integrated into economics, especially in the theory of finance, as can also be seen in the Nobel Prize awarded to Thaler. On the contrary, Simon’s attempt to reforge economic theory on the paradigm of bounded rationality failed. Starting from the birth of the neoclassical paradigm, we’ll describe the attempt to give it psychological foundations with a direct measurement of utility, then the axiomatic turn of the paradigm and its first anomalies. We’ll then sum up the debate on rationality, taking place in the group of economists led by Simon, which brought to the rational expectations hypothesis. Finally, we’ll discuss the development of behavioral economics and its progressive acceptance in economic theory. This historical reconstruction allows us to understand the actual hard core of the neoclassical paradigm and the growing need of the paradigm for practical flexibility that determines how to choose arguments, methods and evidence that can be useful to its development, including psychological ones.

Does ownership structure drive the effect of CEO overconfidence on earnings quality?

PurposeThe purpose of this paper is to examine the effect of chief executive officer (CEO) overconfidence on earnings quality and the moderating role of ownership structure as a crucial corporate governance device.Design/methodology/approachThe paper uses the generalized method of moments (GMM) estimation method to test our models on a sample of 335 French companies between 2009 and 2020, i.e. 4,020 observations.FindingsThe results show that CEO overconfidence negatively affects earnings quality. This result supports the predictions of behavioral finance theory and suggests that CEO overconfidence is a behavioral bias that affects the quality of earnings. The authors also examined the effect of different types of ownership structures on this relationship. The results show the significant role of controlling shareholders, owner-managers, families and institutional investors in mitigating the negative effect of CEO overconfidence on earnings quality.Research limitations/implicationsThis paper has some limitations. First, other types of ownership structures could have been analyzed such as state ownership. Second, we ignored the role of the board of directors as an important governance mechanism in controlling overconfident CEOs’ actions.Practical implicationsCompanies should be aware of the potential risks associated with CEO overconfidence, which can compromise the faithful representation of earnings. This highlights the importance of effective monitoring and internal controls to detect and prevent such practices, which involve the role of ownership structure.Originality/valueThis paper addresses the effect of CEO overconfidence on earnings quality and provides new evidence on the role of different ownership structure types in shaping this relationship. Additionally, this paper sheds new light on how overconfident CEOs may behave in challenging times.

SF-Transformer: A Mutual Information-Enhanced Transformer Model with Spot-Forward Parity for Forecasting Long-Term Chinese Stock Index Futures Prices.

The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in stock price forecasting, they rely solely on historical price data, which, given the inherent volatility and dynamic nature of financial markets, are insufficient to address the complexity and uncertainty in long-term forecasting due to the limited connection between historical and forecasting prices. This paper introduces a pioneering approach that integrates financial theory with advanced deep learning methods to enhance predictive accuracy and risk management in China's stock index futures market. The SF-Transformer model, combining spot-forward parity and the Transformer model, is proposed to improve forecasting accuracy across short and long-term horizons. Formulated upon the arbitrage-free futures pricing model, the spot-forward parity model offers variables such as stock index price, risk-free rate, and stock index dividend yield for forecasting. Our insight is that the mutual information generated by these variables has the potential to significantly reduce uncertainty in long-term forecasting. A case study on predicting major stock index futures prices in China demonstrates the superiority of the SF-Transformer model over models based on LSTM, MLP, and the stock index futures arbitrage-free pricing model, covering both short and long-term forecasting up to 28 days. Unlike existing machine learning models, the Transformer processes entire time series concurrently, leveraging its attention mechanism to discern intricate dependencies and capture long-range relationships, thereby offering a holistic understanding of time series data. An enhancement of mutual information is observed after introducing spot-forward parity in the forecasting. The variation of mutual information and ablation study results highlights the significant contributions of spot-forward parity, particularly to the long-term forecasting. Overall, these findings highlight the SF-Transformer model's efficacy in leveraging spot-forward parity for reducing uncertainty and advancing robust and comprehensive approaches in long-term stock index futures price forecasting.

A Critical Comparison of SEUT and Prospect Theory

This article explores two key theories in behavioral finance: Subjective Expected Utility Theory (SEUT) and Prospect Theory, outlining their development as responses to historic economic paradoxes, challenging traditional notions of rationality in economic decision-making. SEUT extends the concepts of expected value and rational choice, incorporating individual utility functions and subjective probabilities. Despite its comprehensive approach, SEUT struggles to explain certain behaviors under risk and uncertainty. Prospect Theory, conceived by Kahneman and Tversky, offers an alternative. It introduces decision weighting, focusing on how individuals differently perceive gains and losses relative to a reference point. This theory explains human biases like loss aversion and framing effects, which SEUT overlooks. Through practical examples and empirical studies, the article demonstrates scenarios where human behavior deviates from SEUT predictions, influenced by psychological factors. However, both theories provide insights into economic decision-making, they have their respective strengths and limitations. The article suggests a more integrated approach, recognizing the complexities of human decision-making in economic contexts.

The Influencing Mechanism of Managerial Myopic Behavior on Enterprise Sustainable Development Performance: Evidence Based on Listed Companies in China

Corporate sustainability is a key concern in today’s world. This research examines the relationship between managerial myopic behavior and corporate sustainability performance in Chinese A-share listed companies from 2013 to 2022. Based on behavioral finance theory and time orientation theory, our research provides novel insights into how time preference in managerial decisions significantly affects corporate sustainable development. We also investigate how myopic behavior by managers can adversely affect a company’s sustainable development performance. Using a robust dataset, we describe the mechanisms by which managers’ myopic behavior affects sustainability performance, especially in two aspects: financialization of assets and green innovation capability. The results of our research show the following: (1) managers’ myopic behavior adversely affects corporate sustainability performance; (2) asset financialization and green innovation capability play mediating roles; and (3) unlike the findings of the existing research, this study found that the linear moderating effect of media attention plays a significant “inverted U-shaped” moderating role in the relationship between the two influences.

Behavioral traits of fund managers: a systematic literature review

PurposeThe purpose of the study is to investigate, synthesize and critically evaluate empirical research findings on the behavioral traits of fund managers from 1994 to 2024. The ultimate goal is to provide a unified body of literature on three broad topics: first, fund managers' demographic and professional characteristics, such as age, gender, level of education and years of industry experience; second, fund managers' social and political connections; and third, fund managers' behavioral biases that lead to irrational investment decisions.Design/methodology/approachThe relevant papers from selected journals were discovered and manually validated using the Scopus database. From 317 retrieved documents, 57 relevant articles were chosen and analyzed after the forward and backward search of the existing articles.FindingsThis paper presents a categorized summary of behavioral factors that have gained a foothold in influencing the behavior of fund managers in fund management research, with several studies demonstrating their significance leading to improved prediction and model precision, as this review indicates. In addition, the study summarized the contributions of prior empirical studies within the aforementioned three major categories and illustrated their consequences.Originality/valueThe present study contributes to the understanding of the effects of behavioral finance theories on fund managers by providing meaningful explanations of their behavioral traits based on empirical evidence and existing trends and knowledge gaps, both of which can influence the future direction of research.

Unraveling Financial Markets: Deep Neural Network-Based Models for Stock Price Prediction

This paper delves into the potential and challenges of leveraging deep neural networks (DNNs) in stock price forecasting. Traditional econometric models often grapple with the complexities of financial time series data, leading to the exploration of DNNs, especially architectures like Long Short-Term Memory (LSTM) networks, to capture intricate patterns in such data. While these networks present promising results, challenges such as model interpretability, non-stationarity of data, overfitting, and computational demands remain. The financial sector's increasing digitization and influx of alternative data offer a unique opportunity for DNNs, emphasizing their capability for automatic feature extraction. However, the integration of DNNs necessitates a multi-disciplinary approach, involving financial experts, data scientists, and computational specialists. The paper concludes with an optimistic outlook for the synergy between deep learning and traditional financial theories, aiming for a harmonious blend of modern computational techniques with foundational financial principles.

The Influence of Behavioural Factor on Retail Investors Decision Making in IPO Investment

This study explains about the influence of behavioural factors on retail investors in decision-making processes when considering the investments in Initial Public Offerings (IPOs). IPOs represent an attracting investment opportunity for the retail investors, often they are characterized by significant up-and-downs and uncertainty. Understanding the psychological factors for guiding investors decisions in this factor is crucial for both investors and the market regulators. Outlining over the behavioural finance theories and the actual evidence, this research explores various psychological biases and emotional influences that may affect the retail investors during the IPO investment decision-making. psychological biases such as overconfidence, group behaviour, and loosing, along with the emotional factors like fear of missing out (FOMO) and loss aversion, are analyzed in the factors of IPO investments. Here the methodology involves a combination of quantitative analysis, utilizing survey data from the retail investors, and the qualitative examination ofrelevant literature and case studies. By analyzing the behavioural factors influencing the IPO investment decisions this research study intends to give understandings right nto the primary mechanisms driving retail investors behaviour in IPO markets.

The Impact of Sharia Financial Literacy, Religiosity, and Perceived Quality on Investment Decisions on Sharia-Compliant Products

This research examines the influence of sharia financial literacy on investment decisions in sharia financial products, by considering two moderating variables: perceived quality and religiosity. Quantitative methods were used by collecting data through bold questionnaires via Google Form to 190 respondents who understand sharia financial products. Data analysis was conducted using Structural Equation Modeling based on behavioral finance theory. The research results show that there is a positive influence of sharia financial literacy on investment decisions in sharia financial products. Perceived quality and religiosity do not moderate the influence of financial literacy on investment decisions, but have a direct effect. The implication is that sharia financial literacy influences positive investment decisions. Perceived quality and level of religiosity also influence investment decisions in sharia financial products. These findings are important for the Islamic finance industry in developing effective marketing and service strategies, as well as for educational institutions to increase sharia financial literacy.

A Comprehensive Analysis of Behavioral Finance and its Impact on Investment Decisions

This paper thoroughly examines behavioral finance, challenging traditional financial theories by questioning investor rationality and market efficiency. Focusing on five key phenomena—loss aversion, short-term momentum, long-term reversal, framing effect, and endowment effect—the analysis relies on real-world observations to reveal how psychological biases significantly impact investor decisions and financial markets. Loss aversion, especially evident during financial crises, highlights humans' tendency to avoid potential losses rather than pursuing equivalent gains. The study explores short-term momentum and long-term reversal, challenging market efficiency theories by illustrating how past performance influences future returns. Emphasizing cognitive biases like framing and endowment effects, the paper underscores their role in diverse investment decisions and irrational asset overvaluation based on a sense of ownership. It advocates for incorporating these biases in financial models, regulations, and investment strategies, emphasizing their importance for stakeholders to navigate the financial landscape, minimize instability, and capitalize on opportunities. The research establishes a foundation for a holistic finance approach, respecting and leveraging human behavior for market stability and benefit.

Machine Learning and Fama-French Three-Factor Model Applications for Analyzing Stock Price in Technology Enterprises

This study endeavors to forecast the stock prices of the leading U.S. technology entities - Google, Microsoft, Amazon, Meta, and Apple - through the application of diverse machine learning models, complemented by the traditional Fama-French three-factor model. The employed models encompass Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Support Vector Machines (SVM), and Decision Tree models. Initially, historical stock price data is utilized to train these machine learning models, enabling the identification of potential price trends. Subsequently, the integration of the Fama-French three-factor model enhances the analysis by scrutinizing the impacts of market risk, company size, and book-to-market value on stock prices. The outcomes illuminate both the effectiveness and limitations of various models in stock price prediction, highlighting the advantages of machine learning methodologies over traditional financial theories. This research provides financial market analysts and investors with a fresh perspective on the amalgamation of machine learning and traditional financial theories for enhanced stock price prediction.

An Application of Multi-Factor Model on Anomaly in China Stock Market

An Application of Multi-Factor Model on Anomaly in China Stock Market