International Journal of Financial Studies

17 pages, 501 KiB

Open AccessArticle

Financial Development and Economic Growth: Evidence from Low-Income Nations in the SADC Region

by Courage Mlambo

Int. J. Financial Stud. 2024, 12(3), 62; https://doi.org/10.3390/ijfs12030062 - 27 Jun 2024

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The study sought to examine the relationship between financial development and economic growth in low-income nations in the SADC region. Motivated by the observation that numerous states in the SADC region lack adequately developed financial systems, this investigation was undertaken. Many SADC states are low-income countries, and they remain financially underdeveloped, which could compromise their growth prospects. The analysis was quantitative in nature, and used panel data to achieve its objectives. The data period spanned from 2000 to 2022. The dynamic common correlated effects (DCCE) technique was used for estimation purposes. Results showed that there is a positive relationship between financial development and economic growth. The relationship was also found to be causal: financial development is not only a result of economic growth; it also influences growth. The evidence from the findings supports the notion that financial development is needed to increase the effectiveness of resource allocation and consequently promote growth. This calls on the governments in the countries under investigation to create environments that foster financial development. Full article

23 pages, 2599 KiB

Open AccessArticle

Generalized Loss-Based CNN-BiLSTM for Stock Market Prediction

by Xiaosong Zhao, Yong Liu and Qiangfu Zhao

Int. J. Financial Stud. 2024, 12(3), 61; https://doi.org/10.3390/ijfs12030061 - 27 Jun 2024

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Abstract

Stock market prediction (SMP) is challenging due to its uncertainty, nonlinearity, and volatility. Machine learning models such as recurrent neural networks (RNNs) have been widely used in SMP and have achieved high performance in terms of “minimum error”. However, in the context of SMP, using “least cost” to measure performance makes more sense. False Positive Errors (FPE) can lead to significant trading losses, while False Negative Errors (FNE) can result in missed opportunities. Minimizing FPE is critical for investors. In practice, some errors may result in irreparable losses, so measuring costs based on data is important. In this research, we propose a new method called generalized loss CNN-BiLSTM (GL-CNN-BiLSTM), where the cost of each datum can be dynamically calculated based on the difficulty of the data. We verify the effectiveness of GL-CNN-BiLSTM on Shanghai, Hong Kong, and NASDAQ stock exchange data. Experimental results show that although there is no significant difference in the accuracy and winning rate between GL-CNN-BiLSTM and other methods, GL-CNN-BiLSTM achieves the highest rate of return on the test data. Full article

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35 pages, 5247 KiB

Open AccessArticle

AI-Driven Financial Analysis: Exploring ChatGPT’s Capabilities and Challenges

by Li Xian Liu, Zhiyue Sun, Kunpeng Xu and Chao Chen

Int. J. Financial Stud. 2024, 12(3), 60; https://doi.org/10.3390/ijfs12030060 - 27 Jun 2024

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Abstract

The transformative impact of AI technologies on the financial sector has been a topic of increasing interest. This study investigates ChatGPT’s applications in financial reasoning and analysis and evaluates ChatGPT-4o’s effectiveness and limitations in conducting both basic and complex financial analysis tasks. By designing a series of multi-step, advanced reasoning tasks and establishing task-specific evaluation metrics, we assessed ChatGPT-4o’s performance compared to human analysts. Results indicate that while ChatGPT-4o demonstrates proficiency in basic and some complex financial tasks, it struggles with deep analytical and critical thinking tasks, especially in specialized finance areas. This study underscores the need for meticulous task formulation and robust evaluation in AI financial applications. While ChatGPT enhances efficiency, integrating it with human expertise is crucial for effective decision-making. Our findings highlight both the potential and limitations of ChatGPT-4o in financial analysis, providing valuable insights for future AI integration in the finance sector. Full article

20 pages, 459 KiB

Open AccessArticle

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

by Apostolos Ampountolas

Int. J. Financial Stud. 2024, 12(3), 59; https://doi.org/10.3390/ijfs12030059 - 26 Jun 2024

Viewed by 192

Abstract

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets. Full article

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25 pages, 548 KiB

Open AccessArticle

The Moderating Effect of Ownership Structure on the Relationship between Related Party Transactions and Earnings Quality: Evidence from Saudi Arabia

by Abdulaziz Alsultan and Khaled Hussainey

Int. J. Financial Stud. 2024, 12(3), 58; https://doi.org/10.3390/ijfs12030058 - 26 Jun 2024

Viewed by 207

Abstract

This paper seeks to investigate how earnings quality is affected by related party transactions (RPTs). The research also examines the impact of ownership structure as a moderating variable on this relationship. Panel data with the firm fixed effects model are utilized in the paper. A sample of 91 non-financial companies listed on the Saudi Stock Exchange between 2018 and 2022 were included, resulting in 429 observations of company performance over that time period. This paper finds that there is a negative association between RPTs and earnings quality. Furthermore, the study found that the adverse effect of RPTs on earnings quality is intensified when there is managerial ownership and institutional ownership as moderating variables. The study’s conclusions are robust and reliable, as the sensitivity analysis results reinforce those of the basic analysis. To the authors’ knowledge, there is relatively little available evidence on the connection between RPTs and their correlation with earnings quality, particularly in the context of ownership structure acting as a moderating variable. Moreover, the study’s findings hold important implications for enhancing earnings quality in developing economies. To the authors’ knowledge, no studies have been conducted in Saudi Arabia thus far to investigate the impact of ownership concentration, institutional ownership, managerial ownership, foreign ownership, and state ownership on the association between RPTs and earnings quality. Therefore, this paper expands the literature by modeling how the interaction between ownership structure and related party transactions may influence earnings quality. In this way, the authors contribute to the body of knowledge by unveiling a more robust control mechanism, particularly in developing economies with ineffective markets for corporate control. Full article

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Int. J. Financial Stud., Volume 12, Issue 3 (September 2024) – 5 articles

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