Financial Practitioners Research Articles

Systemic Risks in the Derivatives Market: Origins, Impacts, and Mitigation Strategies

Derivative markets play an indispensable role in the contemporary financial system. The global financial markets have been repeatedly riddled with turbulence instigated by derivative trading in recent years, underscoring the urgency and necessity for in-depth study in this field. The subject matter of this paper is to delve into systemic risk in the derivative markets, including its genesis, impact, and mitigation strategies. This paper aims to proffer valuable insights and recommendations to policymakers and financial practitioners through comprehensive analysis. To achieve this objective, this study utilizes research methodologies such as literature review, analysis of historical cases, and empirical analysis. The findings suggest that the systemic risk in the derivative markets primarily stems from high leverage operations, market information asymmetry, and complex financial product structures. These risks became particularly prominent during the financial crisis, leading to an upsurge in market volatility, liquidity crises, and a domino collapse of financial institutions. Based on these findings, the paper proposes a gamut of strategies to mitigate systemic risk, including enhancing market transparency, optimizing risk models, augmenting capital requirements, and fostering international regulatory cooperation. Additionally, technological innovations such as blockchain and big data analysis are considered to hold significant potential in enhancing market transparency and improving the accuracy of risk prediction.

Application and Empirical Analysis of Random Volatility Model in Financial Markets

The Stochastic Volatility Model (SVM), as a key innovation in modern financial engineering, has profoundly changed our understanding of the volatility characteristics of financial markets. This model not only considers the volatility of asset prices as a time-varying random variable, but also simulates this uncertainty by introducing stochastic processes such as Brownian motion or more complex stochastic differential equations, thereby achieving a precise characterization of the dynamic characteristics of volatility. Compared to traditional fixed or historical average volatility assumptions, SVM can capture sudden changes in market sentiment, the impact of news events, and the influence of macroeconomic factors on volatility, providing investors with a more realistic perspective on the market. This article not only deeply analyzes the theoretical basis and construction methods of SVM, but also verifies its effectiveness and applicability in different financial market environments through empirical analysis and actual market data, providing valuable references and insights for financial practitioners, scholars, and policy makers.

Pension funds in Colombia and implications: A bibliometric analysis from Scopus

This study delves into the dynamic landscape of pension funds in Colombia, examining both its current research status and practical implications. Understanding the state of pension systems is crucial for individuals' financial security and a country's overall economic stability, especially given factors such as an aging population and evolving socio-economic dynamics. By comprehensively analyzing scholarly publications on Colombian pension funds using the Scopus database, this research sheds light on key trends and contributors shaping policy formulation and financial planning. The analysis covers a period from 1987 to 2023, identifying 71 relevant articles through keyword searches and filters. Various metrics, including publication trends, citations, international collaboration, and authorship dynamics, were quantitatively assessed. The results reveal an annual growth rate of 5.37%, with research articles comprising 81.69%, review articles 7.04%, and other formats like books, book chapters, and conference papers making up 11.27% of the total documents. Notably, countries such as the UK, the USA, Spain, and Colombia emerge as key contributors to the literature. Among authors, Mesa-Lago, L., stands out as a significant figure, with one of the earliest publications on the topic. The findings underscore the growing importance of pension funds in Colombia, particularly in the context of fund performance research amid financial crises. The prevalence of international collaboration suggests a global interest in understanding and contributing to the development of Colombian pension systems. Furthermore, the study identifies research gaps, particularly in areas concerning the socio-economic impact of pension policies, providing a roadmap for future investigations. Policymakers, financial practitioners, and researchers can leverage these insights to inform strategic decision-making and address critical challenges in optimizing pension fund management in Colombia.

Enhanced Foreign Exchange Volatility Forecasting Using Ceemdan with Optuna-Optimized Ensemble Deep Learning Model

Foreign Exchange (FX) is the largest financial market in the world, with a daily trading volume that significantly exceeds that of stock and futures markets. The prediction of FX volatility is a critical financial concern that has garnered significant attention from researchers and practitioners due to its far-reaching implications in the financial markets. This paper presents a novel hybrid ensemble forecasting model integrating a decomposition strategy and three deep learning (DL) models: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN). This combination addresses individual models' limitations and further improves the accuracy and stability of FX volatility forecasting. The proposed approach utilizes the CEEMDAN technique to decompose volatility into multiple distinct intrinsic mode functions (IMFs) and merges these IMFs with GARCH and EGARCH volatilities to form the input dataset for the DL models. In addition, we employed an attention mechanism to improve the effectiveness of the DL techniques. Furthermore, the hyperparameters for the DL models are optimized using the Optuna algorithm. Finally, a hybrid ensemble model for forecasting exchange rate volatility is developed by combining the predictions of three distinct DL models. The proposed approach is evaluated against various benchmark models using evaluation measures such as MSE, MAE, HMSE, HMAE, RMSE, Q-LIKE, and the model confidence set (MCS) approach. The results demonstrate that our proposed approach provides accurate and reliable forecasts of FX volatility under different forecasting regimes, making it a valuable tool for financial practitioners and researchers.

Legal analysis of derivative transactions in islamic economics

The legal position regarding derivative transactions in Islamic economic practices has attracted significant attention from Islamic financial institutions such as AAOIFI and DSN-MUI. Derivative instruments can be incorporated into the sharia framework as long as they fulfil the core principles of sharia such as being free from elements of riba (interest), gharar (uncertainty), and maysir (gambling). These bodies have issued guidelines and fatwas such as the DSN-MUI fatwa No. 96/DSN-MUI/IV/2015, which permits the use of derivatives for hedging purposes under a number of strict requirements that ensure transparency and fairness and emphasizes that transactions are based on real needs and not speculation. However, implementing these legal guidelines on the ground faces various challenges. Key challenges include correctly applying derivative instruments so that they do not turn into prohibited tools of malicious speculation. Strict supervision and audit systems are needed to ensure compliance with Shariah principles on an ongoing basis, as well as ensuring transparency in the execution of transactions. In addition, in-depth training and education for Islamic finance practitioners is essential to support proper and effective implementation. Thus, the legal position regarding derivative transactions in the Islamic economy is clear and feasible to implement with strict supervision and supported by continuing education.

Stock Market Prediction in Nepali Stock Market using Machine Learning Model

The stock market, a key driver of the global economy, presents a formidable challenge for accurate prediction due to its intricate, chaotic, and dynamic nature. This study explores machine learning approaches, specifically comparing three prediction models—Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The analysis incorporates historical stock trading data, including open, high, low, and close prices, and technical analysis indicators such as moving average, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and Commodity Channel Index (CCI) so on. The evaluation focuses on the performance of these models in predicting future trends in stock prices, utilizing data from the Nepal Stock market spanning from 2013 to 2023. The selected stocks for analysis include two stock NIC Asia Bank Limited and, NMB Bank Limited, two index NEPSE and Hydropower index. Through rigorous assessment, it is determined that LSTM demonstrates superior overall performance compared to GRU and BPNN. This research contributes valuable insights into the effectiveness of machine learning models in forecasting stock market trends, with implications for investors and financial practitioners.

The Effect of Profitability, Liquidity and Company Size on Capital Structure in Companies Listed on the Indonesian Stock Exchange

This study investigates the effect of profitability, liquidity, and firm size on the capital structure of firms listed on the Indonesia Stock Exchange (IDX). Using data from manufacturing firms, the study found that profitability and liquidity have a negative and significant influence on capital structure decisions, with more profitable firms tending to rely on less debt and more internal sources of funds. However, firm size does not consistently have a significant negative impact on capital structure, suggesting that external factors such as capital market conditions and economic policies have a more dominant role in determining a firm's capital structure. These results provide important insights for financial practitioners in designing appropriate funding strategies to maximise firm value and effectively manage financial risk in the Indonesian capital market.

Inklusi Keuangan dan Pemberdayaan Ekonomi: Evaluasi Efektivitas Produk Keuangan Syariah dalam Meningkatkan Akses Keuangan bagi Masyarakat Marginal

Financial inclusion and economic empowerment have become major focal points in efforts to improve financial access for marginalized communities. This is particularly crucial in the current global context where economic inequality and access to financial services remain serious issues. This article aims to evaluate the role of Shariah-compliant financial products in supporting financial inclusion and economic empowerment among marginalized groups. The literature review method used in this article provides a strong foundation for collecting and analyzing various related articles, including policy reviews and market analyses. Through in-depth review, this article illustrates that Shariah-compliant financial products have significant potential to provide more inclusive, transparent, and sustainable solutions in enhancing financial access for marginalized communities. Key factors such as trust, understanding of Shariah principles, and supportive regulations are also emphasized as essential elements in strengthening the effectiveness of these financial products. Additionally, the implications of the findings in this article are crucial in providing guidance for stakeholders, regulators, and financial practitioners. By understanding the potential and advantages of Shariah-compliant financial products, stakeholders can develop more effective strategies to improve financial inclusion and economic empowerment among marginalized communities. Thus, this article not only provides insights but also offers a clear direction for further steps in strengthening financial access for all layers of society, while promoting sustainable and inclusive economic development.

ICC 국제표준은행관행(ISBP 821) 개정사항에 관한 고찰

Purpose: This paper introduces the ICC opinions related to the revision of ISBP 821 and the content of the ISBP revision to trade and finance practitioners who handle letters of credit. Hence, they can apply the latest ISBP when examining documents related to letters of credit and reviewing the presented documents. Research design, data, and methodology: This paper analyzed the ICC opinions and related cases and revised paragraphs of ISBP 821. Results: ISBP 821 is the latest guidance for reviewing documents under the letters of credit, reflecting the practices agreed upon by the ICC National Committee, and is a must-know for letter of credit beneficiaries to prepare and present documents to the nominated bank or issuing bank. Therefore, financial institutions, including banks, and traders that handle letters of credit, must be aware of the revised ISBP 821. Conclusions: ISBP 821 is a rule that must be observed when presenting and reviewing a letter of credit in accordance with UCP 600. If the parties to the letter of credit do not abide by ISBP 821, they will be judged accordingly by the ICC Banking Committee, and there is a high likelihood of losing in court.

Memahami Ancaman Venture Capital Zombie Terhadap Stabilitas Ekonomi: Analisis Dampak Modal Ventura Yang Tidak Produktif Pada Keseimbangan Finansial Global

This research examines the "Venture Capital Zombie" phenomenon and its implications for global economic stability. "Venture Capital Zombie" refers to venture capital firms that continue to operate despite being unable to generate significant profits or innovations. This study employs secondary data analysis, literature review, and policy analysis to minimize the economic impact of zombie venture capital. The research findings indicate that this phenomenon can hinder innovation, create resource allocation distortions, and increase systemic risk. The Financial Services Authority (OJK) in Indonesia has identified this issue and is preparing regulations to address it. This study suggests the need for stricter supervision, investment diversification, and better risk mitigation strategies to reduce the negative impact of "venture capital zombies." The conclusions of this research provide valuable insights for policymakers and financial practitioners in efforts to maintain global economic stability.

Do climate risks affect dirty–clean energy stock price dynamic correlations?

Prior studies have extensively exhibited an interest in exploring the connectedness between dirty and clean energy stock prices alongside the drivers of such price connectedness, shedding light on hedging strategies for finance practitioners. Nevertheless, no empirical research has examined whether climate risks, the emerging indicator for investors to handle the divestment of dirty energy stocks, have affected the time-varying dirty–clean energy equity price nexus. This study fills this gap by innovatively identifying dynamic conditional correlations (DCCs) between dirty and clean energy stock prices. An ARDL/NARDL model is applied to assess whether the climate risks affect such correlations by controlling for business cycles, funding liquidity, USD values, and oil market sentiments. Overall, we detect an undeniable negative impact of climate risks on the positive dirty–clean energy price dynamic correlations. Additionally, the NARDL model results reveal that a rise in federal fund rates exerts higher effects on the dirty–clean energy stock price comovements. Our findings suggest the strengthened potential of hedging clean energy stocks against dirty energy equities in case of escalating climate risks and heightened fossil fuel price volatilities. Furthermore, substantial attention is required to account for monetary policies' asymmetric effects on clean energy investment.

Enhancing Financial Investment Decision-Making With Deep Learning Model

This paper introduces the ISSA-BiLSTM-TPA model to improve financial investment decision-making. Traditional deep learning models face limitations in handling the complexity and uncertainty of financial markets. Our approach incorporates attention mechanisms, Bidirectional Long Short-Term Memory (BiLSTM), and Temporal Pattern Attention (TPA) to enhance accuracy in modeling and forecasting financial time series. The attention mechanism focuses on crucial information, BiLSTM captures bidirectional dependencies, and TPA identifies optimal solutions. Experimental results show higher prediction accuracy compared to traditional models, offering more reliable decision support for financial practitioners. Continuous optimization aims to provide innovative decision-making tools for the finance industry, advancing deep learning technology in finance.

An SME approach to data analytics by management accountants in the transition economy of Vietnam

Purpose Data analytics (DA) is an emerging topic in management science at large organizations; however, accountants in small and medium-sized enterprises (SMEs) are believed to be lagging far behind in the usage of DA. This study aims to provide a deep understanding of the actual DA activities undertaken by management accountants (MA) and to draw on SMEs’ characteristics to take advantage of DA applications. Design/methodology/approach This study used a qualitative approach by conducting in-depth interviews with 31 accounting and finance practitioners at senior levels of SMEs, following the use of the MAXQDA 2022 application for data analysis. Findings Findings in this study suggest variance and trend analysis as the two most popular tasks of DA, and advanced tasks such as contingency analysis, financial modeling, sentiment analysis and regression analysis are unfully performed in SMEs. The outcomes revealed that DA is not anticipated to affect the responsibilities but to expand the role and scope of management accounting. Practical implications This study simultaneously builds a theoretical framework about the antecedents, in terms of the external and internal drives and the characteristics of SMEs’ owner-managers, that are most common in all types of SMEs that encourage MA to use a specific technology, data analysis in a more advanced way instead of searching for determinants that affect the adoption of technology in general, as previous studies have conducted. Originality/value Although there are studies on DA usage, little has approached the mutual interconnections between how DA is applied by MA in SMEs and what changes in management accounting responsibilities by DA affect. Therefore, the point of this study was to look into how SMEs use DA and what activities MA actually do with DA to discover what traits SMEs need to use DA applications effectively as DA applications become more advanced.

Big Data Revolution in Finance: Opportunities, Challenges, and Future Trends

In todays digital era, big data has emerged as one of the most exciting and potentially transformative technological innovations in the financial sector. Its rise has not only altered our understanding of financial markets and institutions but also presented unprecedented opportunities and challenges to the financial industry. As financial transactions, customer interactions, and market behaviors continue to digitize, financial institutions are actively exploring how to harness the power of big data to gain a competitive edge and enhance decision-making. This paper delves into the significance of big data in the financial sector and effective development strategies, analyzing its real-world impact on the industry. It aims to provide valuable insights to financial practitioners, decision-makers, and researchers on how to leverage big data to achieve financial objectives.

Integrating Climate Change Risks and Sustainability Goals into Saudi Arabia’s Financial Regulation: Pathways to Green Finance

This study examines the integration of climate change risks and sustainability goals within Saudi Arabia’s financial regulatory framework to enhance green finance initiatives aligned with Vision 2030. A qualitative research design synthesises insights from a comprehensive literature review, semi-structured interviews with domain experts, and a detailed analysis of critical Saudi green finance frameworks and legislation. This research identifies mechanisms for embedding sustainability in the financial sector and addresses the challenges, opportunities, and strategic directions essential for Saudi Arabia within the global context of sustainable finance. The findings reveal a robust foundation laid by Vision 2030 initiatives yet underscore the need for enhanced regulatory frameworks, increased market readiness, and greater societal engagement. This study highlights a significant literature gap in understanding Saudi Arabia’s unique approach to green finance amid its economic diversification and sustainability goals. Contributing to original insights, this research underscores the critical role of Saudi Arabia in the global energy market and its substantial economic and environmental transformations. It offers detailed analyses and recommendations that enrich the discourse on sustainable finance, impacting policymakers, financial practitioners, and scholars.

Examining the Distributional Characteristics of Daily Returns of Nifty 50: Normality Assessment and Implications

This paper investigates the distributional characteristics of daily returns of the Nifty 50 index, a benchmark index comprising 50 large-cap stocks traded on the National Stock Exchange of India. Utilizing historical data spanning a specified time period, we conduct normality testing to assess the adequacy of the normal distribution assumption underlying many financial models. Our analysis provides insights into the departure from normality. As a result of departure from Normality, it may affect the tail behaviour, and volatility dynamics of Nifty 50 daily returns, offering implications for risk management, option pricing, portfolio management, and market efficiency. By synthesizing empirical findings with theoretical considerations, this research contributes to a deeper understanding of the statistical properties of Nifty 50 daily returns and informs practitioners in finance about the challenges associated with Modeling and analysing stock market data.

Capital Asset Pricing Model: A Renewed Application on S&P 500 Index

Capital Asset Pricing Model: A Renewed Application on S&P 500 Index

Unveiling influential factors in the capital asset pricing model: A novel approach utilizing Taguchi method

This study uses a Design of Experiment (DOE) approach and the Taguchi method to examine how different factors affect the expected return within the framework of the CAPM. The Capital Asset Pricing Model (CAPM) is a financial framework that forecasts an investment’s expected return by taking into account its systematic risk. This model factors elements like the risk-free rate, market risk premium, and beta to ascertain the expected return for a given asset. Typically, historical market data and financial analysis provide the inputs for CAPM. This method, frequently used in manufacturing and engineering, is modified for use in the financial domain to evaluate the importance of variables like beta, market risk premium, and the risk-free rate. The research identifies the factors that influence investment performance and shows how much each factor contributes to the expected returns. It is possible to ascertain each factor's percentage contribution to the expected return through statistical analysis. This work improves knowledge of CAPM and creates opportunities for improving investment decision-making processes by bridging the gap between financial theory and experimental design. Financial practitioners can achieve more reliable and accurate investment return forecasts by incorporating DOE techniques into their existing analytical toolkit.

Behavioral Biases and Regional Diversity: An In-Depth Analysis of Their Influence on Investment Decisions - A SEM & MICOM Approach

The Basic purpose of the study was to understand the impact of behavioral biases on Global Investors investment Decisions and whether there is any significant difference of the impact on investment Decisions of Global investors due to regional diversity. The data was collected from 467 Global investors from China and India SEM was used to test the hypothesis and Followed by Multi- group analysis. The results revealed that Behavioral biases have a significant on investment decisions of Global Investors. The impact was found more on India Global investors than that of China. The insight into how regional diversity can magnify or attenuate these biases offers valuable guidance for global investment firms seeking to tailor their services to a culturally diverse clientele. Understanding the interplay between behavioral biases and regional disparities allows for the development of region-specific financial products and advice, better aligning investment strategies with the unique needs and risk tolerance of investors from different parts of the world. The originality of this research lies in its nuanced examination of how behavioral biases, in conjunction with regional disparities, can offer valuable insights for both investors and financial practitioners, enhancing our comprehension of the intricate dynamics at play in the global investment sphere.

Having a Significant Other Reduced Financial Strain During the COVID-19 Pandemic

Utilizing data collected from the 2021 Perspectives on the Pandemic Survey (n= 6,916), part of the Health and Retirement Study, this article examined the role of having a significant other on subjective and objective measures of financial strain during the COVID-19 pandemic. When controlling for income, net worth, employment status, and demographic characteristics, the empirical results suggest that those who had a significant other, compared with those who did not, experienced less financial strain during the COVID-19 pandemic. The ensuing discussion highlights the importance of the protective role of having a significant other in financial health and offers pragmatic implications for financial practitioners.