Financial Market Research Articles

Improving realised volatility forecast for emerging markets

AbstractAccurate forecasting of realised volatility is essential for financial risk management and investment decision-making in emerging markets, taking the South African financial market as a benchmark. This study examines the predictive performance of four prominent models: HAR (Heterogeneous AutoRegressive), realised GARCH (Generalized AutoRegressive Conditional Heteroscedasticity), Recurrent Conditional Heteroskedasticity (RECH), and the Rough Fractional Stochastic Volatility (RFSV) models. These models are specifically tailored to capture the complex dynamics and long-range dependence observed in financial time series. We illustrate the challenges and limitations of these models outside the context of established markets. Our empirical findings reveal unique strengths for each model. The HAR model excels in capturing long-term volatility patterns, while realised GARCH models effectively capture volatility clustering and persistence. RECH model showcases their ability to forecast Value-at-Risk, while the RFSV model successfully captures irregular and long-memory characteristics. We provide empirical evidence that the South African financial market is rough. Moreover, this study provides valuable insights into forecasting realised volatility in the South African market, and the findings can assist practitioners and investors in making informed decisions and developing robust risk management strategies.

Application of Fintech Financial Technology in the Stock Market in Vietnam

Along with the strong development of science and technology, the application of modern technology in many activities in the financial market, including the stock market, has contributed to promoting and creating new steps. a major turning point in the development and integration of these markets. The application of modern technology has created technological leaps in the financial sector around the world, bringing incredible changes in the fields of securities, banking, and investment. Currently, Fintech is playing an important role in changing the way investors participate in the stock market.The profound impact of the 4.0 Industrial Revolution is and has been taking place strongly on most industries around the world in general and Vietnam in particular, so the stock market is no exception to that trend. Developing the stock market towards integrating modern information technology applications is an inevitable trend. Recognizing the importance of information technology application in the field of securities and stock market, the State Securities Commission has gradually modernized the technology platform to improve management and supervision capacity, operating and serving the development of the market.Regarding the development of securities products and services, since 2017, a number of securities companies have researched and deployed financial technology applications to increase customers' investment experience. Up to now, Fintech has helped new investors easily access stock investment channels with many convenient trading features, low costs, saving time and effort. However, Fintech applications in stock trading in our country are not really diverse, still at a simple level such as supporting buying and selling transactions, providing transaction information for investors. The article provides comments and assessments on the current status of Fintech applications in stock trading in Vietnam and some proposals to widely deploy these applications in the stock market in the near future.

Improving prediction accuracy in agricultural markets through the CIMA-AttGRU model.

In the Chinese futures market, agricultural product futures play a crucial role. While previous studies have primarily relied on historical price data and fundamental financial indicators of agricultural product futures, there is a growing recognition of the value that lies within the vast amounts of textual data generated in the financial domain. Our study specifically focuses on the limitations of existing methods in capturing the complex relationships and rich semantic information embedded in these textual sources. This article designs a CIMA AttGRU (CIMA-AttGRU) model for soybean futures, which is a forecasting method for the agricultural product market. This model uniquely integrates Collective Intrinsic Mode Analysis (CIMA) with an Attention-Gated Recurrent Unit (AttGRU), leveraging the strengths of both techniques to enhance predictive accuracy and adaptability. The rationale behind employing the CIMA-AttGRU model lies in its ability to effectively tackle the inherent challenges of financial market analysis. By incorporating CIMA, the model adeptly filters out market noise, directly addressing the challenge of high volatility. Additionally, with its attention mechanism, the CIMA-AttGRU targets the issue of non-linear patterns by allowing dynamic adjustment to temporal dependencies, offering differential learning capabilities crucial for capturing the nuanced fluctuations in futures prices. Complementing the CIMA and AttGRU, the integration of Class-wise Adversarial Domain Adaptation (CADA) further refines the model's robustness, addressing the critical challenge of domain adaptivity. This aspect is particularly vital for edamame futures, where price determinants can vary significantly over time and across regions. Our empirical results demonstrate a significant improvement in forecasting precision, with the CIMA-AttGRU model achieving a Mean Absolute Error (MAE) reduction of 15% and a Mean Squared Error (MSE) reduction of 20% compared to conventional models. This superior performance, especially in terms of prediction accuracy and handling market fluctuations, highlights the improve of the model compared to existing methods and has made significant explorations in agricultural market forecasting.

Machine Learning-Driven Stock Selection System: A Proposed Multi-Factor Model-Based Navigation Tool for the Chinese Financial Market

In view of the constantly changing and volatile nature of the Chinese financial landscape, characterized by the introduction of high-tech and huge amounts of market data, there is a need for an improved method for stock selection. Conventional stock methods are, however, inadequate due to their tendency to ignore the high volatility of the market and the nonlinear relationship between many financial variables which are at play. They have not been able to deliver predictions that are strong and accurate. This research, therefore, aims to explore how machine learning algorithms can be utilized to improve the accuracy and adaptability of stock selection models, thereby addressing these limitations. Through empirical results, it can be claimed that ML models, especially tree-based algorithms like LightGBM and XGBoost, are superior to traditional models in predicting stock returns. In this case, rolling windows are very well adapted to machine learning methods, which stand out as superior in terms of the adaptability to the changing stock market conditions. In addition, the research reveals the necessity of integrating the market-driven data, for instance, the trading volume and the momentum, into the stock selection models can help to better capture short-term pricing dynamics. The dissertation concludes with a machine learning-based stock selection system that is optimized for the Chinese financial market. The results of this work will increase the knowledge of quantitative finance by showing the machine learning algorithms can be more accurate in stock selection, and at the same time, providing practical solutions to the investors and financial institutions. The research emphasizes the necessity of combining modern computational methods and traditional financial theories to devise more effective and adaptive investment strategies in a complicated financial environment.

The Impact Mechanism of Free Competition Among Financial Institutions on the Credit Accessibility of Farmers

Using data from rural fixed observation points in Zhejiang Province from 2003 to 2022, this study employs a difference-in-differences (DID) model to explore the impact of free competition among financial institutions on the credit accessibility of farmers and its underlying mechanisms. The results show that free competition among financial institutions significantly improves farmers' credit accessibility. The mechanism analysis indicates that such competition leads to an increase in the number of loan officers, promotes the marketization of certain financial functions, and enhances the dissemination of financial knowledge, thereby improving farmers' financial literacy and reducing financing constraints. Specifically, agricultural industrialization and digitalization both indirectly affect the relationship between free competition among financial institutions and farmers' credit accessibility. Based on these developments, financial institutions are able to provide more efficient and convenient financial services, contributing to the formation of a financial ecosystem. Further analysis reveals a noticeable "siphoning effect" of financial competition in Zhejiang Province, where it significantly promotes the growth of rural credit in the region while suppressing the growth of rural credit in neighboring areas. The study’s conclusions offer both empirical insights into rural financial market reform from the perspective of policy changes and practical reference from the perspective of regional spillovers for the development of rural finance.

Adaptive Hybrid Optimized LSTM Models: A Novel Computational Framework for Algorithmic Financial Trading System

In our modern society, with the development of Internet and information system, pre-programing algorithmic trading strategies for online automatic trading has also flourished, especially in the rapidly fluctuating trading market. Using quantitative trading programs that can automatically trade in response to market conditions has attracted the attention of major financial institutions and governments in the financial market. How to use self-adaptive programs to automatically trade in the ever-changing financial market has become a popular research topic for the development and pursuit of all financial markets in recent years. This research proposes an online self-adaptive trading algorithm that can be applied to financial markets such as stock market, currency markets, cryptocurrency markets, futures markets, etc. In the first part of the algorithm, the simplified swarm optimization will be used to optimize the parameters of the newly proposed flexible grid in this research. Then the data will be imported into the artificial neural network model for training in the latter part, helping the trading model automatically select the appropriate parameters for construction flexible grid corresponding to the market conditions. The greatest contribution of the research is to provide a whole new trading algorithm that can adapt to the dynamic trading market, automatically make suitable adjustments to current trading strategy and place real-time orders. The algorithm controlling both profit and risk, which can seem like a balanced trading algorithm that are robust and profitable.

Do financial markets value corporate culture?

This paper examines how financial market participants incorporate corporate culture, an important value-relevant information, into their investment decisions. Utilizing firm-level corporate culture measures derived from the earnings conference call transcripts, we find that firms with stronger cultural values are associated with higher stock liquidity. We identify three channels through which corporate culture affects stock liquidity: reducing information risk, enhancing trust, and increasing investor recognition. In addition, we find that stronger corporate culture is significantly associated with higher stock price informativeness and future stock returns, and lower level of default risk and informed trading. Overall, our findings highlight the importance of corporate culture in enhancing financial market quality.

Oil rents, renewable energy and the role of financial development: Evidence from OPEC+ members

This study examines the threshold and moderating effects of oil rents on renewable energy consumption in OPEC+ members for different financial development indicators. For this, dynamic panel threshold and GMM estimators are employed for the time period 1999-2019. The empirical findings of the analysis reveal significant and negative threshold and moderating effects for financial market and total financial development, but not for financial institution development. Specifically, oil rents are found to reduce renewable energy consumption, with this negative effect becoming more pronounced at higher levels of financial market and total financial development. Overall, the results do not support the energy transition theory for OPEC+ members for which revenues from oil rents are transferred towards renewable energy investments. Rather, the presence of lobbying influences and inadequate support from financial markets and institutions is validated. These findings provide an additional debating point on the role of oil endowments on sustainable growth.

Financial Market Responses to the Policy Language of Forward Guidance: Evidence from China

Forward guidance is an innovative unconventional monetary policy tool used to manage expectations. This study not only investigates whether and how forward guidance influences financial markets but also explores its impact on cross-market correlations. Three key findings include: (1) larger absolute change in tone increases currency market volatility but decreases treasury bond and stock market volatilities. The noise-reducing effect is primarily attributable to forward guidance on economic outlook rather than policy inclination, although their effectiveness has reversed since 2015. (2) A positive change in forward guidance tone decreases currency market interest rate, which is economically negligible, albeit statistically significant. Returns of other financial assets are not responsive to the change in forward guidance tone. (3) Forward guidance significantly influences cross-market correlations though the impacts show heterogeneity. Economic-outlook and policy-inclination forward guidance significantly affect the currency-bond correlation in the same direction while exert differential impacts on bond-stock and currency-stock correlations. Two important implications for expectation management are enhancing coordination between different types of forward guidance as well as improving the efficiency of information transmission in communication.

A Decade Mapping: Economy Impact of Urban Railway in Financial Market

A Decade Mapping: Economy Impact of Urban Railway in Financial Market

Unsupervised learning modeling of the impact of Black Swan events on financial network reconfiguration: New insights from the COVID-19 outbreak and the Russia-Ukraine war

The emergence of Black Swan events, such as the COVID-19 pandemic and the Russian-Ukrainian war, has presented new challenges for stock markets and necessitates a redesign of portfolio management strategies. This work investigates the impact of unexpected and rare events, like the COVID-19 pandemic and the military conflict between Russia and Ukraine, on the structure of the cross-correlation network of stock returns. This research area has not yet been explored in depth. Previous research has primarily focused on the effects on stock returns, volatility, or changes in the topological properties of complex financial networks. In contrast, this study introduces a novel two-phase approach, integrating complex networks and self-organizing maps (the Kohonen network), to analyze changes in network structure and stock classification. A total of 483 US stocks from the S&P 500 index were analyzed. The findings reveal a notable contrast between the networks constructed before and after the onset of the COVID-19 pandemic (895 quotations for each network), along with significant disparities in stock classification according to centrality measures. Moreover, in-depth analysis has revealed significant distinctions between the SOM classification and the inherent partitioning based on economic sectors for both periods. Our research highlights the process of asset reconfiguration within the cross-correlation network, triggered by uncommon and unexpected events that disrupt financial market quotations. These findings are crucial for investors, portfolio managers, policymakers, and researchers.

Digitalization of the financial market and green coal trade

In recent years, fintech advancements have influenced global coal trade patterns. This study analyzes the impact of fintech on coal trade across 80 countries from 2012 to 2019. Findings from the MGARDL model reveal that increased fintech activity reduces coal imports while boosting exports, suggesting enhanced trade efficiency and access to capital. Income inequality and private infrastructure investment negatively impact coal trade, likely due to restricted financial access and infrastructure limitations. Meanwhile, ICT development supports coal trade by streamlining logistics and finance. The study confirms a bidirectional relationship between fintech and coal trade, emphasizing mutual influence. Policy recommendations include promoting fintech adoption, encouraging sustainable investments, improving financial access, and expanding ICT infrastructure to foster a more sustainable coal trade.

Sustainable Synergy: Static and Dynamic nexus between ESG and BRICS Equity Markets

Sustainable finance has become the new frontier in global investments, redefining success beyond mere profit margins. For the BRICS economies, this paradigm shift presents both a challenge and an opportunity to reimagine their role in the evolving world of responsible investing. Therefore, this study examines the connectedness between environmental, social, and governance (ESG) leader indices and the BRICS equity markets using the TVP-VAR model. Our return and volatility connectedness results demonstrate a moderate level of transmission between ESG and conventional equity markets. Moreover, the system integration increased substantially during COVID-19 compared to pre-COVID-19, highlighting the strong impact of global events on financial market behavior. ESG (conventional) stocks predominantly emerge as net transmitters (recipients) of return and volatility shock to the system. The dynamic analysis reveals a notable increase in system connectedness, exhibiting increased transmission during an uncertain market environment. Our portfolio analysis suggests that investors should increase their investment in BRICS equity markets during COVID-19 to get higher diversification benefit, however hedging ESG with conventional stocks becomes expensive (higher hedge ratio) during a turbulent period. These results have substantial implications for portfolio management, suggesting that ESG can effectively mitigate risk and optimize portfolio performance.

Hybrid Preprocessing for Neural Network-Based Stock Price Prediction

In the domain of stock price prediction, the intricate interdependencies within multivariate time series data present significant challenges for accurate forecasting. This paper introduces a groundbreaking hybrid preprocessing technique to tackle this issue. By leveraging the Empirical Wavelet Transform (EWT), we adeptly extract both low-frequency and high-frequency components from the time series. We then apply Dynamic Time Warping (DTW) and Differential Dynamic Time Warping (DDTW) to measure component similarities, identifying correlated patterns within the stock price series. High-frequency components are managed using sliding windows and Principal Component Analysis (PCA), while PCA is directly applied to low-frequency components. Integrating these techniques into neural network models, our approach yields a substantial 30% improvement in prediction accuracy compared to traditional methods. This significant advancement underscores the potential of our hybrid preprocessing method in enhancing stock price prediction accuracy, offering valuable insights for financial market analysis.

A Study on the Antecedent Grouping Pattern of ESG Decoupling in New Energy Vehicle Manufacturing Companies

This study explores the decoupling phenomenon of listed companies in the new energy vehicle manufacturing category in terms of ESG (environmental, social, and corporate governance) by using fuzzy set qualitative comparative analysis (fsQCA) and necessity condition analysis (NCA) methods, and identifies four different groupings. These groupings reveal the complexity and diversity of firms' ESG performance and disclosure, including high gearing and low environmental commitment, high management compensation and low social responsibility, high media attention and selective disclosure, and high market competition and inadequate governance. The results of the study show that financial pressure, management incentives, media attention and market competition all significantly affect firms' ESG behavior. Based on this, policy and management recommendations are proposed, such as strengthening the regulation of ESG disclosure, incorporating ESG performance assessment in executive incentives, and balancing short-term financial goals with long-term sustainable development. Although this study reveals the complexity of ESG decoupling in new energy automobile manufacturing firms, the limitations of data source, sample scope and methodology still exist. Future research should expand the sample scope, conduct dynamic analysis, and synthesize multiple methods to deepen the understanding of corporate ESG decoupling behavior and provide scientific guidance for management and policy making.

Integrating predictive analytics into strategic decision-making: A model for boosting profitability and longevity in small businesses across the United States

The integration of predictive analytics into strategic decision-making has emerged as a transformative approach for small businesses in the United States, offering enhanced pathways to profitability and longevity. This study proposes a conceptual model that explores how predictive analytics can empower small businesses to make data-driven decisions, optimize resources, and mitigate risks. By leveraging advanced machine learning algorithms, data mining, and statistical techniques, small businesses can identify trends, predict customer behavior, and uncover growth opportunities. The proposed model focuses on integrating predictive analytics across key operational areas: financial planning, marketing, inventory management, and customer relationship management. In financial planning, predictive analytics enables more accurate forecasting of revenues and expenses, supporting budget optimization and investment decisions. In marketing, businesses can analyze consumer data to design targeted campaigns, improving conversion rates and customer retention. Inventory management benefits from predictive insights by anticipating demand patterns, reducing overstocking or stockouts. Moreover, customer relationship management is enhanced through personalized recommendations and proactive engagement strategies. This research emphasizes the accessibility of predictive analytics tools, particularly through cloud-based platforms, enabling small businesses to adopt these technologies without incurring substantial costs. Additionally, the study highlights challenges such as data quality, privacy concerns, and the need for technical expertise, proposing strategies to overcome these barriers. Case studies of small businesses that have successfully implemented predictive analytics demonstrate significant outcomes, including increased operational efficiency, reduced costs, and sustained competitive advantage. The findings underscore the importance of fostering a data-driven culture and investing in training to enhance employees’ analytical capabilities. Policymakers and industry stakeholders are encouraged to support initiatives that promote the adoption of predictive analytics in small businesses through funding, education, and technical assistance programs.

Fuzzy MCDM techniques for portfolio selection in the post-COVID Indian mutual fund market: a comparative study of FAHP and entropy methods

Fuzzy MCDM techniques for portfolio selection in the post-COVID Indian mutual fund market: a comparative study of FAHP and entropy methods

What Information Do Investors Care About? Evidence in the Korean Mutual Fund Market

What Information Do Investors Care About? Evidence in the Korean Mutual Fund Market

THE IMPORTANCE OF FINANCIAL EDUCATION AND ITS RELATIONSHIP WITH ACCOUNTING PRACTICE IN DAILY LIFE

Currently, financial management has become of great importance to the financial market, due to its high degree of complexity, not only for companies but for society as a whole.People's lack of information about the financial market can cause impacts such as debt, loss of assets and economic frustration.The respective article aimed to analyze financial education and accounting practices and the incorporation where these possible areas can cooperate for people's daily financial management.The research used the descriptive bibliographic methodology with a qualitative background.Financial understandings with accounting practices showed us that society can.The research contributes to the understanding of the importance of financial education in the development of management skills and more informed financial decision-making.

Barriers for institutional investors to invest in digital financial assets

Subject. The article analyzes barriers to active involvement of institutional investors' funds in digital DFA in the Russian financial market. Objectives. The study aims at identification of the main barriers preventing the active involvement of institutional investors' funds in DFA, development of recommendations for their elimination. Methods. The study employs general scientific methods of research, logical, comparative, and statistical analysis to review foreign practice and academic publications of domestic and foreign authors. Results. We revealed three main groups of barriers. They are legal and regulatory barriers; low volume of the primary market and insufficient liquidity in the secondary market; low level of financial literacy and problems of understanding DFA by private investors. To eliminate them, we offer to amend a number of federal laws to allow institutional investors to include DFA in their investment strategies and portfolios. The problem of low liquidity of the secondary market can be solved by including the main Russian trading platforms in the register of DFA exchange operators of the Bank of Russia, and providing private investors' access to these transactions. Conclusions. It is crucial to develop a risk rating system for DFA and criteria for DFA inclusion in the portfolios of institutional investors.