Major Stock Research Articles

Corporate Sustainability Reporting and Financial Performance: An Empirical Analysis of Indian Listed Companies

This empirical analysis examines the relationship between corporate sustainability reporting and financial performance among Indian listed companies. In recent years, sustainability has gained prominence in corporate strategy due to increasing regulatory requirements, investor interest, and stakeholder expectations. The study explores how sustainability reporting, as disclosed through corporate sustainability reports (CSR), affects the financial performance of companies. Using a sample of Indian companies listed on major stock exchanges, the study analyzes data on sustainability disclosures, financial performance indicators (such as return on assets, return on equity, and market valuation), and various control variables. The analysis employs statistical methods, including regression models, to identify the correlation and potential causal linkages between sustainability reporting and financial performance. The findings suggest a positive relationship between comprehensive sustainability reporting and financial performance, indicating that companies with better sustainability practices tend to experience higher financial returns and market valuation. The results also highlight the role of regulatory frameworks, industry-specific factors, and company size in shaping the strength of this relationship. This study contributes to the growing body of literature on corporate sustainability by providing insights specific to the Indian context, where sustainability reporting practices are evolving. It suggests that companies engaging in transparent and detailed sustainability reporting may achieve not only environmental and social benefits but also financial gains, making sustainability a strategic priority for long-term value creation.

Exploring market-wide herding behavior in the major stock markets of Latin America

PurposeThis paper investigates market-wide herding behavior among investors in the stock markets of Brazil, Chile, Colombia and Mexico from January 2013 to December 2022.Design/methodology/approachWe analyze a survivor-bias-free dataset of daily stock returns, employing a measure reflecting the cross-sectional deviation of stock returns relative to market consensus.FindingsSignificant anti-herding is observed in Brazil and Mexico, while Chile and Colombia show results consistent with rational asset pricing models. The COVID-19 pandemic generally intensifies anti-herding trends. Additionally, significant asymmetries in herding/anti-herding effects are noted during different market trends and volatility levels. Furthermore, we identify the drivers of this phenomenon, revealing that extreme crude oil price movements are associated with more pronounced anti-herding, and herding/anti-herding effects appear synchronized across all four markets.Practical implicationsOur findings regarding synchronization in herding dynamics suggest challenges in realizing the desired benefits of international diversification in the region.Social implicationsThe significant cross-country effects indicate that herding dynamics may play a crucial role in precipitating regional financial crises.Originality/valueFor the first time, we examine various features of herding behavior in the sample markets, including the impact of the COVID-19 pandemic, psychological and economic drivers of the phenomenon and synchronization in herding dynamics among the four markets.

An Empirical Analysis of the Performance of Luxury Watch Portfolios among High-Net-Worth Individuals in India During the COVID-19 Pandemic

This study empirically evaluates luxury watches owned by high-net-worth individuals (HNIs) in India as unconventional alternative assets providing diversifying benefits. During the COVID-19 pandemic, the major stock exchanges in India, NSE and BSE, faced negative returns and high volatility, igniting numerous conservative investors to divest from traditional investments and retain alternative assets, such as luxury watches, due to their low correlation to financial markets. Owing to supply chain disruptions and demand forces, the overall secondary watch market prices surged throughout this period, creating high economic value for investors. Through hypothesis testing and attribution analysis, this paper evaluates the performance of watch portfolios owned by 57 HNIs, comprising of 464 watches, to investigate whether luxury watches are viable investments during economic downturns within the domain of alternative investments. Hence, this study employs suitable attribution tools such as Sharpe ratio, M2 Alpha, Treynor Measure and Jensen’s Alpha to understand the strength of their performance. Using academic literature, market data and primary data, this study found that luxury watches provided substantial portfolio diversification benefits and outperformed traditional assets like the NIFTY50 and gold in terms both nominal and risk adjusted returns during the pandemic. Brands such as Patek Philippe, Rolex, and Audemars Piguet achieved the highest performance, as Patek Philippe demonstrated superior risk-return trade-offs. Despite challenges such as valuation complexities and liquidity constraints, luxury watches provided remarkable upside potential, benefiting from a high retention rate due to their unique emotional features. Ultimately, the findings suggest that luxury watches can be viable alternative investments considering that 65% of these HNIs viewed their watch purchases as investment opportunities.

Extreme downside risk connectedness between green energy and stock markets

This study addresses the need to understand the transmission of tail risk across financial markets, especially in the context of green energy and stock markets. Utilizing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology combined with the Conditional Autoregressive Value-at-Risk (CAViaR) framework, the model analyzes data from September 30, 2013, to October 11, 2023. This study focuses on five major stock indices (S&P500, CSI300, Nikkei225, STOXX50, and FTSE100), West Texas Intermediate (WTI) crude oil, and three sectors within green energy markets (Green bond, Global Clean Energy, and Renewable Energy and Clean Technology), highlighting the significant role of these sectors in risk propagation. The model can capture dynamic changes and asymmetries in financial returns, thus providing a precise estimation of extreme downside risks. Key findings show that three stock indices (S&P500, STOXX50, and FTSE100) and one green energy sector (Renewable Energy and Clean Technology sectors) are the predominant sources of risk, with significant connectedness around events, such as the COVID-19 pandemic. These insights are crucial for developing effective risk-management strategies and supporting the transition to a sustainable energy sector. This study concludes that understanding these risk dynamics is essential for strategic planning and market stability.

Expression of Concern: “Perceived Behavioral Factors and Individual Investor Stock Market Investment Decision: Multigroup Analysis and Major Stock Markets Perspectives”

Expression of Concern: “Perceived Behavioral Factors and Individual Investor Stock Market Investment Decision: Multigroup Analysis and Major Stock Markets Perspectives”

The Dissimilar Market Volatility in Neighboring Financial Markets: An Empirical Study Using A Multivariate Garch Model

ABSTRACT: Conventional knowledge of market volatility represents that two proximate financial markets operated in the same country would display a similar pattern in their time-varying volatility. In this paper, we propose that the heterogeneity in financial policy orientation of stock markets creates significant inter-market differences in terms of the volatility of stock returns, even if they are culturally and geographically linked to each other. For the empirical investigation of our proposition, the volatility data from two major stock exchanges in the Middle East, the Abu Dhabi Securities Exchange (ADX) and the Dubai Financial Market (DFM) are used. Both the ADX and DFM stock exchanges operate in the same country, the United Arab Emirates (UAE). The ADX is based in Abu Dhabi, the administrative capital, while the DFM is based in Dubai, the commercial and business hub of the UAE. They were both established in the year 2000, and their operational headquarters are located about 150 km from each other. This paper uses a multivariate GARCH model, in particular a diagonal VECH GARCH (1, 1) model to estimate volatility measures for stock returns between ADX-listed and DFM-listed stocks. The paper finds that the stock returns are significantly less volatile in the ADX compared to those in the DFM suggesting that the volatility transmission is incomplete between these two neighboring financial markets. This difference in volatility can potentially be attributed to the relatively conservative financial policies adopted by the oil-rich emirate of Abu Dhabi compared to the more market-oriented economic policies of Dubai, the country’s financial and commercial hub. Our findings have considerable implications for portfolio managers in ascertaining risk premiums when allocating investments across the two stock exchanges. For instance, this disparity in volatility should be taken into account by investors and policymakers when designing risk management strategies because it suggests that investors in the DFM may be exposed to higher levels of risk and uncertainty compared to their counterparts in the ADX. Investors operating in the DFM may require more robust risk management strategies, such as asset diversification and hedging, compared to their ASX counterparts.

An efficient hybrid approach for forecasting real-time stock market indices

The stock market’s volatility, noise, and information overload necessitate efficient prediction methods. Forecasting index prices in this environment is complex due to the non-linear and non-stationary nature of time series data generated from the stock market. Machine learning and deep learning have emerged as powerful tools for identifying financial data patterns and generating predictions based on historical trends. However, updating these models in real-time is crucial for accurate predictions. Deep learning models require extensive computational resources and careful hyperparameter optimization, while incremental learning models struggle to balance stability and adaptability. This paper proposes a novel hybrid bidirectional-LSTM (H.BLSTM) model that combines incremental learning and deep learning techniques for real-time index price prediction, addressing these scalability and memory challenges. The method utilizes both univariate time series derived from historical index prices and multivariate time series incorporating technical indicators. Implementation within a real-time trading system demonstrates the method’s effectiveness in achieving more accurate price forecasts for major stock indices globally through extensive experimentation. The proposed model achieved an average mean absolute percentage error of 0.001 across nine stock indices, significantly outperforming traditional models. It has an average forecasting delay of 2 s, making it suitable for real-time trading applications.

Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance’s conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities that represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. We present approaches for a variety of machine learning models – Logistic Regression, Random Forest Classifiers, and Extreme Gradient Boosting models and do a statistical comparison on the balanced accuracies of each machine learning model. This paper aims to detail our understanding of how one can leverage the public sentiment of a stock to gauge its future value.

Does a Change in the ESG Ratings Influence Firms’ Market Value? Evidence from an Event Study

In recent years, the field of “ESG finance” has seen rapid growth, resulting in the emergence and expansion of ESG ratings and rating agencies. This study investigates how financial investors react to updates in ESG ratings provided by two prominent ESG rating agencies, namely MSCI and Refinitiv. The main objective is to determine whether any positive or negative changes in a company’s sustainability ratings directly impact its market value. The Event Study methodology was used for this investigation, which analyses the Cumulated Average Abnormal Returns (CAARs) of economic events to assess their influence on corporate valuations. We analysed over 840 rating updates (events) using a sample of 75 companies across various industries, all listed on major stock exchanges. Our findings indicate that shifts in sustainability ratings, as evaluated by the two rating agencies, do not significantly impact companies’ market capitalisation. Furthermore, these outcomes remain consistent over time, suggesting that financial markets are not assigning increasing significance to ESG ratings. We offer potential explanations for these findings, which are discussed in light of the existing literature on the subject.

Cross-border ripples: investigating stock market responses to Israel-Hamas conflict in trading partner nations using event study method

PurposeThe study will add to the current discourse on the Israel-Hamas conflict by examining the impact of the war on the stock markets of trading partners. Stock market returns inevitably rise as globalization keeps integrating financial markets and economies around the world. Thus, the impact of war is assessed across a range of indicators that are similar in some way, such as geographic location, political climate or economic standing. Thus, the goal of this study is to investigate how the Israel-Hamas war affects trading partner countries' stock performance.Design/methodology/approachEvent study methodology is applied using Morgan Stanley Capital Index (MSCI) as a benchmark index. The influence of the Israel-Hamas war on the world's major stock markets is evaluated using a market model. The study takes into account Israel and its 23 trading partners. To capture the locational asymmetry in the outcome, the countries are further categorized according to their geographic locations. The official declaration of war came on October 7, 2023, a non-trading day. Consequently, October 9, 2023, is designated as the event day in this study. The data was gathered between January 1, 2023, and December 31, 2023, with an estimation period of 140 days taken into account to minimize bias.FindingsAsymmetric response is shown among the nations due to their economic standing, geographic proximity and trading links with Israel. While Austria, Greece, Egypt, Palestine and Israel had the greatest negative effects, Argentina, Japan and Chile saw significant beneficial effects. The remaining nations had little effect. The market quickly adjusted itself, eliminating anomalous returns.Research limitations/implicationsTaking into account the topic's criticality, the current work has certain limits. The study has used the daily data to limit its reach to the stock market exclusively. In the future, academics can combine high-frequency stock market data with data from other macroeconomic variables, such as currency or different commodities markets, to further their research. Furthermore, a cross-national comparison of the impact in terms of direction and intensity regarding developing global groups such as I2U2, LEVANT, BRICS, MIKTA, SCO, NATO, SAARC and OECD can provide a more comprehensive understanding in this context. To gain insight into the durability and adaptation of financial systems over time, longitudinal studies could be conducted to monitor the long-term effects of geopolitical crises on the stock markets of trading partner countries.Practical implicationsBy better managing investment portfolios and evaluating potential risks associated with trading partners involved in such conflicts, investors and businesses can lessen the impact of geopolitical tensions on stock market performance. These results contribute to our understanding of how geopolitical conflicts affect stock markets.Originality/valueThis research provides an extensive analysis of the global impact of Israel-Hamas tensions on stock market volatility by taking into account trading partners. This allows for the investigation of how various market structures and economic systems react to geopolitical turmoil. The present study is one of the first attempts to look into how disturbances in one region might affect continents to better understand the dynamics of global trade and economic interdependencies.

Stock Markets Cycles and Macroeconomic Dynamics

There is a consensus that stock markets are procyclical. However, answers to some important questions remain unclear. Do stock markets lead or lag business cycles? More interestingly, what is the duration with which they lead or lag them? This study uses different time-series filters and time-difference analysis to answer these questions by examining the dynamic interactions between three major stock indices and key macroeconomic indicators in the United States. The findings show that stock markets have been strongly procyclical, lagging industrial production by one to three months in recent decades. There have been noteworthy changes in the relationship between inflation and stock market cycles. The correlations changed from negative in the 1980s and 1990s to positive in the 2000s and 2010s. The results also reveal close associations between the stock indices, offering new insights into the interplay between financial markets and economic cycles.

Giao dịch thuật toán trên thị trường chứng khoán dưới góc nhìn công nghệ

The power of the technological revolution, combining technology and finance, has significantly transformed the way transactions are conducted in the stock market. Algorithmic trading is not only a new trend but also an essential requirement in an increasingly complex market that demands flexible and rapid adaptation. Large financial institutions such as major stock trading companies, investment funds, and individual investors are all seeking to leverage the power of technology to optimize trading performance and maximize profits. In this paper, we provide an insight into automatic algorithmic trading in the stock market from a technological perspective: how algorithms operate, their benefits, as well as the challenges and risks investors have to deal with when using them. The paper employs synthesis, comparison, and contrast methodologies based on published research to evaluate and provide recommendations for algorithmic trading. Our evaluation confirms that algorithmic trading is an inseparable part of the modern stock market.

Modelling Asymmetric Volatility in the Crypto Currency and its Dynamic Relationship with Stock Market

The paper investigates the asymmetric volatility effect of five major cryptocur- rencies and their bilateral linkages with major indices in the Indian stock market. To inves- tigate the bilateral relationship of crypto currency with stock indices, researchers used two major stock indices in the Indian stock market namely, BSE Sensex and NSE Nifty. The study used GARCH, EGARCH and TGARCH models, asymmetric to model the asymmetric volatility effect in the conditional volatility of crypto currencies and stock indices. Johansen’s cointegration and Vector Error Correction Model is used for examining the presence of cointe- gration between selected variables and to analyse the strength of causality among them. The study finds the evidence of cointegration between cryptocurrencies and stock market indices, implying that cryptocurrencies are related to stock indices. Further there is unidirectional relationship among stock and crypto market and crypto currencies having short-lived re- sponse to shocks in stock markets. Even with these currencies’ explosive growth, there are still not many research examining their connection to stock markets. This study will help investor’s those who making investment in currency market or in the stock market to eval- uate the pattern of volatility, interconnection among them, so that they can make crucial investment decisions and diversification strategies. This will help them to gain knowledge about how these two markets move together so they may avoid underestimating risk when building portfolios that contain both kinds of assets.

Utilizing the GARCH Model for Analysis and Prediction of Stock Market Trends

This study utilizes the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model to analyze the Shanghai Stock Exchange Composite Index and the Shenzhen Composite Index. It computes the returns and logarithmic returns, fits the GARCH model with a student-t distribution, and calculates the standardized residuals of the stock. Tests for normality and heteroskedasticity of residuals are conducted, follows by the creation of auto-correlation and partial auto-correlation plots to aid in volatility forecasting. The findings indicate that both returns of stock distributions exhibit multiple peaks and significant fluctuations, revealing that there exists pronounced volatility clustering and strong abrupt changes. Historical fluctuations significantly affect current fluctuations, showing a clustering effect. Volatility in returns has been very persistent, reflecting strong speculation and elevated overall risk. Histograms and skewness assessments show a left-skewed distribution for both indices, with a kurtosis greater than three, indicating a "fat-tailed" distribution. The positive mean values imply that China's economy is growing constantly, while the low standard deviation implies the immaturity of China's stock market. These insights highlight the dynamic behaviors and risk profiles in Chinas major stock indices, offering a deeper understanding of their speculative nature and economic implications. The Advantages and disadvantages of GARCH models and limitations of a single model are acknowledged in the report.

The interdependence and cointegration of stock markets: Evidence from Japan, India and USA

In a rapidly globalizing world, understanding the relationships between major stock markets is of paramount importance for investors and financial analysts. This study explores the interdependence and cointegration of stock markets in Japan, India, and the USA, and explores the dynamics of global financial markets as well as the survival of a long-term and short-term link between these three indices. These leading stock markets were selected because of the researchers’ desire to learn more about the connections between them. From April 2012 through March 2022, we used monthly data from three major stock market indices: the NIKKEI (Japan), theBSE SENSEX (India), and the NASDAQ (USA). Stock market performance in both the United States and India tend to move together. Additionally, the GC test is utilized in an effort to ascertain if the markets have any form of forecasting ability. Based on the results of the tests conducted, it was determined that the NASDAQ index can accurately predict the SENSEX index, but the NIKKEI index. The United States and the Indian stock markets are highly correlated. To further investigate the markets’ potential for foresight, the Granger causality test is applied. Tests showed that while the NASDAQ index predicted the SENSEX index with high precision, the NIKKEI index did not. After a causal relationship has been established, we then look for evidence of a short- and long-term connection.

Comparative Analysis of Spillover Effects in the Global Stock Market under Normal and Extreme Market Conditions

Using the volatility spillover index method based on the quantile vector autoregression (QVAR) model, this paper systematically examines structural changes and corresponding spillover effects within 20 major stock markets under both extreme and normal market conditions, using data spanning from January 2005 to January 2023. The results show that, compared to the traditional volatility spillover index method, which focuses mainly on average spillover effects, the QVAR model-based spillover index better captures spillover effects under extreme and various market conditions among global stock markets. The connections between stock markets are closer in extreme market conditions. The total spillover index of major global stock markets significantly increases in extreme conditions compared to normal conditions. In extreme market conditions, inflow indices show varying degrees of increase, with emerging economy stock markets displaying more significant increases. The outflow indices exhibit heterogeneity; emerging economies show consistent increases, while developed economies show mixed changes.

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.

Volatility Spillover Benefits of the Hang Seng Index and Shanghai Composite Index: A Vector Autoregressive (VAR) Model and Granger Causality Test Analysis

This study aims to explore the volatility spillover benefits between two major stock markets in Hong Kong and Shanghai, namely the Hang Seng Index and the Shanghai Composite Index. Empirical analysis is conducted using a Vector Autoregressive (VAR) model and Granger causality test based on daily return data from 2013 to 2023. The research finds a significant volatility interaction between the Hang Seng Index and the Shanghai Composite Index, which is more pronounced during periods of global economic instability. Impulse response analysis reveals that positive shocks in Hang Seng Index lead to increased volatility in the short term for the Shanghai Composite Index but gradually weaken to a weak negative effect in the long term. Conversely, positive shocks in the Shanghai Composite Index have a short-term inhibitory effect on the Hang Seng Index but transform into a weak promoting effect in the long term. These findings have important implications for investors and policymakers in risk management, forecasting future stock market trends, and capital allocation.

Review of the Impacts of Previous General Elections on Stock Market Performance: Insights for Investors

This paper aims to examine how the announcement of election results affects the NSE (Nifty) and BSE (Sensex) indices. The analysis focuses on measuring the impact of daily average returns and volatility during different time periods before and after the election result announcement: thirty days, fifteen days, and seven days. The paper is based on secondary data as opinions and views of different authors who have conducted such kind of studies are referred for the purpose of writing this research paper and consolidated views are mugged up in the article form. The findings indicate that the impact of election results is predominantly observed in the short term, gradually diminishing over the medium and long term. The study concludes that there is no significant relationship between the announcement of election results and the performance of the stock market. It suggests that the stock market initially reacts unusually to this information, causing short-term anomalies that subsequently correct themselves in the following days. This study aims to analyse the effect of election result announcements on the performance of India's major stock indexes, Sensex (BSE) and Nifty (NSE). By examining previous research and focusing specifically on the election result's impact on these indexes, this study aims to provide insights for investors to navigate the market with agility and caution during election periods, maximizing their investment outcomes..

Spillovers across global stock markets before and after the declaration of Russia’s invasion of Ukraine

Since the financial meltdown, studies on systemic risk and financial contagion have gained currency. Events like the COVID pandemic and the Russian invasion of Ukraine have fueled such an importance. This study examines the impact of the invasion on volatility transmissions across major stock markets worldwide. The stock indices considered in this study are ASX 200, ESTOXX 40, FTSE 100, HNGSNG, NIFTY 50, NIKKIE, and S&P 500. The work uses Vector Auto Regression (VAR) to study the transmission of returns. Later, the work performs Dynamic Conditional Covariance-Generalized Auto Regression Conditional Heteroskedasticity (DCC-GARCH) on the residuals where the transmission of returns was significant. The DCC-GARCH (E-GARCH) shows that all the asymmetric transmissions are negative. The study finds that co-movements of stock returns for the following pairs: ESTOXX 50-S&P 500, NIFTY 50-FTSE100, NIFTY 50-NIKKIE, NIKKIE-ESTOXX 50, S&P 500-NIFTY 50, and SP500-HNGSNG significantly intensified after the declaration of invasion. Such intensification of co-movements does establish the contagion effect triggered by invasion. The study shows that ESTOXX 50, which has the closest geographical proximity to the war zone, happens to be the highest generator of spillovers.