Stock Market Indices Research Articles

Decoding Investor Sentiments in the Indian Stock Market: A Structural Equation Modelling Approach

Background of the study This research examines how psychological and social biases affect individual investors’ investing decisions. Investor sentiment significantly influences financial markets, frequently causing stock prices to deviate from their intrinsic values. In rising economies such as India, where retail investors are significantly affected by psychological factors, comprehending these attitudes is crucial. Methods This study analyses data from a comprehensive questionnaire that was conducted throughout the nation and included 552 retail investors. The investigation employed structural equation modelling (SEM) to identify the elements that influence the decision-making of individuals who invest in the Indian stock market. Findings The research offers insight on the influence that investor attitude has on investment decision-making as well as the factors that precede it. The study demonstrates that investors make financial decisions based on sentiment. In addition to assessing the efficacy of the Indian financial market, this study sought to ascertain the rationality of investors’ choices by exploring the factors that influence their decision-making process. Conclusion The outcome of the study shows that information seeking, anchoring, herding, representativeness, and overconfidence all have a big impact on investors. Moreover, the study has proven investors’ irrationality and stock market inefficiency. The findings may be employed to further examine the trading practices of international investors and encourage further study in the field of behavioural finance.

Searching for safe haven assets against American and European stocks during the Russo-Ukrainian War

Purpose This study aims to evaluate the safe haven property of several assets against the US and European stock markets during the Russo-Ukrainian War in a time–frequency framework. Design/methodology/approach This study uses the wavelet-based dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) methodology and wavelet coherence on daily returns for the S&P500, STOXX600 and 13 possible save haven assets. Findings The results show that wheat and corn are the best assets to use as hedges and safe havens for all types of investors. The second-ranked are energy commodities, which are hedges and safe havens for long-term investors. Gold, silver and palladium display hedging and safe haven qualities for medium- and long-term investment. However, cryptocurrencies, the Dow Jones sustainability index and Islamic indices do not act as safe havens for most holding periods. Practical implications These findings have significant implications for portfolio investment strategies in times of geopolitical risks. Originality/value The contributions of this study are twofold. First, several assets from different classes were analyzed as possible candidates for safe havens. Second, to the best of the authors’ knowledge, this is the first study to analyze safe haven property through different investment horizons for the US and the European stock market indices during the Russo-Ukrainian War.

The cost of political instability in Greece

ABSTRACT This paper examines the relationship between political instability, economic growth, and Greece's overall stock market index (ASE) returns from 1970 to 2018. The aim is to measure and quantify the impact of political instability on economic growth and the overall stock market returns. The analysis includes six different variables as proxies that quantify political instability. A new Socio-Political Instability index is constructed from these variables by employing the principal component analysis. The empirical part employs GARCH, GARCH-M, Granger causality tests and OLS regressions. The paper provides new insightful results and a new measure of political instability for Greece.

Impact of Macroeconomic Variables on Indian Stock Market

This study provides significant understanding of how macroeconomic factors influence the Indian stock market. It highlights the necessity of monitoring these variables to make intelligent investing choices and to better understand the economic trends shaping the stock market

Impact of Foreign Institutional Investors and Inflation Rates on Stock Market Performance in India

The relationship between inflation rates in the Indian stock market from 2014 to 2024 and Foreign Institutional Investors (FII) is examined in this research. Using quarterly data and statistical techniques including correlation analysis and the Augmented Dickey-Fuller (ADF) Unit Root test, it investigates their combined influence on market performance. Poor Correlations FII and Sensex are The relationship between FII inflows and Sensex movements is very weakly negative (-0.08584), suggesting that FII inflows have little effect on stock market performance. The correlation between inflation rates and the Sensex is 0.12846, which is modestly positive, indicating that inflation has little effect on stock performance

An analysis of the effects of aging society on global stock markets

PurposeThe primary purpose of this study is to explore the effects of demographic transition toward aging populations on the performance of stock market indices across various economic developments. The research aims to provide valuable insights into the life-cycle hypothesis on savings patterns, investment behavior and the potential reverberations on global financial markets.Design/methodology/approachThe study adopts a comprehensive global perspective, scrutinizing the effects of aging populations on stock market indices across developed, developing and transitional economies through the panel data analysis. Using annual data spanning the period from 1991 to 2020, encompassing a sample of 10 countries from each economic development level, the study employs the panel autoregressive distributed lag (ARDL) model with fixed effect estimation.FindingsThe findings unveil a statistically significant positive impact of the elderly population proportion on global stock market indices. However, the magnitude and contours of this impact exhibit considerable heterogeneity across different country groups. Specifically, the study finds that while the aging population significantly influences stock market performance in developed nations, its effect is overshadowed by other economic factors, such as consumer price indices and interest rates, in developing countries and economies in transition.Originality/valueThe originality and value of this study lie in its comprehensive global perspective, which encompasses a diverse array of economies at varying developmental stages. The research contributes to an understanding of the effects of demographic transitions on stock market performance on a global scale. The insights derived from this study hold significant implications for policymakers, financial institutions and investors seeking to navigate the challenges and opportunities posed by aging societies in an increasingly interconnected global economy. Additionally, the findings highlight the need for specific strategies and policies that account for the unique economic characteristics and developmental stages of different nations.

Impact of Inflation Stock Market Returns

This research paper examines the impact of inflation on stock market returns, focusing on the Indian stock market. Utilizing a comprehensive methodological approach, we analyse the relationship between inflation and stock market performance through various quantitative techniques including trend analysis, regression analysis, histograms, growth percentage models, and descriptive statistics. The trend analysis reveals patterns and shifts in stock market returns in response to changing inflation rates. Regression analysis is employed to quantify the strength and nature of the relationship between inflation and stock market returns, providing insights into the degree to which inflationary pressures affect market performance. The findings of this study indicate a multifaceted relationship between inflation and stock market returns. While high inflation periods generally correlate with increased market volatility and lower returns, the impact varies across different sectors and economic conditions. Our analysis also includes the significance of monetary and fiscal policies in moderating the effects of inflation on the stock market. This research contributes valuable insights for investors, policymakers, and financial analysts, by having a detailed analysis of Inflation-Stock market study. The study's results can inform investment strategies and policy decisions aimed at mitigating the adverse effects of inflation on stock market performance and enhancing market resilience. The purpose of the study is to investigate he correlation, understand the investor behaviour and recommend the insights from the study.

The Role of Fintech in Increasing Retail Participation in the Indian Stock Market

The rapid growth of financial technology (fintech) has brought substantial changes to retail investor activity in the Indian stock market. This study examines how fintech developments have boosted retail participation from 2017 to 2024, primarily through mobile trading platforms, robo-advisors, and digital payment solutions. Using a quantitative research method with secondary data, the study tracks the year-over-year increase in retail shareholders, noting a significant rise particularly after 2021. The findings point to factors such as the Digital India initiative, growing adoption of fintech, and evolving market conditions as key drivers that have reduced entry barriers for retail investors. The analysis establishes a connection between the advancement of fintech and improved access to the stock market, underlining fintech's significant contribution to democratizing investment opportunities. Nonetheless, ongoing issues like regulatory challenges and financial literacy gaps remain, highlighting the need for continued efforts to maximize fintech's potential in fostering sustainable growth in retail investment.

Asymmetric Effects and Volatility Clustering in NSE NIFTY 50: A Comparative Analysis of GARCH Models

This study aims to analyze the volatility patterns, Clustering, and asymmetric effects in the NSE NIFTY 50 index. It involves using daily returns data from the NSE NIFTY 50 from 01 Jan 2010 to 31 Dec 2023. Daily closing prices are obtained from the official NSE website, and returns are calculated based on these prices. EGARCH (1, 1), TARCH (1, 1), GARCH (1, 1), GARCH-M (1,1), and models are utilized to predict volatility, capturing volatility clustering and leverage effects. Using both the Akaike and Schwarz criteria, EGARCH (1,1) was demonstrated to be the best model. The findings reveal that Fluctuations in the Indian stock market, particularly shown in the NIFTY index of the NSE were the highest in the year 2020 due to the COVID-19 pandemic. Past NIFTY returns demonstrate a GARCH effect on today’s NIFTY returns. The results of the research suggest that the impact of negative and positive shocks on the stock returns series varies. Volatility is largely caused by negative news compared to positive news. It is suggested that the model exhibits a leverage effect and that different types of information produce various types of shock to the volatility of the Nifty index return. The findings show that investors of NSE, particularly those involved with the NIFTY50, face heightened volatility and increased risk, especially during periods of significant events.

Asymmetric Effect of Oil Prices on Kazakhstan’s Stock Market Index and Exchange Rate

In this study, the asymmetric effect of oil prices in Kazakhstan on the exchange rate (USD/KZT) and Kazakhstan stock market index (KASE) returns was analyzed using the non-linear ARDL cointegration (NARDL) method with monthly data for the period January 2010-February 2024. As a result of the findings of the study, both positive and negative changes in oil prices statistically affect stock returns and exchange rates in the long term. The estimated long-term positive (0.115982) and negative (0.111234) coefficients of oil prices on KASE returns were found to be statistically significant at the 5% significance level. Thus, in the examined period, the KASE variable gave different reactions to positive or negative shocks in the oil price. In the model where the exchange rate is the dependent variable, the estimated long-term positive (-0.093875) and negative (-0.092918) coefficients of oil prices were found to be statistically significant at the 95% reliability level.

Decoding the stock market dynamics in the banking sector: Short versus long-term insights

The severity of extreme fluctuations and crises within the global banking sector is escalating. Conventional models, operating on a single time scale, may misinterpret any shift as a change in the long-term trend, distorting market insights. To address this issue, the present paper introduces a hierarchical structure into the standard hidden Markov model, enabling the differentiation of short and long-term trends within the U.S. banking industry. Using NASDAQ Bank stock market index data from January 1, 2007, to July 31, 2023 at two different frequencies, we construct and evaluate different calibrations of the hierarchical hidden Markov model. Results reveal two long-term regimes: turbulent periods with high volatility, instability, and negative returns, and prevalent stable markets. Within each of them, two distinct states representing short-term trends are identified, exhibiting significant differences in persistence, likelihood, expected returns, and risk profiles. The results show that an investor should carefully differentiate between regimes on both hierarchies to make informed investment decisions.

Sentiment-driven deep learning framework for insider trading detection in Indian stock market

Sentiment-driven deep learning framework for insider trading detection in Indian stock market

A causality investigation into stock prices and macroeconomic indicators in the Indian stock market

Background of the study The systematic impact of macroeconomic variables on stock market returns makes it crucial to comprehend the link between macroeconomic variables and the stock market. Stock prices are closely linked to macroeconomic indicators, a crucial aspect for investors, policymakers, and researchers in emerging markets like India, influencing investment decisions and policy formulation. Methods The autoregressive distributed lag (ARDL) model was used in this study to examine the causal links between specific macroeconomic factors and Indian stock prices from April 2009 to March 2023. Results The outcomes of the research suggest that macroeconomic variables exert influence on the Indian stock market, across the short and long term. Moreover, the results of the paired Granger causality test suggest that the domestic macroeconomic variables possess predictive significance for stock prices in the Indian stock market. Conclusion The study reveals that macroeconomic variables significantly impact the Indian stock market, highlighting the need for investors and portfolio managers to monitor these conditions to optimize returns and mitigate risks. The Reserve Bank of India should maintain an optimal money supply to prevent inflation and exchange rate fluctuations, while bolstering the export sector and facilitating imports through initiatives like Atma-nirbhar Bharat Abhiyan and Make in India. Policies focusing on productivity, infrastructure, and a favourable business environment are also crucial. Therefore, it is crucial for investors and portfolio managers to consistently analyse the current macroeconomic conditions in order to maximize their profits and minimize risks. This research has extensive significance for comprehending the intricate connections between the stock market and macroeconomic issues.

Dissemination of Information on Investor Attention, Firm Size, and Year-End Market Dynamics: An Empirical Study of the Indian Stock Market

This paper investigates the dissemination and presence of the “Year-End Market Surge”, commonly referred to as the “Christmas Rally”, in the Indian stock market. In developed nations, this phenomenon describes a notable increase in stock prices typically observed during the last week of December and the first two trading days of January. Recent reports in the popular press suggest that a similar trend has been witnessed in the Indian stock market over recent years. However, there remains a lack of systematic research on this subject. Therefore, this study rigorously examines whether this market surge, which poses a potential challenge to the Efficient Market Hypothesis (EMH), is observable in the Indian context. Furthermore, the paper explores the dissemination of firm-specific trading patterns to identify characteristics of companies that have consistently delivered positive returns during this period over multiple years. The findings reveal that larger stock portfolios in the Indian market consistently benefit from the Year-End Market Surge effect, delivering higher abnormal returns compared to smaller portfolios. These results provide important insights into the role of firm size in capturing the benefits of this seasonal market anomaly.

Sectoral Performance of ESG Enabled Stocks during COVID-19 Pandemic in the Indian Stock Market

Over past one decade, there is significant rise in Environment, Social and Governance (ESG) enabled investment practices across the globe and Indian economy is no exception to that. The ESG-enabled investment options provide better investment avenues for investment for retail and institutional investors by ensuring the long-term sustainability of the business. The present study tries to assess whether there is any impact of ESG scores on performance of stocks at the time of COVID-19 pandemic period. The study compares the performance of stocks with high ESG score vis-à-vis stocks with low ESG scores during COVID-19 period for different sectors of Indian stock market. The study considers the data of 312 sample firms listed on BSE 500 index. The event study methodology is used to assess the impact of COVID-19 on Indian stocks. The stocks were further divided into three categories high score (top 30% of firms), medium (middle 40% of firms) and low (bottom 30% of the firms). The results of the study suggest that the investors were more confident about the performance and revival of firms that carry high ESG scores than firms with low ESG scores. The investors are confident about the long-term prospects and sustainability of the firms, and they tend to hold their investment in the firms with high ESG scores.

Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy

Crude oil price forecasting has been one of the research hotspots in the field of energy economics, which plays a crucial role in energy supply and economic development. However, numerous influencing factors bring serious challenges to crude oil price forecasting, and existing research has room for further improvement in terms of an integrated research roadmap that combines impact factor analysis with predictive modelling. This study aims to examine the impact of financial market factors on the crude oil market and to propose a nonlinear combined forecasting framework based on common variables. Four types of daily exogenous financial market variables are introduced: commodity prices, exchange rates, stock market indices, and macroeconomic indicators for ten indicators. First, various variable selection methods generate different variable subsets, providing more diversity and reliability. Next, common variables in the subset of variables are selected as key features for subsequent models. Then, four models predict crude oil prices using common features as inputs and obtain the prediction results for each model. Finally, the nonlinear mechanism of the deep learning technology is introduced to combine above single prediction results. Experimental results reveal that commodity and foreign exchange factors in financial markets are critical determinants of crude oil market volatility over the long term, as observed in experiments conducted on the West Texas Intermediate and Brent oil price datasets. The proposed model demonstrates strong performance regarding average absolute percentage error, recorded at 2.9962% and 2.4314%, respectively, indicating high forecasting accuracy and robustness. This forecasting framework offers an effective methodology for predicting crude oil prices and enhances understanding the crude oil market.

A Rise in the No. of Initial Public Offer’s in the Indian Public Markets

The research paper is an analysis of the Indian stock market and analyses an increase in Initial Public Offerings (IPOs) made between 2012 to 2024 The growth is also attributed to factors such as positive economic data, growing investor interest, and regulatory softening. No doubt that there has been some change, but the trend has overall remained stronger riding high on bullish market sentiment as well as confidence from investors. The study stresses that this growth is only possible if the continued regulatory oversight and investor protection measures in place remain following the growth.

Interconnected Stock Markets: Analysing Cointegration and Correlation among Global Stock Indices

This study examines the long-term and short-term relationships among five major global stock indices—NSE NIFTY (India), S&P 500 (USA), FTSE 100 (UK), Hang Seng (Hong Kong), and Nikkei 225 (Japan)—over the period from 2008 to 2023. Using advanced econometric techniques such as Johansen cointegration tests, Engle-Granger residual cointegration test and the Vector Error Correction Model (VECM), the research explores the degree of interconnectedness and cointegration among these indices. The results indicate strong positive correlations between NSE NIFTY, S&P 500, and Nikkei 225, suggesting a growing synchronization of these markets due to global economic integration. Johansen's test reveals the presence of multiple long-term equilibrium relationships, affirming that despite short-term deviations, these markets tend to move together in response to global macroeconomic factors. The analysis further highlights that the S&P 500 and FTSE 100 indices exhibit significant error correction mechanisms, underscoring their leadership role in global market dynamics. In contrast, NSE NIFTY shows a weaker adjustment toward long-term equilibrium, indicating its reactive nature to changes in other global indices, particularly the S&P 500. The study finds that while Hang Seng and Nikkei 225 are connected globally, their short-term linkages appear weaker, likely influenced by regional factors. Overall, the results emphasize the critical influence of the U.S. and Japanese markets on NSE NIFTY, with both long-term cointegration and short-term market dynamics shaping the Indian stock market’s movements. This research offers key insights for investors seeking to understand global stock market interdependencies and manage portfolio risk.

Applications of Neuro Deep Learning Models in Predictive Data Analytics for the Movements and Trends of the Indian Stock Market: Financial Data Mining, Nonlinearity, and Quantum Finance

The financial world of today is high-frequency data-driven and characterized by the application and use of information technology for better business development and decision-making. The price movements of stock markets are mainly influenced by micro and macroeconomic variables, legal frameworks, and taxation policies of the respective economies. The crux of the issue lies in exactly forecasting the future stock price movements of individual firms, based on historical prices. Achieving accuracy for forecasting the market trend has become difficult due to the prevalence of stochastic behaviour in the stock market and volatility in the stock prices. This paper analyzes the stochastic movement pattern of the highly volatile thirty individual company stocks (in terms of market capitalization) of BSE-Sensex, using the Neuro Deep Learning method, a model of deep learning approach. The findings of the study would help the investors, to make rational and well-informed investment decisions, and to optimize the returns by investing in the most valuable stocks of listed corporate enterprises.

A Case Study on Fear of Missing Out (FOMO) in Bull Market Strategies and Shifting Investment Opportunities

Akshay, a traditionally conservative investor, embraced the Indian stock market's bullish trend following the 2024 elections, transitioning to a more aggressive strategy. However, global events and market corrections in September 2024 have caused volatility, prompting him to reconsider his investment approach. Now at a crossroads, Akshay must weigh his options—whether to liquidate, re-diversify, or implement risk management strategies to balance short-term uncertainties with long-term financial growth.