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What brings the equilibrium consensus in the stock markets? We hypothesize that the markets' equilibrium consensus depends on the noise of investors' attention mania (NIAM). We refer to the NIAM as investors' attention heterogeneity and explore its impacts on the National Stock Exchange (NSE) Nifty and Bombay Stock Exchange (BSE) Sensex stocks market returns. We use the methodology of the autoregressive distributed lag (ARDL) and augmented generalized autoregressiuve conditional heteroskedacity (GARCH)-X model, and we examine if there is the presence of NIAM at online attention searches within and across the attention layers, and within and across the stated two stock markets from 2004 to 2019 for investors' economic–political attention searches. We have revealed that the impacts of the NIAM on the market returns are diverse in nature at the different attention layers and stock markets as well. Besides the ARCH and GARCH effects, we also document the presence of familiarity bias, attention confidence or confusion, and attention integration in the short run and long run. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to investigate the impact of socially-responsible human resource (SR-HR) practices on organizational sustainability performance (OSP) in Indian business organizations that are mandated to publish business sustainability and responsibility reporting. Design/methodology/approach: Data were obtained from 620 working professionals employed in the organizations listed on National Stock Exchange in India. The proposed hypotheses were tested by deploying the statistical technique of multiple regression analysis using SPSS Version-21. Findings: The results demonstrated that overall, SR-HR practices impact OSP. More precisely, all the dimensions of SR practices are positively associated with the organization's financial performance, environmental performance and social performance (SP). There was a relatively higher significant impact of legal-oriented human resource management (HRM) on organizational economic and ecological performance. However, in the case of SP, a substantial effect of employee-oriented HRM was found. Practical implications: Study findings encourage HR practitioners to invest in SR-HR practices to build and strengthen employees' abilities and contributing to sustainability goals. Originality/value: This study is one of the few studies conducted in the Indian context that highlights the relevance of the convergence of HRM, human resource development and corporate social responsibility to realize sustainability goals. [ABSTRACT FROM AUTHOR]

The article examines the herd behaviour at sector level in the National Stock Exchange of India. The novel objective of the paper is to analyse the presence of herd behaviour during the whole, pre- and post-coronavirus disease 2019 (COVID-19) pandemic outbreak period. The popular model proposed by Chang et al. (2000) has been deployed to examine herd formation in each sector. Five major sectors namely auto, bank, fast moving consumer goods, information technology and pharma have been considered along with NIFTY50 consisting daily closing prices of 106 companies comprising 58 from five sectoral indices and 48 from NIFTY50 for the period from 2011 to 2021 considering 1 January 2011–29 January 2020 as before COVID-19 outbreak period and 30 January 2020–31 December 2021 as after COVID-19 outbreak period. Results revealed significant evidence of anti-herd behaviour before COVID19 outbreak. At the same time, significant evidence of the existence of herd behaviour in FMCG, auto and pharma sectors have been detected after the outbreak of COVID-19 pandemic which assumes that investors tend to imitate the investment decisions of other market participants during such adverse macro events around the globe. The study tried to enhance the knowledge of investors to develop their trading strategies in the volatile market conditions by investing vigilantly and keeping such phenomenon in concern. [ABSTRACT FROM AUTHOR]

Predicting stock price movement and optimizing day-ahead stock portfolios are challenging tasks due to the inherent complexity and volatility of financial markets. This study proposes a novel approach that combines bidirectional long short-term memory (BiLSTM) neural networks with monte carlo simulation (MCS) to enhance day-ahead stock portfolio management. In the proposed methodology, historical data of the topperforming 10 stocks from different sectors of the National Stock Exchange of India (NSEI) is obtained from 1 January 2004 to 30 June 2023 and utilized to train a BiLSTM model. This model effectively extracts intricate patterns and trends from the time series, leading to more accurate and robust stock price predictions. MCS generates different scenarios, considering various market conditions and uncertainties. These scenarios provide a comprehensive view of the portfolio's performance under different conditions, thus mitigating the risk of relying solely on a single prediction. The study evaluates the proposed framework and compares its performance against traditional portfolio management strategies. Results demonstrate that the MCS with the BiLSTM approach outperforms traditional methods in terms of risk-adjusted returns and portfolio stability. [ABSTRACT FROM AUTHOR]

The paper proposes a portfolio selection approach based on cumulative prospect theory (CPT) that integrates data envelopment analysis (DEA). The CPT-based model has emerged as the best model in behavioral portfolio theory for incorporating decision-maker behavior in risk and uncertainty. We are using the quadratic value function suggested in the study of Gazioğlu and Çalışkan (Appl Financ Econom 21(21):1581–1586, 2011), which is the best alternative to the value function proposed by Kahneman and Tversky (Handbook of the fundamentals of financial decision making: Part I, World Scientific, 2013) in the literature. Based on the CPT value of each asset, we bifurcate the assets into two groups, top CPT value assets and bottom CPT value assets. To assess the cross-efficiency of the assets, we consider the CPT value and long-term return of each asset as outputs and the variance of the return as an input. We combine cumulative prospect theory with cross-efficiency and examine the psychological aspects of decision-makers in portfolio selection. The study used thirty listed stocks from the Nifty-50, the National Stock Exchange, India for empirical investigation. The empirical findings elucidate that the portfolios generated by the highest CPT value surpass those generated by the lowest CPT value. We demonstrate that the proposed approach can be a potential tool for portfolio selection by exhibiting that the selected portfolio delivers greater risk-adjusted returns in the financial markets. [ABSTRACT FROM AUTHOR]

Investment decision is one of the most essential factors in today's stock market. People make investment decisions based on different fundamental and technical tools. There are various factors which affect the investor towards investment, but one of the important factors which affect the investor is their emotional intelligence. So, the present study focuses on the how emotional intelligence influences the investment decision. This study is based on the emotional intelligence of investors so that the market can know the beat of the speculator and continue as required. The data was collected from 930 investors who make investment in NSE in Andhra Pradesh. The data was analyzed with the help of correlation analysis. The study reveals that there is a positive relation among the various dimensions of emotional intelligence and investment decisions. This study will help the investment advisors to advise the investors to invest according to their emotional intelligence capability. The usual actuality for stock market investments is subject to not only in making investment decisions in high volatile market circumstances and informational haziness but also by the need to be meticulous in earning something on a consistent basis. The substantial reason is the emotions are part of the evolved mental processes; human beings have for managing uncertainty and competition. Words expressing emotions and feelings such as Dissatisfied, Depressed, Sad, Happy, Cheerful, Satisfied, Fear, Worry, Disappointment, and Trust similarly succeed in the interview scheduling sessions with respondents. Different emotions can be sensed through their connected action tendencies. Fear is, for example, associated with the inclination for flight and love with the trend for an approach. The market moves against the investor. Having never stared at unrealized losses, tell themselves that all their ideas will eventually work. This research examined the relationship between risk-taking behaviour and high levels of extraversion [ABSTRACT FROM AUTHOR]

In this paper, we propose a pairs trading strategy using the theory of topological data analysis (TDA). The proposed strategy is model-free. We propose a TDA-based distance to measure dependence between a pair of stochastic processes. We derive an upper bound of this distance in terms of a function of the canonical correlation of the processes, which allows for interpretability of this distance. We also study Karhunen–Loève expansions of certain processes to qualitatively explore their shape properties. We check the performance of the strategy on simulated data from correlated geometric Brownian motion, correlated Ornstein–Uhlenbeck process and DCC-GARCH. We also examine the profitability of the proposed strategy on high-frequency data from the National Stock Exchange of India in 2018. We compare the method to a Euclidean distance-based method for pairs trading. We propose a pairs trading strategy evaluation framework using a Bayesian model for comparing gains from these two strategies. We find that the proposed approach based on TDA is more profitable and trades more frequently than the Euclidean distance-based strategy. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this article is to explore the phenomena of board gender diversity and its consequences for sustainability performance, as measured by the environment, social and governance (ESG) disclosure score, in the Indian context. Design/methodology/approach: The positivist paradigm influenced the research design for this study. The relationship between firm's ESG performance and female participation on the corporate boards was explored using panel data regression with a fixed effect approach. A total of 712 data points covering the Nifty 100 companies of the National Stock Exchange (NSE) were included in the data set. To add robustness to the findings and to overcome endogeneity bias, authors employed the Dynamic Generalized Method of Moments (GMM). Findings: The results showed that, a relatively small, percentage of women directors has little impact on ESG performance, but when at least three women directors are in place, these relationships become more favourable. Despite the fact that Indian firms trail behind many developed and developing countries in promoting board gender equality, authors conclude that critical mass theory partially applies in the Indian context. Originality/value: This study contributes to the field of corporate governance in the twenty-first century by investigating the subject of women's participation on boards in the context of a rising market and its potential influence on sustainability performance. The use of critical mass theory adds a fresh perspective to the literature. [ABSTRACT FROM AUTHOR]

Option contracts are financial instruments that serve economic purposes for various institutions and individuals. Option plays a crucial role in developing the financial market due to the high innovation and liquidity associated with it. However, due to option contract's increased adaptability and responsiveness, its pricing mechanism has become complicated. The conventional parametric models suffer from various computing restrictions and implausible economic and statistical presumptions leading to deviations from real-world dynamics. Thus, data-driven strategies built upon non-parametric models seems compelling. Machine Learning (ML) serves as a powerful tool that can increase efficiency and productivity by automated processes, decreasing human biases and errors caused by psychological or emotional factors. Most of the existing literature involves only neural networks, whereas alternative algorithms remain undiscovered. This study explores the effectiveness of various ML algorithms through different experimentation. The ML algorithms harnessed for the study are Artificial Neural Networks (ANN), XGBoost, Decision Tree Regression, Support Vector Regression, Random Forest Regression, Long short-term memory (LSTM) Network and Gated recurrent unit (GRU) Network. Furthermore, multi-asset training and ensemble modelling are carried out to enhance predictive performance. A comparison is carried out with the seminal Black-Scholes model to highlight the advantages of the ML approach. The models are evaluated for European option contracts. The underlying assets used are NIFTY50 and BANKNIFTY indices from India's National Stock Exchange (NSE). ML algorithms performed superior to the Black-Scholes model by a significant margin. Additionally, the models are evaluated on data collected following the outbreak of the COVID epidemic to get insight into the effects of abrupt changes in market sentiment. [ABSTRACT FROM AUTHOR]

Financial networks have become increasingly complex and interconnected in the global economy. In this context, the use of data analytics has emerged as a valuable tool for understanding and analyzing the relationships between different financial markets. This paper presents a study using Pearson and partial correlation analysis to explore the interconnections between the National Stock Exchange India (NSEI) and global stock markets. The analysis aims to provide insights into the dynamics and patterns or cross-markets interactions and to identify potential opportunities for investment and risk management that to considering the world’s 43 major indices based on imports and exports. The historical data from 1st January 2008 to 30th June 2022 was obtained from the public domain. In this study, a Python framework was developed to investigate Pearson and partial correlation-based networks in financial markets. The results showed that these two types of networks are significantly different from each other. Furthermore, the study discovered that the NSEI is closely connected to the Singapore market and is integrated with other markets. These findings emphasize the complexities to financial market relationships and have important implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

The present study seeks to examine the implication of iconic investing strategies conceptualized by Graham et al. (1934), Piotroski (2000) and Greenblatt (2006) for significant stock returns on the National Stock Exchange of India (Nifty 500) during the study period 2005-2020. The significance of stock returns, based on the monthly stock prices of sample 500 stocks listed on the NSE from January 2005 through December 2020, was studied using independent t-test at 0.05 level of significance. Based on the research outcomes of this study, Piotroski (2000) value investing strategy reported better stock returns in the Indian stock market during the study period, compared to two other peer value investing strategies. The findings have wider implications for investors, portfolio managers, and investment advisors for superior return generation in the Indian equity market. India being a large-sized emerging economy, these findings also have wider implications for institutional wealth managers and portfolio flows for wealth creation across the globe. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to focus on compliance of workplace sexual harassment-related provisions under Indian companies and securities law, based on an empirical analysis of companies' sexual harassment-related disclosures contained within their directors' annual reports (ARs). Specifically, sections devoted to sexual harassment-related disclosures, inbuilt within directors' ARs for the financial year 2019–2020 for a selected sample of companies listed under the National Stock Exchange, have been analysed. Design/methodology/approach: To examine the nature of companies' disclosures to demonstrate their compliance with statutory requirements under the POSH law, aligned with the Companies (Accounts) Rules, 2014 and Securities and Exchange Board of India's regulations, an empirical-based, descriptive content analysis of ARs of 200 listed companies were used. Findings: This study primarily finds that the majority of companies from the sample have disclosed to have prepared a corporate-level policy, as required under the POSH law. As also required under the POSH law, companies, reportedly, have constituted an Internal Complaints Committee to adjudicate and dispose of incidents related to sexual misconduct reported at their workplaces. However, companies lack in disclosing qualitative information, with sufficient detail, on many important aspects related to prevention and resolution of reported cases of workplace sexual harassment. Originality/value: This paper adds to the broader narrative of the lacunae within the disclosure and reporting requirements on enhancing the liabilities of the companies to prevent and address sexual harassment under India's corporate and securities regulations. [ABSTRACT FROM AUTHOR]

This paper examines whether the Covid-19 pandemic has had a homogeneous or heterogeneous effect on stock returns in India. We consider panel data by using 1,318 companies that are listed on the National Stock Exchange of India. We find that the daily growth rate in Covid-19 cases and Covid-19 deaths are negatively associated with stock returns. Further, we observe that the average stock returns during Lockdown 2 are positive and highly significant, while the returns during Lockdowns 3 and 4 are negative. Moreover, our results show that the chemical, technology, and food and beverage industries earn higher returns. In contrast, the banking and finance, automotive, services, and cement and construction industries yield lower returns for the overall period. Interestingly, all industry groupings in this study earn a positive return during the lockdown period. In particular, the chemical, technology, automotive, metals and mining, and food and beverage industries provide higher returns during the lockdown period. Finally, this study supports the claim that the Covid-19 pandemic has had a heterogeneous effect in the Indian stock markets. [ABSTRACT FROM AUTHOR]

Exchange Traded Funds (ETF's) are one of the beloved passively managed funds that offer both retail and institutional investors an access to highly profitable and wide- range of diversifiable financial assets. The study aims to assess the ability of Indian equity ETF's in replicating the performance of their benchmark indices using a sample of 27 equity ETF's traded on the National Stock Exchange of India during the pre-pandemic period from 01/01/2015 to 31/12/2019. Evaluation of the performance of sample ETF's through risk-return analysis, risk-adjusted performance measures, tracking error analysis and multi-factor regression have revealed that the majority of the sample ETF's outperformed their tracking indices but with notable tracking errors during the study period. Further, the study also indicates that the returns of the sample ETF's have a significant and positive relationship with the returns of the index but are inversely related to risk and management fees. The results of this study will have major implications for investors in evaluating the performance of ETF's and fund managers as well in taking suitable measures to reduce tracking errors that will help in successful replication of the benchmark along with undertaking initiatives that will enable the ETF's to become price efficient. [ABSTRACT FROM AUTHOR]