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This study aims to explore the impact of systematic liquidity risk on the averaged cross-sectional equity return of the Indian equity market. It also examines the effects of illiquidity and decomposed illiquidity on the conditional volatility of the equity market.

The present study employs the Liquidity Adjusted Capital Asset Pricing Model (LCAPM) for pricing systematic liquidity risk using the Fama & MacBeth cross-sectional regression model in the Indian stock market from January 1, 2012, to March 31, 2021. Further, the study employed an exponential generalized autoregressive conditional heteroscedastic (1,1) model to observe the impact of decomposed illiquidity on the equity market’s conditional volatility. The study also uses the Ordinary Least Square (OLS) model to illuminate the return-volatility-liquidity relationship.

The study’s findings indicate that the commonality between individual security liquidity and aggregate liquidity is positive, and the covariance of individual security liquidity and the market return negatively affects the expected return. The study’s outcome specifies that illiquidity time series analysis exhibits the asymmetric effect of directional change in return on illiquidity. Further, the study indicates a significant impact of illiquidity and decomposed illiquidity on conditional volatility. This suggests an asymmetric effect of illiquidity shocks on conditional volatility in the Indian stock market.

This study is one of the few studies that used the World Uncertainty Index (WUI) to measure liquidity and market risks as specified in the LCAPM. Further, the findings of the reverse impact of illiquidity and decomposed higher and lower illiquidity on conditional volatility confirm the presence of price informativeness and its immediate effects on illiquidity in the Indian stock market. The study strengthens earlier studies and offers new insights into stock market liquidity to clarify the association between liquidity and stock return for effective policy and strategy formulation that can benefit investors.

The determinants of investors’ sentiment based on secondary stock market proxies in many empirical studies are reported. However, to the best of our knowledge, no study undertakes investor sentiment drivers developed from primary survey measures by constructing an investor sentiment index (ISI) in relation to market drivers to date. This study aims to fill this research gap by first developing the ISI for the Indian retail investors and then examining which of the stock market drivers impacts such sentiment.

The ISI is constructed using the mean scores of eight statements as formulated based on popular direct investor sentiment surveys undertaken across the world. Then, we use the multiple regression approach overall and for top 33.33% (high-sentiment) and bottom 33.33% (low-sentiment) investors based on the responses of 576 respondents on 18 statements (proxying eight study hypotheses) collected in 2016. Moreover, the demography-based classification based investors’ sentiment is examined to make our results more robust and in-depth.

On an overall basis, the IPO activities/issues and information certainty, trading volume and momentum and institutional investors’ investment activities market drivers significantly and positively impact retail investors is examined. However, only IPO activities/issues and information certainty influences both high- and low-sentiment investors. It is intriguing to report that nature of the stock markets show conflicting results for high- (negative significant) and low- (positive significant) sentiment investors.

The construction of the ISI from primary survey measure is for the first time in Indian context in relation to investigating the stock market drivers influential to retail investors’ sentiment. In addition, hypothesized market drivers are also unique, each representing different fundamental and technical characteristics associated with the Indian market.

This research study aims to design a novel risk-managed time-series momentum approach. The present study also examines the time-series momentum effect in the Indian equity market. Apart from this, the study also proposes a novel risk-managed time-series momentum approach.

The study considers the adjusted monthly closing prices of the stocks listed on the Bombay Stock Exchange from January 1996 to December 2020 to formulate long-short portfolios. Newey–West t statistics were used to test the significance of momentum returns. The present research has considered standard risk factors, i.e. market, size and value, to evaluate the risk-adjusted performance of time-series momentum portfolios.

The present research reports a substantial absolute momentum effect in the Indian equity market. However, absolute momentum strategies are exposed to occasional severe losses. The proposed time-series momentum approach not only yields 2.5 times higher return than the standard time-series momentum approach but also causes substantial enhancement in downside risks and higher-order moments.

The study's outcomes offer valuable insights for professional investors, capital market regulators and asset management companies.

This study is one of the pioneers attempting to test the time-series momentum effect in emerging economies. Besides, current research contributes to the escalating literature on risk-managed momentum by suggesting a novel revised time-series momentum approach.

The study aims at investigating the impact of real earnings management (REM) on the cross-sectional stock return after considering the moderating role of market effect, size effect, value effect and momentum effect.

The study uses weekly and monthly data of 3,085 Bombay Stock Exchange listed stocks spanning over twenty years, from January 2000 to December 2019. REM is measured through metrics developed by Roychowdhury (2006), namely, abnormal levels of operating cash flows, production costs and discretionary expenditure. The study employs univariate and bivariate portfolio-level analysis.

The findings deduced from the empirical results demonstrate that investors perceive downward REM as an element of risk; hence, they discount the stock prices at a higher rate. On the contrary, results show that investors positively perceive upward REM; hence, they hold the stocks even at a lower rate of return. This anomaly is found to be robust for all kinds of considered moderations.

The findings have important managerial implications as investors are found to assign different weights to different forms of REM, depending upon the perception regarding the magnitude of risk involved in different forms. Managers can accommodate this information during their short- and long-term corporate planning.

First, the study is among the earlier attempts to examine the association between REM and stock returns by considering the moderating role of cross-sectional effects. Second, the study considers the direction and endogenous nature of REM while investigating the issue.

The authors aim to examine the mean and volatility linkages between the gold market and the Latin American equity markets in the entire sample period and two crises periods, namely the US financial crisis and the Chinese crash.

To examine the return and volatility spillovers, the authors employ VAR-BEKK-GARCH model on the daily data of four emerging Latin American equity markets which include Peru, Chile, Brazil and Mexico, which ranges from January 2000 to June 2018.

The results show that the return transmissions vary across the stock markets and the crises periods. The volatility transmission is found to be bidirectional between the gold and stock markets of Brazil and Chile during the US financial crisis. Furthermore, the volatility spillover is unidirectional from Brazil to gold and from gold to Peru stock market during the Chinese crash. We also calculate the optimal weights hedge ratios for gold and stock portfolio. The result suggests that portfolio managers need to increase the weight of gold for the equity portfolios of Peru and Mexico during the US financial crisis. Furthermore, during the Chinese crisis, investors may raise the investment in gold for the equity portfolios of Brazil and Chile. Finally, the cheapest hedging strategy is CHIL/GOLD during the US financial crisis, whereas MEXI/GOLD during the Chinese crash.

These findings have useful insights for portfolio diversification, asset pricing and risk management.

The study's outcome provides policymakers and investors with in-depth insights regarding hedging, risk management and portfolio management.

Mutual funds are the second most preferred investment option in India and have garnered considerable research interest. The focus of Indian studies thus far has been restricted to the bottom-up approach of investing which rewards a fund manager for picking winner stocks and generates superior returns. While changing portfolio allocation as per varying macro-trends has been instrumental in generating superior returns, it has not been given the desired attention. This study addresses this important research gap.

The authors analyze the industry selection ability of the fund manager on a robust sample by decomposing alpha into alpha due to industry selection and alpha attributable to stock selection. Alpha estimates are computed on a robust sample of 34 open-ended Indian equity mutual funds for a 10-year duration 2011–2020 using three base models of asset pricing – single-factor, four-factor and five-factor alpha under panel data methodology.

The study leads us to four major findings. One, industry selection explains more than two-fifth of the alpha both in cross-section and time series of returns; two, industry selection exhibits persistence for more than four quarters across asset pricing model; third, younger funds have level playing when alpha from picking right industries is concerned; four, broad industry allocation continues to explain superior returns as sector allocation undergoes consolidation during ongoing COVID-19 pandemic and funds increase exposure to defensive stocks, consistent with folio allocations as per macroeconomic conditions.

The authors find strong evidence of persistence in the case of alpha attributable to the industry selection component, and the findings are consistent with the persistence results reported in the empirical literature. While some funds excel in stock-picking skills and others excel in picking the right industries, both skills together make for winner funds that attract larger investor flows as investors chase superior performance. The authors also find no evidence of diseconomies of scale in the case of industry allocation alpha generated by the fund managers.

The results suggest a fresh approach for investors while making mutual fund investment decisions; the investors can achieve superior returns by assessing industry selection skills as it tends to provide a more holistic picture concerning a perennial question – why some funds outperform and continue to contribute to investor's wealth?

Mutual funds have become a favored investment option for Indian investors more so as a disciplined investment option owing to dismal financial literacy rates. The study throws light on a relatively unaddressed dimension of choosing winner funds. The significance of right sector allocation assumed even more significance with the onset of the pandemic which lends further credence to the findings of the study.

Research has been conducted on secondary data extracted from a well-cited database for Indian mutual funds. Empirical analysis and conclusion drawn are based on authentic statistical analysis and adds to the existing literature.

Owing to the growing evidence of crypto asset connectedness and correlation with traditional financial assets, this study sought to determine if there is a time-varying correlation and/or connectedness between the stablecoin market and the currencies of emerging market and developing economies (EMDEs) with significant cryptocurrency penetration.

This study uses a probabilistic principal component analysis (PPCA) to create stablecoin and EMDEs currency returns and volatility indices for EMDEs with significant cryptocurrency penetration. We then employ a time-varying correlation and time-varying parameter vector autoregressive (TVP-VAR) connectedness measures to document the time-dependent correlation and connectedness between the EMDE currencies and the stablecoin market.

The result points to a spillover of return shocks from the EMDE currencies to the stablecoin market prior to and after the COVID-19 pandemic. This is indicative of a flight-to-safety role of stablecoins for EMDE currencies. This calls for increased attention to the stablecoin market by money market investors and monetary authorities.

The paper contributes to the growing cryptocurrency and finance literature by empirically examining the level of connectedness between stablecoins and emerging market currencies. Knowing the relationship (correlation) and shock spillover (connectedness) between the stablecoins and the EMDE currencies will be valuable to currency investors’ diversification and hedging strategies, and to macroeconomic policymakers in designing and implementing regulation.

The study aims to investigate the persistence of seasonal anomalies during religious holidays in emerging markets.

The authors select the Bombay Stock Exchange and National Stock Exchange stock returns from January 1990 to December 2022. The GARCH family models were adopted to examine the mean-variance returns associated with symmetric and asymmetric effects. The ARIMAX model is used to investigate the exogenous order during the pre-mandated and post-mandated trading holidays.

The results show that the persistence of returns and volatility during religious holidays significantly when subjected to specific religious holidays. The authors also found that volatility during religious festivals dipped during the pre-holiday and gradually increased after the events. The findings suggest that religious holiday anomalies exhibit a trivial significant effect on stock market returns and this effect is waning.

The findings provide investors and market regulators with a better understanding of market anomalies related to religious practices. During these periods, investors may experience substantial fluctuations in their portfolios, potentially leading to significant losses or payoffs. Investors can sustain substantial losses or payoffs and market manipulation by adjusting their strategies around religious holidays to account for potential volatility, albeit temporarily.

This study contributes to behavioural finance literature that suggests that beliefs and cultural aspects determine a country’s stock market inefficiency. To the best of the authors’ knowledge, no previous study has comprehensively examined threshold religious holidays across diverse religions in Indian market using long-memory data.

The Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.

Hybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.

Our study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.

To the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

Despite a significant rise in the adoption of online stock trading applications, the research on measuring and enhancing the service experience of customers is scarce. This paper aims to put forth a credible service performance measurement tool, APPQUAL, customized for stock trading applications.

The paper is based on a multi-method research using qualitative and quantitative research. Further, structural equation modeling is used to establish constructs for measuring service performance.

APPQUAL is conceived and built to measure the service performance of stock trading applications. The APPQUAL measures service performance based on five unique dimensions – Safety Accuracy, Performance, Support and Usability associated with the perceived e-service quality of stock trading applications.

This study extends measurement scales for service quality measurement for a relatively new service of online stock trading applications. The scales are adapted as well as built with the help of an iterative process. The methodology is in line with the previous academic research, and the new scales developed can lead to further extensions to scale for different services.

The APPQUAL tool developed as part of this study can be applied to assess service performance and perceived service quality of stock trading applications, which have leap frogged in terms of adoption but have also been met with criticism on poor customer experience. This study shall add to the body of knowledge of stock trading platforms to enhance service experience by targeting the right factors and underlying items. The efficacy and application of the tool have been demonstrated in this paper by applying it to Zerodha and Upstox, the two of the biggest online stock trading applications in India.

There is no research or tool available currently which focuses on the service quality of the new-age platform application of stock trading apps. This is especially glaring due to the large-scale adoption of these applications by Indian customers.