Purpose: The purpose of this paper is to examine the influence of ownership structure and dividend payouts over firm’s profitability, valuation and idiosyncratic risk. The authors further investigate if corporate performance is sector dependent. Design/methodology/approach: The study employs signaling and bankruptcy theories to evaluate the influence of ownership structure and dividend payout over a firm’s corporate performance. The authors use a panel regression approach to measure the performance of family owned firms against that of widely held firms. Findings: The study confines to firms operating out of emerging markets. The results show that family owned firms are dominant with concentrated ownership. The management pays lower dividend leading to lower valuation and higher idiosyncratic risk. The study further illustrates that family ownership concentration and family control both influence firm performance and level of risk. The findings indicate that information asymmetry and under diversification lead to increased idiosyncratic risk, resulting in the erosion of firm’s value. Results also confirm that firms paying regular dividends are less risky and, hence, command a valuation premium. Originality/value: The evidence supports the proposition that information asymmetry plays a significant role in explaining dividend payouts pattern and related impacts on corporate performance. The originality of the paper lies in factoring idiosyncratic risk while explaining profitability and related valuation among emerging market firms.

The Investor Sentiment Index (ISI) is widely regarded as a useful measure to gauge the overall mood of the market. Investor panic may result in contagion, causing failure in financial markets. Market participants widely use the ISI indicator to understand price fluctuations and related opportunities. As a result, it is imperative to systematically review the compiled literature on the subject. In addition to reviewing past studies on the ISI, this paper attempts a bibliometric analysis (BA) to understand any related publications. We systematically review over 100 articles and carry out a BA on a set of information based on the publication year, the journal, the countries/territories, the deployed statistical tools and techniques, a citation analysis, and a content analysis. This analysis further strengthens the study by establishing interesting findings. Most articles use the Baker and Wurgler index and text-based sentiment analysis. However, an Internet-search-based ISI was also used in a few of the studies. The results reveal the lack of direct measures or a robust qualitative approach in constructing the ISI. The findings further indicate a vast research gap in emerging economies, such as India’s. This study had no limit on the period for inclusion and exclusion. We believe that our current work is a seminal study, jointly involving a systematic literature review and BA, that will enormously facilitate academicians and practitioners working on the ISI.

This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3–5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations.