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.

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.