Top performing stocks recommendation strategy for portfolio

TL;DR

This paper proposes a ranking-based stock recommendation strategy that improves upon traditional regression models by focusing on predicting relative stock performance, using large Indian market datasets and multiple evaluation metrics.

Contribution

It introduces a modified regression approach tailored for stock ranking, addressing limitations of traditional models in capturing market-wide unpredictable events.

Findings

Proposed method outperforms traditional regression models.

Effective in ranking stocks based on expected returns.

Validated on large Indian stock datasets.

Abstract

Stock return forecasting is of utmost importance in the business world. This has been the favourite topic of research for many academicians since decades. Recently, regularization techniques have reported to tremendously increase the forecast accuracy of the simple regression model. Still, this model cannot incorporate the effect of things like a major natural disaster, large foreign influence, etc. in its prediction. Such things affect the whole stock market and are very unpredictable. Thus, it is more important to recommend top stocks rather than predicting exact stock returns. The present paper modifies the regression task to output value for each stock which is more suitable for ranking the stocks by expected returns. Two large datasets consisting of altogether 1205 companies listed at Indian exchanges were used for experimentation. Five different metrics were used for evaluating the different models. Results were also analysed subjectively through plots. The results showed the superiority of the proposed techniques.