  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.
