Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators

Stock return forecasting is of utmost importance in the business world. This has been a major topic of research for many academicians for decades. Recently, regularization techniques have reported significant increase in the forecast accuracy of the simple regression model. Still, more robust techniques are desired which can further help improve stock prices predictions. Furthermore, it is important to recommend top stocks rather than predicting exact stock returns. The technique should be scalable to very large datasets. The present paper proposes a normalization technique which results in a form of regression that is more suitable for ranking the stocks by expected returns. The ranking is done out of the comparison between the stocks in the previous quarter over a big set of important fundamental, technical and general indicators. Two large datasets consisting of altogether 946 unique companies listed at Indian exchanges were used for experimentation. Stochastic Gradient Descent technique is used in this work to train the parameters, which allows scalability to even larger datasets. Five different metrics were used for evaluating the different models. Results were also analysed subjectively through plots. The returns obtained were higher than other popular models and benchmark indices.