Top performing stocks recommendation strategy for portfolio

by   Kartikay Gupta, et al.

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.


page 1

page 2

page 3

page 4


Predicting Stock Returns with Batched AROW

We extend the AROW regression algorithm developed by Vaits and Crammer i...

Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models

We examine the potential of ChatGPT, and other large language models, in...

A New Multivariate Predictive Model for Stock Returns

One of the most important studies in finance is to find out whether stoc...

Empirical Asset Pricing via Ensemble Gaussian Process Regression

We introduce an ensemble learning method based on Gaussian Process Regre...

Dynamic Advisor-Based Ensemble (dynABE): Case Study in Stock Trend Prediction of a Major Critical Metal Producer

The demand of metals by modern technology has been shifting from common ...

Please sign up or login with your details

Forgot password? Click here to reset