Stock Performance Evaluation for Portfolio Design from Different Sectors of the Indian Stock Market

TL;DR

This paper evaluates stock performance across six Indian economic sectors using various predictive models and constructs optimized portfolios to maximize returns with minimal risk.

Contribution

It compares multiple time series, machine learning, and deep learning models for stock prediction and develops sector-based portfolios using modern optimization techniques.

Findings

Deep learning models outperform traditional methods in stock prediction.

Optimized portfolios achieve higher returns with lower risk.

Back testing confirms the effectiveness of the proposed portfolio strategies.

Abstract

The stock market offers a platform where people buy and sell shares of publicly listed companies. Generally, stock prices are quite volatile; hence predicting them is a daunting task. There is still much research going to develop more accuracy in stock price prediction. Portfolio construction refers to the allocation of different sector stocks optimally to achieve a maximum return by taking a minimum risk. A good portfolio can help investors earn maximum profit by taking a minimum risk. Beginning with Dow Jones Theory a lot of advancement has happened in the area of building efficient portfolios. In this project, we have tried to predict the future value of a few stocks from six important sectors of the Indian economy and also built a portfolio. As part of the project, our team has conducted a study of the performance of various Time series, machine learning, and deep learning models in stock price prediction on selected stocks from the chosen six important sectors of the economy. As part of building an efficient portfolio, we have studied multiple portfolio optimization theories beginning with the Modern Portfolio theory. We have built a minimum variance portfolio and optimal risk portfolio for all the six chosen sectors by using the daily stock prices over the past five years as training data and have also conducted back testing to check the performance of the portfolio. We look forward to continuing our study in the area of stock price prediction and asset allocation and consider this project as the first stepping stone.