PORTFOLIO FORMATION BASED ON TECHNICAL INDICATORS FOR INTRADAY TRADING USING DEEP LEARNING TECHNIQUES

Abstract

Portfolio management plays a crucial role in optimizing investment returns while managing risks for both individuals and organizations. With advancements in technology, intelligent models can assist investors and analysts in mitigating investment risks. This study proposes an integrated approach that combines Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) networks with the Black-Litterman model for intraday trading. The study utilizes daily stock data from the Nifty 50 Index spanning from January 2010 to January 2022. The adopted methodology incorporates input features in the form of technical indicators and lagged observations, Principal Component Analysis (PCA) is employed for dimension reduction. The first stage involves predicting the close price of stocks using various deep learning models and ARIMA. Comparisons reveal that the hybrid GRU and LSTM model consistently outperforms other benchmark models across multiple regression metrics for most stocks. The top performing stocks are then selected for further analysis. In the second stage, the Black-Litterman model is utilized to determine the proportional distribution of capital for each individual stock that has been selected, incorporating the predicted results from the GRU_LSTM models as investor views. The performance of the proposed approach is compared against the Modified mean variance model, LSTM model, and equally weighted model. Overall, the hybrid GRU and LSTM model with the Black-Litterman model exhibits superior performance in terms of Sharpe ratio, Maximum Drawdown, Sortino ratio, and Calmar ratio. This research demonstrates the potential of integrating deep learning techniques with statistical portfolio optimization methods for enhanced portfolio management outcomes.