Advanced LSTM Neural Networks for Predicting Directional Changes in Sector-Specific ETFs Using Machine Learning Techniques

TL;DR

This paper evaluates the effectiveness of LSTM neural networks in predicting sector-specific ETF price movements, demonstrating high accuracy and potential for aiding diversification strategies in investment portfolios.

Contribution

It introduces the application of LSTM models to predict directional changes in sector ETFs, showcasing their viability across multiple sectors and stocks.

Findings

Average R-squared of 0.8651 across sectors

High R-squared of 0.942 for VNQ ETF

LSTM models effectively predict sector movements

Abstract

Trading and investing in stocks for some is their full-time career, while for others, it's simply a supplementary income stream. Universal among all investors is the desire to turn a profit. The key to achieving this goal is diversification. Spreading investments across sectors is critical to profitability and maximizing returns. This study aims to gauge the viability of machine learning methods in practicing the principle of diversification to maximize portfolio returns. To test this, the study evaluates the Long-Short Term Memory (LSTM) model across nine different sectors and over 2,200 stocks using Vanguard's sector-based ETFs. The R-squared value across all sectors showed promising results, with an average of 0.8651 and a high of 0.942 for the VNQ ETF. These findings suggest that the LSTM model is a capable and viable model for accurately predicting directional changes across various industry sectors, helping investors diversify and grow their portfolios.