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

2024-09-09 · Rifa Gowani, Zaryab Kanjiani

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.

📄 PDF Abstract BibTeX arXiv:2409.05778

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Tanh Activation 설명 없음

Sigmoid Activation 설명 없음

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning

2024-05-28 · Jaydip Sen, Hetvi Waghela, Sneha Rakshit

This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predict…

Prediction Stock Price Prediction

FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics

2024-11-02 · Mabsur Fatin Bin Hossain, Lubna Zahan Lamia, Md Mahmudur Rahman, Md Mosaddek Khan

Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), fore…

Sentiment Analysis Time Series Time Series Forecasting

Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series

2020-09-10 · Ahmed Ben Said, Abdelkarim Erradi, Hussein Aly, Abdelmonem Mohamed

Background: To assist policy makers in taking adequate decisions to stop the spread of COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. Materials and Methods: This paper pres…

Clustering Time Series Time Series Analysis

Stock Volatility Prediction using Time Series and Deep Learning Approach

2022-10-05 · Ananda Chatterjee, Hrisav Bhowmick, Jaydip Sen

Volatility clustering is a crucial property that has a substantial impact on stock market patterns. Nonetheless, developing robust models for accurately predicting future stock price volatility is a difficult research to…

Deep Learning Time Series Time Series Analysis

Indian Stock Market Prediction using Augmented Financial Intelligence ML

2024-07-02 · Anishka Chauhan, Pratham Mayur, Yeshwanth Sai Gokarakonda, Pooriya Jamie 외

This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidire…

Prediction Stock Market Prediction