Deep Learning in Long-Short Stock Portfolio Allocation: An Empirical Study

ArXiv ID: 2411.13555 “View on arXiv”

Authors: Unknown

Abstract

This paper provides an empirical study explores the application of deep learning algorithms-Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-in constructing long-short stock portfolios. Two datasets comprising randomly selected stocks from the S&P500 and NASDAQ indices, each spanning a decade of daily data, are utilized. The models predict daily stock returns based on historical features such as past returns,Relative Strength Index (RSI), trading volume, and volatility. Portfolios are dynamically adjusted by longing stocks with positive predicted returns and shorting those with negative predictions, with equal asset weights. Performance is evaluated over a two-year testing period, focusing on return, Sharpe ratio, and maximum drawdown metrics. The results demonstrate the efficacy of deep learning models in enhancing long-short stock portfolio performance.

Keywords: Deep Learning, LSTM, Transformers, Long-Short Stock Portfolios, Stock Return Prediction

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders
  • Why: The paper uses established deep learning architectures (MLP, CNN, LSTM, Transformer) applied to financial time-series prediction, requiring some advanced math for model architecture but no novel theoretical derivations. Its empirical rigor is high due to a detailed backtesting framework spanning 8 years of training and 2 years of testing, using specific metrics (Sharpe, MDD) on real market data from S&P500 and NASDAQ.
  flowchart TD
    A["Research Goal<br>Assess Deep Learning Models for<br>Long-Short Stock Portfolios"] --> B["Data Preparation<br>S&P 500 & NASDAQ Decade-Long Data"]
    B --> C["Feature Engineering<br>RSI, Volume, Volatility, Past Returns"]
    C --> D["Model Training<br>MLP, CNN, LSTM, Transformers"]
    D --> E["Portfolio Construction<br>Long Positive Predictions, Short Negative"]
    E --> F["Performance Evaluation<br>Test Period: 2 Years"]
    F --> G["Outcomes<br>Deep Learning Enhances<br>Portfolio Performance (Sharpe, Returns)"]