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Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming.

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This study introduces a novel hybrid Symbolic Genetic Programming-Long Short-Term Memory (SGP-LSTM) model for enhanced stock return prediction in the Chinese market. The SGP-LSTM model significantly improves prediction accuracy and generates substantial excess returns.

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Area of Science:

  • * Computational Finance
  • * Machine Learning in Finance
  • * Quantitative Trading

Background:

  • * Accurate forecasting of cross-sectional stock price returns is crucial for investment strategies.
  • * Traditional models often struggle with the complexity and noise inherent in financial markets.
  • * Enhancing prediction accuracy can lead to improved portfolio performance and risk management.

Purpose of the Study:

  • * To introduce and evaluate a novel hybrid Symbolic Genetic Programming-Long Short-Term Memory (SGP-LSTM) neural network architecture.
  • * To forecast cross-sectional stock price returns using a large dataset of Chinese listed stocks.
  • * To assess the model's performance improvement over existing methods and benchmarks.

Main Methods:

  • * Development of an augmented LSTM architecture integrated with Symbolic Genetic Programming (SGP).
  • * Utilization of data augmentation and feature extraction techniques on the S&P Alpha Pool Dataset for China (2014-2022).
  • * Application of the hybrid model to both fundamental and technical indicators for stock return prediction.

Main Results:

  • * Significant improvements in Rank Information Coefficient (Rank IC) and IC Information Ratio (ICIR) for fundamental indicators (1128% and 5360% increases, respectively).
  • * Substantial gains in Rank IC (206%) and ICIR (2752%) for technical indicators.
  • * Outperformance of major Chinese stock indexes, yielding average annualized excess returns of 31.00% (vs. CSI 300), 24.48% (vs. CSI 500), and 16.38% (vs. average portfolio).

Conclusions:

  • * The hybrid SGP-LSTM model demonstrates superior performance in predicting cross-sectional stock returns.
  • * The integration of SGP with LSTM significantly enhances predictive accuracy and financial forecasting capabilities.
  • * Findings offer valuable insights for fund managers, traders, and financial analysts seeking to improve investment strategies.