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  3. An Efficient Real-time Stock Prediction Exploiting Incremental Learning And Deep Learning.
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An efficient real-time stock prediction exploiting incremental learning and deep learning.

Tinku Singh1, Riya Kalra1, Suryanshi Mishra2

  • 1Department of IT, Indian Institute of Information Technology Allahabad, Prayagraj, U.P. India.

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|April 16, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Offline-Online learning models provide more accurate intraday stock predictions than incremental learning models. These models continuously adapt to live market data for improved forecasting accuracy.

Keywords:
Incremental learningIntraday tradingReal-time forecastingTechnical indicator

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Area of Science:

  • * Financial forecasting and algorithmic trading.
  • * Machine learning applications in quantitative finance.

Background:

  • * Intraday stock trading relies on short-term price fluctuations, necessitating real-time predictions.
  • * Stock market complexity, volatility, and non-stationarity pose significant challenges for accurate forecasting.
  • * Traditional machine learning models require hyperparameter tuning with current data for optimal performance.

Purpose of the Study:

  • * To propose and evaluate novel machine learning strategies for real-time intraday stock price prediction.
  • * To compare the efficacy of incremental learning versus Offline-Online learning for live market forecasting.
  • * To assess model performance on both univariate and multivariate time-series data.

Main Methods:

  • * Implementation of incremental learning: continuous model updates with live data streams.
  • * Implementation of Offline-Online learning: periodic model retraining after each trading session.
  • * Application to univariate (historical prices) and multivariate (prices + technical indicators) time-series data.
  • * Testing on eight liquid stocks from NASDAQ and NSE.

    • Main Results:

    • * Offline-Online learning models demonstrated superior performance compared to incremental learning models.
    • * Forecasting error was significantly lower in models utilizing the Offline-Online approach.
    • * Both approaches were applied to univariate and multivariate time-series data with varying results.

    Conclusions:

    • * Offline-Online learning is a more effective strategy for accurate intraday stock price forecasting in live markets.
    • * Periodic retraining captures market complexities better than continuous incremental updates.
    • * The findings offer valuable insights for developing adaptive algorithmic trading strategies.