Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices

ArXiv ID: 2309.15640 “View on arXiv”

Authors: Unknown

Abstract

This paper proposes a novel approach to hedging portfolios of risky assets when financial markets are affected by financial turmoils. We introduce a completely novel approach to diversification activity not on the level of single assets but on the level of ensemble algorithmic investment strategies (AIS) built based on the prices of these assets. We employ four types of diverse theoretical models (LSTM - Long Short-Term Memory, ARIMA-GARCH - Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity, momentum, and contrarian) to generate price forecasts, which are then used to produce investment signals in single and complex AIS. In such a way, we are able to verify the diversification potential of different types of investment strategies consisting of various assets (energy commodities, precious metals, cryptocurrencies, or soft commodities) in hedging ensemble AIS built for equity indices (S&P 500 index). Empirical data used in this study cover the period between 2004 and 2022. Our main conclusion is that LSTM-based strategies outperform the other models and that the best diversifier for the AIS built for the S&P 500 index is the AIS built for Bitcoin. Finally, we test the LSTM model for a higher frequency of data (1 hour). We conclude that it outperforms the results obtained using daily data.

Keywords: LSTM (Long Short-Term Memory), ARIMA-GARCH, Ensemble Algorithmic Investment Strategies (AIS), Momentum Trading, Contrarian Trading, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning (LSTM) and time series models (ARIMA-GARCH) with sophisticated ensemble construction, indicating moderate-to-high mathematical complexity, while the study features extensive walk-forward backtesting on multiple asset classes and frequencies from 2004 to 2022, demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal: <br>Test Hedging Properties of Algorithmic Strategies"] --> B["Input Data<br>Asset Classes: Equities, Commodities, Crypto<br>Period: 2004-2022"]

    B --> C{"Model Generation<br>Four Algorithmic Types"}
    C --> D["LSTM<br>Deep Learning"]
    C --> E["ARIMA-GARCH<br>Time Series"]
    C --> F["Momentum<br>Price Trend"]
    C --> G["Contrarian<br>Reversion"]

    D & E & F & G --> H["Ensemble AIS Construction<br>Single & Complex Strategies"]
    
    H --> I["Outcome: <br>LSTM outperforms all models<br>Bitcoin is best hedge for S&P 500"]
    B -.->|Hourly Data Test| J["High-Frequency Validation<br>LSTM Daily < Hourly Data"]