Application of Deep Learning for Factor Timing in Asset Management

ArXiv ID: 2404.18017 “View on arXiv”

Authors: Unknown

Abstract

The paper examines the performance of regression models (OLS linear regression, Ridge regression, Random Forest, and Fully-connected Neural Network) on the prediction of CMA (Conservative Minus Aggressive) factor premium and the performance of factor timing investment with them. Out-of-sample R-squared shows that more flexible models have better performance in explaining the variance in factor premium of the unseen period, and the back testing affirms that the factor timing based on more flexible models tends to over perform the ones with linear models. However, for flexible models like neural networks, the optimal weights based on their prediction tend to be unstable, which can lead to high transaction costs and market impacts. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs.

Keywords: Regression Models, Factor Timing, CMA Factor, Neural Networks, Transaction Costs, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders
• Why: The paper employs standard regression models and portfolio optimization formulas rather than advanced derivations, placing it at a moderate math level. It demonstrates strong empirical rigor with detailed backtesting on 60 years of data, out-of-sample validation, transaction cost modeling, and sensitivity analysis on rebalancing frequency.

  flowchart TD
    A["Research Goal<br>Optimize Factor Timing for CMA"] --> B["Model Selection & Data"]
    B --> C["Prediction Phase<br>(Out-of-Sample R²)"]
    B --> D["Backtesting Phase<br>(Investment Performance)"]
    
    subgraph B [" "]
        direction LR
        B1["Inputs<br>Historical CMA Premium"]
        B2["Models<br>OLS, Ridge, RF, NN"]
        B1 & B2
    end
    
    C --> E["Key Finding 1<br>Flexible Models (RF/NN) > Linear Models"]
    D --> F["Key Finding 2<br>Flexible Models Overperform<br>but High Transaction Costs"]
    
    E & F --> G["Optimization<br>Adaptive Rebalancing Frequency"]
    G --> H["Final Outcome<br>Cost-Efficient Factor Timing"]