Application of Deep Learning for Factor Timing in Asset Management (2404.18017v1)

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.

• The paper demonstrates that advanced deep learning models, particularly a three-layer neural network, achieve higher out-of-sample R-squared values than traditional linear regressions.
• It contrasts methodologies using extensive factor datasets from Fama and French and Welch and Goyal, emphasizing key variables like term and default yield spreads.
• The study shows that although complex models improve prediction accuracy, transaction costs and model turnover hinder their practical application in asset management.

Application of Deep Learning for Factor Timing in Asset Management

The research presented in this paper examines the application of various regression models for predicting the Conservative Minus Aggressive (CMA) factor premium and assesses their viability in factor timing for asset management. The analysis involves comparing traditional linear regression techniques such as Ordinary Least Squares (OLS) and Ridge regression with more complex methodologies, namely Random Forest and a three-layer fully-connected neural network (NN3).

Methodology and Data

The paper makes use of extensive factor datasets sourced from the established Fama and French and Welch and Goyal databases. It spans January 1963 to December 2022, with the period from 1963-2002 utilized for training and 2003-2022 reserved for testing. The paper highlights the importance of Term spread and Default Yield spread as critical variables for the CMA factor timing, alongside the factor’s own lagged values to account for autocorrelation.

Predictive Models and Factor Timing

The research contrasts various models, noting that more flexible approaches like NN3 and Random Forest produced higher out-of-sample (OOS) R-squared values. Specifically, NN3 achieved a notable OOS R-squared of 0.102629, outperforming OLS and Ridge regression, which had values of 0.024068 and 0.029150, respectively. The volatility inherent in the predictions from more complex models, however, results in unstable optimal weights for investment, imposing potential challenges such as increased transaction costs.

The portfolio strategy focuses on a two-asset combination between the CMA factor and a risk-free asset, assessed through utility optimization. The paper investigates four distinct economic periods within the testing timeframe, revealing that NN3 could surpass other strategies during early test periods but suffered during volatile phases like the Global Financial Crisis. Random Forest, meanwhile, offered a more consistent performance across different market conditions and achieved the highest Sharpe Ratios, underscoring its potential for stable factor timing.

Performance and Cost Considerations

The exploration of strategies without considering transaction costs showed the neural network's fluctuating performance due to varying interest regimes. Conversely, Random Forest provided stable returns surpassing the linear models and the static optimal weight benchmark. However, when adjusting for transaction costs – including both proportional and quadratic scales – all models underperformed the constant optimal weighting as costs increased, particularly emphasizing the significant role of model turnover.

Optimal rebalancing intervals were studied to mitigate transaction cost erosion. It was demonstrated that for models employing high turnover strategies, such as Random Forest and NN3, optimizing the rebalance frequency could augment annualized returns under specific cost conditions.

Conclusions and Future Considerations

The research indicates that while advanced machine learning models offer robust predictive capabilities, their practical application in factor timing can be compromised by transaction costs and high-frequency trading impacts. The linear OLS, enhanced by previous scholarly techniques, still provides competitive transaction cost performance despite its lower predictive power. Future research directions could include addressing quadratic transaction costs more effectively and refining strategies to balance prediction accuracy with transaction efficiency, further enhancing the operational feasibility of leveraging machine learning for asset management in financial markets.