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Discussion Paper
Hedge Fund Return Prediction and Fund Selection: A Machine-Learning Approach
A machine-learning approach is employed to forecast hedge fund returns and perform individual hedge fund selection within major hedge fund style categories. Hedge fund selection is treated as a cross-sectional supervised learning process based on direct forecasts of future returns. The inputs to the machine-learning models are observed hedge fund characteristics. Various learning processes including the lasso, random forest methods, gradient boosting methods, and deep neural networks are applied to predict fund performance. They all outperform the corresponding style index as well as a ...
Working Paper
Sellin’ in the Rain: Adaptation to Weather and Climate in the Retail Sector
Using novel methodology and proprietary daily store-level sporting goods and apparel brand data, I find that, consistent with long-run adaptation to climate, sales sensitivity to weather declines with historical norms and variability of weather. Short-run adaptation to weather shocks is dominated by changes in what people buy and how they buy it, with little intertemporal substitution. Over four weeks, a one-standard deviation one-day weather shock shifts sales by about 10 percent. While switching between indoor and outdoor stores offsets a small portion of contemporaneous responses to ...
Discussion Paper
The Chen-Tindall system and the lasso operator: improving automatic model performance
Using U.S. monthly macroeconomic data, the automatic model system presented in Chen and Tindall [2016] outperforms the lasso automatic system, but the lasso is improved where Bayesian model averaging is employed to combine its forecasts with those from autoregressive schemes. The best performance is obtained using Bayesian model averaging to combine the Chen?Tindall system, the lasso, and autoregressive schemes. Performance is virtually the same using this combined approach where the elastic-net operator is substituted for the lasso. Similar overall outcomes are found for France and Germany ...
Working Paper
Shrinkage estimation of high-dimensional factor models with structural instabilities
In high-dimensional factor models, both the factor loadings and the number of factors may change over time. This paper proposes a shrinkage estimator that detects and disentangles these instabilities. The new method simultaneously and consistently estimates the number of pre- and post-break factors, which liberates researchers from sequential testing and achieves uniform control of the family-wise model selection errors over an increasing number of variables. The shrinkage estimator only requires the calculation of principal components and the solution of a convex optimization problem, which ...
Working Paper
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigates the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also ...