Today we release the results of our recent research in a paper called Return Predictability and Market-Timing: A One-Month Model. Approximately three-fourths of the model’s predictor variables are fundamental; the rest are technical. Here is the abstract from the paper:
We propose a one-month market-timing model constructed from 15 diverse variables. We use weighted least squares with stepwise variable selection to build a predictive model for the one-month-ahead market excess returns. From our statistical model, we transform our forecasts into investable positions to build a market-timing strategy. From 2003 to 2017, our strategy results in 16.6% annual returns with a 0.92 Sharpe ratio and a 20.3% maximum drawdown, whereas the S&P 500 has annual returns of 10%, a 0.46 Sharpe ratio, and a maximum drawdown of 55.2%. When our one-month model is used in conjunction with Hull and Qiao’s (2017) six-month model, the Sharpe ratio of the combined strategy exceeds the individual model Sharpe ratios. The combined model has 15% annual returns, a Sharpe ratio of 1.12, and a maximum drawdown of 14%. We publish forecasts from our one-month model in our Daily Report.
We believe the paper is unique in that it provides a link to the model’s forecasts. To our knowledge, no other research paper gives access to real time predictions of the equity risk premium.
The One-Month Model paper complements our first paper, A Practitioner’s Defense of Return Predictability, which appears in the Spring 2017 issue of The Journal of Portfolio Management.
As many of our readers know, we publish our forecasts and market exposure daily at the close of trading in our Daily Report. As a result of this new research, we are expanding the daily report to include information about our one-month model forecasts.
In the coming months, we plan to publish a third paper that describes our shorter term models with forecast horizons from one to several days. All good things come in threes.
© 2017 Hull Tactical Asset Allocation, LLC (“HTAA”) is a Registered Investment Adviser.
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