| Abstract: |
Asset-specific factors are commonly used to forecast financial returns and
quantify asset-specific risk premia. Using various machine learning models, we
demonstrate that the information contained in these factors leads to even
larger economic gains in terms of forecasts of sector returns and the
measurement of sector-specific risk premia. To capitalize on the strong
predictive results of individual models for the performance of different
sectors, we develop a novel online ensemble algorithm that learns to optimize
predictive performance. The algorithm continuously adapts over time to
determine the optimal combination of individual models by solely analyzing
their most recent prediction performance. This makes it particularly suited
for time series problems, rolling window backtesting procedures, and systems
of potentially black-box models. We derive the optimal gain function, express
the corresponding regret bounds in terms of the out-of-sample R-squared
measure, and derive optimal learning rate for the algorithm. Empirically, the
new ensemble outperforms both individual machine learning models and their
simple averages in providing better measurements of sector risk premia.
Moreover, it allows for performance attribution of different factors across
various sectors, without conditioning on a specific model. Finally, by
utilizing monthly predictions from our ensemble, we develop a sector rotation
strategy that significantly outperforms the market. The strategy remains
robust against various financial factors, periods of financial distress, and
conservative transaction costs. Notably, the strategy's efficacy persists over
time, exhibiting consistent improvement throughout an extended backtesting
period and yielding substantial profits during the economic turbulence of the
COVID-19 pandemic. |