Investment Sizing with Deep Learning Prediction Uncertainties for High-Frequency Eurodollar Futures Trading

Abstract

In this article, the authors show that prediction uncertainty estimates gleaned from deep learning models can be useful inputs for influencing the relative allocation of risk capital across trades. In this way, consideration of uncertainty is important because it permits the scaling of investment size across trade opportunities in a principled and data-driven way. The authors showcase this insight with a prediction model and, based on a Sharpe ratio metric, find clear outperformance relative to trading strategies that either do not take uncertainty into account or use an alternative market-based statistic as a proxy for uncertainty. Of added novelty is their modeling of high-frequency data at the top level of the Eurodollar futures limit order book for each trading day of 2018, whereby they predict interest rate curve changes on small time horizons. The authors are motivated to study the market for these popularly traded interest rate derivatives because it is deep and liquid and contributes to the efficient functioning of global finance—though there is relatively little by way of its modeling contained in the academic literature. Hence, they verify the utility of prediction models and uncertainty estimates for trading applications in this complex and multidimensional asset price space.

TOPICS: Big data/machine learning, derivatives, simulations, statistical methods

Key Findings

• ▪ The authors model high-frequency Eurodollar Futures limit order book data using state-of-the-art deep learning to predict interest rate curve changes on small time horizons.

• ▪ They further augment their models to yield estimates of prediction uncertainty.

• ▪ In certain settings, the uncertainty estimates can be used in conjunction with return predictions for scaling bankroll allocation between trades. This can lead to clear trading outperformance relative to the case that uncertainty is not taken into account.