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

15 Pages Posted: 10 Sep 2020

See all articles by Trent Spears

Trent Spears

University of Oxford - Oxford-Man Institute of Quantitative Finance

Stefan Zohren

University of Oxford - Oxford-Man Institute of Quantitative Finance

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: July 30, 2020

Abstract

In this work we 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. We showcase this insight with a prediction model and find clear outperformance based on a Sharpe ratio metric, relative to trading strategies that either do not take uncertainty into account, or that utilize an alternative market-based statistic as a proxy for uncertainty. Of added novelty is our modelling of high-frequency data at the top level of the Eurodollar Futures limit order book for each trading day of 2018, whereby we predict interest rate curve changes on small time horizons. We are motivated to study the market for these popularly-traded interest rate derivatives since it is deep and liquid, and contributes to the efficient functioning of global finance -- though there is relatively little by way of its modelling contained in the academic literature. Hence, we verify the utility of prediction models and uncertainty estimates for trading applications in this complex and multi-dimensional asset price space.

Keywords: Financial time-series analysis, high-frequency data, interest rate derivatives, deep learning.

Suggested Citation

Spears, Trent and Zohren, Stefan and Roberts, Stephen, Investment Sizing with Deep Learning Prediction Uncertainties for High-Frequency Eurodollar Futures Trading. (July 30, 2020). Available at SSRN: https://ssrn.com/abstract=3664497 or http://dx.doi.org/10.2139/ssrn.3664497

Trent Spears (Contact Author)

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Stefan Zohren

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

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