Research Repository

Baseline win rates for neural-network based trading algorithms

Krause, Andreas and Fairbank, Michael (2020) Baseline win rates for neural-network based trading algorithms. In: 2020 International Joint Conference on Neural Networks (IJCNN 2020), 2020-07-19 - 2020-07-24, Glasgow. (In Press)

PID6419615.pdf - Accepted Version

Download (451kB) | Preview


Neural networks and other machine-learning systems are used to create automatic financial forecasting and trading systems. To aid comparison of such systems, there is a need for reliable performance metrics. One such metric that may be considered is the win rate. We show how in certain circumstances the win-rate statistic can be very misleading, and to counter this, we propose and define baseline win rates for comparison. We develop empirical and closed-form models for such baselines and validate them against financial data and a neural forecaster.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 04 Jun 2020 12:19
Last Modified: 23 Sep 2022 19:40

Actions (login required)

View Item View Item