Transfer learning approach for financial applications
This work addresses computational efficiency for financial modeling, but it appears incremental as it builds on an existing method.
The paper tackles the problem of reducing training time for neural networks in financial applications by proposing a novel selective breeding technique that extends a prior transfer learning approach with behavioral genetics, and demonstrates its credibility through numerical evidence.
Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process. One way to reduce the amount of time required is to inject preexisting knowledge into the network. To make use of past knowledge, we can take advantage of techniques that transfer the knowledge learned from one task, and reuse it on another (sometimes unrelated) task. In this paper we propose a novel selective breeding technique that extends the transfer learning with behavioural genetics approach proposed by Kohli, Magoulas and Thomas (2013), and evaluate its performance on financial data. Numerical evidence demonstrates the credibility of the new approach. We provide insights on the operation of transfer learning and highlight the benefits of using behavioural principles and selective breeding when tackling a set of diverse financial applications problems.