Incorporating Prior Knowledge About Financial Markets Through Neural Multitask Learning

Abstract

We present the systematic method of Multitask Learning for incorporating prior knowledge (hints) into the inductive learning system of neural networks. Multitask Learning is an inductive transfer method which uses domain information about related tasks as inductive bias to guide the learning process towards better solutions of the main problem. These tasks are presented to the learning system in a shared representation. This paper argues that there exist many opportunities for Multitask Learning especially in the world of financial modeling: It has been shown, that many interdependencies exist between international financial markets, different market sectors and financial products. Models with an isolated view on a single market or a single product therefore ignore this important source of information. An empirical example of Multitask Learning is presented where learning additional tasks improves the forecasting accuracy of a neural network used to forecast the changes of five major German stocks.