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[4Rin1-35] Deep Learning for Multifactor Models in Global Stock Markets
Keywords:Deep learning, Stock price prediction, Multifactor model
Many studies have been undertaken by using deep learning techniques to predict stock returns in terms of time-
series prediction.However, from the viewpoint of the cross-sectional prediction, there are no examples that verify
its effectiveness in the global stock market.This paper implements deep learning to predict stock returns in the
cross-section in the global stock market and investigates the performance of the method.Our results are followings.
1. Deep learning is superior in terms of return / risk as compared with random forest and ridge regression.
2. Especially, in terms of risk, the deep learning model is outstanding.
3. If market efficiency declines, opportunities for return may increase.
series prediction.However, from the viewpoint of the cross-sectional prediction, there are no examples that verify
its effectiveness in the global stock market.This paper implements deep learning to predict stock returns in the
cross-section in the global stock market and investigates the performance of the method.Our results are followings.
1. Deep learning is superior in terms of return / risk as compared with random forest and ridge regression.
2. Especially, in terms of risk, the deep learning model is outstanding.
3. If market efficiency declines, opportunities for return may increase.