[3Rin4-51] RIC-NN: Deep Transfer Learning for Multi-Factor Investment Strategy
Keywords:Deep Learning, Transfer Learning, Multi-Factor Model
Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein.
Although machine learning methods are increasingly popular in stock return prediction, an inference of the stock returns is highly elusive, and still most investors, if partly, rely on their intuition to build a better decision making.
The challenge here is to make an investment strategy that is consistent over a reasonably long period, with the minimum human decision on the entire process.
To this end, we propose a new stock return prediction framework.
Experimental comparison shows that the proposed approach outperforms off-the-shelf machine learning methods.
Although machine learning methods are increasingly popular in stock return prediction, an inference of the stock returns is highly elusive, and still most investors, if partly, rely on their intuition to build a better decision making.
The challenge here is to make an investment strategy that is consistent over a reasonably long period, with the minimum human decision on the entire process.
To this end, we propose a new stock return prediction framework.
Experimental comparison shows that the proposed approach outperforms off-the-shelf machine learning methods.
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