Deep Learning in Finance

Deep Learning in Finance

Sonam Srivastava | Oct. 1, 2017

I am writing this post as a follow up on a talk by the same name given at Re-work Deep Learning Summit, Singapore. In the talk I tried to detail the reasons .

 Which is because,

To solve this, if we look at the research done in Deep Learning in proven fields of image recognition, speech  or sentiment analysis we see that these models are capable of learning from large  unlabelled data, forming non-linear relationships, forming recurrent structures and can be easily tweaked to avoid over-fitting.

If these models find application in the discipline of finance then the applications are far and wide. . So let us tackle a few of these problems.

Return Prediction

Taking the sample problem of predicting daily Gold Prices, 

ARIMA

VAR

If we add related predictor variables to our auto-regressive model and move to a l, we get these results —

Deep Regression

Using the same inputs if I fit a simple deep regression model on the data, I get far better results,

Convolutional Neural Networks

 for the same problem, my  

These results are drastically better. But the best results come next.

Long Short Term Memory (LSTM)

There you go! Using these variations of recurrent neural networks, my results are:

So overall !

Portfolio Construction

The second financial problem we will try to tackle using deep learning is of portfolio construction. The application of deep learning to this problem has a beautiful construct. My study is inspired by a paper titled Deep Portfolios.

What the authors of the paper 

ults are quite good.

The deep neural network here has become a index construction method that replicated the index using stocks.

This technique has a huge potential in the field of portfolio construction!

Conclusion

The current trends in the financial industry are leading the way to more sophisticated and sound models finding their way in. Technology is a huge area of stress for all the banks with a large number of data scientists entering the field. You have hedge funds like  and  that already this technology in their trading. With the superior results shown by these sophisticated models in other fields and the huge gaps open in the field of financial modelling, there is a scope of dramatic innovations!

Better solutions to our critical problems in the field of finance and trading would lead to increased efficiency, more transparency, tighter risk management and new innovations.

I’m planning my next post on deep RL for portfolio management, so keep tuned in!

Any good suggestions are welcome.

Please visit my website http://www.wrightresearch.in /to know more about the investment strategies I manage!


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