Predicting Asset Returns - Machine Learning for Trading

Learn how we designed an intelligent asset allocation system that utilized Deep Learning and Modern Portfolio Theory. The solution (tested over two years period) helped generate an 8% annualized return.

Background

Increased Returns

We designed an intelligent asset allocation system that utilized Deep Learning and Modern Portfolio Theory. The task was to implement an investment strategy that could adapt to rapid changes in the market environment.

The solution (tested over two years period) helped generate an 8% annualized return.

Leverage Deep Learning and Modern Portfolio Theory to provide a robust asset allocation system.

Incorporating Long Short-Term Memory Units to better predict asset returns based on historical data, which led to 8% annualized return over two years period.

Value Delivered

8% Annualized Return

The solution (tested over two years period) helped generate an 8% annualized return.

Risk Minimization

We were able to estimate risks, as well as the uncertainty of our estimates using a novel technique called Variational Dropout.

Solution Overview

In one of our projects, we designed an intelligent asset allocation system that utilized Deep Learning and Modern Portfolio Theory

The task was to implement an investment strategy that could adapt to rapid changes in the market environment.

The base AI model was responsible for predicting asset returns based on historical data. This was accomplished by implementing Long Short-Term Memory Units, which are a sophisticated generalization of a Recurrent Neural Network. This particular architecture can store information for multiple timesteps, which is made possible by a Memory Cell. This property enables the model to learn long and complicated temporal patterns in data. As a result, we were able to predict the asset’s future returns, as well as the uncertainty of our estimates using a novel technique called Variational Dropout.
 
In order to strengthen our predictions, we used a wealth of market data, such as currencies, indices, etc. in our model, in addition to the historical returns of relevant assets. This resulted in over 400 features we used to make final predictions. Of course, many of these features were correlated. This problem was mitigated by Principal Component Analysis (PCA), which reduces the dimensionality of the problem and decorrelates features.
 
We then used the predictions of return and risk (uncertainty) for all the assets as inputs to a Mean-Variance Optimization algorithm, which uses a quadratic solver to minimize risk for a given return. This method determines the allocation of assets, which is diverse and ensures the lowest possible level of risk, given the returns’ predictions.
 
Combining these models created an investment strategy which generated an 8% annualized return, which was 23% higher than any other benchmark strategy tested over a two year period.

Conclusion

By leveraging the novel AI-based approaches, our client was able created an investment strategy which generated an 8% annualized return, which was 23% higher than any other benchmark strategy tested over a two year period.

Other Case Studies In Trading

Learn how we designed an intelligent asset allocation system that utilized Deep Learning and Modern Portfolio Theory. The solution (tested over two years period) helped generate an 8% annualized return.

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