Should Deep Learning Trading Be a Part of Your Hedge Fund Strategy?

The popularity of Deep Neural Networks has drastically increased over the last half a decade or so. Built around the concept of Deep Learning and Biological Neural Networks, it has managed to capture the imagination of one and all. Deep Learning in its brief stint has already made innovative changes and has aided in eliminating or mitigating issues with computer vision, natural language processing, speech recognition, machine translation and more. In fact, in many sectors, it has managed to improve on the performances of the human experts. Hence, it is interesting to analyse its possible impact on the financial sector.

Application of Deep Learning in Hedge Funds is one prospect which has made many financial experts excited. Hedge Fund is a common tool used by different financial institutions to raise funds from investors, which the institutions manage as per their requirements. These typically work with the help of time series data and systematically make certain predictions. In the realm of time-series analysis, Deep Learning offers a special architecture which is called Recurrent Neural Networks or RNNs. In RNNs too there are some specific networks called Long Short-Term Memory networks which can be extremely useful as they have the capability to capture the most vital features from the time series data and by modelling the dependencies.

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One of the biggest challenge, as well as the most exciting part of being involved in the financial industry, is an accurate prediction of the rise and fall of the stock prices. Deep Learning has already proved its mettle in solving some of the most complex tasks, so it is worth the effort to see if it can aid in this endeavour too.

What Makes Hedge Funds Different

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Hedge Fund usually formed either as a limited liability company or a limited partnership. The primary objective of it is to maximise the returns. In its bid to achieve attractive returns, the hedge funds depend on multiple investment strategies which aim to take advantage of the market inefficiencies to make more money. Unlike other investment funds, Hedge Funds are not necessarily registered as funds. As a result of this, they need not publish the various investment strategies and business results, making them a riskier option.

Hedge Funds are primarily targeted towards a small group of wealthy investors. Accreditation is a pre-requisite for Hedge Fund investments. Moreover, one has to have sound investment knowledge to be part of it, eliminating the risk of loss among the small and inexperienced investors. Large corporations and banks too can operate in this type of funds.

Deep Learning In Hedge Funds

Long Short-Term Memory network is the most suitable Recurrent Neural Network architecture for implementation in Hedge Funds behaviour prediction. Although, LSTM have more or less the same structure as that of an RNN, the recurrent neuron has a more complex structure in this case. RNNs like these can be applied to finance based tasks by taking the price of a particular share on five consecutive days. It is to be noted that in such a case there will be five feature vectors and the RNN outputs will have hidden features. These features will typically be more abstract in nature when compared to the input features. Now the LSTM learns the key portions of the input features and subsequently projects them to the space for hidden features. These abstract hidden features are cultivated in the following LSTM cell, which produces more abstract hidden features, this can again be propagated to the next LSTM, forming a cycle.

Upon completion of a chain LSTMs, the linear layer of the neural network comes into play. It maps the hidden features from the latest LSTM in a one-dimensional space which is the ultimate output of the connected network. This is the predicted close price in the mentioned time period.

The Utilisation Of Algorithmic Trading Strategies

Such a system could enable the Hedge Fund investors to speculate better the future share prices of particular companies using algorithmic strategies offered by Deep Learning. The investor could simply provide the system with a certain amount to automatically trade on a daily basis. However, in such a scenario, it is best to have some supervision, ideally by someone with basic knowledge of Deep Learning. Moreover, the supervisor also needs to spot when the system loses its ability to generalise and trade. In such an instance, there is a need for retraining from scratch.

To successfully build a deep learning trading system one needs Deep Learning experts, Hedge Fund data scientists, and Research and Development, engineers. This system is rather basic in operation, and for proper implementation, under real-world circumstances, there is a need for thorough research and development. Improvements can also be made in it by developing better strategies to increase returns. Collection of more data during the training period is a good option but is financially not viable at this stage for many. Using more features is another possible route to improvement.

Above all, significant changes can also be expected in the future with the help of more powerful computers with higher GPUs. Although, at present, the strategy looks to be at a nascent stage the future looks bright as it definitely has the potential to provide more accurate predictions in the share market as a whole. All in all, an exciting time beckons as technology advancements take place at a rapid rate.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Gagan Singla
Gagan Singla is the CMO of Angel Broking, a leading stock broking and wealth management firm in India. He holds a B.Tech degree (Computer Science) from IIT Delhi and MBA from IIM, Lucknow. All in all, he has over 15 years of experience in analytics, consulting and marketing. He has been instrumental in delivering analytics-driven transformations in multiple industry sectors including banks, insurance, eCommerce, AMCs and public sector across numerous geographies including the US, UK, Europe, Malaysia, India & Canada. In the last couple of years, he has taken up leadership roles in digital marketing to drive business growth in the new digital age.

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