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STATS4TRADE Unlocks the Power of Machine Learning for Investors

by on February 2, 2017

The investment industry is an extremely competitive one, where fund managers work hard to demonstrate a strong track record that beats their respective benchmarks in order to be able to justify their fees and to partake in the profits from their assets under management. For retail investors, the recent years have been characterized by a significant shift towards passive investing instruments such as index funds at the expense of actively managed funds that have been struggling to justify higher expense ratios against the backdrop of high volatility markets and easy-money policies that interventionist central bank policies worldwide manufactured in response to the great recession.

In the meanwhile, the abundance of financial market data has given birth to new wave of startups looking to put Machine Learning to good use in order to create sustainable market edge at lower costs. One such exciting company is STATS4TRADE out of France.  We have caught up with the CEO – Founder of STATS4TRADE to see how his company is innovating with advanced analytics.

STATS4TRADE

 

BigML: Congrats on launching your startup Jean-Marc. Can you tell us what was the motivation behind founding STATS4TRADE?

Jean-Marc Guillard: It really starts with my conviction that the financial services industry is faced with drastic change in the coming years and actively-managed equity funds are not immune to that. Investors are rightfully questioning high fees in the face of continued poor performance compared to passive funds with much lower fees. Similar to the disruptive changes now occurring in the transport industry, active-fund managers must contemplate an “Uber-ization” of their business model with software driving innovation to provide investors promised returns at lower cost.

BigML: I understand that active managers are between a rock and a hard place, but what’s wrong with the good old buy-and-hold?

Jean-Marc Guillard: Consider putting your money into a diversified index fund and waiting decades. Would such a traditional buy-and-hold approach yield decent returns with low volatility? Normally yes – but beware. This strategy can yield poor results with rather high volatility for some indices. For example just consider the performance of French CAC 40 over the past two decades.

The CAC 40 index is a diverse weighted stock price average of France’s 40 largest public companies including such internationally famous names as Airbus, L’Oreal and Michelin. As a result, the CAC 40 should serve as an ideal index for the risk averse small investor in France with a buy-and-wait strategy over the long-run. But the performance of the CAC 40 between 1990 and 2015 has been a dismal 3.3% without dividends and 6.5% with dividends. Moreover these returns came with rather high volatility upward of 22%.

Overall we strongly believe that a buy-and-hold strategy is absolutely valid for the risk averse small investor – especially if one considers the cost of active funds. However the recent advent of no fee brokerages like Robinhood in the United States and Deziro in Europe offers investors the ability to actively manage their own investments at costs approaching those of index funds. We want to encourage this democratization process by offering investors an objective way to automatically select stocks that yields better results while bypassing high fees.

CAC40 vs. STATS4TRADE

BigML: That’s very interesting. How is STATS4TRADE’s approach to this problem different? How can the risk averse small investor earn decent returns – say in the range of 6-9% including all fees – with low volatility over shorter time periods than decades?

Jean-Marc Guillard: STATS4TRADE is uniquely positioned to help investors navigate this coming change. With the aid of Machine Learning and cloud computing technologies, we offer investors a new approach for selecting stocks and making buy/sell decisions – a data-driven approach that not only yields consistently better-than-index performance but also minimizes volatility and decreases operational costs while protecting capital.

Our trading applications leverage the power of BigML’s Machine Learning tools and allow investors both private and professional the opportunity to not only select but also simulate different investment strategies based on short-term price forecasts. Once an investor has selected a strategy corresponding to her particular risk-profile, the application automatically provides daily buy/sell signals for trading on no-fee platforms like Robinhood and Deziro.

Of course none of this is magic and our approach is not without its limitations. For example if one expects to become rich quickly, he will be sorely disappointed for no forecast is completely accurate. Normally one needs about six months to begin seeing the benefits of our method. Nonetheless the message is clear: through the power of statistics and data-driven approaches like ours ultimately yield better results at lower cost. The results certainly speak for themselves!

BigML: Thanks for the detailed explanation.  Can you also tell a bit about specifically how Machine Learning comes into play?

Jean-Marc Guillard: Someone once said that predicting the future is a fool’s errand. We agree. However, one can still use stastitics to estimate the likelihood of future events based on past data and an underlying statistical model. In fact, statistical methods have been used extensively for years in activities like consumer research, weather forecasting and of course finance. In our case we use Machine Learning methods powered by BigML to estimate the probability of short-term price movements of selected securities and indices, currencies and commodities. Namely, we aim to identify underlying statistical patterns for a given security, basket of securities, or an index and thereby accurately forecast upcoming movements in price.

BigML: What made you choose to build your models on the BigML platform?

Jean-Marc Guillard: A big part of it was the drastically faster iterative experimentation BigML Dashboard enables, which in turn allowed us to achieve faster time-to-market. One usually doesn’t know what the final Machine Learning workflow will look like when he sets on exploring possibilities in a large hypothesis space a complex problem like ours require. So it is essential that the tools you use afford you very quick and easy iterative exploration.  BigML excels on this front.

In addition, the automation options made available on the BigML platform let’s us decrease ongoing operational costs to a minimum level that can compete with passive index funds while further differentiating from actively-managed funds that rely on manual processes. Lastly, we have had phenomenal support from the BigML team throughout our evaluation, exploration and solutions implementation phases.

BigML: Thanks Jean-Marc. It is very impressive to see how you have been able to ramp up your Machine Learning efforts in such a limited time period despite constrained resources. We hope stories like yours inspire many more startups to realize that they too can turn their data and know-how into sustainable competitive advantages.

For our readers benefit, a downloadable PDF version of the STATS4TRADE case study is also available.

From → Business, Case Study

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