AI and Machine Learning from Hedge Funds to Retail Trading
“From 2006 to 2021, the AI-based hedge funds generated average returns of about 0.75% per month vs. about 0.25% per month for the human-guided hedge funds.”
The above is based on a recent study published on April 22 in the journal Applied Economics called “Man Versus Machine: On Artificial Intelligence and Hedge Funds Performance,” by researchers from Texas A&M University and Finland’s University of Vaasa.
The world’s top hedge funds make deep use of AI and Machine Learning.
Anyone close to the Hedge Fund world has heard about Jim Simons’ iconic hedge fund – Renaissance Technologies (or RenTec). Their flagship Medallion Fund has generated a mind-blowing 66% annualized return (39% net of their hefty fees) from 1988 to 2018. Just from a compounded return perspective this means a Medallion Fund like performance would have converted $1000 invested in 1988 to a whopping $4 Billion in 30 years. What is impressive is the consistency of their return irrespective of the market conditions. They performed even better during the bad market environments like that of 2000-01, 2007-08.
The phenomenal success of Medallion Fund is because of its clever use of AI and Machine Learning. Jim Simons is a mathematician and former codebreaker for the National Security Agency. He assembled a team of mathematicians, physicists, signal processing experts, statisticians, and computer scientists to develop computer-based models to analyze and execute. Interestingly they only recruit people with non-financial backgrounds. Their amazing story is beautifully captured in the book, The Man Who Solved the Market by Gregory Zuckerman.
RenTec isn’t alone in this regard, Ray Dahlio’s Bridgewater Associates, David Elliot Shaw’s D.E.Shaw, David Siegel and John Overdeck’s Two Sigma, Man Group’s Man AHL are among many such examples of quantitative hedge funds that have been successfully using AI and Machine Learning to generate market-beating returns consistently.
Retail traders have been using manual tools and methods to trade for a long time because of the lack of proper AI tools. Most software retail traders are using today was created decades ago, when the focus was mostly on manual trading and charting. To be fair, those tools still work well for the expert traders who have developed a keen eye for charts and a rigorous routine, that they have perfected through trial and error over many years.
But these manual trading research tools don’t suffice the need of the new generation of traders in an extremely competitive trading world today that is increasingly driven by advanced technologies. Millennials, Gen Z, and the upcoming generations of traders cannot afford to learn to trade the difficult manual way by spending decades to master the craft.
But using AI and Machine Learning for trading is not easy. For that matter the application of AI to any domain isn’t easy. It is not a plug-and-play system. Technology giants are employing large teams of data scientists behind the scenes to develop AI systems that blend seamlessly into today’s everyday product experience. For example, the recommender systems of the likes of Amazon and Netflix, the self-driving cars of Tesla, the voice-based assistant like Alexa, Google Assistant and Siri, the fraud detection system of Banks and the list is long and growing.
While it is only the success stories of AI that we hear about in the media most of the time, many failures don’t get talked about. Companies that have failed to leverage AI are the ones that have tried to fit a square peg in a round hole. In other words, AI is not a silver bullet.
There are a lot of critics of AI when it comes to its application in retail trading. And they are right in that AI is not a standard off-the-shelf solution that can be fitted to any problem. Since a lot of use cases of AI and Machine Learning across industries are about prediction, so people assume that the role of AI in trading is to predict the market too. While there has been success stories in market prediction, especially when doing that at extremely small timeframes like milliseconds, seconds and sometimes minute level too. But anyone who has been associated with the trading domain long enough knows that markets are inherently not predictable in the longer timeframe. This is why traders cringe when they hear about algorithms claiming to predict the market.
The AI and Machine Learning usage in large institutions and hedge funds don’t necessarily translate to retail trading use cases. The trading approach at institutions and hedge funds is very different from retail trading. Besides there is a lot of high-frequency trading (HFT) happening at institutions and quantitative hedge funds. The use of algorithms in HFT is very different. So, an institutional-grade AI designed, say, for predictions in an HFT environment when applied at the retail level would be trying to fit a square peg in a round hole.
For AI to be successful for retail traders, it should blend into the trading approach and workflow that retail traders use. This requires learning the data-driven and pattern recognition aspects of the retail traders' workflow. That’s exactly what we have done at Researchfin.ai. We use AI to train a digital eye for data, chart patterns and trade-setups. And we use AI to translate natural language user queries into market scanner rules to enable a fast and a natural way to do iterative market research for trading opportunities. This then acts as assistance for new traders looking to learn how to trade, or for experienced traders to automate part of their workflow to achieve greater productivity.
We have partnered with professional traders and educators like Oliver Kell, Vishal Mehta, and Sofien Kaabar to create this novel AI-powered financial market intelligence platform that is very different from any other platform available in the market today. Read our recent press release here: https://www.prweb.com/releases/2022/6/prweb18725206.htm
The team at Researchfin.ai brings with them decades of experience in AI and Machine Learning and from some of the pioneering companies in the field like Yahoo and IBM. And this isn’t our first rodeo either. The founders of Researchfin.ai had previously created the company DataRPM – one of the award-winning and pioneering AI and Machine Learning platform for Industrial IoT - and has several patents in the field. DataRPM was acquired by Progress Software.
Please visit our website to learn more about how AI and Machine learning can be a game-changer for retail traders and sign up for an invite to this platform that is a gateway to the future-proof your trading: https://www.researchfin.ai