Do robot analysts dream of electric returns?

Our economy is fast moving towards full automation and digitalization. Robots are replacing already or are expected to replace a vast range of functions, from assembly-line workers through drivers to surgeons. Investment industry isn’t different: there are many tasks that can be performed much more efficiently by AI. There’s a chance that soon we’ll invest through an AI-led portfolio manager, or on advice from our personal AI financial analyst.  

AI already has a major role in trading 

“Artificial intelligence is to trading what fire was to the cavemen”, - that’s how industry players describe the impact of a disruptive technology on the industry. AI is changing the stock market game. 

Technology is running large parts of the global markets. More than 60% percent of all trading activity today is run on technology; in some markets the share of machines in trading is above 90%. In fact, the top five hedge funds in the world are all quantitative funds, using some form of technology to develop their trading strategies. As a part of this digital evolution of finance, AI is becoming incredibly influential. AI-powered analytics is already used to generate investment ideas and develop portfolios. 

AI and finance appear to be made for each other: AI can easily find patterns that the human eye might not have recognized otherwise. Because finance is quantitative, it is difficult not to associate it with such AI-enabled activities as data processing, analysis, forecasting, etc.

Financial markets globally have been one of the earliest adopters of AI and machine learning. In 2001, IBM built a team of “robots” that beat humans at trading. Now, AI is riding the S-Curve of technology diffusion. It will replace much of the stock analysis profession - first to go will be the traditional sell-side equity analysts, then AI will overtake the buy-side, too.

For years already, high-frequency trading is of course fully ceded to machines – thousands of trades a second have no place for human involvement.

Technical Analysis is a broader subject that involves studying patterns in price, volume, and other factors to take short-term positions. TA is run by some combination of humans and machines depending on the frequency of trade and data looked at. However, this is increasingly a “solved” problem, and more and more of technical analysis is run by AI.

AI is being ignored in fundamental research 

Currently, most AI research is focused on trading strategies. This is the “lowest hanging fruit” of AI applications in the markets - process large amounts of data quickly to see if you can do a little more than 50% percent of your trades profitably. 

But innovative firms are pioneering the application of machine learning to fundamental investing, finding persistent patterns in company data. Some firms have developed “virtual analysts”; others are building natural language processing (NLP) tools to automate basic research and reporting functions. These innovators understand that market players should prepare for a world where AI runs the markets and eventually algorithms are all trying to “out-algo” each other.

Fundamental investing, i.e. studying a stock, its management, its moat, and future prospects to buy and hold has long been the domain of human investors. Great investors like Graham, Dodd and Buffett have been icons in the space that have helped build the body of work around this subject that has stayed constant for decades. The premise is simple: fundamentals drive value, and this still holds today. However, the number of factors that impact a company’s fundamentals has grown manifold in recent decades, and humans are struggling to beat their benchmarks. 

Today, to do the fundamental research means obtaining and processing large swathes of data, taking into account a myriad of circumstances and parameters, and making the analysis while taking into account many ways in which these factors affect one another and the outcome. Because fundamental research is perceived as difficult (well, it is a complicated task), it is often ignored by traders, who prefer to base their trading decisions on someone else’s advice, technical patterns or even emotions. Therefore most equity investment decisions rely on actual investing performed by human experts - who are not always knowledgeable, cannot always track all market signals, and are mostly biased.  

The answer to this problem is application of AI in fundamental research. AI can evaluate large chunks of data points in real-time, scan over datasets on a scale that a human analyst can’t do in a year, and examine all figures, instantly determining their cross-effects on a company’s stock behavior. AI tools can speed up the in-depth investment research and gauge markets more accurately, letting traders effectively manage risk to deliver higher returns on capital. NLP and machine learning can assist traders and analysts by delivering AI-powered research data curation, helping generate more revenue and enhancing the research experience for traders and investment professionals.  

AI research will democratize trading

“Siri, buy me a stock that has more than a 50% chance of going up 10% this month” - will trading ever be this simple? Maybe not right now. But right at this moment there are powerful AI tools that can make fundamental investing research accessible to all. 

Imagine that in order to drive your car, you have to know exactly how all the parts work with each other. If you didn’t know the “system,” you’d have to drive with a personal mechanic to advise you. Using this analogy, it’s easy to understand why fundamental analysis scares most traders. Instead of learning the meaning of all those heavy terms to just begin the process of investing, traders can use AI advisors, talking to them in easy to understand, human language, leveraging natural language processing. 

Instead of spending years learning how to read financial statements, you could simply ask questions such as “Is Apple a good buy now?”, and  NLP-powered AI would explain the numbers in plain English (or Spanish, or Mandarin, or Norwegian). This can eliminate the high barriers of capital markets and make trading - profitable trading! - accessible to all.

Benefits of AI equity research 

Researchers from the Wharton School say that we need to investigate how we can use machine learning and predictive analytics to help people invest better. This is because we do not receive top-level investment advice: most financial advisers merely point you in a general direction through their analysis of past data. How would this change when there’s a team of robots working for traders and investors?

Coverage: Fundamental equity research is today limited by human resources. Out of the 50,000 globally listed stocks less than 10% are covered by analysts. Under-served assets with little or no analyst coverage provide a well of undiscovered investment opportunities. 

Lack of information: Comparing company financials is difficult and takes a lot of time leaving  investment managers exposed to client questions they cannot answer due to lack of information.

Most traders lack the knowledge and experience to analyze and understand complex earnings reports, which deprives them of applying fundamental research in their investment decisions.

Bias: Stock rankings based on fundamental data are based on human intelligence which makes them biased by default. The NASDAQ 100 lists almost no "underperform" recommendations. 

The traders themselves often make decisions “on a whim”, regretting them afterwards. This is because humans are emotional and suffer from biases, panic, and euphoria, no matter how disciplined.

Time: Fundamental research is prone to latency as it is more time consuming and equity reports take longer to generate which hinders the flow of timely financial information.

Language: Financial information is still predominantly available in English which presents a language barrier for many investors preferring to receive financial information in their native language.

And let’s not forget that machines bring a host of other benefits which will disrupt the investment management space, such as automation which dramatically lowers operating costs (hence lower fees). Also, AI can’t trade inside information or embezzle, defraud, sue, sexually harass, or steal IP. At least not yet …

AI equity research is the answer 

When we are investing in the market, we are all looking for an 'edge'. As the markets are a zero-sum game, an edge is an advantage that will land us on the right side of a decision. Since markets run on data and humans cannot process this as quickly or efficiently as AI systems can – AI has an edge on humans.

Warren Buffett has a quote in his introduction of “The Intelligent Investor” by Benjamin Graham: “To invest successfully over a lifetime does not require a stratospheric IQ, unusual business insights, or inside information. What's needed is a sound intellectual framework for making decisions and the ability to keep emotions from corroding that framework.” The best way to keep emotions out of investing - and bridging all other gaps - is the use of technology.

The AI systems that can run on a thousand machines analyze information from markets, corporate filings, and economic conditions to quickly decipher which trades to make at any given moment in time. They don’t need to please the companies in their analysis, they can get all the information there is in the world and process it in milliseconds, they are not swayed by emotions, they can adapt immediately to changing market conditions, and, while they aren’t totally bias-free (machines are still programmed by humans) - they are as objective as humanly, and inhumanly, possible.

 

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