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Lessons from Finance in AI

WWho are you Early adopters of new technologies? Cutting-edge stuff tends to be expensive, which means the answers are often extremely rich. Early adopters also tend to be motivated by stiff competition to outperform the status quo. So there may be no group more likely to adopt a new vehicle than the ultra-wealthy and hyper-competitive hedge fund industry.

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This rule seems to apply to artificial intelligence (AI) and machine learning, which were first adopted by hedge funds decades ago, long before the recent hype. First came the “quants,” quantitative investors who use data and algorithms to pick stocks and make short-term bets on whether assets will rise or fall. Two Sigma, a New York-based quant fund, has been experimenting with these techniques since its founding in 2001. Man Group, a UK institution with a large quantitative arm, launched its first machine learning fund in 2014. aquarium Capital Management from Greenwich, CT Get Started AI around the same time. Then there are other industries.The experience of hedge funds shows that AIThe ability to revolutionize the business – but also shows that doing so takes time and progress can be disrupted.

AI Machine learning funds appear to be the last step in the robot march. Cheap index funds, which pick stocks by algorithm, have swelled in size, surpassing traditional active funds in assets under management in 2019. human involvement. Renaissance Technologies’ flagship fund, the first-ever quant fund, was founded in 1982 and has averaged annual returns of 66% over decades. In the 2000s, fast cables gave birth to high-frequency market makers, including Citadel Securities and Virtu, which were able to trade stocks in nanoseconds.Newer quantized clothing such as aquarium And two sigma, beating humanity’s rewards and devouring assets.

By the end of 2019, automated algorithms were employed on both sides of the trade; high-frequency traders often face off with quant investors, who have automated the investment process; algorithms manage most of the assets of investors in passive index funds; all the largest and most successful of hedge funds use quantitative methods at least to some extent. Traditional types are throwing in the towel. When celebrity investor Philippe Jabre closed his fund in 2018, he accused computerized models of “unwittingly displacing” traditional players. Because of all this automation, the stock market is more efficient than ever. Execution is lightning fast and costs almost nothing. Individuals can invest savings with fractions of a fraction of a dollar.

Machine learning promises even greater results. The way one investor describes it is that quantitative investing starts with an assumption: the momentum assumption, or the idea that stocks that are rising faster than the rest of the index will continue to rise. This assumption allows individual stocks to be tested against historical data to assess whether their value will continue to rise. By contrast, with machine learning, investors can “start with the data and find hypotheses.” In other words, an algorithm can decide what to choose and why.

However, the great advance of automation has not continued unabated – humans have fought back. By the end of 2019, all major retail brokers, including Charles Schwab, electronic*trading and up to standard Facing competition from new entrant Robinhood, Ameritrade cut commissions to zero. A few months later, retail transactions began to soar, fueled by pandemic boredom and stimulus checks. It peaked in the frantic first few months of 2021, when traders coordinated on social media to pile into out-of-favor stocks, sending their prices spiraling higher. At the same time, many quant strategies appear to be stalling. In 2020 and early 2021, most quants have underperformed the market as well as human hedge funds. aquarium A handful of funds were closed after continued outflows.

Many of these trends reversed when the market turned in 2022. Retail’s share of transactions retreated as losses mounted. The quants are back with a vengeance. aquariumThe longest-running fund returned a whopping 44%, even with a 20% market drop.

This detour, and the growing role of robots, offers lessons for other industries. The first is that humans can respond to new technologies in unexpected ways. The drop in transaction execution costs seemed to empower the investment machines—until the cost fell to zero, at which point it fueled a retail renaissance. Even if the share of transactions by retail investors has not peaked, it is still high compared to before 2019. Retail trading now accounts for one-third of equity trading volume (excluding market makers). Their dominance over stock options, a type of equity derivative bet, is even greater.

Second, not all technologies improve market efficiency.one of the explanations aquarium The firm’s co-founder, Cliff Asness, sees periods of underperformance as how extreme valuations have become and how long “everything is a bubble”. This may in part be the result of retail investor overexcitement. “Getting information quickly doesn’t mean processing it well,” argues Mr Asnes. “I tend to think that things like social media make the market more efficient, not more efficient … People don’t hear dissent, they hear their own, and in politics it might be Leading to some dangerous frenzy that could lead to some really weird price action in the market.”

The third is that the robot needs time to find its place. Machine learning funds have been around for a while, and seem to outperform their human competitors, at least a little. But they haven’t amassed significant assets, in part because they’re hard to sell. After all, few people understand the risks involved. Those who have dedicated their careers to machine learning are acutely aware of this. To build confidence, “we spend more money explaining to clients why we think machine learning strategies are doing what they are doing,” reports Greg Bond of Man Numeric, the quant arm of Man Group.

There was a time when everyone thought the quants had it figured out. This is not how it is viewed today. At least when it comes to the stock market, automation has not been the winner-take-all event that many elsewhere feared. It’s more like a tug of war between man and machine. Although the machines have won, humans have not let go.

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