Artificial intelligence may become the dominant tool for developing financial strategies that were previously considered difficult to predict because traders and hedge fund managers cannot compete with robots that are able to process huge amounts of data and constantly improve their forecasts when making investment decisions. As a sample, you can check out bitcoin360ai here.
In the near future, most of the jobs in the financial markets will be taken by robots, which is good news, because the best university graduates can now go to industries with more tangible benefits for the population and the planet – technology startups, energy, and medicine.
Artificial intelligence and record return on investment
Most of the world’s exchanges use computers that make decisions based on algorithms and corrective strategies based on new data, but some industries, such as bond markets, are slower to automate.
In March, a research team at the University of Erlangen-Nuremberg in Germany developed a series of algorithms that used archived market data to replicate investments in real-time.
One model achieved a 73% return on investment annually from 1992 to 2015, including transaction costs. This is comparable to a real market return of 9% per year. Profits were particularly high during the market turmoil of 2000 (545% return) and 2008 (681% return), proving the increased effectiveness of quantitative algorithms during periods of high volatility when markets are dominated by emotions.
A study by scientists at the University of Erlangen-Nuremberg showed that in their models, the returns on AI investments declined after 2001, as the use of robots in stock trading became more visible and the number of opportunities to exploit market inefficiencies decreased. However, in recent years, returns have fallen and even turned negative from time to time, which researchers attribute to the growing influence of AI on stock trading.
The idea of using computers to trade stocks is not new. Its analog – algorithmic trading or black boxes – has been used for more than a decade and is steadily gaining popularity. In 2012, algorithmic trading accounted for 85% of the market.
If this trend continues, 90% of trading will be conducted through computer programs. Algorithmic trading today is moving towards high-frequency HFT trading, in which stocks are bought and sold in a fraction of a second. The algorithm quickly detects and uses the divergence, the profit is getting smaller and smaller, but the trading volume is not reduced.
A January study by Eurekahedge of 23 AI-powered hedge funds found that they perform far better than those run by humans.
Over the past six years, these funds have achieved an annualized return of 8.44% compared to conventional funds, which ranged from 1.62% to 2.62%. The authors of the study attribute the dominance of artificial intelligence in the industry to the fact that it is constantly re-testing, and not just accumulating data. This may also be due to the shortcomings of traditional quantum approaches and the use of trading models built using unprofitable backtests on historical data that is not capable of generating profit in real-time.
Artificial intelligence processes endless amounts of data, including books, tweets, news, financials, and even TV entertainment. So he learns to understand global trends and constantly improves his predictions about financial markets.
Hedge funds have long been hiring mathematicians who develop statistical models and use historical data to create trading algorithms that anticipate market opportunities, but artificial intelligence does it faster and is constantly improving.
This is why financial giants like Goldman Sachs, which launched the AI-powered trading platform Kensho in 2014, are moving to robotic systems that predict market trends and sell far better than humans. You can read more about it in this handy article
Why artificial intelligence will soon force people out of the stock exchange?
Making more money than average in the stock market is next to impossible—even the most talented investors on Wall Street aren’t consistent. Traders and hedge fund managers are not competitive, but their problem is that they are just people, while all the decisions that robots make are based only on data and statistics.
“People are always biased and emotional, whether they realize it or not,” Babak Hojat, co-founder of financial startup Sentient and one of Apple’s Siri developers, told Bloomberg. Everyone knows that people make mistakes. In my opinion, it’s much scarier to rely on hunches and intuition than data and statistics.”
Systems like the one developed by Sentient can analyze vast amounts of information, including market data, trading volumes, price fluctuations, online SEC filings for all companies, social media data, news, and YouTube videos. The goal is to ensure that the algorithm creates an optimal investment portfolio based on existing knowledge and regularly optimizes it based on the expected new data for each month.
The number of such projects has increased significantly in recent years. According to some estimates, in the financial sector, the number of companies working with artificial intelligence reaches 1,500.
For example, Renaissance Technologies’ Medallion fund, which uses quantitative stock market analysis, boasts some of the best performance in investment history. For 20 years, the fund was able to return + 35% in annual terms. This means that if you had invested $10,000 in 1997, you would have $4.04 million on hand today.
Bridgewater Associates has hired a team to build an autonomous AI system led by David Ferrucci, who previously designed the Watson computer for IBM that won the Jeopardy intellectual game show.
Aidyia Limited, a Hong Kong-based asset manager, has launched a fully AI-driven hedge fund. He can read news in multiple languages, analyze economic data, identify dubious patterns, predict market trends, and then invest.
Some companies use artificial intelligence to generate profitability through algorithmic trading. The Sentinent Technologies Fund, in just a few minutes, can simulate 1800 trading days, pushing trillions of virtual traders against each other.
Many promising hedge funds around the world have been using machine learning for algorithmic trading for a long time because it eliminates any manifestation of irrational feelings such as fear and greed. Investors want artificial intelligence to tell them how to make money in the stock market.