Nowadays, artificial intelligence is highly touted, and it is considered to be omnipotent. In particular, the ability to find investment is beyond humanity. Some people will use artificial intelligence and Buffett to conduct PK. Who will win? Buffett has not commented on this matter, but we can see that the limitations of AI have begun to stand out.
The ability of AI to find investment opportunities is beyond humans, but it also has obvious limitations. The following is the original content:
Large-scale mergers and acquisitions are usually not conducive to the buyer's shareholders, which is a principle that Buffett has insisted on in trading for many years. The London hedge fund Winton designed an AI to test this principle. To this end, the researchers collected and analyzed data on nearly 9,000 transactions since the 1960s.
The test result is: Buffett's principle is untenable, and the large-scale merger itself does not cause value loss.
Buffett has not commented on this matter.
How much potential does AI have?Winton is a $30 billion hedge fund with a team of data scientists, Daniel Mitchell, director of the data scientist, said: "This test prevents us from trading under false signals and avoiding financial losses."
Although there have been many cases of thunder and heavy rain in the past few decades, AI is now occupying the investment community step by step. Companies that use AI as a cornerstone strategy or research tool include not only giants such as Two Sigma and Goldman Sachs, but also small companies like Schonfeld Strategic Advisors.
Luke Ellis, CEO of Man Group, believes that AI will slowly occupy the investment community. The company has used machine learning to invest about $13 billion in several hedge funds. In an interview, Ellis said that 10 years later, AI will be involved in all activities of the company, whether it is to execute transactions or to help select securities.
“If computing power and data volume continue to grow at the current rate, machine learning may be involved in 99% of investment management in 25 years.†Ellis said: “It will participate in all aspects of our lives. I don’t think machine learning is No, but it can help us do a lot better."
AI will change the nature of workThere are 300,000 people in the world engaged in asset management (including fund managers, analysts and back-office workers). Opimas Consulting's survey of financial companies found that by 2025, AI will reduce this number by 90,000.
With the exception of the quantifiers of the Mann Group and Winton, almost all other companies face difficulties.
Only a few scientists can design strategies that are profitable. It is difficult for investors to master this ability, so some people keep a wait-and-see attitude. And the high cost of this technology and data has already put some companies under pressure.
But machine learning's ability to find investment opportunities is beyond the human level, making it impossible to ignore this technology. Some companies are now using AI to sort out messy data on social media and smartphones, predicting revenue and sales quickly (than analysts), interpreting executive emotions from documents, and developing overall strategies.
Vasant Dhar founded one of the first machine learning hedge funds 20 years ago. He said: "The simple things of finding opportunities will be more machine-made. They can generate assumptions, test Hypothesis, then telling humans: 'This opportunity is very interesting. To dig deeper, 'machines can add value, and it changes the nature of human work.'
Limitations of AIAlthough AI is very powerful, its limitations are also obvious. AI lacks imagination and lacks the ability of humans to anticipate events (whether political or macroeconomic) unless such events have occurred many times before. For example, hedge fund manager John Paulson foresees a subprime mortgage crisis, but artificial intelligence is completely unpredictable because it does not have enough relevant historical data to compare and cannot form an opinion.
Vasente O Daha is also a professor of data science and business at New York University. He said: "The machine is difficult to predict the crisis, because each crisis is unique. People are good at explaining things like crisis, and sometimes predict it, but Our predictions are often wrong. Look at the predictions of interest rates over the past few years."
In the AI ​​era, fund managers and their views on the market will play a major role, whether they are right or wrong. The threat faced by fundamental analysts is even greater.
Some experienced machine learning experts who are good at using big data can get a $1 million annual salary from a financial company. And analysts who study the company's fundamentals won't get much money, and they may need to learn programming to keep their jobs.
a caseLet's take a look at the case of asset management company Acadian Asset Management. The company is based in Boston and has seen a 79% increase in assets over the past five years, reaching $93 billion.
The manager's intuition about economic trends is the basis of the company's long-short strategy. They then deployed machine learning to refine the 20 most influential factors, both cash flow and fraud, which can drive better predictions. These factors are then injected into an automated system that holds about 10,000 different stocks in a few months or quarters.
Ryan Stever, head of global macro research at Acadian Quantitative, says the company's managers and analysts are versatile: they have a deep understanding of statistics, and almost everyone writes code and has market experience.
Acadian is investing in artificial intelligence and big data to better predict key performance indicators for a company, such as sales. If Acadian can accurately estimate the number before a company officially releases sales data, this is a big advantage.
“Using machine learning, you can get metrics faster and more accurately,†said Wes Chan, head of stock research at Acadian. “If it does, it’s a big deal.â€
AI has not beaten BuffettFor some companies, the bigger ambition is to get deep learning – behind Google search and Tesla's self-driving cars are artificial intelligence. Deep learning machines mimic the activities of multi-layered neurons in our brains, with less need for human instructions - it can find things, even if humans don't tell what they are looking for.
Jürgen Schmidhuber is the founder of modern artificial intelligence and a consultant for some hedge funds. He said: "You will find that neural networks will become better in various transactions. Predictors and better tools. Many transactions will be executed through self-learning algorithms, requiring only a small number of high-level people to occasionally enter human decisions. This is not far from us."
In the end, the future of AI will depend on its ability to make money. There are also some fully automated AI strategies in operation, and their performance is generally lower than the stock market and more than hedge funds. The data shows that in the six years to 2016, the average annual return of 13 AI funds was 10.6%.
As long as the stock pickers can bring a decent return to investors, there is no job.
Although AI overturned Buffett's one-choice principle. But from 2011 to 2016, Buffett's company's average annual return is 12.5%. The machine has not yet defeated the legendary investor.
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