A more extreme example of academic research entering the field of quantitative trading should be Simmons of Fuxing Technology Company, who is also a practitioner of quantitative trading familiar to readers in China. Simmons received his Ph.D. in Mathematics from the University of California, Berkeley at 196 1. He was only 23 years old. At the age of 30, he became the dean of the School of Mathematics at the State University of New York at Stony Brook. 1978 left school and founded Fuxing technology company, which is famous for quantifying the proud performance of the flagship fund-Medalian Fund. There has been a lot of speculation about the quantitative trading strategy used by Simmons. Many people think that quantitative trading strategy based on hidden Markov model should be used, because Simmons' early partner Baum was one of the founders of hidden Markov model estimation algorithm, and Fuxing Technology Company recruited a large number of speech recognition experts. Hidden Markov model is an important technical tool in the field of speech recognition. The author is skeptical about this statement, but in any case, judging from the different personnel composition of Fuxing Technology Company, it should be a relatively pure hedge fund company, operating with quantitative trading strategy.
Although most trading strategies used by quantitative hedge funds are more or less confidential, there are still some quantitative trading strategies that are gradually familiar to the outside world after years of use, and statistical arbitrage is one of them. The concept of this strategy originated from Morgan Stanley. At that time, it was called a matching transaction. In fact, it uses statistical methods to select a pair of stocks with similar historical price trends. When the price difference between two stocks becomes larger and exceeds a certain threshold, make long and short respectively, and then rely on the price difference to return to the normal level in the following time to obtain income. Because this quantitative trading strategy not only comes from statistical analysis, but also has the arbitrage characteristic of waiting for the spread to return, it is called statistical arbitrage. With the further study of this kind of trading strategy, statistical arbitrage strategy has gone far beyond the scope of paired trading and become more complex and diversified.
Xiao used to be a member of the statistical arbitrage trading group. 1980 obtained a doctorate in computer science from Stanford University, and then stayed in school for academic research. Xiao Yu 1986 joined in charge of the technical department of the group, but two years later, like Bamberg, the founder of statistical arbitrage, he left his job due to political struggles and other reasons, and started his own De Shao Fund Company. De Shao Fund Company combines the research background of Xiao's massively parallel computing and the statistical quantification strategy it has come into contact with, and takes the computer quantification model as the main strategy for trading, which has achieved great success. It is worth mentioning that after Xiao took root in the field of hedge funds, he still did not forget scientific research. De Shao Research Company, which he founded, is committed to making frontier progress in biochemical research fields such as molecular dynamics simulation through powerful computer software and hardware capabilities. This is in stark contrast to Asnes's behavior of publishing academic papers in financial magazines. Of course, Xiao's academic research may be relatively more geek.
Compared with statistical arbitrage, traditional arbitrage strategy is a more well-known and classic quantitative trading strategy. In fact, part of the modern financial framework is based on the assumption principle of "no arbitrage", which shows the popularity and importance of arbitrage strategy. If the focus of statistical arbitrage is to describe and predict the statistical relationship between multiple assets, then traditional arbitrage may pay more attention to the calculation of the value of each asset and the estimation and optimization of transaction costs when implementing strategies. It's just that the quantitative trading strategy has developed to the present, and statistical arbitrage and traditional arbitrage strategies have infiltrated and merged with each other. De Shao Fund Company, which is good at computers, should participate in both arbitrage strategies.
When it comes to arbitrage, I have to mention long-term capital management companies. The lineup of this company is very luxurious, including meriwether, the pioneer of bond arbitrage, Morton and Scholes, two Nobel Prize winners, Mullins, the vice chairman of the Federal Reserve, and many other top practitioners. They are mainly engaged in quantitative arbitrage trading of bonds, and of course they will also include some other forms of strategies. The company performed well in the first three years, but after 1998 Russian government bonds defaulted, the related chain reaction led to huge losses of the company, and it was taken over by several Wall Street companies with the intervention of the Federal Reserve, which was similar to bankruptcy. In fact, long-term capital management companies have not lost much on Russian bonds, but many large financial institutions must ensure sufficient capital in a loss-making environment. Therefore, by selling assets such as the bonds of the seven industrialized countries with good liquidity to reduce risks and increase capital, the prices of major bonds in the world have fallen sharply and fluctuated greatly under the pressure of selling, resulting in huge losses for long-term capital management companies with great leverage in bond arbitrage.