Before the advent of quantitative trading, investment operations in the stock and securities markets were done manually. The famous stock god Warren Buffett, his story investment secret is value investment, that is, through a lot of study of financial reports to select high-quality companies, and long-term holding. Value investment profit is certainly brilliant, but easy to know and difficult to do, the vast majority of investors do not have the patience and perseverance to study each enterprise's information, analyze the fundamentals, and so on. Take the U.S. stock market as an example, 14,000 + companies, each earnings report has hundreds of pages, how to read the end. What's more, many institutions and investors are speculating on the short term, and simply don't have the time to analyze the information according to the idea of value investing.
In this context, a lot of financial innovation came into being. For example, there is a very famous trading strategy in finance called momentum trading (momentum trading), that is, the stock price breaks upward to a certain percentage when buying, down a certain percentage when selling. This principle is easy to say, manual operation is very difficult. And with the computer, traders only need to enter specific clear trading strategy instructions, the rest of the specific operation can be automatically completed by the computer, very easy.
In the 1970s, with the rapid progress of computer arithmetic, the big data analysis of financial data became simple and easy, and then a large number of epoch-making financial theories were born, such as portfolio theory, asset pricing theory, option pricing theory, all appeared in this period, and these theories provide theoretical foundations for the mining of financial data. On the other hand, there is more and more money to manage in the market and more and more types of securities. Computing power, the theoretical foundation of finance, and market demand, these three conditions were realized simultaneously in one era, and quantitative trading was born.
The first to use quantitative trading techniques were the investment banks. They utilized computer technology to mine information from massive amounts of data, designing many complex financial products, enlarging leverage, and reaping incredibly high profits. As a result of the widespread application of computer technology, many IT geniuses have gathered on Wall Street, most of whom are unkempt geeks in T-shirts and jeans, in stark contrast to traditional bankers in suits. 2006, the average annual income of the top "broadcasters" (Quants) from Morgan Stanley, Goldman Sachs, and Deutsche Bank was about $4.6 billion.
The average annual revenue of the top "Quants" from investment banks like Morgan Stanley, Goldman Sachs, and Deutsche Bank was $570 million, and the youngest was only about 30 years old.
After the investment banks to promote the wave after, quantitative trading in the financial markets occupy a considerable share. Currently, quantitative trading accounts for roughly 60% of the U.S. stock market.
The core competency of quantitative trading is to analyze and calculate massive amounts of data, and then refine certain patterns and make predictions accordingly. For example, for a certain agricultural concept stock, in addition to the conventional Kan financial data, historical production, but also the use of satellite data to analyze the weather, and then the historical production of agricultural products and other relevant data are difficult to come, into the integration of the analysis of the future production of this product forecasts, and then the share price of the agricultural stock forecast. In the case of the steady development of the market, the regularity is strong, as long as the precise capture of these laws, invest some capital, and add a certain degree of leverage, you can achieve a very high proportion of profits, can be said to be a book of million, which is also mentioned before a lot of quantitative trading of the IT experts can obtain the secret of the sky income.
This principle does sound tempting, yet it is not easy to do. After all, from the massive and complex data to consistently capture the pattern, and make accurate predictions, is a very complex and brain-burning labor, the fee is generally within the reach of the human force. Therefore, most investment banks are to MIT (Massachusetts Institute of Technology), Princeton and other best colleges and universities to dig the best talent to form a team. These elites also often boast that they are used to simulate the laws of celestial bodies to interpret the financial world. In short, this is an intellectually intensive elite field that is not for the average person to get involved in.
However, the state of the economic world and the financial sector is very different from the laws of astrophysics, chemistry, biology, and other areas of steady state structure, and there is no inevitable and continuous law. Quantitative trading is indeed powerful, but it is not a sure-fire way to make money. In fact, quantitative trading is very risky. The key is that the nature of quantitative trading is based on historical data mining laws, so it relies on past trends. If the conditions on which these trends depend change, the trends cease to exist. In turn, investment strategies made based on these trends are doomed to fail.
The most famous case is the famous investment bank "Salomon Brothers", which has a genius named Messerwilliams, who formed his own famous quantitative fund "Long Term Capital Management". Before 1998, the company's performance was very good, annualized returns of 32%, among the peers. But after the black swan event of the Russian ruble crash, everything went up in smoke.
In 1998, the Russian ruble was devalued dramatically, and Russian bonds were sold off all over the market. Long-term capital management company according to their own set of quantitative models, not only do not sell, but also aggressive bottom, thinking of waiting for the market rebound after a big profit. However, on August 17, 1998, the Russian government issued a statement that it would not repay any more debt. The ruble fell in response, and Long Term Capital Management blew up, losing hundreds of millions of dollars in a single day, and after a month, the genius-studded company went bankrupt and was wound up.
Quantitative trading treats financial markets as if they were homeostatic structures, assuming that everything is in order. However, financial markets are not a celestial world, they are ultimately a human market. Human greed, fear, and desire all change as market conditions change. Therefore, it is a dynamic process of interaction between law and capriciousness, there is no unchanging law, and there is no predictive model that can predict things as if they were God. In the words of Prof. Li Shanyou, widely known in the last two years, called " discontinuity ".
Today's quantitative trading has returned to a normal state: on the one hand, the advantages of quantitative trading in data mining and scientific decision-making are recognized, but on the other hand, it is also recognized that quantitative trading has limitations, especially when dealing with such sudden changes in the law, and that such purely quantitative trading may be subject to greater risk.
As one of the world's most important financial markets, China also has a certain scale of quantitative trading, but it is still in a nascent state of development. As students who have speculated in stocks know, although the Chinese stock market has a good long-term rate of return, it is still generally "news city", "theme city", "concept city", once the policy or the environment is a little windy, China's stock market is still in a state of flux. Once the policy or the environment is a little windy, the Chinese market changes are very, very frequent, and the fluctuation range is particularly large. In the case of the market fluctuation is very big, irregularity is very obvious, quantitative trading strategy is difficult to come to fruition, not to mention earn a lot of money.
In 2013, there was an Everbright "oolong finger" incident in China, which was closely related to quantitative trading. At that time, the Everbright securities traders accidentally lost a wrong number, under a 7 billion skyrocketing buy orders, instantly pulling the stock price up, and then triggered a lot of quantitative trading programs automatically execute the conditions, and soon led to more than 30 billion of funds rushed into the field, within a few minutes, the SSE index pulled up more than 100 points, 59 weighted stocks instantly stop. Many unknown retail investors blindly followed, resulting in heavy losses. In addition to Everbright Securities, many people also accused the organization of using quantitative trading, because quantitative trading several times amplified the effect of the "wulongzhi", obviously affecting the entire stock market, which indirectly contributes to the loss of their follow up.
During the 2013-2014 period, some quantitative trading organizations had good returns, but after the 2015 crash, the mood of the entire A-share market and the capital market changed dramatically, and the strategies that worked well in the past were scrapped, with more than 300 private equity funds centered on quantitative trading falling.
Therefore, the growth of quantitative trading in the Chinese market, the road is long. We ordinary people, or honestly learn Buffett, down-to-earth study of financial reports, engaged in value investment it ^_^