Let's talk about intelligent education. The original purpose of FDT is to train traders, which is a kind of public welfare education. FDT has its own educational philosophy, and intelligent training software, as an educational tool, has a complete set of educational standards and evaluation system. This educational standard and evaluation system is the FDT financial quotient index, which is not only our standard for evaluating traders, but also a tool for personalized education. This financial quotient index is essentially a portrait of users through big data. Our users are traders and retail investors, to deepen our understanding of their trading behavior and trading psychology. We invented FDT financial quotient index on the basis of a large number of simulated trading data. Let's take a look at this photo. The abscissa of this graph is risk control ability, and the ordinate is profitability. With this, we can distinguish the situation of different traders and then educate them individually. We divide traders into four categories. The first category is excellent simulated traders. Compared with the huge FDT users, they are very few, accounting for less than 1%. These traders have good returns and risks, so they can focus on training and even give him a firm offer. The second category is senior simulated traders, accounting for about 9%. They have a strong willingness to trade and can help him improve through personalized intelligent education and training. The third category is medium-pole simulated traders, accounting for more than 40%. They are risk-conscious and can consider passive investment. The fourth category is junior simulated traders. FDT financial quotient index is relatively low, but the number is the largest, accounting for more than 50%. They need to continue to help them with financial education classes.
The innovation of FDT financial quotient index is that it combines artificial intelligence+big data+behavioral economics. Traditional financial methods rely on questionnaires, based on artificially set authority rules, and can't do anything about behavioral characteristics beyond setting. The financial quotient index of FDT is based on artificial intelligence. Through a nonlinear machine learning model and hundreds of transaction characteristics, a large number of decision rules are automatically extracted, and finally the score ranking of the financial quotient index is formed. Traditional finance is based on the "daily" level data after settlement, which is small and very simple, and it is a single computer calculation, so it is impossible to find hidden risks and behavioral characteristics. The financial quotient index of FDT identifies big data according to millisecond market, and carries out real-time step-by-step concurrent processing, which can deeply understand the psychology and behavior of traders. The more data, the clearer the personalized description of traders, so that personalized education and training can be more targeted. In terms of characteristics, traditional financial methods are based on profit or withdrawal data, while FDT financial quotient index is based on behavioral finance to describe users' psychological characteristics and behavioral deviations, which requires the technical support of big data architecture. Generally speaking, the trading behavior characteristics of FDT financial quotient index are based on the close combination of behavioral finance and the experience of hedging experts. This is our report on the FDT financial quotient index provided by each trader. It is a big report with four quadrants, including profit, risk, consistency, activity and so on. There are some specific analyses behind each article. Everything else is easy to understand. Just explaining "consistency" simply means "crossing bulls and bears", flexibly adjusting strategies in changing markets and realizing stable profit output. The following are some rankings of these participating schools according to the financial quotient index.
Let's talk about smart trading first. The core of trading is stop loss, prediction and matching. We must set a stop-loss line in traditional trading. No matter who is in any case, we will clear the position at the stop loss line to avoid unbearable trading losses. This situation actually ignores personality differences. With artificial intelligence, in the case of a large number of historical data, using machine learning model, you can set different stop-loss lines for each trader. For example, you can set different stop-loss lines according to the historical profitability of traders, or you can set them according to the different styles of traders. Some traders like and are good at seizing opportunities in ups and downs. You can set personalized stop-loss lines for them. FDT can set an accurate and detailed stop loss line according to the financial quotient index. Then there is the forecast of fluctuation. Everyone who is engaged in trading knows that the volatility of assets is very important, because it represents both risk and income, so good traders make money from risk fluctuations. How to predict and judge this fluctuation? Now that you have big data and AI, you can predict the volatility of A shares and futures through machine learning. There is also the allocation of resources. For excellent traders, you can give them specific trading opportunities. Just like a marriage agency, we use this evaluation index to evaluate traders and stocks. Different traders make different markets, which can give full play to each trader's talents. This is also an application of artificial intelligence to trading.
Finally, talk about smart investment. China's asset management market is growing rapidly. By 2020, it is estimated that there will be a demand for wealth management of 180 trillion RMB, with a compound annual growth rate of 14%. However, at present, most users invest irrationally and buy and sell at an inappropriate time, which leads to the profit of most fund products, but most users still lose money. So we try to solve it with artificial intelligence. First of all, intelligent users understand. With the help of simulated trading platform and a large number of data, we use FDT financial quotient index to evaluate users' risk preferences from the perspective of financial behavior. Secondly, in cooperation with the FDT Intelligent Asset Management Center of Columbia University, a set of top-level algorithms for intelligent asset portfolio optimization is studied. The third is the risk management of intelligent investment, which estimates the future profit and loss probability of each portfolio. The fourth is intelligent and personalized capital allocation, which gives him different products according to different customers and different risk preferences. This is also an intelligent and personalized fund recommendation, selling the right fund to the most suitable customer. Of course, because China's capital market is still immature, and the market operation is not completely a reflection of market laws, the market environment of smart investment is unstable, and we should create some conditions.
In a word, the structure of our financial trading market is unreasonable, and retail in the United States will take 70 years. We don't want to use it for so many years. We should cultivate excellent traders through FDT innovation factory and explore effective methods. We train traders to master a large number of simulated trading data, and then cooperate with scientific research institutions to tap the value of these data and develop intelligent education, intelligent trading and intelligent investment. It should be said that we have made a preliminary exploration in the application of artificial intelligence in financial markets. I believe we still have a lot of room in this regard. This matter has not only social value, but also commercial value. Thank you.