Unlike most people's imaginations, in the field of machine learning, the energy spent on processing data occupies the most proportion. Typically, for every $1 of data purchased, we need to spend $5-7 to clean up in order to use it for training and reasoning of machine learning models. In this process, data scientists spend 80-90% of the entire development process. If the available data is standardized and directly available, cost and efficiency gains will give technology companies an unprecedented advantage.
The most widely-known investment observation method may be news reports, and the authoritative news comments and opinions provided by them are often the vane of the market. Many short-term investors will make judgments and issue trading orders within a few seconds after the news breaks out. If you let Ai directly determine whether a piece of news is "worthy to trade," you may be able to compete for some valuable time for traders.
AI has evolved to use natural language processing techniques to read news content written by journalists from their own news agencies, and then use mathematical algorithms to calculate emotional data. In addition, information from social media such as Twitter is under control.
With the development of data services, more and more financial institutions are turning their attention to the data platform. For quantitative investments, using the most advanced technology can bring the most benefit. Many customers are using machine learning and artificial intelligence to process financial data to form their investment strategy.
In addition to bringing more expected benefits, there are many ways in which artificial intelligence and machine learning can affect financial markets. In the venture capital field, many people are using the AI algorithm to conduct risk backtesting and look for pressure areas. Others are using AI to identify traders' risk operations. Artificial intelligence may have many new types of applications in the financial sector.
Although the organization does not directly disclose the revenue data obtained through technology, we can already see the trend of artificial intelligence entering the financial industry. Twenty years ago, few investment institutions studied AI, but with the development of deep learning, today we can see that a large number of financial companies are studying artificial intelligence.
If you look at the returns of some technology funds, such as Bridgewater and Renaissance Capital, Digital Dream Technologies, you will find that they have an amazing rate of return, which is the credit of the technology. As financial institutions increase the amount of data, quality requirements, and cost pressures, more and more organizations tend to reduce the number of data providers.
DDT has been in China for some time. With the internationalization of the Chinese market, domestic financial institutions are gradually adopting new technologies, and DDT is also happy to share its global experience with Asian exchangers. In the future, artificial intelligence will change with the development of data services, the world will become highly automated, and people will be connected through a large number of data APIs and data exchanges - all data centers are in the cloud. Cloud services will host applications where people consume and pass data directly in the cloud.