How does intelligent investment research solve the problems of traditional investment research?

Faced with the ever-expanding financial data, traditional investment and research institutions have shown fatigue due to the increase of analysis cost and difficulty. In this respect, the introduction of artificial intelligence has solved the problem of investment and research in a targeted and intelligent way, assisted analysts to make reasonable decisions, and improved the accuracy and analysis efficiency of investment and research.

1. What is intelligent investment research?

With the in-depth application of AI technologies such as natural language processing, machine learning and biometrics in the financial field, our life is undergoing unprecedented changes. For example, personal assistant Siri can help us send text messages, make phone calls, take notes and even chat with users. As an intelligent digital personal assistant, Siri can better understand our natural language problems and requests through machine learning technology.

Such artificial intelligence has been used more and more frequently in the financial field, and has achieved fruitful results in improving the working efficiency of financial institutions, assisting investment decisions and preventing financial risks. Among them, auxiliary fund management has become the biggest application scenario of artificial intelligence in the financial industry. At present, the market scale of wealth management in China has reached 6 trillion US dollars, and the national model of asset management in China has exceeded one million, with broad development prospects.

So, what is intelligent investment research? Use artificial intelligence (deep learning, natural language processing, etc.). ) analyze financial market data and events to help professional practitioners (analysts, fund managers, etc.). ) to improve the accuracy and efficiency of traditional investment research.

There are two points to note here. First, the relationship between events, financial market data and aid. For example, today, February 25th, the China stock market rose sharply, and the Shanghai Stock Exchange rose by 157.05 points. So what caused it? Is it the influence of the RRR cut by the central bank or the continuous rise of offshore RMB? Intelligent investment is to collect all possible related financial events and data, so as to provide auxiliary services. Second, intelligent investment research is a professional employee who serves financial institutions, not our investors, which is also the difference between intelligent investment research and intelligent investment.

Second, the traditional investment and research problems

With the development of the times, traditional investment and research will inevitably appear in the development process. First, with the increase of data, traditional investment and research often rely on the analysis and judgment of professionals, with a strong subjective consciousness; Second, for such a huge amount of data, our utilization rate of all kinds of data is still at a low level.

1) Strong subjective initiative

In the field of financial information, artificial intelligence still has a lot of work to do in empowering financial information processing. From the information source, the public information in the secondary market is relatively comprehensive, but there are still barriers between the primary market data and alternative data information. If the financial data is further liberalized in the future, there will be a lot of financial information to be processed; In this respect, the data processing of traditional professionals is still based on experience, which undoubtedly increases the burden in the analysis process, that is, it can not guarantee the objectivity of analysis, and it has high labor costs.

2) Low data utilization rate

With the increase of financial information data year by year, a large number of data can not be used manually. Even with the application of intelligent investment research, there are a lot of news public opinion and financial information reports, and the voices of investors such as post bars and forums have not been used.

Third, how is the intelligent investment research specifically done?

In the field of intelligent investment research, machines can automatically extract key information from unstructured data such as company announcements, brokerage research reports and news reports. And on this basis, build the relationship, build the domain knowledge map, and optimize the investment decision to some extent.

We take an intelligent investment and research platform in China as an example-Entropy Jane Technology.

Generally speaking, the focus of intelligent investment research lies in the selection of data. As far as the present situation in China is concerned, most intelligent investment and research companies focus on data mining in the pure financial field, such as research reports, bonds, stocks and other information. In addition to financial data, there are huge amounts of data worth mining, such as social media, credit, climate change and other data.

Entropy reduction technology is based on the traditional financial business, adding social media, credit, climate and other data for reasoning and verification. In terms of business classification, entropy simplification has developed a number of business lines such as science and technology, finance, energy and consumption, providing data services for researchers.

Entropy reduction technology has two cores, namely alternative data and knowledge map.

1) alternative data

Entropy Jane technology collects relevant data other than financial data. For example, it will collect the pull/push data of open source projects on Github and continuously monitor it, so that it can actively predict the product development progress and launch date of technology companies. These data are indicators that affect the decision-making of investors in the blockchain and artificial intelligence industries.

In terms of data sources, entropy reduction technology adopts crawler technology to acquire data, and a stable data acquisition system is built through access analysis, which can monitor 1200+ data sources in real time. Take the data set of e-commerce, recruitment and automobile industry as an example:

I e-commerce data set

The platform collects the transaction data of domestic mainstream e-commerce platforms. Taking Tmall as an example, Entropy Jane Technology continuously tracks the sales price data of more than 250,000 brands and 65.438+0.2 billion items in Tmall Mall every day, helping users to continuously track the online sales performance and industry trends of corresponding investment brands at high frequency.

Second, recruitment data set

The platform collects recruitment data covering domestic mainstream recruitment websites, helps investment institutions to judge the development stage and strategy of target industries and enterprises, and even analyzes macro trends.

Three automobile data sets

The platform collects the quotation data of domestic mainstream automobile portals, and realizes the large sample, high frequency, low cost and sustainable coverage tracking of automobile terminal prices through technology, which solves the pain points and difficulties of traditional terminal channel research.

2) Knowledge map

If alternative data is the raw material of intelligent investment research, then knowledge map is the brain of intelligent investment research. The so-called "knowledge map" is to link unstructured data such as entities, attributes and relationships, and then provide logical support for investment decisions. Reflected in the investment industry, that is, researchers can relate related industries, products and companies, and when a certain factor changes, they can infer opinions and forecasts according to the relationship chain to provide support for investment decisions.

A perfect knowledge map is a necessary condition for the application of AI in investment and research. What the financial industry needs most is a large number of high-quality research materials. It is the most important thing for intelligent investment and research to form a self-growing and self-learning knowledge map system through deep mining of text information such as research reports and announcements.

Fourth, the future prospects of intelligent investment research

In China, intelligent investment research is still a new industry. In recent years, a large number of players have poured in, which has cost money and time. However, as far as the domestic situation is concerned, there are very few products that really land. There are several trends in the development of intelligent investment research.

1) Integration of domain experts and scientific and technological experts

In the overall coordination of financial talents and scientific and technological talents, there is a certain cognitive deviation between the two sides in the process of business docking. More and more compound talents are needed to join in order to improve the communication efficiency in the process of business docking between the two parties, so as to strengthen the progress of product research and development and the actual effect after landing.

2) Technology research and development has enhanced investment in research products.

Financial market is a complicated information market, but artificial intelligence technologies such as natural language processing are still in the primary stage, and there is still a lot of room for improvement in deconstructing the correlation between various factors. Intelligent investment and research association will continuously improve the accuracy and effectiveness of investment and research reference through technology research and development.