Accurately predicting the future and making the right decisions at critical moments is a dream scenario for countless people.
Thanks to advancements in algorithms, this reality is getting closer to humanity.
In 2012, Peter Turchin, a professor of ecology and mathematics at the University of Connecticut, published a research paper in the Journal of Peace Research, making a grim prediction: America would experience a peak of social unrest in 2020.
As a result, America indeed faced great turmoil in 2020.
The pandemic, racial conflicts, and financial crises stirred the entire society into chaos.
So how did Professor Turchin predict so accurately?
The reason lies in his staunch belief in “data history”.
“Data history” simply refers to the collection of various key historical data, such as population, income levels, frequency of violent incidents, etc., and then modeling and analyzing them through big data and machine learning to predict future trends.
Using this analytical method, Turchin even calculated that, on average, America would experience a “cycle of unrest” every 50 years.
In addition to analyzing the “rise and fall of nations”, this technical approach has also expanded into a demand-rich field—machine (AI) data analysis.
According to a report by MarketsandMarkets, the global machine data analysis market reached $1.48 billion in 2020 and is expected to grow to $4.54 billion by 2025, with a compound annual growth rate (CAGR) of 28.3% during this period.
Among the many empowered industries, AI+Investment Research has become a hot track.
Data Alchemy
From 2023 to date, there have been a total of 52,400 investment research meetings across the market, about 200 per day. In an age of information explosion, noise reduction and purification have become new necessities for investment researchers.
To this end, many investment research institutions and platforms began using data models to analyze complex financial data in the early days of the internet era.
The most famous of these is Bloomberg’s Bloomberg Terminal, a platform providing real-time financial data, news, and analysis for financial professionals.
Through pre-built financial models and indicators, such as financial ratios, valuation models, risk analysis models, and various charts and visualization tools, Bloomberg Terminal can analyze data from over 300 exchanges and more than 500 data providers, helping investors understand market dynamics in real-time and formulate investment strategies.
In addition, S&P Capital IQ is also an analytical tool developed based on similar technological ideas.
In addition to providing various pre-made financial model templates, such as discounted cash flow (DCF) models and comparable company analysis models, users can also use plugins to directly call S&P Capital IQ data in Excel, utilizing Excel’s formulas and functions for in-depth financial analysis.
Functionally and analytically, these platforms mostly analyze the direction of the financial market using preset models and algorithms, combined with structured data such as financial statements and historical transaction data.
Although these terminals strive to integrate and process large amounts of financial data, they still face various limitations on a technical level, one of the biggest limitations being their relatively weak handling capabilities for unstructured data (such as news, research reports, etc.).
For financial data, structured data (such as financial statements and transaction records) is just a small layer floating on the surface of the iceberg. Much more unstructured text (news, social network information) is the larger iceberg hidden beneath the surface.
This is because, with the continuous proliferation of the internet, a vast amount of text information is generated and stored in cyberspace.
According to a study by the Pew Research Center, from 2008 to 2018, the number of global financial news reports increased by about 40%. These reports cover various market dynamics including stocks, bonds, and foreign exchange.
However, to mine the “gold mountain” composed of these text data, advanced technologies such as natural language processing and big data analysis are required to extract valuable information from unstructured text.
As a result, many AI investment research tools based on natural language processing (NLP) have emerged, ushering in a new phase of financial data analysis.
Currently, many players have entered the large model + financial track.
However, in the absence of absolute differences in NLP technology, to stand out in AI investment research centered on natural language processing technology, high-quality, diverse, and real-time updated financial data sources have become key to competition.
Because the quality and diversity of data directly affect the accuracy and reliability of analysis results.
In this regard, Entropy Simplification Technology attempts to position itself at the forefront of the industry.
“Entropy” describes the degree of disorder or chaos in a system in thermodynamics.
“Entropy Simplification” implies using technical means to simplify the complexity of business data, helping users gain insights from data through a “simplifying complexity” approach.
The newly developed intelligent engine, AlphaEngine, is the best embodiment of this concept.
AlphaEngine not only aggregates a massive amount of quality business intelligence data sources, encompassing three major business databases, but also deeply integrates AI capabilities, enabling users to quickly gain insights from vast amounts of data.
Specifically, the three major business databases included in AlphaEngine are the meeting minutes database, research report database, and industry economic database.
The meeting minutes database not only provides a comprehensive collection of meeting minutes, including mainstream brokerage conference calls, research meeting minutes, and expert interview notes, but also serves as primary research materials.
The research report database covers mainstream brokerage research reports, industry consulting reports, and foreign brokerage research reports, featuring various filters to help users locate the required materials.
Additionally, to help users efficiently find the information they need among vast amounts of data, AlphaEngine provides various filters, utilizing efficient information retrieval technologies and data mining methods, enabling users to easily find the required materials among large datasets.
In AlphaEngine, generative AI based on NLP technology is used in text preprocessing, query processing, and semantic understanding. By utilizing NLP technology, AlphaEngine can better understand user queries and document content, thus improving the accuracy and efficiency of information retrieval.
In terms of text summarization, Entropy Simplification Technology has achieved automated generation of AI summaries through the FinGPT large model, allowing users to conveniently and quickly acquire key information from meetings.
When browsing industry reports, the embedded generative AI in AlphaEngine automatically summarizes and refines the key points and critical information in the reports, saving users’ time while providing a clear understanding of the core content of the reports.
Additionally, for some important meetings and speeches, AlphaEngine can transcribe meeting recordings into text and generate meeting summaries, supporting locating playback and summary tracing functions, facilitating quick access to key meeting information.
Besides providing third-party massive meeting minutes, AlphaEngine can also be used to build a personal AI knowledge base.
In the [Knowledge Base] module, any type of research material can be organized, and the large model will automatically transcribe audio and summarize the full text, including but not limited to PDF, Office documents, audio, video, and other formats.
Users can also ask questions about any material, allowing the large model to provide professional answers based on the information in the materials.
For high-quality research materials found on mobile, simply forward the article to the WeChat assistant, and AlphaEngine will automatically sync the article to the knowledge base for archiving, and settings can be made in [Personal Center] – [WeChat Assistant].
The aforementioned technical features demonstrate the significant advantages of Entropy Simplification Technology in handling unstructured financial data.
With the major advancements in generative AI and the expansion of derivative applications, it is foreseeable that Entropy Simplification Technology will also develop more valuable technologies or products in the AI + data analysis track in the future.
This AI-based data analysis technology is also what the rapidly changing society currently expects and needs.
After all, when epoch-making opportunities arise, only by keenly capturing changes and gaining insights can we find the path to survival and development in the new era.
And now, the opportunity to gain insight is right in front of you:
PC users can log in at (https://www.alphaengine.top)
A fan benefit: 100 users can enjoy 30 days of free VIP trial, first come first served, while supplies last. Please click the link below and register using the invitation code for a trial.
Benefit code: alpha
You need to find the [My] page after registering and logging in, then click [Redeem Benefit Code], enter the benefit code to redeem VIP privileges.