Interesting projects that use Natural Language Processing (NLP) to sparse info from financial statements
- Interesting dicussion of their product in Hacker News
- They use machine learning (NLP) to extract hard-to-find information and assess risk in public company reports (SEC filings). Their platform is used by investors to improve portfolio returns and mitigate downside risks.
- Context: Most public company data is unstructured and textual. Because relevant information is hard to find, a lot of corporate data is radically underused, to the detriment of investors. For example, their research shows it can take 12-18 months for corporate malfeasance to be incorporated into stock price after clear warning signs appear in financial text. Hard-to-find information that they extract includes accounting and governance choices, product defects, regulatory issues, customer/market reliance and much more.
- Their competitors: AlphaSense, Sentieo, InsiderScore, footnoted
- side project on Hacker News
- summarize SEC filings and display the clusters that have a high correlation with non standard price deviation in the next 20 days
- Parse SEC filings for readable information
- This is a side project, appeared in Hacker News
- a web-based Bloomberg Terminal alternative and they monitor SEC filings in real-time to show them to users
- they provide not only Financial and newsmedia data but also real-time data from SEC, Reddit, and Twitter.
- some of their current features
Along with that, additional features include:
- Top trending stocks from the internet in real-time - Sentiment of the discussions using a pytorch model. - Ability to save posts(reddit, twitter, news headline, sec filings) - Ability to create watchlist to watch and monitor a group of tickers like SPACs.
- Similar tools: Gbear.trade, HypeEquity, Gamestonk Terminal
Note: How can we adapt these business model to Vietnam financial market? Since even HOSE didn't implement a standard for filings like EDGAR system of SEC. AFAIK, HOSE use pdf format for filings