I believe there are several post on this general topic but I thought I would start my own thread. I'm a former fundamental hedge fund investor (i.e. modeling a company's financials, forecasting the cashflows and discounting them back at a discount rate to get the valuation) who is interested in learning more about Data Science/AI/Machine learning and how it is used in investing. First off, let me assure some of the people who have posted on different threads that this Data Science/AI/Machine Learning does work. The best hedge funds are actually quantitative hedge funds like Renaissance Technologies, DE Shaw, Two Sigma, and I believe they do this type of work. Before the quantitative investors there were the fundamental investors (who spoke with the company, modeled the financials, qualitatively decided if this was a good investment, etc. i.e. like Warren Buffet) and the technical investors/traders who looked at charts and based on certain movements/volumes of the stock chart, they would invest. Frankly I always thought technical investing was nonsense but a lot of people do it and maybe it works maybe it doesn't. Then came the quants but I believe they were initially using complex mathematical models like Black-Scholes pricing models. Now AI/Machine Learning is what the best quantitative hedge funds are using. For my own edification, I'd like to get people's thoughts on how hedge funds are actually employing AI/Machine Learning to investing.
Are there certain algorithms that are better for investing in the stock market?
What types of data is most useful?
Are there different models for predicting short-term movements and long-term price movements?
Being an investor with 10+ years of experience, I have some general high level thoughts but I'm neither a programmer or a data science. Please correct me if I'm wrong. Essentially, these quantitative hedge funds are trying to find patterns in the stock/stocks/other asset classes (gold, bonds, currency, etc.) that have yet to be exploited. As with the other types of investors, they want to know what will make a stock (or any other asset class) go up or down (most hedge funds both long and short stocks). But while the other types of investors have a particular dogma they are following, these guys just care about the "signal" that will indicate whether the stock will go up or down. Stocks and any other asset class go up and down based on supply and demand. Are there more buyers vs. sellers - then the stock goes up. Are there more sellers vs. buyers, then the stock goes down. So in essence, are these quant funds really just modeling/predicting human behavior and taking advantage of their ability to crunch data are a much large and faster capacity? If that's the case, then from a fundamental perspective, I can easily hypothesize what type of data might move a stock/asset class.
For example, if a stock has a certain trading pattern that indicates technical investors will likely buy it over a certain time frame, wouldn't that work? Would there be a certain algorithm that works best for this type of analysis?
Another example might be, if companies that exhibit certain revenue growth metrics, or margin improvement, would that signal a potential buy opportunity? Or perhaps if certain words in their annual report, quarterly filings, press releases indicate this company is likely to do well?
Or perhaps the holders list (the type of investor that owns the stock) could provide an indication of how the stock might react in the future.
I could go on but I might be totally off base. I'm essentially just noodling how this actually works and therefore I would like to hear any thoughts that people have. Specific examples would be great so I could potentially test them out and learn some Data Science/AI/Machine Learning in the process.
I have no false perception that I could do anything like these quantitative hedge funds. That requires significant capital, use of leverage, hundreds of PH.D data scientist and programmers, costly servers, etc. However, perhaps learning a little bit about their process might improve my fundamental process. Although I'm not even sure about that since perhaps they might be distinctly different. For those wanting to learn more about the investing world, happy to share my knowledge. Also I'm not a data scientist or programmer so while I'd like to learn all these algorithms at the moment this is for my own edification.