Investing

The general ideal behind a DCF model is that the market cap of a business, which controls its stock price, is determined by how much money the business will generates for its shareholders.  Specifically, to get a stock's theoretical price you need to know a business's future earnings.  In order to estimate these earnings, I use an AI that has access to: how the economy is doing, what kind of business it is, how the business has performed recently, and how it is expected to perform in the near future.  The AI trains on twenty years of data from GAAP countries.  Advantages of DCF models are that they are objective to humans feeling about stocks so they are not as susceptible to hype and the subsequent crashes.  Disadvantages of DCFs are that it is difficult to include information into them like the effects of new laws or the brand power of a business.  These pieces of information have measurable, effects on the stock price but are unfortunately neglected by the DCF.

​

​The general investing strategy that I use with the downloaded DCF results is to go to "../Buy_These_Stocks/Misc_ticker_info.txt" to find which stocks my algorithm expects to grow the most over the next year.  After looking into each recommended company to make sure the investment seems sound, I buy the stocks and hold them for one year.  At the end of the year, I sell the stock and then use this money to buy new stocks.  The performance of the algorithm can be tracked once at least one year has passed using the files "../Histogram_Good.png" and "../Histogram_Short.png".  The histograms show out of all recommended stocks which ones had how much gains and final annualized gains.  For instance it answers the question, "out of all recommended stocks, after one year how many had at least 15% gains?"  Note that the "short" stocks are expected to lose market cap as time goes on.