I generally buy and sell weekly as stock prices fluctuate with volatility. I am exploring the idea of using a machine learning algorithm to consider various economic conditions (inputs) and provide insight (output) into whether any given day is a safe day to buy, given market conditions, for a particular stock. Thus I'd like to determine the risk related to investing at any given time. I'm only trying create a model of current conditions; I'm not seeking to predict prices.
For example, it seems more likely that a market correction may occur (and it is bad to buy) when the S&P 500 is pretty high, VIX is low, and the dollar is high.
Similarly, it seems less likely a market correction may occur (and it is good to buy) when indexes are not extremely high, the VIX is relatively higher, and the dollar is relatively lower.
Here is a compiled list of possible factors an algorithm could use as input:
- Fear/volatility index (VIX)
- GDP or GDP growth
- Current interest rate
- Current unemployment rate
- Current Federal funds interest rate
- Employee pay
- Housing market data
- Gold prices
- Dollar value
- Time of year (e.g. September is historically a bad month to invest)
- Oil price
- Bond prices
- Index growth
- Time until next Fed meeting
- Month price change %
- Day price change %
...and other factors (suggestions welcome).