Portfolio formation is a multi-factor optimisation problem, which creates a lot of difficulties for an inexperienced investor, as it is quite heavy on calculations and very technical.
First and foremost, you should understand the rationale behind the upcoming optimisation: you can reach similar "performance" of a portfolio while significantly improving the "defensive" characteristics of a revenue loss in bad cases if several stocks are combined together.
The intuition behind the portfolio optimisation is the correlation (corr
) between stocks in the portfolio. Corr(x,y)
is a measure between two time-series rows x and y, which lies between [-1, 1]. If all stocks are highly correlated (corr(x,y)
~ [0.7..1] - i.e. all stocks are the companies from the same vertical having similar business performance and risks), then there is not much you can do in the portfolio optimisation.
You have many more optimisation possibilities when there are loosely correlated stocks in your selection (corr(x,y) ~ [0..0.3]
), or even better - negatively correlated pairs (corr(x,y) <0
Now let's switch to the portfolio metrics:
- The most common metric of a portfolio performance is a simple return over the period of time (= [Price_end_period]/[Price_begin_period]), which can be compared with some benchmark (like S&P500 returns, or available investment alternatives like bonds or real estate). In this article we cover a short-term investment horizon of 3 days, so we'll use the return_3d signature (similar for all other metrics).
- More advanced metric is return-per-risk (=[return_3d]/[std_deviation_3d]), as it gives a relative number per unit of risk, taking into the account the potential range of price movements (or standard deviation in statistical terms). We will calculate two similar numbers: mean_return_per_volatility_3d and median_return_per_volatility_3d. The reason why we track median_return_per_volatility on top of mean_return_per_volatility is that it is not that sensitive to the outliers (of a big positive/negative price jumps) and gives more realistic picture what to expect.
- Last but not least are the measures of a potential loss from big negative events. Max_drawdown (=[maximum historical drawdown of a portfolio in 3 days]) and 25percent_drawdown (=[drawdown in 25% worst cases]). The logic is that max_drawdown is useful to check to be prepared for the worst-case scenario, and 25percent_drawdown is a measure of smaller negative movements that occur much more often.