The older I become, the less I feel I know anything with certainty. Almost every aspect of life seems to have various degrees of grey rather than being black and white. Most knowledge seems to peel away like an onion at closer inspection and highlights different shades, but rarely an ultimate truth.
For example, eating sushi for lunch feels better from a nutritional and calorie perspective compared to eating a cheeseburger or a pizza. However, tuna tends to contain heavy metals and salmon nasty pesticides that are both likely detrimental to our health. So, is eating sushi good or bad for our health? Or should we prefer the cheeseburger? Tough to say.
Similar questions arise when analysing the performance of investment portfolios. Even simple portfolios, e.g. a traditional one comprised of equities (60%) and bonds (40%), can be dazzling complex when inspected closely. Isn’t the equity allocation responsible for most of the portfolio risk? Which asset class contributed most of the returns? And are those explained by plain beta or a tilt to certain factors?
The classic approach to answering such portfolio analysis questions is by conducting a contribution analysis via factor exposures. However, this often raises more questions than it provides answers to.
In this short research note, we will explore some of the challenging aspects of regression-based factor exposure analysis.
We will explore factor exposure analysis by primarily focusing on six simple portfolios comprising US large-cap stocks (S&P 500), US small caps, US low-risk stocks, international as well as emerging market (EM) stocks, and a multi-asset portfolio.
Comparing the performance of the six portfolios in the period between 2015 and 2020 highlights similar trends. The S&P 500 generated the highest returns, but it depends on the lookback period, e.g. EM stocks outperformed in the first two years. It is interesting to note that even the multi-asset portfolio exhibited the same performance characteristics, which indicates a significant allocation to equities.