Alpha Theory clients are great at fundamental, bottoms-up analysis, and stock picking, as illustrated by continued outperformance over the average hedge fund. However, as recent years demonstrate, non-fundamental factors can have outsized impacts on portfolio performance, leaving funds exposed to unanticipated risks. With that in mind, we teamed up with FactSet’s Quantitative Analytics team to build a five-factor model leveraging FactSet’s Quant Factor Library, providing managers with daily factor exposure and attribution for improved awareness. The five factors we chose for our model are Beta, Growth, Momentum, Value, and Volatility. In this article, we provide examples of the two key use cases from this dataset:
- Measuring unintended factor exposures currently in the active portfolio.
- Highlighting trades to reduce unintended factor exposure that align with fundamental research.
To begin, use the FactSet Risk - Exposures View to investigate current risk in your portfolio. The view contains exposure, optimal and % from optimal exposure for all five factors:
Look at the Totals for Factor Exposure to understand your biggest risk(s) by rearranging the factor exposure columns into an easily comparable view:
This portfolio is highly exposed to the Momentum factor. If there is a desire to cut exposure to the Momentum factor, sort by “% from Optimal Exposure: Momentum” to highlight which optimal trades (trade suggestions based on your fundamental research) can also lead to less Momentum exposure. The same can be true if you would like to increase exposure to a certain factor.
For example, if we wanted to potentially lower our Momentum exposure, we could follow the research recommendation to trim our positions in LOGN3 BZ, UCG IM. and CCJ by 2.8%, 2.6%, and 2.9% and it would reduce our Momentum exposure by 5.5%, 2.6%, and 2.4%, respectively, for a total of a 10.5% reduction.
We can also add to names like CM CN and C, as our fundamental, bottoms-up research suggests, as this will also reduce our exposure to Momentum:
Compare suggested trades against the other factors to determine whether or not the trade makes sense. For instance, if you can only make one trade out of two trades similar in magnitude (CM CN and C), you can compare these trades from a factor perspective to help you determine which trade is most beneficial.
- CM CN decreases exposure to Beta, Growth, and Volatility while increasing exposure to Value.
- C decreases exposure to Growth while increasing exposure to Beta, Value and Volatility.
Your investment strategy and outlook on which factor(s) will be net positive in the market help inform your trade decision.
This dataset is intended to be another tool within the Alpha Theory framework to help managers understand their portfolios better, identify unintended risks, and help better inform trade decisions.