False discovery rate in finance - Thinking out of the box

 

Aha! I found another risk premium. This has been the mantra of finance for well over the last decade, yet perhaps we should comment, "not so fast." While on the one hand, there has been an explosion of research finding new factors, what has been called the factor zoo, there has also been pushback by other researchers who have pointed out the issue of data mining. The argument of data mining is that conventional inferential frameworks are inappropriate when there are endless tests of different model specifications before the right one is reported. We will mine the data until we find the right result. Other researchers have pushed back against the data miner, with analyses showing that the posterior-expected alphas still exist even after the initial results were reported.

A new paper. "The False Discovery Rate in Finance: Identification failure and search-adjusted estimation" outlines how this problem can be solved. There should be a search-and-selection process for identifying factors. Knowing that process will help detect the false discovery rate, but if this search mechanism is unknown, there will be a greater likelihood of underestimating the true false discovery rate. 

Using some lower bounds calculated from their work, the authors suggest that most reported discoveries in finance are likely false. Ouch, that is a strong indictment and has strong implications for academic research and the work of quants who use that research to develop profitable trading strategies.  

Narrative and macro investing

 

An exciting area of macro research is the use of narrative to help explain the weekly movements in equity markets. This work is still in its infancy and seems to be taking several different directions. This work on narratives started with Robert Shiller and his research on narrative memes that may create bubbles. Another analytic approach has been the development of indices that attempt to measure risk by counting mentions of news events. It has expanded with the development of NLP and LLM models.

An interesting application has been developed using the GDELT database to create different narratives. These narratives are then used as input to a macro model that uses key economic data from FRED. See the paper, “Monitoring Narratives: An Applicaiton ot the Equity Market" Using a set of key narratives developed around news themes, the authors find that added narrative information will increase R-squared and reduce error for a model trying to explain equity returns. 

This paper scratches the surface, but it does provide an interesting link between narratives and fundamental data to help explain equity returns. For all the focus on macro quant data, story-telling is still an important driver of markets.

Rationale for trend-following updated

 

The case for trend-following is well established, yet, given the fluctuations in returns during periods without a crisis, it is worth revisiting fundamentals and discussing the rationale for this hedge fund strategy. Meketa, the pension consulting firm, produced a white paper on the topic at the end of the year that provides some new insights. 

One, the dispersion between trend-following fund returns is significant. The difference between the best and worst can be over 25%, and the difference between the 25th and 75th percentile averages around 10%.

 

Two, the difference in dispersion will increase with the extremes in equity returns. When market dislocations are greater, there will be greater dispersion in returns. 

Three,  the Sharpe ratio can be smoothed and increased if an investor chooses a portfolio of managers. It may be hard to say what the right number is, but 4-6 seems to provide the benefits of smoother Sharpe and less dispersion in the return-to-risk trade-off 

Tax loss alpha is getting big

 

There has been an increase in stories about tax alpha and how this has become a big thing in the hedge fund industry. Hedge funds are not tax effciency. The active trading in many funds generates positive returns, but capital gains may be limited, so returns are generally treated as ordinary income. Managed futures will have some tax advanatges, but the general case is that invetsors should compare after-tax returns across strategies. 

The question is who should be generating the tax alpha - the manager or the investor. The answer is to look at some combination of both, There is the old adage by Buffet about the two rules of asset management: Rule 1 protect principal, and rule 2, follow rule 1. 

Of course, the top priority is for any hedge fund is generate return, yet, tax efficicny should be a goal that can provide improved returns without significnat risk. For those who have SMAs, the tax efficiency can be achieved by the investor and viewed more holistically. Wash sales, tax loss harvesting, and forms of tax defferral can all help reduce tax drag. As more "retail" investors get involved in hedge funds, the issue of tax efficiency will come to the forefront.