Quants vs. Discretionary - Numbers vs. Story - Is one better?

Most of our quants have a computer-science background. They can code in either Python or C++. Whereas on the discretionary side most of them come from a more traditional investment-banking background and are digging into 8-Ks and company fundamentals and being able to look at companies from a bottom-up perspective as compared to trying to use many different data sets to help predict the prices of stocks.
-Ryan Tolkin CIO Schonfeld Strategic Advisors 

As the old joke says, "There are only two types of people - those that type others and those who do not." However, this dichotomy may be applicable for current money management. There are two camps of analysis, the quants who are looking for repeatable behavior in data and the discretionary analysts who are looking for unique or special situations across markets. The quant plays the averages and the probabilities while the other places value on what cannot be counted and handicapped. One focuses on the numbers while the other looks for the firm story as a thesis for investing. 

Is one better than the other? I would say that it depends on the problem to be solved and the market to be analyzed, or whether the focus is on counting or the focus is on uniqueness. There is a continuum on research processing. At one extreme will be the focus on large data and an analysis of statistical relationships based on counting versus the other extreme where there is little data that is countable and research requires looking for similarity or a limited number of cases. 

An example of a countable market will be the mortgage-backed securities. There is a lot of data to measure relationships. An example of a countable style will be statistical arbitrage. The alternative is a style that is based on "newness" or a market that has limited data A case-based approach is more applicable for analysis of tech firms and an example of a discretionary style based on uniqueness may be deep value. The approach to research and analysis is affected by the choice of markets and style. In case-based approach, there is a premium on story-telling.

The research approach matches the sale and market. There are reasons for quants focus on some markets and investment-banking types focusing on others; however, an interesting intersection will be those researchers that can marry numbers with story-telling.

Language, perception, and numbers - The translation problem

“March Hare: …Then you should say what you mean.

Alice: I do; at least – at least I mean what I say — that’s the same thing, you know.

Hatter: Not the same thing a bit! Why, you might just as well say that, ‘I see what I eat’ is the same as ‘I eat what I see’!

March Hare: You might just as well say, that “I like what I get” is the same thing as “I get what I like”!

The Dormouse: You might just as well say, that “I breathe when I sleep” is the same thing as “I sleep when I breathe”!Lewis Carroll’s  - “Alice In Wonderland”

“If you cannot say what you mean, your majesty, you will never mean what you say and a gentleman should always mean what he says.” – Reginald Fleming Johnston (The Last Emperor)

 “let your yea be yea; and your nay, nay.” - Matthew 5:37

I meant what I said and I said what I meant. An elephant’s faithful one-hundred percent.  - Dr. Seuss book “Horton Hears a Who”.

When managers or investors use language, there can be a significant amount of uncertainty in what meant. There is little precision in language so quantitative analysis provides more details in the highly competitive money management field.

This issue of imprecise language was first discussed by the CIA analyst Sherman Kent who identified the problem in the reporting by other analysts. If someone says that an event is "likely" what does that really mean? Sloppy language leads to sloppy thinking. We have discussed this issue in the past in our posts Sherman Kent - the godfather of precision in forecasting language and What does failed intelligence tell us about investing, but we came across an enhanced analysis based on survey work. A large number of people were asked to provide probabilities to different word phrases. This information was translated into these beautiful graphs. See these posts for more information on how the graphs were constructed. Look at the high degree of uncertainty associated with these phrases. Be warned; someone who tells you that something is unlikely will still have a median of 20% chance of occurring. A probable outcome has only a 70% median with a range of plus or minus 20%.

The same problem applies with perception of numbers. A lot could mean anything from 10 to 100. Scores could be close to 100 but range from less than 10 to 100,000. The operative phrase is "watch your language". 

How many times have you sat in on an investment meeting only to have those around the table use "squishy" language which has no meaning? (I have been to dozens.) It is a fine way of avoiding accountability. Of course, numbers can be used to provide a false sense of certitude, but given a choice; I will take the precision of a number. The number can be checked and verified. The lesson from "super forecasters" is that the process of adding precision to a forecast improves the forecast.  

Momentum as the big embarrassment to market efficiency

“Momentum is a big embarrassment for market efficiency,” he proclaimed, saying he “hopes it goes away” and that the concept was “not exploitable.” - Eugene Fama from CFA Society of Chicago keynote speech.

“Never let the truth get in the way of a good story.”― Mark Twain

You cannot help but think about Thomas Kuhn and The Evolution of Scientific Revolutions when there is now a discussion of market efficiency. During the 70's and 80's market efficiency studies "proving" this hypothesis took the field of finance by storm only to have alternative studies and work chip away at the theory through the 90's and 2000's only to currently be relegated to a simplifying assumption or a view as "frictionless" market. 

Thousands of finance students and MBA's were indoctrinated with the idea that markets were efficient. We now have behavioral, limits to arbitrage, transaction costs, agent-based, and time varying risk premium stories to explain momentum and trends in prices. The theory of finance is much broader and those that were efficient market supporters have had to adapt and change their views.

Still financial markets are competitive. It is hard to make money in money management. Few have been able to beat benchmarks. Passive low fee investing is a good investment starting point. Perhaps excess returns are mainly compensation for the hard work of finance or the compensation for risk. Nevertheless, the momentum and trend-followers who were outside the mainstream can take pride that their efforts were not in vain. They were rewarded with profits and now the knowledge that those that said it was not possible have to eat crow. 

Should I care if a managed futures fund has a five-star rating?

So you see a manager with a good Morningstar rating. It has five-stars. Should an investor care? Past performance is not indicative of future returns, so should it matter if you had highly rated past risk-adjusted performance? 

Certainly, a rating is not definitive, but as a heuristic on a fund's relative performance, there is positive information to be gleaned from ratings. 

Some simple important facts across mutual fund research:

1. The Morningstar rating is done over a minimum of 3-years so it provides historical perspective.
2. The Morningstar rating is based on risk-adjusted returns that account for utility and not just standard deviation. Hence, it offers and alternative view relative to rankings by Sharpe or information ratios.
3. The rating is based on a category classification, so it is related to peers.
4. The 5-star and 1-star ratings each represent 10% of the sample, so it is hard to maintain  over long periods. 
5. Investors will both punish and reward managers when there is a change in ratings. There are abnormal flows when a rating changes.
6. A 1990's study show that 5-star mutual funds have a fall-off in performance after the rating is given and there is higher risk after the rating is announced.
7. Low-rated funds do predict future poor performance. Higher-rated funds may not outperform the next lower rating category in the future.
7. Mutual fund managers that receive low ratings are likely to be replaced.  

If you expect the 5-star funds to always stay as 5-star funds, you may be disappointed given the 10% star threshold. The signals on performance are mixed, yet the most exhaustive study from Morningstar done last year shows that the rating does make a difference. See the research piece by Jeffery Ptak. There is value in the rating even after accounting for the standard four-factor model in equities. There is also value from a high rating for a fixed income and balanced funds even after account for other risk factors. However, the value associated with alternative investments is not as statistically significant. The author argues that this may be caused by a smaller sample but the general take is that star rating has some marginal meaning.

Research has also found that rankings and performance are tied to costs. Lower cost funds will naturally have an advantage relative to higher cost funds.  A closer analysis may show that the more dispersion around any benchmark will reduce the impact of costs as a driver for rankings. Hence, in alternative investment category where there may be more dispersion around an benchmark and there is less agreement on the benchmark, cost impacts will be less. Still, when in doubt look for lower cost alternatives.

Can this be related to choosing managed futures or alternative investment funds? As a heuristic to help identify potentially better funds, the rating system may be a useful first pass. It is a not a substitute for more exhaustive analysis but there should be strong reasons to bet against the best and worst funds regardless of asset class.