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Alpha Theory Blog - News and Insights

November 30, 2020

Don't Be A Turkey: Optimizing Usage for Optimal Returns

 

This article was co-written by Billy Armfield, Data Scientist of Alpha Theory, and Cameron Hight, CEO of Alpha Theory.

 

Alpha Theory makes it easier for process-oriented investment managers to do their work, from uploading price targets through Excel Connect, to monitoring Optimal Position Size in the application. Previous analyses have shown a strong positive relationship between increased usage of the app and increased ROIC. Those results showed that for usage, like turkey, if some is good, then more is better. In that spirit, we decided to investigate different forms of usage to see what gives the greatest return on investment.

 

Two common ways in which we measure client usage are coverage (how much of the portfolio has research?) and freshness (how recently were your price targets updated?). In the same way we help our clients optimize exposure to their positions, we also want to make sure they are optimizing how they spend time engaging with Alpha Theory.

 

We ask:

1. Which element of usage has the strongest relationship with returns?

2. How much is enough?

 

Coverage

There are several ways to think about coverage. The first way we will examine is to think of it as the percentage of names in the book which has research. Simple enough. But this method can provide a false sense of security. Say, for example, you have a five-position portfolio weighted at 80%, 5%, 5%, 5%, 5%. You have price targets for your smaller positions, but not the big ones, giving you coverage of 80% under this definition. But are you really covered?

 

The second method of measuring coverage is by calculating the ratio of the gross exposure of the portfolio which has research, to the gross exposure of the portfolio as a whole. Both methods have their place, but we were interested to know which was more helpful as a metric to track and optimize.

 

An Experiment

In order to determine which measure of coverage is more helpful, we regressed both forms of coverage against one day forward returns for a sample of Alpha Theory client funds. The results for the first method are in figure 1, and the results for the second method are in figure 2.

 

Figure 1 (% of Positions with Price Targets): 

Picture1

 

Figure 2 (% of Exposure with Price Targets):

Picture2

 

The results for the exposure-based method (figure 2) show a higher coefficient, larger t-statistic, and lower p-value than method 1, giving us a high degree of confidence there is a stronger relationship between one day forward returns and the exposure-based method (p-value < 0.05 is statistically significant for our purposes). This measure of exposure is what we will use going forward.

 

Freshness

We should ask the same questions about freshness that we have for coverage. The first way we can think about freshness is to take the ratio of positions which have had their price targets updated in the last 90 days to all positions with price targets. The second is to take the ratio of gross exposure of positions with research updated less than 90 days to gross exposure of the portfolio.

 

Figure 3 (% of Positions with Price Targets that are Fresh):

Picture3

 

Figure 4 (% of Exposure with Price Targets that are Fresh):

Picture4

 

The exposure-based method is more predictive here as well, with a higher t-stat and lower p-value. These results make sense – small positions can be placeholders and get less attention. The positions with large exposure have targets subjected to greater diligence.

 

Comparing the Variables

We can now compare the results of regression against one day forward returns for the two variables under consideration.

 

Figure 5: Coverage

Picture5

Figure 6: Freshness

Picture6

 

In figures 5 and 6, we see that both coverage and freshness have t-statistics and p-values which are statistically significant, and both variables have positive coefficients. Of the elements of usage examined, higher coverage is most strongly related to higher one day forward returns, followed by freshness.

 

How much is enough?

Unlike at Thanksgiving dinner, we can estimate how much is enough of each. Focusing on Coverage and Freshness, we examined various thresholds to see how the average client performed above and below it. The 85% and 95% thresholds resulted in the greatest difference in annualized ROIC for clients above and below. More clients have been able to reach the upper echelons of coverage than freshness, which makes sense – over time, and given the reasonably low turnover, it isn’t unfathomable that you can make forecasts on > 95% of the exposure of your book.

 

Picture7

 

Keeping everything fresh, however, requires strict discipline, and an emphasis on process. But it appears as if the hard work pays off: the four funds with freshness over 95% have average annualized ROIC 6.6% higher than the average for all the other funds under consideration.

 

What is interesting is the persistent relationship for Coverage where higher Coverage always results in better returns but Freshness reverses course at the 70% level. This phenomenon is counterintuitive given the statistical relationship discovered in the regression. It is clear that more research is required and in next month’s blog post we’ll investigate this discovery and explore what can be learned from examining on a by-position basis instead of by-firm.

 

October 16, 2020

Best Ideas Update

 

The Cohen, Polk, Silli “Best Ideas” paper was first released in 2005 and Alpha Theory incorporated the 2010 draft in the Concentration Manifesto as an empirical proof (#3 to be exact) of why managers should concentrate. An updated version of the “Best Ideas” paper was released in June, it expands the data set from 24 to 37 years and reconfirms the earlier findings that active managers are 1) good at selecting and sizing a few “Best Ideas” and 2) then dilute the “Best Ideas” with a bunch of positions that are basically random noise.

 

The “Best Ideas” portfolio outperforms the rest of the portfolio and benchmarks by 2.8% to 4.5% per year with high statistical significance, across a thousand-plus mutual and hedge fund managers, and with consistency amongst managers and from year-to-year.

 

This abnormal performance appears permanent, showing no evidence of subsequent reversal, even several years later. Interestingly, cross-sectional tests indicate that active managers’ best ideas are most effective in illiquid, growth, momentum stocks, or for funds that have outperformed in the past.

 

Given the strong empirical evidence for concentration, why don’t managers concentrate more on their best ideas? The “Concentration Manifesto” highlights myriad reasons managers should concentrate but does not investigate why they do not. The “Best Ideas” paper does:

 

We identify four reasons managers may overdiversify.

 

1. Regulatory/legal. A number of regulations make it impossible or at least risky for many investment funds to be highly concentrated. Specific regulations bar overconcentration; additionally, vague standards such as the “Prudent man” rule make it more attractive for funds to be better diversified from a regulatory perspective. Managers may well feel that a concentrated portfolio that performs poorly is likely to lead to investor litigation against the manager. Anecdotally, discussions with institutional fund-pickers make their preference for individual funds with low idiosyncratic risk clear. Some attribute the effect to a lack of understanding of portfolio theory by the selectors. Others argue that the selector’s superior (whether inside or outside the organization) will tend to zero in on the worst-performing funds, regardless of portfolio performance. Whatever the cause, we have little doubt that most managers feel pressure to be diversified.

2. Price impact, liquidity, and asset-gathering. Berk and Green (2004) outline a model in which managers attempt to maximize profits by maximizing assets under management. In their model, as in ours, managers mix their positive-alpha ideas with a weighting in the market portfolio. The motivation in their model for the market weight is that investing in an individual stock will affect the stock’s price, each purchase pushing it toward fair value. Thus, there is a maximum number of dollars of alpha that the manager can extract from a given idea. In the Berk and Green model managers collect fees as a fixed percentage of assets under management, and investors react to performance so that in equilibrium each manager will raise assets until the fees are equal to the alpha that can be extracted from their 26 good ideas. This choice leaves the investors with zero after-fee alpha. Clearly in the world of Berk and Green, (and in the real world of mutual funds), managers with one great idea would be foolish to invest their entire fund in that idea, for this would make it impossible for them to capture a very high fraction of the idea’s alpha in their fees. In other words, while investors benefit from concentration as noted above, managers under the most commonly used fee structures are better off with a more diversified portfolio. The distribution of bargaining power between managers and investors may therefore be a key determinant of diversification levels in funds.

3. Manager risk aversion. While the investor is diversified beyond the manager’s portfolio, the manager himself is not. The portfolio’s performance is likely the central determinant of the manager’s wealth, and as such we should expect them to be risk-averse over fund performance. A heavy bet on one or a small number of positions can, in the presence of bad luck, cause the manager to lose their business or their job (and perhaps much of their savings as well, if they are heavily invested in their own fund, as is common practice). If manager talent were fully observable this would not be the case – for a skilled manager, the poor performance would be correctly attributed to luck, and no penalty would be exacted. But when ability is being estimated by investors based on performance, risk-averse managers will have an incentive to overdiversify.

4. Investor irrationality. There is ample reason to believe that many investors – even sophisticated institutional investors – do not fully appreciate portfolio theory and therefore tend to judge individual investments on their expected Sharpe ratio rather than on what those investments are expected to contribute to the Sharpe ratio of their portfolio. For example, Morningstar’s well-known star rating system is based on a risk-return trade-off that is highly correlated with Sharpe ratio. It is very difficult for a highly concentrated fund to get. This behavior is consistent with the general notion of “narrow framing” proposed by Kahneman and Lovallo (1993), Rabin and Thaler (2001), and Barberis, Huang, and Thaler (2006). A top rating even if average returns are very high, as the star methodology heavily penalizes idiosyncratic risk. Since a large majority of all flows to mutual funds are to four- and five-star funds, concentrated funds would appear to be at a significant disadvantage in fundraising. Other evidence of this bias includes the prominence of fund-level Sharpe ratios in the marketing materials of funds, as well as maximum drawdown and other idiosyncratic measures. Both theory and evidence suggest that investors would benefit from managers holding more concentrated portfolios.

Our view is that we fail to see managers focusing on their best ideas for a number of reasons. Most of these relate to benefits to the manager of holding a diversified portfolio. But if those were the only causes, we would be hearing an outcry from investors about overdiversification by managers, while in fact, such complaints are rare. Thus, we speculate that investor irrationality (or at least bounded rationality) in the form of manager-level analytics and heuristics that are not truly appropriate in a portfolio context, play a major role in causing overdiversification.

 

The reasons for diversification (not concentration) are real and will require systematic change and mutual agreement from both funds and LPs. Given the state of flows from active to passive, there may be a strong enough catalyst for that change.