(866)-482-2177

sales@alphatheory.com

REQUEST A DEMO

SYSTEM REQUIREMENTS


Please note the following System Requirements. Further, please limit the number of open applications (particularly price streaming applications) while logged in to Alpha Theory™.


Recommended System Specifications
Processor: Dual Core or Quad-Core 2.4GHz or faster
RAM: 4GB+
Browser: Google Chrome 30+
Screen Resolution: 1280 x 1024 or greater
Internet Access: Business Class High-Speed


Minimum System Requirements
Processor: Intel Pentium-M 2.0Ghz or equivalent
RAM: 2GB+
Browser: Google Chrome, Mozilla Firefox, Internet Explorer 9+ (without Compatibility View), Safari
Screen Resolution: 1024 x 768 or greater
Internet Access: High-Speed

Alpha Theory Blog - News and Insights

October 27, 2021

KISS, or How to Make Money by Following Your Research

 

“It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.” - Charlie Munger

 

Successful traders and investors encourage entrants to the field to find an “edge”, ideally a strategy that has not already been widely adopted by other market participants. This has led to the proliferation of esoteric strategies, especially in the quantitative arena. In order to generate alpha in the increasingly competitive asset management industry, you need an army of PhD’s, complex strategies, and troves of data, right? Well, not necessarily.

 

KISS_Sep2020

 

Analysis of the Alpha Theory dataset shows that if managers simply exit all positions where probability-weighted return is zero or negative, the average manager’s CAGR would improve by 3%!

 

Alpha Theory managers create a probability-weighted value for each position based on price targets and probabilities for the various scenarios which may play out in the market. In an ideal long scenario, the current market price of a security will increase towards the probability-weighted value. As price and expected value converge, probability-weighted return drops to zero, and the analyst should either revise price targets upward, trim, or exit the position all together. If expected return is zero, Optimal Position Size will recommend exiting the position, as there are other investments with greater expected return.

 

Sometimes, however, managers are slow to update price targets, or to reallocate the portfolio to higher expected return investments. We compared the return on invested capital (ROIC or total return/gross exposure) of the manager’s actual portfolios to what ROIC would have been if managers were only invested in positive probability-weighted return positions. This means a long position would only be in the portfolio if the probability-weighted return was positive, and a short position only if the probability-weighted return was negative.

 

The data below shows the improvement in ROIC over actual for simply removing positions with negative probability-weighted returns (blue column) and then for Alpha Theory’s Optimal Position Size (gray column), which layers on additional sizing logic in addition to zeroing out positions with zero probability-weighted return. The sample includes all Alpha Theory clients from January 1st, 2014 to June 30th, 2021.

 

Improvement Over Actual ROIC

Returns on manager portfolios of only the positions which had a directionally accurate positive probability-weighted return had a 3% higher CAGR, and returns on Optimal Position Size, which uses manager research as well as other portfolio constraints, improved CAGR by 6.7% over actual ROIC.

 

Highly intelligent, sophisticated investors look for ways to improve by default, and the temptation to distinguish oneself with new strategies is intense. But our research suggests that it is more important to focus on the fundamentals. John Wooden’s insight that free throws contribute to national championships also applies to portfolio management. Having high research coverage, updating price targets, and being allocated to positive returns are simple rules which contribute to outperformance, but which are often ignored at the expense of alpha.

 

September 29, 2021

The Cost of Volatility – The Path Dependency of Returns

 

In a recent analysis, we were comparing the volatility of a return stream on a daily and monthly basis. We all know that if a portfolio goes down by 10%, it must be up more than 10% to get back to even (11.11% to be exact). The path dependency can cause the differences in return and volatility to be stark. In trying to understand where the differences arise, we compared the outcomes of a perfectly stable return stream (0% volatility) to those of increasing levels of volatility.

 

Cost of volatility

 

The starting point is an ideal 20% return (blue line) over the course of a year (252 days). The most efficient way to create that return is to generate 0.072% of return per day. Any additional volatility, with the same average of 0.072% return, negatively impacts total return. To show the impact, we simulated six return streams that all had an average of 0.072% daily returns but with varying daily volatilities between 0% and 5% (5% daily volatility is 79% annualized volatility).

 

For example, with 1% daily volatility (orange line), the daily return flips between 1.071% and -0.926% (average of 0.072%). For 5% volatility (green line), the daily returns flip between 5.066% and -4.922% (also a 0.072% average).

 

Volatility is a cost that requires a higher average return to get to the same goal. Ex-post (after the goal is reached), if you achieve a 20% return from two assets, you do not care about the path (volatility) that led to the 20% return. On the other hand, ex-ante (before the bet is made), it is harder to get to a 20% return with a 5% vol than a 1% vol.

 

The compounding impact of volatility is difficult to conceptualize but is important to understand when making investment decisions. This graphic is hopefully a helpful tool to remember how volatility causes us to raise the return bar for an asset.

 

As a life-long fundamental investor, acknowledging volatility as a risk is a tough pill to swallow. In my career as an analyst, I made forecasts over long periods of time, and I did not care about the path, just the outcome. However, before the bet is made, if I have two assets with the same probability-weighted return of 20% and one has a volatility of 10% and the other 30%, should I be indifferent or should I factor that in to how I size my bet? And if so, how? These are interesting questions that we’ll continue to explore in future posts.