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Alpha Theory Blog - News and Insights

February 08, 2019

Alpha Theory 2018 Year in Review

 

THE STREAK CONTINUES!  For the seventh consecutive year, Alpha Theory clients have outperformed their peers, more than doubling the returns of the industry average over the same period. This year, our clients beat the primary Equity Hedge index by 3.9% despite missing out on 0.9% of return if they more closely followed the model they built in Alpha Theory. 

 

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From a global perspective, Alpha Theory clients and optimal sizing outperformed major indices.

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Despite the difficult year for equity funds, including our clients, who averaged a decline of 3.0%, they still outperformed their peers who experienced an average decline of 6.9%.

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Clients would have done even better if they would have more closely followed the model they built in Alpha Theory.

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It was a particularly satisfying year to post these results. The streak of momentum fueled markets transitioned into one with much higher volatility in 2018 and it was great to see that our tool is effective at driving alpha in both types of market conditions.    

 

2018 INDUSTRY TRENDS

Some new trends have started to gain traction across the industry in 2018.  While some larger funds have been headed in this direction for years, the change we saw in 2018 was more widespread adoption—even in smaller funds.  As we talked to hundreds of prospective clients and allocators in 2018, we noticed three major trends in how the most successful PMs are changing their investment strategies:

    1. Leveraging more alternative data sources in their research.

    2. Acute focus on repeatable processes around research, risk, and position sizing.

    3. Emphasis on capturing data that can leverage statistical analysis and machine learning.

 

At first, these three trends seemed unrelated. It was only recently that we realized that they are deeply connected by one dominant trend: the reduction in available alpha due to the ubiquity of research data, increased number of analysts, decreased number of publicly available securities, and the rapid rise in computers ability to find market inefficiencies faster than humans. This is making it virtually impossible to gain a sustainable edge through traditional “stock picking.” Put simply-- the largest traditional source of alpha has almost completely dried up.

 

We are seeing this trend in our batting average data as the average of our clients converges towards 50%. The good news is that there is still alpha out there to be harvested and our data bears that out.  Supporting point 2 above, as you will see in the tables below—our most process-driven clients (as represented by our most active clients based on usage) strongly outperform our most passive users.  We also have several clients who are making deep dives into their historical forecasting data to determine which of their analysts have the forecasting best track records and teasing out the strengths and weaknesses of the poor performers so they can target specific areas of improvement. 

 

Our clients are a self-selecting cohort who believe in process and discipline; process orientation goes together with Alpha Theory software that serves as a disciplining mechanism to align best risk/reward ideas with rankings in the portfolio. Shown below, the most active users as measured by frequency of update, research coverage, and correlation with the model have the highest ROIC.

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PROCESS ENHANCES PERFORMANCE

Alpha Theory clients use a process to reduce the impacts from emotion and guesswork as they make position sizing decisions. Alpha Theory highlights when good ideas coincide with largest position sizes in the portfolio. This rules engine codifies a discipline that:

    1. Centralizes price targets and archives them in a database

    2. Provides notifications of price target updates and anomalies

    3. Calculates probability-weighted returns (PWR) for assets and the portfolio as a whole.

    4. Enhances returns

    5. Mitigates portfolio risk 

    6. Saves time

    7. Adds precision and rigor to the sizing process

    8. Enables real-time incorporation of the market and individual asset moves into sizing decisions.

 

DISCIPLINED USAGE REDUCES RESEARCH SLIPPAGE

Alpha Theory’s research not only suggests that adoption of the application by itself leads to improved performance, but actual usage intensity further enhances results.

Usage intensity is determined by:

    1. Percent of Positions with Research

    2. Correlation with Optimal Position Size

    3. Login Frequency

 

OPTIMAL POSITION SIZING REDUCES RESEARCH SLIPPAGE

Comparing clients’ actual versus optimal returns shows:

 

HIGHER TOTAL RETURNS
ROIC is 4% higher.

 

IMPROVED BATTING AVERAGE
Batting Average is 9% higher. Explanation: many of the assets that don’t have price targets or have negative PWRs are held by the fund but recommended as 0% positions by Alpha Theory. Those positions underperform and allow Alpha Theory’s batting average to prevail.

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1. Measured as the average full-year return for clients where full-year data was available, adjusted for differences in exposure, net of trading costs

2. Before trading costs

 

PRICE TARGETS REDUCES RESEARCH SLIPPAGE

Alpha Theory has further found that ROIC for assets with price targets is 4.8% higher than for those without price targets. Some investors chafe at price targets because they smack of “false precision.” These investors are missing the point because the key to price targets is not their absolute validity but their explicit nature which allows for objective conversation of the assumptions that went into them.  Said another way, the requirements of calculating a price target and the questions that targets foster are central to any good process.

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Finding alpha will not become easier. It is imperative that the funds of the 21st century develop plans to evolve into new realities. Data and process are critical to that evolution. Let Alpha Theory help you and your team grow to meet the challenges of tomorrow.

 

January 05, 2019

Valuing Momentum: Part 2

 

I’ll highlight one major article written by Cliff Asness and his team at AQR, published in May 2014 (it’s also worth checking out “What Works on Wall Street” by O’Shaunassy and their fund strategies which combine value and momentum and have solid long-term track records). The AQR piece titled Fact, Fiction and Momentum Investing evaluates some of the most prominent myths regarding Momentum and uses empirical research to refute those myths. In doing so, it gives a compelling account showing why the marriage of value and momentum are potent partners. Here are a few excerpts to give a sense of their conclusions:

 

As we’ll show in this essay, value and momentum work better when used as complements, and it is the combination of the two we stress and most-strongly recommend. We are fans of both momentum and value but bigger fans of their combination (and not fans of myths at all).

 

Evidence for Momentum

The (momentum) return premium is evident in 212 years (yes, this is not a typo, two hundred and twelve years of data from 1801 to 2012) of U.S. equity data,3 dating back to the Victorian age in U.K equity data,4 in over 20 years of out-of-sample evidence from its original discovery, in 40 other countries, and in more than a dozen other asset classes.

 

1

 

88% of returns positive for momentum and 89% for value.

 

2

 

Israel and Moskowitz (2013) show that the long and short side of momentum is equally profitable using 86 years of U.S. data as well as 40 years of international equity data, and another 40 years of data from five other asset classes outside of equities. Everywhere they looked and in every way, they could not find any evidence that the short side profits were systematically larger or more important than the long side.

 

Benefits of Momentum and Value Combined

Sharpe ratio and percent of years with positive returns increase with a 60% value / 40% momentum strategy.

 

Group 2

 

Suppose, despite all of the evidence to the contrary and our strong belief it’s positive, momentum had a zero expected return going forward. Would it still be a valuable investment tool? The answer is clearly, though perhaps surprisingly, yes. The reason is because of momentum’s tremendous diversification benefits when combined with value.

 

The diversification benefits are so great that even a zero expected return would be valuable to your portfolio! The logic is simple. Since value is a good strategy and momentum is -0.4 correlated with it, one should expect momentum to lose money based only on that information. Yet, the fact that it does not lose but in this assumed case breaks even makes it a valuable hedge. (We note that using the definition of value in Asness and Frazzini (2013) dramatically increases the magnitude of this negative correlation (to -0.7) and the power of combining value and momentum. Following their methodology, the results of this section would be far stronger.)

 

But, there’s an even simpler and equally effective way to mitigate these crashes, as we mention repeatedly: combining momentum with value. This combination has effectively eliminated these crashes in our long-term sample evidence — and not just those for momentum but also the crashes that can occur for value investing. In other words, the diversification benefits of combining momentum with value don’t just appear during normal times, but also during these extreme times, which makes their combination even more valuable. For example, Asness and Frazzini (2013) show that the combination of value and momentum did not suffer as badly in 2009. Going the other way, in 1999 momentum helped ameliorate value’s pain. Both factors have worked well over the long-term, but neither has a Sharpe ratio of 10, meaning that both will have hard times occasionally, but when combined together they will have fewer hard times. Using Kenneth French’s data, we can show similarly that these very poor episodes for momentum and value are ameliorated. The diversification benefits between momentum and value are evident, even during these extreme times. For example, the worst drawdown over the full sample is -43% for value, -77% for momentum, but only -30% for a 60/40 combination of value and momentum.

 

By the way, we fully recognize and acknowledge that the past ten years have not been great for momentum, with the 10-year return for UMD (Momentum) falling in the 7th percentile of rolling 10-year returns (going back to 1927). At the same time, the past ten years have not been great for value, either, with the 10-year return for HML (Value) falling in the 5th percentile of rolling 10-year returns. That, of course, makes the prior 10-year return of the 60/40 combination of the two low (2nd percentile), but still positive (12%). You know a strategy has a pretty great history when the 2nd percentile return is still positive.

 

Summing up the points from the AQR paper:

 - Momentum works better with value (negatively correlated with each other)

 - The better the value mechanism the better the whole portfolio performs (see the bolded section in the excerpt above)

 

This is where our clients shine. They are great value estimators and their research is not easily systematized. What should be systematized is the translation of that research into a portfolio and a new push for Alpha Theory will be to give our clients tools to incorporate momentum. 

 

Active manager's search for alpha is more difficult today than it has ever been. There is an existential requirement for active managers to leverage the tools and evidence around them and maximize the return they get from their research. To that end, over the coming months, you will see Alpha Theory develop new functionality to better account for momentum in position sizing. We welcome your input as we embark upon this journey.