Alpha Theory 2018 Year In Review

Feb. 11, 2019 5:03 AM ET1 Like
Cameron Hight profile picture
Cameron Hight
40 Followers

Summary

  • 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.
  • From a global perspective, Alpha Theory clients and optimal sizing outperformed major indices.
  • Clients would have done even better if they would have more closely followed the model they built in Alpha Theory.

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 Reduce 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.

Editor’s Note: The summary bullets for this article were chosen by Seeking Alpha editors.


This article was written by

Cameron Hight profile picture
40 Followers
Prior to founding and developing Alpha Theory in 2005, Cameron worked as an equity research analyst for 10 years, covering primarily technology companies and supporting over $20B in transactions for firms such as Lehman Brothers, CIBC (NYSE: CM) and Credit Suisse First Boston (NYSE: CS) before joining Afton Capital Management, a $200M long-short equity hedge fund, in 2002. He currently sits on the boards of several funds and financial technology start-ups as well as the UNC-Chapel Hill Business School Investment Fund. Cameron earned his Business degree from the University of North Carolina at Chapel Hill and became one of the youngest ever to receive the Chartered Financial Analyst (CFA) designation in 1999. It was on the buy-side that Cameron became aware of the avoidable mistakes investment managers were making causing lost performance and he vowed to solve the problem. When he told his grandmother why he was starting Alpha Theory, she looked at him and asked, "You mean they're not doing this already?" and he knew at that moment he had a responsibility to help bring about a change in the industry. Today, Cameron spends his time deepening his knowledge on important topics in behavioral finance, decision sciences, portfolio management and acting as a thought leader to investment managers on investment process "best practices" through direct communication at conferences and seminars, on TV and Radio, and through the written word. Cameron is an avid golfer, Citi-biker, runner, a technology early-adopter, magician (he taught his three year old and one year old daughters to love sushi) and a die-hard Tarheel fan.
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