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How Omega Point Helped a Value-Oriented Investment Manager

Our firm manages a long short value fund with low net exposure to market indices. We assess businesses for investment by looking at intrinsic value out three to five years yet, like most hedge funds, we report performance monthly and communicate with clients quarterly. Accordingly, most investors judge us monthly and quarterly as we assemble these periods into long-term performance. Even though we are value investors and take an intermediate-term time horizon of three to five years, the reality is we will be overly praised for solid quarters and overly blamed for poor quarters. Accordingly, we apply our intermediate and long-term value investing methodology with an understanding that path dependency matters. A 6-foot person can drown in a river that is on average 3 feet deep if they cannot handle the depths. While a somewhat pithy quote, many investors have let their clients down and/or gone out of business by failing to realize this critical point.

Over the seven years we have been operating our firm, the drivers of this quarterly path dependency have evolved. Accordingly, we completed a multi-quarter process improvement project implementing style factor analysis. The following are key excerpts:

To accomplish our mission statement of compounding capital over lengthy time periods, we launched an effective and replicable strategy: value investing. Based on multiple academic and practitioner studies referenced in this paper, value as a strategy (both as a long / short and long only) has performed well over multi-decade periods. In fact, a May 2018 J.P. Morgan report claims “value stands out as the only style with positive average information coefficient for all factors over the past ~35 years.” Delving further into our value investing methodology, we focus on free cash flow (“FCF”) generation as our primarily research lens. Our focus is well placed: historical studies indicate high free cash flow yield is an all-weather value factor performing well in all phases of the business cycle.

This project is a natural evolution of our investment process in a changing landscape. Fundamental business analysis as the core of our process will remain firmly intact. Our expectation of driving Fund performance in unique securities also will not change. After completing this project, we conclude the following:

  • Our focus on high free cash flow generation relative to current valuation is a proven historical performance generator. We will continue to focus our long portfolio research on companies that generate significant free cash flow to earn the premium over time. Since our inception, the free cash flow factor has not performed commensurately to some factors (such as low volatility). Based on over three decades of data, we expect the free cash flow factor to mean revert and perform over time.
  • Regularly quantify and track portfolio factor exposure. Awareness is the first step in integrating style factor analysis into our regular portfolio and risk management processes.
  • Factor performance is difficult to time as most factors show low persistence (i.e. consistency) quarter to quarter.
  • Short-term portfolio volatility due to factor contributions can be mitigated by investing in a portfolio of securities containing various factor exposures. While not attempting to run a “factor neutral” portfolio, we seek to mitigate portfolio level factor exposure.

As active managers, our core focus will always remain on the identification of and investment in drastically mispriced securities while managing risk to avoid permanent capital losses. However, continual improvement is vital for long-term success in an ever-evolving capital market. The outcome of our factor project has led to the following improvements:

  • Leverage technology to regularly review factor exposures at the following levels: overall portfolio, long and short portfolios, and individual positions.
  • Given the ability to track factor exposures down to the position level, we pragmatically incorporate factor exposures into position sizing.  

The above is “what” we are doing, but the “how” is equally important.  As we were developing a white paper, it became abundantly clear that implementing more rigorous processes around managing factor exposures could benefit overall investment performance, and mitigate both risk as defined as permanent capital impairment and risk as defined by standard deviation. But the increasingly machine-driven trading environment has made factor management substantially more difficult. While just a decade ago there were only a handful of factors tracked by most managers - such as beta, value, momentum, size - now over 1,000 factors and sub-factors have been identified, with no end in sight.

The challenge for our business was that while we wanted to be at a peak level in managing factor exposures, we do not maintain nor have plans to bring on dedicated quant staff. Our core expertise and focus is on developing proprietary research to help identify and invest in undervalued companies. While keeping true to our core value strategy, we wanted to accept less implicit quantitative factor while also minimizing exogenous factors.

We embarked on a search for such a solution, and over the course of several months we evaluated a range of off-the-shelf as well as more bespoke product offerings. Our key requirements were that the product be easy-to-use/implement, offer a deep historical dataset, and fit into our existing workflows. And last but not least, having poured through stacks of historical factor data when researching our white paper, we wanted an intuitive visualization to quickly pinpoint, drill-down, and remedy factor issues as markets change. Our existing data terminal came with built-in factor modules, but their efficacy to meet our evolving needs left much to be desired.

After much research, we chose to engage with Omega Point.  The founder of Omega Point is a former portfolio manager from quant trading firm Two Sigma, and he assembled a team of machine-learning PhDs, developers, and UI designers to build a SaaS factor management platform aimed at fundamental investors. Omega Point is doing some interesting things on the AI and big data fronts, but what really sold us was that it came integrated with Axioma’s global factor datasets, commonly seen as the industry gold standard but not typically offered in a package usable by non-quants.  Implementation was quick and seamless, and we finally had a customizable factor platform to help with our ex ante factor thinking.

Of the many benefits that integrating Omega Point's factor analysis brings to our daily work-flow, it enables us to:

  1. Build portfolios that isolate fundamental views.
  2. Rebalance portfolios in step with changing markets.
  3. Hedge unintended exposure with correlated ETFs or custom baskets.

In conclusion, we say good riddance to 2018 with the added robustness that comes from being “factor aware”. As you invest and/or select managers over the next few years, we encourage you to begin asking these same questions and viewing performance/risk through the lens of factors and factor analysis. We remain focused fundamental value investors yet operate in a complex, factor-driven marketplace. Factor awareness led us to confidence in the back half of 2018 and is a large part of our confidence as we head into 2019.  

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