Equity Risk Models: The Evolution of Predictions Executive Summary In the wake of the economic downturn, assigning blame has become a full-time job for everyone from pundits and politicians to populists and comedians. And financial risk management has been among those pilloried. One of the complaints is that risk models confer a false sense of security by underestimating the likelihood of worst-case scenarios and thus encourage risky behavior. But the reality is that the professionals most familiar with these models—portfolio managers and risk managers—have known for decades that even the best models break

Although few people would lay the quantitative quandary on the doorstep of equity risk models, much should be done to improve the solutions. These tools should enable investment professionals to:

? View timely and relevant portfolio risk analytics (daily updates)
? Align the parameters of the equity risk model to the investment strategy (time-weighting / transparency)
? Understand sources of risk within the portfolio
? Study the performance of the portfolio under different scenarios
? Analyze (and potentially construct) portfolios using a mix of outcomes rather than the most probable outcome.

A foreshadowing of the current crisis occurred in late July and August of 2007. While the markets were operating smoothly and other strategies continued to show decent performance, quantitative strategies alone were tanking. Hedge fund portfolios (and to a lesser extent, some long-only managers) that ran so-called “market-neutral funds”—ostensibly constructed to only take on desirable and known risk factors—began to behave erratically. Losses mounted quickly. Letters were quickly drafted and distributed to clients in an attempt to explain the phenomenon. But what appeared to be a problem specific to quantitative managers was, in fact, a sign of the onset of a larger economic crisis.

By the beginning of 2008, the performance of quantitative strategies began to stabilize and even outperform fundamental strategies, many of which were feeling the initial effects of the looming storm. Then, shortly after September 15th, 2008, when Lehman failed, global asset prices began to plummet, too. A price chart of almost any asset from April 2007 to April 2008 reveals the ensuing collapse. Despite the impressive rally in March and April of this year, prices of most assets are well below the highs of 2007.

But amid the current wreckage are the quantitative strategies that survived the August 2007 scare. Indeed, they have risen from the ashes more quickly than other equity strategies in part because they had already deleveraged and thus protected their funds against volatility—actions that proved almost prescient as the credit crisis kicked into full gear. Enough time has now elapsed to explore the lessons learned by quantitative strategists from the summer of 2007, and to see how this knowledge can be applied to risk management and investment processes to prepare for the next paradigm shift in market dynamics.

Today, the challenge—and opportunity—for risk managers is to find new ways to help both fundamental and quantitative equity managers react to sudden moves in the equity markets. Data from the last 18 months coupled with years of technological and mathematical advancements give quantitative modelers a unique opportunity to uncover short-term factors that arise in a crisis, and determine how those factors can be a source of alpha in the future.

The TABB Group Vision Note on Equity Risk Models:
The Evolution of Predictions
This TABB Group Vision Note discusses how sophisticated risk managers are finding new ways to react to sudden moves in the equity markets, and the characteristics of a risk model necessary to perform that analysis. It also discusses the possible causes of the Quantitative Crisis of 2007, how quantitative managers adjusted their approach in light of that event and what any risk manager can take away from that event. We cover the evolution of equity risk analytics, including daily versus monthly updates, responsiveness to near-term events, handling outliers, fundamental versus statistical approaches, and transparency. Finally, we discuss one of the more vexing problems facing quantitative managers:  there is no smooth handoff between the analysis of the third-party models and a firm’s proprietary model. The note is based on conversations with risk managers and portfolio managers at leading quantitative asset management firms and hedge funds, in addition to equity risk model providers.