Before we dive into this week’s 'Tale of Two Betas', I’d like to take this opportunity to invite you to join Omega Point this Tuesday at 11:30am ET for the kick-off webinar in our 4-Part “Best Practices in Hedging" Series: Creating Custom Hedge Baskets. With sky high market uncertainty forcing practitioners to reexamine their risk management practices, this shapes to be a timely and stimulating series of discussions which we hope will help your organizations meet today's increasingly complex challenges head-on. Discretionary managers whom are expected to deliver Alpha should always be aware that market neutrality doesn’t always equal beta neutrality. Quantifying beta and managing it can be critical to the investment management process. ## Historical BetaHistorical Beta is calculated via a regression, traditionally an ordinary least squares linear regression. Given a time series of asset returns and market returns, one can calculate a regression where the slope is the asset’s beta. While in theory this process seems easy enough, there are a slew of assumptions that must be made in order to arrive at a reasonable beta, with some of the questions being: - What market should I use?
- How much data should I use?
- What frequency of data should I use?
- How should I weight the data?
- Should I trim outliers in the returns data?
- Should I weight recent history more heavily? If so, by how much?
- What regression scheme should I use?
- How do I calculate the beta of an IPO, which doesn’t have historical returns? If I proxy returns, how should I?
All of these are important questions that need to be answered, and the beta you get out of the regression is dependent on it. Slight changes to any of these assumptions can lead to much different beta results, with some choices having a big influence on betas, and other choices having more subtle effect. A better potential indicator of an asset’s beta today would be to only include history where the market acknowledged COVID - clearly Tech, Energy, and Retail have been performing quite differently recently than long term. Skimming over a beta’s assumptions may lead to wrong conclusions and introduce undesired market risk into one’s portfolio. ## Predicted BetaAssuming we’re running an ordinary least squares regression in solving for an asset’s beta, we can also solve for this beta using the following equation: Risk model providers also apply approaches to estimating IPO returns and exposures when they are first listed, and then Bayesian adjusting them as real returns are realized, so you can always have a Beta to analyze for IPOs. ## ConclusionBetas are a fundamental analytic that help investors understand how an asset behaves relative to the broad market. There are many ways to calculate a beta, all of which have various pros and cons, so multiple perspectives of an Asset’s beta is likely warranted. Omega Point is dedicated to working with our risk model providers to help provide these analytics to our clients to help them make better investment decisions. Although we depart from this topic in the next few weeks to focus on Election coverage, rest assured Product enhancements and future Factor Spotlights will dig deeper into this topic. |

US & Global Market Summary |

- The market gained strength in the middle of the week on optimism around impending fiscal stimulus, although news that the US president had tested positive for COVID-19 shook investors on Friday, adding more tumult to a period already fraught with uncertainty.
- Friday’s jobs report (one of the last major economic releases prior to the election) disappointed, as nonfarm payrolls increased by 661,000 last month vs. consensus estimates of 800,000. The unemployment rate fell by more than expected to 7.9%, although much of this looks to have been driven by a decline in labor force participation.
- Several companies (such as Disney, Allstate, American Airlines, and United Airlines) announced major layoffs this week, the impact of which will be seen down the road.
- US-based equity funds received $1.1B, marking the first inflow in seven weeks.
Normalized Factor Returns: Axioma US Equity Risk Model (AXUS4-MH)
**Growth**was again the week’s biggest winner, continuing to rise back towards the mean after hitting a trough of -1.92 SD below the mean on 9/18.**Momentum**also continued to bounce back from a recent bottom of -0.85 SD below the mean on 9/21.**Earnings Yield**headed higher into Overbought space, now sitting at +1.42 SD above the mean.**Profitability**slowly drifted deeper into Oversold territory, now at -1.07 SD below the mean.**US Total Risk**(using the Russell 3000 as proxy) declined by 51bps.
Methodology for normalized factor returns**Growth**continued its rapid ascent from its recent bottom of -1.66 SD below the mean on 9/18, and has exitedOversold territory after its +0.47 SD move this week.**Momentum**also saw some relief, and look poised to lost its Oversold designation this week.**Value**moved further away from its recent bottom of -1.12 SD below the mean on 9/16, up +0.14 SD on the week.**Profitability**saw ongoing weakness, down -0.23 SD and heading deeper into negative space.- Unlike the US,
**Global Risk**(using the ACWI as proxy) declined by 54bps.
Please don’t hesitate to reach out if you’d like to discuss Beta in further detail, or discuss the factor trends that may be impacting your portfolio. |

# A Tale of Two Betas

Chris Martin
October
04,
2020