Measuring Factor Exposures: Uses and Abuses
Ronen Israel and Adrienne Ross (AQR Capital Management)
September 19, 2016
A growing number of investors have come to view their portfolios (especially equity portfolios) as a collection of exposures to risk factors. The most prevalent and widely harvested of these risk factors is the market (equity risk premium); but there are also others, such as value and momentum (style premia).
Measuring exposures to these factors can be a challenge. Investors need to understand how factors are constructed and implemented in their portfolios. They also need to know how statistical analysis may be best applied. Without the proper model, rewards for factor exposures may be misconstrued as alpha, and investors may be misinformed about the risks their portfolios truly face.
This paper should serve as a practical guide for investors looking to measure portfolio factor exposures. We discuss some of the pitfalls associated with regression analysis, and how factor design can matter a lot more than expected. Ultimately, investors with a clear understanding of the risk sources in an existing portfolio, as well as the risk exposures of other portfolios under consideration, may have an edge in building better diversified portfolios.
Forecast Uncertainty Surprises and Financial Markets
Alaa Abi Morshed (Lancaster University)
September 15, 2016
In this paper we study the impact of forecast uncertainty shocks on yields of nominal, inflation-indexed bonds and market-based inflation expectations during the period 1999-2011, in an event-study framework. To that end, we propose a new measure for forecast uncertainty surprises defined as the difference between root mean squared forecast errors (ex-post uncertainty) and forecast disagreement (ex-ante uncertainty). First, we document a significant relationship between uncertainty shocks about the forecasts of some economic fundamentals and financial markets. Second, we explore time variation in the response of asset markets to forecast uncertainty shocks across three macroeconomic regimes: a tranquil regime, a crisis regime and an unconventional monetary policy regime. Third, we conjecture that forecast uncertainty shocks impact asset prices through their effect on inflation and liquidity risk premia.
The Empirics of Long-Term US Interest Rates
Tanweer Akram (Thrivent Financial) and Huiqing Li (CUFE)
May 4, 2016
US government indebtedness and fiscal deficits increased notably following the global financial crisis. Yet long-term interest rates and US Treasury yields have remained remarkably low. Why have long-term interest rates stayed low despite the elevated government indebtedness? What are the drivers of long-term interest rates in the United States? John Maynard Keynes holds that the central bank’s actions are the main determinants of long-term interest rates. A simple model is presented where the central bank’s actions are the key drivers of long-term interest rates through short-term interest rates and various monetary policy measures. The empirical findings reveal that short-term interest rates, after controlling for other crucial variables such as the rate of inflation, the rate of economic activity, fiscal deficits, government debts, and so forth, are the most important determinants of long-term interest rates in the United States. Public finance variables, such as government fiscal balances or government indebtedness, as a share of nominal GDP appear not to have any discernible effect on long-term interest rates.
Seasonality in Government Bond Returns and Factor Premia
Adam Zaremba (Poznań University) and Tomasz Schabek (University of Lodz)
August 29, 2016
The study investigated both the January effect and the “sell-in-May-and-go-away” anomaly in government bond returns. It also tested whether the two seasonal patterns impact the performance of fixed-income factor strategies related to volatility, credit risk, value, and momentum premia. Our examination of government bond markets in 25 countries for years 1992-2016 proved that both the bond returns and factor premia had remained unaffected by the January and “sell-in-May” effects. These seasonal patterns in government bond markets appear to be merely a statistical artefact.
Low Risk Anomalies?
Paul Schneider (University of Lugano), et al.
February 28, 2016
This paper shows theoretically and empirically that beta- and volatility-based low risk anomalies are driven by return skewness. The empirical patterns concisely match the predictions of our model which generates skewness of stock returns via default risk. With increasing downside risk, the standard capital asset pricing model increasingly overestimates required equity returns relative to firms’ true (skew-adjusted) market risk. Empirically, the profitability of betting against beta/volatility increases with firms’ downside risk. Our results suggest that the returns to betting against beta/volatility do not necessarily pose asset pricing puzzles but rather that such strategies collect premia that compensate for skew risk.
A Lower Bound on Real Interest Rates
Jesse Aaron Zinn (Clayton State University)
October 9, 2016
I show that real interest rates can be no lower than -100%. This contrasts with recent commentary suggesting that there is no lower bound on the natural rate of interest. I discuss how using the textbook approximation to the Fisher equation can lead to the erroneous belief that there is no lower bound on real interest rates, so this analysis serves as a reminder to avoid using this approximation when considering the effects of extreme levels of inflation.
How Physics Solved Your Wealth Problem!
Mukul Pal (Orpheus Indices)
October 8, 2016
While Robert Solow suggested not to think of Economics as Science, Andrew Lo warned us about the dangers of using Physics to build economic systems. Physics has been a late entrant to the world of Finance. The subject has reached critical mass to answer some of the biggest challenges of Finance of the last 50 years. The Nobel Prize-winning Finance has failed to reconcile efficiency and inefficiency, forcing the global money to rely on the Efficient Market Hypothesis (EMH) as the last standing hypothesis, which can neither be rejected nor accepted. This has left trillions of dollars in Pension funds clueless regarding what to rely on. The consequence could be an unintended risk, which society is not prepared for.
Though Smart Beta has brought in a rule-based thinking, it is primarily catering as an ‘Active’ solution to changing market cycles. Its foundation of financial theories remains structurally weak, the reason the industry does not have a coherent definition of the term Smart Beta. There is Factor proliferation, conflicting explanations of outperformance, lack of differentiation and a host of unanswered questions like why naive allocation is better than Factor selection? Is Multi-Factor better than Single Factor? Is everything that concentrates exposure of Market Capitalization (MCAP), smart? Whether timing helps or not? And larger questions like what drives the persistence of Premia? Will we ever take on Granger’s (1992) challenge to build a better alternative to EMH? The solution providers are simply riding on the growth of the segment relying on their institutional clients to comprehend the risk and invest.
Finance needs Physics to bring the needed clarity, usher in a simplified Factor thinking, move away from causal thinking and look at markets in a very objective quantifiable way to answer the various questions and explain the need and persistence of Free Lunch (Premia). The author makes the case by restating market Alpha as a process driven by the ‘Reversion – Diversion’ Framework that is at the heart of the ‘Rich Get Richer’ phenomenon. The paper explains how a simple probabilistic classification can address the insufficiencies of a behavioral or market efficiency model, explain how Smart and Dumb Beta work together and how Physics and the science of complex networks can be the savior for Finance.