How Do Investors Measure Risk?
We infer which risk model investors use by looking at their capital allocation decisions. We find that investors adjust for risk using the beta of the Capital Asset Pricing Model (CAPM). Extensions to the CAPM perform poorly, implying that they do not help explain how investors measure risk.
The recent surge of interest in so-called smart beta strategies originates from the apparent shortcomings of both traditional benchmarks and active management. Yet not all investors want to follow passive indexing, no matter how smart. Recent literature has tried to reconcile the need for integrating active views in a given benchmark with the risk discipline of the smart beta approach. In particular, Medvedev (2015) has proposed a procedure of risk optimization based on qualitative views or rankings and a prior portfolio. The current article updates that procedure by making it simpler, more robust and thus better suited for real life applications. Our aim is to present an easily implemented rule that, given a benchmark and a set of views, yields a portfolio that dominates the benchmark both in risk and return. We provide an intuitive interpretation of this algorithm as a hedging procedure where risk budgets allocated to each active view are determined on the basis of their hedging capacity. We demonstrate the working of this algorithm using the Dow Jones Large Caps equity index as an illustration.
What Drives Flight to Quality?
The relationship of equities and bonds is essential in financial markets. The returns of these two asset classes tend to be positively correlated, but in extreme situations this relation reverses. Large negative equity returns co-occur with large positive bond returns. This is potentially caused by investors reassessing their risk preferences and shifting their wealth to less risky asset classes, which is frequently termed flight to quality. We examine macroeconomic factors in order to identify the driving variables. We find that the treasury bill rate is the most significant driver. Furthermore, the growth rates of the gross domestic product and personal consumption expenditures as well as the inflation rate have a significant impact on flight to quality. These results are useful for investors to improve their asset allocation decisions.
The Performance of Asset Allocation Strategies Across Datasets and Over Time
This paper evaluates the ex-ante performance of popular asset allocation strategies including the classical mean variance rule, the widely used naïve diversification and several newly sprouted risk-based techniques. In terms of risk-adjusted return, I find that there are no significant differences between these strategies for country- and industry-based portfolios. For the individual stock portfolio and multi-asset portfolios (stocks and bonds combined), the differences between strategy performances are relatively large, and the superior strategy changes over time. With respect to strategies’ risk loadings, the mean variance rule leads to the most fluctuating exposures in size, value and momentum factors for all four datasets, the other allocation methods are less so. This paper indicates that currently-used allocation strategies are all far from ex-post optimal.
Bond Yield Volatility Innovation and Equity Risk Premium
This paper shows that the monthly innovation of the 10-year Treasury note implied volatility is a strong predictor for short-run aggregate stock market returns. A one standard deviation increase of the innovation of bond yield volatility predicts a 1% decrease of one-month-ahead excess S&P 500 returns. In addition, this predictability is robust in out-of-sample tests and when controlling for commonly used return predictors, and generates over 50% Sharpe ratios using a simple market timing strategy. In contrast, the monthly innovation of CBOE VIX has weak forecasting ability and its information is subsumed by the bond market volatility. The source of this predictive power appears to stem from the ability of higher bond yield volatility to anticipate lower aggregate cash flow innovations.
Does Technical Analysis Beat the Market? – Evidence from High Frequency Trading in Gold and Silver
Previous research has identified that investors place more emphasis on technical analysis than fundamental analysis, however the research has largely been confined to daily data and stock market indices. This paper studies whether intraday technical trading rules produce significant payoffs in the gold and silver market using three popular moving average rules. We find that using the standard parameters previously used in the literature, technical trading rules offer are not profitable. However after utilising a universe of parameters, we find a number of parameter combinations offer significant profits in the gold market, but there remains no significant payoff in the silver market. Our results show that parameters that use longer histories are more successful than the traditional parameters chosen in the literature. Intraday technical trading rules can be profitable in the gold market but offer no significant profit in the silver market.