Information inefficiency in market experiments

February 3, 2018

Experimental research illustrates the mechanics of market inefficiency. If information is costly traders will only procure it to the extent that markets are seen as inefficient. In particular, when observing others’ investment in information, traders will cut their own information spending. Full information efficiency can never be reached. Moreover, business models that invest heavily in information may have higher trading profits, but still earn lower overall profits due to the costs of improving their signals. What seems crucial is high cognitive reflection so as to invest in relevant information where or when others do not.

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Basic theory of momentum strategies

January 27, 2018

Systematic momentum trading is a major alternative risk premium strategy across asset classes. Time series momentum motivates trend following; cross section momentum gives rise to ‘winners-minus-losers strategies’. Trend following is a market directional strategy that promises ‘convex beta’ and ‘good diversification’ for outright long and carry portfolios as it normally performs well in protracted good and bad times alike. It works best if the underlying assets earn high absolute (positive or negative) Sharpe ratios and display low correlation. By contrast, cross section momentum strategies benefit from high absolute correlation of underlying contracts and are more suitable for trading assets of a homogeneous class. The main pitfalls of both momentum strategies are jump events and high costs of ‘gamma trading’ conjoined with high leverage.

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Overconfidence and inattention as asset return factors

January 20, 2018

Overconfidence in personal beliefs and inattention to new trends are widespread in financial markets. If specific behavioural biases become common across investors they constitute sources of mispricing and – hence – return factors. Indeed, overconfidence and inattention can be quantified as factors to an equity market pricing model and seem to capture a wide range of pricing anomalies. This suggests that detecting sources of behavioural biases, such as attachment to ideological views or laziness in the analysis of data, offers opportunities for systematic returns.

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Policy rates and equity volatility

January 13, 2018

Measures of monetary policy rate uncertainty significantly improve forecasting models for equity volatility and variance risk premia. Theoretically, there is a strong link between the variance of equity returns in present value models and the variance short-term rates. For example, there is natural connection between recent years’ near-zero forward-guided policy rates and low equity volatility. Empirically, the inclusion of derivatives-based measures of short-term rate volatility in regression forecast models for high-frequency realized equity volatility has added significant positive predictive power at weekly, monthly and quarterly horizons.

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Clues for estimating market beta

January 6, 2018

A new empirical paper compares methods for estimating “beta”, i.e. the sensitivity of individual asset prices to changes in a broad market benchmark. It analyzes a large range of stocks and more than 50 years of history. The findings point to a useful set of initial default rules for beta estimation: [i] use a lookback window of about one year, [ii] apply an exponential moving average to the observations in the lookback window, and [iii] adjust the statistical estimates by reasonable theoretical priors, such as the similarity of betas for assets with similar characteristics.

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The point of volatility targeting

December 30, 2017

Volatility targeting adjusts the leverage of a portfolio inversely to predicted volatility. Since market volatility is predictable in the short run and returns are not this adjustment typically improves conventional risk-adjusted return measures, such as the Sharpe ratio. An empirical analysis for the U.S. equity market over the past 90 years confirms this point but suggests that the real key benefit of volatility targeting is the reduction of outsized drawdowns in extreme market situations. That is because large cumulative losses mostly occur when market volatility remains high for long. On these occasions volatility targeting has benefits somewhat similar to a momentum strategy, selling risk early into market turmoil, thereby positioning for escalation.

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Gold: risk premium and expected return

December 23, 2017

An empirical paper suggests that the risk premium and excess return on gold have been time-varying and predictable, also out-of-sample. The key predictors have been the variance risk premium and the jump risk premium of gold. Gold has historically also served as a hedge and “safe haven” for equity and bond investments, but this could not have been expected based on forecasting models. Common sense suggests that the hedge value of gold depends on the dominant market shock. For example, gold hedges against inflationary policies but not against rising real interest rates.

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The correlation of equity and bond returns

December 16, 2017

History shows that the correlation of equity and bond returns has been either positive or negative for prolonged periods of time. Monetary policy has played a key role for the direction of equity-bond correlation. In periods of restrictive monetary policy the correlation has been positive. In periods of low inflation and accommodative monetary policy the equity-bond correlation has been negative. The latter regime has predominated since the late 1990s and is critical for performance and sustainability of risk-parity trading strategies.

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The downside variance risk premium

December 9, 2017

The variance risk premium of an asset is the difference between options-implied and actual expected return variation. It can be viewed as a price for hedging against variation in volatility. However, attitudes towards volatility are asymmetric: large upside moves are fine while large downside moves are scary. A measure of aversion to negative volatility is the downside variance risk premium, the difference between options-implied and actual expected downside variation of returns. It is this downside volatility risk that investors want to protect against and whose hedging price is a valid and apparently robust indicator of future returns. Similarly, the skewness risk premium, the difference between upside and downside variance risk premia, is also a powerful predictor of markets.

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The 1×1 of risk perception measures

December 1, 2017

There are two reasons why macro traders watch risk perceptions. First, sudden spikes often trigger subsequent flows and macroeconomic change. Second, implausibly high or low values indicate risk premium opportunities or setback risks. Key types of risk and uncertainty measures include [1] keyword-based newspaper article counts that measure policy and geopolitical uncertainty, [2] survey-based economic forecast discrepancies, [3] asset price-based measures of fear and uncertainty and [4] derivatives-implied cost of hedging against directional risk and price volatility.

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