How salience theory explains the mispricing of risk

March 2, 2019

Salience theory suggests that decision makers exaggerate the probability of extreme events if they are aware of their possibility. This gives rise to subjective probability distributions and undermines conventional rationality. In particular, salience theory explains skewness preference, i.e. the overpricing of assets with a positive skew and the under-pricing of contracts with a negative skew. There is ample evidence of skewness preference, most obviously the overpayment for insurance contracts and lottery tickets. In financial markets, growth stocks with positively-skewed expected returns have historically been overpriced relative to value stocks. This is important for macro trading. For example, a specific publicly discussed disaster risk should pay an excessive premium, and short-volatility strategies in times of fear of large drawdowns for the underlying should have positive expected value.

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Multiple risk-free interest rates

October 6, 2018

Financial markets produce more than one risk-free interest rate. This is because there are several separate market segments where structured trades replicate such a rate. Differences in remuneration arise for two reasons. First, financial frictions can prevent arbitrage. Second, some risk-free assets pay additional convenient yields, typically by virtue of their liquidity and suitability as collateral. Put simply some “safe assets” have value beyond return. U.S. government bonds, in particular, seem to provide a sizable consistent convenience yield that tends to soar in crisis. This suggests that there are arbitrage opportunities for investors that are flexible, impervious to convenience yields and tolerant towards temporary mark-to-market losses.

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A brief history of quantitative equity strategies

September 22, 2018

Understanding quantitative equity investments means understanding a significant portion of market positions. Motivated by the apparent failure of the capital asset pricing model and the efficient market hypothesis, a large share of equity investors follows stylized “factors” that are expected to outperform the market portfolio in the long run. Yet, popularity and past performance of such factors can be self-defeating, while published research is prone to selection bias and overfitting. Big data has introduced greater information efficiency with respect to textual analysis, picking up short-term sentiment but without clear and documented benefits for long-term investment so far. In the future greater emphasis may be placed on dynamic factor models that – in principle – can apply plausible performance factors at the times that they matter.

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Beta herding

September 1, 2018

Beta herding means convergence of market betas of individual stocks that arises from investors’ biased perceptions. Adverse beta herding denotes the dispersion of such betas that arises from a reversal of the bias. A new paper suggests that overconfidence in predictions of overall market direction and positive sentiment are key drivers of beta convergence, while uncertainty and negative sentiment are conducive to beta dispersion. Knowing which trends prevail helps macro trading. First, beta herding implies that directional market moves create price distortions (as the bad rise and fall with the good). Second, adverse beta herding causes low-beta stock returns to outperform high-beta stock returns on a risk-adjusted basis. This reinforces and qualifies what is commonly known as the “low beta bias” of equity returns. (more…)

U.S. dollar exchange rate before FOMC decisions

August 15, 2018

Since the mid-1990s the dollar exchange rate has mostly anticipated the outcome of FOMC meetings: it appreciated in the days before a rate hike and depreciated in the days before a rate cut. This suggests that since fixed income markets usually predict policy rate moves early and correctly their information content can be used to trade the exchange rate. A recent paper proposes a systematic trading rule for trading USD before FOMC meetings based upon what is priced into the each Fed meeting from Fed fund futures and claims that such a strategy would have delivered a respectable Sharpe ratio.

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Identifying asset price bubbles

March 10, 2018

A new paper proposes a practical method for identifying asset price bubbles. First, one estimates deviations of prices from fundamentals based on three different approaches: a structural model, an econometric data-rich regression, and a purely statistical trend filter. Then one computes the first principal component of the three deviation series as an estimate for the common component behind them. As a general approach the method holds promise for detecting price distortions in financial markets and setback risk for ongoing trends.

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The danger of volatility feedback loops

February 17, 2018

There is evidence that the financial system has adapted to low fixed income yields through an expansion of explicit and implicit short volatility strategies. These strategies earn steady premia but bear large volatility, “gamma” and correlation risks and include popular devices such as leveraged risk parity and share buybacks. The total size of explicit and implicit short-volatility strategies may have reached USD2000 billion and probably created two dangerous feedback loops. The first is a positive reinforcement between interest rates and volatility that will overshadow central banks’ attempts to normalize policy rates. The second is a positive reinforcement between measured volatility and the effective scale of short-volatility positions that has increased the risk of escalatory market volatility spirals.

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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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Passive investment vehicles and price distortions

November 11, 2017

The share of passive investment vehicles in financial markets has soared over the past 20 years. In the U.S. equity market it has risen from 12% to 46%. There is reason and evidence suggesting that this will lead to more market price distortions. First, index effects on prices have gained importance relative to other price factors. Second, there has been a reduction of differentials across equity returns within indices. And third, the rise in passive investment vehicles has coincided with the expansion of momentum strategies in active funds, giving rise to vast flows that all explicitly neglect fundamental value.

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Fixed income relative value

October 28, 2017

Relative value can be defined as expected price convergence of contracts or portfolios with similar risk profiles. For fixed income this means similar exposure to duration, convexity and credit risk. The causes of relative value are limited arbitrage capital and aversion to the risk of persistent divergence. Relative value in the fixed income space has been pervasive and persistent. Relative value trades can be based on parametric estimation of yield curves or comparisons of individual contracts with portfolios that replicate their essential features. The latter appear to have been more profitable in the past.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

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