Tracking investor expectations with ETF data

February 8, 2020

Retail investors’ return expectations affect market momentum and risk premia. The rise of ETFs with varying and inverse leverage offers an opportunity to estimate the distribution of such expectations based on actual transactions. A new paper shows how to do this through ETFs that track the S&P 500. The resulting estimates are correlated with investor sentiment surveys but more informative. An important empirical finding is that expectations are extrapolating past price actions. After a negative return shock, investor beliefs become more pessimistic on average, more dispersed, and more negatively skewed.

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Machine learning and macro trading strategies

February 1, 2020

Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or trial-and-error approaches. Within the constraints of pre-set hyperparameters machine learning is continuously and autonomously learning from new data, thereby challenging or refining prevalent beliefs. Machine learning and expert domain knowledge are not rivals but complementary. Domain expertise is critical for the quality of featurization, the choice of hyperparameters, the selection of training and test samples, and the choice of regularization methods. Modern macro strategists may not need to make predictions themselves but could provide great value by helping machine learning algorithms to find the best prediction functions.

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The q-factor model for equity returns

January 25, 2020

Investment-based capital asset pricing looks at equity returns from the angle of issuers, rather than investors. It is based on the cost of capital and the net present value rule of corporate finance. The q-factor model is an implementation of investment capital asset pricing that explains many empirical features of relative equity returns. In particular, the model proposes that the following factors support outperformance of stocks: low investment, high profitability, high expected growth, low valuation ratios, low long-term prior returns, and positive momentum. According to its proponents, the investment CAPM and q-factor model complement the classical consumption-based CAPM and explain why many so-called ‘anomalies’ are actually consistent with efficient markets.

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Risk management shocks and price distortions

January 18, 2020

Risk management relies on statistical metrics that converge on common standards. These metrics can change drastically alongside market conditions. A risk management shock is a large unanticipated market-wide change in statistical risk estimates. These shocks give rise to coerced or even distressed flows, typically subsequent to an initial large move in market prices. Risk management shocks and related flows can team up with other dynamics in the financial system to form feedback loops. Such reinforcing dynamics include dynamic hedging, market price-driven credit downgrades, popular fear of crisis, investment fund redemptions, and forced deleveraging. Feedback loops can trigger large and persistent price distortions and offer special trading opportunities.

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The predictive superiority of ensemble methods for CDS spreads

January 11, 2020

Through ‘R’ and ‘Python’ one can apply a wide range of methods for predicting financial market variables. Key concepts include penalized regression, such as Ridge and LASSO, support vector regression, neural networks, standard regression trees, bagging, random forest, and gradient boosting. The latter three are ensemble methods, i.e. machine learning techniques that combine several base models in order to produce one optimal prediction. According to a new paper, these ensemble methods scored a decisive win in the nowcasting and out-of-sample prediction of credit spreads. One apparent reason is the importance of non-linear relations in times of high volatility.

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How market liquidity causes price distortions

December 28, 2019

Liquidity is a critical force behind market price distortions (and related trading opportunities). First, the cost of trading in and out of a contract gives rise to a liquidity premium. Second, the risk that transaction costs will rise when market conditions necessitate trading commands a separate liquidity risk premium. Third, actual changes in liquidity can precipitate large price changes without any fundamental value consideration. Finally, low liquidity is conducive to ‘run equilibria`, where bids or offers of some institutional investors turn into pricing signals for others, giving rise to self-reinforcing dynamics with feedback loops and margin calls. Examples for liquidity-driven price distortions in the past include breakdowns of covered interest parity across currencies, bond market ‘tantrums’, and ‘fire sales’ in emerging local-currency markets.

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The price effects of order flow

December 20, 2019

Order flow means buyer- or seller-initiated transactions at electronic exchanges. Order flow consumes liquidity provided by market makers and drives a wedge between transacted market price and equilibrium price, even if the flow is based on information advantage. Flow distorts market prices for two reasons. First, the need for imminent transaction carries a convenience charge. Second, the prevalence of informed flow justifies a charge for market risk on the part of the market maker. Standard models suggest that the price impact is increasing in the square root of the order flow, i.e. increases with the order size, but not linearly so. New theoretical work suggests that the price impact function may be “S-shaped”, i.e. increases more than proportionately in the smaller size range and less than proportionately for large sizes. The price effects of order flow are relevant for the design of algorithmic trading strategies, both as signal and execution parameter.

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Rebalancing and market price distortions

December 14, 2019

Price distortions are an important source of short-term trading profits, particularly in turbulent markets. Here price distortions mean apparent price-value gaps that arise from large inefficient flows. An inefficient flow is a transaction that is not motivated by rational risk-return optimization. One source of such inefficient flows is ‘rebalancing’, large-scale institutional transactions that align allocation with fixed targets. Rebalancing flows are detectable or even predictable if one understands their rules. Their motives include benchmarking of portfolios, benchmark changes, regulatory changes, ETF designs, equity parity, capital protection, and – to some extent – high-frequency trading algorithms.

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Equity return anomalies and their causes

December 7, 2019

The vast range of academically researched equity return anomalies can be condensed into five categories: [1] return momentum, [2] outperformance of high valuation, [3] underperformance of high investment growth, [4] outperformance of high profitability, and [5] outperformance of stocks subject to trading frictions. A new empirical analysis suggests that these return anomalies are related to market inefficiencies, such as investor protection, limits-to-arbitrage, and investor irrationality. In particular, the analysis provides evidence that the valuation return anomaly is largely driven by mispricing.

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Basic factor investment for bonds

November 23, 2019

Popular factors for government bond investment are “carry”, “momentum”, “value” and “defensive”. “Carry” depends on the steepness of the yield curve, which to some extent reflects aversion to risk and volatility. “Momentum” relates to medium-term directional trends, which in the case of fixed income are often propagated by fundamental economic changes. “Value” compares yields against a fundamental anchor, albeit some approaches are as rough as medium-term mean reversion. Finally, “defensive” seeks to benefit from some bonds’ status as a “safe haven” in crisis times. A historic analysis over the past 50 years suggests that all of these factors have been relevant in some form. Yet, without more precise and compelling macroeconomic rationale factor investing may lack stability of performance in the medium term. The scope for theory-guided improvement seems vast.

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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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