How loss aversion increases market volatility and predicts returns

March 21, 2020

Loss aversion means that people are more sensitive to losses than to gains. This asymmetry is backed by ample experimental evidence. Loss aversion is not the same as risk aversion, because the aversion is disproportionate towards drawdowns below a threshold. Importantly, loss aversion implies that risk aversion is changing with market prices. This means that the compensation an investor requires for holding a risky asset varies over time, giving rise to excessive price volatility (relative to the volatility of fundamentals), volatility clustering across time, and predictability of returns. All these phenomena are consistent with historical experience and form a useful basis for trading strategies, such as trend following.

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Reward-risk timing

March 14, 2020

Reward-risk timing refers to methods for allocating between a risky market index and a risk-free asset. It is a combination of reward timing, based on expected future risk asset returns, and volatility timing, based on recent price volatility. A new paper proposes to use machine learning with random forests for estimating both risk premia (return expectations) and optimal lookback windows for volatility estimates This method allows for non-linear prediction interaction and averages forecasts across a range of simplistic valid prediction functions. In an empirical analysis with data going back to 1952 the random forest method for reward-risk timing has outperformed other methods and earned significantly higher risk-adjusted returns than a buy-and-hold strategy.

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Lagged correlation between asset prices

March 7, 2020

Efficient market theory assumes that all market prices incorporate all information at the same time. Realistically, different market segments focus on different news flows, depending on the nature of the traded security and their research capacity. Such specialization makes it plausible that lagged correlations arise between securities prices, even though their specifics may change overtime. Indeed, there is empirical evidence for lagged correlation between the price trends of different U.S. stocks. Such lagged correlation can be identified and tested through a neural network. Academic research finds that price trends of some stocks have been predictable out-of-sample based on information about the price trends of others.

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Detecting market price distortions with neural networks

February 22, 2020

Detecting price deviations from fundamental value is challenging because the fundamental value itself is uncertain. A shortcut for doing so is to look at return time series alone and to detect “strict local martingales”, i.e. episodes when the risk-neutral return temporarily follows a random walk while medium-term return expectations decline with the forward horizon length. There is a test based on the instantaneous volatility to identify such strict local martingales. The difficulty is to model the functional form of volatility, which may vary over time. A new approach is to use a recurrent neural network for this purpose, specifically a long short-term memory network. Based on simulated data the neural network approach achieves much higher detection rates for strict local martingales than methods based on conventional volatility estimates.

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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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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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