Natural language processing for financial markets

June 15, 2019

News and comments are major drivers for asset prices, maybe more so than conventional price and economic data. Yet it is impossible for any financial professional to read and analyse the vast and growing flow of written information. This is becoming the domain of natural language processing; a technology that supports the quantitative evaluation of humans’ natural language. It delivers textual information in a structured form that makes it usable for financial market analysis. A range of useful tools is now available for extracting and analysing financial news and comments. Examples of application include machine-readable Bloomberg news, news analytics and market sentiment metrics by Refinitiv, and the Federal Reserve communication score by Cuemacro.

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Market noise

June 1, 2019

The term “market noise” refers to transactions that are erratic and unrelated to fundamental value. Theory suggests that without market noise profitable trading would be impossible. Yet, while irrational and erratic trading may occur, most of what we call “noise” reflects rationality disguised by complexity. Illustrating that point, a new paper shows that the effect of rebalancing cascades on the net demand for individual assets is not predictable, even if we know everything about the underlying rules and if they are fully rational. Predictions become infeasible because of alternating buy and sell orders, feedback loops and threshold-based execution rules. This cautions against dismissing seemingly non-fundamental market flows as irrational and betting against them.

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U.S. Treasuries: decomposing the yield curve and predicting returns

May 25, 2019

A new paper proposes to decompose the U.S. government bond yield curve by applying a ‘bootstrapping method’ that resamples observed return differences across maturities. The advantage of this method over the classical principal components approach would be greater robustness to misspecification of the underlying factor model. Hence, the method should be suitable for bond return predictions under model uncertainty. Empirical findings based on this method suggest that equity tail risk (options skew) and economic growth surveys are significant predictors of returns of government bonds with shorter maturities.

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Systematic trading strategies: fooled by live records

May 11, 2019

Allocators to systematic strategies usually trust live records far more than backtests. Given the moral hazard issues of backtesting in the financial industry, this is understandable (view post here). Unfortunately, for many systematic strategies live records can be even more misleading. First, the survivor bias in published live records is worsening as the business has entered the age of mass production. Second, pronounced seasonality is a natural feature of many single-principle trading strategies. This means that even multi-year live records have very wide standard deviations across time depending on the conditions for the strategy principle. If one relies upon a few years’ of live PnL the probability of investing in a losing strategy or discarding a strong long-term value generator is disturbingly high. This suggests that the use of live record as allocation criterion, without sound theoretical reasoning and backtesting, can be highly inefficient.

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The implicit subsidies behind simple trading rules

April 27, 2019

Implicit subsidies are premia paid by large financial markets participants for reasons other than risk-return optimization (view post here). Their estimation requires skill and a strong “quantamental system”. However, implicit subsidies are behind the popularity and temporary success of many simple trading rules, including those based on variance risk premia, contract hedge value, short volatility bias, and “low-risk effects”. The closest link is between implicit subsidies and cross-asset carry. However, carry is not itself a reliable measure of a subsidy but just correlated with it and – at best – a starting point for estimation The distinction between subsidy and conventional carry is essential for actual long-term value generation of related trading strategies.

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Implicit subsidies paid in financial markets: updated primer

April 20, 2019

Implicit subsidies in financial markets are premia paid through transactions that have motives other than conventional risk-return optimization. They manifest as expected returns over and above the risk-free rate and conventional risk premia. Implicit subsidies are a bit like fees for the service of compliant positioning. They are opaque rather than openly declared, typically for political reasons. Implicit subsidies have valid motives, such as financial stabilization objectives of governments, profit hedging of commodity producers, or downside protection of institutional portfolios. Detecting and receiving implicit subsidies is challenging and information-intensive but creates stable risk-adjusted value. Implicit subsidies are receivable in all major markets, albeit often at the peril of crowded positioning and recurrent setbacks. It is critical to distinguish strategies based on implicit subsidies, which actually create investor value through information efficiency and those that simply receive non-directional risk premia, which are based on rough proxies and do not create risk-adjusted value.

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Survival in the trading factor zoo

March 30, 2019

The algorithmic strategy business likes quoting academic research to support specific trading factors, particularly in the equity space. Unfortunately, the rules of conventional academic success provide strong incentives for data mining and presenting ‘significant’ results. This greases the wheels of a trial-and-error machinery that discover factors merely by the force of large numbers and clever fitting of the data: some 400 trading factors have so far been published in academic journals (called the “factor zoo”). The number of unpublished and rejected tested factors is probably a high multiple of that. With so many tryouts, conventional significance indicators are largely meaningless and misleading. As with the problem of overfitting (view post here), the best remedies are plausible mathematical adjustments and reliance upon solid prior theory.

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The dollar as barometer for credit market risk

March 9, 2019

The external value of the USD has become a key factor of U.S. and global credit conditions. This reflects the surge in global USD-denominated debt in conjunction with the growing importance of mutual funds as the ultimate source of loan financing. There is empirical evidence that USD strength has been correlated with credit tightening by U.S. banks. There is also evidence that this tightening arises from deteriorating secondary market conditions for U.S. corporate loans, which, in turn, are related to outflows of credit funds after USD appreciation. The outflows are a rational response to the negative balance sheet effect of a strong dollar on EM corporates in particular. One upshot is that the dollar exchange rate has become an important early indicator for credit market conditions.

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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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Understanding dollar cross-currency basis

February 9, 2019

Covered interest parity is an arbitrage condition that equalizes costs of direct USD funding and of synthetic USD funding through FX swaps. Deviations are called dollar cross-currency basis and have become a common occurrence since the great financial crisis. A negative dollar basis means direct funding in USD – if accessible – is cheaper than synthetic funding via swaps. An apparent structural cause of the dollar basis has been regulatory tightening, which has increased balance sheet costs of arbitrage. Moreover, research has found several short-term factors. Thus, a negative dollar basis has been linked to aggregate USD strength, rising market volatility, deteriorating FX market liquidity, monetary tightening in the U.S. relative to other countries, and a decrease of funds in the USD money market. In most of these cases, the dollar basis represents dollar funding conditions not captured by published interest rates and is a valid trading signal.

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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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