The power of R for trading (part 2)

August 17, 2019

The R environment makes statistical estimation and learning accessible to portfolio management beyond the traditional quant space. Overcoming technicalities and jargon, managers can operate powerful statistical tools by learning a few lines of code and gaining some basic intuition of statistical models. Thus, for example, R offers convenient functions for time series analysis (characterizing trading signals and returns), seasonal adjustment (detecting inefficiencies and cleaning calendar-dependent data), principal component analysis (condensing the information content of large data sets), standard OLS regression (simplest method to check quantitative relations), panel regression (estimating one type of relation across many countries or companies), logistic regression (estimating the probability of categorical events), Bayesian estimation (characterizing uncertainty of trading strategies comprehensively), and supervised machine learning (delegating the exact form of forecasts and signals to a statistical method).

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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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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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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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Signaling systemic risk

March 23, 2019

Systemic financial crises arise when vulnerable financial systems meet adverse shocks. A systemic risk indicator tracks the vulnerability rather than the shocks (which are the subject of ‘stress indicators’). A systemic risk indicator is by nature slow-moving and should signal elevated probability of financial system crises long before they manifest. A recent ECB paper proposed a practical approach to building domestic systemic risk indicators across countries. For each relevant categories of financial vulnerability, one representative measure is chosen on the basis of its early warning qualities. The measures are then normalized and aggregated linearly. In the past, aggregate systemic risk indicators would have shown vulnerability years ahead of crises. They would also have indicated the depth of ensuing economic downturns.

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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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The fundamental value trap

January 5, 2019

Fundamental value seems like a straightforward investment approach. One simply looks for assets that are “cheap” or “expensive” relative to their rationally expected risk-adjusted discounted cash flows. In reality, conscientious estimation of fundamental value gaps is one of the most challenging strategies in asset management. It requires advanced financial modeling and often long waiting times for payoff. Few managers have the resources and patience for it. In macro trading, cheapness or dearness is commonly inferred from simple valuation metrics, such as real interest rates, real exchange rates or equity earnings yields. However, by themselves, off-the-shelve metrics cannot create much information advantage. Indeed, they regularly confuse forward-looking expectations with mispricing and lure investors into crowded value traps. Fundamental value should be estimated conscientiously or not at all. The minimum requirement for a valid valuation metric is some reasonable integration with related economic states and trends.

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The macro information inefficiency of financial markets

December 22, 2018

There are reason and evidence for financial markets failing to be efficient with respect to macro trends. The main reason is cost: “tradable” economic research is expensive and investment firms will only invest in such research if their fees on expected incremental portfolio returns exceed their expenses. This requires them to concentrate scarce research budgets on areas where they see apparent inefficiency. Professional macro research and macro information efficiency are therefore mutually exclusive. Macro inefficiency is evident in the simplicity of popular investment rules, such as trend and carry, the conspicuous absence of economic data in most strategies, and the bias of financial economics towards marketing rather than trading. Academic papers present ample evidence of herding and sequential dissemination of information. Hence, the great incremental value of “tradable” macro research is that it turns informed macro traders into trendsetters as opposed to trend followers and enhances the social benefit of the investment industry overall.

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