The power of R for trading (part 1)

August 10, 2019

R is an object-oriented programming language and work environment for statistical analysis. It is not just for programmers, but for everyone conducting data analysis, including portfolio managers and traders. Even with limited coding skills R outclasses Excel spreadsheets and boosts information efficiency. First, like Excel, the R environment is built around data structures, albeit far more flexible ones. Operations on data are simple and efficient, particularly for import, wrangling, and complex transformations. Second, R is a functional programming language. This means that functions can use other functions as arguments, making code succinct and readable. Specialized “functions of functions” map elaborate coding subroutines to data structures. Third, R users have access to a repository of almost 15,000 packages of function for all sorts of operations and analyses. Finally, R supports vast arrays of visualizations, which are essential in financial research for building intuition and trust in statistical findings.

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Retail investor beliefs

August 3, 2019

Survey evidence suggests that retail investors adjust positions rather sluggishly to changing beliefs and the beliefs themselves contradict classic rationality. Sluggishness arises from two features. First, the sensitivity of portfolio choices to beliefs is small. Second, the timing of trades does not depend much on belief changes. Contradictions to classic rationality arise because different investors cling stubbornly to different beliefs with little convergence. Also, retail investors associate higher returns with higher economic growth expectations and lower returns with fears of large drawdowns (contradicting the notion of tail risk premia). Overall this suggests that retail investors feel better informed than the market, with no need for updating their beliefs quickly and thoroughly. Opportunities for professional macro trading may arise through front running retail flows and applying more consistent rationality.

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Commodity trends as predictors of bond returns

July 27, 2019

Simple commodity price changes may reflect either supply or demand shocks. However, filtered commodity price trends are plausibly more aligned with demand, economic growth and, ultimately, inflationary pressure. All of these are key factors of fixed income returns. Empirical analysis based on a basket of crude oil prices shows that their common trend is indeed closely associated with empirical proxies for demand and has predictive power for economic output. More importantly for trading strategies, the oil price trend has been able to forecast returns in 20 international bond markets, both in-sample and out-of-sample.

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The relation between value and momentum strategies

July 20, 2019

Simple value and momentum strategies often end up with opposite market positions. One strategy succeeds when the other fails. There are two plausible reasons for this. First, value investors regularly bet against market trends that appear to ‘have gone too far’ by standard valuation metrics. Second, value stocks carry particularly high market risk or ‘bad beta’ and thus fare well when market risk premia are high and the market turns for the better. This typically coincides with ‘momentum crashes’ in oversold markets. As a consequence, value and momentum signals may be complementary. In particular, value strategies are not very profitable in normal times or bull markets but have produced extraordinary profits when being set up in the mature state of a bear market. Similarly, momentum signals can be adjusted by extreme valuation metrics alongside signs of trend exhaustion.

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The mighty “long-long” trade

July 13, 2019

One of the most successful investment strategies since the turn of the century has been the risk-parity “long-long” of combined equity, credit and duration derivatives. In a simple form this trade takes continuous joint equal mark-to-market exposure in equity or credit and duration risk. A simple passive portfolio in the G3 would have outmatched most macro hedge funds since 2000, with a Sharpe ratio well above one and not a single annual drawdown. There have been three apparent contributors to this success: undiversifiable risk premia, implicit subsidies paid by central banks, and great diversification benefits from negative return correlations. These forces remain largely in place, but setback risks bear careful watching: excessive leverage in duration exposure, exhaustion of downside scope for yields, attempts of monetary policy normalization, and the possibility of a fundamental shift in macroeconomic policy regimes.

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The rise in risk spreads

July 6, 2019

A risk spread is a premium for bearing economic risk of an investment, paid over and above the short-term real interest rate. Over the past 30 years, risk spreads in the U.S. have increased significantly and consistently: while real interest rates on ‘safe’ bonds and deposits have collapsed, returns on private capital have remained roughly stable. Macroeconomic research suggests that this secular rise in risk spreads owes mainly to higher risk premia charged by financial markets and higher monopolistic rents extracted by companies. The strategic implication for rational investors would be to receive risk spreads, since they seem to pay an elevated reward for bearing economic uncertainty that is augmented by payoffs for the market power of companies.

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Bad and good beta in FX strategies

June 29, 2019

Bad beta means market exposure that is expensive to hedge. Good beta is market exposure that is cheap to hedge. Distinguishing between these is crucial for FX trading strategies. The market sensitivity of FX positions can be decomposed into a risk premium beta (‘bad beta’) and a real rate beta (‘good beta’). FX positions with risk premium betas are associated with a positive price of risk that increases in crisis periods. FX positions with real rate beta are hedges, whose value increases in crisis times. Many conventional currency trading strategies carry either excessive ‘bad beta’ or too little ‘good beta’ and, thus, fail to produce true investor value.

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Active fund risk premia in emerging markets

June 22, 2019

Security returns, adjusted for market risk, contain risk premia that compensate for the exposure to active fund risk. The active fund risk premium of a security can be modeled as the product of its beta premium sensitivity and price for exposure to active fund risk. Both components change overtime and mutually reinforce each other in episodes of negative fund returns and asset outflows. This explains why securities with high exposure to active fund risk command high expected returns. Active fund risk premia are particularly prevalent in local EM bond markets, where on average 20% of securities are held by foreign institutional investors, many of which are sensitive to drawdowns. Empirical evidence confirms that bonds whose returns positively correlate with active fund returns command substantial premia. The highest premia and expected returns would be offered at times of large capital outflows.

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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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A theory of hedge fund runs

June 8, 2019

Hedge funds’ capital structure is vulnerable to market shocks because most of them offer high liquidity to loss-sensitive investors. Moreover, hedge fund managers form expectations about each other based on market prices and investor flows. When industry-wide position liquidations become a distinct risk they will want to exit early, in order to mitigate losses. Under these conditions, market runs arise from fear of runs, not necessarily because of fundamental risk shocks. This is a major source of “endogenous market risk” to popular investment strategies and subsequent price distortions in financial markets, leading to both setbacks and opportunities in arbitrage and relative value trading.

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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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