We define setback risk as a gap between downside and upside risk of an asset or a trade that is unrelated to its fundamental value proposition. It arises from the market’s “internal dynamics” – as opposed to changes in fundamental value – and is a handicap for valid but popular trading strategies. Setback risk consists of two components: positioning and exit probability. Positioning refers to the “crowdedness” of a trade and indicates the potential size of a setback. Exit probability refers to the vulnerability of positions and indicates the likelihood of liquidations.
What is setback risk?
Setback risk here is defined as the difference between downside and upside risk of a position that is unrelated to its fundamental value. Hence it arises even if the value proposition of a trade is and remains valid. If fact, it is often the fundamentally best trades that incur the highest setback risk. It is also sometimes called “endogenous risk”, because it relates mainly to market dynamics, rather than exogenous shocks, such as changes in “fundamentals”. For example, FX carry trades have historically displayed a strong negative skew in returns, i.e larger negative than positive outliers (forthcoming post).
Setback risk is the natural counterweight to dominant positioning motives, such as implied subsidies, fundamental trends or even simple momentum trading. For momentum strategies there is empirical evidence of setback risk across asset classes (view post here). Its presence means that the probability for future price moves is skewed against dominant positioning motives and has “fat tails”: large adverse outliers relative to standard deviations should be expected.
Information on setback risk can come from various sources, including positioning data, short-term correlation of PnL’s with hedge fund benchmarks, asymmetries of upside and downside market correlation, or simply past performance and the popularity of trades in broker research recommendation. Also, some forms of setback risk can be detected through theoretical models: for example compressed interest rate term premia at the zero lower bound for policy rates are naturally quite vulnerable to any risk of future rates increases (view post here).
Information efficiency on setback risk creates social value by reducing the risk of dislocation and crisis. It creates investor value by reducing the occurrence of large outsized drawdowns and forced expensive position liquidations.
The two components of setback risk
Setback risk can be divided in two components: positioning and exit risks. The former indicated the “crowdedness” of a trade, the latter the probability that the crowd will run for the exit: setback risk is high when a trade is “crowded” and vulnerable to sizeable liquidation. While the positioning component always relates to a specific contract, exit risk can be global factor.
- Positioning relative to market liquidity indicates the potential size of a setback. For some contracts there are outright positioning data being produced by exchanges or custodian banks. However, these are not easy to interpret. In practice, macro traders pay much heed to more informal warning sign, such as
- anecdotal evidence of positioning provided by their brokers,
- the recent historic sensitivity of a price with respect to popular trading styles and hedge fund performance (view post here), and
- recurrent and recent underperformance of positions despite a positive news flow.
More simply, the obviousness and duration of implicit subsidies and past performance of related strategies are often good indirect indicators of positioning. For example, high carry-volatility ratios are a good indicator of the prevalence of implicit and explicit carry trades.
- Exit risk gives the probability of a setback, be it small or large. The most prominent triggers of large-scale unwinding of macro trades are volatility or Value-at-Risk jumps (view post here) and liquidity and funding pressure (view post here and here). Forward looking indicators that condition the likelihood of such events include an estimation of market “complacency” and the tracking of concurrent shocks.
- Complacency here means lack of resilience. Often this lack of resilience arises from implausibly low risk perceptions, which can be measured in a wide range of news-based, survey-based and asset price-based indicators (view post here). For example, one such measure of complacency is the homogeneity of economist forecasts. Empirical analyses point to an important principle: when economists are clustered tightly around a consensus, actual data surprises tend to have stronger market impact (view post here). Generalizing this point, it seems plausible that a strong analyst consensus supporting a macro position makes this position more vulnerable to data surprises.
- Shock effect indicators represent assessments of whether recent events might trigger escalatory price dynamics. A particularly dangerous type of shock is a sharp rise in people’s fear of disaster. Theoretical research shows that a re-assessment of beliefs towards higher disaster risk triggers all sorts of uncertainty shocks, for example with respect to macro variable, company-specific performances and other people’s beliefs (view post here). This can derail both directional and relative value trades.
Also, there is research suggesting that”volatility surprises” (market price changes outside the range of expected variation) provide information on the probable evolution of prices for various risks. These shocks typically draw attention to previously underestimated risks and transmit easily across markets and asset classes (view post here). Also, distinguishing between the long-term fluctuations and short-term swings of volatility helps assessing if, say, high volatility indicates a medium-term risk premium (positive for return) or a short-term de-leveraging trend (negative for return) (view post here).