1/ Impact of Crowding in Alternative Risk Premia Investing (Baltas)
"We create a framework to explore the implications of crowding. Divergence (convergence) premia like momentum (value) are tend to underperform (outperform) following crowded periods."
2/ "When momentum is driven by net inflows, its turnover is likely to fall, as the investor remains positioned in the same assets.
"We hypothesize that crowding is more likely to have a destabilizing effect for divergence premia, as it can drive prices away from fundamentals."
3/ "Investors allocating to convergence premia have a natural anchor that signals the end of a profitable opportunity. Larger investor flows in can bring faster convergence of valuation spreads.
"We hypothesise that anchored strategies are more resilient to incoming flows."
4/ "Synchronous flows into a portfolio (from a large group of investors with similar objectives) should, all else being equal, increase co-movement.
"We estimate the signed average pairwise correlation between all residual returns of the assets in the top and bottom baskets."
5/ "Stocks in the top/bottom momentum and value baskets exhibit time-varying (yet statistically strong) cross-dependence, which can be interpreted as excess demand for these baskets.
"The time-series of the CoMetric is not constant over time but does not necessarily increase."
6/ "To directly relate current levels of crowdedness to future performance, we track buy-and-hold portfolios based on *current* ranks over the entire two-year horizon."
"All divergence premia underperform (outperform) following high (low) levels of asset excess comovement."
7/ "In line with our hypothesis and with the evidence for equity value, FX value benefits from increased asset co-movement."
"Value strategies offer support to our hypothesis that crowdedness has different implications for the profitability of different types of ARP strategies."
8/ "Our findings suggest that crowding is not always a catalyst for underperformance and should not be treated as such by investors.
"In unreported results, we find that divergence (convergent) premia exhibit higher (lower) volatility following crowded periods."
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"Entirely avoidable errors routinely make it past the Maginot Line of peer review. Books, media reports and our heads are being filled with ‘facts’ that are incorrect, exaggerated, or drastically misleading." (p. 5)
2/ "A set of experiments on 1,000 people found evidence for the ability to see the future using ESP. The paper was written by a top psychology professor, Daryl Bem, from Cornell. It was published in one of the most highly regarded, mainstream peer-reviewed psychology journals."
"We sent the paper to the same journal, the Journal of Personality and Social Psychology. The editor rejected it, explaining their policy of never publishing studies that repeated a previous experiment.
2/ Five major asset classes: Developed equity indicces, emerging equity indices, gvt bonds, commodities, real estate
Trend following = long/Tbill based on MA signals, rebalanced monthly
Risk parity = inverse vol weighting using trailing 12-month volatility
No transaction costs
3/ "Trend following shows considerable risk-adjusted performance improvements compared to their equally-weighted portfolios.
"Long-only trend will underperform buy-and-hold during major bull markets. This is the scenario largely witnessed for bonds during the period of study."
"While tail risk of the market index did not move much before the 2020 COVID-19 outbreak, we document that tail risk of less pandemic-resilient economic sectors boomed in advance."
2/ "We compute a measure of [lockdown] resilience based on the capability of a company to implement work-from-home.
"Sectors from low to high resilience are Consumer Staples, Materials, Consumer Disc, Industrial, Energy, Health Care, Utilities, Technology, and Financial."
3/ "In order to not over or underweight the influence of sectors with a really large or little market capitalization, we compute the equally-weighted return of the respective sectors within each specific resilience group."
"Short sellers face unique risks, such as the risk that stock loans become expensive or are recalled. Stocks with more short-selling risk have lower returns, less price efficiency, less short selling."
2/ "We calculate the ln of the variance of the daily Loan Fee for each stock over the past 12 months, then project this variable on a variety of lagged firm and lending market characteristics. The predicted value (ShortRisk) represents a trader’s estimate of short-selling risk."
3/ "Short-selling risk is lower for stocks with traded options and higher immediately following an IPO and for stocks with a large number of failures in the securities lending market.
"We use the predicted value from this model as a forecast of short-selling risk."
For trading, policy decisions, the pandemic, and scientists' conclusions (which should almost always be tentative), there is a wide range of reasonable views.
That range reflects the many things we don't know with respect to both theory and application.