There are several big academic finance conferences that attract the best research and the best researchers in one bullpen — the AFA and the WFA meetings. We chatted about the AFA event last January (be sure to check that out). But now it is the WFA’s turn.
I attended the WFA a few years back in Lake Tahoe (along with my friend Gary Antonacci). We had a blast hobnobbing with the big academic superstars and frying our brains trying to understand what the newest research was all about.
This year’s WFA looks to be even better — and it’s at The Fairmount Chateau in Whistler, BC. Not bad, eh?
I’ll be in the area on a family vacation so there is a good chance I’ll stop by, but I have not cleared this with my “boss.” So I may or may not see you there this year.
Regardless, here is a link to the main website and here is a link to their program (with links to papers).
Here are some of the papers on the program that peaked my interest:
What is the Expected Return on a Stock?
We derive a formula that expresses the expected return on a stock in terms of the risk-neutral variance of the market and the stock’s excess risk-neutral variance relative to the average stock. These components can be computed from index and stock option prices; the formula has no free parameters. We test the theory in-sample by running panel regressions of stock returns onto risk-neutral variances. The formula performs well at 6-month and 1-year forecasting horizons, and our predictors drive out beta, size, book-to-market, and momentum. Out-of-sample, we find that the formula outperforms a range of competitors in forecasting individual stock returns. Our results suggest that there is considerably more variation in expected returns, both over time and across stocks, than has previously been acknowledged.
ETF Arbitrage under Liquidity Mismatch
We provide a theory and empirical evidence showing that the liquidity mismatch between the (liquid) ETF and (illiquid) underlying asset market can make markets less efficient or even fragile. We focus on corporate bond ETFs and their authorized participants (APs). As the only market participants that can trade directly with fund sponsors, APs arbitrage across the bond and ETF markets in a unique way. We identify a conflict between APs’ dual roles as bond-ETF arbitrageurs and as bond market makers. When the magnitude of APs’ bond inventory imbalances is small, APs arbitrage as price discrepancies arise, but the liquidity mismatch limits their arbitrage capacity. When the magnitude of inventory imbalances is large, AP’s inventory management motive strengthens, which may distort ETF arbitrage and lead to large price discrepancies. Our findings suggest new financial stability risks arising from the tension among the rapid growth of corporate bond ETFs, the deteriorating liquidity of the corporate bond market, and the more constrained market-making capacity of bond dealers.
Slow-Moving Capital and Execution Costs: Evidence from a Major Trading Glitch
We investigate the impact of an exogenous trading glitch at a high-frequency market-making firm on standard measures of stock liquidity (effective and realized spreads) as well as on institutional trading costs (Implementation Shortfall and VWAP slippage) obtained from a proprietary data set. We find that stocks in which the firm accumulated large positions as a result of the trading glitch become substantially more illiquid on the day of the glitch. Effective spreads revert very quickly suggesting that market liquidity is resilient. Instead, institutional trading costs remain significantly higher for more than one week. We further document that all stocks for which the firm was a designated market maker become more illiquid, even if they were not heavily traded during the glitch, in the two days prior to being reassigned to another market maker. These findings are broadly consistent with ‘slow-moving capital’ theories and suggest that high-frequency trading ‘flash crashes’ may be associated with significant costs that are difficult to detect using standard liquidity measures
What is the Expected Return on a Stock?
We derive a formula that expresses the expected return on a stock in terms of the risk-neutral variance of the market and the stock’s excess risk-neutral variance relative to the average stock. These components can be computed from index and stock option prices; the formula has no free parameters. We test the theory in-sample by running panel regressions of stock returns onto risk-neutral variances. The formula performs well at 6-month and 1-year forecasting horizons, and our predictors drive out beta, size, book-to-market, and momentum. Out-of-sample, we find that the formula outperforms a range of competitors in forecasting individual stock returns. Our results suggest that there is considerably more variation in expected returns, both over time and across stocks, than has previously been acknowledged.
Can ETFs Increase Market Fragility? Effect of Information Linkages in ETF Markets
We show how inter-market information linkages in ETFs can lead to market instability and herding. When underlying assets are hard-to-trade, informed trading may take place in the ETF. Underlying market makers, then, have an incentive to learn from ETF price when setting prices in their respective markets. We demonstrate that this learning is imperfect: market makers pick up information unrelated to asset value along with pertinent information. This leads to propagation of shocks unrelated to fundamentals and causes market instability. Further, if market makers cannot instantaneously synchronize their prices, inter-market learning can lead to herding, where speculators across markets trade in the same direction using similar signals, unhinged from fundamentals.
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Definitions of common statistics used in our analysis are available here (towards the bottom)
Article by Wesley R. Gray, Ph.D. – Alpha Architect
