Presentation Transcript

1. Price Inflation Due to the 2008 SEC Short-Sale Ban

   Lawrence E. Harris University of Southern California Ethan Namvar University of California – Irvine Blake Phillips University of Waterloo
2. The SEC Ban on Short Selling September 19 to October 8, 2008 (14 trading days) All financial stocks Later, some other stocks with significant financial operations 987 stocks in total
3. The SEC Concerns “We intend these and similar actions to provide powerful disincentives to those who might otherwise engage in illegal market manipulation through the dissemination of false rumors and thereby over time to diminish the effect of these activities on our markets.” SEC Release No. 34-58592
4. The SEC Concerns Price manipulation Short sellers sap confidence Clients withdraw business “Liquidity Death Spirals”
5. Potential Unintended Consequence By preventing short sellers from trading, the SEC created a bias towards higher prices Thus, buyers could have purchased at prices above fundamental values These buyers would face significant losses when prices ultimately adjust downward towards their true intrinsic values
6. Anecdotal Evidence: Fanny Mae and Freddy Mac We estimate the price inflation transferred $597M from buyers to sellers for these stocks
7. Did the Ban Inflate Prices? One-shot event study Short period Lots of other issues Troubled Asset Relief Program in particular! Lehman Brothers collapse also occurred just prior to the ban The answer may not be knowable, but the question is very important
8. Our Paper Take our best shot at estimating inflation Use a factor analytic model to characterize return determinants for the banned stocks Estimate the factors from the not-banned stocks Estimate “but-for” prices for the banned stocks
9. Problems The factor model must work during the crisis The signal must be sufficiently large relative to the extreme noise The noise cannot be specific to the banned stocks
10. The Factor Analytic Approach Use time-series regressions to identify factor loadings for known factors Six return factors Fama-French and Carhart Value-weighted banned stock index TARP-weighted banned stock index
11. The Factor Analytic Approach Use cross-sectional regression to identify factors returns Estimate using only not-banned sample. Three stock characteristic factors Inverse price 10-day rolling volatility 10-day rolling turnover
12. The Factor Model First Stage: Factor Loadings Second Stage: Factor Model Coefficients
13. Sample 987 stocks on the banned list, 88% of which appeared on the original September 19 list 4,812 of 7,639 CRSP stocks with Market cap > $50M on September 18 Complete data over the sample period Includes 676 banned stocks of which 127 received TARP funds in 2008
14. Model Validation The estimated returns for the banned stocks should be highly correlated with the actual returns before and after the period of the ban The difference should have zero mean The estimated factor returns should be highly correlated with the actual factor returns for those factors that are known
15. Model Validation If stock price inflation results from the ban, price correction should be realized in a similar timeframe after the ban Inflationary influences should be greater for stocks realizing more negative investor sentiment Inflation should also be greater for non-optionable stocks
16. Validation Results
17. But What About TARP?
18. Results: Full Sample 10.6%
19. Results: Option Availability 12.8% 1.6%
20. Volatility and the Ban
21. Results: Post-Ban Period
22. Results: Post-Ban Period
23. Results: Post-Ban Period
24. Cost of Inflation Multiply estimated price inflation times volume. Add up across all banned stocks. $4.9B in our sample ($2.3B for negative performance sub-sample)
25. Conclusion Results are indicative of substantial inflation resulting from the short-sale ban. Short sample with high volatility. Arbitrage induces a conservative bias.