When Do Short Sellers Trade? Evidence from Intraday Data and Implications for Informed Trading Models
Description
Replication package for the JFE paper "When Do Short Sellers Trade? Evidence from Intraday Data and Implications for Informed Trading Models". Paper Abstract: Using 2015-2019 intraday short sale data from CBOE, we show that shorting flows near the open, middle, and close all negatively predict future returns, but the shorting flows near the open and middle have stronger predictive power than shorting flows near the close. We relate our findings to three informed trading models with different predictions on the timing of the trades. The long term predictive power of shorting flows near the open and midday is consistent with Kyle’s (1985) model of steady trading; the intraday variation in shorting flows’ predictive power is more consistent with Holden and Subrahmanyam’s (1992) aggressive trading model, in the sense that predictive power of shorting flows is stronger when there is greater urgency to trade at open and when the securities lending market is more competitive; and the liquidity timing hypothesis from Collin-Dufresne and Fos (2016) is also supported by the finding that opening shorting flows increase for firms with better liquidity conditions.
Files
Steps to reproduce
1. Raw data 1.1 CBOE file includes the sample data of the intraday and daily short sales from CBOE exchanges and the code to process the data. 1.2 CRSP file includes the sample data of CRSP and COMPUSTAT. 1.3 Insider file includes the sample data of insider trading and the code to process the data. 1.4 Markit file includes the sample data of borrower concentration and the code to process the data. 1.5 Ravenpack file includes the sample data of Ravenpack news and the code to process the data. 1.6 TAQ file includes the sample data of TAQ and the code to calculate the intraday effective spread, lambda, retail trading, price error and autocorrelation measures. 1.7 TSP file includes the data of Tick Size Pilot stocks and the code to process the data. 2. Analyses Step 1.1 prepares the short sales data. Step 1.2 prepares the stock returns and variables of controls. Step 1.3 constructs the main data, and provides a pseudo data of the main sample. Step 1.4 generates the pseudo results of Table 1 and 2, Figure 1 Panel B, and Figure 2. Step 1.5 generates the pseudo results of Figure1 Panel A, and Figure 4. Step 2 generates the pseudo results of Table 3, and Figure 3. Step 3.1 generates the pseudo results of Table 4 Panel A, B, and C. Step 3.2 generates the pseudo results of Table 4 Panel D. Step 4 generates the pseudo results of Table 5. Step 5 generates the pseudo results of Table 6 and 7. Step 6.1 generates the pseudo results of Table 8 Panel D. Step 6.2 generates the pseudo results of Table 8 Panel A, B, C. Step 7.1 and Step 7.2 generate the pseudo results of Table 9. Step 8 generates the pseudo results of Table 10. Step 9 generates the pseudo results of Table 11.
Institutions
- Columbia University Business School
- Central University of Finance and Economics
- Tsinghua University
- Peking University