According to Weitzner (2016), short-selling costs will be higher if negative information is likely to arrive. Moreover, the arbitrageurs do not need to borrow stocks to be able to short them. It is possible to replicate a short position or enter into a “synthetic” short by simultaneously buying a put option and shorting a call option with the same strike price and time to maturity. These synthetic shorting costs could be estimated and obtained from Borrow Intensity Indicators by the CBOE. The borrow intensity equals the risk-free rate minus the lending spread (lending fee) and corresponds to the rebate rate in stock loan transactions. The rebate rate is the rate earned on collateral by borrowers for equity loans. Consequently, the rise (fall) of the borrow intensity corresponds to the decrease (increase) in lending fees or decrease (increase) in shorting costs.
Padysak (2021) hypothesizes that tight short constraints and higher shorting costs lead to negative sentiment and market returns, and vice versa, lower shorting costs lead to positive sentiment and market returns. The tightening of the shorting costs refers to a situation where the difference of aggregate borrow intensity defined as aggregate borrow intensity at day t minus aggregate borrow intensity at day t-1 is negative. Conversely, the relaxing of shorting costs refers to a situation where the daily difference of aggregate borrow intensity is positive. The aggregate borrow intensities are proxied by the mean (equally-weighted) borrow intensity for each share in the sample provided by the CBOE. The research finds a highly statistically significant positive correlation of 0.137 (t-stats 4.17) between the difference of intensities (and implicitly also lending fees) at the end of the trading day t (15:57) and the end of the trading day t-1, and subsequent daily return of SPY. On average, the rise in the aggregate shorting costs predicts a negative next day’s market return, and the fall in the aggregate shorting costs predicts a positive next day’s market return. As a result, it is possible to construct a trading strategy that aims to time the market by taking a long position in the SPY after a rise in aggregate borrow intensity and a short position in SPY after a fall in aggregate borrow intensity. Last but not least, it is important to highlight that the strategy is the most profitable during crises such as the end of 2018 or the corona crisis, but it lags behind passive market investment during calm periods.
The functionality is based on the finding that the difference in synthetic lending rates derived from the options market and provided by the CBOE contains valuable information about the subsequent market’s return. If the difference of borrow intensities is positive (negative), it signals a decrease (increase) in lending fees, and the subsequent return tends to be also positive (negative). In line with expectations and previous literature, as the shorting costs rise (fall), negative (positive) sentiment proxied by negative (positive) returns follows. Although the average next day’s average return is highly positive, the effect is short-lived and reversed during the subsequent two days.
While the previous section was mostly related to the empirical observations, there are also two additional hypotheses. Firstly, the borrow intensity change might be a sentiment indicator that signals a positive sentiment after the closing of the short positions (and thus decrease in shorting fees because of the lower demand) or a negative sentiment after the opening of the short positions (and by the increase in short demand, increase in shorting fees). This theory could be supported by the correlation of the difference in borrowing intensities of days t and t-1 and the difference in VIX of days t+1 and t. The difference in borrow intensities is negatively correlated with the VIX (commonly described as the fear index): the increase (decrease) of borrow intensity translates to a decrease (increase) in shorting fees, and as a result, the decrease (increase) in shorting fees signals a subsequent decrease (increase) in the VIX. Secondly, the research has identified that albeit the difference in borrow intensities is positively correlated with the next day’s market return, it is negatively correlated with the same day’s intraday return. Therefore, the difference in borrow intensities could be related to the short-term reversal, and the exceptional performance after negative returns might be connected with the liquidity provision, which is commonly one of the main explanations of the short-term reversal effect
data from Table 1, Ret (15.47)
data from Table 1, Sd (19.52)
SPY ETF
data from Table 1, Max DD (-28.51)
The investment universe consists of SPY ETF. Synthetic shorting costs data are obtained from Borrow Intensity Indicators by the CBOE (and includes 4877 stocks/ETFs). The paper utilizes the constant maturities of 45 days. Intraday SPY data are obtained from FirstRate Data. The aggregate (mean) borrow intensity is calculated as equally weighted borrow intensity of each stock/ETF in the sample at day t. The shorting costs data are estimated at a timestamp of 15:57. Calculate the change in the aggregate intensity at day 𝑡 as the difference of aggregate borrowing intensity at day t and t-1. Buy the SPY ETF at 15:59 if the difference is positive and short the SPY if the difference is negative. The positions are held for one day and are closed at 15:58 at next day.
Yes - The strategy performs the best during crises such as the end of the 2018 or the corona-crisis (see Table 3).
