[The returns of short-term reversal strategies in equity markets can be interpreted as a proxy for the returns from liquidity provision. Using this approach, this article shows that the return from liquidity provision is highly predictable with the VIX index. Expected returns and conditional Sharpe ratios from liquidity provision spike during periods of financial market turmoil. The results point to withdrawal of liquidity supply and an associated increase in the expected returns from liquidity provision, as a main driver behind the evaporation of liquidity during times of financial market turmoil, consistent with theories of liquidity provision by financially constrained intermediaries.]

Short-sale constraints are most likely to bind among stocks with low institutional ownership. Because of institutional constraints, most professional investors simply never sell short and hence cannot trade against overpricing of stocks they do not own. Furthermore, stock loan supply tends to be sparse and short selling more expensive when institutional ownership is low. Using institutional ownership as a proxy, I find that short-sale constraints help explain cross-sectional stock return anomalies. Specifically, holding size fixed, the under-performance of stocks with high market-to-book, analyst forecast dispersion, turnover, or volatility is most pronounced among stocks with low institutional ownership. Ownership by passive investors with large stock lending programs partly mitigates this under-performance, indicating some impact of stock loan supply. Prices of stocks with low institutional ownership also underreact to bad cash-flow news and overreact to good cash-flow news, consistent with the idea that short-sale constraints hold negative opinions off the market for these stocks.

The returns of short-term reversal strategies in equity markets can be interpreted as a proxy for the returns from liquidity provision. Analysis of reversal strategies shows that the expected return from liquidity provision is strongly time-varying and highly predictable with the VIX index. Expected returns and conditional Sharpe Ratios increase enormously along with the VIX during times of financial market turmoil, such as the financial crisis 2007-09. Even reversal strategies formed from industry portfolios (which do not yield high returns unconditionally) produce high rates of return and high Sharpe Ratios during times of high VIX. The results point to withdrawal of liquidity supply, and an associated increase in the expected returns from liquidity provision, as a main driver behind the evaporation of liquidity during times of financial market turmoil, consistent with theories of liquidity provision by financially constrained intermediaries.

This article examines the link between the opportunity cost of money and time-varying liquidity premia of near-money assets. Higher interest rates imply higher opportunity costs of holding money and hence a higher premium for the liquidity service benefits of assets that are close substitutes for money. Consistent with this theory, short-term interest rates in the United States, United Kingdom, and Canada have a strong positive relationship with the liquidity premium of Treasury bills and other near-money assets over periods going back to the 1920s. Once the opportunity cost of money is taken into account, Treasury security supply variables lose their explanatory power for the liquidity premium, except for transitory short-run effects. These findings indicate a high elasticity of substitution between money and near-money assets. As a consequence, a central bank that follows an interest rate operating target not only elastically accommodates and neutralizes shocks to money demand, but effectively also shocks to near-money asset supply and demand.

At the end of 2017, our team of editors completed its first full year at the helm of the Journal of Finance. Even so, many of the numbers and successes that I highlight in this report reflect the excellent work of our predecessors. For example, virtually all papers published in the Journal in 2017 were handled by the previous editorial team headed by Ken Singleton. The majority of submissions accepted for publication in 2017 were accepted by Ken’s team. And many of the articles that contribute to the most recent available impact factors for 2016 include the impact of papers initially submitted prior to 2013 and accepted by Campbell Harvey. I am pleased to report that 2017 was a good year for the Journal. Table I details the number and timing of submissions received throughout the year. We received 1,166 submissions, of which 1,030 were new manuscripts and 136 were resubmissions. The number of new submissions fell slightly compared with 2016, when it was 1,081. Two policy changes that we implemented recently contributed to this decline. First, for reasons that I explained in my report last year, we discontinued the use of “reject-and-resubmit” editorial decisions beginning in July 2016. One of the (intended) consequences of this change is a greater degree of transparency in the editorial statistics—for example, by eliminating the double counting of reject-and-resubmits as new submissions (once on the initial submission, and once when the “rejected” paper is resubmitted as a “new” submission). Prior to this policy change in 2016, about 60 “new” submissions per year were actually reject-and-resubmits. This number decreased to 12 in 2017 and is expected to fall to zero soon since we have not issued a reject-and-resubmit since July 2016. The second change that materially affected new submission numbers is an update to the Journal’s submission fee schedule in March 2017 that led to a reclassification of several countries from lowto middle-income status, eliminating their eligibility for free submission. This change reduced the number of submissions from the affected countries from around 60 per year to close to zero. Turnaround remains good with little change from previous years. As can be seen in Table II, in 2017, 70% of editorial decisions took less than 70 days and only 11.3% took over 100 days. The median turnaround time in 2017 was 45 days. Figure 1 compares turnaround over the 2013 to 2017 period.