1. Closing Time: The Effects of Closing Mechanism Design on Market Quality Nicolas Cordi (University of Sydney), Sean Foley (University of Sydney), Talis Putnins (UTS) Discussion par Carole Gresse (Université Paris-Dauphine)
12th Annual Central Bank Workshop on the Microstructure of Financial Markets

2. Contributions
Analysis of the impact of the introduction of different closing mechanisms
Introduction events on 32 echanges from June 1996 to January 2015 for 7,199 stocks
2 closing mechanisms
Batch call auction
On-close facility
4 features
Randomized closing time (RANDOM)
Price stabilization mechanism (STAB)
Transparency on the indicative closing price (TRANSP)
Order flexibility in the pre-auction period (FLEX)
Impressive empirical work
Well written / Clear contribution to the literature

3. Findings Optimal = ON-CLOSE + RANDOM + STAB
Les différents équilibres sont générés par la possibilité de sortie du carnet pour traiter en OTC (lambda<L).
Optimal = ON-CLOSE + RANDOM + STAB

4. First comment on empirical measures
Price efficiency measures
Closing spreads
TWCS (not really time-weighted ! Based on 9 points every 15 min over the last 2h before close)
Daily continuous volume standardized by its average
Price synchronicity = R² of the market model
Pricing errors between the closing price and benchmark price (VWAP past 2 days or next open)
Variance of return reversals from the close to the next open (not so different from the pricing error based on the next open price!)
Volatility
Price range
Average residual from the market model
Market integrity measure
Closing price manipulation index by Comerton-Forde and Putnins (2011)

5. Modify your classification

6. Main comments
1) Optimal case = On-close + STAB + RANDOM does not exist in the sample.
On-close introduction: only one exchange among the 32 (TSX)
FLEX + STAB
Concern: generalization? Could be specific to TSX or related to the effect of STAB?
Why can’t you incorporate the Nasdaq case in the sample?
2) How general are your findings?
Small stocks vs. large stocks?
Small/Emerging exchanges vs. large exchanges?
Fragmented markets vs. concentrated markets?

7. Main comments – cont’d
3) Negative impact of the call auction on liquidity interpreted as resulting from a redistribution of the order flow
Negative impact  from the literature (Nasdaq, ASX, Euronext), so dig deeper in the analysis
Redistribution of the order flow
Which traders? Informed? Liquidity traders?
Information asymmetry story: check with an IA measure (price impact e.g.)
Is there only redistribution or are there new traders participating in the closing auction?
Test on total volume: continuous + auction
4) Robustness check
Reduce the observation window to [-3M;+3M] around the event

8. Comments on the empirics
The model: yjtm = a + b’.dummies + q’.Controls + ejtm
Controls includes average volume, average price range, illiquidity while yjtm can be daily volume, price range, spreads
Regression of spreads: drop illiquidity, include price level (as in the literature)
Regression of price range: drop average price range
Regression of volumes: drop average volume (daily volumes are already standardized)
FE per stock could replace fixed variables per stock
Illiquidity = % days with no trades / could use the zero-return ratio by Lesmond, Ogden, and Trzcinka (1999)

9. Quibbles Table 2: for exchange NSE, no event, all dummies = 0
Table 1: the caption defines a variable that is not in the table (Value traded in the closing mechanism)
Section 3.1 (description of the call auction): last 2 paragraphs = literature review, should be moved there.
Maybe easier for the reader to describe the mechanisms first and then present the literature review and the related hypotheses.
Hypo.2 is on price discovery and stability but the explanations preceding it (sub-section 2.2.2) very much relate to liquidity. Restructure
Hypo. on liquidity / literature on liquidity
Hypo. price quality / literature on price quality
Hypo. on integrity / literature on integrity