It’s COVID-19 chaos! Is the temporary restriction on short selling in Thailand effective?

Figures & data

Table 1. Descriptive statistics

Panel A. Treatment group
	Pre				Post
	Mean	Max	Min	SD	Mean	Max	Min	SD
Price (THB)	17.1	18.9	13.4	1.1	14.4	16.0	10.6	1.5
Transaction	981	1,798	412	244	1,592	2,871	933	368
Volume (Thousand Baht)	7,600	18,186	2,465	3,159	12,382	27,103	5,213	4,123
Market Cap (Million Baht)	19,304	20,937	15,411	1,096	16,311	18,120	11,757	1,699
Market Quality Proxies
Quoted Spread	0.102	0.120	0.081	0.010	0.087	0.101	0.064	0.009
Relative Quoted Spread	0.741	0.909	0.682	0.049	0.731	0.998	0.670	0.059
Bid Size	355.1	786.5	153.5	100.0	338.9	489.5	121.5	78.6
Ask Size	287.6	459.9	119.0	67.5	296.8	586.7	112.6	77.9
Buy Imbalance	-123.2	3373.7	-3241.8	839.3	160.1	3581.9	-2351.7	967.5
Relative Buy Imbalance	-9.2	19.0	-34.5	10.3	-4.9	18.9	-28.9	9.7
Daily Volatility Measures
High Low Trade Price	3.141	6.954	2.070	0.826	4.211	8.172	2.920	0.964
Volatility Trade CC	7.576	62.153	1.741	8.424	12.772	132.967	3.228	13.894
Volatility Trade OC	5.923	43.289	1.530	4.769	9.664	42.563	2.949	5.981
SemiVariance (Positive)	6.861	32.843	0.485	5.317	15.682	71.730	3.524	10.320
SemiVariance (Negative)	8.395	103.650	1.433	12.128	9.558	134.764	1.188	13.101
High Low Quote Price	2.613	6.511	1.624	0.817	3.687	8.032	2.398	0.998
Volatility Quote CC	6.789	55.288	1.572	7.786	12.133	140.061	2.858	15.180
Volatility Quote OC	4.727	39.209	1.068	4.281	8.029	42.602	2.112	5.886
Intraday Volatility Measures
Trade Volatility	0.516	0.918	0.416	0.087	0.600	1.442	0.486	0.125
SemiVariance (Positive)	0.819	4.987	0.524	0.442	1.177	5.686	0.617	0.913
SemiVariance (Negative)	0.800	2.597	0.526	0.376	0.942	7.400	0.547	0.884
Quote Volatility 5min	0.309	0.606	0.212	0.071	0.402	0.712	0.298	0.080
Quote Volatility 10min	0.387	0.810	0.263	0.092	0.514	0.919	0.379	0.106
Quote Volatility 15min	0.444	1.025	0.295	0.110	0.592	1.102	0.430	0.131
Quote Volatility 20min	0.466	1.108	0.313	0.118	0.624	1.162	0.450	0.137
Panel B. Treatment group
	Pre				Post
	Mean	Max	Min	SD	Mean	Max	Min	SD
Price (THB)	14.5	17.3	10.8	1.2	13.6	15.1	11.2	0.9
No. of Transaction	330	953	133	165	525	959	253	170
Volume (Thousand Baht)	3,416	22,219	783	2,783	4,507	12,397	1,511	2,004
Market Cap (Million Baht)	12,823	14,468	10,378	879	10,666	11,792	9,123	523
Market Quality Proxies
Quoted Spread	0.260	0.501	0.133	0.078	0.148	0.448	0.095	0.063
Relative Quoted Spread	1.824	3.012	1.276	0.320	1.633	3.613	1.074	0.477
Bid Size	190.7	323.8	113.3	45.3	264.3	481.0	118.2	80.8
Ask Size	152.5	327.0	66.8	46.6	221.2	520.5	96.2	76.4
Buy Imbalance	-87.8	1541.3	-3960.6	590.8	67.3	3251.1	-2718.4	756.3
Relative Buy Imbalance	-11.3	12.6	-47.7	12.3	-6.2	19.2	-33.9	10.9
Daily Volatility Measures
High Low Trade Price	2.590	6.549	1.429	0.872	3.320	6.390	2.129	0.830
Volatility Trade CC	7.531	50.524	0.788	8.253	8.446	72.327	1.676	7.807
Volatility Trade OC	6.832	33.820	0.853	6.283	8.036	29.938	2.458	5.039
SemiVariance (Positive)	7.592	56.448	0.246	8.657	11.901	46.263	1.907	7.181
SemiVariance (Negative)	9.789	56.518	1.369	11.681	8.001	82.437	1.099	8.824
High Low Quote Price	2.268	5.548	1.205	0.806	2.736	5.554	1.752	0.743
Volatility Quote CC	5.623	41.037	0.704	6.135	6.681	66.027	0.986	6.894
Volatility Quote OC	5.253	30.863	0.787	4.530	5.136	21.275	1.458	3.508
Intraday Volatility Measures
Trade Volatility	0.812	2.131	0.363	0.259	0.954	2.337	0.558	0.300
SemiVariance (Positive)	3.505	20.326	0.634	3.381	3.624	16.413	0.927	2.696
SemiVariance (Negative)	3.502	19.724	0.516	3.473	3.471	16.243	0.851	2.702
Quote Volatility 5min	0.453	1.028	0.257	0.123	0.472	0.896	0.310	0.115
Quote Volatility 10min	0.498	1.137	0.296	0.135	0.535	1.005	0.347	0.128
Quote Volatility 15min	0.523	1.022	0.290	0.140	0.582	1.113	0.379	0.144
Quote Volatility 20min	0.542	1.284	0.332	0.152	0.596	1.112	0.391	0.144

This table reports descriptive statistics for our key variables of interests. We define the period from 2 September 2019 to 10 March 2020 as the ‘pre’ period, and 18 March to 30 September 2020 as the ‘post’ period. The treatment group is defined as all stocks that are eligible and having short-selling transactions in both pre and post event periods. The sample in the control group are selected from the remaining stocks that have the trading information in both periods. We match the propensity score matching (PSM) scores based on firm size, turnover and price and results in equal number of 76 stocks. Panels A and B report the statistics of the treatment and control groups, respectively. The column ‘mean’ is the time-series average of the cross-sectional average of the treatment group. The max, min and std is the maximum, minimum and standard deviation of the time-series average. The Price is in Baht. Transaction is the daily number of transactions per stock. Volume is the daily number of shares traded per stock in Thousand shares. Size is the market capitalization of each stock in million Bath. Quoted spread (relative spread) is measured as the time-weighted average of the outstanding ask price minus bid price (ask minus bid price divided by mid quote price). Bid (ask) size is the time-weighted average of the market depth on the bid (ask) size. BuyImb (RelBuyImb) is the daily order imbalance measured by buy minus sell volume (buy minus sell volume divided by total volume). The high-low price volatility, squared absolute return of daily return, and the semi-variance. The high-low price volatility is calculated by highest minus lowest prices divided by the highest price on each day and each stock. The close-to-close return (open to close) volatility is computed as the squared absolute return of the logarithm of the closing price of day t divided by closing price of day t-1 (closing price of day t divided by opening price of the same day). The positive semi-variance is calculated as $\left(1/N\right){\sum }^{\hspace{0.17em}}max{\left[0,log\left(\frac{p_t}{p_{t-1}}\right)\right]}^2$ and the negative semi-variance is computed as $\left(1/N\right){\sum }^{\hspace{0.17em}}min{\left[0,log\left(\frac{p_t}{p_{t-1}}\right)\right]}^2$ where N is the number of observations and P_t is a closing price on day t. The daily high low quote range, and squared absolute return are computed in the same manner as the trade-based measures but using the mid-quote prices instead of the actual trade price. For the intraday measures, we use the 5-, 10-, 15-, and 20-minutes price changes for the high frequency proxies. Likewise, the positive (negative) semi-variance is calculated as an average of positive (negative) return with the same period as the standard deviation proxies

Figure 1. Plot of daily trading activity panel A: daily number of transactions panel B: Daily trade volume.

The figures show graphs of the daily number of transaction and volume trades in Thousand from our sample. Daily number transactions (TRANS) and volume trades (VOLUME) are collected on a daily basis (regardless of being short-sale transaction or not). The suffix “_S.H.”, “_NOSH” denote that the variables are from the trading activity treatment and control group, respectively. “SHSELL” denotes that the variables account for the short-sale transactions only. Panel A presents the average daily number of transactions, while the panel B presents the average number of shares traded per day. The dotted blue line is the treatment group, and the dotted green line is the control group. The solid red line is transaction and volume from the short-sale only. The left vertical axis is for the dotted lines, and the right vertical axis is for the solid line. The solid vertical line depicts the 13 March 2020 when the uptick rule is imposed by the SET.

Table 2. Market quality measures and short selling uptick rules

	Quoted Spread	Relative Spread	Relative Spread	Bid Size	BuyImb	RelBuyImb
Intercept	-1.008***	10.093***	-2458.736***	-2075.358***	150.623***	-55.965***
	0.046	0.263	100.455	70.552	524.624	5.874
Post	-0.086***	-0.318***	108.87***	91.87***	93.132**	4.659***
	0.011	0.042	12.816	10.299	80.687	0.851
Short	-0.219***	-0.781***	-23.511***	-24.664***	-186.366**	1.009
	0.013	0.034	10.88	8.867	89.627	0.736
Post x Short	0.091***	0.256***	-164.173***	-126.986***	-145.085*	-0.93
	0.012	0.042	16.2	12.52	127.662	0.944
Size	0.054***	-0.436***	115.656***	97.372***	-32.103	1.811***
	0.002	0.012	4.821	3.351	26.31	0.253
Turnover	-0.025***	-0.064***	183.07***	187.173***	975.324***	2.657***
	0.002	0.004	8.677	8.697	209.621	0.176
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes
Number Obs	36409	36409	36409	36409	35738	35738
R-square	0.0805	0.1702	0.1347	0.1768	0.0385	0.0144

This table reports the regression results of market quality proxies on the short-selling stocks and control samples. The study period covers from 2 September 2019 to 30 September 2020. Our sample includes treatment and control groups. For the treatment group, we include all eligible stocks listed in the SET during our study period. These stocks must have short-selling transactions in both pre and post event periods. The control group is selected from the propensity score matching (PSM) scores based on firm size, turnover and price. The dependent variables for market quality include quoted spread in Baht, and in per cent, bid size, ask size, buy imbalance in shares and in per cent. The Post dummy variable is equal to 0 for the dates from 2 September 2019 to 10 March 2020 and 1 for the date from 18 March—30 September 2020. The Short dummy variable is equal to 1 for the treatment group and 0 for the control group. Size is the market capitalization of each stocks in the sample. Turnover is calculated from the daily number of shares traded divided by outstanding shares. Short x Post is the DID coefficient. We estimate the regression with cluster standard errors by date and stocks. The values in the parenthesis presents are the standard errors and ***, **, * denote the significant level of the coefficients.

Table 3. Daily volatility

Panal A: Daily Quote Measures
Variables	Daily Quote Range (High-Low)/High	Volatility (Close to Close)	Volatility (Open to Close)
Intercept	8.4184***	47.2891***	38.9996***
	(0.326)	(3.763)	(2.862)
Post	0.2727***	-0.2790	-1.0165***
	(0.041)	(0.440)	(0.363)
Short	0.2437***	-0.4020	-1.1356***
	(0.038)	(0.480)	(0.338)
Post x Short	0.4671***	2.1947***	2.1293***
	(0.050)	(0.590)	(0.420)
Size	-0.2792***	-2.1244***	-1.6839***
	(0.014)	(0.178)	(0.130)
Turnover	0.8873***	9.5822***	5.9785***
	(0.040)	(1.064)	(0.534)
Industry F.E.	Yes	Yes	Yes
Number Obs	43454	43284	43284
R-square	0.2015	0.1030	0.0919
Panal B: Daily Trade Measures
Variables	Daily Trade Range (High-Low)/High	Volatility (Close to Close)	Volatility (Open to Close)	Semivariance Positive	Semivariance Negative
Intercept	9.2492***	56.6837***	54.5330***	66.0826***	71.6508***
	(0.328)	(4.500)	(4.004)	(5.699)	(8.485)
Post	0.5912***	-0.2813	0.2175	3.2778***	-4.0515***
	(0.041)	(0.555)	(0.521)	(0.761)	(1.077)
Short	0.5834***	-0.5597	-0.7297	-0.4507	-1.7705
	(0.038)	(0.562)	(0.485)	(0.668)	(1.093)
Post x Short	0.1140**	2.0760***	0.9974*	1.6588*	3.1191**
	(0.050)	(0.658)	(0.568)	(0.935)	(1.239)
Size	-0.3216***	-2.5913***	-2.4816***	-3.0908***	-3.1593***
	(0.014)	(0.214)	(0.191)	(0.263)	(0.404)
Turnover	0.9044***	8.6082***	6.1232***	8.8325***	8.2017***
	(0.042)	(0.764)	(0.526)	(0.593)	(1.252)
Industry FE	Yes	Yes	Yes	Yes	Yes
Number Obs	37436	37436	37436	14675	15856
R-square	0.1944	0.0768	0.0582	0.1359	0.0437

This table report the regression results of the daily volatility measures. Panel A shows the results for the daily quote measures. The daily quote range calculated as the highest price minus the lowest price of the day scaled by the highest price. The close-to-close (open-to-close) return volatility is computed as the squared absolute return of the logarithm of the closing price of day t divided by closing price of day t-1 (closing price of day t divided by opening price of the same day). The positive semi-variance is calculated as $\left(1/N\right){\sum }^{\hspace{0.17em}}max{\left[0,log\left(\frac{p_t}{p_{t-1}}\right)\right]}^2$ and the negative semi-variance is computed as $\left(1/N\right){\sum }^{\hspace{0.17em}}min{\left[0,log\left(\frac{p_t}{p_{t-1}}\right)\right]}^2$ where N is the number of observations and P_t is a closing price on day t. Short_i,t is a dummy variable setting to 1 for stocks in the treatment group and 0 otherwise. In order to mitigate any potential omitted bias in our analysis, we include the size, turnover and industry dummy variables. Size_i,t is firm size, measured as the natural logarithm of market capitalization. Turnover_i,t is the daily number of shares traded divided by outstanding shares. Industry dummy variables are also controlled for but not reported for conserving space. We estimate the regression with cluster standard errors by date and stocks. The values in the parenthesis presents are the standard errors and ***, **, * denote the significant level of the coefficients.

Table 4. High-frequency volatility

Panel A: Quote Measures
	5Min	10Min	15Min	20Min
Intercept	2.0347***	2.0905***	2.1539***	2.2388***
	(0.056)	(0.062)	(0.065)	(0.073)
Post	-0.0238***	-0.0092	0.0080	0.0020
	(0.009)	(0.009)	(0.010)	(0.010)
Short	-0.1049***	-0.0798***	-0.0544***	-0.0537
	(0.008)	(0.008)	(0.008)	(0.009)
Post x Short	0.0857***	0.0950***	0.0914***	0.1041***
	(0.009)	(0.010)	(0.010)	(0.011)
Size	-0.0786***	-0.0791***	-0.0799***	-0.0821***
	(0.002)	(0.003)	(0.003)	(0.003)
Turnover	0.0513***	0.0800***	0.1029***	0.1114***
	(0.003)	(0.004)	(0.005)	(0.005)
Industry FE	Yes	Yes	Yes	Yes
Number Obs	36262	36233	36195	36165
R-square	0.1109	0.1087	0.1170	0.1144
Panel B: Trade Measures
Parameter	Volatility Trade-to-Trade	Semivariance Positive	Semivariance Negative
Intercept	15.8366***	19.4012***	26.5055***
	(1.256)	(1.650)	(2.533)
Post	-0.1558***	-0.1931	-0.4390
	(0.009)	(0.242)	(0.316)
Short	-0.8683***	-1.1181***	-1.5846***
	(0.118)	(0.242)	(0.317)
Post x Short	0.0285	0.1639***	0.3042***
	(0.158)	(0.023)	(0.027)
Size	-0.7475***	-0.8928***	-1.2217***
	(0.059)	(0.076)	(0.117)
Turnover	0.0607***	0.0533	0.0983***
	(0.013)	(0.046)	(0.017)
Industry F.E.	Yes	Yes	Yes
Number Obs	35701	34451	34460
R-square	0.0359	0.0242	0.0283

This table report the regression results from the intraday measures as the dependent variables. In panel A, the 5-, 10-, 15-, and 20-minute volatilities are calculated as the standard deviation of the mid-quote price changes. In panel B, the trade-to-trade volatility is calculated as the standard of the trade-to-trade return. The positive semi-variance is calculated as $\left(1/N\right){\sum }^{\hspace{0.17em}}max{\left[0,log\left(\frac{p_t}{p_{t-1}}\right)\right]}^2$ and the negative semi-variance is computed as $\left(1/N\right){\sum }^{\hspace{0.17em}}min{\left[0,log\left(\frac{p_t}{p_{t-1}}\right)\right]}^2$ where N where N is the number of observations and P_t is the actual trade price t. Short_i,t is a dummy variable setting to 1 for stocks in the treatment group and 0 otherwise. In order to mitigate any potential omitted bias in our analysis, we include the size, turnover and industry dummy variables. Size_i,t is firm size, measured as the natural logarithm of market capitalization. Turnover_i,t is the daily number of shares traded divided by outstanding shares. Industry dummy variables are also controlled for but not reported. We estimate the regression with cluster standard errors by date and stocks. The values in the parenthesis presents are the standard errors and ***, **, * denote the significant level of the coefficients.

Table 5. Return on the event days

Parameter	Returns[-3, +3]	Returns[-2, +2]	Returns[-1, +1]	Returns[+1, +3]	Returns[+1, +5]
Intercept	-0.2939	-0.1535	-0.2797	-1.0899	-1.5795
	(1.182)	(1.175)	(1.171)	(1.201)	(1.200)
Event	-2.6301***	-3.8927***	-5.8232***	-1.8458***	-0.331
	(0.257)	(0.314)	(0.425)	(0.370)	(0.304)
Short x Event	-1.3964***	-1.9397***	-1.876***	-1.9949***	-0.4952
	(0.371)	(0.442)	(0.591)	(0.507)	(0.453)
Size	0.1199	0.0745	0.1135	0.368	0.5255
	(0.389)	(0.387)	(0.385)	(0.395)	(0.395)
Turnover	0.3057***	0.3084***	0.3121***	0.3009***	0.3005***
	(0.043)	(0.043)	(0.043)	(0.044)	(0.044)
Industry FE	Yes	Yes	Yes	Yes	Yes
Number Obs	38158	38158	38158	38158	38158
R-square	0.03975	0.05587	0.06263	0.01986	0.01017

This table examines the stock returns reaction around the event period using the pool regression of daily returns on the event dummy variable. We construction the regression analysis as follows.

$R_{i,t}=\alpha +{\beta }_{1}Event+{\beta }_{2}Short\times Event+{\beta }_{3}Siz{e}_{i,t}+{\beta }_{4}Turnove{r}_{i,t}+{\beta }_{5}Industr{y}_{i,t}+{\epsilon }_{i,t}$

Where R_it is a daily return from day -m to +n which m, n range from 1 to 5 days around the 13 March 2020, Short is a dummy variable that equals one if the given stocks is in the treatment group, and zero otherwise, event is a dummy variable that equals one if the returns of a given stocks is from between day – m to +n, inclusive, and zero otherwise. The regression cluster standard errors by both day and stock. We also control for firm size, turnover and industry dummy (not reported). The value in the parenthesis presents in the standard errors and ***, **, * denote the significant level of the coefficients.

Table A1. Sample stocks and size (in million Baht) in the treatment group and control group

Treatment Group				Control Group
AAV	9,952.75	MALEE	1,864.19	AHC	2,237.55	PRG	7,170.79
AEONTS	37,580.66	MBK	29,920.83	AJ	4,270.02	PRINC	12,175.72
AMATA	18,523.72	MC	6,771.77	AMARIN	10,770.15	PYLON	3,413.85
ANAN	6,847.62	MEGA	26,856.54	AMC	750.62	RAM	35,142.51
AP	19,318.55	MONO	6,279.98	APCS	2,915.06	RCL	2,719.27
ASIAN	3,241.74	ORI	15,243.82	BGC	8,277.38	RWI	809.16
AU	8,214.87	PLANB	24,762.73	BGT	331.52	SAUCE	8,421.57
BA	13,320.82	PLAT	9,864.77	CCET	7,763.77	SCG	3,687.55
BAFS	16,491.06	PRM	18,048.02	COLOR	631.90	SEAOIL	1,793.29
BCP	30,712.40	PSH	29,266.92	COTTO	7,832.63	SISB	8,301.80
BEAUTY	5,641.00	PSL	9,163.69	DRT	5,572.16	SKR	10,122.26
BEC	10,648.00	PTL	14,049.00	DTC	7,092.69	SMIT	1,970.11
BGRIM	128,052.90	RS	13,474.20	FLOYD	437.18	SNP	6,231.24
BLA	29,715.51	S	14,558.85	FPT	27,288.78	SPC	18,665.22
CENTEL	32,551.30	SAT	5,519.50	GLAND	15,387.96	SPI	36,093.25
CHG	27,333.96	SAWAD	77,376.17	GRAMMY	7,790.56	SQ	2,054.52
CPF	245,454.53	SEAFCO	4,331.95	HFT	1,979.18	SST	1,577.08
DCC	14,918.54	SGP	15,638.83	ICC	10,069.66	STANLY	12,123.95
DOHOME	19,022.88	SPALI	35,651.14	ICN	762.54	STARK	48,777.17
EPG	15,588.60	STA	25,275.60	IHL	1,850.45	STI	1,419.14
ERW	10,770.15	STEC	23,205.50	ILINK	2,155.14	SUTHA	785.52
ESSO	23,726.73	STPI	8,558.02	KTIS	13,409.49	TCCC	12,033.55
GFPT	15,965.40	TASCO	33,731.33	LALIN	4,484.36	TFMAMA	62,283.58
GGC	10,127.42	TFG	23,141.50	LDC	521.82	TIP	14,234.72
GL	5,971.42	THAI	12,296.28	LEE	1,976.15	TK	4,246.32
GLOBAL	66,952.00	THANI	18,561.35	LHFG	24,429.96	TKS	2,686.43
GUNKUL	24,209.42	THCOM	4,666.58	LIT	855.82	TMILL	1,140.69
HANA	25,380.72	THG	18,717.09	MACO	4,889.25	TMT	3,620.38
ICHI	8,629.84	TIPCO	3,393.26	METCO	3,044.10	TMW	1,482.81
INTUCH	183,759.58	TKN	13,144.01	MITSIB	954.42	TNH	5,743.60
ITD	7,136.79	TQM	24,790.47	MPIC	1,882.49	TOG	2,055.55
JMART	8,799.22	TSE	5,687.22	NOK	4,884.08	TPAC	3,178.11
JMT	19,507.46	TTCL	2,748.71	OTO	791.10	TRT	541.33
KCE	24,144.63	TVO	21,603.95	PB	30,353.42	TSTE	2,686.73
KTB	185,037.78	UNIQ	7,065.85	PICO	784.00	UOBKH	1,531.98
LH	101,459.83	VIBHA	22,105.29	PL	1,406.37	VIH	3,475.91
LPN	6,951.77	WHAUP	19,264.29	POST	866.96	VNG	5,693.49
MAJOR	17,325.83	WORK	5,618.40	PRAKIT	558.26	WACOAL	5,539.33