Short Sale Ban during Financial Crisis: Impact on Volatility, Liquidity, and Market Efficiency

Transcription

1 hort ale Ban during Financial risis: Impact on Volatility, Liquidity, and Market fficiency lesya Lobanova* hahid. Hamid** Arun J. rakash*** * Department of Finance, RB, ollege of Business, Florida International University, Miami, FL. el:, oloba@fiu.edu ** rofessor, Department of Finance, RB, ollege of Business, Florida International University, Miami, FL. el:, hamids@fiu.edu *** rofessor, Department of Finance, RB, ollege of Business, Florida International University, Miami, FL. el:, prakasha@fiu.edu JL lassification: G

2 hort ale Ban during Financial risis: Impact on Volatility, Liquidity, and Market fficiency Abstract During the financial crisis in eptember the ecurities and xchange ommission put a temporary ban on the short sales of financial stocks. his unprecedented measure allows us to explore the impact of short-sale restrictions on the U.. stocks. In this study we investigate the effect of short-sale constraints on volatility, liquidity and market efficiency. he results suggest there were statistically significant changes in liquidity, volume, and return volatility while the ban was in effect. We find that market liquidity deteriorated substantially, t quoted spread and percent quoted spread increased significantly, while turnover ratio, trading volume, and dollar volume declined during the ban. Furthermore, returns decreased and volatility of returns measured by squared returns increased during the restriction on shorting activity. herefore, a significant drop in market efficiency is documented. Introduction hort- selling has been controversial topic in financial research. ome regulators and academicians argue that short-selling causes high volatility and market crashes. n the other hand, many researchers believe that short-selling increases liquidity, improves market efficiency, and facilitates price discovery. n eptember,, in response to the financial crisis, the ecurities and xchange ommission put a temporary ban on the short sales of financial stocks, stating that this measure will restore equilibrium to markets (he New York imes, ). his unexpected measure came as a surprise to the community of traders and investors around the globe. he U.. ban was finally lifted on ctober,. he restriction on shorting became one of the multiple measures taken by the to prevent the panic in the financial markets. he temporary ban provides a rare opportunity to examine some of the important he published a list of banned stocks on its website stating that the list contains stocks but in fact, there were only stocks

3 relationships between short sales and market behavior and efficiency, as represented by the traditional metrics of liquidity, volatility, spread, and return. he spectrum of potentially contrasting market behavior during the ban may in turn provide significant insights into the broader questions of how and why short sales matter. he goal of this study is to examine the impact of short-sale restrictions on volatility of stock returns, liquidity of stocks, and overall market efficiency. he main hypotheses tested are whether short sale ban resulted in the deterioration of e market efficiency, decrease in volume and liquidity, increase in spread, and increase in volatility.. We investigate the banned stocks listed on the NY/AMX and NADAQ exchanges to see if the trading platforms and their associated mechanics and regulations matter in how short sales impact the market.. he results suggest that market liquidity substantially declined for all stocks in the initial banned list. he shorting ban also led to a significant decline in trading volume, dollar volume, and turnover ratio. he volatility of returns also increased significantly. All this suggests that even during financial crisis, market efficiency and market conditions deteriorated significantly due to the absence of short sales. he results support the importance of the existence of short sales to market efficiency and completeness. he remainder of this paper is organized as follows. ection I discusses relevant literature on short-sale constraints. ection II describes the data. Hypotheses and empirical methodology are discussed in ection III and ection IV, respectively. Results and issues to consider are offered in ection V. ection VI presents the conclusions.

4 I. Literature Review here have been a number of theoretical models developed and empirical tests performed for a better understanding of the effects of short selling and short- sale constraints. For example, Miller () finds that stock prices tend to reflect a more optimistic valuation when short sales are not permitted. hus, pessimistic investors do not trade in the presence of short sale constraints and as a result some negative information is not reflected in prices. Many researchers concentrated on the effect of short sales constraints on the volume, liquidity, skewness, and market efficiency. Diamond and Verrecchia () developed a theoretical model that predicts a decline in liquidity when short sales are prohibited as prices adjust more slowly to new information. Bris, et al. () studied how short-sale constraints affect market efficiency and found that restriction on short sales decreases market efficiency. In addition, Bris et al. () analyzed forty-six equity markets around the world and found evidence supporting the hypothesis that markets are more efficient in countries where short sales are allowed. hey found that stock prices incorporate information faster in those markets where shorting is allowed, and that markets are more efficient when investors can take short positions. Another relevant study was conducted by hang, heng and Yu (). hey examined short sales restrictions in the Hong Kong market and found that short-sales lead to higher volatility and less positive skewness of individual returns. Henry and McKenzie () also studied the Hong Kong market and showed that asymmetric responses to positive and negative innovations to returns are exacerbated by short selling. he implication is that short sale existence causes higher volatility in returns. onsiderable controversy exists on whether short sales cause or exacerbate market crashes. Hong and tein () developed a model that predicts a higher frequency of extreme

5 negative returns when short sales are allowed. Allen and Gale () showed that short sales can potentially destabilize an economy. While Bris et al. () argue that short sales do not cause market crashes but rather exacerbate them. hey find that the distribution of stocks returns is more negatively skewed when short selling is allowed. ontrarily, Bernando and Welch () suggest that restricting short sales can effectively prevent financial crises. Another interesting area of research on short sales includes short selling and informed trading. For example, Atken, et al. () showed that short sellers are well informed. Also, Boehmer, Jones, and Zhang () found that on average, short sellers are important contributors to efficient stock prices. While most studies look at the effects of short selling on individual stocks, haroenrook and Daouk () performed an analysis of aggregate market returns when short sales are restricted. hey also found that short sales improve overall market quality. herefore, the literature is not always conclusive on the impact of short sales and short sale restrictions on the market. In a study on the shorting ban of by Boehmer et al (AFA, ) the authors create a matching sample of non-banned stocks to identify the effects of the short sales ban in the U.. on stock prices, market quality, and shorting activity. hey find that market quality deteriorated and shorting activity substantially decreased as a result of the ban. However, the impact of the ban on stock prices is unclear because, according to the authors, an increase in stock prices might have been an outcome of the roubled Asset Relief rogram (AR). More recently, Autore, Billingsley and Kovacs () showed the short sale ban reduced valuation of stocks that became illiquid. In another study Grundy, Lim and Verwijmeren () found the short sale ban

6 restricted trading in options. A summary of some key studies on the effects of short sales restrictions are presented in able. ur study contributes to the existing literature in the following ways. First, many of the previous research on short-sale restrictions were done using the data from emerging countries. his study concentrates on the impact of short-sale constraints in the U.. stock market, during a financial crisis. econd, we examine banned stocks listed on three main exchanges separately which allows us to examine the impact of short-sale restrictions on different types of exchanges. hird, the effects of the shorting ban on liquidity, market efficiency, and volatility are examined using a variety of different measures and tests that provide us with robust results. II. Data We limit our study to the initial list of banned stocks provided by the. o be included in our sample, stocks must have R data for each day of the fourteen trading days before the ban, during the ban, and fourteen trading days after the ban. We keep only ordinary common stocks (with R share code, and ) which means that ADRs, RIs, and closed-end funds are not included in our sample. he resulting sample of common stocks is subdivided into three categories depending on which stock exchange the stock is listed on. hus, there are banned common stocks that are listed on New York tock xchange (NY) and American tock xchange (AMX), and banned stocks listed on NADAQ. From this sample, five stocks listed on NY, five stocks listed on NADAQ, and three stocks listed on AMX were delisted during the ban or during the fourteen trading days after the ban and were removed from our sample. he final sample includes common stocks listed on the three major exchanges. able summarizes our process of creating the final sample.

7 he sample period contains fourteen trading days before the ban which runs from // to //, fourteen trading days during the ban from // to //, and fourteen trading days after the ban from // to // as a robustness check. Following hordia et al. (), we construct the following variables to measure the impact of the shorting ban on liquidity, volume, and volatility. Quoted spread is daily closing ask minus daily closing bid. %Quoted spread is quoted bid-ask spread divided by the midpoint of the bid-ask quote (bid plus ask divided by two). urnover ratio is daily number of shares traded to the number of shares outstanding. Returns are calculated from close to close. rading volume is the total daily share volume. $Volume is the total dollar volume which is number of shares traded multiplied by the transaction price at close. III. Hypotheses Based on previous studies, several hypotheses on the impact of short sale restrictions emerge. For instance, Diamond and Verrecchia () developed a theoretical model to examine the impact of short-sales on the distribution and speed of adjustment (to private information) of security prices. he model predicts that short-selling constraints lead to a reduced liquidity. herefore, we examine the effects of the short-sale ban on liquidity. Hypothesis : here is less liquidity (smaller turnover, trading volume, and larger bid-ask spread) when short selling is restricted. he study by Bris et al. () found that short-sale restrictions result in a higher level of stock prices co-movement. Bris et al. also use cross-autocorrelation of returns to show that the restrictions of short-sale slows price discovery in downturn markets. herefore, the second hypothesis can be formulated as:

8 Hypothesis : he degree of co-movement (a measure of market efficiency) in stock prices increases during the short-sale ban. Lastly, a number of previous studies have examined the effects of short sale restrictions on volatility but a consensus has not yet been reached. herefore, we develop the third hypothesis: Hypothesis : Volatility changes when short selling is restricted. IV. Methodology In order to test our hypotheses, we construct a variety of measures to test the effects of the shorting ban on market liquidity, volatility, and market efficiency. Following hordia et al. (), we use quoted spread and %quoted as the measures of liquidity. We additionally employ turnover ratio, Volume, and $Volume to investigate the impact of the shorting ban on trading activity. he measures of return volatility include close-to-close volatility, which is the volatility of returns computed from previous day close to current day close, and squared daily returns. We employ the non-parametric Wilcoxon matched-pairs signed rank test in the following way. First, we find the averages of each variable described above for each stock in the sample over the fourteen pre-ban trading days, the fourteen ban trading days, and the fourteen post-ban trading days, separately. We then create three vectors of the differences in means: D Ban-reBan, D ostban-reban,and D Ban-ostBan. We test three hypotheses that the median of each of three vectors is equal to zero against a non-directional alternative hypothesis using Wilcoxon matched-pairs signed rank test. In addition, a one-tailed paired t-test is employed to test the direction of changes in the measures of liquidity, trading activity, and return volatility.

9 o test if short-sale restrictions have had any impact on volatility of returns, we utilize the t-test for homogeneity of variance for two dependent samples. Moreover, we apply a paired t-test to test the changes in means of each variable from the pre- ban to the ban, from the ban to the post ban, and from the pre-ban to the post-ban periods for each stock individually. We find the averages of each variable for each stock over fourteen trading days before the ban, fourteen trading days during the ban, and fourteen trading after the ban; we test whether there have been any significant changes in means. We report the number of stocks that have rejected the null hypothesis that there is no difference in means at % significant level. Also, a chi-square test of the aggregate significance of several independent tests is employed to check how significant are the overall results from each individual test. We also run dummy variable regressions with panel data. Following the methodology by Nguyen et al. (Working paper), we use the bid-ask spread and the percent quoted spread as the depended variables. Retsq, volume, and excvolume (excess trading volume) are used as independent variables and serve as proxies for the three components of bid-ask spread: inventory holding component, order processing component, and adverse selection component. pread it =β retsq it +β volume it +β excvolume it +β reban it +β DuringBan it +β ostban it +ε it () We define dummy variables as follows: reban is a dummy variable equal to for the pre-ban period of trading days, and equal to otherwise. DuringBan is a dummy variable equal to for the ban period of trading days, and equal to otherwise. ostban is a dummy variable equal to for the post-ban period of trading days, and equal to otherwise. We also regress returns on the following variables: Return it = β Quotedpread it + β urnover it +β reban it +β DuringBan it +β ostban it +ε it ()

10 Where dummy variables are defined the same way as described above. In order to calculate the co-movement of prices and returns, which is a proxy for market efficiency, we follow the method developed by Morck et al. () to measure stock price synchronicity. We run a time-series regression of individual stock returns on market returns over each period. We then compute and average R-squared across each group of banned stocks based on the stock exchange they are listed in. he process is repeated to run regression with only positive market returns, and then negative market returns. We also compute crossautocorrelations between individual stock returns and the -day lagged market returns. his procedure is also repeated for positive market return and negative market return separately. V. Results We first perform the Wilcoxon matched-pairs signed rank test on each of three vectors of differences in means: D Ban-reBan, D ostban-reban, D Ban-ostBan able reports the results. anel A contains the median of the differences for three groups with corresponding p-values for the entire sample of stocks. All the results are statistically significant at the % level. his suggests that there have been statistically significant changes in quoted spread, percent quoted spread, returns, squared returns, turnover ratio, dollar volume, and trading volume as a result of the restriction on short sales. anel B and anel provide the results of the Wilcoxon matched-pairs signed rank test for NY/AMX listed stocks and for NADAQ, respectively. he results for NADAQ listed stocks are all significant. While, there are several insignificant results for the measures of trading activity for stocks listed on NY/AMX. verall, this suggests there have been statistically significant changes in the measures of market liquidity, trading activity, and return volatility once the ban was implemented.

11 In order to test the direction of changes in these variables, we perform a one-tailed paired t- test. able reports the results. anel A shows the results for the entire sample for the measures of liquidity, which are quoted spread, percent quoted spread, and the results for the measure of volatility, which is daily return squared. he results imply that on average Quoted spread, and %Quoted spread and squared returns for the entire sample of banned stocks became statistically significantly larger during the ban period compared to the period before the ban. anel A and anel A presents the results for NY/AMX and NADAQ listed stocks, respectively. All examined variables are significant for NADAQ stocks for pre-ban versus ban period. his implies that quoted spread, %quoted spread and squared returns have increased after the short sales ban. However, NY/AMX stocks do not exhibit significant results for quoted spread. anel B offers the results for the measures of trading activity and returns. Group one, re-ban versus Ban, shows statistically significant results for each variable tested. anel B and anel B provides results for NY/AMX and NADAQ stocks. NADAQ stocks demonstrate significant changes in return, turnover ratio, dollar volume, and trading volume after ban was implemented. While stocks listed on NY/AMX do not show any significant changes in trading volume after the ban on short selling activity. n average, the results suggest that turnover ratio, dollar volume, trading volume, and returns have decreased after the ban was put into effect. In order to test if there were any significant changes in volatility of returns, we employ the standard measure of close-to-close volatility of returns. he t-test for homogeneity of variance between two dependent samples is performed for each stock individually for the three groups. able present the results. he number of stocks that have rejected the null hypothesis at the % level is given in the table together with p-values for a chi-square test of the aggregate

12 significance of multiple independent tests. tatistically significant chi-square values for all three groups provide evidence of significant changes in volatility of returns after the short-selling activity was banned. In addition, we run the paired t-test on each stock individually and also perform the chisquare test of the aggregate significance of several independent tests. able offers the results. he number of stocks that have rejected the null hypothesis at the % significance level is given in the table while the values of chi-square test are in parenthesis. he results provide more evidence on the significant changes in the measures of trading activity, market liquidity, and return volatility in terms of squared returns for banned stocks that have resulted from the constraints on short sales. We also run dummy variable regressions using the panel data of banned stocks. he results of the regressions are given in able and able. able uses quoted spread and % quoted spread as the dependent variables. he coefficients on each dummy variable reban(.), Ban(.), and ostban(.) are positive, and statistically significant. able gives the results for the dummy variable regression with daily returns as a dependent variable. he salient points are the following. he coefficients on dummy variables DuringBan(-.) and ostban (-.) are negative and statistically significant. able and able provide some evidence on the impact of the short sale ban on the market quality. able shows the results of the regressions of individual banned stock returns on the market return estimated for the pre-ban, the ban, and the post-ban periods separately. High R-squared reflect the high level of co-movement between individual stock returns and the market. he R-squared are calculated from the regressions run on positive market

13 returns and negative market returns separately to form a measure, upside R-squared and downside R-squared based on Bris et al.() and Morck et al.(). ome interesting results emerge from these measures. he R-squared are decreasing during the ban which implies there is less co-movement between individual stock returns and market returns. able provides additional evidence on the measure of market efficiency. We compute cross-autocorrelations between individual stock returns and the -day lagged market return. Higher crossautocorrelations also correspond to higher level of co-movement and imply decreased market efficiency. he results suggest that cross-autocorrelations have also decreased for banned stocks across all three stock exchanges examined in this study. hese results suggest that banned stocks returns became less correlated with market returns. ne of the explanations for this can be given in terms of the changes in systematic risk measure, the coefficient on market return. reliminary results are given in able anel D. he coefficients decrease for NY/AMX and NADAQ stocks from the pre-ban period to ban and to post ban periods. herefore, the results suggest that there have been significant changes in the measures of liquidity, measures of trading activity, and return volatility after the ban was implemented on eptember. We find that market quality and market liquidity has deteriorated substantially as a result of the short-sale restrictions. he results show that quoted spread and percent quoted spread have significantly increased during the ban, turnover ratio has declined together with trading volume and dollar volume. Returns have decreased while volatility of returns measured by squared returns have increased during the ban. herefore, the sudden imposition of the shortsale restrictions caused a substantial drop in market quality and efficiency.

14 VI. onclusion hort sales play an important role in the U.. stock market. he s decision to put a ban on shorting was met with shock and surprise around the globe. It was a drastic step and the last time a similar measure was put into effect was during the Great Depression. his sudden restriction on short sales provides us a rare opportunity to examine the impact it had on the U.. financial markets using daily data. he results suggest that market quality measured in terms of liquidity and volume declined as a result of the restriction. he quoted spread and percent quoted spread significantly increased, while turnover ratio, volume, and dollar volume all decreased. Returns also declined, and volatility of returns measured by squared returns increased during the ban. herefore, the results suggest that the measure by the to restore equilibrium to the U.. financial markets resulted in the deterioration of market efficiency and quality.

15 References: Aitken, Michael, Alex Frino, Michael. Mcorry, and eter L. wan,, hort sales are almost instantaneously bad news: vidence from the Australian tock xchange, Journal of Finance, -. Allen, Franklin, and Douglas Gale,, Arbitrage, short sales, and financial innovation, conometrica, -. Autore, Don, Randall Billingsley, and unde Kovacs,, he short sale ban: Liquidity, dispersion of opinion, and the cross section of returns of U.. financial stocks, Journal of Banking and Finance, -.. Bernardo, Antonio, and Ivo Welch,, Liquidity and financial market runs, Quarterly Journal of conomics, -. Boehmer, kkehart, Jones, harles M. and Zhang, Xiaoyan,Which horts are Informed? (February, ). AFA hicago Meetings aper. Boehmer, kkehart, Jones, harles M. and Zhang, Xiaoyan,hackling, hort ellers: he horting Ban. Johnson chool Research aper eries No. - Bris, A., W.N. Goetzmann and N. Zhu.. fficiency and the bear: hort sales and markets around the world. Journal of Finance (): Bris, Arturo, William N. Goetzmann, and Ning Zhu,, hort sales in global perspective, in Frank Fabozzi, ed.: he heory and ractice of hort-elling (Wiley). haroenrook, Anchada, and Hazem Daouk,, he world price of short selling, Working paper, ornell University.

16 hang,.., J.W. heng, and Y. Yu.. hort-sales constraints and price discovery: vidence from the Hong Kong Market. Journal of Finance ():. hordia,., R. Roll, and A. ubrahmanyam,, Market liquidity and trading activity, Journal of Finance, -. Diamond, D.W. and R.. Verrecchia,, onstraints on short-selling and asset price adjustment to private information, Journal of Financial conomics, - Grundy, Bruce, Bryan Lim, and atrick Verwijmeren,, Do option markets undo restrictions on short sales? vidence from the short sale ban, Journal of Financial conomics, -. Henry,.. and M. McKenzie,. he impact of short selling on the price-volume relationship: vidence from Hong Kong, Journal of Business, No., -. Hong, Harrison, and Jeremy. tein,, Differences of opinion, short sales constraints and market crashes, Review of Financial tudies, - Lo, Andrew W. and Wang, Jiang,rading Volume: Definitions, Data Analysis, and Implications of ortfolio heory(march ). NBR Working aper No. W. - Miller,.M.,, Risk, uncertainty, and divergence of opinion, Journal of Finance, Mørck, Randall, Bernard Yeung, and Wayne Yu,, he information content of stock markets: Why do emerging markets have synchronous stock price movement? Journal of Financial conomics, -. he New York imes,, Vikas Bajaj and Graham Bowley,

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18 able. ffects of hort-ale Restrictions he table presents the summary of some studies on the effects of short sales prohibitions. tudy ample Frequency of data and ample eriod When short sales are not allowed: Miller () heoretical model Results rices reflect more optimistic valuation ome negative information is not reflected in prices Diamond and Verrecchia () heoretical model Market liquidity declines rices do not adjust to new information fast enough Bris et al. () quity Markets Weekly to Markets are less efficient hort sale exacerbate crashes Boehmer et al. (Working paper) he U.. Market Daily and Intraday Market quality deteriorates When short sales are allowed: Hong and tein () heoretical Model Higher frequency of extreme negative returns is observed Allen and Gale () heoretical Model conomy destabilization haroenrook and Daouk () equity markets Monthly to Market quality improves hang,heng,yu () Hong-Kong market Daily to Returns have higher volatility Henry and McKenzie () Hong Kong market Daily Returns have higher volatility

19 able : ecurities on the initial ban list anel A: Initial ample Number of stocks on initial ban list Number of ordinary common stocks with available data or anel B: Distribution of initially banned common stocks across exchanges NY/AMX Delisted during ban or trading days after ban Removed due to data issues Remaining common stocks on NY/AMX NADAQ Delisted during ban or trading days after ban Removed due to data issues Remaining common stocks on NADAQ otal number of common stocks in the sample

20 able : Variables Description Variable name Return rading Volume $Volume urnover Ratio Quoted pread %Quoted pread Returnquared xcvolume Description Daily return from close to close otal daily share volume Number of shares traded multiplied by the transaction price at a close Daily number of shares traded divided by the number of shares outstanding Daily closing ask minus daily closing bid Quoted bid-ask spread divided by the midpoint of the bid-ask quote (closing bid plus closing ask divided by two) quared daily returns xcess daily trading volume (defined as the difference between actual daily trading volume and its daily average over trading days from May to November

21 able : he Non-arametric Wilcoxon matched-pairs signed-rank test for the median of the differences in means hree groups are created: pre-ban vs. during ban, pre-ban vs. post-ban, and during ban vs. postban. he values are first averaged over pre-ban, ban, and post-ban periods for each stock in the sample. he differences between individual stock averages are calculated for each group. (D i pre-ban vs. ban, D i pre-ban vs post- ban, D i ban vs.post- ban,i= to ) D is the median of the differences in means for each group. Null hypothesis is tested for each group that D = (the median of the differences in means is zero) using the non-parametric Wilcoxon matched-pairs signed-rank test. he median of the differences is reported in the table. -values are given in parenthesis. he same procedure is repeated for stocks listed only on NY/AMX (anel B), and only on NADAQ (anel ). anel A: otal ample Number of stocks = re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : D = Ban reban H o : D = ostban reban H o : D = Ban ostban H : D Ban reban H : D ostban reban H : D Ban ostban Quoted pread.*(.).*(.).*(.) %Quoted pread.*(.).*(.).*(.) Return -. *(.) -.*(.) -.*(.) urnover ratio -.*(.) -.*(.) -.*(.) Dollar Volume -,.*(.) -,.*(.),.*(.) rading Volume -,.*(.) -.*(.) -.*(.) Returnquared.*(.).*(.) -.*(.) *significant at % level **significant at % level ***significant at % level

22 anel B:NY/AMX Number of stocks = re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : DBan reban = H : D Ban reban H o : DostBan reban = H : D ostban reban H o : DBan ostban = H : D Ban ostban Quoted pread.* (.).*(.).*(.) %Quoted pread.*(.).*(.).*(.) Return -.*(.) -.*(.) -.*(.) urnover ratio -.*(.).(.) -.*(.) Dollar Volume -,,.*(.) -,,.*(.) -,. (.) rading Volume -,.*(.). (.) -,.*(.) Returnquared.*(.).*(.) -.*(.) *significant at % level **significant at % level ***significant at % level

23 anel : NADAQ Number of stocks = re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : D = Ban reban H o : D = ostban reban H o : D = Ban ostban H : D Ban reban H : D ostban reban H : D Ban ostban Quoted pread.* (.).*(.).*(.) %Quoted pread.*(.).*(.).*(.) Return -.*(.) -.*(.) -.*(.) urnover ratio.*(.) -.*(.) -.**(.) Dollar Volume -.*(.) -,.*(.),.* (.) rading Volume.*(.) -.* (.) -.*(.) Returnquared.*(.).*(.) -.*(.) *significant at % level **significant at % level ***significant at % level

24 able : ne-tailed paired-t-test hree groups are created: pre-ban vs. during ban, pre-ban vs. post-ban, and during ban vs. postban. he values are first averaged over pre-ban, ban, and post-ban periods for each stock in the sample. he differences between individual stock averages are calculated for each group. (D i pre-ban vs. ban, D i pre-ban vs post- ban, D i ban vs.post- ban,i= to ). is the mean of the differences in means for each group. Null hypothesis is tested for each group that = (the mean of the differences in means is zero) using the parametric paired t-test against directional alternative hypothesis. he mean value of the differences is given in the table. -values are given in parenthesis for a one-tailed test. Number of stocks is. he same procedure is repeated for stocks only listed on NY/AMX (anel A, anel B), and NADAQ (anel A, anel B). anel A: otal ample: stocks re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : = Ban reban H : > Ban reban H o : = ostban reban H : > ostban reban H o : Ban ostban = H : > Ban ostban Quoted pread.**(.).(.).*(.) %Quoted pread.*(.).*(.).*(.) Returnquared.*(.).*(.).(.) *significant at % level **significant at % level ***significant at % level

25 anel A: NY/AMX: stocks re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : = Ban reban H : > Ban reban H o : = ostban reban H : > ostban reban H o : Ban ostban = H : > Ban ostban Quoted pread.(.).(.).**(.) %Quoted pread.*(.).*(.).***(.) Returnquared.*(.).*(.) -.(.) *significant at % level **significant at % level ***significant at % level anel A: NADAQ: stocks re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : = Ban reban H : > Ban reban H o : = ostban reban H : > ostban reban H o : Ban ostban = H : > Ban ostban Quoted pread.* (.).*(.).*(.) %Quoted pread.*(.).*(.).*(.) Returnquared.*(.).*(.).(.) *significant at % level **significant at % level ***significant at % level

26 anel B:otal ample: stocks re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : H : = Ban reban < Ban reban H o : = ostban reban H : < ostban reban H o : Ban ostban = H : < Ban ostban Return -. *(.) -.*(.) -.*(.) urnover ratio -. *(.) -.*(.) -.(.) Dollar Volume -,,.*(.) -,,.*(.),,.(.) rading Volume -,. ***(.) -,.**(.),.(.) *significant at % level **significant at % level ***significant at % level

27 anel B:NY/AMX: stocks re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : H : = Ban reban < Ban reban H o : = ostban reban H : < ostban reban H o : Ban ostban = H : < Ban ostban Return -.*(.) -.*(.) -.*(.) urnover ratio -.*(.) -.*(.) -. (.) Dollar Volume -,,.*(.) -,,.*(.),,. (.) rading Volume -,.(.) -,,.***(.),,. (.) *significant at % level **significant at % level ***significant at % level anel B:NADAQ: stocks re-ban re-ban Ban Variable name Vs. Ban Vs. ost-ban Vs. ost-ban H o : H : = Ban reban < Ban reban H o : = ostban reban H : < ostban reban H o : Ban ostban = H : < Ban ostban Return -.*(.) -.*(.) -.*(.) urnover ratio -.*(.) -.*(.) -.**(.) Dollar Volume -,,.*(.) -,,.*(.) -,.(.) rading Volume -,.*(.) -,.* (.) -,.*(.) *significant at % level **significant at % level ***significant at % level

28 able : Results of the -test for homogeneity of variance his table presents the results of the t-test two for homogeneity of variance for two dependent samples. he test is performed for each firm individually. he number of firms that have rejected null hypothesis at % level is presented in the table. otal sample size is firms. hree hypotheses tested for each firm. he values of χ (test of aggregate significance of several independent tests) is in parenthesis. Hypothesis tested H :σ before=σ during H :σ before=σ after H :σ during=σ after Variable name H : σ before σ during H : σ before σ after H : σ during σ after Volatility of daily returns (,.*) (,.*) (,.*) *significant at % level **significant at % level ***significant at % level

29 able : he aired t-test results using the means of each stock individually his table reports the results of the paired t-test. he test is performed for each firm and each variable individually. he means for each variable( µ ) are found by averaging the values over pre-ban, ban, and post-ban periods for each firm individually. hree hypotheses tested for each firm. he number of firms that have rejected null hypothesis at % level is given. otal sample size is stocks. he values of χ (test of aggregate significance of several independent tests) is in parenthesis. Hypothesis tested H o :µ before ban =µ during ban H :µ before ban µ during ban H o :µ before ban =µ after ban H :µ before ban µ after ban H o :µ during ban =µ after ban H :µ during ban µ after ban Variable name Quoted pread (,.*) (,.*) (,.*) %Quoted pread (,.*) (,.*) (,.*) ffective pread (,.*) (,.*) (,.*) %ffective pread (,.*) (,.*) (,.*) urnover ratio (,.*) (,.*) (,.*) Dollar Volume (,.*) (,.*) (,.*) rading Volume (,.*) (,.*) (,.*) Returnquared (,.*) (,.*) (,.*) *significant at % level **significant at % level ***significant at % level

30 able : Dummy Variable Regression Results pread it =β retsq it +β volume it +β excvolume it +β reban it +β DuringBan it +β ostban it +ε it he table presents the results of the pooled regression above. retsq is a daily return squared, volume is a trading daily volume, excvolume is the excess daily trading volume (defined as the difference between actual daily trading volume and its daily average over trading days from May to November). reban is a dummy variable equal for the pre-ban period of trading days and equal otherwise.duringban is a dummy variable equal for the ban period of trading days and equal otherwise. ostban is a dummy variable equal for the post-ban period of trading days and equal otherwise. Quoted pread Quoted pread ercent Variable oefficient p-value oefficient p-value Retsq...*. Volume.. -.*. xcvolume *. reban.*..*. DuringBan.*..*. ostban.*..*. *significant at % level **significant at % level ***significant at % level

31 able : Dummy Variable Regression Results Return it = β Quotedpread it + β urnover it +β reban it +β DuringBan it +β ostban it +ε it he table presents the results of the pooled regressions above. return is a daily return close to close, turnover is a trading daily volume divided by number of shares outstanding. reban is a dummy variable equal for the pre-ban period of trading days and equal otherwise. DuringBan is a dummy variable equal for the ban period of trading days and equal otherwise. ostban is a dummy variable equal for the post-ban period of trading days and equal otherwise. Return (lose to lose) Variable oefficient p-value Quotedpread.. urnover.*. reban.. DuringBan -.*. ostban -.*. *significant at % level **significant at % level ***significant at % level

32 able : Measures of Market fficiency anel A: R-squared Model R it =α i +βr mt +ε it We run a time-series regression of individual stock returns on the market index returns (&) separate in each period: pre-ban, ban, and post-ban.we compute R-squared which are then averaged by a stock exchange on which banned stocks are listed (NY/AMX and NADAQ). re-ban trading days Ban trading days ost-ban trading days NY/AMX... NADAQ... anel B: Upside R-squared Model R it =α i +βr + mt+ε it We run a time-series regression of individual stock returns on only positive market index returns (&) separate in each period: pre-ban, ban, and post-ban. We compute R-squared which are then averaged by a stock exchange on which banned stocks are listed (NY/AMX and NADAQ). re-ban trading days Ban trading days ost-ban trading days NY/AMX... NADAQ...

33 anel : Downside R-squared Model R it =α i +βr - mt+ε it We run a time-series regression of individual stock returns on only negative market index returns (&) separate in each period: pre-ban, ban, and post-ban. We compute R-squared which are then averaged by a stock exchange on which banned stocks are listed (NY/AMX and NADAQ). re-ban trading days Ban trading days ost-ban trading days NY/AMX... NADAQ... anel D: -coefficient Model R it =α i +βr mt +ε it We regress individual stock returns on the market index returns (&) separate in each period.we compute coefficients on market return which are then averaged by a stock exchange on which banned stocks are listed(ny/amx and NADAQ). re-ban trading days Ban trading days ost-ban trading days NY/AMX... NADAQ...

34 able : Measures of Market fficiency: o-movement anel A: ross-autocorrelation ρ ij =corr(r ijt,rm jt- ) ross-autocorrelation between individual stock returns and one-day lagged market return (daily return on & index).he cross-autocorrelations are averaged over pre-ban, ban and postban periods across stocks in each group (NY/AMX listed and NADAQ listed). re-ban trading days Ban trading days ost-ban trading days NY/AMX NADAQ anel B: Upside ross-autocorrelation ρ + ij=corr(r ijt,rm + jt-) ross-autocorrelation between individual stock returns and one-day lagged positive (greater or equal to zero) market return (daily return on & index).he cross-autocorrelations are averaged over pre-ban, ban and post-ban periods across stocks in each group (NY/AMX listed and NADAQ listed). re-ban trading days Ban trading days ost-ban trading days NY/AMX NADAQ...

35 anel : Downside ross-autocorrelation ρ - ij=corr(r ijt,rm - jt-) ross-autocorrelation between individual stock returns and one-day lagged negative (less than zero) market return (daily return on & index).he cross-autocorrelations are averaged over pre-ban, ban and post-ban periods across stocks in each group (NY/AMX listed and NADAQ listed). re-ban trading days Ban trading days ost-ban trading days NY/AMX NADAQ

36 Figure urnover ratio is plotted for pre-ban,ban and post ban periods. he ratio is averaged daily across all stocks in each group of banned stocks (NY listed,nadaq listed, and AMX listed) urnover Ban Ban Lifted Date A U G A U G A U G Daily urnover for 'Banned' stocks on NY,NADAQ and AMX NADAQ NY AMX

37 Figure Quoted Bid-Ask pread is plotted for pre-ban, ban, and post ban periods. he spread is averaged daily across all stocks in each group of banned stocks (NY listed,nadaq listed, and AMX listed) pread Value Ban Ban Lifted Date A U G A U G A U G Daily Quoted pread for 'Banned' stocks on NY,NADAQ and AMX NADAQ Quoted pread NY Quoted pread AMX Quoted pread

38 Figure Returns (close-to-close) are plotted for pre-ban,ban, and post ban periods. Returns are averaged daily across all stocks in each group of banned stocks (NY listed,nadaq listed, and AMX listed).daily returns on & are used as market proxy. Return Ban Ban Lifted Date A U G A U G A U G Daily Returns for 'Banned' stocks on NY,NADAQ and AMX and Index NADAQ NY AMX Market

39 Figure Volatility of Returns (close-to-close) are plotted for pre-ban,ban, and post ban periods. Volatility is averaged on a daily basis across all stocks in each group of banned stocks (NY listed,nadaq listed, and AMX listed) Volatility Ban Ban Lifted Date A U G A U G A U G Volatility of Returns for 'Banned' stocks on NY,NADAQ and AMX NADAQ NY AMX