Corporate disclosure and price discovery associated with NYSE temporary trading halts

Corporate disclosure and price discoveryassociated with NYSE temporary

trading halts*

RONALD KING Washington University

GRACE POWNALL Washington University

GREGORY WAYMIRE Emory University

Abstract. This paper examines the properties of corporate disclosure and price discoveryassociated with NYSE temporary trading halts. We address the hypothesis that managersrelease highly informative disclosures outside of trading hours or seek a trading haltto ailow investors greater opportutiity to assess the implications of new informatioti.We investigate whether: (a) disclosures associated with trading halts are highly pdceinformative, and (b) the process of price discovery as reflected in specialist indicationsis more protracted and difficult for extreme and bad news halts.

We find that halts arise frooi non-routine highly informative disclosures for whichprice discoveiy'is more uncertain and protracted. First, most disclosures associated withour sample of trading halts are ones whose arrival investors cantiot predict but whichhave large valuation effects (e.g., corporate takeovers and leveraged buyouts). Second,halts associated with large price changes exhibit more uncertain and protracted pricediscovery during the halt. Specialist indications for extreme news halts have (1) biggerdifferences between high and low prices, (2) poorer predictive accuracy with respect toopening price, and (3) greater frequency. Finally, similar comparisons for bad and goodBews only weakly support the conjecture that bad news is associated with more certainand protracted price discovery..

Resume. Les auteurs examinent les proprietes des renseignements foomis par les societeset de la supputation des cours en periode d'arret temporaire des operations de la Boursede New York. Us se penchent sur I'hypothese selon laquelle les gestionnaires publient desrenseignements tres informatifs en dehors des heures d'activite ou en periode d'arret desoperatioHs, de fagon a donner aux investisseurs tout le loisir d'evaluer les consequencesde ces renseignements. Les auteurs se demandant 1) si I'information publiee en perioded'arret des operations est tres eciairante sur les cours et b) si le processus de supputation

EDITOR'S NOTE: S.P. Kothari advises me that this version of the paper, together with Charles Lee'sdiscussion adequately deals with his comments made at the 1990 CAR Conference. Consequently,he has rtot written a discussion.

* We are gratefu} for comments from Carol Frost, Teiry Shevlin, and workshop participants at0iike, Emory, Florida, Illinois, North Carolina, Ohio State, Purdue, Wisconsin, the 1990 CARConference and the two Conference discussants (Charles Lee and S. P. Kothari). The John M.Olin School of Business at Washington University, the KPMG Peat Marwick Foundation (forKing and Waymire), and the Kiannert Graduate School of Management at Piffdue University(for Waymire) provided generous financial support. We are grateful to the New York StockExchange for providing data for this study and to SooYoung Kwon for providing computerassistance.

Contemporary Accounting Research Vol. 8 No. 2 pp. 509—53!

510 R. King G. Pownall G. Waymire

du cours tel que I'illustrent les indications des specialistes est plus long et plus difficilelorsque les arrets sont associes a des renseignements qui entrainent des variations ducours d'une grande amplitude ou des variations du cours negatives.

Selon les autetirs, il y a arret des operations lorsque les renseignements publies sortentde r ordinaire et que leur contenu en information est eleve, si bien que le processus de sup-putation du cours est plus incertain et plus long. Premierement, la plupart des declarationsassociees a notre echantillon d'arrets des operations sont de nature telle qu'il etait impos-sible pour les investisseurs d'en predire l'occurrence, mais ont des consequences majeuressur revaluation de I'entreprise (par exemple, les prises de controie et les prises de controieadossees). Deuxiemement, les arrets des operations correspondant a d'importantes varia-tions du cours sont caracterises par un processus de supputation du prix plus incertain etplus long. Les indications des specialistes en ce qui a trait aux arrets des operations as-socies a des renseignements qui entrainent des variations du cours d'une grande amplitudepresetttent 1) un ecart plus grand entre cours eleve et cours faibie, 2) moins de precisiondans les predictions relatives au cours d'ouverttire et 3) une frequence superieure. Enfin,des comparaisons analogues en ce qui a trait aux renseignements positifs et aux ren-seignements negatifs corroborent seulement faiblement l'hypothese selon laquelle lesrenseignements negatifs rendent la processus de supputation du cours plus certain et pluslong.

IntroductionThe purpose of this paper is to provide evidence on (1) the characteristics ofcorporate public disclosures associated with the New York Stock Exchange(NYSE) temporary trading halts and (2) the static and dynamic properties ofthe "price discovery process" during trading halts as reflected in specialist indi-cations.^ Price discovery refers to the market adjustment process whereby newinformation is consolidated into security prices.^ Indications are range forecastsof reopening prices disseminated by the market specialist during trading halts.These forecasts of the reopening price can be used by traders to base buy or sellorders for the halted security. Various properties of these indications (e.g., thespread) reflect market uncertainty about eventual transaction prices and, hence,constitute empirical proxies for market adjustment processes during trading haltperiods.

Institutions such as the NYSE incorporate trading features (e.g., trading halts),which are designed to enhance economic efficiency. Highly itiformative corpo-rate disclosures may be subject to exchange-initiated trading halts to reduce the

Prior studies document abnormal retums associated with trading halts but generally ignore thenature of contemporaneous news arrival. Hopewell and Schwartz (1976 and 1978) found ab-normal stock retums associated with teinporary suspensions in NYSE securities. Kryzanowski(1978 and 1979) studied price behavior for suspensions on major Canadian exchanges andexplored the nature of information releases for suspended Canadian securities. Howe andSchiarbaum (1986) documented significant abnornwl retums associated with trading sus-pensions initiated by the SEC. Schwartz (1982) studied the intrasuspension pdce discoveryprocess by analyzing specialist indications. Finally, Fabozzi and Ma (1988) studied OTCactivity in stocks for which trading was halted on the NYSE.See Cohen, Maier, Schwartz, and Whitcomb (1986, p. 160) and Schwartz (1988, pp. 36, 394-400, and 514-527) for a discussion of the price discovery process. See Pincus (1983), Patelland Wolfson (1984), Jennings and Starks (1985), and Lee (1990) for empirical analyses ofprice discovery following corporate public disclosures.

Corporate Disclosure and Price Discovery 511

oumber of trades at prices that do not fully reflect the information. ^ Pricesmay not adjust to new information instantaneously because of institutional con-straints and various market frictions (e.g., short sale restrictions, price continuit̂ i?mles).* If exchanges call halts in response to news releases because price dis-covery is not instantaneous, we expect to observe a relationship between thedifficulty of price discovery during halt periods as reflected in specialist indica-tions and the information content of news arrivals. Hence, we investigate whetherprice discovery is more protracted and characterized by greater uncertainty forlarge magnitude (extreme news) and negative (bad news) price changes.

The study by Patell and Wolfson (1982) is closely related to this study. Theytested the "market wisdom" that firms hold bad news for announcement untilafter the close of the trading day. They performed their tests on predictable an-noaacements (earnings reports aod dividend declarations) over which managershave discretion as to intraday timing. They found that bad news is released aftertrading more often than goods news, although they cautioned that this evidenceis consistent with explanations other than managerial attempts to minimize theexposere of adverse information. Their evidence also suggested that extremenews releases are more likely to be disclosed while the exchange is closed.^Patell and Wolfson (1982) offered one altemative conjecture that evaluation andconsolidation are more difficult for some disclosures and that managers takeadvaatage of the lack of trading to provide greater opportunity for investors toevaluate these announcements.

Casual observation of stock exchange policies pertaining to corporate disclo-sure suggests that the conjecture by Patell and Wolfson (1982) is plausible. Themajor U.S. exchanges subject listed firms to disclosure requirements that includethe timely dissemination of any information that is expected to have a materialimpact on its stock price (see Sheffey, 1982). The NYSE also suggests that listedlinns inform the Department of Stock List prior to public disclosures so that theexchange can decide wbether to halt trading while the announcement is beingmade. Our empirical tests provide additional evidence pertinent to this conjec-ture since we can directly assess the relationship between properties of price

See King, Pownali, and Waymire (1990) for a discussion of this in the context of the "expec-tations adjustment hypothesis."Short sales cannot be executed on down ticks (i.e., negative price changes) pursuant to SECRale lOa-l. The specialist is constrained from making large changes in quoted bid and askprices on successive transactions. Presisniabiy, this is done to ensure that market orders placedby traders will actually execute at or near prices anticipated when orders are made. See Cohenet al. (1986) and Schwartz (1988) for detailed discussion of trading procedures and constraintsOE the NYSE.Although tins latter point is not directly addressed by Patell and Wolfson (1982), the evidenceleported in their Table 11 leads us to believe it holds. The average squared standardizedresidual across the Broad Tape disclosure date (day-1) and The Wall Street Journal disclosuredate (day 0) is approximately 2.49 for releases made outside of trading hours (before 10A.M. and after 4 P.M.) and 2.06 for releases dtiring trading (between 10 A.M. and after 4 P.M.).This effect is most pronounced for disclosures occurring before the opening of trade with anaverage across the two days of 3.06.


discovery during nontrading periods (as reflected in specialist indications duringtemporary trading halts) and characteristics of corporate public disclosures.

The primary results of our analysis are as follows. First, we document thatthe bulk of disclosures associated with NYSE trading halts are nonroutine andextremely price informative. Specifically, the majority (79.3 percent) relate todisclosures about corporate takeovers £ind leveraged buyouts, which cannot bepredicted by investors but have substantial valuation effects. Routine armounce-ments of eamings and dividends do not in general precipitate trading halts.Second, our primary results strongly suggest that trading halts associated withextreme news are characterized by more uncertain and protracted price discoveryprocesses. Third, our evidence provides only weak support for the same con-jecture for bad news. Overall, we interpret our evidence as consistent with theprediction that trading halts are more likely to be called in response to highlyinformative events that are characterized by more tincertain and protracted pricediscovery processes.

The rest of the paper is organized as follows. The second section presentssome institutional details of NYSE trading halts. The sample of trading haltsis described in the third section. Empirical evidence on the characteristics ofcorporate disclosures associated with trading halts is provided in section four.The fifth section provides evidence on the relationship between properties ofprice discovery as reflected in specialist indications and characteristics of newsarrivals. Some extensions to our primary tests are reported in the sixth section.A seventh section concludes the paper with a brief summary.

Institutional aspects of trading haltsNYSE officials may temporarily halt trading in a listed security in two cases.In the first case—called a news halt—the listed company calls the Departmentof Stock List and informs the exchange that it plans to make a press release noearlier than concurrently with the phone call. If exchange officials (includingthe specialist for the security^) deem the pending disclosure sufficiently mate-rial, trading in the security is halted. In the second case^—called an imbalancehalt—the specialist initiates a halt when he observes an abnormally high levelof buy or sell orders. In either case, the halt can be associated with corpo-rate disclosures. In the case of a news halt, the link is explicit. An imbalancehalt can also arise from corporate disclosures for which the exchange initiallydeemed it unnecessary to halt trading or because of abnormal trading promptedby forthcoming news releases.^

Our description of the specialist's involvement in initiating trading halts implicitly assumesthat he does not engage in any sort of strategic behavior but simply acts as a market maker.Consideration of the economic determinants of the specialist's decision to halt trading isbeyond the scope of this paper.See Cohen et al. (1986) for current research on the microstructure of security markets. Eco-nomic analysis of the value of trading halts has been done only in the larger research query ofthe design of efficient trading institutions.


On the NYSE, the market specialist has an affirmative obligation to createa "fair and orderly" market. This obligation is fulfilled not only by buyingand selling activities, but also by behavior in identifying market-clearing pricesduring trading halts and at the beginning of the trading day. During the haltperiod, the specialist announces indications, which are sequential forecasts of theupper aad lower bounds of the security's pdce at the resumption of trade. Tradersmay chaage their orders during the halt in response to specialist indications orother information, but no trades are executed dudng the halt. Once order flowsindicate a market-clearing price, trade resumes. At both the opening trades ofthe day and following a trading halt, the specialist batches all market orders forexecution at a single price, as in a "call" market. Subsequent to opening, tradingtakes place in a continuous auction market.

Timely disclosure requirements by the exchange mandate listed firms to dis-close any information that can have a material effect on their stock price. Suchrequirements enhance the operation of a fair and orderly market. The timelydisclosure of material information when investors cannot transact reduces theincidence of privately informed trading, and, hence, information-based transac-tion costs associated with secondary security trading (see King, Pownall, andWaymire, 1990). These transaction costs include both the charges for market-maMng services (e.g., bid-ask spreads) and direct expenditures for private in-formation acquisition.

Stock exchange policies that allow for temporary trading halts for materialnews releases are also an integral part of institutional arrangements that mini-mize costs associated with privately informed trading. Trading halts serve thisfunction by limiting trades (1) before public disclosure based on foreknowledgeof the content of forthcoming news releases or (2) immediately following publicdisclosures at disequilibrium prices. Both institutional and informational reasonscan cause price discovery following public disclosure not to be instantaneous.Institutional constraints include those associated with short-selling restrictionsand price continuity requirements that minimize successive tr,ansaction pricechanges. Informational reasons can exist because not all traders have immediateaccess to corporate public disclosures. Halting trade enables brokers on the floorto assess the implications of new information and contact clients to determinetrading preferences. These arguments suggest that trading halts are likely to becalled for material disclosures that are characterized by more uncertain, difficult,and protracted price discovery processes.

Trading halts sampleOur initial sample consists of the 216 trading halts on the Daily Halts Log ofthe New York Stock Exchange during the period August 1 to October 20, 1988(58 trading days). We deleted 10 halts because they were permanent delistingsand 17 because the halt applied exclusively to noncommon securities. The finalsample of 189 temporary trading halts represents an average of 3.26 per tradingday. The maximum number of halts on any day is eight, and the minimum is


zero. The frequency of halts is greatest on Monday relative to other days of theweek, with 50 sample halts on Mondays (4.55 halts per Monday on average).^

The sample halts apply to 135 common stocks. One hundred three firms arerepresented by one sample halt, 20 firms by two halts, and 12 firms by threeor more halts. No firm experienced more than seven halts dtuing the sampleperiod. The exchange called 140 sample halts for an actual or pending newsrelease (news halts), and 26 (21) for buy (sell) order imbalances (imbalancehalts). The Halts Log did not classify two sample halts by type.

Throughout the sample period, the NYSE opened at 9:30 AM (EST) andclosed at 4 PM (EST). The exchange initiated 139 of the sample halts within thefirst half hour of the trading day: 129 are delayed openings, and in 60 cases,trading was halted after at least one trade was executed. The mean (median)halt duration for the full sample is 67.8 (51) trading minutes. The Exchangeterminated 180 halts on the same day they were initiated, and the other 9 onthe next day. Only 17 halts lasted more than two hours. The imbalance halts arematerially shorter in duration than the news halts: the mean halt duration forthe 47 imbalance halts is 27.3 minutes compared to 82.0 minutes for the newshalts.

Evidence on the characteristics of disclosures associated with trading halts

Identification of disclosuresWe identified public disclosures associated with the sample halts via on-linesearch of the Dow Jones News Retrieval Service. Our search considered onlydisclosures appearing on the Dow Jones News Wire (i.e.. Broad Tape), whichcould be timed as to hour and minute of arrival. The Broad Tape operates dailybetween 7:30 A.M. and 7 P.M. For the 47 imbalance halts and two halts withoutspecified reason, we confined our search from the close of trade on the tradingday preceding the halt to the end of the trading halt. For halts initiated due to apending news release, we examined only the period of the halt. For halts calledbecause of an actual news release, we considered the period from the close ofthe prior day to the begiiuiing of the halt.^ News releases that confirmed priorannouncements and cases for which management knew of no reason for the haltwere discarded (six cases). This sampling procedure resulted in a total of 179disclosures.

Table 1 provides infonnation on the frequency of multiple disclosures asso-ciated with halts. In 12 cases, we identified two or more disclosures associatedwith a given halt, making it difficult to draw inferences about the disclosureinducing the halt. However, since these cases constitute a small portion of the

The frequency of halts is significantly greater on Monday, Tuesday, and Thursday than onWednesday and Friday (the difference is sigtiificant using various statistical methods).In a few cases, we were unable to locate disclosures during this period for these halts butfound disclosures during the halt (in tnost cases, within the first few tninutes of the halt). Weretained these disclosures in the sample.


TABLE 1Frequency of multipie disclosures for 189 sample halts

No. disclosuresassociated with halts

0

1

2

3

4

Fullsample

25(13.2%)

152(80.4%)

10(5.4%)

1(0.5%)

1(0.5%)

189''(100.0%)

Newshalts"

7(5.0%)

125(89.3%)

7(5.0%)

1(0.7%)

0(0.0%)

140(100.0%)

IBhalts'"

10(38.5%)

14(53.8%)

2(7.7%)

0(0.0%)

0(0.0%)

26(100.0%)

IShalts"

8(38.1%)

11(52.3%)

1(4.8%)

0(0.0%)

1(4.8%)

21(100.0%)

"Halts initiated for actual or pending news release''Hatts initiated due to imbalance of buy orders"Halts initiated due to imbalance of sell orders''Only .187 of the 189 halts were classified as news or imbalance halts.

sample, they do not pose a significant confounding events problem. '** We wereunable to locate specific releases for 25 trading halts, the majority of which wereimbalance halts.*^

Categorization of sample disclosure and stock price effectsWe initially classified each of the 179 disclosures into one of the followingseven categories: (1) acquisitions, (2) capital stmcture changes, (3) divestitures,(4) earnings/dividend, (5) legal—unrelated to takeovers, (6) leveraged buyouts,and (7) takeover targets. Acquisitions include disclosures when the halted com-pany is acquiring another firm. Stock repurchases, offerings, and restmcturingare classified under capital structure changes. Divestitures include sales of busi-ness segments and spin-offs; the earnings/dividend category includes eamingsannouncements, management forecasts, and dividend declarations. Legal an-nouacements unrelated to takeovers consist of disclosures about regulatory orlegal decisions typically related to the finn's products. Leveraged buyouts en-'s 0 The confounding events problem is also reduced because in four cases with multiple disclo-

sures, all disclosures associated with a given halt are from the same category as described insection four.

11 If a news release is not forthcoming during a news-pending halt, the exchange can reopentrading. Of the seven news halts for which we could not identify a disclosure, three were forpending releases. The Halts Log lists the remaining four cases as arising from news releasesalready disclosed. These could be attributable to (1) errors in the coding halt reason in theHalts Log, (2) a news release timed on Dow Jones outside the period we examined, or (3) arelease that was not reported on Dow Jones or was reported without a time.


compass disclosures about going private transactions, and takeover targets in-clude disclosures when the halted company is being acquired. Further details ofthe specific disclosures in each category are provided in the Appendix.

The evidence in Table 2, panel a suggest that the disclosures associated withtrading halts are nonroutine. Note that for the full sample, 128 of 179 disclosurespertain to corporate control activities, either acquisitions by the halted firm (18),divestitures (11), leveraged buyouts (24), or takeover targets (75). For instance,the most routine form of disclosure is a periodic eamings announcement ordividend declaration. However, of the 142 disclosures for news halts, only threeare related to eamings or dividends. Of these three, one is a dividend cut and theother two are earnings announcements released with information about either adivestiture or takeover proposal. Hence, the evidence in panel a suggests thedisclosures associated with halts are largely nonroutine in nature.

Panel b of Table 2 provides summary statistics on retums and absolute retumsfor the full sample and subsamples based on type of disclosure. Retums arecomputed based on the opening and closing prices in the NYSE Halts Log. Theclosing (opening) price refers to the first transaction price immediately before(after) the halt.̂ ^ The retum over the halt period is the difference between thesetwo prices divided by the closing price. For each of the 189 sample trading halts,we computed a retum over the halt period. If the retum was positive (negative),the halt was classified as good (bad) news. We classified halts as extreme newsif the absolute retum over the halt period was greater than or equal to 5 per-cent. '̂ The sample is characterized by large magnitude price changes over thehalt period. The mean (median) absolute rettim for the full sample is 7.95 (3.94)percent, and 39.7 percent of the sample halts are classified as extreme news.*'*The average retum for the full sample is positive, and 56.7 percent of the haltsare classified as good news. This is not unexpected since a large portion ofthe disclosures relate to leveraged buyouts and takeover targets, for which priorstudies have documented large positive abnormal retums.'^

Evidence on the relationship between price discovery and disclosurecharacteristics

Data on specialist indicationsThe NYSE Halts Log also provides information on specialist indications issued

12 In six cases, no trades were executed at the reopening, so we estimated returns using theaverage of reopening bid and ask quotes.

13 We are unable to specify models of investor expectations, and therefore cannot estimate thesurprise component associated with many of the disclosures associated with the halts.

14 The magnitude of absolute rettuns is also strongly associated with transaction volume atreopetiing. For the full sample, the Spearman rank-order correlation between absolute retumand the number of shares traded at reopening is 0.4456 (significant at the 0.0001 level). Therelationship between price and volume is discussed in Karpoff (1987), Grundy and McNichols(1989), and Kim (1989).

15 See Jensen and Ruback (1983) for a review of this evidence.


TABLE 2Frequency of altemative disclosures and summary statistics on stock retums during tradinghalt by disclosure category

Category

A;i.

2,

3,

4.

5,

6.

7.

Number of disclosures.Acquisitions

Capital structure changes

Divestitures

Eamings/Dividends

Legal—Unrelated to takeover

Leveraged buyouts

Takeover targets

Category

B.I.

2.

3,

4,

5,

6.

7.

ImbalanceNews halts" halts"

13 (9,2%) 5 (14,

26(18.3%) 4(11

8 (5,6%) 3 (8,f

3(2,1%) 8(22,

7 (4,9%) 1 (IS

,3%)

,4%)

>%)

,9%)

20(14,1%) 4(11.4%)

65 (45.8%) 10 (28,,5%)

142 (100,0%) 35 (100.0%)

MeanNumber of (Median)halts'* return^

• Stock returns over halt periodAcquisitions 16

Capital structurechanges

Divestitures

Earnings/Dividends

Legal—Unrelatedto takeover

i,,«veraged buyouts

Takeover targets

Full sample

30

11

12

8

24

71

189

-1,42 (-2,15)

0,11 (0)

2.33(4.19)

-1,62 (-6,21)

-1,09 (-0,14)

8,19(2,14)

8.18(3,74)

3,64(1,15)

Percentgoodnews*

18,8

40.0

72,7

33,3

37,5

62,5

78,9

56,7

Othei^ Full sample

18(10,1%)

1 (50,0%) 3! (17,3%)

11(6,1%)

1 (50,0%) 12 (6,7%)

8 (4.5%)

24(13.4%)

75 (41,9%)

2(100,0%) 179(100,0%)

Mean(Median) Percentabsolute extremereturn" news^

5,14(3,59) 31,3

4.64 (3,20) 23.3

7,48 (4,92) 45,5

10,84 (8.41) 75,0

3,38(2,35) 12,5

13,39 (4,97) 50.0

9.44 (417) 47.9

7,95 (3,94) 39,7

"Halts called for actual or pending news release,''Halts called due to imbalance of buy or sell orders."Halts not classifiable according to news or imbalance.''Refers to number of halts in sample with at least one disclosure from a given category. The numberof halts is less than the total number of disclosui^s in the full sample due to multiple disclosures,

"ReUims and absolute retums are expressed as percentages,'Good news are cases where retum is greater than zero,"Extreme news are cases where absolute retum is greater than or equal to 5 percent.

during the trading halts. The indications on the Halts Log are range forecastsof the reopening price and include the hour and minute the specialist releasedthe indicatioa. Each sample halt is associated with at least one indication. Themaximum number of iodications for a given halt is four. For the full sample,57 halts are associated with one indication, 97 have two indications, 25 havethree indications, and the other 10 are associated witb, four indications. The


Figure 1 Time line depicting sequence of events in selected trading halt

8/31/884:30 P.M.XYZCo.stock closesat $25 1/2

6:27 P.M.XYZ reportsfirst-quartersales coulddecline by15-20% andearningswill likelyexperience agreater decline

9/1/889:30 A.M.Openingdelayeddue tosell orderimbalance

9:38 A.M.FirstspecialistindicationLow = 21High = 24

9:44 A.M.SecondspecialistindicationLow = 20High = 23

9:49 A.M.ThirdspecialistindicationLow = 19High = 22

10:07 A.M.FourthspecialistindicationLow = 19High = 21

10:28 A.M.XYZ opensat $19 3/4

mean (median) number of trading minutes between halt initiation and the firstindication is 36.5 (21). The mean (median) number of trading minutes beforetrade resumes after the final indication is 17.31 (15).

Measuring price discovery during trading halts using specialist indicationsOur tests assess the relationship between sign and magnitude of news (as re-flected in halt period retums) and the properties of price discovery during tradinghalts. We measure the price discovery process using the properties of specialistindications as a proxy for market adjustment during halt periods. An exampleis useful for illustrative purposes. Figure 1 shows a time line depicting theactual events in a trading halt called for a sell order imbalance on 9/1/88. ^̂The company's stock closed at $25 1/2 the prior day. After closing on 8/31, thefirm issued a press release appearing on the Broad Tape at 6:27 P.M. stating itexpected a substantial decline in future earnings and sales. On the morning of9/1, the opening of the stock was delayed due to a market imbalance of sellorders. The market specialist disseminated four indications during the halt. Thefirst occurred 8 minutes into the halt, and the last was released 21 minutes priorto the opening of trade at 10:28 A.M. This halt is classified in our tests as both badand extreme news since the retum over the halt period equals —22.55 percent.

The primaiy conjecture we seek to test is whether price discovery duringhalts is more uncertain or difficult for extreme and bad news. These aspectsof price discovery are manifested in several measurable properties of specialistindications, such as the spread in indications, ex post accuracy of price fore-casts, and various dynamic measures such as revisions in indications or thetotal number of indications released. The spread in indications should be widerwhen the specialist is more uncertain about the opening price. Note in Figure1 the $3 difference between the low and high prices at the first indication. In

16 The events in Figure 1 apply to an actual trading halt appearing in our sample. We havechanged the name of the halted security to maintain data confidentiality pursuant to our agree-ment with the NYSE. The trading halt in Figure 1 was not randomly selected but was chosenfor purposes of illustration.


cross-section, we expect this difference to be greater for extreme and bad news.The ex post accuracy of price forecasts in specialist indications should be

poorer when price discovery is uncertain. Thus, we expect indications to beless accurate predictors of opening price for bad and extreme news. Given thatindications are specified as bounded range forecasts, there are several ways tomeasure their ex post accuracy. One method is to compute a point forecastequal to the midpoint of the low and high indication prices and then measureits deviation from the actual opening price. For example, the midpoint of lovfand high pdce for the first indication in Figure 1 equals $22 1/2. The deviationbetween this forecast and the opening price of $19 3/4 is $2.75. The magnitudeof this difference should be greater, on average, for bad and extreme news.

A secood measure of ex post accuracy is the frequency of cases whentlie opening price falls outside the bounds of the indication. The first indica-tion in Figure 1 specifies a low price of $21 whereas the opening price was$19 3/4. The frequency of this event should be greater for halts classified as bador extreme news.

A third way to measure ex post accuracy is to impute probability estimatesto various events for a given indication and then assess the specialist's abilityto recognize a particular event prior to its realization. For example, the haltdepicted in Figure 1 can be characterized as a bad news event. Assuming thatthe probability mass characterizing the specialist's beliefs follows a particulardistributional form, it is straightforward to compute the probability of bad newsat each indication. Note in Figure 1 that the high price at the first indicationis less than the closing price of $25 1/2. Assuming that the specialist's priorbeliefs ai"e bounded at the high and low prices, he assigned a probability of 1.0at tile first indication to the event that the halt is bad news. If price discovery ismore uncertain for bad news, we expect the probability assigned to the eventualsign of the price change will be lower for bad news compared to good news

Price discovery processes that are more difficult should also be characterizedby a greater number of total indications and indication revisions when the high(low) price is revised upward (downward). Cases when a high (low) price isrevised upward (downward) suggest that the specialist is moving the supportof Ms distiibution rather than shrinking distributional support in convergencetoward an opening price. For illustrative purposes, compare the revisions at thethird and fourth indications in Figure 1. At the third indication, the specialistshifts the entire distribution down by lowering both the low and high prices. Atthe final indication, the distributional support of the distriibution is reduced via adownward revision in the high price. We expect to observe distributional shiftsof the type apparent at the third indication in Figure 1 more frequently for badand extreme news. We also expect to observe more total indications for haltsclassified as bad or extreme news.

To measure some of these effects, we assume the specialist's beliefs aboutopening price at the A''' indication are uniformly distributed between the high


price, Pnok, and low price, Piok-^^ Let Pc and Po represent closing and openingprices associated with a given halt. ^̂ Given the uniformity assitmption, themean and variance of the specialist's beliefs about Po at the fe* indication are,respectively:

^ ( l a )

and

" 12

We use the variance estimate in (lb) as a measure of the dispersion in specialistbeliefs and test whether it is, on average, greater for bad and extreme news."

We also used these assumptions for two measures of ex post accuracy. Thefirst is an absolute forecast error (AFE) representing the scaled absolute deviationbetween actual opening price and the mean of the specialist's prior beliefs atthe fe* indication:

(2)

We call the second ex post accuracy meastire the "probability of correctsign" measure. For all good news (Pg > Pc) halts, the probability of correctsign equals the implied probability of good news at the fe* indication:

> P.) = ^-MZJ^p2t:^lsill!^ (3)Puok ~ Plok

Similarly, the probability of correct sign for a bad news (Pg < Pc) halt at thefe* indication is equal to Pr^XPo < Pc) or 1 - Pr^ (Po > Pc)'-

Empirical resultsTable 3 provides summary statistics and significance tests on the variance ofspecialist beliefs for various sample partitions by sign and magtiitude of news.Recall that the variance is a measure of the dispersion of specialist beliefssince its magnitude depends on the difference between high and low indicationprices. We report results separately for the first indications during halts and final

17 The conclusions from analysis based on the uniformity assumption are qualitatively similarto those from a more general assumption that the specialist's priors are described by anystable synunetric distribution. For example, assuming that the specialist's beliefs are normallydistributed and that he communicates a 95 percent confidence interval by his indication wouldnot materially alter our predictions.

18 Po is a random variable as it is unknown until trading resumes.19 We replicated all tests that employ the variance measure using the coefficient of variation with

similar results.


TABLE 3Suaiitiary statistics and significance tests for variance of specialist beliefs at first and final indicationsfor sample partitioBs according to sign and magnitude of news

First indications" Final indications"

Mean(Median)variance'' T-statistic'=

Mean(Median)variance T-statistic

Extreme news (N = 75)" 0.816 (0.750)Non-extreme news (N = 114)'* 0.451 (0.333)Bad news (N = 69)'* 0.524 (0.188)Good news (N = 107)* 0.691 (0.521)Extreme bad news (N = 25) 0.739 (0.333)Extreme good news (N = 50) 0.854 (0.750)Non-extreme bad news (N = 44) 0.401 (0.159)Noa-extreme good news (N = 57) 0.548 (0.333)

3.01

-1.35

-0.45

-1.27

0.283 (0.083)0.188(0.083)0.197 (0.083)0.264(0.188)0.235 (0.083)0.307(0.188)0.175(0.083)0.226(0.188)

1.85

-1.32

-0.75

-1.60

'Variance equals (Puak - Pu,kfll2."First (inal) indications refer to the first indication released after halt initiation (beforetrading resumes). For the 57 halts with one indication, the first and final indications are the same.

"^T-statistics apply to a test of differences in cross-sectionai means. When appropriate, thisstatistic is computed assuming unequal variances.

''Extreme (non-extreme) halts are those with absolute retums greater than or equal to (lessthan) 5 percent. Bad (good) news ate cases with negative (positive) retums.

indications before the resumption of trade. We base our infere.iices on a ?-testfor differences in cross-sectional average variance. ̂ ^

The evidence in Table 3 strongly supports the conjecture that specialist beliefsas represented in indications are more diffuse for extreme news. At the firstindication, the mean (median) variance is 0.816 (0.750) for the extreme groupcompared to 0.451 (0.333) for the non-extreme sample. A ^test for differencesin means rejects the null of no difference at 0.01 for a two-tailed test. The samepattem is observed at final indications although the difference is much lesssignificaat. At the final indication, the difference in means is only marginallysignificant at the 0.10 level. Finally, note that in no case are we able to rejecttlie hypothesis of equal cross-sectional average variance for comparisons of badand good

20 Many of the tests we conducted are not independent since some halts have only one indicationand the tests employ overlapping sample partitions. Also, where we could reject an F-test onthe equality of variances at 0.05, we computed the /-statistics assuming unequal cross-sectionalvariances. We also replicated our f-tests using nonparametric altematives (e.g., the Wilcoxontest), and our inferences are robust to the use of altemative tests. Our sample is not likely tobe materially affected by cross-sectional dependence since the halts exhibit little btmching incalendar time. Of the 58 trading days in the sample period, only one has zero trading halts. Inaddition, we replicated the analysis on Tables 3 through 6 and Table 8 on the imbalance haltsonly, with results very similar to those reported on the tables.We employ multiple partitions comparing bad and good news because in our sample, goodaews halts are also more likely to be extreme news. Of the good news halts, we classified46.7 petceat as exlrem.e compared to only 36.2 percent of the bad news halts. Hence, wealso report comparisons of bad vs. good news holding the classification of extreme vs. non-extreme constant.

21


Summary statistics and significance tests for ex post accuracy measures forfirst and final indications are reported in Table 4. We used the same samplepartitions and identical statistical tests. The accuracy measures are the absoluteforecast error and probability of correct sign variables as defined above. Weused the latter only for the comparison between bad and good news. At firstindications, the midpoint of high and low prices tends to be a relatively poorpredictor of opening price for the extreme news group. The mean absolute fore-cast error at the first indication is 0.063 (0.019) for the extreme (non-extreme)group. The difference between these means is significant at 0.01 using a two-tailed /- test. This difference is not significantly different from zero at the finalindication, suggesting that accuracy differences have largely dissipated prior tothe resumption of trade.

The evidence in Table 4 also suggests that specialist indications are poorerpredictors of opening price for bad news. However, this effect occurs only fornon-extreme news. The mean and median absolute forecast error at the firstindication are greater for the non-extreme bad news group relative to the non-extreme good news group. The t-test rejects the null of equal mean absoluteforecast errors for this comparison at the 0.05 level (two-tailed test). The prob-ability of correct sign measure provides confirming evidence. At both first andfinal indications, the specialist is significantly poorer at identifying the natureof news for non-extreme bad news compared to non-extreme good news.

As an alternative accuracy measure, we also investigated the frequency ofcases for which the actual opening price fell outside the bounds of the specialist'sfirst indication. For the full sample of 189 halts, in 25 cases where the openingprice is either greater than the high price or less than the low price in thespecialist's first indication. This tendency is most pronounced for extreme news.For the 75 halts classified as extreme news, in 17 (22.7 percent) cases, theopening price fell outside the bounds of the first indication. Only eight of the 114(7.0 percent) non-extreme halts are cases in which the opening price fell outsidethe specialist's first indications. A Chi- square test of these frequencies rejectsthe null of no association at the 0.005 level. We found no substantial differencesin the frequency with which opening price fell outside the first indication incomparing bad and good news.

A consistent pattern in the results is that specialists have more diffuse beliefsabout opening price and provide relatively poor predictions of opening price atfirst indications for halts classified as extreme news. Further, these differenceshave largely (but not entirely) dissipated by the time of the final indication.This suggests that later indications in a multiple indication halt are likely to bebetter predictors of opening prices. We provide evidence consistent with thisconjecture in Table 5, which shows the frequency of opening prices that falloutside the bounds of first and final indications in the 132 multiple indicationhalts in the sample. At first indications, the eventual opening price falls outsidethe indication bounds in 24 cases compared to only 4 cases at the final indicationbefore trading resumes.

i 8 8 8 8

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•oa

t i

£3

ol y

-ctH'3CM)

sign

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m

S

ls l l511

^^ "̂ ^ o ' >n' <ON O

^ r̂ - o\ o^ o^ r~~ co\ df cS d> d' <z> d

r ^ o c 4 nO O_ O O O_ O_ Od d d d d d d d

m O ' — i ' — t r ^ r O i — " Oooooooopd

rnONooooooooood d d d d d d d

— zS«-HIC S a) S .aj ia > 5p x S e

•S •S

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TABLE 5Frequency of cases when opening pdce fails outside of indication bounds for first andfinal indications in mtiltiple indication halts"

Number of caseswhen Pi_sPa< PH"

Ntimber of caseswhen Po < PL or Pa > PH'

Total

First indicationduring trading halt

108(81.8%)

24(18.2%)

132(100.0%)

Final indicationbefore trading resumes

128(97.0%)

4(3.0%)

132(100.0%)

Chi-squarestatistic**

14.873"

"Multiple indication halts are cases in which the specialist issues more than one indicationduring the trading halt.

''Chi-square statistic for 2 x 2 contingency table with continuity correction."Cases when opening price (P^) between low {Pi) and high {PH) indication price"'Significant at 0.001 level

when opening price (PJ less than low price (Pi) or greater than high price (P^)

The evidence in Table 6 strongly suggests that halts classified as extremenews are characterized by more protracted price discovery processes. Of the75 extreme news cases, only 10 (13.3 percent) are halts where the specialistissues only one indication. Forty-seven of the 114 (41.2 percent) non-extremecases are single indication halts. A Chi-square test of association rejects thehypothesis that magnitude of news and frequency of single indication haltsare independent at the 0.001 level. In 34 of the 91 (37.4 percent) indicationrevisions, the high price was increased or the low price was decreased for theextreme news sample, compared to 20 of 86 (23.3 percent) indication revisionsfor the non-extreme group. However, the Chi-square test rejects the hypothesisof independence at only the 0.10 level. In no case is the frequency of singleindication halts or indication revisions with high price increases or low pricedecreases proportionately greater for bad news.

In summary, our results suggest that trading halts associated with extremenews are associated with more uncertain and protracted price discovery pro-cesses. Specialists' first indications in extreme news halts exhibit greater mag-nitude differences between low and high prices and are relatively poor predictorsof opening price. Further, extreme news cases are associated with a greater totalnumber of specialist indications and revisions in indications when high price isincreased or low price decreased. The evidence for comparisons between badand good news is far weaker. The evidence suggests that first indications in haltsclassified as bad news are poorer predictors of opening price, but this effect isrestricted primarily to nonextreme price changes.

ExtensionsWe investigated several other aspects of our data to provide evidence on ad-

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TABLE 7Properties of specialist indications by type of disclosure

Disclosure category

1. Acquisitions2. Capital structure changes3. Divestitures4. Earnings/Dividends5. Legal6. Leveraged buyouts7. Takeover targets

Rank-order correlation withpercentage of extreme news

Percentage of haltsclassified as extreme"

31.323.345.575.012.550.047.9

halts

Average variance atfirst indication''

0.5430.4800.1960.4060.1440.7660.764

.4643''

Average numberof indications"

1.751.702.001.921.631.922.13

.7028'

"Percentage of halts when absolute retum is greater than or equal to 5 percent•"Cross-sectional average of variance in first specialist indication as defined in (lb)"^Cross-sectional average of the total number of specialist indications•"Not significant at 0.10 level, two-tailed test"Significant at 0.10 level, two-tailed test

ditional issues that arise from either the design or results of our primary tests.First, our price discovery tests classify disclosures according to observed pricechanges rather than characteristics measured independently of price changes. Toensure that these results are associated with the actual disclosures, we exam-ined properties of indications for altemative disclosure classes. Table 7 reportsthe cross-sectional average variance of the first specialist indication and thetotal number of indications for all seven disclosure categories. There is a posi-tive rank-order correlation between these variables and the percentage of haltsclassified as extreme news. This suggests a relationship between the actual dis-closures and properties of specialist indications, but only one of the correlationsis marginally significant.

Second, our tests are partly motivated by those in Patell and Wolfson (1982),who document that bad news releases are more likely to be released after tradingcloses. Their evidence also suggests that highly informative disclosures are morelikely to be released when the exchange is closed. Our disclosure sample exhibitssimilar properties. Of the 75 extreme news disclosures associated with halts, 45(60.0 percent) were released while the exchange was closed. Only 37 to 100disclosures associated with non-extreme halts were released while the exchangewas closed. Finally, 10 of 21 (47.6 percent) sample disclosures released aftertrading closed on the day prior to the halt are associated with bad news haltscompared to 52 of 158 (32.9 percent) disclosures released on the day of thetrading halt.

We also investigated the association between quoted bid-ask spreads at theresumption of trade and the halt period retum. The bid-ask spread provides anadditioned measure of the extent to which uncertainty about prices has beenresolved during the halt period since these quoted prices reflect those at which


TABLE 8Summary statistics for quoted bid-ask spread at the resumption of trade

Extreme news (N = 75)'Non-extreme news (N = 114)'

Bad news (N = 69)'Good news (N = 107)'

Extreme bad news (N = 25)'Extreme good news (N = 50)'

Non-extreme bad news (N = 44)'Non-extreme good news (N = 57)"

(Ask price —

Percentage bid-ask spread''

Mean

0,0180.014

0,0180,013

0,0240.016

0.0150.011

Bid price)

Median

0.0160,009

0.0110,009

0.0220.013

0,0100.007

r-statistic''

2.09

2,00

1.7B

1,42

[(Ask price + Bid price)/2]T-statistics apply to a test of differences in means. When appropriate, the /-statistic iscomputed assuming unequal cross-sectional variances,

'Extreme (non-extreme) news is defined as cases when the absolute retum is greater thanor equa! to (less than) 5 percent. Bad (good) news is defined as cases when the retum is less(greater) than zero.

tbe specialist or limit order traders are willing to transact. ̂ ^ Table 8 reportssummary statistics on the percentage bid-ask spread for various subsamples.We are able to reject the hypothesis of equal mean spreads at the 0.05 levelfor a comparison of extreme and non-extreme news. Also, the average bid-askspread is larger following bad news halts. Although consistent with much ofour prev,ious evidence, these results are only suggestive since we employ nobenchmark to identify normal bid-ask spreads on halted securities.

SummaryIn this paper, we examine the empirical properties of corporate disclosure andprice discovery associated with NYSE temporary trading halts. We investigatedwhether (1) disclosures associated with trading halts are highly price informativeand (2) the process of price discovery as reflected in specialist indications is moreprotracted and characterized by greater uncertainty for extreme and bad newshalts. We examined bad news disclosures in our price discovery tests becauseof the Pateli and Wolfson (1982) paper, which documents a greater frequencyof bad news releases after the close of trading.

Oar empirical results generally support the proposition that trading haltsarise from nomroutine highly informative news releases that are characterized by

22 The bid and ask quotes are not perfect indicators of the specialist's beliefs since these canbe based on existing limit orders on the specialist's book. When quotes are based on existinglimit orders, the spread characterizing the specialist's willingness to trade will be wider thanthe quoted bid-ask spread.


more uncertain and protracted price discovery processes. First, the majority ofdisclosures associated with our sample of trading halts are ones whose arrivalcannot be predicted by investors but can have substantial valuation effects (e.g.,corporate takeovers and leveraged buyouts). Second, our results provide strongevidence that halts associated with large magnitude price changes exhibit moreuncertain and protracted price discovery processes during the halt. Specialistindications for extreme news halts are characterized by (1) greater magnitudedifferences between high and low prices, (2) poorer predictive accuracy withrespect to opening price, and (3) greater frequency. Finally, similar comparisonsfor bad and good news provide only weak support for the conjecture that badnews is associated with more uncertain and protracted price discovery processes.

The relationships between financial market microstructure, corporate disclo-sure, and the dynamics of stock price behavior constitute a rich area for futureempirical work. Empirical analyses that directly investigate the impact of alterna-tive institutional arrangements can potentially improve our understanding of thecomplex nature of security price dynamics. Although our focus in this paper hasbeen on temporary trading halts, future analyses could investigate the impacton price discovery of (1) multiple markets in the stock or related contingentclaims (e.g., options or stock futures), (2) heterogeneous investor beliefs, (3)differences in corporate disclosure practices or information environment (e.g.,security analyst following), or (4) alternative forms of corporate disclosure (e.g.,releases about accounting earnings relative to other information).

ReferencesAjinkya, B. and M. Gift, "Corporate Managers' Earnings Forecasts and Syxnmetrical

Adjustment of Market Expectatiotis," Journal of Accounting Research (Autumn1984) pp. 425-44.

Cohen, K., S. Maier, R. Schwartz, and D. Whitcomb, The Microstructure of SecuritiesMarkets (Englewood Cliffs, N.J.: Prentice-Hall, 1986).

Fabozzi, F. atid C. Ma, "The Over-the-counter Market and New York Stock ExchangeTrading Halts," The Financial Review (November 1988) pp. 427-437.

Gmndy, B. and M. McNichols, "Trade and the Revelation of Information throughPrices and Direct Disclosure," The Review of Financial Studies, vol. 4 (1989) pp.495-526.

Hopewell, M. and A. Schwartz, "Temporary Trading Suspensions in Individual NYSESecurities," Journal of Finance (December 1978) pp. 1355-1373.

, "Stock Price Movement Associated with Temporary Trading Suspensions:Bear Market versus Bull Market," Journal of Financial and Quantitative Analysis(November 1976) pp. 577-590.

Howe, J. and G. Schlarbaum, "SEC Trading Suspensions: Empirical Evidence,"Journal of Financial and Quantitative Analysis (September 1986) pp. 323-333.

Jennings, R. and L. Starks, "Information Cotitent and the Speed of Stock Price Adjust-ment," Journal of Accounting Research (Spring 1985) pp. 336-350.

Jensen, M. and R. Ruback, "The Market for Corporate Control: The Scientific Evi-dence," Journal of Financial Economics (1983) pp. 5-50.

Karpoff, J., "The Relation between Price Changes and Trading Volume: A Stirvey,"Journal of Financial and Quantitative Analysis (March 1987) pp. 102-126.


Kim, O., "Trade Volume and Price Reactions to Public Annouticetnetits," WorkingPaper, University of Pennsylvania (February 1989).

King, R., G. Pownall, and G. Waymire, "Expectations Adjustment Via Timely Earn-ings Forecast Disclosure: Review, Synthesis, and Suggestions for Future Research,"Journal of Accounting Literature (1990) pp. 113-144.

Kiyzanowski, L., "The Efficacy of Trading Suspension: A Regulatory Action De-signed to Prevent the Exploitation of Monopoly Information," Journal of Finance(December 1979) pp. 1187-1200.

.^ "Misinfonnation and Regulatory Actions in the Canadian Capital Markets:Some Empirical Evidence," Bell Journal of Economics (Autumn 1978) pp. 355-368.

Lee, C , "Information Dissemination and the Small Trader: An Intraday Analysis of theSmall Trader Response to Anaouncements of Corporate Earnings," Working Paper,University of Michigan (January 1990).

Patell, J. and M. Wolfson, "The Intraday Speed of Adjustment of Stock Prices toEamings and Dividend Announcements," Journal of Financial Economics (June1984) pp. 223-252.

, "Goods News, Bad News, and the Intraday Timing of Corporate Disclosures,"The Accounting Review (July 1982) pp. 509-527

Pincus, M., "Information Characteristics of Eamings Announcements and Stock MarketBehavior," Journal of Accounting Research (Spring 1983) pp. 155-183.

Schwartz, A., "The Adjustment of Individual Stock Prices during Periods of UnusualDisequilibda," The Financial Review (1982) pp. 228-239.

Schwartz, R., Equity Markets: Structure, Trading and Performance (New York: Harperand Row, 1988).

Sheffey, J., "Security Law Responsibility of Issuers to Respond to Rumors and OtiierPublicity: Re-examination of a Continuing Problem," The Notre Dame Lawyer(June 1982) pp. 755-796.

Appendix: Initial categorization of disclosures and observed frequencies

Category

1

Sabcategory

101

102103104

201

202

Description

AcquisitionsPreliminary disclosure of negotiations

or equity investmentPreliminary negotiations terminatedFormal proposal/agreementUpdate on status of proposal or

agreement

Capital structure changesAdopted/extended stock purchase rights

planDelaved distribution of stock Burchase

No. ofdisclosures

418

5

is

4

rights 1


No. ofCategory Subcategory Description disclosures

203 Announced intent to repurchase stock,initiate self-tender offer, or indicatedprogress of stock buyback 17

204 Initiated restmcting or announcedresults/progress of restmcturing 7

205 Common stock issues 2

31

3 Divestitures301 Release on divestitures/spin offs 9302 Revision/cancellation of divestiture

401402403

501

502503

agreement

Earnings/Dividend informationEamings atuiouncementsManagement eamings forecastsDividend reductions

Legal unrelated to takeoversRegulatory/Legal decision in favor

of firmRegulatory/Legal decision against firmAnnouncement of plans to appeal

2Ti

552

12

431

Leveraged buyouts601 Preliminary disclosure of possible

buyout 3602 Proposal offered, terms of existing

proposal increased, or agreementreached 12

603 Proposal rejected, withdrawn or notbeing considered 3

604 Update on status of proposal oragreement 6

24


No. ofCategory Subcategory Description disclosures

7 Takeover targets701 Preliminary disclosure of possible

takeover or equity investment 23702 Proposal offered, terms of existing

proposal offered, terms or agreementreached 38

703 Proposal rejected/Withdrawn ornegotiations terminated 5

704 Update on status of proposal oragreement 9

75

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Corporate disclosure and price discovery associated with NYSE temporary trading halts

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