The relationship between stock liquidity risk and financial information quality criteria in Tehran Stock Exchange

Transcription

1 Iranian Journal of Managemen Sudies (IJMS) hp://ijms.u.ac.ir/ Vol. 8, No. 4, Ocober 2015 Prin ISSN: pp: Online ISSN: The relaionship beween sock liquidiy risk and financial informaion qualiy crieria in Tehran Sock Exchange Mahmoud Mousavi Shiri 1, Masomeh Roshandel 2 1. Deparmen of Economics, Managemen and Accouning, Payame Noor Universiy, Iran 2. Deparmen of accouning, Islamic Azad Universiy of Mashad, Mashad, Iran (Received: 27 March, 2014; Revised: 13 March, 2015; Acceped: 21 March, 2015) Absrac The curren sudy aims o invesigae he relaionship beween sock liquidiy risk and financial informaion qualiy crieria (i.e. he imely dividends announcemen, accruals qualiy and he percenage of profiabiliy predicion error) of companies lised on he Tehran Sock Exchange. For his purpose, 148 cases of daa from lised companies, colleced from 2007 o 2012, were employed in order o es he hypoheses during The resuls of he sudy reveal ha here is a significan relaionship beween liquidiy risk (he dependen variable) wih qualiy of accruals, percenage of profiabiliy predicion error and imely dividends announcemen (independen variables). High levels of accruals qualiy and imely dividends announcemen, cause reducion in sock's liquidiy risk, and high percenages of profiabiliy predicion error increase he socks' liquidiy risk. Keywords accruals qualiy, liquidiy risk, percenage of profiabiliy predicion, qualiy of informaion, imely dividends announcemen. Corresponding Auhor, mousavi1973@yahoo.com

2 504 (IJMS) Vol. 8, No. 4, Ocober 2015 Inroducion The role of liquidiy in asses pricing process is imporan because invesors would consider his issue if hey inend o sell heir asses, regardless of he exisence of a reasonable marke for hem. Empirical evidence shows ha liquidiy facors can play an imporan role in making he decision (e.g., Jeffrey 2011; Liang and Wei 2012; Lin e al., 2013). However, based on several sudies conduced on he subjec of he sudy, here is no accepable approach among he scholars (Zhang e al., 2009). Marke liquidiy represens he abiliy of rapidly rading he high volumes of shares wihou he curren price and low cos ransacion being affeced (Pasor and Sambaugh, 2003).A imes of reduced marke liquidiy, companies have differen degrees of efficiency and his efficiency considering he degree of risk aversion and marke volailiy is moderaed beween invesors and marke makers (Acharya and Pedersen, 2005; Brunnermeier and Pedersen, 2009; Chordia e al., 2000; Pasor and Sambaugh, 2003). A causal mechanism hrough which liquidiy may discipline managemen is idenified in Admai and Pfleiderer (2006) and Palmier (2002). If managemen s compensaion is ied o curren sock prices, hen increased liquidiy increases he cos of opporunism for managers by faciliaing informed selling or dumping. Therefore, liquidiy allows small shareholders o become major shareholders, righs and benefis of managemen will improve, and informed invesors will be encouraged owards invesmen. Thus, a posiive relaionship beween liquidiy and he performance of he company and he value of he company would no be far-feched (Fang e al., 2009).The purpose of his sudy is o find evidence concerning he relaionship beween informaion qualiy and liquidiy risk. Jeffrey (2011) saes ha he socks' liquidiy risk is defined as he sensiiviy of sock reurns o unexpeced changes in sock marke liquidiy, and he crieria used for he qualiy of financial informaion include accruals qualiy, he percenage of profiabiliy predicion and imely dividends announcemen (dividends announcemen is imely if released before July, he 22 nd, 2007).

3 The relaionship beween sock liquidiy risk and financial informaion Relaed lieraure Lamber e al. (2007) sugges ha higher informaion qualiy, ha is, more precise signals, lowers marke risk and hus he cos of capial in he radiional Capial Asse Pricing Model (CAPM) framework. CAPM assumes perfec liquidiy, which means ha here are always marke paricipans willing o ake he opposie posiion of any rade a he curren price. Consequenly, a firm s share price is simply a funcion of expecaions abou he firm s cash flow. Wih imperfec liquidiy, he demand and supply of shares by some marke paricipans could affec prices if ohers are no willing o rade a he curren prices. While marke risk exiss in boh perfecly and imperfecly liquid markes, liquidiy risk is an addiional and imporan sysemaic risk ha invesors face when markes are no perfecly liquid (Pasor and Sambaugh, 2003; Acharya and Pedersen, 2005; Sadka, 2006). Acharya and Pederson (2005) sudied he asses pricing process by liquidiy risk. They used capial asse pricing model and defined liquidiy risk as an independen variable. The model presens a unified framework for undersanding differen channels hrough he liquidiy risk ha may affec asse prices. Experimenal resuls clarify he roles conribuing o liquidiy in he expeced reurn on capial asses and i's pricing. Sadka (2006) showed ha liquidiy risk ha is measured by he covariance yields wih unexpeced changes in aggregae liquidiy is a decisive facor in deermining he marke price of securiies. His research resuls indicae he imporance of sysemaic risk in sock liquidiy performance securiies. Johnson (2009) invesigaed he relaionship beween urnover volume, liquidiy risk and liquidiy paymen. His findings revealed ha he volume and liquidiy have been non-relevan bu rading volume is posiively relaed o he liquidiy variance or liquidiy risk. Johnson (2009) poins ou ha empirical evidence from he Unied Saes Governmen bonds and financial markes confirms his new predicion. Lee (2011) examined world price of liquidiy risk in 50 counries in a period from 1988 o He indicaed ha he Unied Saes is an influence marke for global liquidiy risk. In addiion, he

4 506 (IJMS) Vol. 8, No. 4, Ocober 2015 saed ha he pricing process of liquidiy risk in differen counries according o he geographical, economic and poliical environmen is differen. His findings showed ha Sysemic aspecs of liquidiy offer Reasons for inernaional diversiy in an invesing porfolio and ha liquidiy risk is an iem ha specifies expeced reurn of capial asses. Jeffrey (2011) conduced a sudy o check how daa qualiy affeced he cos of capial hrough liquidiy risk. His inerpreaion of he liquidiy risk was sensiiviy of sock reurns o unexpeced changes in marke liquidiy. His research resuls indicae ha high qualiy of he daa is associaed wih low liquidiy risk. He also saed ha negaive relaionship beween financial reporing qualiy and liquidiy risk in he case of large shocks is sronger in he marke liquidiy. Lin e al., (2011) reviewed he pricing of liquidiy risk in corporae bonds for he period beginning January 1994 o March They found a posiive relaionship beween he expeced reurn on bonds and he bea of corporaion liquidiy, despie he level of liquidiy and some of he securiy feaures. The resuls showed ha liquidiy risk is a deermining facor in expeced reurn on corporae bonds. Liang and Wei (2012) examined he relaionship beween liquidiy risk and sock reurns in 21 developed counries in which foreign invesors may freely conver heir currencies. They saed ha by conrolling marke facor, value and size, global liquidiy risk is an imporan facor among all invesing porfolios in developed counries. Lin and Wu (2013) exended he marke iming lieraure o show ha seasoned equiy offerings (SEO( iming can be characerized by he dynamics of liquidiy risk. Tha is, firms end o issue SEOs when liquidiy risk declines o he poin where invesors have leas concern of he risk. In he absence of liquidiy risk, marke risk rises righ before SEOs and gradually falls aferwards. However, once hey incorporae liquidiy risk facor ino he model for expeced reurns, issuing firms' marke risk behaves like ha of mached non-issuers, suggesing an omied risk facor problem in SEO sudies ha does no ake ino accoun he effec of liquidiy risk on sock reurns. Their resuls imply ha, insead of iming alpha (i.e., exploiing overpricing, as behavioral finance has suggesed), issuing firms ime liquidiy bea o minimize

5 The relaionship beween sock liquidiy risk and financial informaion heir cos of equiy capial. Brandon and Wang (2013) sudied he effec of liquidiy risk on reurn predicion and performance of he fund during he years The fund daa used in his aricle were provided by he Lipper TASS daabase. They saed ha wihou he impac of liquidiy risk, muual funds' porfolios ha have benefied from he experience and skill of heir forecass managers have a good performance. Hypoheses According o he objecive of he sudy, he following hypoheses were posulaed: H 1 : There is a significan relaionship beween he qualiy of accruals and sock liquidiy risk. H 2 : There is a significan relaionship beween he percenage of profiabiliy predicion and sock liquidiy risk. H 3 : There is a significan relaionship beween imely dividends announcemen and sock liquidiy risk. Research Mehod Research mehod refers o a collecion of rules, insrumens, as well as reliable and sysemaic ways o analyze facs, discover unknown iems and find soluions for problems. Scienific research is divided ino hree caegories based on he purpose including: fundamenal research, pracical research, and research and developmen. The presen research is a pracical one in erms of purpose ha is o develop pracical knowledge in a cerain field. Measuring Research Variables Sock liquidiy risk: Pasor and Sambaugh (2003) saed ha i can be measured by esimaing he co variaion of a firm s sock reurns o unexpeced changes in aggregae liquidiy (i.e., hey develop a liquidiy bea ). They hen consruc an empirical asse pricing model ha includes liquidiy risk by exending he Fama and French (1993) hree-facor model o include a marke liquidiy facor:

6 508 (IJMS) Vol. 8, No. 4, Ocober 2015 M S H L r i, i i, MKT i, SMB i, HML i, LIQ i, where r i, is he monhly reurn in excess of he risk-free rae for sock i in monh, MKT, SMB, and HML are he Fama and French(1993) risk facors, and LIQ is he marke liquidiy facor in monh. LIQ is he marke liquidiy facor ha capures unexpeced changes in marke liquidiy. A higher liquidiy bea means a higher co variaion beween a sock s reurn and unexpeced changes in marke liquidiy, ha is, i indicaes higher liquidiy risk. MKT: is he excess expeced reurns of marke porfolio o he risk-free rae of reurn per monh (he marke facor). Tehran Sock Exchange index of yields and prices (TEDPIX) as he average marke reurn was used. The ineres rae bonds (monhly) wih a governmen guaraneed as risk-free rae of reurn was used in he calculaions. SMB: is he difference beween monhly porfolios reurns for small size company and monhly porfolios reurns for large size, in cases ha he raio of book value o marke value is conrolled. In fac, he concep of his variable is he sensiiviy of he expeced reurn on a share o he difference performances of small and large companies (Jeffrey, 2011). HML: is difference beween monhly porfolios reurns for high raio of book value o marke value and reurns of shares for low raio of book value o marke value, in cases ha he size facor is conrolled. Indeed This variable accouned for he sensiiviy of expeced reurns for a share in differen performances in valuable companies (high B/M) and growing companies (low B/M). In he classificaion based on variable-sized companies' breakdown of he porfolio was moderae. In he classificaion based on B/M also he breakdown of he porfolio was perceniles 30 and 70. The companies ha form he basis of he observed variable were pu below he 30 percenile of he porfolio, in low porfolios, and beween 30 and 70 in he average porfolios and over 70 in high porfolios. To calculae he size facor and he value facor, based on he mehod Fama and French's (1993) mehod independen classificaion ables based on size facor and B/M are as follow: (1)

7 The relaionship beween sock liquidiy risk and financial informaion Table 1. Independen classificaion ables based on size facor and B/M HIGH B/M MIDDLE B/M LOW B/M SMALL Small value Small Middle Small Growh BIG Big Value Big Middle Big Growh According o he ables, SMB and HML are calculaed as: SMB Toalreurn of smallporfolio 3 Toalreurnof big porfolio 3 HML Toalreurnof valualeporfolio 3 Toal reurn of grohing porfolio 3 : Sensiiviy of facors in relaion o marke facor, size and he raio of book value o marke value. : Sensiiviy of sock reurns o unexpeced changes in marke liquidiy which reflecs Liquidiy Risk Facor. LIQ: Monhly marke liquidiy is obained by aggregaing he individual sock liquidiy in each monh. LIQ represens innovaions (i.e., unexpeced changes) in monhly marke liquidiy. The liquidiy risk premium is an esimae of he cos of capial effecs arising from exposure o LIQ. The monhly liquidiy (у) for an individual sock Monhly liquidiy for sock "i" in monh "" resul from he leas squares esimaes, in he following regression model: e e r i, d, 1 i, i, ri, d, i, sign( ri, d, ) i, d, i, d 1, m 1,..., D (2) where: : Reurn of sock "i" on day "d" of monh "". : Excess daily reurn of sock "i" o he marke reurn on day "d" of monh "". : Trading volume (measured in million Rials) for sock "i" on day "d" of monh "". (Monhly Marke Liquidiy): he monhly weighed average liquidiy of companies in each year, calculaed as follows: N 1 (3) i, N i 1

8 510 (IJMS) Vol. 8, No. 4, Ocober 2015 To obain unexpeced changes in marke liquidiy, he following regression model was used: m a b ) where: 1 c( 1 m1 u N m 1 ( ) ( i, i, 1) (5) m N 1 i 1 (4) In hese models, is used o weigh and m is he oal moneary value of ransacions in monh -1 for shares ha are included in calculaion of he annual average, and m 1 also refers o oal moneary value of ransacions compued in he firs average monh. Finally, U in he above-menioned model represens unexpeced changes in marke liquidiy ha weighed up hrough being divided by 100 and liquidiy facor is hus obained: Independen variables (measures of daa qualiy): Accruals Qualiy: To measure he accruals qualiy based on Dechow & Dichev (2002), hyseresis model is calculaed as: (6) TCA i, = Toal accruals in year "" for conrol firm "i". CFO = Cash follows from operaions. = Income changes during he period of year -1 o, for conrol firm i. = Changes in receivable accouns and noes during he period of year -1 o, for conrol firm i. = Changes in he gross value of propery, machinery and equipmen during he period of year -1 o, for conrol firm i. The percenage of profiabiliy predicion error on he accrual process based on he above-menioned model called Residues (7)

9 The relaionship beween sock liquidiy risk and financial informaion Volailiy (sandard deviaion), is he resul of he regression during he sudy period. Logically since represens he accruals esimaed error o cash flow, higher variabiliy beween cash flow and profi shows lower qualiy accruals, because considering he accruals in profis reduced earning qualiy. Profiabiliy predicion error: o calculae he profiabiliy predicion error, we deduced he real benefi from he firs profi predicion and hen resul divided o he firs profi predicion. Timely dividends announcemen: sauory deadline for reporing earnings (forming he general meeing) for companies whose financial year ends in March is July 22 of he following year. Dividends announcemen is imely if released before July, he 22 nd, For his purpose, he number of days before he legal deadline o repor formal earnings is used for he calculaion of he variable (he abovemenioned daes are he legal daes for Iranian companies). Conrol variables Marke characerisics: Some of he characerisics of he marke are expeced o affec a company's sock liquidiy risk in his research, including sock liquidiy, volume of shares raded, and can be pas reurns and firm size, and are presened in he model as a conrol variable affecing sock liquidiy risks. Company's characerisics: The company has some characerisics expeced o be affecing is sock liquidiy risk, in his research, including marke value o book value of equiy and achieved sales growh, which as a conrol variable affecing sock liquidiy risks, are presen in he model. Research hypoheses esing Models Model o es he firs hypohesis:

10 512 (IJMS) Vol. 8, No. 4, Ocober 2015 Model o es he second hypohesis: Model o es he hird hypohesis: where: : A measure of sock Liquidiy risk of company i in year. AQ (A measure of informaion qualiy): Accruals qualiy firm i in year. LIQ: Liquidiy sock i in year. Sales Grows: Sales growh of company i in year over he previous year. M/B: Marke value o book value of sock i in year. Size: Size of company i in year. Reurn: Sock reurns of company i in year. EP: Profi predicion error of company i in year. ON: Timely dividends announcemen of company i in year. The Resuls Descripive saisics of he variables The following able shows he descripive saisics such as mean, sandard deviaion, skewness and sreching and coefficien of variaion for he daa. According o he able, he highes coefficien of variaion was relaed o accruals qualiy and he lowes changes were relaed o he firm sizes. According o Coefficien of Variaion, he mos variables have normal disribuion, bu he size of companies is more normally disribued han he oher variables.

11 The relaionship beween sock liquidiy risk and financial informaion Variables Re-2 Re-1 TA Size MB Sg LIQ AQ EP ON Table 2.Variables and descripive saisics Coefficien of Variaion Kurosis Skewness S.d Mean The resuls for he firs hypohesis: Firs, Chow es is used by he sofware Eveiws for deecion of using ordinary leas squares (OLS) or panel regression. If he Chow es were no significan, he OLS regression based on he year-firms would be used. Chow es resuls: Based on Chow Tes, he model did no have differen laiudes in he effec of ime. Therefore i does no require o using panel daa mehods in he form of asymmeric regression model and he linear cross-secional regression model was used. Table 3. Chow Tes Null Hypohesis: No breaks a specified breakpoins F-saisic Prob. F Table 4. Regression Saisics of he relaionship beween he qualiy of accruals and sock liquidiy risk Iem Regression Saisics The correlaion coefficien The coefficien of deerminaion Adjused coefficien of deerminaion The resuls of fiing his model show ha abou 21% of he variabiliy is explained by he model.

12 514 (IJMS) Vol. 8, No. 4, Ocober 2015 Model Error Toal P_value Table 5. ANOVA able of regression model F_value Mean square Degrees of freedom Sum of squares The ANOVA able and F-es indicae ha he probabiliy of he model is saisically significan. Variable (Consan) AQ TA Ri-1 Ri-2 size1 MB Sig. Table 6. Parameer Esimaion T_value Sandard deviaion Esimae P_value The final model fied o he firs hypohesis is as follows: Based on he probabiliy values of he model and compared wih a significance level of α=0.05 and also according o regression coefficien associaed wih accrual qualiy(aq) in he regression equaion, he null hypohesis can be rejeced a he 5% level and i could be claimed wih 95% confidence ha: There is a relaionship beween he qualiy of accruals and sock liquidiy risk. Resuls of residuals in he esing model of he firs hypohesis show ha based on he Kolmogorov-Smirnov es (K-S, d=1.196, P=0.115), normaliy of residuals canno be rejeced. Fixed variance is proved by he diagram agains he residual esimaion saionary and Durbin Wason Saisics (D-V=1.572) also expresses he lack of residual correlaion.

13 The relaionship beween sock liquidiy risk and financial informaion The resuls for he second hypohesis: Firs, Chow es is used by sofware Eveiws for deecion of using cross-secional or panel regression. If he Chow es were no significan, he OLS regression based on he year-companies would be used. Chow es resuls: Based on Chow es, he model does no have differen laiudes in he effec of ime. Therefore i is no required o use panel daa mehods in form of asymmeric regression model and he linear cross-secional regression model is used. Table 7. Chow Tes Null Hypohesis: No breaks a specified breakpoins. F-saisic Prob. F Table 8. Regression Saisics of he relaionship beween he percenage of profiabiliy predicion and sock liquidiy risk Facor Regression Saisics The correlaion coefficien The coefficien of deerminaion Adjused coefficien of deerminaion 0.03 The resuls of fiing his model show ha abou 31% of he variabiliy can be explained by he model. Model Error Toal P_value Table 9. The resuls of ANOVA Tes F_value Mean Degrees of square freedom Sum of squares The ANOVA Table and F-es indicae ha he probabiliy of he model is saisically significan. Variable (Consan) EP TA Ri-1 Ri-2 size1 MB Sg Table 10. Parameer Esimaion T_value Sandard deviaion Esimae P_value

14 516 (IJMS) Vol. 8, No. 4, Ocober 2015 The final model fied o he second hypohesis is as follows: Based on he probabiliy values of he model and compared wih a significance level of α=0.05 and also according o regression coefficien associaed wih profi predicion error (EP) in he regression equaion, he null hypohesis can be rejeced a he 5% level and i could be claimed wih 95% confidence ha: There is a relaionship beween he percenage of profiabiliy predicion and sock liquidiy risk. Resuls of Residuals in he esing model of he second hypohesis show ha, based on he Kolmogorov-Smirnov es (K-s d=1.137, P=0.150), normaliy of residuals canno be rejeced. Fixed variance is proved by he diagram agains he residual esimaion saionary and Durbin Wason Saisics (D-V=1.540) also expresses he lack of residual correlaion. The resuls for he hird hypohesis: Firs, Chow es is used by sofware Eveiws for deecion of using cross-secional or panel regression. If he Chow es were no significan, he OLS regression based on he year-companies would be used. Chow es resuls Based on Chow es, he model does no have differen laiudes in he effec of ime. Therefore i is no required o use panel daa mehods in form of asymmeric regression model and he linear cross-secional regression model is used. Table 11. Chow Breakpoin Tes Null Hypohesis: No breaks a specified breakpoins. F-saisic Prob. F Table 12. Regression saisics of relaion beween he imely dividends announcemen and sock liquidiy risk The correlaion coefficien The coefficien of deerminaion Adjused coefficien of deerminaion

15 The relaionship beween sock liquidiy risk and financial informaion The resuls of fiing his model show ha abou 19% of he variabiliy can be explained by he model. Model Error Toal P-value Table 13. The resuls of ANOVA es F-value Mean Degrees of square freedom Sum of squares The ANOVA able and F-es indicae ha he probabiliy of he model is saisically significan. (Consan) ON TA Ri-1 Ri-2 size1 MB Sg Table 14.Parameer esimaion. T-value Sandard deviaion Esimae P-value The final model fied o he hird hypohesis is as follows: Based on he probabiliy values of he model and compared wih a significance level of α=0.05 and also according o regression coefficien associaed wih Timely dividends announcemen (ON)in he regression equaion, he null hypohesis can be rejeced a he 5% level and i could be claimed wih 95% confidence ha: There is a relaion beween imely dividends announcemen and sock liquidiy risk. The resuls of residuals in he esing model of he hird hypohesis show ha The Kolmogorov-Smirnov es (K-s d=1255, P=0.086), normaliy of residuals canno be rejeced. Fixed variance is proved by he diagram agains he residual esimaion saionary and Durbin Wason Saisics (D-V=1.565) also expresses he lack of residual correlaion.

16 518 (IJMS) Vol. 8, No. 4, Ocober 2015 Conclusions and discussion Financial reporing should provide informaion o help presen and poenial invesors, crediors and oher sakeholders in assessing he amouns, iming and uncerainy of prospecive cash receips from dividends or ineres and he proceeds from he sale, redempion or mauriy of securiies or loan (SFAC No 1, Para 37). Therefore, his quesion is se forh for discussion of wha ype of informaion should be given o help user o make decisions and wha facors are appropriae for decision making models. In his sudy, he auhors conduc an exploraory analysis o deermine how informaion qualiy relaes o liquidiy risk and o marke risk in differen periods of unexpeced changes in marke liquidiy. This analysis, while no guided by any clear ex-ane predicion of how he relaionships would differ in differen periods, is moivaed by he fac ha exreme marke liquidiy evens, paricularly exreme negaive evens, significanly affec invesors welfare. For example, Pasor and Sambaugh (2003) highligh ha exposure o liquidiy risk doomed Long-Term Capial Managemen during a period of widespread deerioraion in marke liquidiy precipiaed by he Russian deb crisis. Oher scholars have also noed ha porfolio managers are concerned abou freezes in liquidiy (or liquidiy black holes ) in he equiy markes due o he disappearance of invesors or marke makers (Moorhy, 2003; Morris and Shin, 2003). Furhermore, he prior lieraure has documened ha liquidiy risk ends o be more pronounced during exreme negaive marke condiions (Brunnermeier and Pedersen, 2009; Hameed e al., 2010). Building on he lieraure, Lang and Maffe (2010) examined he relaionship beween ransparency and liquidiy risk during crisis periods. The presen research examined he relaionship beween informaion qualiy and liquidiy. A possible explanaion for he resuls relies on he noion ha large negaive/ (posiive) liquidiy shocks are, on average, associaed wih a significan fligh of invesors from/ (o) he equiy markes (Pasor and Sambaugh, 2003). During such shocks, informaion qualiy could have a greaer influence on invesors

17 The relaionship beween sock liquidiy risk and financial informaion decisions if invesors consider socks wih poor informaion qualiy o be risky, hey prefer o exi from hese socks when marke liquidiy declines and are only willing o inves in hem when marke liquidiy improves. This explanaion is based on he idea ha informaion qualiy affecs liquidiy risk because, condiional on changes in marke liquidiy, i has differen influences on he demand for individual socks. In his sudy, he relaionship beween informaion qualiy and liquidiy risk of companies sock lised on he Tehran Sock Exchange was examined. The resuls indicae ha here is a saisically significan relaionship beween he qualiy of accruals and sock liquidiy risk. Thus based on he fied model, one could say ha high levels of accruals qualiy decrease sock liquidiy risks and he relaion beween percenage of profiabiliy predicion error and sock liquidiy risk is saisically significan. In oher words, high percenages of profiabiliy predicion error increase sock liquidiy risks. Also, he relaionship beween imely dividends announcemen and sock liquidiy risk is saisically significan. So, i can be said ha high levels of imely profi repors reduced sock liquidiy risks.

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