Directory UMM :Data Elmu:jurnal:M:Multinational Financial Management:Vol11.Issue1.2001:

eJournal of Multinational Financial Management 11 (2001) 105 – 115

The effects of stock market rumors on stock

prices: evidence from an emerging market

Halil Kiymaz *

Finance and Economics-SBPA,Uni6ersity of Houston-Clear Lake,Houston,TX77058,USA

Received 5 June 1999; accepted 18 February 2000

Abstract

The purpose of this study is to investigate the effects of stock market rumors on the prices of stocks traded at the Istanbul Stock Exchange. The sample consists of 355 favorable rumors mentioned in the HOTS column of ‘Ekonomik Trend’. While positive significant abnormal returns are observed in each of the 4 days prior to the publication date, negative insignificant abnormal returns are detected in the post-publication period. The findings in the pre-publication period refute the strong form of market efficiency while the findings in the post-publication period suggest that investment decisions based on the published rumors would not benefit investors. A further analysis based on the content of rumor shows that earning expectations’ rumors, and purchases by foreign investors rumors generate greater impact on stock prices than other rumors. © 2001 Elsevier Science B.V. All rights reserved. JEL classification:G14; G15

Keywords:Stock market rumors; Emerging market; Istanbul stock exchange

www.elsevier.com/locate/econbase

1. Introduction

Although the behavior of stock market prices has been investigated extensively, the question of whether trading based on a particular set of information can lead investors to obtain excess returns remains as an interesting topic to study. Studies investigating effects of analysts’ recommendations or rumors on stock prices are

* Tel.: +1-281-2833208; fax: +1-181-2833951. E-mail address:kiymaz@cl.uh.edu (H. Kiymaz).

1042-444X/01/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S 1 0 4 2 - 4 4 4 X ( 0 0 ) 0 0 0 4 5 - 1

mainly related to the market efficiency hypothesis. The strong form of the efficient market hypothesis assumes that all information, whether public or private, is rapidly incorporated into security prices that no investor can use it to earn excess returns. Although the initial studies (i.e. Diefenback, 1972; Logue and Tuttle, 1973) argue that information based on market rumors does not have any economic value, later studies report statistically significant stock price reactions to stock market rumors. The majority of studies in literature seem to refute the market efficiency hypothesis in its strongest form. These studies document the existence of significant abnormal returns following analysts’ recommendations or rumors. Among them Lloyd-Davies and Canes (1978), Syed et al. (1989), Liu et al. (1990), Barber and Loeffler (1993) report abnormal stock price performance following recommenda-tions reported in the Dartboard column of the Wall Street Journal (WSJ).

The objective of this study is to investigate whether stock market rumors have any impact on common stocks traded at the Istanbul Stock Exchange (ISE) by examining the ‘Heard on the Street’ (HOTS) column of ‘Ekonomik Trend’ (ET) weekly. Furthermore, the impact of the content of the rumors on stock prices is investigated. The results of this study provide additional international evidence on effects of rumors on stock prices. The empirical findings show the existence of positive and significant abnormal returns in each of the 4 days prior to the publication date, and negative insignificant abnormal returns in the post-publica-tion period. These results suggest that rumors and gossips contained in the HOTS column have been disseminated prior to their publication. A further analysis based on the content of rumor reveals that the earning expectation rumors and purchases by foreign investors rumors generate greater impact on stock prices than other rumors.

2. Literature review

The question of whether trading based on recommendations and/or rumors published in newspaper or magazines would benefit investors has been investigated extensively. The empirical studies in this subject report mixed results. Diefenback (1972) and Logue and Tuttle (1973) are two initial studies reporting that analysts’ recommendations have no value for investors. Later studies, on the other hand, report that information provided by the Wall Street Journal Heard on the Street column or analysts contain valuable information to investors. Lloyd-Davies and Canes (1978) focus on financial analysts’ recommendations as discussed in the HOTS column of the WSJ. They report that buy recommendations provide significant positive abnormal returns, while sell recommendations are associated with significant negative abnormal returns on the day of publication. They conclude that analysts and investment advisors provide valuable service to investors. Liu et al. (1990) extend the Lloyd-Davies and Canes (1978) study with more recent sample and further analyze the effects of the single-company versus multi-company recom-mendations, and the trading volume around the publication day. Their findings are in the line with the Lloyd-Davies and Canes (1978) study. Moreover, the results

H.Kiymaz/J.of Multi.Fin.Manag.11 (2001) 105 – 115 107

indicate that investors respond earlier to the information and single company recommendations have greater impact on the stock prices than those of multi-com-pany recommendations. Finally, they report higher trading volume around the publication day.

There are also studies investigating the factors influencing the magnitude of stock market reaction to analysts’ information or recommendation provided by various publications. Beneish (1991) investigates explanations for the significant stock price reaction to analysts’ information reported in the ‘HOTS’ column of the WSJ. The results indicate that market reaction persists after controlling for confounding releases. Furthermore, stock prices adjust prior to publication when recommenda-tions are reported on a single firm. Huth and Maris (1992) examine the same issue in terms of the usefulness of recommendations in short term trade decision making and firm size. The findings indicate that information obtained from the HOTS column can produce statistically significant stock price movements. Firm size is found to be important only for negative comments in the column. Barber and Loeffler (1993) analyze the stock price and volume behaviors using recommenda-tions published in the Dartboard column of the WSJ. They report average positive abnormal returns of 4% in 2 days following the publication. Furthermore, average volume doubles normal volume level in the same period.

More recently, Mathur and Waheed (1995) investigate the stock price behavior of firms that are favorably mentioned in the ‘Inside Wall Street’ column of Business Week. The results reveal the existence of positive significant abnormal returns on the day before the publication date, the publication date, and 2 days after the publication date. The study suggests that information provided by the column is valuable to short term traders if transaction costs are low. Moreover, the results indicate that investors who invest long term based on the information obtain rate of returns below market returns.

In general, the studies on stock market rumors or analysts’ recommendations support the view that information provided to investors is valuable. This paper aims to investigate the effects of stock market rumors on stocks traded at the ISE to provide evidence from an emerging market.

3. Data and methodology

The study uses the stock market rumors published in the HOTS column of the ‘ET’ weekly magazine during the period of July 21, 1996 and August 17, 1997.1

The HOTS page is published every week in ET. Topics covered in the page include information about both single firms and a group of firms. The purpose of the page is to inform investors about market developments influencing stock prices. Informa-tion provided by the HOTS, with rare excepInforma-tions, is favorable.

Table 1 reports the sample selection and the division of final sample based on the content of rumors/gossip. During this period, a total of 614 favorable gossip/rumor are reported.2_{From this sample, the rumors published on the same topic and about}

the same firm in subsequent weeks are eliminated (186 rumors). Furthermore, firms with missing stock price data (73 rumors) are eliminated. The net sample consists of 355 rumors/gossip.

I, then, classify the net sample according to the content of the rumors. I identify six groups of rumors. They are earning expectations rumors; firm sales/export rumors, undervalued stocks rumors, purchases by foreign investors’ rumors, un-classified rumors, and rumors without any content. The distribution of net sample based on the content of rumors is outlined in the Panel B of Table 1. The most favorite topic of rumors is earnings expectations with 128 rumor, followed by unclassified topics3

with 108 rumors. Rumors without any content4

(68 rumors) are in the third place. The remaining topics include undervalued stocks (23 rumors), purchases by foreign investors (22 rumors), and sales/export expectations (six rumors).

Table 1

Sample selection and content of rumorsa

No. rumors

Panel A:Sample selection

All rumors published 614

186 Less: subsequently published rumors

Less: missing data 73

Net sample 355

Panel B:Classification of net sample based on the content of rumors

Content of rumors No. rumors

128 Earnings expectations

Firm sales/export 6

Undervalued stocks 23

22 Purchases by foreign investor

Unclassified 108

Rumors without content 68

355 Total

a_{This table presents the sample selection and the contents of rumors. The sample consists of favorable} stock market rumors published in the HOTS column of ‘‘Ekonomik Trend’’ weekly magazine during the period of 1996–1997. The magazine is published and distributed on Sundays.

2_{During the study period, there were only three unfavorable rumors/gossips in the HOTS column,} and they were not included in the study.

3_{These rumors are the ones, which do not fall into any of identified rumors/gossips group. However,} they do have contents.

4_{These rumors do not contain any reason. A typical rumor in this case would state that an increase} in stock prices is expected based on conversations on the street.

H.Kiymaz/J.of Multi.Fin.Manag.11 (2001) 105 – 115 109

Table 2

Daily average abnormal returns (AARs) surrounding the publication of rumorsa

Event days AARs (%) t-value

−0.99

−10 −0.17

−9 −0.03 −0.02

−8 −0.26 −1.29

−7 0.21 1.49

−6 −0.08 −0.05

−5 −0.20 −0.21

−4 0.52 3.29***

−3 0.29 2.14**

−2 0.76 4.89***

−1 0.78 3.60***

0 – –

+1 0.07 0.36

+2 −0.43 −2.65**

+3 −0.04 −0.06

+4 −0.37 −1.12

+5 −0.04 −0.44

+6 −0.21 −1.44

+7 0.21 1.31

+8 −0.04 −0.01

+9 −0.10 −0.09

+10 −0.42 −1.42

a_{This table presents the abnormal returns surrounding the publication datet=0 in theEkonomik} Trend. Abnormal return is calculated as the difference between the actual and expected return. Expected return is generated from the market model parameters. The ISE Composite index used as a proxy for market. The t-statistics tests the null hypothesis that the average abnormal returns are equal to zero.

*** Statistically significant at 1%. ** Statistically significant at 5%.

The event study methodology is employed to analyze the effects of rumors/ gos-sips on stock prices as surveyed by Brown and Warner (1985). The analysis period extends from day −30 to +30 relative to publication date t=0.5

4. Empirical results

To determine how stock prices are influenced by rumors, I pose three period analyses. The first examines the effect of rumors around the publication date. The

5_{Standard event methodology is used to measure the stock price reaction forNfirms in each of the} group of firms. The standard event study methodology is not spelled out here.

second looks for unusual activity prior to publication of rumors. Finally, I analyze price movement after the publication date.

The empirical results are reported on Tables 2 – 4. The daily average abnormal returns (AARs) for all rumors are calculated over −30 and +30 period relative to the event day 0. Only AARs for −10 and +10 period are reported on Table 2. The results indicate that the sample experiences statistically significant positive abnormal returns prior to publication of rumors. The AARs are 0.52, 0.29, 0.76 and 0.78% for the days −4, −3, −2 and −1, respectively. These results are statistically significant at 1% level. The AARs following the publication day are mostly negative and statistically insignificant. For example, AARs are 0.07% on day +1, −0.43% on day +2, −0.04% on day +3, and −0.37% on day +4. Only AARs on day +2 is statistically significant at 5% level.

The average cumulative abnormal returns (CARs) for all rumors are reported on Table 3. In the top part of table, the CARs around the publication of rumors are reported. The results indicate that firms experience positive abnormal returns just before and after publication date. For example, during (−1, +1), (−2, +2), and

Table 3

Cumulative abnormal returns (CARs) surrounding the publication of rumorsa

t-value CARs (%)

Windows

Combined periods

0.85 2.80***

(−1,+1)

3.09*** 1.19

(−2,+2)

3.42*** 1.35

(−5,+5)

1.89* 0.44

(−10,+10)

−0.75

(−20,+20) −0.28

−1.22

(−30,+30) −0.49

Prior to publication date

2.16

(−5,−1) 6.13***

1.82 4.06***

(−10,−1)

1.48

(−20,−1) 2.60**

After publication date

−0.81

(+1,+5) −1.31

−1.38 −1.39

(+1,+10)

−1.78

(+1,+20) −1.54

a_{This table presents the cumulative abnormal returns in combined, pre, and post publication period} relative to publication datet=0 in theEkonomik Trend. Abnormal return is calculated as the difference between the actual and expected return. Expected return is generated from the international market model parameters. Thet-statistics tests the null hypothesis that the cumulative abnormal returns are equal to zero.

*** Statistically significant at 1%. ** Statistically significant at 5%. * Statistically significant at 10%.

H . Kiymaz / J . of Multi . Fin . Manag . 11 (2001) 105 – 115 111 Table 4

Daily average abnormal returns (AARs) based on the content of rumorsa

Undervalued stocks

Earnings expectations Unclassified rumors Rumors without content

Days Sales/export expectations Purchases by foreign investor

(n=68) (n=22)

(n=23)

(n=6) (n=108)

(n=128)

t-value AARs

AARs t-value AARs t-value AARs t-value AARs t-value AARs t-value

0.15 −0.62 −0.56 −0.15 −0.35

0.81 0.36

−10 −0.52 −1.55 0.72 0.30 0.30

−0.40 −0.79 1.13 −1.15 −1.94** 0.10 0.27 0.24 0.82 0.46 0.66

−9 1.17

−0.37 −0.11 −0.16 0.11 −0.15 −0.32 −0.36

−8 −0.58 −1.49 0.04 −0.31 −0.30

0.43 0.38 1.05 0.58 1.37 −0.09

0.23 0.04

−7 0.02 0.45 0.13 −0.02

−0.34 0.87 1.13 −0.11 0.29 −0.07

−6 −0.29 −0.78 0.44 0.40 0.08 0.21

−0.11 0.75 1.74* −0.33 −0.04 −1.03

−0.14 −1.93*

−5 0.13 0.19 0.57 0.50

0.75

0.65 2.44** −0.51 −0.47 1.13 0.09 −0.41 0.83 2.94*** −0.02 −0.19

−4

0.71 0.97 1.97* 0.30 1.46 0.47

0.32 1.35

0.21

−3 0.51 −2.58 −2.27**

0.21

0.67 2.38** 0.15 0.15 0.35 −0.55 −1.38 1.96 6.32*** −0.30 0.11

−2

0.22

−0.12 −0.89 −0.46 −0.56 0.37 1.41 2.17** 2.18 5.88*** 0.37 0.89

−1

– – – – – –

– –

–

0 – – –

0.81 −1.46 −2.40** 0.20 0.60 0.21 0.04

+1 0.15 0.84 −1.10 −1.05 0.40

−0.28 −0.09 −0.51 −0.35 −1.04 −0.87

−0.11

−1.60

+2 −0.35 −0.79 −0.68

−2.34**

0.24 −0.98 −1.40 0.10 0.13 −0.24 −0.49 −0.56 −1.07 0.40 0.56

+3 −1.16

0.63 −0.60 −0.99 −0.21 −0.09 −0.20

0.24 0.42

−0.62

+4 −2.07** −1.74 −1.29

0.30

−0.07 −0.22 1.58 −1.36 0.50 1.04 1.65 −0.13 −0.12 −0.44 −0.80

+5

0.02 −0.39 −0.95 −0.20 −0.12 −0.34 −0.74 −0.21 −0.68

+6 −0.08 −0.74 0.06

0.24 −0.04 −0.10 0.08 0.79 0.64

0.12 1.37

0.00 0.21

+7 0.16 0.41

−0.53

0.21 1.05 −0.57 −0.52 −0.93 −0.40 −0.74 −0.08 −0.06 −0.11 −0.17

+8

−1.05 1.07 2.01** −0.46

+9 0.21 1.16 −0.22 −1.75* −0.80 −1.17 −0.07 −0.06

0.68 0.39 0.68 −0.34 −0.26 −1.33

0.31

−0.35 −1.15 0.81 0.76

+10

−2.08**

a_{This table presents the average abnormal returns based on the content of rumors, surrounding the publication datet=0 in theEkonomik Trend. Abnormal return is calculated as the difference between}

the actual and expected return. Expected return is generated from the market model parameters. The ISE Composite index used as a proxy for market. Thet-statistics tests the null hypothesis that the average abnormal returns are equal to zero. Event day (0) represents the day of publication of rumors/gossips and corresponds to Sundays.

*** Statistically significant at 1%. ** Statistically significant at 5%. * Statistically significant at 10%.

(−5, +5) periods, CARs are 0.85, 1.19 and 1.35%, respectively. All of them are statistically significant at 1% level. In longer time periods firms experience negative insignificant abnormal returns. For example, during (−20, +20) and (−30, +30) periods, CARs are −0.28 and −1.22%, respectively. Based on these results, one would conclude that stock market rumors provide valuable information to in-vestors. Trading based on rumors would provide statistically significant abnormal returns.

I further analyze the behavior of stock prices in the period prior and after the publication of rumors. In the middle part of Table 3, the CARs in the pre-publica-tion period for several windows are reported. The CARs in the pre-publicapre-publica-tion periods are positive and statistically significant. For example; during (−20, −1), (−10, −1) and (−5, −1) periods, the CARs are 1.48, 1.82 and 2.16%, respec-tively. While the results of (−20, −1) period is statistically significant at 5%, others are statistically significant at 1% level. The significant positive stock price reaction in the pre-publications days may be explained by two interpretations. The first one is related to the possible use of information by those who initially posses it. These may include insiders, who may use information for their trading, and stock analysts, who may supply information to their clients for trading. The second interpretation can be attributed to the nature of the HOTS column itself. Typically, the stocks mentioned in HOTS are those that recently have been performing well. This may be one of the reasons why almost all HOTS rumors/gossips are favorable. Finally, in the bottom part of Table 3, CARs in the after-publication period are analyzed. The CARs for (+1, +5), (+1, +10) and (+1, +20) windows are

−0.81, −1.38 and−1.78%, respectively. None of these results is statistically significant. The negative gains in the after publication period would support the view that trading based on the rumors would not benefit to investors and informa-tion does not have any value. These results are contrary to those of recent US studies, which reports a positive price reaction after the publication date. The results of this study seem to suggest the possible dissemination of information prior to the publication date.

To further analyze the differences in stock price reaction with respect to the contents of rumors, I classify gossips into six sub-groups. The results of AARs are reported on Table 4. When the pre-publication period is examined,earning expecta

-tions rumors, purchases by foreign in6estor rumors, and unclassified rumors groups

have positive significant abnormal returns. For example; earning expectations rumors group experiences abnormal returns of 0.65 and 0.67% on days −4 and

−2. Both findings are statistically significant at 5% level.Unclassified rumorsgroup shows the highest abnormal returns in pre- publication period. The AARs are 2.18, 1.96 and 0.83% for the days −1, −2, and −4, respectively. All of them are statistically significant at 1% level. The rest of the groups do not show any statistically significant specific patterns. In the period following the publication of rumors, the AARs are mostly negative and some of them are statistically signifi-cant. For example; earning expectations group has a return of −0.62% on day

+4, which is statistically significant at 5% level. Similarly, purchases by foreign investors group experiences a return of −1.46% on the day +1.

H.Kiymaz/J.of Multi.Fin.Manag.11 (2001) 105 – 115 113

Table 5 reports for CARs for three periods based on the subjects of rumors. In pre-publication period positive CARs are observed forearning expectations’rumors,

purchases by foreign in6estors rumors, andunclassified rumorsgroups. For example;

earning expectations’ rumors group experience CARs of 1.54% during (−5, −1) windows,purchases by foreign in6estors rumorsandunclassified rumorsgroups have

CARs of 2.48 and 4.94% in the same time period. While the first two results are statistically significant at 5% level, the last one is highly significant at 1% level.

In the after publication period, all CARs are negative but none of them is statistically significant. For combined periods, only CARs for other topics are statistically significant. For example; CARs are 2.37, 3.99, 3.89% in the windows (−1, +1), (−2, +2) and (−5, +5) respectively. The analysis of content of rumors reveals that there are differences in abnormal returns with respect to the content of stock market rumors. Clearly rumors related toearning expectationsand

purchases by foreign in6estors have greatest impact on stock prices, while others

have statistically insignificant effects.

Overall, the empirical results indicate the existence of positive statistically signifi-cant abnormal returns in the pre-publication period of rumors. Such findings would refute the strong form of market efficiency, and are in line with the existing literature. The findings pertaining to the post-publication of rumors, on the other hand, show that there are statistically insignificant negative abnormal returns. This suggests that investment strategies based on the published rumors would not generate any wealth gains to investors, implying that information provided by column does not have any value at all. The results seem to suggest the possible dissemination of information prior to publication, and are contrary to those of US studies.

5. Summary and conclusions

The question of whether the trading based on a particular set of information can lead investors to obtain abnormal returns continues to receive attention from researchers and investors. A vast majority of these studies on analysts’ recommen-dations and stock market rumors reports statistically significant stock price reaction to the publication of information and, hence, concludes that information has value. This study investigates the effects of stock market rumors/gossips on the prices of stocks traded at the ISE by using 355 favorable rumors mentioned on the HOTS column of ‘ET’. The empirical findings suggest that there are statistically significant abnormal returns around the publication date. While positive, significant abnormal returns are observed in each of the 4 days prior to the publication date, negative insignificant abnormal returns are detected in the post-publication period. The significant stock price reaction in pre-publications days may be interpreted in two ways. The first one is related to the possible use of information by either insider, who may use information for their trading, or stock analysts, who may supply information to their clients for trading. This interpretation suggests the dissemina-tion of informadissemina-tion prior to publicadissemina-tion. The second interpretadissemina-tion can be

at-H . Kiymaz / J . of Multi . Fin . Manag . 11 (2001) 105 – 115 Table 5

Cumulative abnormal returns (CARs) based on the content of rumorsa

Undervalued stocks Purchases by foreign investor Unclassified rumors Rumors without content Sales/export expectations

Earnings expectations

(n=68)

(n=128) (n=6) (n=23) (n=22) (n=108)

t-value CARs t-value CARs t-value CARs t-value CARs

CARs t-value CARs t-value

Prior to publication

−1.06 1.43 0.70

(−20,−1) 0.14 0.50 3.44 0.74 −3.19 4.10 3.37*** 1.27 1.11

0.12 3.11 1.95* 5.62 5.86***

0.51

−0.05 −0.16

−0.36 −0.34

−0.19 (−10,−1) −0.22

1.09 2.48 2.00** 4.94 7.41***

1.54 −0.56

(−5,−1) 2.10** −2.82 −0.99 1.35 −0.10

After publication

0.80 −1.34 −1.02 −1.05

−0.65 −0.69 −0.69 −0.90 −0.94

(+1,+5) −3.45 −1.26 0.92

−0.06 −0.52 −0.11 −0.22

(+1,+10) −0.49 −0.26 −5.36 −1.29 −0.35 −0.95 −1.99 −1.17

0.48 −1.33 −0.19 −3.68 −1.56 −1.97 −1.13 1.35

−0.62 −0.31 −3.19 −0.45

(+1,+20) Combined periods

−1.14 0.62 0.83 −0.05 −0.15 2.37 4.58***

0.08 0.58 0.63

(−1,+1) 0.03 −1.55

0.62 −0.69 −0.82 3.99 5.88***

0.35 0.71 −0.57

(−2,+2) 0.39 −2.19 1.07 −0.64

1.34 1.13 0.69 3.89 4.74***

0.88 −1.40 −0.59

(−5,+5) 0.99 −6.29 −1.59 2.28

0.03 2.59 1.30 3.42 3.47***

0.15

−0.95 −2.15

−0.45 −5.74 −0.58

−0.72 (−10,+10)

−0.48 −0.13 0.24 −1.83 −0.41 0.09 0.36 0.42 1.28 −0.69 −0.12

(−20,+20) 0.21

0.15 −2.06 −0.34 2.43 0.77 −5.01 −0.39 −0.95 −0.19 1.28

(−30,+30) 1.05 0.58

a_{This table presents the cumulative abnormal returns based on the content of rumors in combined, pre, and post publication period relative to publication datet=0 in theEkonomik Trend. Abnormal return}

is calculated as the difference between the actual and expected return. Expected return is generated from the market model parameters. The ISE Composite index used as a proxy for market. Thet-statistics tests the null hypothesis that the cumulative abnormal returns are equal to zero.

*** Statistically significant at 1%. ** Statistically significant at 5%. * Statistically significant at 10%.

H.Kiymaz/J.of Multi.Fin.Manag.11 (2001) 105 – 115 115

tributed to the nature of the HOTS column itself. Typically, the stocks mentioned in HOTS are those that recently have been performing well.

The negative insignificant abnormal returns in the post-publication period may suggest that investment decisions based on the published rumors would not benefit investors. Hence, information provided by such columns may not have any value. A further analysis based on the content of rumors/gossips reveals that earning expectations’ rumors, purchases by foreign investor rumors generate higher abnor-mal returns than other rumors.

Acknowledgements

I thank the participants at the 1999 meeting of Financial Management Associa-tion InternaAssocia-tional, the editor, and an anonymous referee for helpful suggesAssocia-tions on an earlier draft.

References

Barber, B.M., Loeffler, D., 1993. The dartboard column: second-hand information and price pressure. Journal of Financial and Quantitative Analysis 28, 273 – 283.

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Brown, S.J., Warner, J.B., 1985. Using daily stock returns: the case of event studies. Journal of Financial Economics 14, 3 – 31.

Diefenback, R., 1972. How good is institutional brokerage research. Financial Analyst Journal 28, 54 – 60.

Huth, W.L., Maris, B.A., 1992. Large and small firm stock price responses to heard on the street recommendations. Journal of Accounting and Auditing 7, 27 – 47.

Liu, P., Smith, D., Syed, A., 1990. Stock reaction to the Wall Street Journal’s securities recommenda-tions. Journal of Financial and Quantitative Analysis 25, 399 – 410.

Lloyd-Davies, P., Canes, M., 1978. Stock prices and publication of second-hand information. Journal of Business 51, 43 – 56.

Logue, D., Tuttle, D., 1973. Brokerage houses investment advises. The Financial Review 8, 38 – 54. Mathur, I., Waheed, A., 1995. Stock price reactions to securities recommended in Business Week’s inside

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H.Kiymaz/J.of Multi.Fin.Manag.11 (2001) 105 – 115

110

second looks for unusual activity prior to publication of rumors. Finally, I analyze price movement after the publication date.

The empirical results are reported on Tables 2 – 4. The daily average abnormal returns (AARs) for all rumors are calculated over −30 and +30 period relative to the event day 0. Only AARs for −10 and +10 period are reported on Table 2. The results indicate that the sample experiences statistically significant positive abnormal returns prior to publication of rumors. The AARs are 0.52, 0.29, 0.76 and 0.78% for the days −4, −3, −2 and −1, respectively. These results are statistically significant at 1% level. The AARs following the publication day are mostly negative and statistically insignificant. For example, AARs are 0.07% on day +1, −0.43% on day +2, −0.04% on day +3, and −0.37% on day +4. Only AARs on day +2 is statistically significant at 5% level.

The average cumulative abnormal returns (CARs) for all rumors are reported on Table 3. In the top part of table, the CARs around the publication of rumors are reported. The results indicate that firms experience positive abnormal returns just before and after publication date. For example, during (−1, +1), (−2, +2), and

Table 3

Cumulative abnormal returns (CARs) surrounding the publication of rumorsa

t-value CARs (%)

Windows

Combined periods

0.85 2.80***

(−1,+1)

3.09*** 1.19

(−2,+2)

3.42*** 1.35

(−5,+5)

1.89* 0.44

(−10,+10)

−0.75

(−20,+20) −0.28

−1.22

(−30,+30) −0.49

Prior to publication date

2.16

(−5,−1) 6.13***

1.82 4.06***

(−10,−1)

1.48

(−20,−1) 2.60**

After publication date

−0.81

(+1,+5) −1.31

−1.38 −1.39

(+1,+10)

−1.78

(+1,+20) −1.54

a_{This table presents the cumulative abnormal returns in combined, pre, and post publication period} relative to publication datet=0 in theEkonomik Trend. Abnormal return is calculated as the difference between the actual and expected return. Expected return is generated from the international market model parameters. Thet-statistics tests the null hypothesis that the cumulative abnormal returns are equal to zero.

*** Statistically significant at 1%. ** Statistically significant at 5%. * Statistically significant at 10%.

H . Kiymaz / J . of Multi . Fin . Manag . 11 (2001) 105 – 115 111 Table 4

Daily average abnormal returns (AARs) based on the content of rumorsa

Undervalued stocks

Earnings expectations Unclassified rumors Rumors without content Days Sales/export expectations Purchases by foreign investor

(n=68) (n=22)

(n=23)

(n=6) (n=108) (n=128)

t-value AARs

AARs t-value AARs t-value AARs t-value AARs t-value AARs t-value

0.15 −0.62 −0.56 −0.15 −0.35

0.81 0.36

−10 −0.52 −1.55 0.72 0.30 0.30

−0.40 −0.79 1.13 −1.15 −1.94** 0.10 0.27 0.24 0.82 0.46 0.66

−9 1.17

−0.37 −0.11 −0.16 0.11 −0.15 −0.32 −0.36

−8 −0.58 −1.49 0.04 −0.31 −0.30

0.43 0.38 1.05 0.58 1.37 −0.09

0.23 0.04

−7 0.02 0.45 0.13 −0.02

−0.34 0.87 1.13 −0.11 0.29 −0.07

−6 −0.29 −0.78 0.44 0.40 0.08 0.21

−0.11 0.75 1.74* −0.33 −0.04 −1.03

−0.14 −1.93*

−5 0.13 0.19 0.57 0.50 0.75

0.65 2.44** −0.51 −0.47 1.13 0.09 −0.41 0.83 2.94*** −0.02 −0.19

−4

0.71 0.97 1.97* 0.30 1.46 0.47

0.32 1.35

0.21

−3 0.51 −2.58 −2.27** 0.21

0.67 2.38** 0.15 0.15 0.35 −0.55 −1.38 1.96 6.32*** −0.30 0.11

−2

0.22

−0.12 −0.89 −0.46 −0.56 0.37 1.41 2.17** 2.18 5.88*** 0.37 0.89

−1

– – – – – –

– –

–

0 – – –

0.81 −1.46 −2.40** 0.20 0.60 0.21 0.04

+1 0.15 0.84 −1.10 −1.05 0.40

−0.28 −0.09 −0.51 −0.35 −1.04 −0.87

−0.11

−1.60

+2 −0.35 −0.79 −0.68

−2.34**

0.24 −0.98 −1.40 0.10 0.13 −0.24 −0.49 −0.56 −1.07 0.40 0.56

+3 −1.16

0.63 −0.60 −0.99 −0.21 −0.09 −0.20

0.24 0.42

−0.62

+4 −2.07** −1.74 −1.29 0.30

−0.07 −0.22 1.58 −1.36 0.50 1.04 1.65 −0.13 −0.12 −0.44 −0.80

+5

0.02 −0.39 −0.95 −0.20 −0.12 −0.34 −0.74 −0.21 −0.68

+6 −0.08 −0.74 0.06

0.24 −0.04 −0.10 0.08 0.79 0.64

0.12 1.37

0.00 0.21

+7 0.16 0.41

−0.53

0.21 1.05 −0.57 −0.52 −0.93 −0.40 −0.74 −0.08 −0.06 −0.11 −0.17

+8

−1.05 1.07 2.01** −0.46

+9 0.21 1.16 −0.22 −1.75* −0.80 −1.17 −0.07 −0.06 0.68 0.39 0.68 −0.34 −0.26 −1.33

0.31

−0.35 −1.15 0.81 0.76

+10

−2.08**

a_{This table presents the average abnormal returns based on the content of rumors, surrounding the publication datet=0 in theEkonomik Trend. Abnormal return is calculated as the difference between} the actual and expected return. Expected return is generated from the market model parameters. The ISE Composite index used as a proxy for market. Thet-statistics tests the null hypothesis that the average abnormal returns are equal to zero. Event day (0) represents the day of publication of rumors/gossips and corresponds to Sundays.

*** Statistically significant at 1%. ** Statistically significant at 5%. * Statistically significant at 10%.

H.Kiymaz/J.of Multi.Fin.Manag.11 (2001) 105 – 115

112

(−5, +5) periods, CARs are 0.85, 1.19 and 1.35%, respectively. All of them are statistically significant at 1% level. In longer time periods firms experience negative insignificant abnormal returns. For example, during (−20, +20) and (−30, +30) periods, CARs are −0.28 and −1.22%, respectively. Based on these results, one would conclude that stock market rumors provide valuable information to in-vestors. Trading based on rumors would provide statistically significant abnormal returns.

I further analyze the behavior of stock prices in the period prior and after the publication of rumors. In the middle part of Table 3, the CARs in the pre-publica-tion period for several windows are reported. The CARs in the pre-publicapre-publica-tion periods are positive and statistically significant. For example; during (−20, −1), (−10, −1) and (−5, −1) periods, the CARs are 1.48, 1.82 and 2.16%, respec-tively. While the results of (−20, −1) period is statistically significant at 5%, others are statistically significant at 1% level. The significant positive stock price reaction in the pre-publications days may be explained by two interpretations. The first one is related to the possible use of information by those who initially posses it. These may include insiders, who may use information for their trading, and stock analysts, who may supply information to their clients for trading. The second interpretation can be attributed to the nature of the HOTS column itself. Typically, the stocks mentioned in HOTS are those that recently have been performing well. This may be one of the reasons why almost all HOTS rumors/gossips are favorable. Finally, in the bottom part of Table 3, CARs in the after-publication period are analyzed. The CARs for (+1, +5), (+1, +10) and (+1, +20) windows are

−0.81, −1.38 and−1.78%, respectively. None of these results is statistically significant. The negative gains in the after publication period would support the view that trading based on the rumors would not benefit to investors and informa-tion does not have any value. These results are contrary to those of recent US studies, which reports a positive price reaction after the publication date. The results of this study seem to suggest the possible dissemination of information prior to the publication date.

To further analyze the differences in stock price reaction with respect to the contents of rumors, I classify gossips into six sub-groups. The results of AARs are reported on Table 4. When the pre-publication period is examined,earning expecta

-tions rumors, purchases by foreign in6estor rumors, and unclassified rumors groups

have positive significant abnormal returns. For example; earning expectations

rumors group experiences abnormal returns of 0.65 and 0.67% on days −4 and

−2. Both findings are statistically significant at 5% level.Unclassified rumorsgroup shows the highest abnormal returns in pre- publication period. The AARs are 2.18, 1.96 and 0.83% for the days −1, −2, and −4, respectively. All of them are statistically significant at 1% level. The rest of the groups do not show any statistically significant specific patterns. In the period following the publication of rumors, the AARs are mostly negative and some of them are statistically signifi-cant. For example; earning expectations group has a return of −0.62% on day

+4, which is statistically significant at 5% level. Similarly, purchases by foreign investors group experiences a return of −1.46% on the day +1.

H.Kiymaz/J.of Multi.Fin.Manag.11 (2001) 105 – 115 113

Table 5 reports for CARs for three periods based on the subjects of rumors. In pre-publication period positive CARs are observed forearning expectations’rumors,

purchases by foreign in6estors rumors, andunclassified rumorsgroups. For example;

earning expectations’ rumors group experience CARs of 1.54% during (−5, −1)

windows,purchases by foreign in6estors rumorsandunclassified rumorsgroups have

CARs of 2.48 and 4.94% in the same time period. While the first two results are statistically significant at 5% level, the last one is highly significant at 1% level.

In the after publication period, all CARs are negative but none of them is statistically significant. For combined periods, only CARs for other topics are statistically significant. For example; CARs are 2.37, 3.99, 3.89% in the windows (−1, +1), (−2, +2) and (−5, +5) respectively. The analysis of content of rumors reveals that there are differences in abnormal returns with respect to the content of stock market rumors. Clearly rumors related toearning expectationsand

purchases by foreign in6estors have greatest impact on stock prices, while others

have statistically insignificant effects.

Overall, the empirical results indicate the existence of positive statistically signifi-cant abnormal returns in the pre-publication period of rumors. Such findings would refute the strong form of market efficiency, and are in line with the existing literature. The findings pertaining to the post-publication of rumors, on the other hand, show that there are statistically insignificant negative abnormal returns. This suggests that investment strategies based on the published rumors would not generate any wealth gains to investors, implying that information provided by column does not have any value at all. The results seem to suggest the possible dissemination of information prior to publication, and are contrary to those of US studies.

5. Summary and conclusions

The question of whether the trading based on a particular set of information can lead investors to obtain abnormal returns continues to receive attention from researchers and investors. A vast majority of these studies on analysts’ recommen-dations and stock market rumors reports statistically significant stock price reaction to the publication of information and, hence, concludes that information has value. This study investigates the effects of stock market rumors/gossips on the prices of stocks traded at the ISE by using 355 favorable rumors mentioned on the HOTS column of ‘ET’. The empirical findings suggest that there are statistically significant abnormal returns around the publication date. While positive, significant abnormal returns are observed in each of the 4 days prior to the publication date, negative insignificant abnormal returns are detected in the post-publication period. The significant stock price reaction in pre-publications days may be interpreted in two ways. The first one is related to the possible use of information by either insider, who may use information for their trading, or stock analysts, who may supply information to their clients for trading. This interpretation suggests the dissemina-tion of informadissemina-tion prior to publicadissemina-tion. The second interpretadissemina-tion can be

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Table 5

Cumulative abnormal returns (CARs) based on the content of rumorsa

Undervalued stocks Purchases by foreign investor Unclassified rumors Rumors without content Sales/export expectations

Earnings expectations

(n=68) (n=128) (n=6) (n=23) (n=22) (n=108)

t-value CARs t-value CARs t-value CARs t-value CARs

CARs t-value CARs t-value

Prior to publication

−1.06 1.43 0.70

(−20,−1) 0.14 0.50 3.44 0.74 −3.19 4.10 3.37*** 1.27 1.11

0.12 3.11 1.95* 5.62 5.86*** 0.51

−0.05 −0.16

−0.36 −0.34

−0.19 (−10,−1) −0.22

1.09 2.48 2.00** 4.94 7.41***

1.54 −0.56

(−5,−1) 2.10** −2.82 −0.99 1.35 −0.10

After publication

0.80 −1.34 −1.02 −1.05

−0.65 −0.69 −0.69 −0.90 −0.94

(+1,+5) −3.45 −1.26 0.92

−0.06 −0.52 −0.11 −0.22

(+1,+10) −0.49 −0.26 −5.36 −1.29 −0.35 −0.95 −1.99 −1.17 0.48 −1.33 −0.19 −3.68 −1.56 −1.97 −1.13 1.35

−0.62 −0.31 −3.19 −0.45 (+1,+20)

Combined periods

−1.14 0.62 0.83 −0.05 −0.15 2.37 4.58***

0.08 0.58 0.63

(−1,+1) 0.03 −1.55

0.62 −0.69 −0.82 3.99 5.88***

0.35 0.71 −0.57

(−2,+2) 0.39 −2.19 1.07 −0.64

1.34 1.13 0.69 3.89 4.74***

0.88 −1.40 −0.59

(−5,+5) 0.99 −6.29 −1.59 2.28

0.03 2.59 1.30 3.42 3.47*** 0.15

−0.95 −2.15

−0.45 −5.74 −0.58

−0.72 (−10,+10)

−0.48 −0.13 0.24 −1.83 −0.41 0.09 0.36 0.42 1.28 −0.69 −0.12 (−20,+20) 0.21

0.15 −2.06 −0.34 2.43 0.77 −5.01 −0.39 −0.95 −0.19 1.28

(−30,+30) 1.05 0.58

a_{This table presents the cumulative abnormal returns based on the content of rumors in combined, pre, and post publication period relative to publication datet=0 in theEkonomik Trend. Abnormal return} is calculated as the difference between the actual and expected return. Expected return is generated from the market model parameters. The ISE Composite index used as a proxy for market. Thet-statistics tests the null hypothesis that the cumulative abnormal returns are equal to zero.

*** Statistically significant at 1%. ** Statistically significant at 5%. * Statistically significant at 10%.

H.Kiymaz/J.of Multi.Fin.Manag.11 (2001) 105 – 115 115

tributed to the nature of the HOTS column itself. Typically, the stocks mentioned in HOTS are those that recently have been performing well.

The negative insignificant abnormal returns in the post-publication period may suggest that investment decisions based on the published rumors would not benefit investors. Hence, information provided by such columns may not have any value. A further analysis based on the content of rumors/gossips reveals that earning expectations’ rumors, purchases by foreign investor rumors generate higher abnor-mal returns than other rumors.

Acknowledgements

I thank the participants at the 1999 meeting of Financial Management Associa-tion InternaAssocia-tional, the editor, and an anonymous referee for helpful suggesAssocia-tions on an earlier draft.

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