Analyst Recommendation Bias andBrokerage House Shareholding
Masahito Kato
October 2016
Discussion Paper No.1635

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANAnalyst Recommendation Bias and Brokerage House Shareholding

Masahito Kato
Graduate School of Economics, Faculty of Economics, Kobe University
1-1 Rokkodai-cho Nada-ku, Kobe, Japan
kato_masahito@econ.kobe-u.ac.jp

Abstract
When a brokerage house owns a firm’s stock, a capital relationship is created between thebrokerage house and that firm. This paper examines the effect the capital relationship between abrokerage house and a firm has on the content of a recommendation, and the results are as follows.
First, the capital relationship between a brokerage house and a firm creates an optimistic bias inrecommendations. Second, the stronger the capital relationship between a brokerage house and afirm, the larger the optimistic bias in recommendations.

JEL Classification: G10, G14, G24
Keywords: Analyst, Recommendation, Conflicts of interest, Brokerage house, Shareholdings

11. Introduction
It is required that a security analyst (hereinafter “analyst”) should be a fairness. However, thefairness of analysts employed by brokerage houses is sometimes hindered by the variety ofpressures they face within the brokerage house.
A brokerage house consists of various departments. One of these departments, the investmentbanking department, primarily performs underwriting services such as equity financing andadvisory services for M&As. The investment banking department sometimes uses analysts todevelop friendly business relationships with firms providing such works. Lin and McNichols(1998) and Michaely and Womack (1999) have shown that analysts make optimistic
recommendations to firms with which the investment banking department is entering into anunderwriting contract1. The same is true of underwriting contracts for debt financing and advisory
contracts for M&As, with analysts making optimistic recommendations to firms that are clients of
the investment banking department (Kolasinski and Kothari (2008), Ljungquvist et al. (2007)). In
addition to using analysts to develop friendly business relationships with client firms, investmentbanking departments sometimes use them to acquire new client firms. Ellis et al. (2011) and
Boudry et al. (2011) have shown that the analyst behavior (in particular, offering optimistic
recommendations) prior to equity financing is a factor that affects how firms decide on anunderwriter2. Cliff and Denis (2004) have shown that when the underwriter’s analyst does notcover the firm after equity financing, there is an increased chance that the underwriter will bechanged in the subsequent equity financing.

New York Attorney General, Elliot Spitzer revealed that US Merrill Lynch analysts
had provided optimistic investment information to client firms of the investmentbanking department. Why did these analysts release false investment information tomarket participants? An investigation by Elliot Spitzer showed that analyst
compensations and promotions at Merrill Lynch were connected to the results of theinvestment banking business. (Nihon Keizai Shimbun, 5/10/2003).
2 There is also a research that optimistic recommendations do not affect the obtainingof underwriting services (Ljungquvist et al. (2006)).
1

2

Analyst fairness is also sometimes damaged by pressure within the brokerage department of abrokerage house. The primary job of a brokerage department is to distribute market participant
orders in the stock market. As brokerage departments receive a brokerage commission that
corresponds to market participant orders, increasing the number of orders leads to higher earnings
for the brokerage department. Therefore, analysts sometimes make optimistic recommendations toincrease market participant orders (Cowen et al. (2006), Agrawal and Chen (2008)).
This paper focuses on how the pressure within the self-dealing departments of a brokerage houseaffects analysts. The primary job of self-dealing departments is to make stock investments andbond investments with the brokerage house’s own funds to obtain investment income. If the market
price of an owned share becomes higher than it was at the time of purchase, a self-dealing
department can take the margin as investment income 3. In addition, holding a share whose value
has gone up compared to its purchase price increases the value of a brokerage house’s assets, even
if the share is not sold off. Just as in investment banking departments and brokerage departments,
self-dealing departments are likely to put pressure on analysts to make optimistic recommendations
to increase investment income or boost asset value in the stock market.
This paper investigates the correlation in Japanese firms between brokerage house shareholding
and optimistic bias in recommendations. The stock owned by brokerage houses is primarily
obtained through self-dealing by self-dealing departments. Japan relaxed regulations regarding
self-dealing through the financial system reforms of 1998, and self-dealing has since emerged as
one of the core businesses of brokerage houses. With this background, Japan is an extremely
appropriate subject for this paper to conduct its investigation. Moreover, plenty of detailed data
regarding brokerage house shareholding can be obtained in Japan. Accordingly, this paper
investigates the correlation between brokerage house shareholding and optimistic bias in
In the case of short selling, if the market price of an owned share becomes lower thanit was at the time of purchase, a self-dealing department can take the margin as
investment income.
3

3

recommendations using recommendations made to firms that were listed on the Japanese stock
market from March 2003 to February 2015 (the period for which data were available).
The results of the investigation are as follows. First, brokerage house shareholding creates anoptimistic bias in recommendations. Second, the more the value of shares (value base) held by abrokerage house, the larger the optimistic bias in recommendations. Third, the more the percentageof shares held by a brokerage house and the more its shareholder rights are strengthened, the larger
the optimistic bias in recommendations. All these results demonstrate that analysts change thecontent of recommendations to be optimistic for the benefit of the self-dealing department, andsuch analyst behavior may have been influenced by the pressure in the self-dealing department.
Fourth, recommendations made for firms in which the brokerage house is a shareholder are lessinformative than recommendations made for firms in which the brokerage house is not ashareholder. This result can be interpreted to mean that market participants are aware thatbrokerage house shareholding leads to optimistic bias in recommendations.
Prior research has revealed that analysts employed by brokerage houses experience pressure
from various departments within the brokerage house, rendering them unable to makerecommendations from a fair perspective. This paper investigates the correlation between pressure
in self-dealing departments and optimistic bias in recommendations, focusing on self-dealing
departments, which have not been addressed by such existing research. The results show that whena brokerage house owns a certain firm’s stock and a capital relationship forms between thebrokerage house and that firm, analysts create optimistic bias in recommendations. The said pointis this paper’s contribution.
IOSCO4 has offered a variety of opinions meant to increase analyst fairness. In the Global
Analyst Research Settlement of September 2003, proposals for measures to eliminate pressure
IOSCO stands for the International Organization of Securities Commissions, aninternational organization made up of institutions such as securities regulators andsecurity exchanges from various countries around the world.
44

primarily in investment banking departments and brokerage departments were presented. Kadan etal. (2009) and Corwin et al. (2016) compared optimistic bias in recommendations before and after
the Global Analyst Research Settlement, and the results revealed that there was less optimistic bias
following the settlement. Meanwhile, most of the recommendations used as samples for this paper
were released after the Global Analyst Research Settlement. However, the analysis results include
recommendations in which the optimistic bias was due to pressure in the self-dealing department.
In addition, it was found that market participants are aware of the bias and take theserecommendations with skepticism when evaluating. These issues must be improved in the future.
This paper is structured as follows. The next chapter explains data and samples, while Chapter 3
analyzes them. Chapter 4 provides additional analysis, and the final chapter gives conclusions.

2. Data and Samples
This paper primarily uses two databases: Nikkei Needs Financial Quest and Bloomberg. Nikkei
Needs Financial Quest is a database that records shareholder data for firms listed on the Japanesestock market. It was used to obtain the names of the top 30 shareholders and the number of shares
held at the settlement term (full-year settlement and midterm settlement) for each firm from March
2003. Bloomberg is a database that records analyst recommendations. It was used to obtain
information on recommendations released from 1996, and it included the content of therecommendation, the analyst’s name, and the name of the brokerage house in which the analyst was
employed.
The data set was created through the following steps. First, it was decided that the data set would
be formatted so that the brokerage house, firm, and settlement term (full-year settlement andmidterm settlement) were differentiated. Nikkei Needs Financial Quest was used to identify thebrokerage houses that owned a firm’s stock. This revealed that 13 brokerage houses owned a firm’s
5stock through self-dealing. However, among these 13 brokerage houses, the amount of self-dealing
was extremely small in 5. Since, for the purpose of the analysis, it was more appropriate to use
brokerage houses in which self-dealing had a certain amount of influence on the earnings structureof the brokerage house, the data set was created using 8 brokerage houses and excluding those 5 5.
The firms and their settlement terms were identified using Nikkei Needs Financial Quest. Second,
recommendation data were obtained from Bloomberg and merged into the corresponding areas ofthe data set. Recommendations can be released at a variety of times other that settlement terms. For
settlement term recommendations, Ljungquvist et al. (2007) and Corwin et al. (2016) looked atrecommendations released in the one-year period before the settlement term and selected therecommendation that was released at the time closest to that settlement term. This paper also adopts
their method and specifies recommendations at the settlement term. Among the set of firms, there
are some firms that are not the subject of analyst recommendations and some for which, by
coincidence, no recommendations were released in the one-year period before the settlement term.
Samples for which these circumstances apply are excluded from the data set. Furthermore, samples
for which relative recommendations (to be defined later) could not be created are excluded fromthe data set. Lastly, brokerage house shareholding data were obtained from Nikkei Needs Financial
Quest and merged into the corresponding areas of the data set. Table 1 shows the data set used in
the analysis. From March 2003 to February 2015, the period in which the use of Nikkei Needs
Financial Quest. shareholder date could be maximized, there were 46,768 recommendations
regarding listed Japanese firms. Of these, 1,800 (3.85%) recommendations were about firms whose
shares the brokerage house held.
Recommendations are defined using either the five ranks StrongBuy (5), Buy (4), Hold (3), Sell(2), and StrongSell (1) or the three ranks Buy (5), Hold (3), and Sell (1). These qualitative data

Even when 13 brokerage houses are included in the analysis, the results are similar
to the analysis results that will be shown later.
5

6

were converted into the numerical values shown next to the recommendations above in parentheses,
and then used for the analysis. The correlation between brokerage house shareholding andrecommendations is shown in the second column of Table 2. Recommendations for firms in which
the brokerage house held stock were scored 3.80 (mean value), while recommendations for firms inwhich the brokerage house did not hold stock were scored 3.71. This score shows thatrecommendations released were between Buy and Hold, and it reveals that analysts gave
recommendations that were about 0.09 points closer to Buy for firms in which the brokerage house
held stock.
The correlation between brokerage house shareholding and relative recommendation is shown in
the third column of Table 2.

relative recommendation ＝ recommendation － consensus recommendation

A relative recommendation is the difference between a recommendation and a consensus
recommendation. It is also an indicator that shows how recommendations by a certain analyst andanother analyst differ. A consensus recommendation is the median value of a recommendation
released by other analysts during the same settlement period. However, if multiple
recommendations were released by other analysts in the same period, the recommendation that was
released at the time closest to that settlement term was used. The third column of Table 2 showsthat analysts release recommendations that are more optimistic than the consensus recommendation
for firms whose shares the brokerage house holds.

3. Analysis
This paper examined the effect that a brokerage house shareholding has on recommendations,7

controlling for the characteristics of the analyst who released the recommendation, thecharacteristics of the brokerage house that employed the analyst, and the characteristics of the firmthat was the subject of the recommendation.

𝐼′

𝑅𝑒𝑙𝑅𝑒𝑐𝑖𝑗𝑘𝑡 = 𝛼 + 𝛽 × 𝐵𝑟𝑜𝑘𝑒𝑟 𝑠 𝑆ℎ𝑎𝑟𝑒ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑗𝑘𝑡 + ∑ 𝛾𝑖 × 𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝐶ℎ𝑎𝑟𝑖
𝑖=1
𝐽

𝐾

+ ∑ 𝛿𝑗 × 𝐵𝑟𝑜𝑘𝑒𝑟𝐶ℎ𝑎𝑟𝑖 + ∑ 𝜃𝑘 × 𝐹𝑖𝑟𝑚𝐶ℎ𝑎𝑟𝑘 + 𝜀𝑖𝑗𝑘𝑡
𝑗=1

・・・（A）

𝑘=1(The meaning of the subscripts is as follows: i is analyst, j is brokerage house, k is firm, and t issettlement term.)

The explained variable 𝑅𝑒𝑙𝑅𝑒𝑐𝑖𝑗𝑘𝑡 is the relative recommendation. The explanatory variable
𝐵𝑟𝑜𝑘𝑒𝑟 ′ 𝑠 𝑆ℎ𝑎𝑟𝑒ℎ𝑜𝑙𝑑𝑖𝑛𝑔𝑗𝑘𝑡 shows the capital relationship between the firm and the brokerage
house and is the variable being focused on most. This paper uses three variables: ① a dummy
variable (𝑆ℎ𝐷𝑢𝑚𝑚𝑦𝑗𝑘𝑡 ) that takes the value of 1 if the brokerage house held stock in the firm andtakes the value of 0 if it did not; ② the market value of the shares held by the brokerage house(𝑆ℎ𝑉𝑎𝑙𝑢𝑒𝑗𝑘𝑡 ); and ③ the percentage of shares held by the brokerage house (𝑆ℎ𝑂𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝𝑗𝑘𝑡 ).
Other explanatory variables, which were used as control variables, include variables related toanalyst characteristics, brokerage house characteristics, and firm characteristics.
𝑆𝑒𝑛𝑖𝑜𝑟𝑖𝑡𝑦𝑖𝑡 , 𝑆𝑒𝑎𝑠𝑜𝑛𝑖𝑛𝑔𝑖𝑡 , 𝑅𝑎𝑛𝑘𝑖𝑛𝑔𝑖𝑡 , 𝑁𝑓𝑜𝑙𝑙𝑜𝑤𝑖𝑡 , and 𝐽𝑜𝑏𝑚𝑜𝑣𝑒𝑖𝑡 are used to represent
analyst characteristics6. 𝑆𝑒𝑛𝑖𝑜𝑟𝑖𝑡𝑦𝑖𝑡 is the length of an analyst’s career, while 𝑆𝑒𝑎𝑠𝑜𝑛𝑖𝑛𝑔𝑖𝑡 is theHong and Kubik (2003), Ljungquvist et al. (2007), and Corwin et al. (2016) use theprecision of analyst earnings forecasts (Relative Forecast Accuracy) to control for theeffect of analyst reputation building (Reputational Concern) on recommendations.
However, as analyst earnings forecasts cannot be obtained from the Bloomberg
database, Relative Forecast Accuracy has been excluded from the variables.
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length of an analyst’s career for a firm; both are indicators of analyst experience. Hong and Kubik(2001) have demonstrated that analyst experience is positively correlated with the boldness ofearnings forecasts. 𝑅𝑎𝑛𝑘𝑖𝑛𝑔𝑖𝑡 is a dummy variable that takes the value of 1 if the analyst is in the
top 3 of the analyst rankings in his/her industry and takes the value of 0 if that is not the case.
Jackson (2005) has demonstrated that when there is pressure within an investment banking
department or brokerage department, an analyst with a good reputation (all-star analysts and thelike) provides accurate investment information rather than biased investment information.
𝑁𝑓𝑜𝑙𝑙𝑜𝑤𝑖𝑡 is the number of firms an analyst is responsible for and is an indicator of an analyst’s
workload. Clement (1999) has demonstrated that the analyst workload is negatively correlated withthe accuracy of investment information. 𝐽𝑜𝑏𝑚𝑜𝑣𝑒𝑖𝑡 is a dummy variable that takes the value of 1
if an analyst is transferred from one brokerage house to another during the settlement term andtakes the value of 0 if that is not the case.
𝐵𝑟𝑜𝑘𝑒𝑟𝑠𝑖𝑧𝑒𝑗𝑡 , 𝐼𝐵𝑅𝑒𝑙𝑗𝑘𝑡 , and 𝐼𝐵𝑆ℎ𝑎𝑟𝑒𝑗𝑡 are used to represent brokerage house characteristics.
𝐵𝑟𝑜𝑘𝑒𝑟𝑠𝑖𝑧𝑒𝑗𝑡 is the total number of analysts working in a brokerage house and is a proxy variableshowing the brokerage house size. Cowen et al. (2006) has demonstrated that investment
information (provided by analysts) employed at major brokerage houses has less affiliation bias.
This is because major brokerage houses are averse to damaging their reputation by misleading
market participants with optimistic recommendations. 𝐼𝐵𝑅𝑒𝑙𝑗𝑡 is a dummy variable that takes thevalue of 1 if an underwriting contract exists between a firm and a brokerage house within thethree-year period before the settlement term and takes the value of 0 if that is not the case.
𝐼𝐵𝑆ℎ𝑎𝑟𝑒𝑗𝑡 is the proportion of equity financing that the brokerage house was involved in as anunderwriter out of all equity financing in the one-year period before the settlement term. 𝐼𝐵𝑅𝑒𝑙𝑗𝑡
and 𝐼𝐵𝑆ℎ𝑎𝑟𝑒𝑗𝑡 are both proxy variables for the pressure put on an analyst by the investmentbanking department. Corwin et al. (2016) has demonstrated that pressure from the investmentbanking department is positively correlated with the optimism of recommendations.
9𝑀𝑉𝑘𝑡 , 𝐴𝑁𝐹𝑘𝑡 , 𝑃𝑟𝑜𝑐𝑒𝑒𝑑𝑠𝑘𝑡 , and 𝐼𝑛𝑠𝑡𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑘𝑡 are used to represent firm characteristics.
𝑀𝑉𝑘𝑡 is the market value of equity for the firm that is the subject of the recommendation. 𝐴𝑁𝐹𝑘𝑡is the analyst coverage for that firm in the one-year period before the settlement term. 𝑃𝑟𝑜𝑐𝑒𝑒𝑑𝑠𝑘𝑡
is the total value of equity financing that the firm conducted in the three-year period before thesettlement term. 𝐼𝑛𝑠𝑡𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑘𝑡 is the percentage of shares held by institutional investors at thetime of the settlement term. Ljungquvist et al. (2007) has shown that optimistic bias inrecommendations decreases when the pressure from institutional investors is strong.
Table 3, panel A shows the full sample, while Table 3, panel B shows descriptive statistics for the
sample divided based on whether or not the brokerage house held stock in the firm. RecBias was
0.17 for the entire sample. Dividing the sample based on whether or not the brokerage house is ashareholder demonstrates that analysts release optimistic recommendations for firms whose sharesthe brokerage house holds (0.28 vs. 0.17). ShValue and ShOwnership always take the value of 0
when ShDummy takes a value other than 1. When the characteristics of these variables werechecked only for the sample in which brokerage houses were shareholders, the mean value of
ShValue was 8.8 billion yen and ShOwnership was 1.06%. This means that, on average, a single
brokerage house owns 8.8 billion yen worth of a firm’s stock and owns 1.06% of all the shares ofthat firm, making the brokerage house a major shareholder. Trends like the following can be seen in
the other variables. In the sample in which brokerage houses were shareholders, InstHoldings,
Brokersize, IBShare, and Ranking values were higher than in the sample in which brokerage
houses were not shareholders, while Proceeds and Seniority values were lower. Table 4 shows thecorrelations between variables.
Table 5 shows the results of an analysis based on the model (A) regarding the effect of brokerage
house shareholding on the recommendations. In (1)–(3) in Table 5, ShDummy is used as thevariable representing the capital relationship between the firm and the brokerage house. (1)-(3)show the results when the fixed effects of (1) firm, (2) analyst, and (3) brokerage house are10

analyzed through model (A). The coefficient of ShDummy is always a positive value in (1)-(3).
These results show that even when controlling for a variety of factors that affect recommendations,analysts release recommendations that are more optimistic than the consensus recommendation forfirms whose shares the brokerage house holds.
In (4)-(6) in Table 5, ln(ShValue) is used as the variable representing the capital relationshipbetween the firm and the brokerage house. (4)-(6) show the results when the fixed effects of (4)firm, (5) analyst, and (6) brokerage house are each analyzed through model (A). In each of these
sections, the coefficient of In(ShValue) is a positive value. Firth et al. (2013) has shown that whena business relationship exists between a brokerage house and a mutual fund, analysts release
optimistic recommendations for the firms that are heavily weighted in the fund’s stock portfolio.
The ln(ShValue) is the market value of shares owned by a brokerage house. Accordingly, the resultsof (4)-(6) show that analysts make more optimistic recommendations as the market value of shares
held by the brokerage house increases.
In (7)-(9) in Table 5, ShOwnership is used as the variable representing the shareholding
relationship between the firm and the brokerage house. (7)-(9) show the results when the fixed
effects of (7) firm, (8) analyst, and (9) brokerage house are each analyzed through model (A). In
each of these sections, the coefficient of ShOwnership is a positive value. These results show thatanalysts make more optimistic recommendations as the brokerage house continues to increase thenumber of shares it holds and strengthens the influence it has over the firm as a major shareholder.

4. Additional Analysis
The previous chapters have shown that analysts release recommendations that are more
optimistic than the consensus recommendation due to the capital relationship between thebrokerage house and the firm. In that case, how do market participants—the users of analyst
11

recommendations—react to recommendations that include this optimistic bias? Recommendations
were tested for informativeness using the below model (B).

𝐴𝐵𝑆_𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙𝑅𝑒𝑡𝑢𝑟𝑛(0 𝑡𝑜 2) = 𝛼 + 𝛽 × 𝐵𝑟𝑜𝑘𝑒𝑟 ′ 𝑠 𝑆ℎ𝑎𝑟𝑒ℎ𝑜𝑙𝑑𝑖𝑛𝑔 + ∑ 𝛽 × 𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝐶ℎ𝑎𝑟+ ∑ 𝛽 × 𝐵𝑟𝑜𝑘𝑒𝑟𝐶ℎ𝑎𝑟 + ∑ 𝛽 × 𝐹𝑖𝑟𝑚𝐶ℎ𝑎𝑟+ ∑ 𝛽 × 𝑅𝑒𝑐𝐶ℎ𝑎𝑟 + 𝜀𝑖𝑗𝑘𝑡

・・・（B）The explained variable 𝐴𝐵𝑆_𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙𝑅𝑒𝑡𝑢𝑟𝑛(0 𝑡𝑜 2) is the absolute value of the difference
between the cumulative share price returns for the day the recommendation was released and twodays later, and the cumulative market returns for the same periods. 𝐵𝑟𝑜𝑘𝑒𝑟 ′ 𝑠 𝑆ℎ𝑎𝑟𝑒ℎ𝑜𝑙𝑑𝑖𝑛𝑔 uses
ShDummy, ShValue, and ShOwnership. The definitions of these variables are omitted here because
they were explained in the previous chapter. 𝐵𝑟𝑜𝑘𝑒𝑟𝐶ℎ𝑎𝑟𝑖 , 𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝐶ℎ𝑎𝑟, 𝐵𝑟𝑜𝑘𝑒𝑟𝐶ℎ𝑎𝑟, and
𝐹𝑖𝑟𝑚𝐶ℎ𝑎𝑟 also use the same variables as in the previous chapter. 𝑅𝑒𝑐𝐶ℎ𝑎𝑟, which is a new
addition here, uses Tier5, Change, Horizon, RecBias, and Rec. Tier5 is a dummy variable that takes
the value of 1 if recommendations are defined by the five ranks StrongBuy, Buy, Hold, Sell, andStrongSell, and takes the value of 0 if that is not the case. Change is the value of change from theprevious recommendation to the most recent recommendation. Horizon is the number of months
until settlement term t. RecBias is the difference between the recommendation and the consensus
recommendation. Rec is the content of the recommendation.
Table 6 shows the analysis results. In (1), ShDummy is the variable that represents the capital
relationship between the firm and the brokerage house, while ShValue is the variable that
represents this in (2). The coefficient of ShDummy is -0.193 and the coefficient of ShValue is-0.009, both of which are statistically significant under the 5% standard. Meanwhile, the coefficientof ShOwnership in (3) took a negative value just as in (1) and (2), but this value was notstatistically significant. Consistent results could not be obtained from these three variables.
12However, (1) shows that recommendations made to firms in which the brokerage house is ashareholder are less informative than recommendations made to firms in which the brokerage
house is not a shareholder. (2) shows that the informativeness of a recommendation drops as themarket value of shares held by the brokerage house goes up. These results suggest that when abrokerage house owns shares in a certain firm and analysts from the brokerage house release
recommendations about that firm, market participants will use the recommendations while viewing
them with a fair amount of skepticism.
In Table 7, the sample is separated into buy recommendations (StrongBuy or Buy), neutral
recommendations (Hold), and sell recommendations (Sell or StrongSell). The table shows theresults of an investigation that studied the correlation between share price response torecommendations in each category and brokerage house shareholding. In panel A, the coefficientsof (1) ShDummy, (2) ShValue, and (3) ShOwnership are all negative values, and all of these values
are statistically significant. These results show that the informativeness of buy recommendations
drops due to brokerage house shareholding. Meanwhile, panels B and C demonstrate that neutral
and sell recommendations are not affected by brokerage house shareholding.
Market participants can recognize that the capital relationship between a brokerage house and afirm creates optimistic bias in recommendations. However, market participants cannot perfectly
identify the recommendations that have optimistic bias. In such cases, market participants are likely
to take the capital relationship between a brokerage house and a firm as a signal of optimistic bias
and use recommendations for all firms that have a capital relationship with the brokerage house by
undervaluing the portion with optimistic bias. Therefore, as the same undervaluing ends up being
applied even to fair recommendations when there is a capital relationship between the brokerage
house and the firm, recommendations may not be used effectively in the pricing process of thestock market.

13

5. Conclusions
Since the regulations on self-dealing were relaxed, self-dealing departments have gained a large
amount of influence within Japanese brokerage houses. The goal of a self-dealing department is tomake stock investments with the brokerage house’s own funds to obtain investment income. Prior
research has reported that analysts release optimistic recommendations to increase the earnings ofthe brokerage house’s investment banking department or brokerage department. This paper
examined the effect the capital relationship between a brokerage house and a firm had on
recommendations, focusing on self-dealing departments, which have not been studied so far. The
results of this study demonstrated that the capital relationship between a brokerage house and afirm creates an optimistic bias in recommendations. Furthermore, they showed that the stronger thecapital relationship, the larger the optimistic bias in recommendations.
Although market participants are aware of such optimistic bias in recommendations, they cannot
accurately distinguish the recommendations that have an optimistic bias. As a result, they perceive
the capital relationship between a brokerage house and a firm as a signal of optimistic bias and use
the recommendations with some skepticism. These conditions mean that when there is a capital
relationship between the brokerage house and the firm, the same suspicions are applied to thecontent of fair recommendations as well. There is an urgent need for future measures that willallow recommendations to be effectively used in the pricing process of the stock market.

14

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recommendations”, The Journal of Law & Economics 51(3), pp. 503-537.
Boudry, W. I., J. G. Kallberg, and C. H. Liu, (2011), “Analyst behavior and underwriter choice”,
The Journal of Real Estate Finance and Economics 43(1), pp. 5-38.
Clement, M.B., (1999), “Analyst forecast accuracy: Do ability, resources, and portfolio complexity
matter?”, Journal of Accounting and Economics 27(3), pp. 285-303．
Cliff, M., and D. Denis, (2004), “Do initial public offering firms purchase analyst coverage with
underpricing?”, The Journal of Finance 59(6), pp. 2871–2901.
Corwin, S. A., S. Larocque, and M. Stegemoller, (2016), “Investment Banking Relationships andAnalyst Affiliation Bias: The Impact of the Global Settlement on Sanctioned and
Non-Sanctioned Banks”, Journal of Financial Economics, forthcoming.
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forecast?”, Journal of Accounting and Economics 41(1・2), pp. 119-146．
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Firth, M., Lin, P. Liu, and Y. Xuan, (2013), “The client is king: Do mutual fund relationships bias
analyst recommendation”, Journal of Accounting Research 51(1), pp. 165-200.
Hong, H., and J.D. Kubik,(2003), “Analyzing the analysts: career concerns and biased earnings
forecasts”, The Journal of Finance 58(1), pp. 313-351.
Kadan, O., L. Madureira, R. Wang, and T. Zach, (2009), “Conflicts of interest and stock
recommendations：The effects of the Global Settlement and related regulations,” The
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Kolasinski, A. C., and S. P. Kothari, (2008), “Investment banking and analyst objectivity: Evidence
from analysts affiliated with mergers and acquisitions advisors”, Journal of Financial and15

Quantitative Analysis 43(4), pp. 817-842.
Jackson, R. A., (2005), “Trade generation, reputation, and sell-side analysts”, The Journal of
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Lin H.W., and M. McNichols, (1998), “Underwriting relationships, analysts’ earnings forecasts andinvestment recommendations”, Journal of Accounting and Economics 25(1), pp. 101-127.
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mandates: Banking relationships and analyst recommendations”, The Journal of Finance61(1), pp. 301-340.
Ljungqvist A., F. Marston, L.T. Starks, K.D. Wei, and H. Yan, (2007), “Conflicts of interest in
sell-side research and the moderating role of institutional investors”, Journal of Financial
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Michaely, R., and K. Womack, (1999), “Conflict of interest and the credibility of underwriter
analyst recommendation”, The Review of Financial Studies 12(4), pp. 653-686.

16

Table 1

Samplesbrokerage house

Available data period# of recommendations# of recommendations forthe firm shareheld by thebrokerage house%

Nomura Securities

Mar. 2003 ~ Feb. 2015

12042

944

7.84%

Daiwa Securities

Mar. 2003 ~ Feb. 2015

9160

115

1.26%

Mizuho Securities

Mar. 2003 ~ Feb. 2015

5600

125

2.23%

Mitsubishi UFJ Securities

Mar. 2003 ~ Feb. 2015

8251

216

2.62%

Shinko Securities

Mar. 2003 ~ Mar. 2010

3967

40

1.01%

JPMorgan Securities Japan

Mar. 2003 ~ Feb. 2015

3519

176

5.00%

Barclays Security Japan

Mar. 2003 ~ Feb. 2015

1075

19

1.77%

Deutsche Securities Japan

Mar. 2003 ~ Sep. 2013

3154

165

5.23%

46768

1800

3.85%

Total

17

Table 2

Differences in recommendations for firms in which the brokerage house is a shareholder

and firms in which it is not

Raw Recommendation（Rec）

Relative Recommendation（RelRec)

3.80

0.28

3.71

0.17

0.09 ***

0.11 ***

3.28

3.40

Shareheld firm by broker house(N=1800)
Non-shareheld firm by broker house(N=44968)
Difference
t-valuez-value
3.29***, **, * indicate significance at the 1%, 5%, 10% level, respectively.

18

3.61

Table 3, Panel A

Full sample summary statistics

N=46768

Mean

MedianMinimum

Maximum

Std. Dev.

RecBias

0.17

0.00

-4.00

4.00

1.37

SHDummy

0.04

0.00

0.00

1.00

0.19

SHValue (billion yen)

0.34

0.00

0.00

257.43

4.04

SHOwnership (%)

0.04

0.00

0.00

8.42

0.25

Seniority (months)

78.98

74.00

1.00

213.0044.96

Seasoning (months)

46.95

36.00

1.00

209.0040.25

Ranking

0.25

0.00

0.00

1.00

0.43

Nfollow

16.66

16.00

1.00

108.008.56

Jovmove

0.02

0.00

0.00

1.00

0.13

Brokersize

53.52

55.00

13.00

85.00

17.38

IBRel

0.02

0.00

0.00

1.00

0.13

IBShare (%)

15.61

11.57

0.00

64.53

15.37

MV (billion yen)

676.49265.430.9227255.48

1369.82

ANF

9.93

9.00

1.00

30.00

5.17

Proceeds (billion yen)

2.50

0.00

0.00

537.16

16.24

InstHoldings (%)

27.61

26.65

0.08

90.95

12.47119

Panel B Sub-sample summary statistics
the firm shareheld by the the firms non-shareheld bybrokerage housethe brokerage house(N=1800)(N=44968)

p-value for difference

RecBias

0.28

0.17

0.00

SHDummy

1.00

0.00

0.00

SHValue (billion yen)

8.80

0.00

0.00

SHOwnership (%)

1.06

0.00

0.00

Seniority (months)

74.06

79.18

0.00

Seasoning (months)

48.01

46.91

0.26

Ranking

0.27

0.25

0.01

Nfollow

16.62

16.66

0.85

Jovmove

0.02

0.02

0.94

Brokersize

60.48

53.24

0.00

IBRel

0.02

0.02

0.42

IBShare (%)

21.61

15.37

0.00

MV (billion yen)724.58674.57

0.13

ANF

10.02

9.93

0.46

Proceeds (billion yen)

1.53

2.54

0.01

InstHoldings (%)

31.39

27.46

0.00

20

Table 4

Correlation matrix① RecBias

1.00② SHDummy

0.02

1.00

③ SHValue

0.03

0.42

1.00

④ SHOwnership

0.02

0.81

0.55

1.00

⑤ Seniority

0.01

-0.02

0.00

-0.01

1.00

⑥ Seasoning

-0.01

0.01

0.02

0.01

0.65

1.00

⑦ Ranking

0.02

0.01

0.03

0.01

0.29

0.31

⑧ Nfollow

-0.01

0.00

0.00

0.01

0.20

0.12

0.02

⑨ Jovmove

0.01

0.00

0.00

0.00

0.00

0.01

-0.01 -0.01⑩ Brokersize

0.01

0.08

0.06

0.08

-0.04 -0.11

0.08-0.10 -0.05

1.00⑪ IBRel

0.02

0.00

-0.01

0.00

-0.01 -0.03

0.01-0.02 -0.01

0.06

1.00

⑫ IBShare

0.03

0.08

0.04

0.07

0.00

-0.10

0.09

-0.06 -0.09

0.47

0.09

⑬ MV

0.07

0.01

0.13

0.02

0.02

0.08

0.02

-0.08

0.02

-0.02 -0.03 -0.08

1.00

⑭ ANF

0.03

0.00

0.07

0.03

0.19

0.21

0.06

-0.09

0.02

-0.03 -0.02 -0.18

0.46

1.00

⑮ Proceeds

0.02

-0.01

0.00

-0.01

0.00

0.02

0.01

-0.02

0.00

0.00

-0.01

0.07

0.09

1.00

⑯ InstHoldings

0.05

0.06

0.03

0.05

-0.07

0.02

-0.02 -0.02

0.00-0.02 -0.02 -0.08

0.08

0.18

-0.03

①

②

③

④

⑤

⑥

⑦

⑧

⑨

⑩

⑪

⑫

⑬

⑭

⑮

⑯

1.00

21

1.00
1.00

0.27

1.00

1.00

Table 5

Analysis results: Effect of brokerage house shareholding on recommendations
Dependent Variable: RelRec

Independent variables

(1)

(2)

(3)

Intercept-8.299*** -3.482*** -2.825***(0.413)(0.256)(0.246)

ShDummy0.111***(0.033)0.060*(0.032)

(4)

(5)

(6)

-8.287*** -3.478*** -2.822***(0.413)(0.256)(0.246)

(7)

(8)

(9)

-8.279*** -3.478*** -2.823***(0.413)(0.256)(0.246)0.078**(0.033)ln(ShValue)

0.005*** 0.003**(0.002)(0.001)0.004***(0.002)

ShOwnership

0.094*** 0.068*** 0.073***(0.025)(0.024)(0.024)ln(Seniority)

0.008(0.011)0.038*** 0.027**(0.015)(0.011)0.008(0.011)0.039*** 0.027**(0.015)(0.011)0.008(0.011)0.039*** 0.027**(0.015)(0.011)ln(Seasoning)

0.005(0.006)-0.022*** -0.027***(0.007)(0.006)0.005(0.006)-0.022*** -0.027***(0.007)(0.006)0.005(0.006)-0.022*** -0.027***(0.007)(0.006)

Ranking

0.052*** 0.063***(0.016)(0.022)ln(Nfollow)-0.015(0.015)-0.070*** -0.036**(0.023)(0.014)-0.015(0.015)-0.070*** -0.036**(0.023)(0.014)-0.015(0.015)-0.070*** -0.036***(0.023)(0.014)

Jovmove

0.058(0.052)0.019(0.054)

0.109**(0.052)0.058(0.052)0.019(0.054)

0.109**(0.052)0.058(0.052)0.019(0.054)

0.109**(0.052)ln(Brokersize)-0.020(0.021)0.074(0.046)0.130***(0.048)-0.021(0.021)0.074(0.046)0.131***(0.048)-0.021(0.021)

0.075(0.046)0.132***(0.048)

IBRel

0.005
(0.053)0.133**(0.055)0.113**(0.054)0.005(0.053)0.133**(0.055)0.113**(0.054)0.004(0.053)0.133**(0.055)0.113**(0.054)

IBShare0.003***(0.000)0.000(0.001)-0.001*(0.001)0.003***(0.000)0.000(0.001)-0.001*(0.001)0.003***(0.000)0.000(0.001)-0.001*(0.001)ln(MV)

0.313*** 0.132*** 0.098***(0.015)(0.007)(0.006)

0.313*** 0.132*** 0.098***(0.015)(0.007)(0.006)

0.313*** 0.132*** 0.098***(0.015)(0.007)(0.006)ln(ANF)-0.010(0.020)-0.097*** -0.095***(0.015)(0.014)-0.010(0.020)-0.097*** -0.095***(0.015)(0.014)-0.010(0.020)-0.097*** -0.095***(0.015)(0.014)ln(Proceeds)

0.003**(0.001)0.004*** 0.005***(0.001)(0.001)0.003**(0.001)0.004*** 0.005***(0.001)(0.001)0.003**(0.001)0.004*** 0.005***(0.001)(0.001)

InstHoldings

0.009*** 0.006*** 0.006***(0.001)(0.001)(0.001)0.020(0.016)

0.052*** 0.063***(0.016)(0.022)0.020(0.016)0.009*** 0.006*** 0.006***(0.001)(0.001)(0.001)

0.052*** 0.063***(0.016)(0.022)0.020(0.016)0.009*** 0.006*** 0.006***(0.001)(0.001)(0.001)

Fixed effects

firmanalystbrokerfirm

analystbrokerfirm

analystbrokerAdjusted R-squared

0.127

0.062

0.019

0.127

0.062

0.019

0.127

0.062

0.019

N

46768

46768

46768

46768

46768

46768

46768

46768

46768

The value in parenthesis is robust standard error. ***, **, * indicate significance at the 1%, 5%, 10% levels, respectively.

22

Table 6

Informativeness of recommendation

Independent variables
Intercept
ShDummyln(ShValue)
ShOwnershipln(Seniority)ln(Seasoning)
Rankingln(Nfollow)
Jovmoveln(Brokersize)
IBRel
IBShareln(MV)ln(ANF)ln(Proceeds)
InstHoldings
Tier5
ChangeHorizonRecbiasRec
year dummy

Dependent Variable: ABS_AbnormalReturn(0 to 2)
(1)
(2)
(3)
5.320***
5.303***
5.317***(0.450)(0.450)(0.450)-0.193**(0.080)-0.009**(0.004)-0.084(0.069)0.061**
0.061**
0.061**(0.028)(0.028)(0.028)-0.020-0.020-0.021(0.015)(0.015)(0.015)
0.003
0.003
0.003
(0.037)(0.037)(0.037)-0.052-0.052-0.052(0.036)(0.036)(0.036)-0.121-0.120-0.122(0.126)(0.126)(0.126)
0.014
0.015
0.010
(0.051)(0.051)(0.051)-0.122-0.122-0.122(0.134)(0.134)(0.134)0.009***
0.009***
0.009***(0.001)(0.001)(0.001)-0.085***-0.085***-0.084***(0.017)(0.017)(0.017)0.089**
0.089**
0.089**(0.036)(0.036)(0.036)-0.002-0.002-0.001(0.003)(0.003)(0.003)-0.001-0.001-0.001(0.001)(0.001)(0.001)-0.060*-0.060*-0.058(0.036)(0.036)(0.036)-0.028-0.028-0.027(0.025)(0.025)(0.025)
0.001
0.001
0.001
(0.008)(0.008)(0.008)
0.011
0.011
0.011
(0.019)(0.019)(0.019)
0.027
0.027
0.027
(0.024)(0.024)(0.024)
yes
yes
yes

0.005
0.005
0.004
46768
46768
46768
The value in parenthesis is robust standard error. ***, **, * indicate significance at the 1%, 5%, 10% levels, respectively.
Adjusted R-squaredN

23

Table 7, Panel A
Independent variables
ShDummyInformativeness of buy recommendations(1)-0.310*(0.175)

Dependent Variable: AbnormalReturn(0 to 2)(2)ln(ShValue)(3)-0.016**(0.008)

ShOwnership
Analyst Characteristics
Broker Characteristics
Firm Characteristics
Rec Characteristics

yes
yes
yes
yes

yes
yes
yes
yes

-0.458***(0.153)
yes
yes
yes
yes

Adjusted R-squared
0.004
0.004
0.004
N
18980
18980
18980
The value in parenthesis is robust standard error. ***, **, * indicate significance at the 1%, 5%, 10% levels, respectively.

Panel B

Informativeness of neutral recommendations

Independent variables
ShDummy(1)-0.004(0.164)

Dependent Variable: AbnormalReturn(0 to 2)(2)ln(ShValue)(3)-0.002(0.008)

ShOwnership
Analyst Characteristics
Broker Characteristics
Firm Characteristics
Rec Characteristics

yes
yes
yes
yes

yes
yes
yes
yes

0.043
(0.116)
yes
yes
yes
yes

Adjusted R-squared
0.001
0.001
0.001
N
25075
25075
25075
The value in parenthesis is robust standard error. ***, **, * indicate significance at the 1%, 5%, 10% levels, respectively.

Panel C

Informativeness of sell recommendations

Independent variables
ShDummy(1)
0.015(0.527)

Dependent Variable: AbnormalReturn(0 to 2)(2)ln(ShValue)

0.001(0.025)

ShOwnership
Analyst Characteristics
Broker Characteristics
Firm Characteristics
Rec Characteristics

(3)

yes
yes
yes
yes

yes
yes
yes
yes

0.215
(0.495)
yes
yes
yes
yes

Adjusted R-squared
0.011
0.011
0.011
N
2713
2713
2713
The value in parenthesis is robust standard error. ***, **, * indicate significance at the 1%, 5%, 10% levels, respectively.

24