Analyst recommendations and abnormal returns

Bachelor Thesis

Analyst recommendations and
abnormal returns
An event study on OMX Stockholm 30

 Authors: Krenare Salihu & Ludwig Flank
 Zetterström
 Supervisor: Christopher von Koch
 Examiner: Håkan Locking
 Term: VT21
 Subject: Finance
 Level: Bachelor Thesis
 Course code: 2FE32E

Abstract
The main purpose of this study is to contribute to the previous literature by evaluating
positive changes in analysts' consensus recommendations of the stocks listed in
OMXS30. We analyze if new positive changes in consensus recommendations
correspond with lower abnormal returns. By conducting an event study and
performing a series of different statistical tests, we find that positive changes in
analyst consensus provide a short lived negative mean abnormal return in certain
cases. We argue that this implies that investors might interpret positive changes as a
sell signal. Furthermore, we find some pieces of evidence to suggest that it may
actually be changes in the mean target price rather than changes in recommendations
that causes the movements in abnormal returns.

Key words
Finance, event study, recommendations, abnormal returns, behavioral
finance

Acknowledgments
We would like to send a very special thank you to Christopher von Koch for
his key role in the creation of this thesis. We are most grateful for his
continuous support and guidance throughout the writing of this thesis. We
would also like to extend our gratitude to our opponents and our examiner
Håkan Locking for their feedback on our work. Finally, we would like to thank
master’s student Nathalie Sommar Lindskog for her guidance in STATA.

Table of contents
1 Introduction 1
 1.1 Background 1
 1.2 Problem Discussion 2
 1.3 Purpose and Contribution 3
 1.4 Disposition 4
2 Theoretical framework 4
 2.1 Efficient Market Hypothesis 4
 2.2 Adaptive Market Hypothesis 5
 2.3 Behavioral Finance 6
 2.4 Noise Trading 7
 2.5 Piggyback Theory 8
3 Literature Review 8
 3.1 Stock Recommendations 8
 3.1.1 A Critical View on Recommendations 10
 3.1.2 The Aspect of Noise 11
4 Data and Research Method 11
 4.1 Data 11
 4.2 Research Method 12
 4.2.1 Significance Test 15
 4.2.2 Spearman Rank Correlation 15
 4.2.3 Calculations and Variables 16
 4.2.4 A Critical View on the Method and Data 21
5 Empirical Results 23
 5.1 Descriptive Statistics 23
 5.2 Abnormal Returns 24
 5.2.1 Cumulative Abnormal Returns 25
 5.3 Bivariate Analysis 27
 5.4 Regression Analysis 29
 5.4.1 Positive Change 31
 5.4.2 Negative Change 32
6 Discussion 33
7 Summary and Conclusions 38
8 References 40
 8.1 Scientific Articles 40
 8.2 Literature 42
 8.3 Media and News Outlets 42
 8.4 Websites 43
9 Appendix 45

1 Introduction

1.1 Background
Buy low and sell high is perhaps the most repeated phrase when talking about the
stock market and how to approach it. While it is the basic goal of investors, buying
low and selling high isn’t always easy, why? Because investors may lack the time,
resources and knowledge required in predicting stock price and market movements.
There are strategies with varying degrees of accuracy an investor can implement to
forecast movements, most of which are difficult, time consuming and require
extensive knowledge of both business and economics.
 This is not only true for individual investors, but firm’s as well, which may
have the resources but lack the time, knowledge or manpower necessary to analyze
large numbers of financial statements, predicting micro- and macroeconomic trends
and ultimately translate it to a stock price. This is where financial analysts come in.
Everyone from banks, to financial institutions, to newspapers hire analysts to produce
forecasts of such things as growth, earnings per share and future cash flows. As
mentioned above, an analyst will also analyze microeconomic factors such as the
specific sector the firm is in and the macroeconomic trends of the market as a whole
to determine how they may affect the company now and, in the future (McClure,
2020). Usually, analysts will produce a recommendation based on either fundamental
or technical analysis. Fundamental analysis means that private and public information
is a part of the valuation basis. The technical aspect means that historical data is
studied and used as a valuation basis to predict future share prices. The analysis may
also come with a strategic tool called a SWOT-analysis which assesses the firm's
strengths, weaknesses, opportunities and threats going forward. Based on these
variables the analyst will produce a recommendation whether an investor should buy,
sell or hold the analyzed asset. The scale and terminology used for stock
recommendations depends on which bank or firm issues the recommendation, but the
purpose is the same, recommendations exist to provide investors with information, in
order to make value estimates of companies more accurate (Jegadeesh et al., 2004).

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Larger companies tend to receive recommendations more frequently than
smaller companies. This may take place in connection with annual reports and interim
reports, but also major events that may affect the value of shares. The analysts will
revise their forecast and valuation. However, they can repeat themselves with an
identical recommendation again, which means that they remain at the same position.

1.2 Problem Discussion
These earnings forecasts and recommendations of stocks from analysts at big banks,
financial institutions and even newspapers seem to be plentiful and very easily
accessible on the internet nowadays. In March of 2021 the Wall Street Journal
reported on the increasing number of so-called “stock influencers”. These influencers
include everyone from business leaders, fund managers and billionaires like Elon
Musk to more “common” people, for example Keith Gill also known as Roaring Kitty,
who use social media platforms to relay trading strategies, recommended stock picks
and stock analysis. Gill is most known for his analysis of GameStop which ultimately
influenced a number of people on Reddit’s WallStreetBets, an online trading forum,
to “declare war” on the hedge funds with short positions in the company. The author
describes an almost cult following of these influencers where every comment is taken
as gospel (Otani, 2021).
 This increase in adherence to stock recommendations, be it an institutional
analyst or a “stock influencer”, could very well be correlated with the fact that in just
the last ten years, the market activity of retail investors on the U.S. stock market has
doubled. In 2020 retail investors accounted for 19,5% of all market activity, with daily
peaks reaching as high as 25% (Osipovich, 2020). One explanation for this increase
of traders may be because “free” trading apps, such as RobinHood in the U.S. and
Avanza and Nordnet in Sweden, have made equity trading more easily accessible for
the “common” man and woman. Just in 2020, Avanza saw a surge of 300 000 new
users on its platform to a total number of 1,3 million users (Avanza, 2021).
 The usefulness and accuracy of analysts' stock recommendations is an
extensively studied subject within finance academia. However, there is still no clear
consensus from the existing literature on how well, if at all, the analysts' stock
recommendations actually correlate with stock price movements.

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Previous studies such as Jegadeesh et al. (2004) have found that in most cases
stocks with new favorable recommendations outperformed the less favored stocks,
but in some cases the opposite could be found, where the analysts most favored stocks
underperformed both the market and the least favored stocks. Other studies have
ended up with similar results, where favored stocks usually outperform those less
favored but far from always. For instance, Barber et al. (2003) found that for two
straight years the stocks most favored by analysts underperformed the analysts least
favored stocks. Whether or not analysts' recommendations are deemed effective
seems dependent on the chosen time periods, the method used, and which variables
are considered.
Considering the absence of a clear consensus over the effectiveness of analyst stock
recommendations coupled with the increase in retail trader activity, we feel that a
follow up to previous works, such as Barber et al. (2001 & 2003) and Jegadeesh et al.
(2004), is needed. Our thesis will focus on the Swedish stock market and with help
from previous studies in the field as well as established economic theories, we will
seek to answer the following research question; Can a positive, new, consensus
recommendation have an adverse effect on abnormal return? If it does have an
adverse effect; In which cases and during which periods may this occur?

1.3 Purpose and Contribution
The purpose of this study is to evaluate changes analysts' consensus stock
recommendations of stocks listed on the OMXS30 index and analyze if they correlate
with higher or lower abnormal returns.
 While our thesis is not the first of its kind, it contributes to a better
understanding of the Efficient Market Hypothesis (EMH) and how information, in
this case new recommendations, may influence stock prices. Furthermore, our thesis
contributes to the behavioral finance field by analyzing both analyst and investor
behaviors and providing a better understanding of herding behavior, loss aversion and
piggybacking. We also contribute to the concept of noise trading and the effects it has
on markets and market prices, in this case the Swedish stock market. We provide an
increased understanding of if, how and when analysts’ favorable stock
recommendations may have adverse effects on the price of a stock.

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Our contribution, per sei, is not to explicitly construct an optimal portfolio or to offer
investment advice, instead we seek to provide the growing number of novice investors
a clearer understanding of how this specific kind of information may affect returns
and why.

1.4 Disposition
The thesis consists of seven chapters, where the first part explains our problem, the
relevancy, purpose and background. The second and third part contains our theoretical
framework and a review of previous literature. Part four presents the data and
methodology used to answer the research question. Part five and six is a presentation
and analysis of the results obtained from our study and a discussion, and the final part,
part seven explains the conclusions we draw from the results.

2 Theoretical framework

2.1 Efficient Market Hypothesis
The effective market hypothesis is fundamental in financial theory and assumes that
the financial markets are efficient. A market that is efficient means that all available
information is reflected in the share price. To predict future share prices, investors
will implement the information that is available and change the share price to the new
correct level. In order for the share price to be adjusted at the time of advertising,
there must be unexpected information available. If the information is expected, the
price will already contain the new information at the time of advertising (Bodie et al.,
2009). If analysts possess knowledge that enables them to beat the market, it disputes
the theory of the effective market hypothesis (Fama et al. 1979). There are three
grounds for a market to be seen as effective. The first is rationality, which means that
new information is released on the market and investors adjust their estimates of the
price in a rational way. The second basis is deviations from rationality, which is since
people do not always act rationally but that emotions can influence. The third basis is
arbitrage which means that there are imbalances between valuations of assets in
markets.

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Since all available information is reflected in the price directly, the possibility of
arbitrage wins is limited. Depending on how accessible the information is, the
market’s efficiency can be divided into three different categories.

Weak
Weak market efficiency means that by studying historical share prices it is not
possible to take advantage of risk-adjusted excess returns. By applying technical
analysis, for example, risk-adjusted excess returns cannot be achieved (Fama, 1970).

Semi
The semi-strong form of market efficiency means that all available public information
is taken into account in the share price i.e., annual reports and shared analysis.
Creating excess returns with technical or fundamental analysis is not possible under
semi efficient market conditions. The only reliable way to generate excess returns is
by obtaining insider information. Insider information is information that has not been
announced to the public (Bodie et al, 2009).

Strong
The strong form of market efficiency means that all information, historical-, public-
and insider information about a share is reflected in the price. Because the share price
already reflects all the information that analysts could possibly have, they cannot add
any value to investors. Therefore, the strong efficient market hypothesis cannot give
investors an excess return (Bodie et al, 2009).

2.2 Adaptive Market Hypothesis
The theory is based on the basic assumptions within EMH but includes behavioral
finance as an additional aspect. The AMH believes investors to be rational but that
increased market volatility can lead to irrational behavior. Cognitive factors from the
behavioral finance-field are applied to explain irrational behavior among investors.
Examples of these cognitive factors are that investors are loss averse and overstating
their ability to generate profits (Lo, 2004). Investors are believed to adapt to events
that affect market conditions and the cognitive factors described in behavioral finance
may strengthen the argument that investors are not necessarily rational (Lo, 2004).

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An example of this is that if an investor were to make an incorrect investment
decision, he or she will most likely start to form an alternative investment strategy at
a later time i.e., adapting their behavior (Lo, 2004).

2.3 Behavioral Finance
Behavioral finance is a field that uses cognitive factors to explain financial market
behaviors that cannot be explained by traditional, economic or quantitative models
(Ritter, 2003). Related aspects to the cognitive have to do with psychology, how
human behavior can influence investment decisions and how restrictions on arbitrage
for an investor can arise.
 A common behavior in finance is loss aversion, meaning that people in
general are more sensitive to losses than gains. In other words, investors are less
likely to choose higher gains with high risk than lower gains and low risk (Benartzi
and Thaler, 1995). Since investors are believed to be loss averse, they may also tend
to sell “winners'' too early and hold on to “losers” for too long. For the winners this
means that investors will be quick to secure a profit and sell the asset to avoid the risk
of the profit diminishing or disappearing. For the losers the opposite is likely to
happen, the investor will instead hold on to the asset for far too long in order to avoid
realizing the loss (Shefrin and Statman, 1985). In theory this means that an investor
who buys a stock based on a new positive consensus recommendation is less likely to
hold on to the stock if the stock price increases a lot, since the investor wants to
“secure” the profit.
 Another common behavior in finance is herding behavior. This refers to the
instances where investors start to follow other individual investors or groups of
investors, instead of relying on their own analysis they rely on the wisdom and
knowledge of others. An investor who shows a herd instinct will therefore often
attract the same, or similar, investments as other investors. This may either be because
investors receive the same information as others around them, or because they start
to imitate the investment strategies of more profitable investors (Jegadeesh and Kim,
2010).

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One reason this behavior occurs is due to the fear of missing an opportunity
to make money. The fear of missing a good investment opportunity can lead to
impulsive actions and in the end, this can be a risky and wrong investment that one
would not have made otherwise (DeMarzo et al., 2008).
 Both investors and analysts may exhibit herding behavior. Analysts usually
do not stray too far from the consensus recommendation of a stock, especially when
downgrading. Analysts’ herding is sometimes said to introduce increased noise on
markets. However, assuming that the EMH holds, markets will expect herding from
analysts’ and prices will already have adjusted to the correct level when a revision is
made (Jegadeesh and Kim, 2010).

2.4 Noise Trading
Noise traders are a group of investors who trade stocks independendent of the firm’s
fundamentals or new information. In the literature they are usually described as
uninformed traders who spend no time on research or due diligence and thus are
unaware of the information investors usually require to assess stocks (Black, 1986).
They are however aware that market prices should reflect all current information
about the firms and thus the market as a whole (Grossman, 1976).
 Noise traders are what makes financial markets possible but also what makes
them imperfect. Without noise trading, the trading of individual assets would be
almost non-existent. People would still hold assets, such as stocks, but be unwilling
to trade them. If one investor has information that makes them willing to sell a stock,
the investor at the other side of the trade must have information that makes him or her
willing to buy the stock. As such, one of them must have faulty information (Black,
1986). Noise traders solve this market problem by trading on noise they believe to
be information. For the informed traders this creates an incentive, because they can
now use their information and trade with uninformed investors, who as opposed to
actual informed traders are willing to trade even though they, objectively, will be
worse off (Black, 1986) Other benefits on markets include increased trade volumes,
reduced ask-bid spread and a reduced price impact from trades. However, they also
prevent market prices from efficiently adjusting to new information (Bloomfield et.
al, 2009). This noise effect may cause stock prices to drift away from the actual value,
the farther away it drifts the faster it will adjust back. (Black, 1986).

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2.5 Piggyback Theory
The piggyback theory suggests that analysts' recommendations often “piggyback” on
other news or announcements from or about the company. When accounting for the
effects of the news or announcement, analysts’ recommendations have no
measurable effect on the stock price and the reason why the price moves at all is
because of the “piggybacked” event (Altinkilic and Hansen, 2009) If true, the
piggyback theory would disprove a large number of previous studies on the effects
analyst recommendations presented in the next chapter.

3 Literature Review
3.1 Stock Recommendations
In the article Discrete Expectational Data and Portfolio Performance, written by
Edwin J. Elton, Martin J. Gruber and Seth Grossman (1986), the authors analyzed
over 10 000 recommendations per month and found out that an investor will benefit
by investing in new buys rather than sells, by as much as +4,5%. Dividing stocks into
different portfolios with similar values for beta, based on receiving positive or
negative updates to their recommendation level, they can observe how different
changes affect the stocks. Their study differs from past studies, since Elton et al.
(1986) utilizes a much larger sample than what was commonly used prior to the
release of their study and applies a more robust method when adjusting for risks. By
removing the highest beta stocks from portfolios until the betas are within just 0.01
of each other, their procedure only assumes that beta is simply a measure of risk. They
go on to conclude that there is indeed information value in analysts’
recommendations and that buying from “buy-lists” can generate excess returns,
however, not as great as the excess returns generated by acting on changes in
recommendations.
 Ten years later Womack (1996) found a similar pattern in the article Do
Brokerage Analysts’ Recommendations Have Investment Value? By analyzing 14
brokerage firms he found that new buy recommendations had an immediate positive
effect on the stock price, described as a “short lived modest drift” of +2,4%.

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One interesting observation was that when a stock received a new sell
recommendation, the effect was much larger and more long lived. The observed drift
for the stocks receiving a sell-recommendation in Womack’s study was -9,1% and
the effects of the new information were prevalent for six months. He also points out
that analysts were more likely to issue buy than sell recommendations.
 A key aspect of Womack’s study was the fact that only 9% of
recommendations were issued in conjunction with quarterly earnings reports. He goes
on to conclude that analyst recommendations have a significant effect on stock prices.
 Womack’s results are put to test by Barber, Lehavy, McNichols and Trueman
(2001 & 2003) who take a more “practical” approach by forming investment
strategies based on the findings published by, among others, Womack (1996). Barber
et al. (2001) found that if an investor employs a strategy based on recommendations,
he or she can make a profit greater than 4%, if they invest in the analyst's most favored
stocks compared to their least favored. They do, however, conclude that none of the
investment strategies yield positive abnormal returns since they all require a lot of
trading and thus high transaction costs. This means that none of the strategies reliably
can generate abnormal net returns larger than zero. The authors also believe that the
results show that the market efficiency is semi-strong, excluding transactions costs. It
would not be possible to take advantage of abnormal risk-adjusted returns if strong
market efficiency prevailed. This depends on that all available information is already
included in the price.
 In their follow up study from 2003 they can observe a negative correlation
between analysts' most favored stocks and their least favored stocks in both 2000 and
2001. The most favored stocks by analysts significantly underperform, compared to
the least favored stocks but also the index. They do however conclude that this could
be a case of analysts doubling down on their positive view on small cap, high growth
stocks which significantly underperformed the market in these years.
 From the groundwork provided by the articles above the study by Jegadeesh,
Kim, Krische and Lee (2004) examines when analysts’ recommendations add value.
They note that when comparing value stocks, growth stocks and “glamor-stocks''
seems to be more popular among analysts. According to the results of the study, this
is claimed to lead to “noise trading” in the financial market.

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In short, noise trading means that actions such as selling and buying in the
market are done irrationally. Parts of the study’s results also show that issued
recommendations can be useful when considering different investment strategies.
They find similar results as the studies by Elton et al. (1986), Womack (1996) and
Barber et al. (2001) which means that it can be of value for an investor to act on
analysts' stock recommendations. Like the previous studies they also find that stocks
favored by analysts tend to outperform the less favored stocks.
For a more “local” perspective on the Swedish stock market the dissertation by Lidén
(2005) discusses the effects of stock recommendations in print media in Sweden
between 1995 and 2000. The dissertation is mainly based on two economic theories
called the price-pressure hypothesis and the information hypothesis. An interesting
observation is that Lidén received advice from Brad Barber on how to conduct the
event study used in the dissertation. Lidén finds evidence that journalist
recommendations are much more influential than analyst recommendations. He also
finds evidence that buy recommendations were more likely to be released on a
weekday, while sell recommendations were more likely to be released on a weekend.

3.1.1 A Critical View on Recommendations
A critical view on the impact of recommendations is provided by Altinkilic and
Hansen (2009). They analyze a number of studies, among others Elton et. al (1986)
and Womack (1996) and claim that recommendations tend to “piggyback” on
earnings announcements and firm specific news. When removing these effects, the
recommendations do not produce any meaningful stock price reaction. Another study
that puts analysts in a critical light, is the study Do Analysts Herd? An Analysis of
Recommendations and Market Reactions by Jegadeesh and Kim (2010). The authors
find that analysts tend to herd around the consensus estimate, especially when they
are analysts at a big firm. They find evidence that the market reacts more strongly to
new recommendations that are away from consensus. The question of the effect of
recommendations is further debated by Loh and Stulz (2011) who found that just 12%
of recommendations can be considered influential given that they are from an
influential analyst, accompanied by earnings forecasts from high growth firms.

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The observation that they must be accompanied by an earnings forecast validates
Altinkilic and Hansen’s piggyback theory. Similar to Jegadeesh and Kim (2010) they
found that new recommendations away from consensus have the biggest impact.
Meaning that herding could potentially hurt analyst reliability.

3.1.2 The Aspect of Noise
In Grossman’s (1976) study On the Efficiency of Competitive Stock Markets Where
Trades Have Diverse Information an insight is brought to light. If the market prices
reflect all available information, there is no incentive to do any further research than
to just observe prices. Grossman states that some form of noise is required for
informed traders to hide information from uninformed traders. A further development
of the noise theory is provided by Black (1986) in his article simply called Noise.
Black (1986) states that noise traders are what makes financial markets possible, but
also what makes them imperfect. Without noise trading, the trading of individual
assets would be almost non-existent. Black (1986) explains that people would still
hold stocks, but there would not be any trade. Black (1986) also states that noise
traders solve this market problem by trading on noise they believe to be information.
For the informed traders this creates an incentive, because they can now use their
information and trade with uninformed investors, who as opposed to actual informed
traders are willing to trade even though they, objectively, will be worse off.

4 Data and Research Method

4.1 Data
In order to answer our research question daily price and volume data was collected
via a Thomson Reuters Eikon terminal for the 30 Swedish stocks in OMXS30 (as of
Q1 2020, hence SSAB A for example will be included instead of Evolution Gaming),
the full list of stocks can be found in Appendix 1. All of the data on prices, volumes,
indices and analysts’ recommendations are obtained from the Thomson Reuters Eikon
terminal with a daily frequency and then summarized weekly. This in order to make
it easier for the reader and easier to track changes between weeks as well as within
weeks. The week in which a change occurs will later become our event window.

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Our data spans three full years (2017, 2018 and 2019) and two quarters, one on either
side of the primary time period (Q4 2016 and Q1 2020).
This is done in order to avoid the market effects of the COVID-19 pandemic and the
ability to obtain entry values for the time period.
 We will use the all-share index OMXS PI as the benchmark for the economic
climate in Sweden in each time period. The new recommendations will not be
analyzed individually nor will the type of analysis used to produce the
recommendation or be analyzed. We will instead use changes in the analyst consensus
recommendation during the period and study how they correlate with abnormal stock
returns. We will also touch briefly on mean target price changes and other variables
related to analysts, but these will not be the primary focus in this thesis.

4.2 Research Method
This thesis applies a deductive approach, meaning that based on existing models and
theories, we develop a hypothesis which is then tested. The chosen research strategy
in this thesis is a quantitative analysis, more specifically an event study based on panel
data. This is the most appropriate way to obtain an answer to our research question.
It means that we will use changes in analyst consensus recommendations as our events
and the week where the event occurs as our event window.

 Timeline 1. The timeline for the event study (in weeks). The estimation period is the weeks prior to a change that
 falls outside of the observation period of a previous change. The observation period for an event will always be
 replaced by the observation period of a more current event in the case where two (-or more) events overlap.

We have taken inspiration from the methods and tests used by Womack (1996),
Barber et al. (2001 & 2003), Jegadeesh et al. (2004) and Lidén (2005), in order to
discover if and how analyst recommendations correlate with abnormal returns.

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Like Lidén (2005) we have chosen abnormal returns based on a market model as our
dependent variable, for the simple reason that it shows if returns for our sample are
unusually high or low when compared to the expected returns.
The mean analyst consensus recommendation is calculated by using a weighted
average of all outstanding recommendations for every stock during every week. Like
Jegadeesh et al. (2004) we have assigned the recommendations a numerical weight
on a five-point scale where 5 is the highest (strong buy recommendations) and 1 is
the lowest (strong sell recommendations). The number is then divided with the total
number of outstanding recommendations for the stock in the period, in this case
during the week. For example, if a stock has three strong-buy recommendations,
seven buy recommendations, ten hold recommendations and five sell
recommendations, the analyst consensus recommendation for the stock would be
 1
3.32 . If the consensus recommendation for the stock in the example changes to 3.42
in the following week, there would be an increase of 3% in consensus compared to
 2
the previous week . Worth nothing is that we will focus solely on changes in analyst
consensus and not the actual level of the aforementioned analyst consensus stock
recommendation.
 Since we have a large number of analyst changes with multiple overlapping
events, we have decided to do the following; the changes in analyst recommendations
happen in week t=0, this will be our event window. The estimation period, seen in
Timeline 1, is used to estimate performance during the weeks where a change is not
present, and the week is not within a 4-week period after the latest change. The
observation period consists of the event window and evaluation period and is used to
evaluate the returns in week t=0 to week t=4. If a change takes place in t=0 week and
a new change happens in week t=1, the more current change will override the previous
period. For example, in week 8 the analyst consensus recommendation for stock A
increased, thus opening the event window. In week 9 there is no change in the
consensus, so the evaluation period of the event in week 8 is still ongoing.

1
 A specification of the equation is presented in section 4.2.3.
2
 A specification of the equation is presented in section 4.2.3.

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However, in week 10 the analyst consensus recommendation decreases for
stock A, this means that a new event window and evaluation period begins, replacing
the evaluation period of the change that happened in week 8.
 In order to find out if the change in analyst consensus recommendations has
a significant effect, this thesis will conduct a number of statistical tests to determine
the relationship between abnormal returns and changes in analyst consensus
recommendations for the chosen stocks. First, we will use a two tailed t-test to see if
mean abnormal returns, when changes are positive or negative, are significantly
different from the mean returns of the index. This test will be conducted for both
abnormal returns and cumulative abnormal returns. This should give us a first clue of
whether or not the changes actually produce a reaction.
 Secondly, we will conduct a bivariate analysis to see if we can observe a
statistically significant correlation between abnormal returns and the changes in
analyst consensus recommendations. A bivariate analysis is a test of correlation
between two variables. In this thesis we have chosen to test for correlation with the
Spearman rank correlation-test, described and motivated in detail in the next section.
When the data from the tests is obtained, we can assess if (positive) changes in
analysts’ recommendations indeed show signs of (negative) correlation for our chosen
sample.
 Third, we will conduct a multiple linear regression analysis with abnormal
returns as our dependent variable in order to see if and how much of its variation is
explained by the changes in analyst consensus recommendations, and if so at which
significance level(s). Although we focus on recommendations, we will include a few
other variables used in previous studies that are believed to be relevant.

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4.2.1 Significance Test
In order to discover if the mean abnormal returns and the mean cumulative abnormal
returns of the chosen stocks are statistically different from the mean returns of the
index, we will conduct a two tailed t-test. In this thesis we have gone for a two tailed
t-test which assumes that the two groups are unpaired and have unequal variances.
The null hypothesis is that the two samples have a mean difference of zero, and the
equation is formulated as following;

H0: The two samples have a mean difference of 0
H1: The two samples do not have mean difference of 0

Where 1 denotes the mean of the first group and 2 denotes the mean of the second
group. The variables 21 and 22 denotes the standard deviation of group 1 and 2
respectively, 1 and 2 denotes the sample size in each of the two groups. The tests
will show if there is a statistically significant difference and at which level, meaning
that if the p-value is less than significance level, there is a significant possibility that
the null hypothesis (H0) is untrue. However, it does not automatically mean that the
alternative hypothesis (H1) is true, nor does it mean that H0 is automatically true if
we cannot reject it.

4.2.2 Spearman Rank Correlation
Like Jegadeesh et al. (2004) we have opted to use The Spearman rank correlation-test
as a test for correlations. The Spearman rank-test allows us to test for monotonic
relationships between variables, in this case between the abnormal return and changes
in analyst consensus stock recommendations. We will run the test with all stocks as
one group but with changing conditions, so we will look at each year individually and
if there is a report or not. The Spearman rank correlation-test is often used if there is
a monotonic relationship and no linear relationship between the variables, this is
however not required to run the test (Laerd Statistics, 2018).

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The difference between a monotonic relationship and a linear relationship is that in a
monotonic relationship the two variables do not have to move in the same direction
at a constant rate like in a linear relationship, so it imposes fever restrictions on the
relationship.
 The coefficient, rho, obtained from the Spearman-test takes a value between
-1 and +1, where -1 indicates a perfect negative relationship and +1 indicates a perfect
positive relationship between the variables (Laerd Statistics, 2018). The hypothesis
test of the Spearman rank correlation-test in this thesis is formulated by STATA as:

H0: Variable A is independent of Variable B
H1: Variable A is not independent of Variable B

The test shows if the correlation coefficient between the chosen variables is
statistically significant and at which significance level. Meaning the same thing as
described in the previous section 4.2.1.

4.2.3 Calculations and Variables
This section provides a presentation of the equations used to calculate the variables,
and a presentation of said variables, that will be used in this thesis.

 *+,-'./01 2*+,-'./0134
Realized return3: '( =
 *+,-'./0134

Realized return for stock i is the difference between closing price on the last trading
day in week t and closing price on the last trading day in week t-1, in relation to the
closing price on the last trading day in week t-1. It gives us a percentage return for
the stock(s) and OMXS PI index.

Abnormal return: '( = '( − '( + ' :(
Abnormal return is based on the market model used by Lidén (2005), where
 '( denotes the abnormal return for stock i in week t. '( denotes the stocks realised
return in week t and :( denotes the market’s return in week t.

3
 Used for both stocks and OMXS PI, the return for OMXS PI is denoted Rmt.

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The values for a (intercept) and b (slope) are estimated through running an OLS
regression using the stocks realized return and the markets return. This provides us
with the abnormal return for the stock given its level of risk. The mean abnormal
return ( is simply the abnormal return in relation to the number of observations in
period t. The mean cumulative abnormal return is the sum of the mean abnormal
returns between two points in time. In this thesis it will be measured each week for
the 4 weeks following a change in analyst consensus.

 ?01 2?0134
Change in volume: ∆ '( =
 ?0134

The change in volume is calculated the same way as the realized return. The change
in volume for stock i is the difference between the volume in period t and volume in
period t-1 in relation to the volume in period t-1. This will provide us with a
percentage change.

Abnormal volume: '( = '( − '( + ' :(
Abnormal volume is, as the abnormal return, based on the same model used by Lidén
(2005), where '( denotes the abnormal volume for stock i in week t. '( denotes the
stocks trade volume in week t and :( denotes the market’s trade volume in week t.
The values for a (intercept) and b (slope) are estimated through running an OLS
regression using the stocks trade volume and the markets trade volume. The mean
abnormal volume ( is the abnormal volume in relation to the number of
observations in period t.

Analysts change variables
Analyst consensus recommendation:

 B.DEF 5 + HEF 4 + J,+K '( 3 + BM++ '( 2 + B.-M++ '( 1
 '( '(
 '( =
 NMO '(

The analyst consensus recommendation is the weighted average of all outstanding
recommendations for stock i in period t. A recommendation is assigned a weight
between 1 and 5, depending on their level.

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Then multiplied by the number of recommendations in that category, the total is then
divided by the total number of outstanding recommendations for stock i in period t.

Change analyst consensus recommendation:

 '( − '(2P
 ∆ '( =
 '(2P

The analyst consensus recommendation changes for stock i is the difference between
consensus in period t and period t-1 analyst consensus recommendation in relation to
period t-1 analyst consensus recommendation. As mentioned before, this variable is
the main focus in this thesis. Mean analyst consensus recommendation change is the
sum of the changes in relation to the number of observations.

Change analyst mean target price:

 '( − '(2P
 ∆ '( =
 '(2P

The mean analyst target price change for stock i is the difference between mean target
in period t and period t-1 mean analyst target price in relation to period t-1 mean
analyst target price. The change in the analyst mean target price is calculated in order
to include it in the regression analysis, to see if it can offer any additional explanatory
power.

Change total number of analyst recommendations:

 ∆ *,. = *,.1 − *,.134

The change in the total number of analyst recommendations is simply the difference
between the total number of analyst recommendations in the current period and the
previous period. The change in the analyst mean target price is calculated in order to
include it in the regression analysis.

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As was the case for the change in the analyst mean target price, the change in the total
number of analyst recommendations is calculated in order to include it in the
regression analysis, to see if it can offer any additional explanatory power.

STATA variables

 Table 1. Variables used in STATA, with hypothesised relationship to the dependent variable.

Table 1 above presents the variables that will be used in this thesis in order to run our
significance test, Spearman rank correlation-test and our regression analysis. The first
two tests will only be between abnormal returns and the analyst consensus
recommendation change. The regression analysis will include all variables. The
second left most column is the abbreviation for the variable when analyzed in
STATA. The center column is the name of the variable as it appears in the section
above and the rightmost column shows the hypothesized relationship with the
dependent variable. For example, we hypothesize that an increase in volume will have
a negative relationship with the dependent variable abnormal return, as stated by
Jegadeesh et al. (2004). If an annual report and/or interim report is released, we expect
it to have a positive relation to abnormal returns. We expect both mean analyst
consensus changes and mean target price changes to be negatively related to abnormal
returns, since a number of investors may try to “make a quick” buck on recently
upgraded stocks, thus hinder it from rising and send it the other way. The change in
the total number of analyst recommendations is hypothesized to be positive since an
increased number of analysts studying a stock might, at least in theory, lead to better
estimates and may give legitimacy to the stock. We also hypothesize that positive
market returns will provide a better basis for stocks to generate abnormal returns.

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We have chosen the variables based both on the intuition that they are all the
most relevant in explaining abnormal returns in the immediate to short run, but also
since the previous studies used as a base for this thesis use these variables or some
form of variation of them. Abnormal return and abnormal volume is used by for
instance Lidén (2005) in determining if stock recommendations generate price
reactions, however our abnormal volume is based on the change in the total number
of trades rather than turnover, this is discussed in further detail in section 4.2.4.
Jeegadesh et al. (2004) use volume changes as an indicator of momentum and to
determine an expected direction of returns, since they state that high volume stocks
should correspond with lower returns. They also use the variable analyst consensus
recommendation changes to track if consensus changes have an effect rather than just
the consensus level.
 The variable mean target change isn’t commonly used in the studies that
serves as a base for our thesis, however we felt it was relevant to include it in the
regression analysis since target prices often seem to accompany a new or repeated
recommendation. The change in the total number of recommendations is often
mentioned in previous works but is rarely included as an explanatory variable in the
models. Again, we felt that it would be relevant to analyze this effect to some extent
as well in our regression. The variable will only change in positive or negative
integers, meaning that it does not measure a percentage change. As previously stated,
an increased number of analysts studying a stock might, at least in theory, lead to
better estimates and may give legitimacy to the stock. The market return variable
serves as our benchmark during the period and may provide us with information about
why the stocks might not react as expected to firm specific phenomenon’s. Based on
the variables presented in this section we have formulated a multiple linear regression
for the regression analysis as follows:

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4.2.4 A Critical View on the Method and Data
The quantitative method chosen offers a possibility to mathematically assess the
relationship between abnormal returns and changes in analysts’ recommendations, we
will touch briefly on mean target prices as well, but it will not be of focus in this
thesis. Event studies are widely used for this type of study, see for instance Womack
(1996), Barber et al. (2001 & 2003), Jegadeesh et al. (2004) and Lidén (2005). Using
the Spearman rank correlation-test to assess correlation is also a previously used
method, see Jegadeesh et. al (2004). We have also opted to use a t-test to see if mean
abnormal returns differ from each other and the index under different scenarios, which
is a widely used practice.
 Since we have opted to calculate our analyst consensus recommendation
based on a weighted average of all outstanding recommendations in the period, and
then calculating a percentage change either up or down, and not a dummy variable
for increase or no increase, our results may differ from previous studies. However,
the method using a weighted average of recommendations to calculate changes in
consensus is used by Jegadeesh et al. (2004).
 A key difference to previous studies is that our estimation-, event- and
evaluation windows are constructed in a different manner. Our estimation period is
simply the weeks where no change is present, and the week falls outside the 4-week
evaluation period of a previous change. When two (-or more) events overlap, we have
chosen to use the evaluation period of the more recent event and the 4 weeks
following that event, unless a new event occurs in that time, then the procedure is
repeated. This is described in more detail in previous sections. Since we have
accounted for both negative and positive changes with a large number of overlapping
events, this could cause our estimation windows to be significantly shorter than in
previous studies, giving us fewer observations to base our estimation of normal
performance on. This may lead to different results from previous studies. Transaction
costs are not accounted for in this thesis, this could provide us with differing results
from other studies.
 The data obtained from the Thomson Reuters Eikon terminal might also be
constructed in a different manner than previous and other studies which could give
differing results.

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The chosen time periods are different from previous works and might return
different results. Furthermore, our sample contains a number of 5 490 observations
over three full years and two quarters and the number of observations may differ from
the previously mentioned studies which also might cause our results to differ.
 Since annual reports are few and far between, there were only a handful of
observations in this category which limits our ability to get meaningful results and
draw conclusions. Interim reports are more frequent, but this category also suffers
from few observations.
 Our variable for volume is based on the average number of stocks traded each
week rather than the average value of the traded stocks. Which could potentially
provide us with different results from previous studies. Furthermore, the regressions
could potentially suffer from omitted variable bias, since there could be relevant
variables which affect the outcome of the regressions that are not accounted for in our
thesis. There may also be non-linearity and/or correlation between independent
variables which could bias the results.

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5 Empirical Results
5.1 Descriptive Statistics
In table 2 below our summary statistics of the data sample can be observed. The table
shows the mean values for our sample in the first column as all periods combined and
the following column presents the mean values each year.

 Table 2. Summary statistics from STATA for entire sample

The analyst changes in Table 2 were on average small, with the average analyst
consensus change only being 0.0004. The only year it took on a negative value was
in 2017 with an average of -0.0012, meaning that during 2017 the average change in
analysts’ consensus recommendation was negative. Mean target price was on average
changed in bigger steps than the average consensus change. The change in the total
number of recommendations is expected to be larger since it only moves in positive
or negative integers. The biggest change was in 2019 when the average change in the
total number of recommendations was -0.0468.
There was a total of 5 490 observations meaning that the stocks were each measured
on average over 183 weeks. However, the total number of observations suggests that
Essity B also was observed over a total of 183 weeks, this is not the case, the stock
only has 147 observations in total. This is accounted for in the tests and analysis later
on, where 2017 returns an accumulated 1537 weeks for our sample.

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Table 3. Total number of observed analyst changes in consensus recommendation for all stocks.

Table 3 shows our total number of observed changes in analyst consensus
recommendations since it is the main focus on this thesis. As the table shows, there
were a total of 2 416 changes in analyst consensus recommendations for the entire
sample across all periods. This means that there were on average 0.44 changes in
analysts’ consensus recommendations per week. The changes overall were slightly
positive at 51.7384% positive changes and 48.2616% negative changes. This should
indicate that analysts during the analyzed period were somewhat more likely to
upgrade a stocks recommendation in our sample, than to downgrade it.

5.2 Abnormal Returns
The difference between mean abnormal returns and the mean returns of OMXS PI are
presented below. Table 4 only includes the mean difference between the groups and
a more detailed presentation can be found in table 1 and 2 in appendix 2.

 Table 4. Two tailed t-test of difference between mean abnormal returns for the stocks and the mean return for
 OMXS PI during our observation window. Unpaired and with unequal variances. 2020 and 2016 only contain Q1
 and Q4 respectively.

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As shown in table 4 the mean abnormal returns of our stocks shows significant mean
difference to the index in the event week and 3 weeks after the analyst
recommendation change when looking at the left most column. The fact that the mean
difference is negative in the event week may indicate that the stocks on average
underperformed the index in the week where they were upgraded. The same thing can
be found in 2020 where the mean difference in returns between the stocks and OMXS
PI was almost -4%, followed by a mean difference of -2,34% in the first week after
the analyst consensus recommendation was changed. We are not surprised by this,
and we will discuss why this happened later on. A somewhat similar pattern can be
found for the weeks following a decrease. For the most part, the weeks after a decrease
did not exhibit much of a statistically significant difference, on any level, to the
returns of the index. The only case where this happened was in week 3 in 2018.

5.2.1 Cumulative Abnormal Returns
Mean cumulative returns are summarized from the mean abnormal returns. The mean
cumulative abnormal returns for the stocks and the mean cumulative returns of
OMXS PI are presented in a slightly different manner to before, the table does not
include the difference between the two. The mean cumulative (abnormal) returns for
the stocks and the index is presented in Table 5, a more detailed presentation can be
found in table 3 in appendix 3.

Table 5. Two tailed t-test of mean cumulative abnormal returns for the stocks and the mean cumulative return for
 OMXS PI during all of our observation windows of an increase or decrease. Unpaired samples and with unequal
 sample variances.

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Looking at the summary of all years combined in table 5, one can see that only the
mean cumulative abnormal returns following a decrease in analyst consensus
recommendation shows a statistically significant difference from the mean
cumulative returns of the index. On average the stocks generated a mean cumulative
abnormal return very close to zero after 4 weeks following a decrease, with the biggest
changes happening in week 3. The weeks following an increase did not return a mean
cumulative abnormal return statistically different from the mean cumulative return of
OMXS PI. As can be observed from graph 1 below, the cumulative abnormal return
for the stocks seems to almost move exactly the opposite to the cumulative return of
the index. When seeing the lines in graph 1 it becomes clearer why it should lead to
an overall difference between the mean values that is not statistically different from
0.

 Graph 1. Shows the mean cumulative abnormal returns for the stocks from week 0 to week 4 and OMXS PI mean
 cumulative returns, when there is an increase.

The stocks in graph 1 seem to generate a higher total mean cumulative abnormal
return than the mean cumulative returns of the index when comparing the results in
week 4. If we compare it to the mean cumulative abnormal returns from graph 2,
following the negative changes to analyst consensus recommendations, it seems to
indicate a sharper increase in returns following the initial decline.

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Graph 2. Shows the mean cumulative abnormal returns for the stocks from week 0 to week 4 and mean cumulative
 returns of OMXS PI, when there is a decrease.

Turning to graph 2 we can see a visualization of the mean cumulative abnormal
returns of the stocks and the mean cumulative returns index in the weeks following a
decrease in analyst consensus recommendation. Instead of exhibiting a pattern like in
graph 1, that seemed like a mirror opposite to the index, the stocks seem to follow the
general movements of the index more closely. As one can see, there are a few
differences, most obvious is in week 2 following a decrease, where the stocks seem
to show an increase in returns compared to both the previous week and the index,
which shows a pattern of a decline in the week. After a sharp decline in week 3, there
seems to be a rather steep incline in the last week.

5.3 Bivariate Analysis
In order to check for correlation between abnormal returns and the analyst change
variables we conducted a number of Spearman rank correlation-tests with different
time frames and scenarios.
 Table 6 below shows the obtained rho-value, number of observations and p-
value for the Spearman Rank Correlation-test for the entire sample for each time
period as well as all periods combined. The null hypothesis is that abnormal return is
independent of changes in analyst consensus recommendations.

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The decision to accept or reject the null hypothesis is given in the left most
column and the stars show at which significance level the null hypothesis is rejected.

Table 6. Spearman rank correlation coefficient-test between abnormal return and the observation periods of changes
analyst consensus recommendations for all stocks, with increase and decrease. 2020 and 2016 only contain Q1 and
 Q4 respectively.

Overall, from what can be seen from table 6, the statistically significant correlations
between abnormal returns and our change-variable are very weak. As the rho-values
indicate, there is only a statistically significant relationship between the variables in
two of the years when a positive change happens, 2020 and 2017. The only negative
correlation between the abnormal returns and a positive change in analyst consensus
recommendations happens in 2020 (Q1), this may be expected however, we will
discuss why later on. When there is a negative change 2017 and the combined period
exhibits a statistically significant correlation, albeit both very weak.
 Table 7 shows the results from the Spearman rank correlation-test when we
only include observation periods where an annual or interim report was made public
or exclude all observation periods where an annual or interim report was made public.
The first column only shows correlations between abnormal return and our change-
variables during observation periods where an annual report was released. The second
column only shows observation periods where an interim report was released and the
third and fourth column excludes observation periods where annual and interim
reports were made public from the sample.

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Table 7. Spearman rank correlation test between abnormal return and changes in analyst consensus
 recommendations, with regards to the disclosure of annual and interim earnings reports.

For the entire sample during observation periods where annual reports are released
and there is a decrease in analyst consensus recommendation, there is a moderate
correlation of 0.5131 between abnormal returns and changes in analyst consensus
recommendations. There is also a very faint correlation for the sample when no
report is released and there is a decrease in analyst consensus recommendation. The
relationships are positive, indicating that abnormal returns and changes in analyst
consensus should move in the same direction, for example a negative change in
analyst consensus recommendation correlates with lower abnormal returns in that
period.

5.4 Regression Analysis
The point of the regression analysis is to analyze how much and to what degree the
changes in the dependent variable are explained by the independent variables. A total
of 5 454 observations were obtained from our regression analysis. Table 8 below
shows the results from our regression with abnormal return as the dependent variable.
The table shows that for all stocks across the entire period, the variables abnormal
volume, changes in mean target price, changes in the total number of
recommendations, and interim reports all have a significant effect on the changes in
abnormal return, at the one percent significance level.

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