Is it possible to outperform the market by making use of analyst recommendations? : an analysis of the Dutch stock market

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Is it possible to outperform the market by making use of analyst

recommendations?

An analysis of the Dutch stock market

UNIVERSITY OF AMSTERDAM

BSc Economics & Business

Bachelor Specialisation Economics & Finance

Author: S. Sturkenboom

Student number: 10432795 Thesis supervisor: J.J.G. Lemmen Finish date: 2018-06

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PREFACE AND ACKNOWLEDGEMENTS

This document is written by Student Sem Sturkenboom who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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ABSTRACT

The purpose if this study is to examine whether the use of analyst stock recommendations could contribute to excess stock returns. According to the efficient market hypothesis (EMH) all available information is already priced in the stocks, which would make it impossible to benefit from this information. The study made use of stock data of the last ten years for stocks in the Dutch AEX index, resulting in 3654 data points for 21 different stocks. Although the impact of analyst recommendations was not significant, it was positive. This means that investors are presented with opportunities of making higher returns and thus higher profits by carrying out investments in the market that are based on credible analyst recommendations. Keywords:

G14, Analysts ratings, Efficient Market Hypothesis, Stock Returns, Analyst recommendations, Stock Picking

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LIST OF TABLES

Table 1 Codes for stocks that are part of the analysis 9

Table 2 Descriptive Statistics 10

Table 3 Collinearity Test 10

Table 4 Variance Inflation Factor 11

Table 5 Test for Heteroscedasticity 11

Table 6 Test for Normality 11

Table 7 Augmented Dickey Fuller Test for Stationarity 16

Table 8 Johansen Test for Co-integration 16

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LIST OF FIGURES

Figure 1 Normal Probability Graph for Stock Prices 12

Figure 2 Scatter Plot for Stock Prices 12

Figure 3 Normal Probability Graph for Analyst Rating 13

Figure 4 Scatter Plot for Analyst Rating 13

Figure 5 Normal Probability Plot for Market Capitalization 14

Figure 6 Scatter Plot for Market Capitalization 14

Figure 7 Scatter Plot for Traded Volume 15

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1. INTRODUCTION

This section will cover the background of the study, problem statement, research objectives, research questions, justification and value of the study and an overview of the thesis.

1.1 Background

The efficient market hypothesis, as an investment theory, presumes that it is almost impossible for individual firms or individual analysts to beat the market in making predictions about what prices shares will take both in the short and long term. The reasoning behind this assumption is that stock markets are efficient and so, they include all the relevant and available market information in determining equilibrium market prices. According to Fama (1991), the hypothesis is an assertion that the prices of stocks at any given particular moment fully reflect all the available market information. The implication of this widely acknowledged hypothesis is that financial markets are characterized by symmetry of information. In such markets, information is abundantly available at no cost to any investor that seeks to make rational decisions to buying and selling of securities in the stock market. Another version of the hypothesis suggests that prices of securities in the market reflect market information up to a certain level where the marginal profits of using the information are just equal to the marginal costs associated with using such market information to make investment decisions in the stock market.

From this can be deduced that the efficient market hypothesis means that it is not possible for investors to make abnormal profits by undertaking investment actions in the stock market. Additionally, another implication is that, under efficient market conditions, no investor would pay for information from stock analysts as this information is made easily and freely available by the market itself.

In reality, however, it is increasingly a usual behavior by investors and institutional brokers to pay stock analysts to obtain stock market recommendations, which are strictly speaking upgrade and downgrade information, with the motive of beating the market and realizing abnormal returns. Fama (1991) recognizes this reality and agrees that based on observed behavior of investors in the securities market, the strong version of the efficient market hypothesis, that is based on the assumption that investors don’t want to make any costs to get information that fairly reflects the market prices, must be unsustainable. It is true that market information on prevailing prices is not costless. Similarly, information on what prices will be in future is not costless. There are costs associated with access to information that fairly and reasonably predicts price trends in the securities market.

The semi-strong efficient market hypothesis seems to better explain the how prices fairly adjust to various kinds of information in the stock market. The hypothesis asserts that investors will always engage in making payments to access reliable market information on stock prices so long as the marginal profits obtained from such a behavior do not exceed the marginal costs incurred to access the information.

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1.2 Problem Statement

This research seeks to examine how recommendations by stock analysts influence stock prices in the Dutch stock market. Based on the findings, the study shall be able to conclude whether or not investors can profit from information obtained from stock analysts and whether it makes sense for brokerage companies to hire expensive stock analysts for their companies. Beneish (1991) observes that the market for stock analysts has been on an upward trajectory across the world. The demand for the services of stock analysts, specifically dissemination of stock prices information through the popular analyst downgrade and upgrade recommendations, has been on the rise. This is a factor that contrasts the efficient market hypothesis. According to Beneish (1991), stock prices, on average, outperform the market on the days when securities analysts release their recommendations.

It is being realized that securities markets are not efficient, in that all useful information about stock prices in the market is not entirely made free and accessible to stock market investors (Fama, 1991). Owing from this observation therefore, there is a case for stock market analysts to carry out in-depth research on individual securities in the market and advising their customers accordingly. This might generate information that is not readily and freely made available by the market itself to investors. 1.3 Research Objectives

The general objective for the study is determining whether there is an empirical relationship between stock analysts’ recommendations and expected stock prices in the Dutch stock market and whether analysts can actually beat the market by making use of these analyst recommendations. The specific objectives for the study are:

i. To determine whether stocks that are recommended by analysts perform better than those that

are not and the underlying reason.

ii. To determine whether informed stock selection leads to better stock performance.

iii. To determine whether recommended stocks are undervalued and discouraged stocks

overvalued. 1.4 Research Question

Is it possible to outperform the market by making use of analyst recommendations?

An answer on the above research question could be given by stating the earlier given research objectives in two testable hypotheses as shown below:

H0: Stock analyst recommendations have no significant impact on stock prices.

H1: Stock analyst recommendations have significant impact on stock prices.

The set of hypotheses will be tested at the 5% level of significance. The null hypothesis will be rejected if the p-value is less than the given level of significance, otherwise the study will fail to reject it. The

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methodology of Ordinary Least Squares estimation that uses regression analysis will be used to determine the causal relationship between analyst recommendations and stock prices. Thereafter a conclusion will be made pertaining the possibility of investors in the securities market being able to use analyst recommendations to earn abnormal returns and profits.

1.5 Justification and value of the study

The study has relevance as that its findings will shed light on the credibility of the efficient market hypothesis in real world application. If investors are found to pay for information whose marginal profits are greater than the marginal costs incurred to pay analysts, in order to gain access to useful and reliable information about features and prices of stocks, then the efficient market hypothesis in the securities markets will be understood to be inefficient.

Another justification for this study is that markets are nowadays much more efficient than in the past due to higher volumes mainly generated by high frequency trading companies. As this study is using data of the last ten years it might return a different result compared to previous studies.

The study is valuable as it will decide whether analysts’ jobs are valuable or whether analysts actually don’t add any value, which might decide brokerage companies to get rid of their analysts. The findings will further inform whether the actions of investors, to continue paying for information about the features and expected prices of stocks, in a bid to realize abnormal profits, are rational or not.

1.6 Main findings

In this paper the semi strong efficient market hypothesis is being questioned by testing whether analyst recommendations have an effect on actual stock prices. Although the findings were not significant on a 5% level, the regression coefficient is positive, which means investors might have opportunities earning excess returns while making use of analyst recommendations.

1.7 Research overview

The thesis is organized in the form of sections. The current section is an introduction. It introduced the topic of study, stated the problem being investigated, the research objectives, research questions, justification and its value. Section 2 contains the literature review which is an extensive review of relevant existing literature and earlier research. Section 3 contains the methodology which will describe the research design and the dataset which is being used in this research. Section 4 contains the results of the research. Section 5 contains a summary of the results and a conclusion whether analyst recommendations have a positive impact on stock prices and whether investors in the stock market can rely on recommendations from analysts in order to make higher returns and profits.

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2. LITERATURE REVIEW

2.1 Introduction

In this section, existing and relevant literature on the effect of analysts’ recommendations on stock returns will be reviewed. This is to determine the direction of the research, identify existing research gaps that may be filled by this study as well as document data and methodological approach that similar studies previously used.

2.2 Empirical Literature Review

Panchenko (2007) undertook to examine the relationship between the recommendations of stock analysts and returns of stocks using a sample of thirty-six large cap securities traded in the securities market in the United States for a period of seven years ending in 2003. The study found out that there is a significant relationship between recommendations by analysts and the returns from stocks.

Murg et al.(2016) examined how analyst recommendations impacted stock prices in Austria for a period of fourteen years ending in 2014. The data used was obtained from a sample of eight thousand reports on analyst recommendations in the small and thinly traded Austrian stock market. Recommendations were categorized into downgrades and upgrades. The results indicated that sell to buy recommendation changes were associated with a 1.232% increase in stock returns while a buy to sell recommendation changes were associated with a 1.534% decrease in returns from stocks. Although investors were found to trust recommendations from analysts, they were more skeptical of such recommendations during times of financial crisis.

Gleichmann and Stattin (2011) attempt to examine how recommendations by stock analysts translate to abnormal returns. They also seek to find how such analyst opinion can be used to predict future returns in the market. Ordinary Least Squares method is used in the analysis where the size and existence of abnormal returns is examined as the outcome variable influenced by recommendations from analysts as the explanatory variable. The findings indicate a strong positive relationship between recommendations from analysts and abnormal stock returns. Specifically, there is a more than 1% increase for every upgrade or downgrade recommendation made. This leads to a conclusion that analyst recommendations have a significant impact on stock returns in the short term.

Sorescu and Subrahmanyam (2006) carried out a study to examine how investors reacted to stock price revisions by analysts. They used experience and reputation of analysts as proxies for the ability of an analyst to make reasonably reliable recommendations. The findings found out that recommendations on stock revisions from experienced and reputable analysts are superior and more reliable compared to recommendations from less experienced and reputable analysts. Furthermore, stock price recommendations from more experienced and reputable analysts were found to have persistently higher returns compared to those made by less experienced and reputable analysts. The study was generally

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found to have importance in helping investors make more informed decisions and thus realize higher returns from stock investment.

Jegadeesh et al. (2004) found that analyst recommendations can be reliable in predicting price movements of stocks, and should be embraced. They however note that caution is needed pertaining the extent to which such information is used. They argue that in most instances, the sell-side analysts tend to give recommendations that are greatly reliable for high growth, high momentum, expensive and high volume stocks. If the securities in question do not have these feature, then blind adherence to such recommendations can be highly costly. Generally, they arrive at the conclusion that stock analysts can relay useful information through recommendations that is in effect related to higher stock prices and returns.

Bradley et al. (2008) sought to examine how 7400 recommendations made analysts influence stock pricing during an IPO between 1999 and 2000. The study found out that the securities market rarely discounts information from analysts while controlling for the timing and characteristics of the recommendations. This further provides more light into the relationship between analyst recommendations and stock prices. Investors were found to heavily rely on the recommendations made by the analysts and these recommendations were found to affect price adjustments of stocks thus affecting profits obtained by investors.

Barber et al. (2001) undertook to find out the relationship between recommendations from stock analysts and stock returns received by investors in the stock market. They argued that purchasing stocks that have the most favorable recommendations was associated with abnormal annual returns that were higher than 4%. They further observed that investors using analysts’ recommendations had to react fast to information given to them so that they could realize supernormal profits. Delay in responding to the information given to them was associated with stock performance that did not yield significant excess returns to the investors. As opposed to the claims of the efficient market hypothesis (Fama, 1991), they find that trading in the securities market is associated with heavy transaction costs for voluminous transactions that are necessary in order for investors to realize abnormal returns from the market. In a similar evaluation, Barber et al. (2003) note that it is actually true that some securities that are highly rated by analysts perform better than the market. They however note that analysts need to ensure that they factor as many features and aspects of securities as possible, so that they can be able to come out with recommendations that are fairly and reasonably representative and reflective of market realities. According to them, some stocks that are poorly rated by analysts have the potential to perform better than those rated favorably in those instances where material information about the stocks was left out during the stock analysis and the recommendations (Barber et al., 2003).

Nicholas (1968) examined how recommendations by analysts affected prices of stocks and how that was related to the earnings received by investors. He observes that the securities market is sensitive to any

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information, whether large or small. As such, information in form of recommendations from research carried out by analysts which is publicly published and made available to everyone in the market, has the potential to create price adjustments that further influence the amount of profits that investors can earn. The prices taken by stocks at any given moment is a reflection of the information that is available to the market at that particular time. As such, any new information that is made public by stock analysts is quickly absorbed and reflected in the stock prices of the market. This means that if securities analysts know some information that is not known by the market and thus privately sell this information to private customers, then they have the potential of taking advantage of information they have that the market does not have in order to earn abnormal profits.

Clarke et al. (2006) carried out a similar research that sought to find out whether analyst recommendations have an effect on stock prices and how investors can take advantage of information provided by analysts in order to realize higher profits. Specifically, they inquired whether stock analyst recommendations were biased, and whether they could be relied in making investment decisions that yield returns that are higher than what making investments based on market information would yield. The findings indicated that recommendations can be biased if the analysts do not do rigorous research into all the features and characteristics of the stocks. They note that rigorous research by stock analysts can yield information that is unbiased and reasonably reflective of the hidden features of stocks that the market left out while pricing stocks. If this information is made available to investors before the market becomes aware of the new information, then investors can earn abnormal returns. They further note that however analyst recommendations can be relied to some extent, the securities market is greatly subject to shocks that neither the analysts nor the market itself could have foreseen. As such, information from analysts can only be reliable in predicting the actual value of a stock, and the price it is likely to take in the future, if the securities market does not face unexpected external or internal shocks as these have the potential to create massive stock price fluctuations.

Desai et al. (2000) examine how securities that were recommended by analysts in the Wall Street Journal performed in comparison to those whose trading was basically depended on the information provided by the market. They concluded that stocks that had been recommended by analysts performed better than those that were not recommended and that traded based on the information provided by the general market. Additionally, the study found out that securities that had been recommended by industry-specific analysts yielded superior returns than those recommended by analysts that concentrated on a multiplicity of sectorial securities. They observed that herding behavior was common among analysts in the industry where smaller and less experienced analysts tended to mimic and imitate recommendations that were made by well-established analysts in the industry. This observation meant that recommendations by analysts, can greatly cost investors’ money, if the less experienced and reputable analysts imitates recommendations made by the more reputable and experienced ones, while the more reputable one had made some errors in their research, analysis, results and recommendations

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thereof. Specifically, their findings pointed towards the conclusion that outperformance of stocks recommended by analysts was possible for both large-cap and small-cap securities.

Green (2006) undertook to find whether recommendations by analysts offer any value to investors in the stock market. The study found out that an investor who gains access to information, pertaining certain aspects of stocks that the entire market does not capture in the prevailing stock prices, presents investors with an opportunity to realize abnormal returns from such information. More specifically, he finds that when transaction costs are taken into account, early access to recommendations and acting on them immediately, presented investors with profitable opportunities that were valued at between 1.02% for upgrades and 1.50% for downgrades within a duration of two days. More interestingly, he observes that the lucrative opportunities exist for a very short duration of time, that last up to two hours after the analysts shared the information with the investors. Keen execution of trading activities based on reasonably reliable analyst recommendations presented investors with opportunities to make abnormal profits that range up to 30% per year. On realizing that new information has been released, that markets had not effectively factored in their pricing, securities move swiftly to adjust their prices, either upwards or downwards, presenting evidence as to why clients continually pay stock analysts to get recommendations. They do so because such an action presents them with the advantage of having access to information that the market does not have, thus taking actions that yield higher returns and profits before the market becomes aware of the new information and thus adjusts accordingly.

Groysberg et al. (2011) examined whether there is a rational and empirical explanation as to why analysts exist and whether there is rational profit motivation for investors that come to them seeking for recommendations. They find that the actions of analysts, especially in the banking sector, are rational and demand-driven. Investors who aim at making higher profits from investing in the securities market come looking for unique information that is not readily available in the entire market. As such, analysts, being rational economic agents that seek to maximize their own incomes, undertake to carry out rigorous and directed research into intrinsic and hidden aspects of securities. These aspects are normally not currently captured by the market and thus, analysts are in a position to take actions, in the form of upgrades or downgrades in order to reposition themselves strategically to earn more profit once the market notices the new information and adjusts the prices either upward or downward to reflect the value of the stock.

Howe et al. (2009) examined how over three hundred and fifty thousand analyst recommendations affected stock prices and whether investors could take advantage of such information to make abnormal profits from the securities market. Specifically, the recommendations were for the sell side of the market and spanned between 1994 and 2006. They find that aggregate analyst recommendations in the industry have reliable predictive potential for stock returns in the future after adjusting for external shocks taking place in the macro-economic environment. Generally, they observe that aggregate recommendations in the market carry information that has the potential to predict future stock returns and investor earnings.

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The implication of their study is that investors have a rational motivation to pay for analyst recommendations as they are associated with greater returns. Their major findings is that rigor and thorough analysis of intrinsic information contained in hidden features and characteristics of the stocks has the potential of realizing above 25% of returns that would have been realized without using such information. As such, the study finds empirical support for the relationship between analyst recommendations and the stock prices and goes a step further to argue that investors can actually rely on the information contained from the recommendations given by analysts to make abnormal profits. This is so because any profit that is way above the marginal cost incurred to gain access to analyst recommendations is actually considered abnormal.

Kakebeeke (1999) examined whether following the advices from the monthly magazines distributed by Dutch banks to private investors would yield excess return or not. The dataset contains analyst advices from 120 monthly distributed magazines of the four biggest Dutch banks. He found that following the analyst ratings didn’t result in excess returns, in contrast, following the advices would lead to a significant negative excess return. The main reason for this negative excess return is the time-lag between the publication of the analyst advises and the release of the actual magazines.

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3. DATA AND METHODOLOGY

3.1 Data

The study uses time series data of the 25 biggest Dutch stocks per 1 January 2018, which are part of the

AEX index. The data comprises of daily closing prices starting from 1st_{January, 2008 to 1}st_January,

2018. That makes a sample of 3,654 observations. The AEX index dataset contains 25 stocks, but some stocks have missing data in the data set. The variables stock price, analyst recommendation, traded volume and market capitalization each had 21 stocks with complete data, translating to 84% of the entire market. That percentage is large enough to fairly represent the entire market. Stocks with missing data values in various variables were removed from the data set because using them in the analysis with missing values would yield biased results. The stocks which are removed from the dataset are ABN NA, ATC NA, NN NA and PHIA NA. These stocks had incomplete data due to the fact they were listed on an exchange after the start date of the dataset.

Table 1: Codes for stocks that are part of the analysis

AALB NA AKZA NA DSM NA INGA NA RAND NA

AD NA ASML NA GLPG NA MT NA RDSA NA

HEIA NA SBMO NA GTO NA KPN NA UNA NA

AGN NA BOKA NA WKL NA REN NA VPK NA

UL NA

The dependent variable is the stock price. Since there are 21 securities, their daily prices are added and average obtained with an aim of obtaining average stock market prices that fairly and reasonably represent the prevailing market prices. The independent variable is analyst recommendations consensus that are measured by consensus recommendations for the entire market. The proxy is average analyst rating in a scale of 1-5 with values of 4-5 representing a buy recommendation, 3 representing neutral or a hold recommendation and 1-2 representing a sell recommendation. In order to ensure that the study adequately controls for other factors that may explain stock price movements and adjustments, control variables on market capitalization and traded volume are included in the model in order to control for these other factors.

The descriptive statistics for the three variables used in the model are shown in Table 2 below. There are 3654 observations for each variable. Average stock price has (M=35.1287, SD=9.0023), Analyst

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Rating has (M=3.7943, SD=.1198), market capitalization has (M=25612, SD=5863.1940) while traded volume has (M=4235893, SD=1622183).

Table 2: Descriptive Statistics

Variable Observations M SD MIN MAX

Average Stock Price 3,654 35.1287 9.0023 15.7026 52.5155 Analyst Rating 3,654 3.7943 0.1198 3.4989 4.1067 Market Capitalization 3,654 25612.52 5863.19 12977.48 39613.98 Traded Volume 3,654 4235893.00 1622183.00 264615.00 14900000.00

Collinearity between the explanatory variables was carried out using pairwise correlation. The correlation coefficient was -0.2334 between market capitalization and analyst ratings, 0.2297 between traded volume and analyst rating and -0.2822 between traded volume and market capitalization as shown in Table 3 which implies that the two explanatory variables are weakly negatively correlated.

Table 3: Collinearity Test

Variable Analyst Rating Market Capitalization Traded Volume Analyst Rating 1.0000

Market Capitalization -0.2334 1.0000

Traded Volume 0.2297 -0.2822 1.0000

The variance inflation factor test for collinearity was 1.09, 1.12 and 1.12 as shown in Table 4. This implies that the variance of either coefficient is only 9% and 12% higher than what would be expected if there was no collinearity. This collinearity value is however, too small to affect the reliability of the regression results.

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Table 4: Variance Inflation Factor Variable VIF Analyst rating 1.09 Market Capitalization 1.12 Traded Volume 1.12

The Breusch-Pagan test for heteroscedasticity as shown in Table 5 had a p-value of 0.0000 which is smaller than the 0.05 level of significance. This implies that the variance of the error terms varies across observations. Heteroscedasticity is therefore a major problem as it affects the efficiency of the parameter estimates. To address the problem, robust standard errors will be obtained.

Table 5: Test for Heteroscedasticity

Chi2(1) 94.82 Prob>chi2 0.0000

The Shapiro-Wilk test for normality as shown in Table 6 indicates that the error terms of average stock price, average analyst rating, market capitalization and traded volume had a normal distribution. The data therefore satisfies the requirement for normality.

Table 6: Test for Normality

Variable Observations W V z prob>z

Average Stock Price 3,654 .9669 67.6010 10.9460 .0000

Average Analyst Rating 3,654 .9778 45.4580 9.9150 .0000

Market Capitalization 3,654 .9768 47.4070 10.0240 .0000

Traded Volume 3,654 .9001 204.4310 13.8200 .0000

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Additionally, the graphs and scatter plots shown in Figures 1-8 further provide more credence to the conclusion arrived using the p-value criterion on normality of the variables used in the study.

Figure 1: Normal Probability Graph for Stock Prices

Figure 2: Scatter Plot for Stock Prices

0.00 0.25 0.50 0.75 1.00 No rm al F[ (st ock pri ce -m )/s] 0.00 0.25 0.50 0.75 1.00

Empirical P[i] = i/(N+1)

10 20 30 40 50 St ock Pri ce 0 1000 2000 3000 4000 Day

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Figure 3: Normal Probability Graph for Analyst Rating

Figure 4: Scatter Plot for Analyst Rating

0.00 0.25 0.50 0.75 1.00 No rm al F[ (a na lyst ra tin g-m )/s] 0.00 0.25 0.50 0.75 1.00

3. 4 3. 6 3. 8 4 4. 2 A na lyst R at in g 0 1000 2000 3000 4000 Day

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Figure 5: Normal Probability Plot for Market Capitalization

Figure 6: Scatter Plot for Market Capitalization

0. 00 0. 25 0. 50 0. 75 1. 00 No rm al F [(m arke tca p-m )/s ] 0.00 0.25 0.50 0.75 1.00

10

000

20

000

30

000

40

000 Ma

rke

tC

ap

0 1000 2000 3000 4000 Day

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Figure 7: Scatter Plot for Traded Volume

Figure 8: Normal Probability Plot for Traded Volume

0 50 000 00 1. 00 e+ 07 1. 50 e+ 07 Vo lu me 0 1000 2000 3000 4000 Day 0. 00 0. 25 0. 50 0. 75 1. 00 No rm al F [(vo lu me -m )/s] 0.00 0.25 0.50 0.75 1.00

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The augmented Dickey-Fuller test as shown in Table 7 was used to test for stationarity of the three data series. The test indicates that the series are non-stationary when the test is executed on the raw data without differencing. The series become stationary after undertaking the first differencing, meaning they have one unit root each. In order to avoid loss of information associated with attempts to make the series stationary through differencing, the Johansen test for cointegration is carried out with the aim of combining short-run and long-run information and testing whether the series of the estimated residuals from the cointegrating residuals are integrated of order zero.

Table 7: Augmented Dickey Fuller Test for Stationarity

Variable Test statistic 1% Critical value 5% Critical value 10% Critical value Average Stock Price -0.447 -3.430 -2.860 -2.570 Average Analyst Rating -1.765 -3.430 -2.860 -2.570 Market Capitalization -0.401 -3.430 -2.860 -2.570 Traded Volume -21.325 -3.430 -2.860 -2.570

The results of the Johansen cointegration test indicate that the series are cointegrated of order 1. The trace and max statistics are greater than their corresponding critical values and the null hypothesis of non-cointegration is thus rejected. The short-run causality running from the independent variables to the dependent variable is then tested using the vector error correction model.

Table 8: Johansen Test for Co-integration

Maximum Rank Trace statistic 5% Critical value 0 406.7950 47.21 1 27.7854* 29.68 2 5.6283 15.41 3 .4275 3.76

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3.2 Research Design

This paper embraces a quantitative research design. The design will establish the quantitative causal relationship between the dependent variable, average stock market price, and the key independent variable, analyst consensus rating, while controlling for other factors that may have influence on stock prices.

The research design should achieve validity and reliability in order to produce results that are credible and reproducible. As such, a number of tests have been carried out to ensure that the data is adequate in ensuring that the research questions and hypotheses are adequately tested and satisfied. These tests were carried out in section 3.1 above.

3.3 Empirical model

The study estimates a linear model. The dependent variable is the average stock market price while the key independent variable is analyst rating. Market capitalization is included in the model as a control variable. The model is as shown below:

Stock price = β0 +β1 Analyst Rating + β2 ln(Market Capitalization) + β3 ln(Trading Volume) + ε

where Y is the stock price and stands for the dependent variable, X1 is analyst rating, X2 is market

capitalization and X3 is trading volume, β0 is the estimation constant which is normally the intercept, β1

is the coefficient for analyst rating that measures the impact of analyst rating on stock prices, β2 is the

coefficient for market capitalization while β3 is the coefficient for trading volume. ε is the error term of

the model.

The hypotheses could be tested by making use of the results of the model.

H0: Stock analyst recommendations have no significant impact on stock prices.

H1: Stock analyst recommendations have significant impact on stock prices.

If Analyst Rating is significantly positive and so statistically affects the stock price, we can reject the null hypothesis and accept our alternative hypothesis.

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4. RESULTS

In this section, the results obtained from the vector error correction model (VECM) model that was estimated will be presented and interpreted. The interpretation of the results will be linked to the objectives, research questions and hypotheses that were stated in section one.

4.1 Results

The aim of this research study was to determine whether there is an empirical relationship between analyst recommendations indicated by analyst ratings and the stock prices. The study further aimed at making a decision as to whether investors in the securities market can rely on recommendations given to them by stock analysts to make higher returns and profits.

A linear model was estimated with average stock market price as the dependent variable and the average analyst rating as the key independent variable. Market Capitalization and Traded Volume were used control variables with an aim of catering for variations in stock prices that may not be explained by

analyst rating. Daily time series data starting from 1st_{of January 2008 to 1}st_{of January 2018 was used}

in the analysis.

The null hypothesis that stock market recommendations have no significant impact on stock prices was tested at the 5% level of significance. At this level of significance, the study has a 95% confidence interval that the estimated parameters validly and reliably measure what the study aimed to measure. Due to presence of cointegration among the error terms of the four series, the short-run and long-run causality running from average analyst rating, traded volume and market capitalization to average stock price, was tested using the vector error correction model.

The results indicated that analyst recommendations have a positive impact on stock prices. The impact was however not statistically significant as analyst ratings had a p-value of 0.303 which was greater than the 5% significance level.

The fitted model is:

Average Stock Price = 0.00507 + 1.27923 AnalystRating - 0.01763 ln(MarketCapitalization) - 0.02488 ln(Trade Volume)

The results could be interpreted as follows: Every additional point to the analyst consensus rating on a scale of 1-5 is associated with a 1.27923 raise in stock prices in the market, holding the effect of market capitalization and traded volume on stock prices constant.

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Table 9: Results

Average Stock Prices β Robust SE β z-statistic P-Value 95% CI

Constant .0051 .0058 .88 .377 [-.0062, .0164]

Analyst Rating 1.2792 1.2425 1.03 .303 [-1.1885, 3.6809] ln(Market Capitalization) -.017637 1.0213 -0.02 .986 [-2.0193, 1.98411]

ln(Traded Volume) -.0248878 .0232 -1.07 .284 [-.0704, .0206]

Based on the results in Table 9, the null hypothesis that asserted analyst recommendations have no significant impact on stock prices was not rejected. Despite being insignificant, there was a positive relationship between analyst recommendations and stock prices. The implication is that the behavior of investors in the market to pay analysts in exchange for upgrade, downgrade or hold recommendations is not only rational in theory but also supported by data.

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5. SUMMARY AND CONCLUSION

5.1 Introduction

In this section, a summary on the results will be provided. Conclusion will also be made pertaining whether analyst recommendations have a positive impact on stock prices and whether investors in the stock market can rely on recommendations from analysts in order to make higher returns and profits. 5.2 Summary and Conclusion

The objective of the study was to find out whether analyst recommendations have a positive impact on stock prices and whether investors in the stock market could rely on these recommendations to realize higher returns and profits.

The motivation to carry out the study was to find out whether for sure the securities markets are efficient as suggested by the efficient market hypothesis. The semi strong efficient market hypothesis opines that stocks in the market reflect all the available information in the prices.

It further postulates that information if freely available in the market, meaning that investors in the securities market do not need to incur any cost to pay stock analyst for upgrade or downgrade recommendations as this information would readily and freely be available to them.

The null hypothesis that analyst recommendations have a positive impact on the stock prices was estimated at the 5% level of significance using a Vector Error Correction Model (VECM). The results indicated that analyst recommendations have a positive impact on stock prices. This explains the observed behavior of investors in the real world where they approach stock analyst and pay them in exchange for upgrade, downgrade or hold recommendations.

The implication is that the efficient market hypothesis is just true in theory, it is a simplified version of the reality as it does not hold in reality. In the real world, the securities markets are not efficient and all the information about securities is not accurately captured and factored in pricing of stocks. The stock market, however, is conscious and readily notes any new information that is breaking out in the market and factors it in the prices, meaning that stock prices normally do exhibit price adjustments after analysts have released the recommendations.

Although the impact of analyst recommendation was not significant, it was positive. This means that investors in the market are presented with opportunities of making higher returns and thus higher profits by carrying out investments in the market that are based on credible analyst recommendations.

The channel for making higher returns and profits is through investors in the securities market getting recommendations from the analysts and taking quick actions to either buy, sell, or hold their securities before the information held by the analysts finally leaks into the market and the market adjusts prices

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accordingly. This approach can prove lucrative for investors who swiftly and quickly implement recommendations given to them by the analysts.

5.3 Suggestions for Future Research

This study succeeded in shedding light into the rationale behind investors paying huge sums of money to stock market analysts in exchange for recommendations. It tested the hypothesis that analyst recommendations have no significant impact on stock prices. The gap that had been identified by Beneish (1991) that there was still no empirical relationship between analyst recommendations and stock prices is now fully filled. This study established an empirical evidence on the impact of analyst recommendations on stock prices.

Suggestions on future research in this topic are to examine whether unanimous analyst ratings are more powerful than analyst consensus ratings which are not unanimous. Additional data as individual analyst rating data would be required for this research. In other words, would an analyst consensus rating with a smaller standard deviation be more predictive than a consensus rating with a higher standard deviation?

Other interesting research topics would be whether experienced analysts influence less experienced analysts’ ratings, and whether smaller stocks with lower trading volumes would be more sensitive to analyst ratings than stocks with a higher market capitalization.

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