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«PhD Thesis submitted to the Faculty of Economics and Business Institute of Financial Analysis University of Neuchâtel For the degree of PhD in Finance by ...»

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PhD Thesis submitted to the Faculty of Economics and Business

Institute of Financial Analysis

University of Neuchâtel

For the degree of PhD in Finance



Members of the dissertation committee:

Michel Dubois, Université de Neuchâtel, thesis director

Peter Fiechter, Université de Neuchâtel

Bernard Raffournier, Université de Genève

Thomas Jeanjean, ESSEC Business School, Cergy-Pontoise, France Gilles Hilary, INSEAD, Fontainbleau, Singapore Defended on 23 February 2016 Faculté des sciences économiques Avenue du 1er-Mars 26 CH-2000 Neuchâtel www.unine.ch/seco ii iii iv Acknowledgments First of all, I am grateful to Michel Dubois, my advisor. His dedicated approach in directing my thesis enabled me to grow on multiple levels. Not only did he guide me in my research endeavors with a critical eye, but he motivated me to surpass myself continuously. I was lucky and honored to meet and work with such a person.

I would like to thank the members of the jury who kindly accepted to review this manuscript: Peter Fiechter, Gilles Hilary, Thomas Jeanjean, and Bernard Raffournier. Their invaluable comments and suggestions helped me to improve my work and will undoubtedly contribute to the development of my ongoing projects.

Several persons contributed to the development of this thesis at different stages. I am especially indebted to Carolina Salva and Luzi Hail for their feedback, to Ines Chaieb for her advices on research, and to Žana Grigaliūniene for her collaboration. My special thanks go to Andreea Semenescu, a passionate professor and lifelong friend, for her encouragements.

I am very thankful to the entire IAF team and to my former and current Ph.D. colleagues. In particular, Ivan Guidotti, Ashkan Marami and Xiqian Zhang kept my research interests alert on many exciting topics through our talks and shared curiosity. I am grateful to the University of Neuchâtel for providing me with excellent work conditions. I thank the Swiss National Research Foundation and the Swiss Federal Commission for Scholarships for Foreign Students for financial support.

The boundless support from my family was crucial over these years. I thank my mother for her patience and constant reminder to value my time. Adrian, my husband and best friend, has been an exceptional partner in both joy and pain: I would have not managed to become the person I am today without you being by my side and loving me unconditionally.

Finally, my deepest gratitude goes to my father. I can only hope that, by the values he left me, I’ll continue to grow and travel each day as far as my mind will take me. I dedicate this thesis to his memory.

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This dissertation is composed of three chapters. The first chapter explores the importance of previously identified factors in explaining the variation in analysts’ earnings forecast error. As earnings forecasts are the main input in determining price targets and consequently stock recommendations, much of the process through which analysts process their input remains in a so-called “black box”. This study attempts to shed light on these inputs. First, it reveals that forecast errors are stable over time, and analysts do not efficiently integrate past information in their forecasts. Second, analysts do not factor in expectations related to the macroeconomic conditions for the underlying forecast horizon. Analysts overreact (underreact) to positive (negative) macroeconomic expectations on both GDP and consumer sentiment index. Third, this study decomposes analysts’ forecast errors variance by observable characteristics and fixed effects. Importantly, the analysis shows that there is an unobserved, time-invariant component related to the firm-analyst dimension that explains much of the variance in the forecast errors. This component is not yet captured by the existing observable characteristics which, at date, have a trifling effect on their own in explaining the variation in analysts’ forecast error.

In the second chapter, I investigate the role of financial reporting frequency in analysts’ earnings forecasts. I addresses two questions. First, does mandatory quarterly reporting benefit financial analysts in decreasing their earnings forecast error and dispersion? Second, to what extent common accounting standards increase the convergence of analysts’ information set for firms with different reporting frequencies? I find little support to the claim that regulation forcing firms to issue more frequent financial information benefits financial analysts. Compared to a control sample of semiannual reporting firms in the European market, analysts issuing earnings forecasts for firms with mandatory quarterly frequency experience higher forecast error and dispersion. When firms are mandated to report not only on a quarterly frequency, but also under International Financial Reporting Standards (IFRS), analysts’ both forecast error and dispersion decrease. However, while IFRS does benefit analysts by increasing the quality of their

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quarterly statements.

The third chapter focuses on how financial analysts adapt to the passage of regulations aiming at limiting conflicts of interest in the investment banking industry. This last chapter investigates analysts’ price targets and recommendations, and unravels a new form of conflicts of interest. Specifically, it investigates whether affiliated brokers issue unfavourable ratings on their clients’ competitors in the product market (rivals). The findings document an important gap between ratings for affiliated and rival firms. Specifically, brokers issue persistently higher ratings on firms with which they are affiliated compared to their rivals. Importantly, the Sarbanes-Oxley Act and the related financial regulations aiming at curbing the conflicts of interests had no significant impact in reducing this gap. As such, affiliated brokers continue to indirectly favour their clients. This form of conflict was devoid of adequate attention in prior research. Furthermore, investors are unaware of the existence of such conflict in the short-run.

Key words: financial analysts, earnings forecasts error, reporting frequency, financial statements, price targets, stock recommendations, Sarbanes-Oxley act, conflicts of interest, investment banking.

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Chapter 1: Decomposing Analysts’ Forecast Error: What are the Key Factors?


Background and related literature

1.2.1. Bias in earnings forecasts Analyst characteristics Firm characteristics Forecast characteristics Institutional environment

1.2.2. Unexpected changes in macroeconomic factors

1.2.3. Forecast error dynamics

Data and sample selection

The role of the forecast error components

1.4.1. Model specification Past forecast error Unexpected changes in macroeconomic variables Bias

1.4.2. Descriptive statistics

1.4.3. Dynamic panel data estimators

Variance decomposition of analysts’ forecast error

1.5.1. One-way analysis

1.5.2. Multivariate analysis



Chapter 2: Quality versus Quantity: The Role of Reporting Frequency on Financial Analysts’ Information Environment


The harmonization process of financial reporting in Europe

2.2.1. The historical context

2.2.2. Recent developments

Literature review and hypotheses development

2.3.1. Mandatory interim financial statements

2.3.2. Mandatory IFRS adoption

Data and methodology

2.4.1. Sample construction

2.4.2. Empirical design


2.5.1. Univariate results

2.5.2. Multivariate results

ix 2.5.3. Differences in the mandated quarterly reporting Different enforcement levels Distance to IFRS

Robustness tests

2.6.1. Matching sample

2.6.2. Transparency Directive

2.6.3. Mandatory versus voluntary samples



Chapter 3: Hype My Stock or Harm My Rivals? Another View on Analysts’ Conflicts of Interest


Prior research and hypotheses development

3.2.1. Conflicts of interest in sell-side equity research

3.2.2. The Sarbanes-Oxley Act and related regulations

3.2.3. Persistence of conflicts of interest

Data and methodology

3.3.1. Sample construction Identification of affiliated recommendations and price targets Identification of rival recommendations and price targets Final sample

3.3.2. Research design

Empirical results

3.4.1. Descriptive evidence

3.4.2. Negative ratings on rival stocks

3.4.3. Evaluating alternative hypotheses Selection hypothesis Information hypothesis

Short-term market reaction to affiliated and rival bias



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Chapter 1: Decomposing Analysts’ Forecast Error: What are the Key Factors?

Table 1.1: Descriptive statistics

Table 1.2: Sample correlations

Table 1.3: Regression results

Table 1.4: Analysts’ forecast error variance decomposition (one-way)

Table 1.5: Variance decomposition by observable characteristics and fixed effects

Table 2.1: Distribution of firm-year observations by country

Table 2.2: Descriptive statistics

Table 2.3: Summary of analysts’ information environment by reporting frequency

Table 2.4: Baseline results

Table 2.5: Differences in country institutional regimes

Table 2.6: Robustness results

Table 2.7: Voluntary versus mandatory quarterly and IFRS reporting

Table 3.1: Descriptive statistics

Table 3.2: Percentage distribution of recommendations and price targets by affiliation

Table 3.3: Brokers’ bias and SOX

Table 3.4: The affiliation bias relative to rival and neutral recommendations and price targets.

........ 131 Table 3.5: Alternative explanations to conflicts of interests

Table 3.6: Short-term abnormal returns by affiliation

Table 3.7: Multivariate tests on short-term market reaction to biased recommendations

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Chapter 1: Decomposing Analysts’ Forecast Error: What are the Key Factors?

Figure 1.1: Timing of EPS forecast

Figure 1.2: Variance decomposition by observable characteristics and fixed effects (one-way).

......... 41 Figure 1.3: Variance decomposition by observable characteristics and fixed effects

Chapter 2: Quality versus Quantity: The Role of Reporting Frequency on Financial Analysts’ Information Environment Figure 2.1: Milestones in the reporting harmonization process in Europe

Figure 2.2: Annual forecast error cut-off dates during the fiscal year

Figure 2.3: Analysts’ annual information environment

Chapter 3: Hype My Stock or Harm My Rivals? Another View on Analysts’ Conflicts Of Interest Figure 3.1: The affiliation bias over time

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In financial markets, investors generally rely on the experts - and particularly on financial analysts – to obtain information on what the future may hold. Accurate predictions are paramount for dealing in a world fraught with uncertainty, since they improve the decision making process and help optimizing resource allocations. What equities should one invest in over the long run? Which sectors will outperform? How will the market react to the next regulatory initiatives? The role of financial analysts is to provide investors

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to value companies, research and issue professional reports on firms’ operations, and provide an investment rating to their clients.

The research interest on financial analysts has grown exponentially over the last three decades. While in the early years the academic literature tackled analysts’ expectations to investigate market efficiency, it later explored the investment value of their output. Along this way, the spectrum of analysis has extended from solely earnings forecasts to stock recommendations and, eventually, price targets. This work has certainly increased our understanding on analysts’ relevance and how they operate in financial markets as intrinsic agents.

Nevertheless, we still lack a great deal of knowledge in two particular areas. The first one relies on analysts’ input in producing forecasts. Despite hundreds of studies, the academic research has made limited breakthrough in what constitutes the analysts’ toolbox for valuing firms. What are the key determinants of analysts’ forecast errors? Do analysts efficiently integrate past information in their forecasts? The process through which analysts forecast earnings remains by and large a “black-box” and warrants further investigation.

The second strand of research concerns the role of financial regulations on analysts’ output. As the crisis of 2008 set back the financial industry in a state of ill-repute, financial regulators responded with an unprecedented growth in reforms to increase confidence in capital markets. To illustrate, the number of See, e.g., “14 Predictions for 2016 from the brightest minds in finance”, Bloomberg, November 20, 2015;

“Analysts' top sectors and stock picks for 2016”, CNBC, December 31, 2015.

regulatory restrictions issued by the U.S. Security and Exchange Commission increased by 19% from 1997 to 2012.1 2 Financial regulations have expanded on a global scale and show no signs of abating. For F example, in 2014, financial regulators issued on average 155 alerts per day concerning worldwide updates,

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changed the way analysts interact with firms, mix private and public information, and conduct their research. Do analysts issue more accurate forecasts? Do price targets and stock recommendations better reflect stocks’ expected performance? How these regulations affect analysts’ role as intermediaries in financial markets? Did they have unintended consequences?

This dissertation aims to contribute to these lines of inquiry. The first essay examines the determinants of analysts’ forecast errors. In a variance decomposition model, this study checks to what extent the determinants previously identified in the literature are important in the cross-section and time-series variation of analysts’ forecast errors. The analysis reveals that the importance of the existing determinants is dismal, and that forecast errors are rather stable over time. Firm and analyst fixed effects explain much of the variance in analysts’ forecast errors, suggesting that there is an important, unobserved firm and analyst related component in the forecast error, not yet captured by the existing variables. This timeinvariant component reveals that much of what constitutes analysts’ forecast error is currently left unmeasured. As such, the inherent firm-analyst dimension plays a critical role in explaining the forecast error. This first essay complements recent findings showing that private and public communication between analyst and management constitutes one of the most valuable sources of information for the earnings forecasts.

The second essay investigates whether financial analysts benefit from the mandatory quarterly financial reporting in Europe. Specifically, do firms with mandated quarterly reports and semiannual reports differ in terms of analysts’ annual earnings forecast error, dispersion and following? On one hand, if mandated quarterly reports decrease information asymmetry, the quality of analysts’ information Source: Mercatus Center, RegData, http://regdata.org.

The corresponding numbers for 2013 and 2012 were 103 and 68 respectively. Source: “The peak of regulatory change may be some way off “, Thomson Reuters Regulatory Intelligence, March 16, 2015.

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