Informativeness of stock prices after IFRS adoption in Brazil

Accepted Manuscript Title: Informativeness of stock prices after IFRS adoption in Brazil Author: F. Henrique Castro Verˆonica Santana PII: DOI: Reference:

S1042-444X(18)30160-9 https://doi.org/doi:10.1016/j.mulfin.2018.09.001 MULFIN 564

To appear in:

J. of Multi. Fin. Manag.

Received date: Accepted date:

19-4-2018 1-9-2018

Please cite this article as: F. Henrique Castro, Verˆonica Santana, Informativeness of stock prices after IFRS adoption in Brazil, (2018), https://doi.org/10.1016/j.mulfin.2018.09.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Informativeness of stock prices after IFRS adoption in Brazil F. Henrique Castroa,∗ , Verˆonica Santanaa,∗ de S˜ ao Paulo. Av. Prof. Luciano Gualberto, 908, FEA-3 Bldg., S˜ ao Paulo/SP, 05508-010, Brazil.

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a Universidade

Abstract

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This study investigates the effects of the adoption of the International Financial Reporting Standards (IFRS) on prices’ informativeness in the Brazilian capital market. Consistent with the hypothesis that IFRS increases

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the amount and quality of firm-specific information available to the market, we found that after the adoption, prices in the Brazilian stock market are less synchronous and that their firm-level volatility increased relative to total volatility. This means that prices move more according to firm-specific shocks than to market-wide

Key words: Synchronicity, volatility, IFRS, Brazil

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JEL: F65, M41, M48

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events, indicating that prices became more informative and, thus, more useful for investment decision-making.

Funding acknowledgements

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Pessoal de N´ıvel Superior (Capes).

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• Verˆ onica Santana gratefully acknowledges financial support from Coordena¸c˜ao de Aperfei¸coamento de

• F. Henrique Castro gratefully acknowledges financial support from Funda¸c˜ao de Amparo `a Pesquisa

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do Estado de S˜ ao Paulo (Fapesp).

∗ Corresponding

author Email addresses: [email protected] (F. Henrique Castro), [email protected] (Verˆ onica Santana)

Preprint submitted to Journal of Multinational Financial Management

August 21, 2018

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Informativeness of stock prices after IFRS adoption in Brazil

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Abstract

This study investigates the effects of the adoption of the International Financial Reporting Standards (IFRS)

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on prices’ informativeness in the Brazilian capital market. Consistent with the hypothesis that IFRS increases the amount and quality of firm-specific information available to the market, we found that after the adoption,

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prices in the Brazilian stock market are less synchronous and that their firm-level volatility increased relative to total volatility. This means that prices move more according to firm-specific shocks than to market-wide

Key words: Synchronicity, volatility, IFRS, Brazil JEL: F65, M41, M48

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1. Introduction

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events, indicating that prices became more informative and, thus, more useful for investment decision-making.

This study aims to investigate whether the adoption of the International Financial Reporting Standards

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(IFRS) has affected stock prices’ informativeness in the Brazilian market, measured both through stocks’ comovement and idiosyncratic volatility. There are several studies analyzing the economic and financial effects of IFRS adoption around the world, such as accounting quality (Ahmed et al., 2013; Barth et al.,

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2008) and firms’ cost of capital (Daske et al., 2008; Li, 2010). However, the mechanism via which IFRS

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adoption affects risk calculations remains poorly explored, especially in emerging economies. IFRS adoption is expected to impact financial markets mainly because it improves the information environment (Beuselinck et al., 2010; Dasgupta et al., 2010; Kim & Shi, 2012), interacts with the institutional 10

and legal systems (Christensen et al., 2013), and/or affects some features of the financial market and the behavior of agents (La Porta et al., 1998). The greater amount and quality of firm-specific information believed to be brought by IFRS adoption (Ahmed et al., 2013; Barth et al., 2008) may have some implications for the behavior of individual stocks because it affects the relation between two basic components of stock prices: (i) factors related to the economy as a whole (market-level shocks), which reflect the systematic risk,

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and (ii) factors related to specific features of each firm (firm-level shocks), which reflect the idiosyncratic risk. The more representative the systematic risk is, the less firm-specific information is incorporated into stock prices and the more stocks tend to move together in the market and to vary according to the market, once stock prices basically reflect market-wide events. To capture how much individual stock prices capture firm-specific information, we use two different

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measures. The first one measures the extent to which stocks move together with market-wide factors, referred in the literature as stock price synchronicity, proposed by Morck et al. (2000), which is a logistic Preprint submitted to Journal of Multinational Financial Management

August 21, 2018

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transformation applied to the explanatory power (R2 ) of the market model. It represents the relative amount of firm-specific information seized by stock prices. Higher synchronicity means that it is more difficult to price firm-specific fundamental information because investors take too much account of market factors (Roll, 25

1988; Hsin & Tseng, 2012). The second measure, still unexplored in the financial accounting literature, is stocks’ relative amount of idiosyncratic volatility. While synchronicity is focused on returns and relative

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volatility on risk, the two measures are conceptually related. Campbell et al. (2001), for instance, explore these two concepts of prices’ informativeness. Analyzing stock prices in the United States from 1962 to 1997,

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the authors found that while the market and industry variances were relatively stable, firm-level variance had a significant positive trend. Consistent with the increase in the idiosyncratic risk, they found that

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the correlations among individuals stocks, in addition to the explanatory power of the market model, have declined.

A more developed financial market is believed to improve capital allocation (Bena & Ondko, 2012; Pang &

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Wu, 2009; Wurgler, 2000) because the informativeness of stock prices facilitates efficient investment (Durnev et al., 2004; Habib, 2008). If market prices are better able to seize firm-level information they are better able to sign investment opportunities. Therefore, both stock returns synchronicity and relative idiosyncratic

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volatility can be considered evidence of information efficiency. The more prices vary according to firmspecific events, the less they move with the market and higher is the proportion of idiosyncratic volatility. Therefore, assuming that IFRS, as a high-quality financial reporting system, is capable of influencing the information environment by increasing the amount and quality of firm-specific information available to the

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and idiosyncratic volatility.

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market, thus increasing its relevance to stock price formation, it can affect both the levels of synchronicity

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Previous findings relate corporate transparency to an improvement in the informational environment (Beuselinck et al., 2010; Dasgupta et al., 2010; Habib, 2008; Kim & Shi, 2012). There is also evidence that 45

emerging markets exhibit low financial development, highly concentrated ownership structures, and weak institutions, investor protection and enforcement mechanisms (Daske et al., 2008; Li, 2010; Lopes & Alencar, 2010). Therefore, the effects of IFRS adoption on an emerging economy are a sensible issue. Gordon et al. (2012); Kim & Shi (2012), for instance, argue that countries with poorer governance have greater potential to increase transparency through the adoption of the international standards because these countries are more

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opaque to begin with. On the other hand, Daske et al. (2008) and Li (2010) argue that the positive results of accounting harmonization hold only for countries with strong enforcement mechanisms and where firms already have enough incentives to prepare transparent financial statements. Furthermore, Christensen et al. (2013), analyzing the response of market liquidity to IFRS adoption, found the significant effects only hold for countries where the adoption was supported by changes in enforcement, leading them to conclude that the

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effects of IFRS are most likely to be due to a bundle comprising IFRS adoption and changes in enforcement. According to the authors, separating the two effects is very difficult because they might reinforce each other. If the effect only holds where there are changes in the enforcement, IFRS might be a precondition to such

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changes taking place, or the effect would be smaller without the change in the accounting standards. Considering this literature, we investigate the effects of IFRS adoption on prices’ informativeness in the 60

Brazilian capital market. Arguing in line with the hypothesis that IFRS can improve firms’ financial statements transparency despite the weaker governance mechanisms in the country (Anderson, 1999; Kaufmann et al., 2007; La Porta et al., 1998), we expect IFRS adoption in Brazil to decrease stock price synchronicity

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and increase the proportion of idiosyncratic volatility in individual stocks. We claim that IFRS has improved the information environment in the Brazilian capital market, increasing the amount of firm-specific information incorporated into stock prices and, thus, reducing stock price synchronicity and making idiosyncratic

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volatility more intense relative to systematic volatility.

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Brazil started adopting IFRS in 2008 and 2009, when firms could incorporate some of the international standards into their accounting practices. The full adoption, when all the international standards became mandatory, started in 2010. Using a sample of firms listed on the S˜ao Paulo Stock Exchange through the years of 2004 to 2013, we examine what happens to their level of stock price synchronicity and their relative

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level of idiosyncratic volatility in the transition years (2008 and 2009) and in the full adoption years (from 2010 to 2013). We measure stock price synchronicity according to Morck et al. (2000), and we gauge the

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idiosyncratic volatility relative to the total volatility according to the decomposition developed by Campbell et al. (2001). 75

The results confirm the hypothesis that the incorporation of firm-specific information into stock prices is

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higher after IFRS adoption. We found that already in the period of transition (2008 and 2009), the level of

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stock price synchronicity decreased significantly, and the effects became stronger and more significant with the full adoption (from 2010). Accordingly, we found that the relative level of idiosyncratic volatility has

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significantly increased over the same period, also getting stronger and more significant with the full adoption. These results indicate that IFRS is associated with higher incorporation of public firm-specific information into stock prices, making them more informative (Durnev et al., 2004) and thereby making the market less obscure and better able to allocate resources (Habib, 2008; Wurgler, 2000). Our results are important for two main reasons. First we add to the literature on IFRS and stock prices informativeness by bringing the discussion to emerging economies. While the previous literature is mainly 85

focused on developed countries in the European Union, we focus on Brazil, the most important economy in Latin America and the second largest of the BRICS group (after China). This adds to the work of Beuselinck et al. (2010), for instance, who studied the role of IFRS adoption in 14 European Union developed countries from 2003 to 2007. Further, our results also question the V-shaped pattern of synchronicity found by Beuselinck et al. (2010). The authors argue that synchronicity falls at the moment of adoption, indicating

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new firm-specific information is revealed then, but increases later because it lowers the surprise of future disclosure. Nevertheless, this results is likely due to the imminence of the 2008 financial crisis. Accordingly, we find synchronicity is higher in periods of crisis, but we properly control for this effect with a larger period of analysis and year fixed effects. Second, we bring a volatility based measure for stock price informativeness,

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allowing to extend the literature on IFRS discussing a practical implication of the international accounting 95

standards for risk management evaluating its effects on the levels of systematic and idiosyncratic risks, which may affect investors’ abilities to diversify their portfolios. Taken together, we provide evidence that IFRS adoption has some macroeconomic effects, being capable of bringing positive changes to the functioning of a financial market in an emerging country.

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The paper is organized as follows. Section 2 discusses some of the history of accounting practices in Brazil, highlighting the practical changes brought by IFRS with greater potential to make financial statements more

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useful to investors. Section 3 explores the empirical models of the analyses, in addition to the fundamentals of the volatility decomposition. Section 4 describes and discusses the results, and finally, section 5 presents

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our concluding remarks.

2. IFRS in the Brazilian Context

Similar to other countries with code-law origins, accounting in Brazil has always been strongly influenced

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by regulation, especially income tax regulations, which has limited the accounting principles evolution or, at

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least, constrained their application (Martins et al., 2013). As an example, tax legislation dictated a ceiling for registering expected losses from receivables, which harmed the disclosed information regarding the quality of firms’ asset (Lopes, 2002). Using tax settings while reporting the financial and economic conditions of the firm in the capital market harms the discretionary process of accounting choice, causing damage to the

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communication between the firm and its investors and thus increasing information asymmetry (Lopes, 2002).

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The first comprehensive legislation addressing financial accounting in Brazil dates from 1940. This legislation established specific procedures and practices for corporations, comprising rules for asset measurement,

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profit retention and dividend distribution. In the 1970s, a crash in the stock exchanges took place in Rio de Janeiro and S˜ ao Paulo, revealing the weaknesses regarding investor protection and corporate disclosure. This crash led to new legislation enacted in 1976 that reformed the corporate law and created the Brazilian Securities Commission (CVM). This new corporate law provided the main accounting concepts that should be followed by publicly held companies in Brazil. This accounting model contrasts with the ones from countries such as the United States, the United 120

Kingdom, Canada and Australia, where accounting is mainly regulated by private initiative without direct intervention from the government. Under the Brazilian model of corporate reporting, the main user of accounting was the government (Lopes, 2002), and accounting was seen simply as a system to calculate taxes and dividends. Therefore, the demand for accounting information was influenced more by the payout preferences of agents for labor, capital and government and less by the demand for public disclosure (Ball

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et al., 2000). For Lopes (2002), such a direct and strong influence from the government limits the evolution of accounting by cutting off the participation of diverse market sectors. In other words, accounting used to be a mere legal obligation with which companies should comply, in such a manner that accounting information could not be considered relevant for decision-making in the capital market. 4

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However, an attempt to change this scenario was made with the public commitment to move Brazilian 130

accounting standards towards IFRS. According to Carvalho & Salotti (2012), this was possible due to three reasons: the control of inflation, the balance of the federal budget and the increase of market capitalization of the stock exchange, which amounted to about 25% of GDP, as a result of the improvement of the country’s economic condition. These factors convinced Brazilian businesspeople that a new accounting regime would

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be beneficial for the expanding and increasingly internationalized economy of that time. In December 2007, a new piece of legislation was enacted, requiring a country-wide convergence to the international accounting

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standards, both for listed and non-listed firms and large (as legally defined) private firms (Carvalho & Salotti, 2012). One of the most important steps taken towards IFRS adoption in Brazil concerns the implications for

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income tax calculations. When debating the new legislation, it was decided that any accounting adjustment due to IFRS would not have any tax impact. Thus, in 2008, legislation established the Transition Tax 140

Regime (RTT), specifying that IFRS would be “tax neutral”, that is, revenues and expenses to be registered

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according to IFRS would not count for tax calculation purposes (Cardoso et al., 2009). As a result of this process, since 31 December 2007, the Brazilian accounting standards were partially converged to IFRS due to alterations in the corporate law. Since 2010, IFRS has been mandatory for the financial statements of

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firms whose debt or equity securities are publicly traded (IFRS Foundation, 2017). The institution responsible for translating and introducing each new standard issued by the International Accounting Standards Board (IASB) is the Committee of Accounting Pronouncements (CPC), created in

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2005 and organized by professionals in accounting and the capital market, auditors and academics. The

et al., 2013).

Brazil had two important moments configuring IFRS adoption. Starting 2008, the first International

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committee has formal and strong support of the government through CVM and the Central Bank (Martins

Accounting Standards (IAS) and IFRS translated and inserted by the CPC were adopted by some Brazilian firms. From 2010, all the standards issued by the IASB were converged and all firms were obligated to publish their financial statements according to them. Some considerations must be made regarding the early adoption period (2008 and 2009). The first international standards adopted in Brazil did not follow the 155

original order in which they were issued by IASB. Among the first standards valid for the period were the Technical Pronouncements CPC 01, equivalent to IAS 36 (Impairment of Assets); CPC 04, equivalent to IAS 38 (Intangible Assets); CPC 06, equivalent to IAS 17 (Leases); and CPC 14, equivalent to IAS 39 and 32 (Financial Instruments). These first standards were expected to cause the greatest changes in the Brazilian accounting. CPC 01,

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for example, brought the concept of “economic substance”, stating that it is not conceptually possible to keep an asset registered for a value greater than its economic capacity. Regarding the intangible assets, a new group inside the balance sheet was created, and the Anglo-Saxon principle of “arm’s length” was introduced to define what should be recognized as an asset. CPC 06 also brought important changes to accounting for leases. The main change was the idea that an asset should be registered according to the

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“transference of benefits, risks and control”, not simply the legal ownership. Before this standard, all leases contracts only affected their current exercise through accounting expenses. After CPC 06, every contract should be examined to evaluate whether it implied transference of risk, benefits and control; if so, it should be recognized as an asset on the balance sheet (Martins et al., 2013). Finally, the standardization process of financial instruments is worth noting because it happened in different phases. The CPC 14 was initially issued as valid for only the years of 2008 and 2009. In 2009, the CPC issued other three standards for

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financial instruments (CPC 38, CPC 39 and CPC 40) to be adopted from 2010, when the initial CPC 14

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was transformed into a technical orientation (OCPC 03) (Martins et al., 2013).

Therefore, several changes to the Brazilian accounting method were implemented at different moments.

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It is important to highlight that besides the differences brought by the standards per se and their new concepts, in addition to the general unfamiliarity with the new accounting procedures, other factors related to the early adoption affected the business environment at the time, mainly the uncertainties about the tax

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effects. Therefore, the years of 2008 and 2009 had some particularities that made them different from the years after 2010, when the full adoption occurred. In our analysis, we consider these two different moments.

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3. Methodology 3.1. Data sample

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Our sample consists of all Brazilian firms listed on the S˜ao Paulo Stock Exchange whose stocks were traded on at least 80% of the trading days of each year from 2004 to 2013. We assume that actively traded

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firms are better able to incorporate public information into stock prices. We then obtain 1,545 firm-year observations. The sample is highly unbalanced, ranging from 73 firms in 2004 to 184 in 2008 to 201 in 2013. However, it is important to note that in the empirical models, the final number of observations is

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much smaller, because not all of the variables for all firms that satisfy the 80% criterion are available for the whole period. For instance, ownership data for a firm may not be available for a specific year even though its stock is frequently traded in that year. The accounting and market data are from Economatica, and the data about firms’ auditors are from Bloomberg. We also consider a less restrictive threshold for including 190

firms in our sample, establishing a ceiling of 50% of negotiation, which increases our initial sample to 1, 921 firm-year observations, and discuss the results accordingly. 3.2. Stock Price Synchronicity Stock price synchronicity is measured by the r-squared (R2 ) of the market model, where stock returns are regressed on a market index. The market model is estimated for each firm in each year using daily data. Since market returns reflect macroeconomic information, the greater the R2 is, the greater the weight of market-wide information on stock returns is (Barberis et al., 2005; Beuselinck et al., 2010; Jin & Myers, 2006; Kim & Shi, 2012; Morck et al., 2000). When the market model for all individual stocks has a large R2 value, it is an indication that stock prices frequently move together; that is, they are more synchronized. 6

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Morck et al. (2000) suggest adding the U.S. stock market index return as an additional factor in the market model, arguing that most economies are at least partially open to foreign capital. Since part of the risk for Brazilian stocks may be due to international shocks, we added the equal-weighted S&P 500 index to our

BR US Rjt = α + β1 Rmt + β2 Rmt + εjt ,

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market model. The market model we used to measure stock price synchronicity is (1)

BR where Rjt is the daily stock return for firm j on day t, Rmt is the daily return of Ibovespa (the main market

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US index of the S˜ ao Paulo Stock Exchange) and Rmt is the daily return for the S&P 500 index. We also conduct

our analysis while including only the domestic market index as a robustness check, as well as including three

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lags for the market indexes in the model.

We estimate equation (1) for each firm on each year. The estimated R2 for each regression is denoted

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2 Rjt , where j identifies the firm and t identifies the corresponding year. To circumvent the bounded nature 2 of Rjt , we apply a logistic transformation to obtain our measure of stock price synchronicity (Durnev et al.,

2004):

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2 Rjt 2 1 − Rjt

Synjt = ln

!

.

(2)

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To investigate the role of IFRS adoption on stock price synchronicity, we entertain an empirical model regressing Syn on two dummy variables: (i) T rans, the transition period, identifying the years of early IFRS

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adoption, and (ii) P ost, identifying the years of full adoption, from 2010 on. Our intention is to allow for different effects in these two time windows, because the two periods are very different, as discussed in section

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2. To isolate the effect of IFRS, we add some control variables that represent individual firm aspects that may affect the process of incorporating information into their stock prices. We also control for variables related to corporate transparency that may affect specific information about them available to the market, related to managers’ and auditors’ incentives to report transparent financial statements. Further, we add 205

firm fixed effects to control for unobserved time-constant firm characteristics as well as time dummies to control for macroeconomic shocks.

As factors that may affect the ability to incorporate information into stock prices, we control for trading volume, T radV ol (Chan & Hameed, 2006); ownership concentration, OwnStruct (Boubaker et al., 2014; Durnev & Kim, 2005); and industry and firm concentration (Chan et al., 2007; Roll, 1992), measured by 210

Herfindahl-Hirschman indexes. Industry concentration, Hi , is defined as the ratio between combined sales for firms in the industry and combined sales for all firms in the sample. Firms’ concentration, Hj , is defined as the ratio between the sales for each firm and the sales for all firms in the sample. To account for manager and auditors’ incentives to report transparent information (Ball et al., 2003), we add firms’ size, Size (Durnev & Kim, 2005); debt structure, Lev (Lima, 2011); profitability, ROA (Larcker & Richardson, 2004); annual

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percentage growth in total revenues, Grow (Barth et al., 2008; Chen et al., 2010; Lee & Hutchison, 2005); market-to-book ratio, M T B (Durnev & Kim, 2005); a dummy variable for firms that issued American Depository Receipts (ADR), ADR (Fernandes & Ferreira, 2008); and a dummy variable for firms audited by non Big-4 audit firms, Aud (DeAngelo, 1981).

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The empirical model for measuring the effect of IFRS adoption on stock price synchronicity is Synjt = β0 + β1 T ranst + β2 P ostt + Xjt β T + δt + γj + εjt ,

(3)

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where β is the vector of parameters for the control variables in X. The estimation uses firm-fixed effects approach for panel data in order to control for the unobserved individual effects γj constant over time, and we

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add time dummies to control for δt . These controls are crucial for minimizing the biases on the IFRS effects since our measures of stock prices’ informativeness may simply evolve with time and with firms’ inherent

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characteristics, regardless of whether they are observable or non-observable. 3.3. Relative Idiosyncratic Volatility 3.3.1. Volatility Decomposition

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The next step in the analysis concerns the volatility measures and the behavior of the systematic and idiosyncratic risk from 2004 to 2013. Based on Campbell et al. (2001), we decompose the volatility of stock

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returns into three components: market-wide, industry-specific and firm-specific. Based on this decomposition, we construct volatility measures that sum to the total return volatility of a typical firm, without the need to estimate betas for firms and without the need to keep track of covariances.

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We denote firms by the j index and industries by i such that the daily excess return of firm j from

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industry i in period t is rjit . We use the CDI (Certificado de Dep´ osito Interfinanceiro), which is the rate of P the interbank lending market, as the risk-free rate. The excess return of industry i is rit = j∈i wjit rjit , P and the excess market return is rmt = i wit rit . In the following steps, we demonstrate the composition performed by Campbell et al. (2001), showing that since all firms in the sample are aggregated into industries and all industries are aggregated into the market, there is no need to worry about firms individual betas; thus, they may freely vary over time, which is a much less restrictive assumption for the market model than fixed betas. The aggregation is valid for any weighting scheme, and we use weights based on market capitalization.

Setting the intercepts for industries of the CAPM to zero, rit = βim rmt + ˜it ,

(4)

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and for individual firms: rjit = βji rit + η˜jit , rjit = βji βim rmt + βji ˜it + η˜jit ,

where βim is the beta for industry i with respect to the market, βji is the beta for firm j with respect to

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(5)

its industry, ˜it is the industry-specific residual, and η˜jit is the firm-specific residual. By construction, η˜jit

If so, applying variance to the models (4) and (5), we obtain

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2 Var(rit ) = βim Var(rmt ) + Var(˜ it ) + 2βim Cov(rmt , ˜it )

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is orthogonal to rit , and we also assume that it is orthogonal to rmt and ˜it .

2 Var(rit ) = βim Var(rmt ) + Var(˜ it );

(6)

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2 Var(rjit ) = (βji βim )2 Var(rmt ) + βji Var(˜ it ) + Var(˜ ηjit )+

2 + 2βji βim Cov(rmt , ˜it ) + 2βji βim Cov(rmt , η˜jit ) + 2βji Cov(˜ it , η˜jit ), 2 2 Var(rjit ) = βjm Var(rmt ) + βji Var(˜ it ) + Var(˜ ηjit ).

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(7)

However, equations (6) and (7) require knowledge about firms’ betas. To avoid this requirement, Campbell et al. (2001) work with a simplified model dropping the betas and show it is equivalent to equations (6) and

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(7) since all firms are aggregated into the industry and market portfolios.

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Using the market-adjusted model (Campbell et al., 1997) rit = rmt + it ,

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(8)

and comparing it with equation (4), we have the following relation between their error terms: it = ˜it + (βim − 1)rmt .

(9)

From equation (9), rmt and it are no longer orthogonal, so it is necessary to compute their covariances: Var(rit ) = Var(rmt ) + Var(it ) + 2Cov(rmt , it ) = Var(rmt ) + Var(it ) + 2Cov [rmt , (βim − 1)rmt ] = Var(rmt ) + Var(it ) + 2(βim − 1)Var(rmt ),

(10)

and we introduce betas again in the decomposition. However, when all firms of the sample are aggregated

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into the portfolios, the weighted sum of all betas equals one: X

X

wit βim = 1,

i

wjit βji = 1,

(11)

j∈i

X

wit Var(rit ) =

X

i

wit Var(rmt ) +

X

i

wit Var(it ) +

i

X

wit 2(βim − 1)Var(rmt )

i

! wit Var(it ) + 2

i

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i

X

wit Var(it ) + 2 (1 − 1) Var(rmt )

i

= Var(rmt ) +

X

(13)

(14)

(15)

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2 2 = σmt + σt ,

i

(12)

wit Var(it )

i

P

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wit Var(rit ) = Var(rmt ) +

2 2 ≡ ≡ Var(rmt ) and σt where σmt

wit βim − 1 Var(rmt ),

i

we are again free of individual covariances: X

X

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= Var(rmt ) +

X

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such that aggregating equation (10) over the industries,

wit Var(it ). The proceeds are similar for the individual firms’ returns.

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Dropping the betas from (5), (16)

ηjit = η˜jit + (βji − 1)rit ,

(17)

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rjit = rit + ηjit ,

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where

thus, that the variance of individual firms’ returns is Var(rjit ) = Var(rit ) + Var(ηjit ) + 2Cov(rit , ηjit ) = Var(rit ) + Var(ηjit ) + 2(βji − 1)Var(rit ).

(18)

Averaging over firms in each industry, X

2 wjit Var(rjit ) = Var(rit ) + σηit ,

(19)

j∈i 2 where σηit ≡

P

j∈i

wjit Var(ηjit ) is the weighted average of the firm-level volatility of each industry i. Com-

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puting the weighted average across industries, X

wit

i

X

wjit Var(rjit ) =

j∈i

X

wit Var(rit ) +

i

X i

= Var(rmt ) +

X

wit

X

wit Var(it ) +

= P

i

2 wit σηit =

P

i

wit

P

jit

+

+

2 wit σηit

i

2 σηt ,

(20)

Var(ηjit ) is the weighted average of firm-level volatility across all

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2 where σηt ≡

2 σt

X

ip t

i 2 σmt

wjit Var(ηjit )

j∈i

firms.

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3.3.2. Estimation

The sample of firms used in this analysis is the same one that we used to calculate stock price synchronic-

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ity. Using daily returns, we estimate their volatility components from (20). Following the methodology in Campbell et al. (2001), we use returns of intervals s (daily) and construct volatility estimates aggregated at 2 intervals t (monthly). The sample volatility of the market return at time t, σ ˆmt , is

X (rms − µm )2 ,

M

2 σ ˆmt =

(21)

s∈t

section 3.3.1, we construct the market return as the value-weighted average using all firms in the sample. As

te

Campbell et al. (2001) note, this measure is, of course, different from the market index, but as in their case, they are very correlated. The correlation between our constructed market index and Ibovespa throughout the sample is 0.9779.

Ac ce p

250

d

where µm is the mean of the market return rms over the sample. To be consistent with what is presented in

For estimating industry volatility, we sum the squares of industry-specific residuals in equation (8) within each month t:

X

2 σ ˆit =

2is .

(22)

s∈t

To ensure that industry covariances are cancelled out, we average over industries: 2 σ ˆt =

X

2 wit σ ˆit .

(23)

i

Similarly, for estimating firm-specific volatility, we sum the squares of the firm-specific residual in equation (16), obtaining a measure for each firm in each industry at each period t: 2 σ ˆηit =

X

2 ηjis .

(24)

s∈t

11

Page 12 of 25

We compute the weighted average to obtain the firm-level volatility of each industry and then aggregate once more to obtain the average firm-level volatility of the market: 2 σ ˆηt =

X

wit

i

X

2 wjit σ ˆηit .

(25)

j∈i

255

ip t

If the synchronicity analysis provides evidence that stock prices and the market joint movements have substantially changed over the period of analysis, we expect to see changes in the volatility behavior – we

cr

expect the firm-level volatility to become more accentuated compared to the other components. That is, whereas a firm’s total volatility may have decreased or increased, we expect a relatively higher firm-level

us

volatility, which is a reflection of higher incorporation of firm-specific information into stock prices. To perform this analysis, we start analyzing the evolution of the three decomposed volatility series, which 260

are averages of the market (our sample). To do so, we conduct a trend analysis of the series generated by

an

equations (21), (23) and (25), testing for stochastic and deterministic trends via the Augmented Dickey-Fuller (ADF) test (Dickey & Fuller, 1981) and the Phillips-Perron (PP) test (Phillips & Perron, 1988). Further, to avoid that structural instabilities in the series affected the two tests into misinterpreting a trend-stationary

265

M

time series (deterministic trend) with a difference-stationary series (stochastic trend), as discussed by YinWong & Chinn (1996), we also apply the KPSS test (Kwiatkowski et al., 1992), whose null hypothesis is trend-stationarity.

d

After this trend analysis, we also conduct a regression analysis because controlling for those factors

te

that may affect the ability to incorporate information into stock prices and for firms’ incentives to be transparent, as in the synchronicity analysis, is likely relevant. If the univariate analysis of the trend analyses does not show any evolution throughout time, this may be because other macroeconomic shocks

Ac ce p

270

or firms’ characteristics that may have offset the effect of IFRS adoption. Therefore, it is also important to perform an disaggregated analysis controlling for such factors. For the volatility regression analysis, we first define a variable to account for the proportion of idiosyncratic volatility in the total volatility of each firm at each year, which we denote as

RelV oljit = ln

2 σ ˆηit 2 σ ˆjit

! ,

(26)

2 2 where σ ˆηit comes from equation (24) and σ ˆjit is the realized volatility of firm j at month t, calculated as

the variance of each firms’ stock daily returns inside each month. We also apply the natural logarithm to eliminate the squares and to normalize the variable. We obtain monthly measures, which are then aggregated by year, so we obtain measures of RelV ol for each firm at each year, which are then used in the regression: RelV oljt = β0 + β1 T ranst + β2 P ostt + Xjt β T + δt + γj + εt ,

(27)

where j denotes firms and t denotes years, repeating the specification of the synchronicity analysis. The 12

Page 13 of 25

difference is that since we expect idiosyncratic volatility to have increased relatively to the total volatility, 275

we expect positive coefficients for β1 and β2 . If equation (27) is run only with the IFRS dummy variables, we gauge results equivalent to the aggregated trend analysis. Incorporating the variables on X and including individual and time effects (δt and γj ) is important for minimizing the confounding effects to better identify

ip t

the role of IFRS on idiosyncratic volatility.

4. Empirical Results

We start our analysis studying the average relative information content of stock prices in our sample,

cr

280

according to the model in equation (1). Figure 1a shows the time evolution of the average R2 of the models,

us

representing market-wide information, and the average 1 − R2 , representing firm-specific information. The average R2 is approximately 20%, and it has slightly decreased over time, but it increases during financial crises, in line with the idea that macroeconomic information becomes more salient during these periods

an

(Peng et al., 2007). If we consider only the domestic index, the average R2 is still 20%, and if we consider the threshold of 50% of negotiation, the proportion is slightly smaller, 18%. If we include lags, the average

M

R2 increases 10 p.p.. Nevertheless, regardless of the market model specification, the downward trend for the average R2 with peaks in 2008 and 2011 is the same over the years. Average Proportion of Market−wide and Firm−specific Information

te

75

50

25

0

Average Proportion of Market, Industry and Firm Volatility

100

d

100

Ac ce p

285

2004

2006

2008

2010

Firm−Specific Information

75

50

25

0 2012

2004

2006

Market−wide Information

2008

Market Volatility

(a) Market-wide and Firm-specific Information

Industry Volatility

2010

2012

Firm Volatility

(b) Volatility Decomposition

Synchronicity and Relative Volatility

Synchronicity

−1.5

−2.0

2.40 Relative Volatility

2.35 2.30 2.25 2.20 2004

2006

2008

2010

2012

(c) Synchronicity and Relative Volatility Figure 1: Stock prices’ informativeness measures over time

13

Page 14 of 25

Figure 1b shows the average proportion of the total volatility of a firm that is due to market, industry 290

and idiosyncratic shocks, according to the decomposition from Campbell et al. (2001). From it, we can see the idiosyncratic shocks account for an average of 47% of the total volatility, whereas industry shocks represent an average of 17%, and market-wide shocks represent approximately 36%. The figure also shows that idiosyncratic volatility starts with a downward trend, hitting its minimum in 2008, again reflecting the

295

ip t

effects of the financial crisis, but starts increasing from then on. If we consider the series estimated using the 50% of trading days threshold, this trend is the same, but the proportion of firm-level volatility is slightly

cr

higher, 49%, and the proportion of market-level volatility is slightly lower, 34%. Finally, figure 1c plots the evolution over time of our two measures for price informativeness, showing that the synchronicity variable

us

decreases over the years of analysis, whereas the relative volatility variable increases.

2 2 Figure 2 shows the series of each component of volatility, defined by the terms σηt as firm volatility, σt 300

2 as industry volatility, and σmt as market volatility in equation (20), in addition to their sum, which accounts

an

for total volatility. Firm volatility is usually higher than the volatility due to market-wide shocks, whereas industry volatility is always lower. Firm Volatility

30

M

30

Industry Volatility

20

10

2006

2008

2010

2012

te

2004

d

20

10

2014

2004

2006

Ac ce p

Market Volatility

30

20

20

10

10

2006

2008

2010

2012

2010

2012

2014

2012

2014

Total Volatility

30

2004

2008

2014

2004

2006

2008

2010

Figure 2: Firm, Industry, Market and Total Volatility

Following Campbell et al. (2001), we search for trends in the series because we expect firm volatility to have increased relatively to the other series. To do so, we apply the ADF (Dickey & Fuller, 1981), the PP 305

(Phillips & Perron, 1988) and the KPSS (Kwiatkowski et al., 1992) unit-roots tests to identify trends in the series. The results are presented in Table 1. One can see none of the tests were able to identify significant trends; thus, simply analyzing trends we could not infer any increase in the firm-level volatility following IFRS adoption. This same conclusion is obtained if we use the series estimated with the 50% of trading days threshold. However, this lack of results may be due to failing to control for factors that affect the

14

Page 15 of 25

310

incorporation of information by stock prices. We deal with this issue later in our regressions. Table 1: Tests for Deterministic and Stochastic Trends Test

Market Volatility

Industry Volatility

Firm Volatility

−3.556∗∗ (p = 0.040) Unit root. No stochastic trend.

−3.591∗∗ (p = 0.037) Unit root. No stochastic trend.

−3.742∗∗ (p = 0.024) Unit root. No stochastic trend.

PP

Test. Stat. Null Hyp. Conclusion:

−5.556∗∗ (p = 0.010) Unit root. No stochastic trend.

−4.000∗∗ (p = 0.012) Unit root. No stochastic trend.

−4.911∗∗ (p = 0.010) Unit root. No stochastic trend.

KPSS

Test. Stat. Null Hyp. Conclusion:

0.191∗∗ (p = 0.0361) Trend Stationarity. No deterministic trend.

0.163∗∗ (p = 0.048) Trend Stationarity. No deterministic trend.

0.149∗∗ (p = 0.019) Trend Stationarity. No deterministic trend.

us

cr

ip t

ADF

Test. Stat. Null Hyp. Conclusion:

Table 2 presents the evolution of both the synchronicity and relative volatility variables along with the controls for each period of analysis (pre-adoption, transition and post-adoption), computing their means and

an

standard deviations, in addition to a test for the statistical significance of the changes in means over the three periods. Synchronicity is significantly lower in the post-adoption period compared to the pre-adoption and

M

transition periods. There is no statistical difference among the three periods for the relative volatility. From the control variables, trading volume and size increased over the periods, whereas ownership structure and the proportion of non Big-4 decreased. Further, the industry concentration decreased from the pre-adoption

d

period to the transition and to the post-adoption period, and firms’ growth decreased from the transition to

te

the post-adoption period.

Table 2: Descriptive Statistics by Period of Analysis Pre-Adoption (1)

Transition (2)

Ac ce p

315

Mean

Synch RelVol TradVol OwnStruct Hi Hj Size Lev ROA Grow MTB ADR NonBig4

−1.522 2.280 5.521 0.631 0.193 0.003 13.460 0.576 0.005 0.301 3.122 0.048 0.401

SD

Mean

1.307 −1.501 1.233 2.310 3.727 6.721 0.275 0.580 0.133 0.170 0.011 0.003 2.391 13.902 3.655 0.506 1.714 −0.127 2.923 0.433 12.874 3.215 0.202 0.042 0.514 0.379

SD 1.302 0.693 3.527 0.278 0.133 0.011 2.391 3.655 1.714 2.923 12.874 0.202 0.514

Difference

(2) − (1)

Post-Adoption (3) Mean

0.022 −2.021 0.030 2.370 1.199∗∗∗ 7.166 −0.052∗∗∗ 0.548 −0.023∗∗∗ 0.169 0.000 0.003 0.442∗∗∗ 14.185 −0.069 0.641 −0.132 −0.268 0.132 0.190 0.093 2.611 −0.006 0.038 −0.022∗ 0.232

SD 1.331 1.249 3.805 0.285 0.141 0.011 2.492 6.011 23.100 0.842 5.685 0.191 0.578

Difference

Difference

(3) − (2)

(3) − (1)

−0.521∗∗∗ 0.060 0.445∗∗∗ −0.032∗∗∗ −0.001 0.000 0.283∗∗∗ 0.135 −0.141 −0.243∗∗ −0.604 −0.005 −0.147∗∗∗

−0.499∗∗∗ 0.090 1.645∗∗∗ −0.084∗∗∗ −0.025∗∗∗ 0.000 0.7256∗∗∗ 0.065 −0.273 −0.111 −0.511 −0.010∗ −0.169∗∗∗

Synch: Stock price synchronicity; RelVol: relative volatility; TradVol: trading volume; OwnStruct: ownership structure; Hj : Herfindahl-Hirschman index by industry; Hi : Herfindahl-Hirschman index by firm; Size: size of a firm; Lev : leverage; ROA: return on assets; Grow : revenue growth; MTB : market-to-book ratio; ADR: dummy variable indicating firms with ADR; NonBig4 : dummy variable indicating firms audited by a non Big-4 audit firm. Detailed variables definitions are in Table A1. The test for the statistical differences of the differences between the periods is a t-test for the continuous variables and a chi-squared for the dummy variables. Significance levels: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

15

Page 16 of 25

Table 3: Correlation Matrix

1

(3)

−0.37∗∗∗ 1

(4)

(5)

0.67∗∗∗−0.08∗∗∗

(6)

(7)

0.31∗∗∗−0.07∗∗

(8)

0.56∗∗∗−0.05∗

(9)

(10)

(11)

0.19∗∗∗

(12)

(13)

0.34∗∗∗

0.02 −0.04 0.26∗∗∗ −0.38∗∗∗ 0.12∗∗∗−0.24∗∗∗ 0.20∗∗∗−0.35∗∗∗ 0.03 −0.08∗∗∗ 0.02 −0.10∗∗∗−0.16∗∗∗−0.14∗∗∗ 1 −0.33∗∗∗ 0.29∗∗∗−0.18∗∗∗ 0.62∗∗∗−0.07∗∗∗ 0.13∗∗∗ 0.06∗∗∗ 0.06∗∗∗ 0.36∗∗∗ 0.44∗∗∗ 1 −0.03 0.09∗∗∗−0.14∗∗∗ 0.05∗∗ −0.08∗∗∗−0.02 −0.05∗∗ −0.05∗∗ −0.12∗∗∗ 1 0.06∗∗ 0.37∗∗∗−0.02 0.00 −0.01 −0.02 0.45∗∗∗ 0.12∗∗∗ 1 0.00 0.07∗∗∗−0.01 −0.07∗∗∗−0.06∗∗∗ 0.03∗ −0.10∗∗∗ 1 −0.14∗∗∗ 0.03∗ −0.00 −0.06∗∗∗ 0.38∗∗∗ 0.43∗∗∗ 1 −0.03 −0.01 −0.00 −0.02 −0.07∗∗∗ 1 −0.00 0.01∗∗ 0.00 0.03∗∗∗ 1 0.03 −0.02 0.05∗ 1 −0.03 0.05∗∗ 1 0.16∗∗ 1

ip t

Synch RelVol TradVol OwnStruct Hi Hj Size Lev ROA Grow MTB ADR NonBig4

(2)

cr

(1)

320

an

us

Synch: Stock price synchronicity; RelVol: relative volatility; TradVol: trading volume; OwnStruct: ownership structure; Hj : Herfindahl-Hirschman index by industry; Hi : Herfindahl-Hirschman index by firm; Size: size of a firm; Lev : leverage; ROA: return on assets; Grow : revenue growth; MTB : market-to-book ratio; ADR: dummy variable indicating firms with ADR; NonBig4 : dummy variable indicating firms audited by a non Big-4 audit firm. Detailed variables definitions are in Table A1. Significance levels: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Table 3 presents the simple Pearson correlations between synchronicity, relative volatility and the control

M

variables. The variables of interest are significantly correlated with several of the controls, highlighting the need to control for them in the analysis. For instance, larger and more profitable firms whose stocks are more frequently traded in the domestic country and are also traded in the United States have higher levels

with the market have lower levels of idiosyncratic volatility relative to total volatility because the two

te

325

d

of synchronicity and lower levels of relative volatility. As expected, stock returns that are more synchronized

variables are negatively correlated (−0.370).

Ac ce p

As mentioned in section 3.1, we restricted our sample to firms whose stocks were traded in at least 80% of the the trading days at each year but also considered a less restricted threshold of 50%. Further, we calculated synchronicity including the U.S. stock market index in the market model but also considered 330

using only the domestic index ans well as including lags in the market model. The correlation between the synchronicity measure in Table 3 with its alternatives including lags and considering the 50% of negotiation threshold, as well as considering only the domestic market index is around 0.95, indicating little difference among them. Similarly, the volatility series are estimated according to the 80% selection criterion, but we also consider using the 50% threshold; however, the lower threshold generates little difference because the

335

correlation between the measures for relative volatility is greater than 0.99. The results of the empirical regression model (3) for the synchronicity analysis and (27) for the volatility analysis are presented in Table 4. We estimate three different versions of each model with different sets of controls. All models are estimated using the within transformation to get rid of firm-fixed effects, and all coefficients are presented with clustered standard errors at the firm level. We start with the most naive

340

specification in models (1) and (4), regressing synchronicity and relative volatility only against the IFRS adoption dummies and thus ignoring confounding effects. Doing so, we see the results already presented by

16

Page 17 of 25

the descriptive statistics: synchronicity increases in the transition period and decreases in the post adoption period; relative volatility does not show any significant differences. However, the increase in synchronicity seen in the transition period is mostly likely due to the financial crisis that hit the Brazilian market in 2008, 345

which is then offset when we add time dummies in models (2) and (5) to control for such macroeconomic shocks. We find a significant decrease in the transition period for synchronicity but relative volatility remains

ip t

non-significant.

Synchronicity

Trans Post

Relative Volatility

(2)

(3)

0.186∗∗

−0.577∗∗∗

−1.020∗∗∗

(0.077) −0.275∗∗∗ (0.086)

(0.145) −0.950∗∗∗ (0.141)

(0.251) −1.853∗∗∗ (0.277) 0.374∗∗∗ (0.054) −0.296 (0.418) −2.586 (13.250) 2.739 (2.565) 0.235 (0.159) 0.358 (0.457) 0.482 (0.574) −0.081∗∗∗ (0.024) −0.012 (0.010) −0.090 (0.386) −0.050 (0.149)

OwnStruct Hi

Size Lev

d

ROA

te

Grow MTB

Ac ce p

ADR

Year-fixed effects Firm-fixed effects Firm-clustered s.e. N R2 Adjusted R2 F Statistic ∗p

0.016 (0.070) 0.108 (0.081)

M

Hj

NonBig4

−0.046 (0.081) 0.037 (0.109)

(5)

an

TradVol

(4)

us

(1)

cr

Table 4: Regression Results

(6) 0.263∗ (0.154) 0.407∗∗ (0.158) −0.099∗∗∗ (0.026) 0.312 (0.205) −0.997 (3.270) 0.818 (1.530) −0.062 (0.054) 0.075 (0.175) −0.144 (0.161) 0.026∗ (0.013) −0.008 (0.006) 0.919∗∗∗ (0.197) 0.001 (0.048)

No Yes Yes

Yes Yes Yes

Yes Yes Yes

No Yes Yes

Yes Yes Yes

Yes Yes Yes

1,545 0.049 −0.176 32.238∗∗∗

1,545 0.257 0.076 47.748∗∗∗

785 0.384 0.133 17.389∗∗∗

1,541 0.001 −0.235 0.922

1,541 0.007 −0.235 0.979

785 0.124 −0.233 3.945∗∗∗

< .1; ∗∗ p < .05; ∗∗∗ p < .01 TradVol: trading volume; OwnStruct: ownership structure; Hj : Herfindahl-Hirschman index by industry; Hi : HerfindahlHirschman index by firm; Size: size of a firm; Lev : leverage; ROA: return on assets; Grow : revenue growth; MTB : market-to-book ratio; ADR: dummy variable indicating firms with ADR; NonBig4 : dummy variable indicating firms audited by a non Big-4 audit firm. Detailed variables definitions are in Table A1.

Finally, since synchronicity and relative volatility are correlated with several firms’ characteristics that may have evolved throughout the period of the analysis, in models (3) and (6), we add our set of firm-level 350

controls. Doing so, we see the effects associated with the adoption are actually higher for synchronicity, and relative volatility shows positive significant results. In these last models, where we properly account for both macroeconomic shocks and firm-level factors, we are able to see strong and significant effects already 17

Page 18 of 25

in the transition period, which become stronger in the post-adoption period, indicating the incorporation of firm-specific information into stock prices significantly increased, reducing their comovement and increasing 355

the proportion of idiosyncratic volatility. If we use synchronicity calculated with only the domestic market index, the transition and post-adoption dummies in model (3) in Table 4 have higher coefficients (−1.063 and −2.082, respectively), whereas including

ip t

lags in the market model for calculating synchroncity yields smaller coefficients (−0.715 and −1.464) as well as decreasing the 80% of trading days threshold to 50% (−0.850 and −1.632). If we consider relative volatility estimated according to the lower trading days threshold, we obtain coefficients slightly higher (0.270 and

cr

360

0.470, for the transition and post-adoption periods, respectively, where 0.470 is significant at the 1% level).

us

However, the differences are small, and the significance levels remain the same, so our conclusions are the same, regardless of which approach we take to measure our dependent variables.

These results are in line with our hypothesis that IFRS was capable to improve the information environment in the Brazilian capital market, increasing the amount and quality of firm-specific information available

an

365

to the market. These results complement other studies about the economic consequences of IFRS in Brazil that also found positive effects, such as Lima (2011), who analyzed the cost of equity capital and market

M

liquidity, and Silva (2013), who studied the cost of capital and information quality. However, it conflicts with Grecco (2013), who could not find any significant effect regarding accounting information quality. A 370

possible reason for this result is the manner in which prices’ informativeness interacts with institutional

d

factors. Although accounting quality depends on a strong institutional environment, the reduction effect of

view of Kim & Shi (2012).

te

IFRS in informativeness could have been actually strengthened by this weak environment, in line with the

375

Ac ce p

Therefore, our results confirm our hypothesis of higher incorporation of firm specific information into stock prices, which is reflected both in the decrease of prices’ comovement and in the increase of the proportion of idiosyncratic shocks in prices’ movements. The results show that despite the lower level of property rights protection and governance compared to some other IFRS adopters, IFRS seems to have significantly improved the functioning of the Brazilian stock market. A reason for that could either lie in the argument that a pre-existing poorer information environment 380

has more room for improvements, or the substitute effect argument, where the improvement in the firmlevel information environment acts as a substitute for the legal environment, as explained by Kim & Shi (2012). The argument of Christensen et al. (2013) that IFRS adoption is usually accompanied by wider regulatory reforms, so its effects are due to this bundle and not just to the change in accounting standards per se, may also be responsible for this effect, once the efforts of the regulatory boards to properly enforce

385

IFRS might have increased corporate monitoring. This may be an indication of the effects of accounting and legal enforcement to IFRS adoption, specially in emerging countries. Although works such as those by Daske et al. (2008) and Li (2010) argue that the economic effects of IFRS are only seen in countries with stronger enforcement mechanisms, future research comparing emerging and developed countries and properly

18

Page 19 of 25

accounting for the interactions of IFRS and the enforcement environment could provide better understanding 390

about this issue, which is not clear yet. 5. Concluding Remarks

ip t

This research aimed to evaluate whether the adoption of IFRS in Brazil increased the incorporation of firm-specific information into stock prices, making them more informative and therefore more useful for investment decision-making. Stock prices move according to two basic factors: (i) factors related to the market as a whole, reflecting the systematic risk, and (ii) factors related to firms’ specific events, reflecting

cr

395

the idiosyncratic risk. As more market-wide information is reflected into stock prices, the more they tend

us

to move together in the market, making them more synchronous and increasing the proportion of risk that is systematic. We argue that IFRS can increase firms’ information transparency, increasing the amount and quality of firm-specific information available to the market. To evaluate this claim, we investigated whether IFRS adoption is associated with a decrease in the extent to which stocks move with the market, measured

an

400

by stock price synchronicity (Morck et al., 2000), and with an increase in the level of idiosyncratic volatility relative to total volatility.

M

Our results indicate a significant decrease in stock price synchronicity and an increase in firm-level volatility relative to total volatility. The results hold for the adoption transition period (2008 and 2009) 405

and become stronger and more significant for the full adoption period (from 2010). This is consistent with

d

our hypothesis that the increase in the quality of firms’ financial statements with the adoption of IFRS has

te

increased the ability of prices to reflect information about firms, making them more informative (Durnev et al., 2004) and thus contributing to a healthier and more efficient financial market (Habib, 2008; Wurgler,

410

Ac ce p

2000). References

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A. Variables definitions

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Variables definitions are in Table A1.

Table A1: Variables definitions Definition

Synchronicity Relative volatility

The logistic transformation of the R2 of the market model according to equations (1) and (2). The ratio between idiosyncratic volatility and total volatility according to the decomposition developed by Campbell et al. (2001). Dummy variable equal to one for years 2008 and 2009, when IFRS adoption in Brazil started. Dummy variable equal to one for years 2010 to 2013, when full IFRS adoption in Brazil was mandatory. The log of the median of weekly trading volumes measured in thousand Brazilian Reais. The percentage of shares held by the largest shareholder. The ratio between combined sales for firms in the industry and combined sales for all firms. The ratio between the sales for each firm and the sales for all firms in the sample. The log of total assets measured in thousand Brazilian Reais. The ratio between debt and total assets. Return on assets. Percentage change in sales. Market to book ratio. Dummy variable equal to one if a firm is cross-listed in the United States. Dummy variable equal to one if a firm is not audited by one of the big four audit firms (Deloitte, EY, KPMG, and PwC).

T radV ol OwnStruct Hi Hj Size Lev ROA Grow MT B ADR N onBig4

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T rans P ost

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Variable

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a cross-country analysis. Oxford Economic Papers, 48 , 134–162. doi:10.1093/oxfordjournals.oep.

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Highlights • We study the effect of IFRS adoption on stock price informativeness in the Brazilian stock market.

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• After IFRS adoption, we find that stock prices are less synchronous.

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• We also find that firm-level volatility increased relative to total volatility.

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• The results show that stock prices incorporate more firm-specific information after IFRS adoption.

1

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