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Fair Value Accounting

Implications for Users of Financial Statements


Kristian Bachert

Fair value accounting is viewed as a major feature of IFRS and several standards either require assets to be measured at fair value or at least provide an option to fair value measurement instead of applying historical cost. While it is argued that fair values provide more timely and relevant information, the global financial crisis led to a considerable debate about the usefulness of fair value accounting. The study examines the implications of fair value accounting for financial analysts and nonprofessional investors. It provides evidence that, even if financial analysts find it challenging to produce accurate forecasts under a fair value regime, nonprofessional investors make larger investments and are more confident with their judgments for fair value firms.


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5 Analysis of financial analysts’ forecasts


5.1 Methodological approach This chapter describes the methodological approach used to investigate financial analysts’ forecasts. It presents the research design and the variables that are used within the regres- sion analyses. The chapter also presents some procedures for additional analyses which are intended to both support the main results and strengthen the confidence that is placed into the main findings. The additional analyses refer to a matched sample design and to a difference in differences analysis. 5.1.1 Research design Regression analysis and assumptions Similar to the majority of the studies which are presented in the state of the research sec- tion (chapter 3) the study presented in this chapter uses an archival-based approach. To provide evidence on the association between fair value accounting and financial analysts’ ability to forecasting (i.e. forecast accuracy and forecast dispersion) and analyst following, and to address the hypotheses, the method of multiple regression analysis is used. Rep- resenting a statistical method, regression analyses help to understand the changes of a cer- tain dependent variable when any one of the independent variables (explanatory variables) is modified in some way.138 In general, the multiple regression model with the number of k independent variables is stated as follows (Eckstein (2010), p. 337): (5.1) , where i indicates the number of observations (i = 1,2,…,N) and y represents the depend- ent variable and x the independent variables. 0 indicates the constant and 1 – k repre- sent the coefficients for the regarding values of the independent variables. The factor...

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