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Journal ArticleDOI

Using daily stock returns: The case of event studies

01 Mar 1985-Journal of Financial Economics (Elsevier)-Vol. 14, Iss: 1, pp 3-31
TL;DR: In this paper, the authors examine properties of daily stock returns and how the particular characteristics of these data affect event study methodologies and show that recognition of autocorrelation in daily excess returns and changes in their variance conditional on an event can sometimes be advantageous.
About: This article is published in Journal of Financial Economics.The article was published on 1985-03-01. It has received 6651 citations till now. The article focuses on the topics: Event study.
Citations
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Journal ArticleDOI
TL;DR: A review of the market efficiency literature can be found in this article, where the authors discuss the work that they find most interesting, and offer their views on what we have learned from the research on market efficiency.
Abstract: SEQUELS ARE RARELY AS good as the originals, so I approach this review of the market efficiency literature with trepidation. The task is thornier than it was 20 years ago, when work on efficiency was rather new. The literature is now so large that a full review is impossible, and is not attempted here. Instead, I discuss the work that I find most interesting, and I offer my views on what we have learned from the research on market efficiency.

5,506 citations

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TL;DR: An overview of some of the developments in the formulation of ARCH models and a survey of the numerous empirical applications using financial data can be found in this paper, where several suggestions for future research, including the implementation and tests of competing asset pricing theories, market microstructure models, information transmission mechanisms, dynamic hedging strategies, and pricing of derivative assets, are also discussed.

4,206 citations

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TL;DR: A review of the scientific literature on the market for corporate control can be found in this paper, where the authors argue that corporate control is best viewed as an arena in which managerial teams compete for the rights to manage corporate resources.

3,821 citations

Journal ArticleDOI
TL;DR: In this paper, the empirical power and specification of test statistics in event studies designed to detect long-run (one to five-year) abnormal stock returns were analyzed and three reasons for this misspecification were identified.

2,946 citations

Posted Content
TL;DR: The third edition has been updated with new data, extensive examples and additional introductory material on mathematics, making the book more accessible to students encountering econometrics for the first time as discussed by the authors.
Abstract: This bestselling and thoroughly classroom-tested textbook is a complete resource for finance students. A comprehensive and illustrated discussion of the most common empirical approaches in finance prepares students for using econometrics in practice, while detailed case studies help them understand how the techniques are used in relevant financial contexts. Worked examples from the latest version of the popular statistical software EViews guide students to implement their own models and interpret results. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Building on the successful data- and problem-driven approach of previous editions, this third edition has been updated with new data, extensive examples and additional introductory material on mathematics, making the book more accessible to students encountering econometrics for the first time. A companion website, with numerous student and instructor resources, completes the learning package.

2,797 citations

References
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Book
01 Jan 1970
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Abstract: From the Publisher: This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.

19,748 citations

Journal ArticleDOI
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Abstract: From the Publisher: This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification, such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.

12,650 citations

Book
01 Jan 1979
TL;DR: In this paper, the convergence of distributions is considered in the context of conditional probability, i.e., random variables and expected values, and the probability of a given distribution converging to a certain value.
Abstract: Probability. Measure. Integration. Random Variables and Expected Values. Convergence of Distributions. Derivatives and Conditional Probability. Stochastic Processes. Appendix. Notes on the Problems. Bibliography. List of Symbols. Index.

6,334 citations

Journal ArticleDOI
TL;DR: Time Series Analysis and Forecasting: principles and practice as mentioned in this paper The Oxford Handbook of Quantitative Methods, Vol. 3, No. 2: Statistical AnalysisTime-Series ForecastingPractical Time-Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series analysisTime Series analysisElements of Nonlinear Time Series analyses and forecastingTime series analysis and forecasting by Example.
Abstract: Advances in Time Series Analysis and ForecastingThe Analysis of Time SeriesForecasting: principles and practiceIntroduction to Time Series Analysis and ForecastingThe Oxford Handbook of Quantitative Methods, Vol. 2: Statistical AnalysisTime-Series ForecastingPractical Time Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series AnalysisTime Series AnalysisElements of Nonlinear Time Series Analysis and ForecastingTime Series Analysis and Forecasting by ExampleIntroduction to Time Series Analysis and ForecastingTime Series Analysis and AdjustmentSpatial Time SeriesPractical Time Series Forecasting with RA Very British AffairMachine Learning for Time Series Forecasting with PythonTime Series with PythonTime Series Analysis: Forecasting & Control, 3/EIntroduction to Time Series Forecasting With PythonThe Analysis of Time SeriesTime Series Analysis and Its ApplicationsForecasting and Time Series AnalysisIntroduction to Time Series and ForecastingIntroduction to Time Series Analysis and ForecastingTime Series Analysis in the Social SciencesPractical Time Series AnalysisTime Series Analysis and ForecastingTheory and Applications of Time Series AnalysisApplied Time SeriesSAS for Forecasting Time Series, Third EditionTime Series AnalysisPredictive Modeling Applications in Actuarial ScienceIntroductory Time Series with RHands-On Time Series Analysis with RAdvances in Time Series ForecastingTime Series Analysis and Forecasting Using Python & RAdvanced Time Series Data Analysis

6,184 citations

Journal ArticleDOI
TL;DR: In this article, it is argued that income numbers cannot be defined substantively, that they lack "meaning" and are therefore of doubtful utility, and the argument stems in part from the patchwork development of account-based theories.
Abstract: Accounting theorists have generally evaluated the usefulness of accounting practices by the extent of their agreement with a particular analytic model. The model may consist of only a few assertions or it may be a rigorously developed argument. In each case, the method of evaluation has been to compare existing practices with the more preferable practices implied by the model or with some standard which the model implies all practices should possess. The shortcoming of this method is that it ignores a significant source of knowledge of the world, namely, the extent to which the predictions of the model conform to observed behavior. It is not enough to defend an analytical inquiry on the basis that its assumptions are empirically supportable, for how is one to know that a theory embraces all of the relevant supportable assumptions? And how does one explain the predictive powers of propositions which are based on unverifiable assumptions such as the maximization of utility functions? Further, how is one to resolve differences between propositions which arise from considering different aspects of the world? The limitations of a completely analytical approach to usefulness are illustrated by the argument that income numbers cannot be defined substantively, that they lack "meaning" and are therefore of doubtful utility.' The argument stems in part from the patchwork development of account-

6,043 citations


"Using daily stock returns: The case..." refers background in this paper

  • ...A direct way of addressing variance increases is to partition the sample based on an economic model of the effects of the event, such as whether the event is ‘good news’ or ‘bad news’ [e.g., Ball and Brown (1968) ]....

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