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Analyzing Event Statistics in Corporate Finance provides new alternative methodologies to increase accuracy when performing statistical tests within corporate finance.



Event Study Methodology I


Chapter 1. Data Collection in Long-Run or Short-Run Format?

In this chapter, a critical question is raised for the empirical finance of corporate event studies. That is, what kind of data set should one apply? Should the short-run data set such as daily returns (or even high-frequency data) be applied? Or, should one try with the longer horizon data? A painful browse through all related literature shows that it is easy to find that there is no definite rule applied to this issue. One question often asked is whether the short-run returns contain more updated information or, the longer horizon data that may provide more insightful views since the impacts of corporate events may be persistent over time.
Jau-Lian Jeng

Chapter 2. Model Specifications for Normal (or Expected) Returns

For corporate finance event studies that look into abnormal returns, robust model specifications for normal (expected) returns are needed. However, to verify the model specifi-cation, one needs to be cautious about the included explanatory variables. Although many candidate variables seem useful in forecasting the returns, they are not necessarily genuine systematic variables that explain the capital market equilibrium. Common-sense reasoning may be considered for filtering the returns thoroughly with all seemingly significant variables to provide cleaner abnormal returns. Yet, inclusion of nonsystematic firm-specific variables in the expected rates of returns may, in fact, result in incorrect conclusion due to possible overrejection in statistics applied. This chapter introduces some new arguments for specification of normal returns.
Jau-Lian Jeng

Chapter 3. Cumulative Abnormal Returns or Structural Change Tests?

In this chapter, it is shown that if the impact of event(s) is considered as permanent, the cumulative abnormal return statistics in event studies coincides with the CUSUM statistics in the tests for parameter changes of regressions such as market models. Namely, the applications for the tests on abnormal returns are closely related with the model specification of normal returns, especially with the regression models assumed for the normal (expected) returns. If the statistical approach is reterospective (where the studies of interest are to identify the possible (permanent) change in parameters within the given history of stock returns), and if the presumed initial date for event window is the correct time period where parameter changes, the hypotheses testings of the conventional CARs and CUSUM statistics are almost identical except for the asymptotic distributions applied. The CARs tests apply the (asymptotic) normality, while CUSUM tests are based on Brownian motion or Brownian bridge.
Jau-Lian Jeng

Event Study Methodology II


Chapter 4. Recursive Estimation for Normal (or Expected) Returns

Given that the updating information continue flowing into the capital market, modification on the model specifications of systematic components of normal returns are necessary for further discussions on firm-specific abnormal returns. In this chapter, since all models that approximate normal returns are prone to time-varying parameters, some recursive estimation methods are shown to cope with this nature. Given that the systematic components of asset returns can be approximated by proposed (time-varying coefficient) theoretical models of nondiversifiable variables or proxies, all the model specifications are similar to the adaptive filters for the data stream.
Jau-Lian Jeng

Chapter 5. Time Will Tell! A Method with Occupation Time Statistics

In this chapter, an alternative method is introduced to assess the impact of corporate events such as mergers and acquisitions on the firms. The method differs from the conventional event study tests in that, instead of testing the parameter changes over time, the durability of the parameter changes and persistence of the impacts is idscussed. In other words, the method considers the intensitity of the impacts from announcements or events may last over time. In terms of properties of stochastic processes, this persistence over time can be represented by the so-called occupation time (or sojourn time) of the underlying stochastic processes constructed by the statistics of interest.
Jau-Lian Jeng


Event studies in corporate finance are so critical for verification on the capital market efficiency and the speed of adjustments in stock returns. The contents of this book merely touch the surface of this gigantic territory of intellectual expertise. Although the issues in event studies of corporate finance are not as spectacular as the space wonderment of galaxies, their varieties and depths are enormous. For the purpose of continuing research, certain extended works are required. For instance, the model search procedures can be extended with further works in statistics for long dependence. Given that the concept of long (or strong) dependence in stochastic processes (either for time series or cross-sectional observations) is more extensive than the specification of unit root(s), developments of robust statistics for long dependence is in need to elaborate the model selection (or variable selection) in empirical asset pricing models. Various definitions of strong dependence can be introduced to provide better verifications on the essential feature of nondiversifiable pricing kernels that describe the benchmark normal (or expected) returns of risky securities.
Jau-Lian Jeng


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