A Bayesian Approach for Testing the Debt Signaling Hypothesis in a Transitional Market: Perspectives from Egypt

2004 ◽  
Author(s):  
Tarek Ibrahim Eldomiaty ◽  
Mohamed A. Ismail
Keyword(s):  
Bayesian Approach ◽  
Signaling Hypothesis

The Effects of Share Repurchase Announcements on Returns in the Malaysia Stock Market

2018 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Tze San Ong ◽  
Pei San Ng
Keyword(s):  
Stock Market ◽  
Abnormal Return ◽  
Small Firms ◽  
Abnormal Returns ◽  
Share Repurchase ◽  
Before And After ◽  
Positive Abnormal Return ◽  
Repurchase Announcements ◽  
Signaling Hypothesis ◽  
Firm Characteristic

This paper examines the market response surrounding the share repurchase announcements of Malaysia Listed Companies from years 2012 to 2016. One sample T-test was carried out to identify the abnormal return in the range before and after 20 days from share repurchase announcements. The result shows a significant positive abnormal return in the day of repurchase announcements and continuously until day 1 after the announcements. Multiple regression analysis was performed in order to identify the firm characteristic of share repurchase. The finding is supported with information asymmetric, which shows that stock market reacts more favorably through the repurchase announcements by small firms than large firms. This study is consistent with the signaling hypothesis that shows share repurchase announcement can be an effective tool in stabilizing the stock market in Malaysia. The finding of this study acts as a useful tool for managers and investors to improve their decisions on share repurchase announcements in Malaysia. Company’s managers can conduct share repurchase announcements that are able to make the stock market react positively in order to generate positive abnormal returns.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

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