Evidence of Stock Price Bubbles: The Case of the Malaysian Second Board Index

2008 ◽  
Author(s):  
Gary J. Rangel
Keyword(s):  
Stock Price ◽  
Price Bubbles

Periodically collapsing stock price bubbles: a robust test

1998 ◽  
Vol 61 (2) ◽  
pp. 221-228 ◽  
Author(s):  
Mark P. Taylor ◽  
David A. Peel
Keyword(s):  
Stock Price ◽  
Robust Test ◽  
Price Bubbles

scholarly journals Do stock price bubbles influence corporate investment?

2005 ◽  
Vol 52 (4) ◽  
pp. 805-827 ◽  
Author(s):  
Simon Gilchrist ◽  
Charles P. Himmelberg ◽  
Gur Huberman
Keyword(s):  
Stock Price ◽  
Corporate Investment ◽  
Price Bubbles

Determinants of stock price bubbles

2013 ◽  
Vol 35 ◽  
pp. 661-667 ◽  
Author(s):  
Paresh Kumar Narayan ◽  
Sagarika Mishra ◽  
Susan Sharma ◽  
Ruipeng Liu
Keyword(s):  
Stock Price ◽  
Price Bubbles

scholarly journals Confining value from neural networks

2019 ◽  
Vol 45 (10/11) ◽  
pp. 1433-1457 ◽  
Author(s):  
Ioannis Anagnostopoulos ◽  
Anas Rizeq
Keyword(s):  
Neural Networks ◽  
Firm Value ◽  
Stock Price ◽  
Predictive Accuracy ◽  
Target Identification ◽  
Methodological Approach ◽  
Market Uncertainty ◽  
Content Type ◽  
Financial Variables ◽  
Price Bubbles

Purpose This study provides valuable insights to managers aiming to increase the effectiveness of their diversification and growth portfolios. The purpose of this paper is to examine the value of utilizing a neural networks (NNs) approach using mergers and acquisition (M&A) data confined in the US technology domain. Design/methodology/approach Using data from Bloomberg for the period 2000–2016, the results confirm that an NN approach provides more explanation between financial variables in the model than a traditional regression model where the NN approach of this study is then compared with linear classifier, logistic regression. The empirical results show that NN is a promising method of evaluating M&A takeover targets in terms of their predictive accuracy and adaptability. Findings The findings emphasize the value alternative methodologies provide in high-technology industries in order to achieve the screening and explorative performance objectives, given the technological complexity, market uncertainty and the divergent skill sets required for breakthrough innovations in these sectors. Research limitations/implications NN methods do not provide for a fuller analysis of significance for each of the autonomous variables in the model as traditional regression methods do. The generalization breadth of this study is limited within a specific sector (technology) in a specific country (USA) covering a specific period (2000–2016). Practical implications Investors value firms before investing in them to identify their true stock price; yet, technology firms pose a great valuation challenge to investors and analysts alike as the latest information technology stock price bubbles, Silicon Valley and as the recent stratospheric rise of financial technology companies have also demonstrated. Social implications Numerous studies have shown that M&As are more often than not destroy value rather than create it. More than 50 percent of all M&As lead to a decline in relative total shareholder return after one year. Hence, effective target identification must be built on the foundation of a credible strategy that identifies the most promising market segments for growth, assesses whether organic or acquisitive growth is the best way forward and defines the commercial and financial hurdles for potential deals. Originality/value Technology firm value is directly dependent on growth, consequently most of the value will originate from future customers or products not from current assets that makes it challenging for investors to measure a firm’s beta (risk) where the value of a technology is only known after its commercialization to the market. A differentiated methodological approach used is the use of NNs, machine learning and data mining to predict bankruptcy or takeover targets.

scholarly journals On the dynamics of stock price bubbles: comments on a model by Miao and Wang

2019 ◽  
Vol 28 (2) ◽  
pp. 521-537 ◽  
Author(s):  
Gerhard Sorger
Keyword(s):  
Risk Aversion ◽  
Stock Price ◽  
Local Dynamics ◽  
Collateral Constraints ◽  
Price Bubbles ◽  
Risk Neutral

Abstract We consider the model by Miao and Wang (Am Econ Rev 108:2590–2628, 2018), in which endogenous collateral constraints may generate stock price bubbles. Whereas Miao and Wang (2018) characterize the local dynamics around stationary equilibria only under the assumption of risk neutral households, we extend this characterization to the case of risk aversion.

Bubbles and Total Factor Productivity

2012 ◽  
Vol 102 (3) ◽  
pp. 82-87 ◽  
Author(s):  
Jianjun Miao ◽  
Pengfei Wang
Keyword(s):  
Total Factor Productivity ◽  
Stock Price ◽  
Credit Constraints ◽  
Infinite Horizon ◽  
Factor Productivity ◽  
Productivity Shocks ◽  
Price Bubbles ◽  
Production Economies

This paper presents an infinite-horizon model of production economies in which firms face idiosyncratic productivity shocks and are subject to endogenous credit constraints. Credit-driven stock price bubbles can arise which can relax credit constraints and reallocate capital more efficiently among firms. The collapse of bubbles causes a fall of total factor productivity.

Empirical evidence on periodically collapsing stock price bubbles

2004 ◽  
Vol 11 (1) ◽  
pp. 61-69 ◽  
Author(s):  
G. Capelle-Blancard ◽  
H. Raymond
Keyword(s):  
Empirical Evidence ◽  
Stock Price ◽  
Price Bubbles
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