Search results

We tested for evidence of stock price bubbles in the Malaysian stock market from 1978 to 2004. Four different tests were used namely excess volatility tests, unit root/co-integration tests, duration dependence tests, and the intrinsic bubbles model. All four tests indicate that during the sample period, there was evidence of stock price bubbles. All tests results conform to the theoretical literature on asset price bubbles except for the results on the intrinsic bubbles model, which concludes that Malaysian investors under react to information on dividends. We find this result hardly surprising as anecdotal evidence does indicate that Malaysian investors place more importance on capital gains as compared to dividends. Although we do not go into a debate on whether authorities should be prick the bubble to stem its negative effects, we argue that transparent information dissemination will ensure that the stock market becomes more efficient in pricing stocks.

This chapter introduces the best linear predictor (BLP) with the asymptotic minimum mean squared forecasting error (MSFE) among linear predictors of variables in cointegrated systems. Accordingly, the authors show that (i) if the autocorrelation coefficient of the cointegration error between the prediction time and the predicted targeting time is larger than ½ (representing a short prediction period), then the BLP is deduced from the random walk model; and (ii) in other cases (representing a long prediction period), the BLP is deduced from the cointegration model. Under this scheme, we suggest a switching predictor that automatically selects the random walk or cointegration model according to the size of the estimated autocorrelation coefficient. These results effectively explain the superiority reversal in the short- and long-term prediction of the exchange rate between the random walk and the structural/cointegration model (known as the Meese–Rogoff or disconnect puzzle).

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

We analyze the sizes of standard cointegration tests applied to data subject to linear interpolation, discovering evidence of substantial size distortions induced by the interpolation. We propose modifications to these tests to effectively eliminate size distortions from such tests conducted on data interpolated from end-of-period sampled low-frequency series. Our results generally do not support linear interpolation when alternatives such as aggregation or mixed-frequency-modified tests are possible.

The ratio between share price and current earnings per share, the Price Earning (PE) ratio, is widely considered to be an effective gauge of under/overvaluation of a corporation’s stock. Arguably, a more reliable indicator, the Cyclically Adjusted Price Earning ratio or CAPE, can be obtained by replacing current earnings with a measure of permanent earnings i.e. the profits that a corporation is able to earn, on average, over the medium to long run. In this study, we aim to understand the cross-sectional aspects of the dynamics of the valuation metrics across global stock markets including both developed and emerging markets. We use a time varying DCC model to exploit the dynamics in correlations, by introducing the notion of value spread between CAPE and the respective Market Index from 2002 to 2014 for 34 countries. Value spread is statistically significant during the 2008 crisis for asset allocation. The signal can be utilized for better asset allocation as it allows one to interpret the common movements in the stock market for under/overvaluation trends. These estimates clearly indicate periods of misvaluation in our sample. Furthermore, our simulations suggest that the model can provide early warning signs for asset mispricing in real time on a global scale and formation of asset bubbles.

The economic crisis that began in 2008 represents the end of two experiments in social organization in the United States: the corporate-centered society, in which corporate employers were the predominant providers of health care and retirement security, and the “Ownership Society,” which aimed to vest the economic security of individuals directly in the financial markets. The first experiment lasted for most of the 20th century, while the second hardly got off the ground before imploding. The result is that economic and health security and social mobility in the United States have become increasingly unmoored. Organizational sociologists can contribute to a constructive solution by facilitating, documenting, and disseminating locally based experiments in post-corporate social organization.

Recent research in cognitive and social psychology finds that individual change is more emotional than rational. Further evidence suggests that the contagious power of emotions explains how groups may overcome obstacles and behave in unified ways. We offer a neuropsychological model of emotion-driven change in organizations that explains these findings and predicts conditions in which contagion effects will be successful in facilitating rapid change. Our model posits that emotive precursors to conscious action enable goal alignment and overcome cognitive resource limitations necessary to sustain organizational change over time. Our model adapts the findings from social and cognitive neuroscience to bring new insights into the mental mechanisms underlying the change process. The chapter concludes with tentative suggestions for developing new methods for research and practice that improve our predictive capability for creating rapid large-scale organizational change.