Stock exchange

About: Stock exchange is a research topic. Over the lifetime, 39566 publications have been published within this topic receiving 612044 citations.

Modeling the dynamic interdependence of major european stock markets

Abstract: The growing globalization of financial markets has been accompanied by a growing body of empirical research attempting to describe and quantify the ways in which financial markets within and across countries interact. Better understanding of the nature of cross market linkages and interactions could be of help to investors and policy makers alike. With respect to policy, aspects of market interaction that promote efficiency could, in principle, be facilitated whereas, those with undesirable side effects could be controlled. Likewise, investment and hedging strategies could be more effective if the nature of market interactions were better understood. The extant literature provides convincing evidence that financial markets do influence each other. For example, Koch and Koch (1991) provide evidence on the evolution of contemporaneous and lead/lag relationships among eight national stock markets. They suggest that regional interdependencies have grown over time. Becker, Finnerty, and Gupta (1990) show that information generated in the US stock market could be used to trade profitably in Japan, contrary to the market efficiency hypothesis. However, when transaction costs and transfer taxes are included into the analysis, excess profits vanish. Eun and Shim (1989) document that markets around the globe respond to innovations in a way that is consistent with the notion of informationally efficient international stock markets. King and Wadhwani (1990) use a rational expectations model with asymmetric information to test for 'contagion effects' i.e., the notion that valuation mistakes in one market can be transmitted to other markets.' More recent papers extend the scope of market interaction to include second moment linkages. This extension allows testing ofthe hypothesis that information generated in a given market at time / is useful in terms of predicting the conditional mean and variance in another market at time t+l. Hamao, Masulis and Ng (1990) examine first and second moment interdependencies in the three major stock markets (New York, Tokyo, and London) using univariate GARCH models. For the period after the October 1987 worldwide stock market crash, they find that innovations coming from

The effects of behavioural factors in investment decision-making: a survey of institutional investors operating at the Nairobi Stock Exchange

Abstract: This study investigated the role of behavioural finance and investor psychology in investment decision-making at the Nairobi Stock Exchange with special reference to institutional investors. Using a sample of 23 institutional investors, the study established that behavioural factors such as representativeness, overconfidence, anchoring, gambler's fallacy, availability bias, loss aversion, regret aversion and mental accounting affected the decisions of the institutional investors operating at the NSE. Moreover, these investors made reference to the trading activity of the other institutional investors and often exhibited an institutional-herding behaviour in their investment decision-making.

Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese Stock Market Bubbles

Abstract: By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the logperiodic power law (LPPL) model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. We use the LPPL model in one of its incarnations to analyze two bubbles and subsequent market crashes in two important indexes in the Chinese stock markets between May 2005 and July 2009. Both the Shanghai Stock Exchange Composite index (US ticker symbol SSEC) and Shenzhen Stock Exchange Component index (SZSC) exhibited such behavior in two distinct time periods: 1) from mid-2005, bursting in October 2007 and 2) from November 2008, bursting in the beginning of August 2009. We successfully predicted time windows for both crashes in advance [24, 1] with the same methods used to successfully predict the peak in mid-2006 of the US housing bubble [37] and the peak in July 2008 of the global oil bubble [26]. The more recent bubble in the Chinese indexes was detected and its end or change of regime was predicted independently by two groups with similar results, showing that the model has been well-documented and can be replicated by industrial practitioners. Here we present more detailed analysis of the individual Chinese index predictions and of the methods used to make and test them. We complement the detection of log-periodic behavior with Lomb spectral analysis of detrended residuals and (H, q)-derivative of logarithmic indexes for both bubbles. We perform unit-root tests on the residuals from the log-periodic power law model to confirm the Ornstein-Uhlenbeck property of bounded residuals, in agreement with the consistent model of ‘explosive’ financial bubbles [16].

The Determinants of Capital Structure Choice: Evidence from Polish Companies

Abstract: The main objective of this paper is to investigate which of the two competing capital structure theories – the pecking order of financing choices or the traditional static trade-off model – better describes the financing decisions in Polish companies traded on the Warsaw Stock Exchange (WSE). The data come from financial statements of the companies and cover a 5-year period, 2000–2004. First, a correlation is run in order to separate a set of significant factors influencing the capital structure from the list of the following independent variables: assets structure, profitability, growth opportunities, liquidity, firm size, product uniqueness, earnings volatility, non-debt tax shields, dividend policy, and the effective tax rate. Next, in order to test the relationship between capital structure and its potential determinants, multiple regression is run. The evidence generally suggests the relevance of the pecking order hypothesis in explaining the financing choices of Polish firms.