Permanent And Temporary Components Of Stock Prices Using The Vector Error Correction Estimation Method

Capital markets provide a function which facilitates the buying and selling of long-term financial securities to increase liquidity and their value, Watson & Head (2013). Hence, the Efficient Market Hypothesis (EMH) explains the relationship that exists with the prices of the capital market securities, where no individual can beat the market by regularly buying securities at a lower price than it should be. This means that in order to be an efficient market prices of securities will have to fairly and fully reflect all available information, Fama (1970). Consequently, Watson & Head (2013) believe that market efficiency refers to the speed and quality of how share price adjusts to new information. Nevertheless, the testing of the efficient markets has led to the recognition of three different forms of efficiency in which explains how information available is used within the market. In this essay, the EMH will be analysed; testing of EMH will show that the model does provide strong evidence to explain share behaviour but also anomalies will be discussed that refutes the EMH. Therefore, a judgment will be made to see which structure explains the efficient market and whether there are some implications with the EMH, as a whole.

In order to study how stock prices react to these events, approximate three years of continuous daily stock price are chose, beginning at 17th March 2008 and ending more than three months after the final event at 22nd April 2011. In addition, SHANGHAI Stock Exchange Index (SSE) is adopted as a proxy of the market portfolio.

In this project we first checked consistency and seasonality of S&P500 index stock performance by splitting its recent twenty years historical data into ten two year data and built ARIMA and GARCH models for each sub-period. We found that the models are considerably consistent before 2007-2008 sub-period, and there exists some minor seasonality in several subperiods, but no particular pattern can be identified for the whole period. We then tried to predict future return, volatility and VaR using the model we built for the last sub-period based on rolling forecast procedure. Though the fitted values of 10th sub-period model are

Regulation of financial markets is very significant for investors in the stock markets, policy-makers, practitioners, and academicians who explore the topic very rigorously. This paper contributes to the field by examining the volatility among stock returns for firms in different segments of the financial services industry. My findings are useful to investors and have significant implication for policy-makers and federal regulators in the United States.

14. The index model has been estimated for stocks A and B with the following results:

We believe, however, that returns response uses only partial market information about stocks. The Traditional Capital Asset Pricing Model (CAPM) holds the opinion that systematic risk would be the only factor that influences asset prices since idiosyncratic risk could be eliminated away by holding a well-diversified investment portfolio. However, recent empirical studies have found that the risk intensity of stocks is mainly due to the idiosyncratic risk of individual stocks. This conclusion was different from CAPM’s argument that only systematic risk would have an effect on returns. Volatility, in many studies, has also been found to carry current and future information. A recent study that attempted to break through the traditional method of calculating returns was performed by Campbell, Lettau, Malkiel and Xu (2001). They successfully separated the volatility of stock returns into market, industry and firms’ idiosyncratic volatility by use of a disaggregated approach. Xu and Malkiel (2003) applied a decomposition method and a disaggregated approach method to decompose volatility of stock returns into systematic volatility and idiosyncratic volatility and found that corporate private information could be reflected to its stock price faster when the institutional investors held a higher percentage of that company’s stock. Hatemi-J, A. and M. Irandoust

sider BACKGROUND Efficient market theory examines how accurately stock prices signal resource allocation alloc and fully reflect all available information. Fama (1970) introduced the efficient market hypothesis stating there are three forms of efficiency: weak, semi strong, and strong. A market semi-strong, that incorporates all historical information is said to be weak form efficient, while one that responds to all publicly available informatio is semi-strong efficient. In a semiinformation -strong efficient market, prices instantly change to reflect publicly available information. A strong form market, strong responds to all information, both public and private. The hypothesis claims that achieving above average returns on a risk adjusted basis is impossible (Fama 1970). (Fama, The lowest level of market efficiency, weak form, states that the market only reacts to historical information. This means that no one can earn above normal returns based on published historical information; however, the market does not quickly react to new public or private information. It may be possible then, in a weak form efficient market, to obtain abnormal returns form using either new publicly available or private insider information (Fama 1970). (Fama, A semi-strong form market is more efficient that a weak form, as it reacts to publicly strong available new information quickly and share prices adjust to reflect the market’s reaction. share Obtaining

We have used the data from Jan.2007-Dec.2011 (60 data points) to re-estimate βi’s which were averaged across securities within portfolios in order to obtain 10 initial portfolio βpt using the following regression

Efficient Market Hypothesis has been controversial issues among researcher for decades. Until now, there is no united conclusion whether capital markets are efficiency or not. In 1960s, Fama (1970) believed that market is very efficient despite there are some trivial contradicted tests. Until recently, both empirical and theatrical efficient market hypothesis was being disputed by behavior finance economist. They have found that investor have psychological biases and found evidences that some stocks outperform other stocks. Moreover, there are evidences prove that market are not efficient for instance financial crisis, stock market bubble, and some investor can earn abnormal return which happening regularly in stock markets all over the world. Therefore, the purpose of this essay is to demonstrate that Efficient Market Hypothesis in stock (capital) markets does not exist in the real world by proofing four outstanding unrealistic conditions that make market efficient: information is widely available and cost-free, investor are rational, independent and unbiased, There is no liquidity problem in stock market, and finally stock prices has no pattern.

The key idea of vector error correction model is that there are the short term dynamic which generates stock prices deviations, and the long term equilibrium component that makes mean reverting. In the error correction model, the short term dynamics of stock prices changes are influenced by the deviation from the long term equilibrium. If the stock prices are integrated order of one, I(1), the model would be,

In article of 1971, Fama categorised market efficiency into three main forms. Weak form is based on the historical data of the stock prices. The semi-strong form tests how efficiently prices adapt to the publically available information. The third form is concerned whether any given market participants having monopolistic access to the creation of the stock prices.

Even though there are flaws in the CAPM for empirical study, the approach of the linearity of expected return and risk is readily relevant. As Fama & French (2004:20) stated “… Markowitz’s portfolio model … is nevertheless a theoretical tour de force.” It could be seen that the study of this paper may possibly justify Fama & French’s study that stated the CAPM is insufficient in interpreting the expected return with respect to risk. This is due to the failure of considering the other market factors that would affect the stock price.

During the third quarter, the US stock market finally succumbed to selling pressure that has been affecting financial markets across the globe. For over a year volatility has been a feature of the world 's currency, credit and commodity markets. In addition, many world stock markets have been in correction mode, especially emerging markets. Financial markets are interconnected so as poor economic news piled up last quarter all stock markets headed lower. The S&P 500 fell by -6.44% but that was better than just about any other equity asset class. US small capitalization stocks and most international indices fell by over 10% while emerging market stocks got clobbered, falling -17.9%.

Mukhejee and Naka (1995): Author in his article says that relationship between Tokyo stock prices and six macroeconomic variables using a vector error correction model (VECM). Their study covered 240 monthly observations for each variable in the period from January 1971 to December 1990. The results of the study showed that the relationship between Tokyo stock prices, the exchanges rate, money supply and industrial production is positive, whereas the relationship between Tokyo stock price and inflation and interest rates is mixed.

The efficient market hypothesis exists in three forms, weak, semi-strong, and strong form. The first form is considered weak because it assumes the stock’s price is a reflection of its historical relevance, which includes accounting data (financial statements) and other publicly available information. At this level, such information is considered immaterial in causing the price of a stock to change. The second form is semi-strong, and it’s concerned with market adjustments. In other words, how do the markets react to all available information? At this level, all “accessible” information is assumed be reflected in the stock’s price, which can range from historical to future oriented information made available through public