Testing Moving Average Rules Using The Dow Jones Index

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.

When investing in a company, the goal is to buy shares at a low price and then sell them at a higher price. Individual stocks may go up or down independent of how “The Market” is doing overall. Stock market indices such as the Dow Jones Average, the NASDAQ, and the Standard and Poors 500 report how “The Market” is doing “on average.” To check

In this article, “Is the efficient market hypothesis day-of-the-week dependent,”, Stephen Pop reveals significant evidence that the efficient market hypothesis is day-of-the-week-dependent. Overall, for only 62% of firms, the unit root null hypothesis is rejected on all the five trading days. He also discovers that when investors do not account for unit root properties in devising trading strategies, they obtain spurious

A Study on S&P 500 Index Stock Return and Volatility using ARIMA and GARCH Modeling

Since the great depression, the global economy has been facing a number of ups and downs. With the markets being built rapidly and crashing down in the other instant, elements that actually make up the skeleton of the international economy seems to have correlated variables. These variables seems to have been correlated with each other in numerous combinations and their correlation is what makes the market fluctuate. The volatility of the global market becomes more apparent in times of recession when the correlation between variables like stocks, bonds, US dollar, Euro, gold and oil becomes more apparent. Therefore, it is of paramount importance that these correlated variables should be studied with a very keen eye. Since the link between currencies, commodities, stocks an bonds runs very deep, change in any one of them seems to have a profound impact on the rest hence changing the face of the market.

The stock market as measured by the Dow 30 industrials bottomed in March at which time the Dollar index negatively correlated by peaking in price. In all the retracements and minor corrections that occurred, since then, in the Dow the dollar also experienced a similar but opposite reaction.

The stock market is one of the leading influences on price setting and trade markets. It can have both positive and negative effects on prices and movement in trade both domestically and internationally. The United States stock market has had a long history of influence not only on our economy, but also on a global scale. Currently, the stock market has been slight downturn, due to falling oil prices and agricultural commodities. Which allows for cheaper products for the consumers. It is clear to see that the stock market has and will continue to influence how the average person goes about their day to day life.

Table: 1, represents the results of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted R² and pronounced as R squared, indicates how well data points fit a statistical model and the adjusted R² values in the analysis are fairly good which is more than 60%, indicates the considered model is fit for analysis. Also, the F-Statistics which provides the statistical significance of the model and its probabilities which are below 5% level and hence proves the model’s significance.

The 3-month T-bill rates and Dow Jones index are really close to the whole economic environment; the 3-month T-Bill rates are the preeminent default-risk-free rates in the US money market that is often used by

Proponents of market efficiency (or academics) would say that technical analysis is a waste of time and cannot result in abnormal profits. This is because in a weak form, semi-strong or strong form efficient market, share prices should have already reflected historical price and volume data, and no-one can earn an abnormal return from strategies based in this information. Thus, we could also say that technical analysts or chartists do not believe in any form of market efficiency.

The momentum strategy in market trading is widely known as the strategy of short selling past losers and buying past winners. This strategy relies on the past pricing as trading indicators. Interestingly, there are several evidences provided in the

By analyzed the link between financial asset prices and monetary policy is highly important to achieve a better understanding in the transmission mechanism of monetary policy, as the changes in financial asset prices is a main role in several channels. We contribute empirical confirmation data on the relationship between monetary policy and the financial stock market. In the economy, stock prices are among the most strictly monitored asset prices in the market and stock prices are also frequently seen as being strongly sensitive to economic situation. Bernanke and Kuttner (2005) stated that some researchers view the stock market as a separate source of macroeconomic volatility to which governmental may wish to acknowledge. Stock prices often known as volatility and boom-bust cycles and leading to the interest about preserved deviations from their fundamental values. Hence, the authorize quantitatively the continuation of a stock market respond to the monetary policy changes will not only be significantly related to the study of the stock market purpose but will also assign to a further research and understanding of the element of monetary policy and further discussion of the economic impact on stock market.

The purpose of this paper is concentrated on relationship between Vietnamese stock price relative to exchange rate and United State stock market. In order to have a better view about this relationships, the suitable econometrics model will be used in the research are OLS and ARMA. To determine the correlation, coefficients among the variables from the test we will be able to find out the β, R2, P-value, Standard Error, Durbin-Watson stat statistic etc... With the time series dataset, in other to get a good forecast, the regressions will be run and tested on EVIEW program. The main model will be use is:

The purpose of this study is to investigate the presence of January effect in emerging stock markets of four Southeast Asia countries: Malaysia, Thailand, Philippine and Indonesia, for the period of January 2012 until December 2015, which is the most recent period after the financial crisis in 2007-2008. The financial crisis would affect the behaviour of the stock markets and thus the stock price might not reflect its true value. As the most recent economic crisis is believed to have ended in Fall 2011 (Elliott 2011; Weisenthal 2013), this study will focus on the most recent 4-year period, from January 2012 until December 2015. The four Southeast Asia countries are selected because there are limited studies about them. Furthermore, they are the only Southeast Asia countries being included in MSCI Emerging Markets Index as of 2016. Thus it is worth examining the efficiency of the stock markets of these high growth emerging markets.