Efficiency of Stock Exchange Markets in G7 Countries： Bootstrap Causality Approach

Market efficiency is based on efficient market hypothesis （EMH）. EMH claims that market totally contains the available information. In case of EMH, valid investors who take position will not gain abnormal profits. If the efficiency can not be established, that is, if markets are not efficient, investors will have the opportunity of abnormal profits. This paper investigates the causality relations to determine validity of EMH among G7 （Canada, France, Germany, Italy, Japan, United Kingdom, and United States） countries＇ stock exchange markets for the period from July 2003 to October 2014. To find out whether the variables cause each other or not provides knowledge about the market efficiency. The implication of this analysis is twofold. One implication is that if the markets are informationally efficient, the possibility of abnormal returns through arbitrage is ruled out and investors can reduce the risk of their investment for the same expected returns, if they establish portfolios that consist of both markets rather than consisting of only one market. Based on this, Hacker-Hatemi-J. bootstrap causality test that is newer and has many advantages contrary to other tests was used. Results showed that EMH is valid among each G7 countries＇ stock exchange markets. Also portfolio diversification benefits exist among these markets.

Carbon trading thickness and market efficiency in a socialist market economy

Unlike the European Union emission trade system(EU ETS), China s pilot ETSs implemented diversified policy designs instead of using a uniform framework. Variance ratio test is used to evaluate the Efficient Market Hypothesis(EMH) in China's carbon trading markets. The results of two versions of variance ratio tests indicate that the carbon trading market in Hubei is considered weak form efficient, and the socialist market economy does not necessarily lead to market inefficiency in carbon trading markets. Thin trading activities generate market frictions and bias the Efficient Market Hypothesis(EMH) tests.

An Empirical Analysis of the Efficient Market Hypothesis in China's Stock Market

The efficient market hypothesis is one of the most important theories in finance.According to this hypothesis,in a stock market with sound laws,good functions,high transparencies,and extensive competitions,all valuable information is timely,accurately,and fully reflected in the trend of stock prices including the current and future values of enterprises.Unless there are market manipulations,it would be impossible for investors to gain more above the average profits in the market by analyzing former prices.Since the efficient market hypothesis has been introduced,it has become an interest in the empirical research of the security market.It is one of the most controversial investment theories and there are many evidences supporting and also opposing this hypothesis.Nevertheless,this hypothesis still holds an important status in the basic framework of mainstream theories in modem financial markets.By analyzing simulated investment transactions in regard to stock trading of three different enterprises,this paper verified that the efficient market hypothesis is partially valid.

Nonlinearity in Stock Exchange Markets: The Case of Bist 100 Indices

In this paper,using data for the Bist 100 index,we investigate the presence of nonlinearities by employing several nonlinearity tests.The Brock,Dechert,and Scheinkman(BDS)and runs tests were first applied to the series to show an initial indication of nonlinearity.The findings for the BDS and runs test of randomness were followed by other sets of direct nonlinearity tests developed by White(1989),Terasvirta(1993),Keenan(1985),and Tsay(1986).Also,the Threshold Autoregression(TAR)test is employed as a final test to confirm the existence of nonlinearity in the Turkish stock exchange market.From the results of the nonlinearity test,it is concluded that the Bist 100 index is characterised by the presence of nonlinearities and cycles.This finding is in contrast with the efficient market hypothesis(EMH)implying that the Turkish stock exchange market is inefficient.

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss.The proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different sectors.The dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next quarter.Our model uses 3 main concepts for forecasting results.Thefirst one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning Factor.The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm.The second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop.The third concept is Recommendation System whichfilters and predict the rating based on the different factors.

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

This study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange.To achieve the objectives,the study uses descriptive statistics;tests including variance ratio,Augmented Dickey-Fuller,Phillips-Perron,and Kwiatkowski Phillips Schmidt and Shin;and Autoregressive Integrated Moving Average(ARIMA).The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series,using the ARIMA model.The results reveal that the mean returns of both indices are positive but near zero.This is indicative of a regressive tendency in the longterm.The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values,with few deviations.Hence,the ARIMA model is capable of predicting medium-or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

R/S Analysis of China Securities Markets

This paper presents the law of changes of indices and stocks in the Shanghai and Shenzhen Stock Exchanges by using rescaled range analysis in nonlinear time series analysis. The Hurst exponents of the stock indices and of all stocks listed in the Shanghai and Shenzhen Stock Exchanges are estimated. The results show that the changes of indices and stocks in the last period have positive impact in the next period in the short run, but this impact disappears for long time.

ROBUST TRADING RULE SELECTION AND FORECASTING ACCURACY

Trading rules performing well on a given data set seldom lead to promising out-of-sample results, a problem which is a consequence of the in-sample data snooping bias. Efforts to justify the selection of trading rules by assessing the out-of-sample performance will not really remedy this predica- ment either, because they are prone to be trapped in what is known as the out-of-sample data-snooping bias. Our approach to curb the data-snooping bias consists of constructing a framework for trading rule selection using a-priori robustness strategies, where robustness is gauged on the basis of time- series bootstrap and multi-objective criteria. This approach focuses thus on building robustness into the process of trading rule selection at an early stage, rather than on an ex-post assessment of trading rule fitness. Intra-day FX market data constitute the empirical basis of the proposed investigations. Trading rules are selected from a wide universe created by evolutionary computation tools. The authors show evidence of the benefit of this approach in terms of indirect forecasting accuracy when investing in FX markets.