We develop a new class of artificial stock market that apply general equilibrium price clearing. Our model is the first multi-asset artificial stock market to use exogenously generated dividends from a geometric vector autoregressive model and is therefore the first truly multi-asset artificial stock market. Our model does not require the concept of a risk free asset and agents are able to form...

This paper investigates the interactive relationships among China energy price shocks, stock market, and the macroeconomy using multivariate vector autoregression. The results indicate that there is a long cointegration among them. A 1% rise in the energy price index can depress the stock market index by 0.54% and the industrial value-adding growth by 0.037%. Energy price shocks also cause infl...

This paper uses a quantile impulse response approach to investigate the impact of oil price shocks on Chinese stock returns. This process allows us to uncover asymmetric effects of oil price shocks on stock market returns by taking into account the different quantiles of oil price shocks. Our results show that the responses of Chinese stock market returns to oil price shocks differ greatly, dep...

This paper studies the dynamic behavior of risks and returns in Chinese stock markets. We characterize the time-series properties of stock-market return and volatility and test the market e–ciency hypothesis. We estimate an empirical model that captures the efiect of local and global information variables on the conditional mean of stock-market returns and characterize the second order conditio...

Twitter is a widely used online social media. One important characteristic of Twitter is its real-time nature. In this paper, we investigate whether the daily number of tweets that mention Standard & Poor 500 (S&P 500) stocks is correlated with S&P 500 stock indicators (stock price and traded volume) at three different levels, from the stock market to industry sector and individual company stoc...

In the paper we study the relationship between macroeconomic and stock market volatility, using S&P500 data for the period 1970-2001. We find weak evidence of long memory in volatility once structural change is accounted for and a twofold linkage between stock market and macroeconomic volatility: macroeconomic volatility explains the persitent dynamics in stock market volatility, while stock ma...

Stock market represents an essential part of the economy in the Middle East, it is significant for shareholders and investors to estimate the stock price and select the best trading opportunity accurately in advance. This paper utilizes artificial neural network in the modeling of stock market exchange prices. The network was trained using supervised learning. Simulation was conducted for seven...

Classification of Indian stock market data has always been a certain appeal for researchers. In this paper, first time combination of three supervised machine learning algorithms, classification and regression tree (CART) , linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are proposed for classification of Indian stock market data, which gives simple interpretation o...

In this study, we focused on Tehran stock exchange market analysis based on applying moving average rules. The Tehran stock exchange in the Middle East has evolved into an exciting and growing marketplace where individual and institutional investor trade securities of over 420 companies. In an attempt to examine the ability to earn excess return by exploiting moving average rules, the average a...