We describe the application of a statistical technique known as Multidimensional Scaling to analyze & model a stock market. We have reviewed a set of novel papers in this field, and report on the current state of the art. The strength of this approach is in it’s ease of implementation, effective visualization and dynamic nature.We give a brief introduction to MDS, and state the assumptions & pr...

In terms of transfer entropy, we investigate the strength and the direction of information transfer in the US stock market. Through the directionality of the information transfer, the more influential company between the correlated ones can be found and also the market leading companies are selected. Our entropy analysis shows that the companies related with energy industries such as oil, gas, ...

in this paper we investigate the effect of oil price shocks on stock market index in iran, by using of a structural var (svar) approach. we used four variables in the model namely kilian index, global oil supply, real oil price and real stock market index. the data are monthly and spanning the period 1997m10-2014m12. we identify the effect of four different shocks on stock market including oil ...

Trading in stock market indices has gained unprecedented popularity in major ®nancial markets around the world. However, the prediction of stock price index is a very dif®cult problem because of the complexity of the stock market data. This study proposes stock trading model based on chaotic analysis and piecewise nonlinear model. The core component of the model is composed of four phases: The ...

In this study, we establish a network structure of the Korean stock market, one of the emerging markets, with its minimum spanning tree through the correlation matrix. Base on this analysis, it is found that the Korean stock market doesn’t form the clusters of the business sectors or of the industry categories. When the MSCI (Morgan Stanley Capital International Inc.) index is exploited, we fou...

The paper examines the extent to which the conditional volatility of stock market returns in a small, internationally integrated stock market are related to the conditional volatility of financial and business cycle variables. It employs a low frequency monthly dataset for Australia including stock market returns, interest rates, inflation, the money supply, industrial production and the curren...

There have been several efforts in the literature to extract as much information as possible from the financial networks. Most of the research has been concerned about the hierarchical structures, clustering, topology and also the behavior of the market network; but not a notable work on the network filtration exists. This paper proposes a stock market filtering model using the correlation - ba...

Stock market decision making is a very challenging and difficult task of financial data prediction. Prediction about stock market with high accuracy movement yield profit for investors of the stocks. Because of the complexity of stock market financial data, development of efficient models for prediction decision is very difficult, and it must be accurate. This study attempted to develop models ...