Data Mining (DM) is a relatively recent technology that is employed in inferring useful knowledge that can be put to use from a vast amount of data. This paper presents the data mining processes applied to the seemingly chaotic behavior of stock markets which could be well represented using the enhance GMDH, and we compared its results with published results using neural network, TS fuzzy syste...

Stock trading is an important financial activity of human society. Machine learning techniques are adopted to provide trading decision support by predicting the stock price or trading signals of the next day. Decisions are made by analyzing technical indices and fundamental analysis of companies. There are two major machine learning research problems for stock trading decision support: classifi...

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods...

Systemic risk events constitute an important issue in current financial systems. A leading course of action used to mitigate such events is identification of systemically important agents in order to implement the prudential policies in a financial system. In this paper, a bi-level cross-shareholding network of the stock market is considered according to direct and integrated ownership structur...

the process of economic globalization and integration has had a tremendous growth in recent years and the convergence of various economic sectors, including the financial markets is increasingly in the spotlight. this increasing trend has strengthened investors' interests in the subject of convergence among the world's stock markets and investors experimentally show a passion for unde...

We use daily price indices obtained from the Morgan Stanley Capital International to construct realized volatility for 18 individual stock markets, including the US, and the world stock market. In contrast with the CAPM, we find that volatility by itself does not forecast excess returns in most countries; however, it becomes a significant predictor when combined with the US consumptionwealth ra...

Purpose of the paper The Basel Committee regulations require the estimation of Value-at-Risk at 99% confidence level for a 10-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real-estate portfolio returns. The purpose of the paper is to estimate accurate 10-day-ahead 99% VaR...

We use daily price indices obtained from the Morgan Stanley Capital International to construct realized volatility for 18 individual stock markets, including the US, and the world stock market. In contrast with the CAPM, we find that volatility by itself does not forecast excess returns in most countries; however, it becomes a significant predictor when combined with the US consumptionwealth ra...