Asymmetric Influence Detection and Forecasting of Global Stock Markets Based on the Copula Theory

Correlation analysis of financial markets is an important starting point for modern financial theory of financial market risks. Along with the deepening of financial globalization, global financial markets have become more and more interdependent. Correlation analysis of global financial markets has become a hot issue for many scholars. On the basis of an in-depth study of Copula theory, this paper applies the theory to the asymmetric correlation analysis of the global major stock market indexes. First, asymmetric correlations among the selected stock indexes are modeled and detected using the relevant metrics of the Copula function on the logarithmic yield of stock indexes; The detected asymmetric correlations are put together to form a directed acyclic graph. Then, artificial neural networks (ANN) are used as a nonlinear model to predict the nearest future of the target stock index; the prediction accuracy is measured in terms of hit rate and mean square error. Test is done on historical daily data with the results showing that the Copula correlation coefficients are more informative for finding the influential leading markets for the predefined target market better than the traditional linear correlation coefficients. The hit rate of the ANN prediction using the detected leading markets found by Copula correlation coefficients is about 3% to 10% higher than that by the linear correlation coefficients

Nonparametric statistical inference via metric distribution function in metric spaces

The distribution function is essential in statistical inference and connected with samples to form a directed closed loop by the correspondence theorem in measure theory and the Glivenko-Cantelli and Donsker properties. This connection creates a paradigm for statistical inference. However, existing distribution functions are defined in Euclidean spaces and are no longer convenient to use in rapidly evolving data objects of complex nature. It is imperative to develop the concept of the distribution function in a more general space to meet emerging needs. Note that the linearity allows us to use hypercubes to define the distribution function in a Euclidean space. Still, without the linearity in a metric space, we must work with the metric to investigate the probability measure. We introduce a class of metric distribution functions through the metric only. We overcome this challenging step by proving the correspondence theorem and the Glivenko-Cantelli theorem for metric distribution functions in metric spaces, laying the foundation for conducting rational statistical inference for metric space-valued data. Then, we develop a homogeneity test and a mutual independence test for non-Euclidean random objects and present comprehensive empirical evidence to support the performance of our proposed methods. Supplementary materials for this article are available online

A computable theory for learning Bayesian networks based on MAP-MDL principles

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The Influence of the Reputation of Finance Intermediaries on IPO Underpricing in China Growth Enterprize Markets (GEM)

Growth Enterprize Markets (GEM) in China was established in October 30, 2009. The paper analyses the underpricing effects and IPO stock’s first month, first quarter return in GEM of China, researches nearly 150 listed company in GEM. Its finds weak reputation effects of finance intermediaries on IPO underpricing and periods returns. This shows the failure of information generation function and failure of certificate function of finance intermediaries in new emerging market in China.Key Words: Growth enterprize market; Underpricing; Reputation of finance intermediaries; Information generation; Information certificatio

Forecasting model for crude oil prices based on artificial neural networks

This paper presents short-term forecasting model for crude oil prices based on three layer feedforward neural network. Careful attention was paid on finding the optimal network structure. Moreover, a number of features were tested as an inputs such as crude oil futures prices, dollar index, gold spot price, heating oil spot price and S&P 500 index. The results show that with adequate network design and appropriate selection of the training inputs, feedforward networks are capable of forecasting noisy time series with high accuracy

Study of Characteristic and Period of Communication and Electronics Industry in Chinese Securities Market

Purpose: This study aims to analyze the characteristics of communication and electronics industry in Chinese stock market and calculate the average periods of it. Design/methodology/approach: We use R/S analysis method to study the characteristics of communication and electronics industry in Chinese stock market, and use Matlab software and Eviews software to calculate some representative exponents of this industry. Findings: The results show that the probability distribution of the communication and electronics industry in the Chinese stock market is nearly non-normal distribution, but a partial distribution, showing a peak, thick tail, migraine and other features. Hurst exponent calculated shows that the communication and electronics industry in the Chinese stock market has obvious fractal characteristics, and does not follow a random walk assumption, but follows persistent trend. The average big circulation period is about 400 days; the average small circulation period is about 200 days. Research limitations/implications: We use R/S analysis method to study the characteristics including periods and venture.of communication and electronics industry in Chinese stock market. Practical implications: The average periods and related venture can give investors properly suggestions. Originality/Value: We use R/S analysis method and Matlab software and Eviews software to analyze the characteristics of communication and electronics industry in Chinese stock market which has barely been studied. The results unfold the characteristics of this industry and can give investors properly suggestions as well.Key words: Stock market; Fractal; R/S analysis; Hurst exponent; Periods; Communication and electronic