We study auto-correlations and cross-correlations of daily price changes and daily volume changes of thirteen global stock market indices, using multifractal detrended fluctuation analysis (MF-DFA) and multifractal detrended cross-correlation analysis (MF-DXA). We find rather distinct multifractal behavior of price and volume changes. Our results indicate that the time series of price changes a...

We explore the relationship between price points and price rigidity using two datasets. One is a large weekly transaction price data set, covering 29 product categories over an eight-year period from a large Midwestern United States supermarket chain. The other comes from the Internet and includes daily prices over a two-year period for a variety of consumer electronic products with a wide rang...

In this paper we compare market price fluctuations with the response to fundamental price drops within the Lux-Marchesi model which is able to reproduce the most important stylized facts of real market data. Major differences can be observed between the decay of spontaneous fluctuations and of changes due to external perturbations reflecting the absence of detailed balance, i.e., of the validit...

herd behavior by investors in capital markets is a behavioral bias that can cause to undesirable effects such as bubble, crash and high fluctuation in stock price. this anomalies can disturb the equilibrium relations in stock market and lead to market inefficiency. herd behavior is a condition that investors with rational or in rational reasons ignore private information and imitate from others...

This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price f...