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...

this paper employs a general non-linear analysis tool to analyse the nature of time series associated with the price (returns) of a particular company in tehran stock exchange. it is shown that the behavior of the process associated with the price (returns) time-series of this company is weakly chaotic, and due to the non-random behavior of the process, short term prediction of stock price is p...

The stock price e€ects for the domestic competitors of foreign acquisition targets in the US are found to be signi®cantly positive. These results imply that signals of favorable industry conditions conveyed through cross-border acquisitions dominate any perceived changes in competitive balance. Consistent with the information-signaling hypothesis, the stock price e€ects are more favorable for r...

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 aims to apply the quantile regression analysis to explore the impacts of the stock market trading value, change in international oil prices, and the US implementation of Quantitative easing monetary policy on Taiwan’s and Korea’s stock index returns. This study is in accordance with the 2008 US implementation of quantitative policy to conduct research on 53-month data collected from ...

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

In Taiwan stock market, it has been accumulated large amounts of time series stock data and successful investment strategies. The stock price, which is impacted by various factors, is the result of buyer-seller investment strategies. Since the stock price reflects numerous factors, its pattern can be described as the strategies of investors. In this paper, pattern recognition concept is adapted...

Accurate and effective stock price prediction is appealing for investors due to the potential of obtaining a very high return. However, it is still a challenging task in the modern business world because of the complex, evolutionary, and nonlinear nature of stock market. Therefore, we proposed two hybrid models, which are Harmony Search (HS) based Extreme Learning Machine (ELM) that is denoted ...

This paper is concerned with valuation of stock loans. The underlying stock price is assumed to follow a mean reverting uncertain differential equation driven by canonical Liu process in this paper. The price formulas of standard stock loan and capped stock loan are derived by using method of uncertain calculus within the framework of uncertainty theory.

Stock prices have the characteristics of nonlinearity, randomicity and uncertainty, so It is difficult to accurately depict the change rules of stock prices using traditional linear forecasting methods, which lead to low stock price prediction accuracy. In order to improve the stock price prediction precision , this paper proposed a stock price predicting model using SVM optimized by particle s...