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...

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...

In this paper we use a nonlinear processing technique based on mathematical morphology to develop a simple day trading system that automatically decides the timing to commute the marked strategy in terms of sort/long positions. In this short paper we show preliminary results.

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFE...

In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of auto regressive moving average (ARMA) (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state’s change...

We propose improved methods to identify stock groups using the correlation matrix of stock price changes. By filtering out the market-wide effect and the random noise, we construct the correlation matrix of stock groups in which nontrivial high correlations between stocks are found. Using the filtered correlation matrix, we successfully identify the multiple stock groups without any extra knowl...

We propose a model for the aggregate stock market together with its dividend yield and earnings yield so that the ex-ante risk premium could be extracted in an unobserved component modelling framework. We posit the model as a linked stochastic differential equation system and the linking variable is the ex-ante risk premium. By hypothesising a realistic dynamic structure for the ex-ante risk pr...

In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of t...