A new procedure in stock market forecasting based on fuzzy random auto-regression time series model

2018 ◽  
Vol 441 ◽  
pp. 113-132 ◽  
Author(s):  
Riswan Efendi ◽  
Nureize Arbaiy ◽  
Mustafa Mat Deris
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Model ◽  
Stock Market Forecasting ◽  
Auto Regression ◽  
Fuzzy Random

scholarly journals Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Wuyang Cheng ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Statistical Physics ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Contact Process ◽  
Composite Index ◽  
Return Time ◽  
Time Series Model ◽  
Financial Time

We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI) and Hang Seng Index (HSI) are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.

scholarly journals Stock Market Prediction

2020 ◽  
Vol 9 (9) ◽  
pp. 506-510
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Regression Model ◽  
Current System ◽  
Efficient Algorithms ◽  
Time Series Model ◽  
Stock Market Prediction ◽  
Preferred Stock ◽  
Regression Algorithms ◽  
Future Value

The Stock Market is a challenging forum for investment and requires immense brainstorming before one shall put their hard earned money to work. This project aims at processing large volumes of data and running comprehensive regression algorithms on the dataset; that will predict the future value of a stock using the regression model with the highest accuracy. The purpose of this paper is to analyze the shortcomings of the current system and building a time-series model that would mitigate most of them by implementing more efficient algorithms. Using this model, anyone can monitor the preferred stock that they want to invest in; and maximize profit by purchasing volume at the lowest price and liquidating the stock when it’s at its highest.

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