Surveying the best volatility measurements in stock market forecasting techniques involving small size companies in Bursa Malaysia

2012 ◽  
Author(s):  
S. N. Z. Abidin ◽  
M. M. Jaafar
Keyword(s):  
Stock Market ◽  
Stock Market Forecasting ◽  
Forecasting Techniques

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

A simulation model for evaluating the effectiveness of various stock market strategies

SIMULATION ◽  
1969 ◽  
Vol 13 (6) ◽  
pp. 299-305 ◽  
Author(s):  
P. Krolak ◽  
R. Berquist ◽  
R. Conn ◽  
H. Gilliland
Keyword(s):  
Stock Market ◽  
Simulation Model ◽  
Review Of The Literature ◽  
Preliminary Results ◽  
Stock Market Forecasting ◽  
Market Strategies

This paper develops a simulation model which can be used to investigate a wide variety of stock market invest ment strategies. A brief review of the literature of stock market forecasting is given. The paper describes the de tails that any simulation of a stock market investor would have to include if the model is to be realistically com pared to the performance of real investors. An outline of the necessary features of any program which is to be used to investigate may different combinations of invest ment strategies and forecasting devices is also given. The program is described in detail and a few preliminary results are given.

scholarly journals Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 13099-13111
Author(s):  
Khaled A. Althelaya ◽  
Salahadin A. Mohammed ◽  
El-Sayed M. El-Alfy
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Multiresolution Analysis ◽  
Stock Market Forecasting
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