Stock Market Forecasting Algorithm Based on Improved Neural Network

2016 ◽  
Author(s):  
Bihui Luo ◽  
Yuan Chen ◽  
Weichen Jiang
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Market Forecasting

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.

An investigational analysis on forecasting intraday values

2019 ◽  
Vol 27 (2) ◽  
pp. 592-605
Author(s):  
Jeevananthan Manickavasagam ◽  
Visalakshmi S.
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Future Price ◽  
Support Vector ◽  
Content Type ◽  
Stock Market Forecasting ◽  
Statistical Measures ◽  
Stock Indices ◽  
Long Run ◽  
Using Data

Purpose The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results. Design/methodology/approach This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data. Findings MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models. Practical implications Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market. Originality/value This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.

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