Forecasting model of global stock index by stochastic time effective neural network

By Zhengwen Liao, J. Wang. 2008

159 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Omer Berat Sezer
2. A. Ozbayoglu
3. Jie Wang
4. Jun Wang
5. Sung-Bae Cho
6. Chang-Seok Lee
7. Kee-Hoon Kim
8. Arash Bahrammirzaee

In Electrical and Electronic Engineering

1. A. Ozbayoglu
2. Omer Berat Sezer
3. Sung-Bae Cho
4. Jie Wang
5. Jun Wang
6. Arash Bahrammirzaee
7. Longbing Cao
8. Weiwei Jiang

In Management Science and Operations Research

1. Omer Berat Sezer
2. A. Ozbayoglu
3. Weiwei Jiang
4. Jie Wang
5. Jun Wang
6. Sung-Bae Cho
7. Arash Bahrammirzaee
8. M. Ugur Gudelek

Citations in the corpus

• Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, methodology): “ML approaches can also be used to analyze financial time-series data [28, 24, 14, 8].”
• Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks (2015, methodology): “Multilayer perceptron (MLP) is one of the most prevalent neural networks, which has the capability of complex mapping between inputs and outputs that makes it possible to approximate nonlinear function [19,20].”
• Predicting the success of bank telemarketing using deep convolutional neural network (2015, background): “Neural networks have self-learning and feature extracting abilities, a strong anti-jamming capability, hence had widely used for predicting stock prices, profits, exchange rates and risk analysis [4].”
• Financial time series forecasting with machine learning techniques: a survey (2010, unknown): “Day 31 [1], [2], [3], [4], [6], [7], [8], [9], [10], [13], [14], [17], [19], [20], [21], [22], [24], [27], [28], [31], [32], [33], [34], [35], [36], [37], [40], [41], [42], [44], [45] Week 3 [18], [23], [43] Month 3 [26], [38], [39] Multiple / Other 9 [5], [11], [12], [15], [16], [25], [29], [30],…”
• A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[104] Global stock index forecasting BP stochastic time effective NN with numerical experiment on the data of SAI, SBI, HSI, DJI, IXIC and SP500, and the validity of the volatility parameters of the Brownian motion ANN performs better Neural Comput & Applic (2010) 19:1165–1195 1171”