Using artificial neural network models in stock market index prediction

By Erkam Güresen, Gülgün Kayakutlu, Tugrul U. Daim. 2011

781 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research, Time-Series & Forecasting

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. Saman Haratizadeh
4. Ehsan Hoseinzade
5. Chih-Feng Liu
6. Chi-Yuan Yeh
7. Shie-Jue Lee
8. Michal Tkác

In Economics and Econometrics

1. Omer Berat Sezer
2. Saman Haratizadeh
3. Ehsan Hoseinzade
4. A. Ozbayoglu
5. Michal Tkác
6. Henry Leung
7. Chi-Yuan Yeh
8. Robert Verner

In Management Science and Operations Research

1. A. Ozbayoglu
2. Omer Berat Sezer
3. Weiwei Jiang
4. M. Ugur Gudelek
5. Saman Haratizadeh
6. Ehsan Hoseinzade
7. Chih-Feng Liu
8. Chi-Yuan Yeh

In Time-Series & Forecasting

1. Omer Berat Sezer
2. A. Ozbayoglu
3. Weiwei Jiang
4. M. Ugur Gudelek
5. Gopalakrishnan E.A.
6. Michal Tkác
7. Chi-Yuan Yeh
8. Robert Verner

Citations in the corpus

• Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks (2019, background): “Guresen et al. [11] used dynamic artificial neural networks and multi-layer perceptron (MLP) to forecast NASDAQ stock index.”
• NSE Stock Market Prediction Using Deep-Learning Models (2018, background): “LONG SHORT TERM MEMORY LSTM [19] is a special type of RNN.”
• A Review of Deep Learning Models for Time Series Prediction (2019, methodology): “The training process for RNNs is a well-known strategy named as Backpropagation-ThroughTime (BPTT) [58]–[62].”
• Neural networks performance in exchange rate prediction (2016, background): “The largest number of NN applications in finance is for exchange rate [4], stock price [3] and sales [5] predictions.”
• Fintech Model: The Random Neural Network with Genetic Algorithm (2018, background): “[13] evaluates the effectiveness of neural network models in stock market predictions.”
• Artificial neural networks in business: Two decades of research (2016, background): “Several approaches have also been suggested to improve the performance of neural networks in stock markets prediction, including normalization of data, use of hyperbolic tangent instead of logistic activation function, addition of hidden neurons and hidden layers, or integration with metaheuristic m”
• Financial Time Series Prediction Based on Deep Learning (2017, background): “In recent years, scholars have made a lot of meaningful explorations in financial time series prediction based on different machine learning models such as support vector machine (SVM) [4], BP neural network [5].”
• Expert Systems With Applications (positive): “Stock market prediction is of great importance since, as Guresen, Kayakutlu, and Daim (2011) note, better prediction is always crucial for making better economic decisions.”