Selective presentation learning for neural network forecasting of stock markets

Kohara, Kazuhiro; Fukuhara, Yoshimi; Nakamura, Yukihiro

doi:10.1007/bf01414874

Cited by 17 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We trained the four neural network algorithms by varying the structure of the network; that is by changing the number of hidden layers as well as the number of neurons per hidden layer. The best four prediction results corresponding to the four networks were obtained when the number of hidden layers equal to one is and, the number of neurons per hidden layer is equal to two results are shown in Tables 12,13,14,15 . Therefore, only the results relevant to networks with two hidden neurons are presented in this section.…”

Section: Results Obtained From Network Trainingmentioning

confidence: 99%

Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

Tilakaratne

Mammadov

Morris

2009

Journal of Applied Mathematics and Decision Sciences

View full text Add to dashboard Cite

The aim of this paper is to present modified neural network algorithms to predict whether it is best to buy, hold, or sell shares (trading signals) of stock market indices. Most commonly used classification techniques are not successful in predicting trading signals when the distribution of the actual trading signals, among these three classes, is imbalanced. The modified network algorithms are based on the structure of feedforward neural networks and a modified Ordinary Least Squares (OLSs) error function. An adjustment relating to the contribution from the historical data used for training the networks and penalisation of incorrectly classified trading signals were accounted for, when modifying the OLS function. A global optimization algorithm was employed to train these networks. These algorithms were employed to predict the trading signals of the Australian All Ordinary Index. The algorithms with the modified error functions introduced by this study produced better predictions.

show abstract

Section: Results Obtained From Network Trainingmentioning

confidence: 99%

Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

Tilakaratne

Mammadov

Morris

2009

Journal of Applied Mathematics and Decision Sciences

View full text Add to dashboard Cite

show abstract

“…Neural network learning [7] is a highly complex nonlinear artificial intelligence system. It is a supervised learning model that simulates the memory of the human brain.…”

Section: Introductionmentioning

confidence: 99%

An improved long short-term memory neural network for stock forecast

Kong

et al. 2018

MATEC Web Conf.

View full text Add to dashboard Cite

This paper presents an improved long short-term memory (LSTM) neural network based on particle swarm optimization (PSO), which is applied to predict the closing price of the stock. PSO is introduced to optimize the weights of the LSTM neural network, which reduces the prediction error. After preprocessing the historical data of the stock, including opening price, closing price, highest price, lowest price, and daily volume these five attributes, we train the LSTM by employing time series of the historical data. Finally, we apply the proposed LSTM to predict the closing price of the stock in the last two years. Compared with typical algorithms by simulation, we find the LSTM has better performance in reliability and adaptability, and the improved PSO-LSTM algorithm has better accuracy.

show abstract

“…Abdelaziz et al (2014);Adebiyi et al (2014);Baek and Cho (2001);;Bildirici and Ersin (2009); Chung and Shin (2020); de Faria et al (2009); De Oliveira et al (2013); Fernández-Rodríguez et al (2000); Fong et al (2005); Ghasemiyeh et al (2017); Göçken et al (2016); Guresen et al (2011); Halliday (2004); Inthachot et al (2016);Kara et al (2011);Kim and Shin (2007);Kohara et al (1996);Laboissiere et al (2015);Liu et al (2020);Safi and White (2017);Sagir and Sathasivan (2017);Wu et al (2021) …”

mentioning

confidence: 99%

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

Chopra

Sharma

2021

JRFM

View full text Add to dashboard Cite

The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study; and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship.

show abstract

Selective presentation learning for neural network forecasting of stock markets

Abstract: This paper proposes a selective presentation learning

Cited by 17 publications

References 11 publications

Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

Modified Neural Network Algorithms for Predicting Trading Signals of Stock Market Indices

An improved long short-term memory neural network for stock forecast

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

Contact Info

Product

Resources

About