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Pooyan Nayyeri

Other affiliations: Ryerson University
Bio: Pooyan Nayyeri is an academic researcher from University of Tehran. The author has contributed to research in topics: Deep learning & AdaBoost. The author has an hindex of 3, co-authored 10 publications receiving 119 citations. Previous affiliations of Pooyan Nayyeri include Ryerson University.

Papers
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Journal ArticleDOI
TL;DR: Results show that for the continuous data, RNN and LSTM outperform other prediction models with a considerable difference, and results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models’ performance in the second way.
Abstract: The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Four stock market groups, namely diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange, are chosen for experimental evaluations. This study compares nine machine learning models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN)) and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing them. Firstly, calculating the indicators by stock trading values as continuous data, and secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on the input ways. The evaluation results indicate that for the continuous data, RNN and LSTM outperform other prediction models with a considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models' performance in the second way.

181 citations

Journal ArticleDOI
30 Jul 2020-Entropy
TL;DR: In this paper, the authors used decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM).
Abstract: The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

154 citations

Journal ArticleDOI
TL;DR: In this article, the authors focused on the future prediction of stock market groups and employed decision tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM).
Abstract: Prediction of stock groups' values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all the algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

30 citations

Posted ContentDOI
16 Mar 2020

12 citations


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Book ChapterDOI
13 Aug 2010
TL;DR: All rights reserved.
Abstract: All rights reserved. No part of the publication may be reproduced in any form by print, photoprint, microfilm or any other means without written permission from the publisher.

430 citations

Journal ArticleDOI
TL;DR: The practical findings confirm this claim and indicate that CNN alongside LSTM and CEEMD or EMD could enhance the prediction accuracy and outperform other counterparts and the suggested algorithm withCEEMD provides better performance compared to EMD.
Abstract: Nonlinearity and high volatility of financial time series have made it difficult to predict stock price. However, thanks to recent developments in deep learning and methods such as long short-term memory (LSTM) and convolutional neural network (CNN) models, significant improvements have been obtained in the analysis of this type of data. Further, empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD) algorithms decomposing time series to different frequency spectra are among the methods that could be effective in analyzing financial time series. Based on these theoretical frameworks, we propose novel hybrid algorithms, i.e., CEEMD-CNN-LSTM and EMD-CNN-LSTM, which could extract deep features and time sequences, which are finally applied to one-step-ahead prediction. The concept of the suggested algorithm is that when combining these models, some collaboration is established between them that could enhance the analytical power of the model. The practical findings confirm this claim and indicate that CNN alongside LSTM and CEEMD or EMD could enhance the prediction accuracy and outperform other counterparts. Further, the suggested algorithm with CEEMD provides better performance compared to EMD.

111 citations

Journal ArticleDOI
TL;DR: This study empirically proves that a successful prediction performance largely depends on a deliberate combination of feature engineering processes with a baseline learning model to make a good balance and harmony between the curse of dimensionality and the blessing ofdimensionality.
Abstract: The stock market has performed one of the most important functions in a laissez-faire economic system by gathering people, companies, and flows of money for several centuries. There have been numerous studies on the stock market among researchers to predict stock prices, and a growing number of studies employed machine learning or deep learning techniques on the stock market predictions with the advent of big data and the rapid development of artificial intelligence techniques. However, making accurate predictions of stock price direction remains difficult because stock prices are inherently complex, nonlinear, nonstationary, and sometimes too irrational to be predictable. Despite the wealth of information, previous prediction systems often overlooked key indicators and the importance of feature engineering. This study proposes a hybrid GA-XGBoost prediction system with an enhanced feature engineering process consisting of feature set expansion, data preparation, and optimal feature set selection using the hybrid GA-XGBoost algorithm. This study experimentally verifies the importance of feature engineering process in stock price direction prediction by comparing obtained feature sets to original dataset as well as improving prediction performance to outperform benchmark models. Specifically, the most significant accuracy increment comes from feature expansion that adds 67 technical indicators to the original historical stock price data. This study also produces a parsimonious optimal feature set using the GA-XGBoost algorithm that can achieve the desired performance with substantially fewer features. Consequently, this study empirically proves that a successful prediction performance largely depends on a deliberate combination of feature engineering processes with a baseline learning model to make a good balance and harmony between the curse of dimensionality and the blessing of dimensionality.

72 citations

Journal ArticleDOI
TL;DR: In this article , a review article presents important 4D technologies in conjunction with the underlying functionalities of stimuli-responsive polymer composites, and elucidates the future opportunities of 4D-printed SMPCs in terms of preprogramming knowledge, multi-way SMPC, multimaterial printing, sustainability, and potential applications.

66 citations