Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques

By Jigar Patel, Sahil R. Shah, Priyank Thakkar, Ketan Kotecha. Expert systems with applications, 2015

1,055 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/81be22bd4cf4e7ebb6ff5aa2a415e6b9aaf712fc

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In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Developing an approach to evaluate stocks by forecasting effective features with data mining methods (2015, methodology): “, 2011; Wang & Chan, 2006) and negative and positive return trend (Enke & Thawornwong, 2005) are used (see also (Patel et al., 2014; Yu et al., 2014; Zhang, Hu, Xie, Wang, et al., 2014)).”
Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020, methodology): “Details about the way of calculating indicators are presented here [26]–[28]: The average of values in a particular range computes SMA indicator, and this helps investors to decide if a price will go further in the same trend.”
CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, background): “Another study on the eﬀect of variables on the performance of prediction models has been reported in (Patel et al., 2015).”
A Double-Layer Neural Network Framework for High-Frequency Forecasting (2017, background): “Kara et al. [2011] compared single-layer NN with SVM in stock prediction and concluded that NN was better than SVM. Recently, Patel et al. [2015] compared four machine learning methods in stock prediction: Random forests, SVM, single-layer NN, and naive-Bayes, and suggested that Random Forests was t”
Restricted Boltzmann machine based stock market trend prediction (2017, methodology): “Inspired by work of Patel et al. [9], the trend deterministic data preparation are incorporated with RBM to improve the performance of RBM.”
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019, methodology): “, 2002) and was used in time series prediction (Patel et al., 2015).”
Deep Learning for Stock Market Prediction (2020, neutral): “The formula is shown in Equation (13) [50,51].”
Stock prediction using deep learning (2016, background): “Several mathematical models have been developed but the results are still dissatisfying [21].”