research-article

Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models

Authors:

Ching-Seh Mike Wu,

Katerina Potika,

Marjan OrangAuthors Info & Claims

ICSIM '20: Proceedings of the 3rd International Conference on Software Engineering and Information Management

Pages 85 - 92

https://doi.org/10.1145/3378936.3378972

Published: 07 March 2020 Publication History

Abstract

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random guess, they consider nothing but a proper selection of stocks and time interval in the experiments. In this paper, a novel approach is proposed using graph theory. This approach leverages Spatio-temporal relationship information between different stocks by modeling the stock market as a complex network. This graph-based approach is used along with two techniques to create two hybrid models. Two different types of graphs are constructed, one from the correlation of the historical stock prices and the other is a causation-based graph constructed from the financial news mention of that stock over a period. The first hybrid model leverages deep learning convolutional neural networks and the second model leverages a traditional machine learning approach. These models are compared along with other statistical models and the advantages and disadvantages of graph-based models are discussed. Our experiments conclude that both graph-based approaches perform better than the traditional approaches since they leverage structural information while building the prediction model.

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Cited By

Chang VXu QChidozie AWang H(2024)Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning TechniquesElectronics10.3390/electronics1317339613:17(3396)Online publication date: 26-Aug-2024
https://doi.org/10.3390/electronics13173396
Khandelwal VVarshney HMunjal G(2024)Sentiment Analysis based Stock Price Prediction using Machine Learning2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT)10.1109/InCACCT61598.2024.10551038(182-187)Online publication date: 2-May-2024
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Raut RSaratkar SThakre GThute TChaudhari AGourshettiwar P(2024)A Review of Social Media Sentiment Analysis Using Machine Learning To Enhance Stock Market Prediction2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)10.1109/IDICAIEI61867.2024.10842801(1-6)Online publication date: 29-Nov-2024
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Index Terms

Stock Market Prediction Using Ensemble of Graph Theory, Machine Learning and Deep Learning Models
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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ICSIM '20: Proceedings of the 3rd International Conference on Software Engineering and Information Management

January 2020

258 pages

ISBN:9781450376907

DOI:10.1145/3378936

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Science and Technology of China: University of Science and Technology of China

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Published: 07 March 2020

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ICSIM '20: The 3rd International Conference on Software Engineering and Information Management

January 12 - 15, 2020

NSW, Sydney, Australia

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Cited By

Chang VXu QChidozie AWang H(2024)Predicting Economic Trends and Stock Market Prices with Deep Learning and Advanced Machine Learning TechniquesElectronics10.3390/electronics1317339613:17(3396)Online publication date: 26-Aug-2024
https://doi.org/10.3390/electronics13173396
Khandelwal VVarshney HMunjal G(2024)Sentiment Analysis based Stock Price Prediction using Machine Learning2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT)10.1109/InCACCT61598.2024.10551038(182-187)Online publication date: 2-May-2024
https://doi.org/10.1109/InCACCT61598.2024.10551038
Raut RSaratkar SThakre GThute TChaudhari AGourshettiwar P(2024)A Review of Social Media Sentiment Analysis Using Machine Learning To Enhance Stock Market Prediction2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI)10.1109/IDICAIEI61867.2024.10842801(1-6)Online publication date: 29-Nov-2024
https://doi.org/10.1109/IDICAIEI61867.2024.10842801
Liu MWang XLi YWang J(2024)Research on the Construction of Efficient Application System of Financial Market Prediction Model Based on Deep Learning2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)10.1109/ICEDCS64328.2024.00192(1045-1050)Online publication date: 23-Sep-2024
https://doi.org/10.1109/ICEDCS64328.2024.00192
Kulkarni NPotikas PPotika K(2024)Learning to Play the Trading Game: Exploring Reinforcement Learning-Based Stock Trading Bots2024 IEEE 10th International Conference on Big Data Computing Service and Machine Learning Applications (BigDataService)10.1109/BigDataService62917.2024.00011(27-34)Online publication date: 15-Jul-2024
https://doi.org/10.1109/BigDataService62917.2024.00011
Ansari Y(2024)Multi-Cluster Graph (MCG): A Novel Clustering-Based Multi-Relation Graph Neural Networks for Stock Price ForecastingIEEE Access10.1109/ACCESS.2024.347615912(154482-154502)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3476159
Garcia RFerreira JZanotti LRamachandran GEstrella JUeyama J(2024)An automated decision-making system employing complex networks and blockchain for the decentralized stock marketExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125131257:COnline publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.125131
Bodaghi AOliveira J(2024)A financial anomaly prediction approach using semantic space of news flow on twitterDecision Analytics Journal10.1016/j.dajour.2024.10042210(100422)Online publication date: Mar-2024
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Yilmaz MKeskin MOzbayoglu A(2024)Algorithmic stock trading based on ensemble deep neural networks trained with time graphApplied Soft Computing10.1016/j.asoc.2024.111847163(111847)Online publication date: Sep-2024
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Ahmed WAshraf TAlMutairi DZaman SAhmed SEhsan H(2024)A Deep Dive into Machine Learning: The Roles of Neural Networks and Random Forests in QSPR AnalysisBioNanoScience10.1007/s12668-024-01710-815:1Online publication date: 10-Dec-2024
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