Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market

Nowadays, Machine Learning (ML) can serve as one of the solutions to accelerate the process of decision-making in forecasting daily stock market price movements. Nonetheless, inadequate number of research and lack of extensive data analysis using various ML models had limit the investors to apprecia...

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Main Author:	Hazirah Halul
Format:	Thesis
Language:	en_US
Subjects:	Machine Learning Walk-Forward Analysis traditional ML deep learning time series forecasting
Online Access:	https://oarep.usim.edu.my/bitstreams/eb59f9ae-5075-4f6c-a7dd-d106ab802454/download https://oarep.usim.edu.my/bitstreams/2d6c01cf-8b55-48ec-acde-534f6767cf37/download https://oarep.usim.edu.my/bitstreams/3d7f769a-33c8-43af-9756-9121ebd4c278/download https://oarep.usim.edu.my/bitstreams/4c37d170-ded2-4c43-a020-10f018d1ac20/download https://oarep.usim.edu.my/bitstreams/c60b5dc0-b39b-41a2-a7d3-c9e163c4db29/download https://oarep.usim.edu.my/bitstreams/0f616eca-b603-4d19-829e-ccb4123024be/download https://oarep.usim.edu.my/bitstreams/6c382b10-8d4a-4fac-bc6e-22d03d8fa4a0/download https://oarep.usim.edu.my/bitstreams/10cb59c0-ded0-44b3-9034-fd5e1725bfcb/download
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spelling	my-usim-ddms-123172024-05-29T19:54:32Z Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market Hazirah Halul Nowadays, Machine Learning (ML) can serve as one of the solutions to accelerate the process of decision-making in forecasting daily stock market price movements. Nonetheless, inadequate number of research and lack of extensive data analysis using various ML models had limit the investors to appreciate the efficiency and capability of these models. Previous studies usually concentrate on the forecasting stock index or selecting a few stocks with restricted features. Therefore, this study focused to contribute on evaluating different algorithm models such as traditional ML and deep learning models with big stock data of multiple parameters from selected companies in Bursa Malaysia. The three traditional ML selected includes Logistic Regression (LR), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), while another three deep learning models selected are Deep Belief Network (DBN), Multilayer Perception (MLP), and Stacked Auto-Encoder (SAE). By setting the ML algorithms and their parameter along with using Walk-Forward Analysis (WFA) method, the algorithm design of trading signal was evaluated based on two groups of evaluation indicators, namely directional and performance. Comparative analysis of evaluation indicators for all trading algorithms has been assessed and discussed. For stock trading in Malaysian stock market particularly, the experimental results of this study demonstrate that deep learning models have better performance in directional evaluation indicator compared to traditional ML in time series forecasting. However, traditional ML models are more efficient than deep learning in performance evaluation indicators in terms of profitability and risk assessment. Universiti Sains Islam Malaysia 2021 Thesis en_US https://oarep.usim.edu.my/handle/123456789/12317 https://oarep.usim.edu.my/bitstreams/4eafe61f-0141-4e91-a8ea-9aadc40abdc1/download 8a4605be74aa9ea9d79846c1fba20a33 https://oarep.usim.edu.my/bitstreams/eb59f9ae-5075-4f6c-a7dd-d106ab802454/download 776240ebb8cf95f08a98f6cf5af28ce2 https://oarep.usim.edu.my/bitstreams/2d6c01cf-8b55-48ec-acde-534f6767cf37/download cb0e726ab7a9fd6e77ed4aa720505943 https://oarep.usim.edu.my/bitstreams/3d7f769a-33c8-43af-9756-9121ebd4c278/download 3474d8881dcfb5e7bb23f3f18a649ec1 https://oarep.usim.edu.my/bitstreams/4c37d170-ded2-4c43-a020-10f018d1ac20/download b5b32b37138cda3e1e442b24f469183f https://oarep.usim.edu.my/bitstreams/c60b5dc0-b39b-41a2-a7d3-c9e163c4db29/download 0023e062122a371a128ef525007c385c https://oarep.usim.edu.my/bitstreams/0f616eca-b603-4d19-829e-ccb4123024be/download 4007f580179c82d3128ccbdf47625ba2 https://oarep.usim.edu.my/bitstreams/6c382b10-8d4a-4fac-bc6e-22d03d8fa4a0/download 43ab7f88df6ce2168ba35758c1c89411 https://oarep.usim.edu.my/bitstreams/10cb59c0-ded0-44b3-9034-fd5e1725bfcb/download 57d1ed3e4eb9b63e5217bb3965278e15 https://oarep.usim.edu.my/bitstreams/07560312-2645-4311-9d9a-57b377070d8b/download d7ee6719e6c29198087c9d205c2f3804 https://oarep.usim.edu.my/bitstreams/3c5182b2-2579-4d8d-be2d-e0449b00258c/download c8245e12710c908ebbace1b91f26565e https://oarep.usim.edu.my/bitstreams/d7cc37cd-5438-46e8-97e7-a6c75fb22331/download 0ada730e47ee2e36eae144f2e392f639 https://oarep.usim.edu.my/bitstreams/c548540b-14f2-4cf0-9d78-ce64de7afaac/download 809cb19ca046d0ea280a6a9d41b3b967 https://oarep.usim.edu.my/bitstreams/ca9f1f02-b1b0-480c-bf29-b93556daa8b3/download 4e3b3776a35c09e5d26cb6cb83f37eed https://oarep.usim.edu.my/bitstreams/8b236a83-82a3-4c83-9dd4-0d4a1026d4ba/download 5fc9a6480296c627b4714387d5ddf217 https://oarep.usim.edu.my/bitstreams/082dab57-52ed-4f02-8357-81db00931e64/download b319131c4e27ad68320193a22f7abbb0 https://oarep.usim.edu.my/bitstreams/e4534653-1264-425e-a5e2-d1445192983d/download e2f1068508aba0f5b84a05b218ae9c6e Machine Learning, Walk-Forward Analysis, traditional ML, deep learning, time series forecasting Computer science, Algorithms, Artificial intelligence
institution	Universiti Sains Islam Malaysia
collection	USIM Institutional Repository
language	en_US
topic	Machine Learning Walk-Forward Analysis traditional ML deep learning time series forecasting Machine Learning Walk-Forward Analysis traditional ML deep learning time series forecasting
spellingShingle	Machine Learning Walk-Forward Analysis traditional ML deep learning time series forecasting Machine Learning Walk-Forward Analysis traditional ML deep learning time series forecasting Hazirah Halul Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
description	Nowadays, Machine Learning (ML) can serve as one of the solutions to accelerate the process of decision-making in forecasting daily stock market price movements. Nonetheless, inadequate number of research and lack of extensive data analysis using various ML models had limit the investors to appreciate the efficiency and capability of these models. Previous studies usually concentrate on the forecasting stock index or selecting a few stocks with restricted features. Therefore, this study focused to contribute on evaluating different algorithm models such as traditional ML and deep learning models with big stock data of multiple parameters from selected companies in Bursa Malaysia. The three traditional ML selected includes Logistic Regression (LR), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), while another three deep learning models selected are Deep Belief Network (DBN), Multilayer Perception (MLP), and Stacked Auto-Encoder (SAE). By setting the ML algorithms and their parameter along with using Walk-Forward Analysis (WFA) method, the algorithm design of trading signal was evaluated based on two groups of evaluation indicators, namely directional and performance. Comparative analysis of evaluation indicators for all trading algorithms has been assessed and discussed. For stock trading in Malaysian stock market particularly, the experimental results of this study demonstrate that deep learning models have better performance in directional evaluation indicator compared to traditional ML in time series forecasting. However, traditional ML models are more efficient than deep learning in performance evaluation indicators in terms of profitability and risk assessment.
format	Thesis
author	Hazirah Halul
author_facet	Hazirah Halul
author_sort	Hazirah Halul
title	Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
title_short	Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
title_full	Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
title_fullStr	Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
title_full_unstemmed	Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
title_sort	analytical study of machine learning models for stock trading in malaysian market
granting_institution	Universiti Sains Islam Malaysia
url	https://oarep.usim.edu.my/bitstreams/eb59f9ae-5075-4f6c-a7dd-d106ab802454/download https://oarep.usim.edu.my/bitstreams/2d6c01cf-8b55-48ec-acde-534f6767cf37/download https://oarep.usim.edu.my/bitstreams/3d7f769a-33c8-43af-9756-9121ebd4c278/download https://oarep.usim.edu.my/bitstreams/4c37d170-ded2-4c43-a020-10f018d1ac20/download https://oarep.usim.edu.my/bitstreams/c60b5dc0-b39b-41a2-a7d3-c9e163c4db29/download https://oarep.usim.edu.my/bitstreams/0f616eca-b603-4d19-829e-ccb4123024be/download https://oarep.usim.edu.my/bitstreams/6c382b10-8d4a-4fac-bc6e-22d03d8fa4a0/download https://oarep.usim.edu.my/bitstreams/10cb59c0-ded0-44b3-9034-fd5e1725bfcb/download
_version_	1812444835593846784

Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market

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