MERAL Myanmar Education Research and Learning Portal
Item
{"_buckets": {"deposit": "34306115-7a08-4526-83be-5f84378745e8"}, "_deposit": {"id": "4074", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4074"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4074", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Big Data Analytics for Price Prediction", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Big Data Predictive Analytics isinfluenced in the financial market mainly instock exchange with its emergingtechnologies. Stock Market Prediction hasalways been one of the hottest topics inresearch, as well as a great challenge due toits complex and volatile nature. Stock orshare prices are considered to be verydynamic and quick changes because of theunderlying nature of financial domain.Therefore, there is a critical need inprediction approaches to be effective andefficient utilization of large amount ofmarket data (Big Data) to analyze futureprediction in stock price movement. In thispaper, a hybrid prediction model isproposed for predicting daily basis stockprice changes or movements. It is based onthe combination of historical stock pricedata and text mining techniques which takethe textual contents of Financial NewsWebsites that have highly impacts on pricemovement."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Big Data"}, {"interim": "predictive analytics"}, {"interim": "prediction model"}, {"interim": "stock price movement"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-03"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "20161.pdf", "filesize": [{"value": "98 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 98000.0, "url": {"url": "https://meral.edu.mm/record/4074/files/20161.pdf"}, "version_id": "ad7e81cf-adaf-46f9-9c3d-a549c1e735e4"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fourteenth International Conference On Computer Applications (ICCA 2016)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Khine, Kyi Lai Lai"}, {"subitem_authors_fullname": "Nyunt, Thi Thi Soe"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2016-02-25"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/178"}, "item_title": "Big Data Analytics for Price Prediction", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004074", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "4074", "relation": {}, "relation_version_is_last": true, "title": ["Big Data Analytics for Price Prediction"], "weko_shared_id": -1}
Big Data Analytics for Price Prediction
http://hdl.handle.net/20.500.12678/0000004074
http://hdl.handle.net/20.500.12678/0000004074329e6ae3-afd5-450a-baf5-3ebe734b8bb8
34306115-7a08-4526-83be-5f84378745e8
Name / File | License | Actions |
---|---|---|
20161.pdf (98 Kb)
|
|
Publication type | ||||||
---|---|---|---|---|---|---|
Article | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Big Data Analytics for Price Prediction | |||||
Language | en | |||||
Publication date | 2016-02-25 | |||||
Authors | ||||||
Khine, Kyi Lai Lai | ||||||
Nyunt, Thi Thi Soe | ||||||
Description | ||||||
Big Data Predictive Analytics isinfluenced in the financial market mainly instock exchange with its emergingtechnologies. Stock Market Prediction hasalways been one of the hottest topics inresearch, as well as a great challenge due toits complex and volatile nature. Stock orshare prices are considered to be verydynamic and quick changes because of theunderlying nature of financial domain.Therefore, there is a critical need inprediction approaches to be effective andefficient utilization of large amount ofmarket data (Big Data) to analyze futureprediction in stock price movement. In thispaper, a hybrid prediction model isproposed for predicting daily basis stockprice changes or movements. It is based onthe combination of historical stock pricedata and text mining techniques which takethe textual contents of Financial NewsWebsites that have highly impacts on pricemovement. | ||||||
Keywords | ||||||
Big Data, predictive analytics, prediction model, stock price movement | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/178 | |||||
Journal articles | ||||||
Fourteenth International Conference On Computer Applications (ICCA 2016) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |