2026-09-19T11:57:31Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/34447 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

2014-09-18T10:00:50Z 2012-08 2012-09-18T21:17:38Z 2012-08 Zhai, ChengXiang Zou, Jing 2012-09-18T21:17:38Z 2012-09-18T21:17:38Z News can influent the market. It has been proven that using text mining techniques, financial news can be used to predict market trend and volatility. In this thesis, we study some existing prediction algorithms that are based on Naive Bayes Classifier and Adjusted Document Frequency-Inverse Document Frequency(ADFIDF) weighting. Although occurrence of features selected by ADFIDF weighting can usually represent volatility bursts in financial market, it has been unclear whether it is also effective for market trend or trading volume. We conduct experiments that test the effectiveness of ADFIDF feature selection algorithm in finding correlation between news, market volume and trend. Our experiement result shows that a thin positive correlation exists between ADFIDF features occurrence and market volume. However, features occurrence and market trend are not directly correlated. We also propose a novel algorithm of finding correlation between news and financial market. The proposed algorithm is based on topic model and adjust TF-IDF weighting. It allows us to identify a few factors that could influence the performance of a prediction algorithm, such as number of topics of a model and adjustment. of IDF value. Our experiment results also show that grouping stocks together does not necessarily improve the performance of a prediction algorithm. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-07-19T14:37:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 report.tex: 41274 bytes, checksum: 8851c544ea1fdf034bd65b3c5485df3d (MD5) Zou_Jing.pdf: 208474 bytes, checksum: ec5a188f277dae29c7e1725a8927e698 (MD5) Made available in DSpace on 2012-09-18T21:17:38Z (GMT). No. of bitstreams: 3 Zou_Jing.pdf: 208463 bytes, checksum: c785b61d1cd71811489ca0d2a42952cc (MD5) report.tex: 41274 bytes, checksum: 14070d4642fd8a659e2652ea7e655d3c (MD5) license.txt: 4058 bytes, checksum: 3fbdc16675d8379ae93c61ea51e6ff6c (MD5) Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Seth Robbins (srobbins@illinois.edu) on 2012-09-18T21:21:04Z Item is restricted until 2014-09-18T21:21:01Z Restriction data tranferred 2014-07-01T11:11:20-05:00 Original Data Group with Access UIUC Users [automated] Release Date: 2014-09-18 16:21:01 UTC Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 34721 on 2014-09-18T10:00:50Z. http://hdl.handle.net/2142/34447 en Copyright 2012 Jing Zou News Adjusted Document Frequency-Inverse Document Frequency (ADFIDF) Market prediction Topic model News and financial market Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. MS:CS:BS/MS Program - UIUC 10KS1854MS