| { | |
| "paper_id": "O07-2007", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:07:54.299168Z" | |
| }, | |
| "title": "Predicting Trends of Stock Prices with Text Classification Techniques", | |
| "authors": [ | |
| { | |
| "first": "Jiun-Da", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Aletheia University National Chengchi University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tai-Ping", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Aletheia University National Chengchi University", | |
| "location": {} | |
| }, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "\uf9c7 \uf9f3 Chao-Lin", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Aletheia University National Chengchi University", | |
| "location": {} | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Stocks' closing price levels can provide hints about investors' aggregate demands and aggregate supplies in the stock trading markets. If the level of a stock's closing price is higher than its previous closing price, it indicates that the aggregate demand is stronger than the aggregate supply in this trading day. Otherwise, the aggregate demand is weaker than the aggregate supply. It would be profitable if we can predict the individual stock's closing price level. For example, in case that one stock's current price is lower than its previous closing price. We can do the proper strategies(buy or sell) to gain profit if we can predict the stock's closing price level correctly in advance. In this paper, we propose and evaluate three models for predicting individual stock's closing price in the Taiwan stock market. These models include a na\u00efve Bayes model, a k-nearest neighbors model, and a hybrid model. Experimental results show the proposed methods perform better than the NewsCATS system for the \"UP\" and \"DOWN\" categories.", | |
| "pdf_parse": { | |
| "paper_id": "O07-2007", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "Stocks' closing price levels can provide hints about investors' aggregate demands and aggregate supplies in the stock trading markets. If the level of a stock's closing price is higher than its previous closing price, it indicates that the aggregate demand is stronger than the aggregate supply in this trading day. Otherwise, the aggregate demand is weaker than the aggregate supply. It would be profitable if we can predict the individual stock's closing price level. For example, in case that one stock's current price is lower than its previous closing price. We can do the proper strategies(buy or sell) to gain profit if we can predict the stock's closing price level correctly in advance. In this paper, we propose and evaluate three models for predicting individual stock's closing price in the Taiwan stock market. These models include a na\u00efve Bayes model, a k-nearest neighbors model, and a hybrid model. Experimental results show the proposed methods perform better than the NewsCATS system for the \"UP\" and \"DOWN\" categories.", | |
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| "section": "Abstract", | |
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| "text": "\ufa08 \uf9d0 \uf976 NewsCATS \uf976 \uf976\uf901 \uf961 0% NewsCATS \uf961 0.11% Mittermayer NewsCATS \uf9d0 \uf901 \uf92d \ufa08 \uf9e4 \uf99c\uf978 [9] (Dictionary-Based Approach) (Statistical Approach) \uf9f7 \ufa08 \uf97e \uf96b\uf969 \uf96b\uf969 \uf96b\uf969 \uf961 \uf961 \uf92d \uf9e4 \uf9fc \uf92d \ufa08 \uf906 \uf9e4 \uf96f \uf9e0 k \uf9d0 \uf9d0 \uf92d \uf9f7 \uf9e0 \ufa08 \uf96b \uf9b5\uf92d \uf96f \uf974 \uf9e0 \uf974 \uf9e0 \uf974 \uf9dd 1 \uf967 \uf9d0 \uf92d \uf9f7 \uf9be \uf9e4 \uf92d \ufa08 \uf9d0 \uf92d \uf9be \uf9d0 \uf9d0 \ufa08 \uf9be \uf996 \uf9be \uf9be\uf978 \uf9d0 \uf9dd \uf996 \uf9be\uf92d \uf996 \uf9d0 \uf9be \uf92d \uf9d0 \uf9be \uf9d0 \uf92d \ufa08 \uf9be \uf9be \uf9be \uf9be \uf996 \uf9be \uf996 \uf9d0 \uf9d0 1. \uf9d0 \uf9d0(classification)[15][19] \uf9d0 \uf9d0 \uf9be \uf967 \uf92d \uf9be \uf9d0 \uf9d0 \uf996 \uf9be\uf92d \uf9d0 \uf9d0 \uf996 \uf965 \uf9d0 \uf9be \ufa08 \uf9d0 \uf9f7 \uf9be \uf9d0 \uf9d0 \uf967 \uf9d0 \uf9d0 \uf9e0 (", | |
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| "text": "1 \uf9e0 \uf9e0 \uf9e4 \uf961 \uf92d \uf9d0 \uf9be \uf9d0 \uf961 \uf9d0 \uf9e0 \uf996 \uf9be \uf92d \uf9f7 \uf9d0 \uf961 \uf9be \uf9d0 \uf961 \uf961 \uf9d0 \uf9be \uf9d0 \uf96f \uf9d0 \uf9d0 { k C C C , \u2026 , , 2 1 } \uf961 \u2211 = = k i i C x P x P 1 ) ( ) ( I \uf961 \uf9d0 \uf961 ) ( ) ( ) | ( x P x C P x C P i i I = \uf961 \uf9d0 \uf961 ) ( ) | ( ) ( ) ( ) ( ) | ( x P C x P C P x P x C P x C P i i i i \u2022 = = I \uf9f7 \uf9d0 ) , \u2026 , , ( 2 1 d x x x \uf961 \u220f = = d j i j C x P x x P 1 i d 2 1 ) | ( ) C | x , \u2026 , , ( \uf9d0 \uf9e0 \uf9d0 \u2211 \u220f \u220f = = = \u239f \u239f \u23a0 \u239e \u239c \u239c \u239d \u239b \u2022 \u2022 = k i d j i j i d j i j i d i C x P C P C x P C P x x x C P 1 1 1 2 1 ) | ( ) ( ) | ( ) ( ) , \u2026 , , | ( \uf9ea\ufa01\uf97e \ufa01 \uf967 \ufa01\uf97e \uf967 \uf9d0 \ufa01 \uf92d \uf92d \uf967 \uf901 \uf92d \uf9d0 \uf9a3\uf92d \uf9d0 \uf97e \uf9d0 \uf97e \uf92d \uf9be \uf9d0 \uf967 ) | ( ) ( ) , ( a Ex H Ex H a Ex IG \u2212 = \uf9e0 \uf9be \ufa08 \uf9e4 \uf92d \uf9be \ufa08 \uf92d \uf969\uf97e\uf92d \uf962 \uf969\uf97e \uf92d \uf997 \uf9be \uf9b5 \uf997 \uf9be\uf92d \uf9be \uf9be \uf98e \uf98e \uf9be\uf92d \uf9be \uf9be \uf9e4 \uf969\uf97e \uf962\uf967 \uf997 \uf9be \uf9be \uf978 \uf9f7 \uf974 \uf9d0 \uf969 \uf9d0 \uf9d0 \uf9d0 \uf996 \uf9be \uf9d0 \uf961 \uf9d0 \uf961 \uf9d0 \uf9b2 \uf961 \uf961 \uf961 \uf9b2 \uf941 \uf961 \uf961 \uf9b2 \uf969 \uf969 1 \uf92d \uf9b2 \uf961 \uf9d0 \uf9be \uf92d \uf9d0 \uf961 \uf9d0 \uf9d0 \uf961 \uf9d0 2 k \uf9d0 \uf9d0 \uf9d0 \uf9d0 \uf974 \uf97e \uf92d \uf9d0 \uf9ea \uf9d0 \uf9be \uf996 \uf9be \uf9be \uf9d0 \uf92d \uf9d0 \uf9be \uf9d0 \uf9d0 \uf969 \uf974 \uf92d \uf9d0 \uf967 \ufa0a \uf9be \uf9d0 \uf92d \ufa08 \uf92d \uf9d0 \uf9d0 \uf92d \uf9d0 \uf9be \uf9d0 \uf92d \uf996 \uf9be \uf9be \uf9ea \uf9d0 \uf9d0 \uf969 \uf9d0 \uf9be \uf9d0 \uf969 \uf9be \uf9d0 \uf9d0 \uf969 \uf9d0 \uf9ea \uf9e9 \uf9ea \uf97e \uf978 ) ,... , ( 2 1 n p p p P = ) ,..., , ( 2 1 n q q q Q = \uf9e9 \uf9ea \u2211 = \u2212 = n i i i Euclidean q p D 1 2 ) ( \uf9be \ufa08 \uf9e4 \uf9f7 \uf97e \uf97e \uf9d0 \uf9be \uf996 \uf9be \uf9ea \uf9be \uf969 \uf9d0 \uf9be \uf9d0 3 \uf9e0 k \uf9be \uf9d0 \uf92d \ufa08 \uf9d0 \uf96f \uf9be \uf92d \uf9d0 \uf9be \uf9e0 \uf9d0 \uf961 \uf961 C i C j \uf9d0 \uf961 \uf9d0 \uf978 \uf961 \uf9b5 \u2206p \u2206p ( 8) } ,..., , { , ) ( ) ( ) ( 2 1 k j i i j i C C C C C C P C P C P P \u2208 \u2212 = \u2206 \u2026\u2026\u2026\u2026\u2026\u2026\u2026( 8) \u2206p \uf9e0 \uf961 \uf9d0 (C i ) \uf961 \uf9d0 (C j ) \uf9be \uf967 \uf96f \uf9be \uf9d0 \uf967 \uf9d0 C i \uf9be C j \uf9d0 \uf961 \uf967 \u2206p k \uf92d \uf9be \ufa08 \uf9d0 \u2206p \uf9e0 \uf92d \uf9be \ufa08 \uf9d0 \ufa0a 9 \u23a9 \u23a8 \u23a7 \u2264 \u2206 > \u2206 = \uf974 \uf9e0 \uf974 k P P \u03b5 \u03b5 \u2026\u2026\u2026\u2026\u2026( 9) \uf9e0 \uf9d0 \uf961 \uf9d0 \uf9d0 \uf961 \uf9d0 \uf961 \uf9d0 \uf9be\uf92d \uf9be\uf92d \uf9d0 \uf9d0 \uf9d0 \uf92d \ufa08 \uf9be \uf9be \uf9be (\uf997 \uf9be \uf9be \uf9be \uf9f7 \uf9be ) 747 \uf9be \uf9be\uf92d Yahoo! \uf9be [1] \uf9be 95 \uf98e 9 96 \uf98e 4 \uf9be\uf92d \uf9e0 \uf9be [4][10] \uf967 \uf9be \uf92d \uf9e0 k \uf967 \uf9be \uf967 \uf962 \ufa01 (3-fold cross-validation)[20]\uf92d \uf9be \ufa00 \uf9be \uf996 \uf9be \uf9be \uf9be \uf9be \uf961 \uf961 \uf996 \uf9be\uf9d7\uf9ca \ufa08 3 \uf9be \uf9be \uf969 \uf961(Precision) \uf961(Recall) F-measure \uf92d [19] TP \uf9d0 \uf9d0 \uf9d0 \uf969 FP \uf9d0 \uf9d0 \uf9d0 \uf969 TN \uf9d0 \uf9d0 \uf9d0 \uf969 FN \uf9d0 \uf9d0 \uf9d0 \uf969 \uf961 \uf9d0 \uf9d0 FP TP TP Precision \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026( 10) \uf961 \uf9d0 \uf9d0 FN TP TP Recall \u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026( 11) F-measure \uf961 \uf961\uf978 \ufa0a 12 \u03b1 \uf92d F-measure \uf961 \uf961 \ufa01 \uf96b\uf969 F-measure \u03b1 1 \uf961 \uf961 F-measure \uf98a \ufa01 0 Recall Precision Recall Precision ) 1 ( measure - F \u2265 + \u2022 \u2022 \u2022 + = \u03b1 \u03b1 \u03b1 \u03b1 \u2026\u2026\u2026\u2026\u2026\u2026( 12) \uf9d0 \uf9d0 \" \" \" \" \uf92d \uf9ea \uf9d0 \uf92d \uf9d0 \uf962 \ufa01 [5] \uf9d0 \uf9d0 \" \" \uf9d0 \" \" \uf9d0 \" \" \uf9d0 \uf961 \uf961 F-measure \uf92d \uf97e \ufa08 \uf96b 13 14 15 } \" \" , \" \" , \" {\" 3 Precision 3 1 \uf9d0 \uf9d0 \uf9d0 \ufa1d \uf961 = = \u2211 = i i i \u2026\u2026\u2026\u2026( 13) } \" \" , \" \" , \" {\" 3 Recall 3 1 \uf9d0 \uf9d0 \uf9d0 \uf961 = = \u2211 = i i i \u2026\u2026\u2026\u2026\u2026( 14) } \" \" , \" \" , \" {\" 3 measure - F 3 1 measure - F \uf9d0 \uf9d0 \uf9d0 = = \u2211 = i i i \u2026\u2026( 15) \uf9d0 \" \" \" \" \uf9d0 \" \" \uf9d0 \" \" \uf978 \uf9d0 \uf961 \uf961 F-measure \uf92d \uf97e \uf9dd \uf9d0 \" \" \uf9d0 \" \" \uf978 \uf9d0 \uf9d0 \" \" \uf9d0 \" \" \uf978 \uf9d0 \uf9ba \ufa08 \uf901 \uf9dd 16 17 18 } { 2 Precision 2 1 \uf9d0 \uf9d0 \uf9d0 \ufa1d \uf961 = = \u2211 = i i i \u2026\u2026\u2026( 16) } { 2 Recall 2 1 \uf9d0 \uf9d0 \uf9d0 \uf961 = = \u2211 = i i i \u2026\u2026\u2026\u2026( 17) } { 2 measure - F 2 1 measure - F \uf9d0 \uf9d0 \uf9d0 = = \u2211 = i i i \u2026( 18) \uf9d0 \uf92d \uf97e \uf9d0 \uf962 \ufa01 \uf9d0 \uf9d0 \ufa08 \uf96b \uf974\uf9d0 \uf9d0 \uf918 \uf9e0 \uf9d0 \uf967 \ufa08 \uf96b \uf9d0 \uf9d0 \uf9d0 \uf9d0 \uf9d0 19 20 21 } \" \" , \" \" , \" {\" 3 ) Precision Precision ( 3 1 = i i Precision \uf9d0 \uf9d0 \uf9d0 \uf9d0 = = \u2211 i \u2026\u2026( 19) } \" \" , \" \" , \" {\" 3 ) Recall Recall ( 3 1 = i i Recall \uf9d0 \uf9d0 \uf9d0 \uf9d0 = = \u2211 i \u2026\u2026( 20) } \" \" , \" \" , \" {\" 3 ) measure - F measure - F ( 3 1 = i i measure - F \uf9d0 \uf9d0 \uf9d0 \uf9d0 = = \u2211 i \u2026\u2026( 21) NewsCATS \uf92d \ufa08 \ufa08 Mittermayer Mittermayer NewsCATS NewsCATS \uf962 \uf9d0 \" \" \uf961 98% \uf9d0 \" \" \" \" \uf961 5% 6% \uf9d0 \" \" NewsCATS \uf9d0 \" \" \uf92d \uf96b \uf901 \uf9dd \uf9d0 \" \" \uf9d0 \" \" \uf978 \uf9d0 NewsCATS \uf967 \uf96b Mittermayer \uf967 \uf967 \uf967 \uf967 \u2026 \uf962 Mittermayer \uf9be \uf9d0 NewsCATS \uf92d \ufa08 NewCATS \uf92d NewsCATS \uf9e0 k \uf92d \ufa08 NewsCATS NewsCATS \uf92d \uf967 \uf967 \uf962 \ufa01 \uf967 \uf9be \uf9be \uf997 \uf9be \uf9be \uf9be \uf9f7 \uf9be 1 \uf997 \uf9be \uf9be \uf9be \uf9f7 \uf9be \uf9e0 k", | |
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| "text": "\uf997 \uf9be \uf9be \uf9be \uf9f7 \uf9be \uf967 \uf9be \ufa08 \uf9be \uf9d0 \uf9d0 \" \" \" \" \uf9d0 \uf92d \uf9e0 k NewCATS \uf9d0 \uf9d0 \" \" \" \" \uf9dd \uf9d0 \" \" \uf9d0 \" \" \uf9d0 \uf9d0 \uf962 \ufa01 2 3 4 \uf997 \uf9be \uf9be \uf9be \uf9f7 \uf9be \uf9e0 \uf961 \uf961 F-measure NewsCATS \uf91d \uf969 \uf9be NewsCATS \uf9be NewsCATS \uf9d0 \uf92d \uf9ba \uf9be \uf961 F-measure NewsCATS 2.66% 0.61% \uf9be NewsCATS \uf9dd \uf9d0 \" \" \uf9d0 \" \" \uf9d0 \" \" \" \" \uf997 \uf9be \uf9be \uf9be \uf9f7 \uf9be \uf9be NewsCATS \uf961", | |
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