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研究生:陳俊甫
研究生(外文):Chun-Fu Chen
論文名稱:透過社會大眾情緒預測台灣股市
論文名稱(外文):Predicting Taiwan Stock Market Using Social Moods
指導教授:雷欽隆雷欽隆引用關係
指導教授(外文):Chin-Laung Lei
口試委員:顏嗣鈞郭斯彥黃秋煌莊文勝
口試日期:2016-07-15
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:英文
論文頁數:39
中文關鍵詞:台灣股市情緒分析股市預測批踢踢
外文關鍵詞:Taiwan Stock MarketSentiment AnalysisStock Market PredictionPTT
相關次數:
  • 被引用被引用:4
  • 點閱點閱:559
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
近年來有許研究再探討透過分析社群網站上的資訊來預測未來的可能性當然,不管在什麼時期,投資者的情緒和股市的波動常常被放再一起討論,而隨著社群網站的快速發展,越來越多人願意在網路上宣洩他們的情緒,當然投資者也不例外,因此藉由分析他們的情緒來預測股市成為近幾年的熱門議題。
在這篇論文研究中,PTT是我們要分析的社群網站,有許多投資者在PTT的股市版上發表的他們對於股市的看法還有給予其他投資者的建議,透過分析裡面的資料可以了解到台灣投資者的行為與情緒,我們透過台灣大學編訂的NTUSD跟大連理工大學編定的DUTIRSD這兩個情緒字典來量化投資者的情緒並預測台灣兩個代表性的股市指數,分別為台股期貨指數(TX)與台股加權指數(TAIEX)。我們利用的移動視窗的概念並在每一個移動式窗內選取固定數量的特徵來進行預測,我們認為隨著時間的變化,造成股市震盪的因素也會改變,因此不應該選特定特徵來預測未來的每一天,我們透過均方根誤差(RMSE)來決定移動視窗的大小和選定特徵的數量,越小的RMSE代表當下的移動視窗大小和選定特徵數量有較大的預測能力。
每一天的股市都記錄著開盤價、最高價、最低價、收盤價,我們透過K-means分群演算法將四個價格區分成三類並透過投資者的情緒來預測隔日的股市的四個數值分別屬於哪一種狀態。


In recent years, mining social media data to forecast the future has been a popular research. The stock market behavior and investor emotions are always bonded together. With the development of social media, people are willing the share their feelings on the social media including investor.
In our study, we select PTT stock board as our platform, a forum gathering investors sharing their opinions, and crawl data on it. We calculate the emotion score through NTUSD and DUTIR sentiment dictionary and predict two representative stock market indices: Taiwan Futures Index and Taiwan Capitalization Weighted Stock Index. The concept of fixed-sized rolling window and fixed feature size are adopted in this thesis. That is, if the emotion cause the variation of stock market, the main causality might be different in different time span. The rolling window size and feature size are selected to our prediction model through lower Root Mean Square Error.
There are four value recorded each day: opening value, intra-day highest value, intra-day lowest value and closing value. We classify these four value into three groups through K-means clustering algorithm and then conduct prediction.


致謝 i
中文摘要 ii
Abstract iii
Contents iv
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
Chapter 2 Related Work 4
Chapter 3 Background 6
3.1 PTT Stock Board 6
3.2 Sentiment Dictionary 6
3.2.1 DUTIR 6
3.2.2 NTUSD 7
3.3 Jieba 8
3.4 Scikit-learn 8
Chapter 4 Datasets 10
4.1 Stock Market Data 10
4.2 Online Emotions 11
Chapter 5 Methodology 17
5.1 Rolling window 17
5.2 Feature Ranking 18
5.2.1 Random Forest Based Feature ranking 18
5.3 Model Selection 19
Chapter 6 Case study: Taiwan stock market 21
6.1 Discretization of Stock Market 21
6.2 Prediction in Taiwan Future (TX) 25
6.3 Prediction in Taiwan Capitalization Weighted Stock Index 30
Chapter 7 Conclusion 36
Bibliography 38


[1]Antweiler, W., and Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259-1294.
[2]Zhang, X., Fuehres, H., and Gloor, P. A. (2011). Predicting stock market indicators through twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, 55-62.
[3]Bollen, J., Mao, H., and Zeng, X. (2011). Twitter mood predicts the stock market.Journal of Computational Science, 2(1), 1-8.
[4]Mao, H., Counts, S., and Bollen, J. (2011). Predicting financial markets: Comparing survey, news, twitter and search engine data. arXiv preprint arXiv:1112.1051.
[5]Sprenger, T. O., Tumasjan, A., Sandner, P. G., and Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926-957.
[6]Oliveira, N., Cortez, P., and Areal, N. (2013, September). On the predictability of stock market behavior using stocktwits sentiment and posting volume. InPortuguese Conference on Artificial Intelligence (pp. 355-365). Springer Berlin Heidelberg.
[7]Chen, H., De, P., Hu, Y. J., and Hwang, B. H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies, 27(5), 1367-1403.
[8]Mao, H., Counts, S., and Bollen, J. (2015, July). Quantifying the effects of online bullishness on international financial markets. In ECB workshop on using Big Data for forecasting and statistics, Frankfurt, Germany.
[9]Zhou, Z., Zhao, J., and Xu, K. (2016). Can Online Emotions Predict the Stock Market in China?. arXiv preprint arXiv:1604.07529.
[10]PTT Stock Board https://www.ptt.cc/bbs/Stock/index.html
[11]NTUSD http://academiasinicanlplab.github.io/
[12]DUTIR Sentiment Dictionary http://ir.dlut.edu.cn/EmotionOntologyDownload
[13]Jieba https://github.com/fxsjy/jieba
[14]Scikit-learn http://scikit-learn.org/stable/index.html
[15]PTT-BOT https://github.com/mbilab/ptt-bot


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