政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/135943

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/135943

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113648/144635 (79%)
Visitors : 51640908 Online Users : 584

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大典藏 > College of Commerce > Department of Money and Banking > Theses > Item 140.119/135943

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/135943

Title:	運用Google Trends情緒萃取建構人工智慧量化交易策略：以台灣加權指數期貨為例 Devising Quantitative Trading Strategies with Artificial-Intelligence using Google Trends Sentiment Extraction：The Case of TAIEX Futures
Authors:	王德諭 Wang, De-Yu
Contributors:	江彌修 Chiang, Mi-Hsiu 王德諭 Wang, De-Yu
Keywords:	Google Trends 機器學習隨機森林市場情緒萃取台灣加權指數期貨下方風險 Google Trends Machine learning Random forest Market sentiment extraction TAIEX futures Down-side risk
Date:	2021
Issue Date:	2021-07-01 18:09:32 (UTC+8)
Abstract:	基於Google Trends的投資人情緒萃取，本文提供一具情緒表徵學習能力的集成預測框架。以隨機森林模型建構台灣加權指數期貨量化交易策略為例，本文探究輔以情緒萃取的分類器特徵生成之於模型預測能力及其量化交易策略之影響。本文的研究發現，輔以市場負面情緒(FEARS指數)以及股市關注度(Company_SVI)特徵生成，能有效提高隨機森林模型之陰性預測能力，其量化交易策略於測試區間之累積損益與風險比率皆勝出於大盤。特別地，我們發現2020年新冠疫情之後，輔以情緒特徵生成之模型預測能力及交易策略績效都能夠有效提升，在獲得與大盤相同績效的同時，承受虧損的幅度以及時間皆呈現大幅縮減。另外，當允許市場情緒萃取作近一步正負面之區分，本文發現陰性預測率雖能更有效提升，然而對下方風險的趨避能力下降，從而減損其量化交易策略之績效。 By extracting public investor sentiment from Google Trends, this thesis provides an ensemble prediction framework that allows for sentiment representation-learning. Based on random forest models, TAIEX futures trading strategies are devised to examine the impacts of the added sentiment dimension on the random forest models’ predictive abilities and the trading strategies’ risk-reward performances. Our numerical findings show that, sentiment assisted representation-learning, when attributed by FEARS and Company_SVI indices, can effectively improve the downside predictive ability of random forest models, resulting in higher cumulative returns and better risk-return profiles relative to simple buy-and-holds. Further evidence suggests that, adopting sentiment assisted representation learning, especially during the post-pandemic era (after 2020), helps to maintain a comparable risk-return profile relative to that of a buy-and-hold while at the same time significantly reduces the extent of losses and the time endured for losses. In addition, upon further categorizing market sentiment as positive or negative, the random forest models’ downside predictive power is found to increase while the strategies’ downside-risk-aversive ability seems to decrease, leading to an overall detrimental effect on trading performance.
Reference:	一、中文部分林哲鵬, 李春安, & 葉智丞. (2012). 投資人情緒與價格動能之關聯性. 管理與系統, 19(4), 729-759. 鄭仁杰, & 江彌修. (2019). 漫步於隨機森林-輔以多數決學習的台股指數期貨交易策略. 經濟論文, 47(3), 395-448. 二、英文部分 Ahundjanov, B. B., Akhundjanov, S. B., &Okhunjanov, B. B. (2020). Information Search and Financial Markets under COVID-19. Entropy, 22(7), 791. Antweiler, W., & 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. Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert systems with Applications, 42(20), 7046-7056. Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The review of financial studies, 21(2), 785-818. Beer, F., Hervé, F., & Zouaoui, M. (2012). Is big brother watching us? Google, investor sentiment and the stock market. Economics Bulletin, Forthcoming. Blume, L., D. Easley, and M. O’Hara (1994), “Market Statistics and Technical Analysis: The Role of Volume,” Journal of Finance, 49(1), 153–181. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press. Carneiro, H. A., &Mylonakis, E. (2009). Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clinical infectious diseases, 49(10), 1557-1564. Cutler, D. M., Poterba, J. M., & Summers, L. H. (1988). What moves stock prices? (No. w2538). National Bureau of Economic Research. Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499. Da, Z., Engelberg, J., & Gao, P. (2015). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32. De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of political Economy, 98(4), 703-738. Go, A., Bhayani, R., and Huang, L. (2009). Twitter sentiment classification using distant supervision. Technical report, Stanford University. Huang, M. Y., Rojas, R. R., & Convery, P. D. (2019). Forecasting stock market movements using Google Trend searches. Empirical Economics, 1-19. Khaidem, L., S. Saha, and S. R. Dey (2016), “Predicting the Direction of Stock Market Prices Using Random Forest,” arXiv preprint arXiv:160500003. Kumar, M. and M. Thenmozhi (2006), “Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest,” Working Paper, The Ninth Indian Institute of Capital Markets Conference Mondria, J., Wu, T., & Zhang, Y. (2010). The determinants of international investment and attention allocation: Using internet search query data. Journal of International Economics, 82(1), 85-95. Ren, N., M. Zargham, and S. Rahimi (2006), “A Decision Tree-Based Classification Approach to Rule Extraction for Security Analysis,” International Journal of Information Technology and Decision Making, 5(1), 227–240. Richards, A. (2005), “Big Fish in Small Ponds: The Trading Behavior and Price Impact of Foreign Investors in Asian Emerging Equity Markets,” Journal of Financial and Quantitative Analysis, 40(1), 1–27. Sen, J. and T. Chaudhuri (2017), “A Robust Predictive Model for Stock Price Forecasting,” Working Paper, The 5th International Conference on Business Analytics and Intelligence. Shiller, R. J., Fischer, S., & Friedman, B. M. (1984). Stock prices and social dynamics. Brookings papers on economic activity, 1984(2), 457-510. Simon, D. P. and R. A. Wiggins (2001), “S&P Futures Returns and Contrary Sentiment Indicators,” Journal of Futures Markets, 21(5), 447–462. Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of finance, 62(3), 1139-1168. Tetlock, P. C., Saar‐Tsechansky, M., &Macskassy, S. (2008). More than words: Quantifying language to measure firms` fundamentals. The Journal of Finance, 63(3), 1437-1467. Vu, T. T., Chang, S., Ha, Q. T., & Collier, N. (2012). An experiment in integrating sentiment features for tech stock prediction in twitter.
Description:	碩士國立政治大學金融學系 108352030
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0108352030
Data Type:	thesis
DOI:	10.6814/NCCU202100594
Appears in Collections:	[Department of Money and Banking] Theses

Files in This Item:

File	Description	Size	Format
203001.pdf		2448Kb	Adobe PDF2	0	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback