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Review on financial document sentiments

Review on financial document sentiments. 24-8-2017 David Ling HSMC Deep Learning Center. Contents. Financial sentiment examples Brief history Performance Dictionary based Machine learning/ deep learning Our plan. Wh at is financial document sentiment.

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Review on financial document sentiments

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  1. Review on financial document sentiments 24-8-2017 David Ling HSMC Deep Learning Center

  2. Contents • Financialsentimentexamples • Briefhistory • Performance • Dictionarybased • Machine learning/ deep learning • Our plan

  3. Whatis financial document sentiment • Financial document sentiment – getting tones or emotions from the document • Annualreports,news,twitters,analystreports,forumposts,etc. • Predictionsonstock prices and returns,financial distress, risks, and volatility(Feng Li, SSRN 2006; Gabriele Ranco, PLOS ONE 2015;Steven L.,FFJ2017; Shimon Kogan, NAACL 2009; Petr Hájek, EANN 2013; Y Liu, arXiv 2017) This is good! “匯控獲大摩唱好，公司回購股份力度加碼，再升3.3%，收65.5元，推動恒指升逾78點，即…” “《收市總結》港股連續兩日成交不足500億元 全周累跌172點…” This is bad!

  4. L. Iliadis, H. Papadopoulos, and C. Jayne EANN 2013 • Texts store views of the past and future • Annualreports->Extractnumericaldataandsentimentscores • Investment grade (IG) and non-investment grade(NG) • Assignedby a highly regarded Standard & Poor’s rating agency • 520UScompaniesin2010, predicting results in 2011

  5. Tim Loughran, Journal of Financial Economics 2013 • S-1 Filling-a form filled for the exchange before going public in US (~50000 words) • Calculated the correlation between S-1 sentiment scores and first day returns • First day return = closing price – offer price • Includes 1,887 completed U.S. IPOs with an offer price of at least $5 per share during the 1997–2010 time period • Weak modal and positive tones have relatively higher correlation => will have a higher return • Weak modal: may, could, might, possible Correlations to IPO first day returns

  6. Newssentiment Steven L, Financial analysis Journal, 2017 • Reuters NewScope Sentiment Engine • Data service provided by Reuters (non-free) • Real time news updating with sentiment scores • 3 indices: Pos, Neut, Neg • Tagged with date and related company http://share.thomsonreuters.com/assets/elektron/news-analytics-flyer.pdf

  7. Newssentiment • Trade base on the news sentiment score • A company score is obtained by averaging the related news score • Long the top 20% and short the bottom 20% according to the scores (daily or weekly) • x day after news means using scores x days before. x = 1 means using yesterday’s score • 0.17% for 1 day after, 0.32% for 1 week after • Positive return for x < 0 implies news stories may lag events that affect stock prices • Daily news can predict 1 day return only, while weekly based shows a better result Daily return Weekly return

  8. Newssentiment • 優礦 • A mainland big data company • Provide newssentimentscoresofacompanyviaAPI • 40k news threats per day • Sentiment score: [-1,1]

  9. TwitterSentiment Gabriele PLOS 2015 • Analyse 15 months, 30 companies in DJIA, total 1.5M tweets from users (eg. McDonald’s, Visa, Coca-cola) • The machine learning classifier tells you whether a tweet is negative, neutral, or positive • Two time series, daily sentiment score and daily stock return • Small non-zero linear correlation is obtained Trivago 3M Part of the correlations for different companies (Gabriele PLOS 2015)

  10. Twitter sentiment Event study • Events (eg. Earning announcement) are classified according to the number of positive and negative tweets • returns are affected by events the significantly • An even can be classified by using tweets sentiment Event day • Result obtained by averaging across all companies and events.

  11. Brief history on financial document sentiment • Early financial document sentiment (from ~2006) • Official documents: annual/quarter reports (10-Ks), analyst reports • Newspaper: The Wall Street Journal, New York Times • Dictionary based Feng Li, SSRN 2006; Henry, SSRN 2009; Tim Loughran, JFE 2013; Petr Hájek, EANN 2013; Li Xiaodong, 2014 • Recent years • Online news (Reuters) • Social media (Tweeters, StockTwits, forum messages) • Machine learning/ Deep learning Matthias, JBF 2014; Gabriele Ranco, PLOS ONE 2015; Steven L. Heston, FAJ 2017; Y Liu, arXiv2017 • Growing number of companies are implementing the technology, but mainly for English, and not for Hong Kong

  12. Dictionary based • Sentiment scores are calculated by Keyword frequency in the text • Positive: excellent, nice, agree, etc. • Negative: against, afraid, etc. • Common keyword lists: • Harvard IV-4 categories: positive, negative, strong, weak, active, passive • Loughran & McDonald word lists (2011) • 知网 (2007) • Keyword usages are often different across disciplines and regions • Eg. Taiwan news and Hong Kong news • Eg. Financial reports and general text • Subjective, not accurate, and not sophisticated

  13. Dictionary based • 台股新聞情緒指標 • Ming ChuanUniversity, Taiwan, 2013 • Website format • Two sentiment scores ITDC and SR • Scores are obtained from Taiwan financial news • Provide recommendation of companies • Provide news sentiment testing “指標試算”

  14. Dictionary based News sentiment 2: News sentiment 1: Incorrect, the tone should be negative. Correct, the tone is negative. Dictionary seems not so sophisticated. Like “蝕”is not detected.

  15. Deeplearning/Machinelearning • Deeplearningandmachinelearning • Latest technology in extracting textual sentiment • Don’thavetospecifyrulesmanually • Abletounderstandsemanticmeaning • Moreaccurate,evenbetterthanhuman • With more data, system becomes more sophisticated

  16. Machinelearning Machinelearningclassifier performance • Annotator agreement: comparing results of two human annotators • Sentiment classifier: comparing results of the machine and a human • Machine has a comparative accuracy, and a slightly higher score (excluding the neutral class) • = Gabriele PLOS 2015

  17. Deeplearning • Deeplearningallowsmachinetolearnsemanticmeaning • Prepared 30000 pieces of online financial news from Quamnet (華富財經) • Sample news: 長和今天放榜，早前大摩預測長和中期比只升5%，主要受英英鎊貶值等外匯因素影響，預測長和上半年經營溢利同比升5%至309億元 Nearest to 跌: 升, 挫, 倒跌, 微跌, 股亦收升, 現跌, 無升, 微升, Nearest to 同比: 按年, 去年同期, 之後高見, 僅減, 連特別息, 遠洋報, 此負, Nearest to 對: 認為, 家會員, 讓, 運費, 他們將, 將對, 令電能, 與, Nearest to 涉及: 共, 成交, xhand, 涉資約, 光啟, bcm_energy_partners, 對換, Nearest to 公布: 公佈, 宣布, 公告, 放榜, 發布, 止, 公在, Nearest to 在: 於, 或, , 預期, 將在, 將於未來, 資源予, 與, Nearest to 而: 但, 另外, 表示, 或, 九鐵, 至於, 認為, 他稱,

  18. Deeplearning • Betterperformancethansimplymachinelearningmethod • Published by google in 2014 • Results on classifying movie reviews:12.2%, much lower than the other Movie review sentiment comparison Quoc Le, Tomas Mikolov 2014

  19. Our planandproject • Toimplementing deeplearningtechnologiestofinancial industry • ProvidingsentimentscoresofHong Kong’s Chinese news • Moreaccurateandobjective • Handlehugeamountofdailynewsandreports • Recommendation • Perfectrighttime(havenotseeninHongKongmarket) • Bigmarket(webelievemainlandanalystswillalsobeinterested)

  20. Thankyou • Selectedreferences: • [Steven L. 2017]News vs. Sentiment: Predicting Stock Returns from News Stories,Financial Analysts Journal,73(3) • [Gabriele 2015]The Effects of Twitter Sentiment on StockPrice Returns,PLoS ONE 10(9) • [Petr Hájek2013]Evaluating Sentiment in Annual Reports for FinancialDistress Prediction Using Neural Networks and SupportVector Machines,EANN 2013, Part II, CCIS 384, pp. 1–10 • [Jonas 2017] How the Market Can Detect Its Own Mispricing - A News Sentiment Index to Detect Irrational Exuberance, Proceedings of the 50th Hawaii International Conference on System Sciences • [Shimom 2009] Predicting Risk from Financial Reports with Regression, NAACL • [Matthias 2014] Reuters Sentiment and Stock Returns, The Journal of Behavioral Finance, 15: 287–298 • [Tim Loughran2013]IPO First-Day Returns, Offer Price Revisions, Volatility, and Form S-1 Language,Journal of Financial Economics (JFE), Forthcoming • [Xiao 2015]Deep Learning for Event-Driven Stock Prediction, IJCAI • [Quoc Le2014]Distributed Representations of Sentences and Documents,arXiv:1405.4053 [cs.CL]

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