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1 27 EM

2 textream ( )

3 Comtemporary regression A 23 B :

4 A.1 textream ( : ) A.2 textream ( ) A.3 textream ( )

5 A.1 insample accuracy(c=buy, Hold or Sell) A.2 out of sample accuracy(3-fold cross validation, C=Buy, Hold or Sell) A.3 out of sample accuracy(4-fold cross validation, C=Buy, Hold or Sell) A.4 insample accuracy(c=buy or Sell) A.5 out of sample accuracy(3-fold cross validation, C=Buy or Sell) A.6 out of sample accuracy(4-fold cross validation, C=Buy or Sell) A.7 ( ): A.8 ( : Large) A.9 ( : Mid) A.10 ( : Small) A.11 (Large) A.12 (Mid) A.13 (Small) A.14 ( ): A.15 ( ): A.16 ( ) A.17 ( )

6 1 SNS( ) SNS ( 1/7 ) SNS SNS SNS SNS ( ) SNS SNS 2 textream Twitter ( ) 2 1 Twitter textream Yahoo! textream 2 2 Yahoo! Japan ID 2 textream ( textream ) 4

7 textream 2 Twitter web Twitter 2 textream ( ) ( ) ( ) ( ) Twitter 2 textream ( ) 2 textream Twitter Twitter 2 textream textream textream

8 2 Wysocki(1999) Wysocki(1999) Yahoo! ROE NYSE Tumarkin and Whitelaw(2001) Raging Bull Antweiler and Frank(2004) Yahoo! Raging Bull Sabherwal et al.(2011) Leung and Ton(2015) 2142 (HotCopper) Fama and Macbeth(1992) Mackinlay(1997) (2005) (2008) (2012) (2005) (2005) Yahoo! 2003 (2008) Yahoo! 50 ( ) 110 (2012) Yahoo! ( ) 390 Fama and French(1992) 6

9 Tetlock(2007) Tetlock et al.(2008) Tirunillia and Tellis(2012) Yu et al.(2013) (2013) Tetlock(2007) Wall Street Journal column Tetlock et al.(2008) S&P Tirunillia and Tellis(2012) 15 Amazon.com Eopinion.com Yahoo!shopping web Yu et al.(2013) Twitter (2013) : 2: 3: 1,2 Leung and Ton(2015) Antweiler and Frank(2004) Sabherwal et al.(2011) 3 7

10 3 (Naive Bayes classifier) Antweiler and Frank(2004), Das and Chen(2007), Sabherwal et al.(2011), Leung and Ton(2015) (corpus) (training data) W k k K (Message ) Message = {W k } K k=1 (3.1) 6 C C W k P (W k C) = m kc /n c (3.2) m kc C W k n c C C {,, } C {, } 2 P (C Message) = P (C) P (Message C) P (Message) = P (C) K k=1 P (W k C) P (Message) (3.3) (Naive) (Message) P (C Message) C P (Message) C P (C) K k=1 P (W k C) C P (W k C) = 0 P (C) K k=1 P (W k C)=0 (3.2) P (W k C) = n c/n + m kc n c /n + n c (3.4) 8

11 n Mecab Mecab Mecab B 69 B , (2008), (2012) textream 9

12 4 textream( A.1 textream textream textream twitter ( ) ( 6758) ( ) ( ) ( ) (2008), (2012) Antweiler and Frank(2004), Leung and Ton(2015) ( A ) Python textream Web HTML Python Web HTML MySQL MySQL MySQL Python Mecab 3 Mecab Mecab 2 -ifinance( ( Python MySQL ( ) 4.1 Yahoo! ( NEEDS FinancialQUEST 10

13 1 4.2 ( ( ) ( MySQL ( 1 ) A A ( ) ( ) ( ) ( ) ( ) ( ) ( ) , :00-15: :00-15:00 11

14 K- (K-fold cross-validation) K- K 1 K-1 ( ) K K K A (in sample accuracy) (0- ) Buy Hold Sell (95.9%) 23 (2.4%) 16 (1.7%) (11.2%) 1186 (83.1%) 82 (5.7%) (3.7%) 16 (2.6%) 585 (93.7%) A.2 3 (out of sample accuracy) (65.7%) 231 (24.4%) 94 (9.9%) (30.6%) 796 (55.7%) 195 (13.7%) (27.9%) 212 (34.0%) 238 (38.1%) A K 2 A (in sample accuracy) 12

15 (95.9%) 74 (4.1%) (11.7%) 1058 (88.3%) A (out of sample accuracy) (80.1%) 359 (14.9%) (47.7%) 626 (52.3%) A Comtemporary regression R i,t = α + β 1 Message i,t + β 2 Bullishness i,t + β 3 MarketRet i,t + β 4 R i,t 1 + ε i,t (5.1) V olume i,t = α + β 1 Message i,t + β 2 Bullishness i,t + β 3 Market i,t + β 4 V olume i,t 1 + ε i,t (5.2) R i,t i t V olume i,t 1 Message i,t i t ( (15:00) 15:00 ) Bullishness i,t i t MarketRet t t ( ) Market t t ( ) Market t A Antweiler and Frank(2004) i t C {,, } Mi,t C (Bullishness index) Bullishness i,t = M i,t M i,t + M i,t + M i,t (5.3) Antweiler and Frank(2004), Sabherwal et al.(2011), Leung and Ton(2015) Bullishness i,t = M i,t M i,t M i,t + M i,t ln (1 + M i,t + M i,t + M i,t ) (5.4) 5.1 out of sample 1/3 (5.4) 1 Message i,t (5.3) Bullishness Measurement Error 2 Measurement Error 13

16 ( : 3386) ( : 4661) ips Measurement Error 1 (5.3) A.7 Bullishness t , , Bullishness t , , (Large) 100 (Mid) 200 (Small) 700 Leung and Ton(2015) A.8 A.9 A.10 (Large) (Mid) (Small) Bullishness Median 25%quantile 0 0 A.11 A.12 A.13 (Large) (Mid) (Small) Return 1 1 A.11,

17 A.14 (Large) (Mid) (Small) (5.1) (t =-2.95) (t =16.98) (Large: t =11.23, Medium: t =12.71, Small: t =11.91) A.15 (Large) (Mid) (Small) (5.2) 2 (Large: t =18.74, Medium: t =39.98, Small: t =52.70) (Large: t =5.04, Medium: t =4.30, Small: t =8.29) t Large: -1.74, Medium: t =-7.04, Small: t = , , Machinlay(1997) (event window) 5 ( ) :00 24:00 (estimation window) 2 (5.5) R i = X i θ i + ε i (5.5) R iτ i, τ ι R m,τ τ α i, β i, σε 2 i R i = [R it 125,..., R it 6 ] (5.6) X i = [ι, R m ] R m = [R mt 125,..., R mt 6 ] (5.7)

18 θ i = [α i, β i ] (5.8) ε i = [ε it 125,..., ε it 6 ] (5.9) E(ε iτ ) = 0 V ar(ε iτ ) = σε 2 i (5.10) 120 OLS ˆθ i = (X ix i ) 1 X ir i (5.11) ˆσ 2 ε i = ˆε iˆε i (5.12) ˆε i = R i ˆα i ι ˆβ i R m = R i X i ˆθ i (5.13) R i = [R it 5,..., R it+5 ] (5.14) X i = [ι, R m] R m = [R mt 5,..., R mt+5 ] (5.15) ˆθ i = [ˆα i, ˆβ i ] (5.16) (Cumulative Abnormal Return) E[ˆε i X i ] = E[R i X ˆθ i i X i ] = E[(R i X i θ i ) X i (ˆθ i θ i ) X i ] = 0 (5.17) V i = E[ˆε i ˆε i X i ] = E[ε i ε i ε i (ˆθ i θ i ) X i X i (ˆθ i θ i )ε i X i (ˆθ i θ i )(ˆθ i θ i ) X i X i ] (5.18) = Iσ 2 ε i + X i (X ix i ) 1 X i σ 2 ε i ˆε i N (0, V i ) (5.19) 16

19 γ τ 1 τ (11 1) τ 1 τ 2 3 ĈAR i (τ 1, τ 2 ) γ ˆε i (5.20) V ar[ĉar i (τ 1, τ 2 )] = σi 2 (τ 1, τ 2 ) = γ V i γ (5.21) ĈAR i (τ 1, τ 2 ) N (0, σi 2 (τ 1, τ 2 )) (5.22) 1 4 (5.13) ˆε i N N ε = 1 N N i=1 ˆε i V ar[ ε ] = V = 1 N 2 N V i (5.23) i=1 CAR(τ 1, τ 2 ) γ ε (5.24) V ar[car(τ 1, τ 2 )] = σ 2 (τ 1, τ 2 ) = γ V γ (5.25) N (5.20) (5.21) (5.26) (5.27) CAR(τ 1, τ 2 ) = 1 N N ĈAR i (τ 1, τ 2 ) (5.26) i=1 V ar[car(τ 1, τ 2 )] = σ 2 (τ 1, τ 2 ) = 1 N 2 N σi 2 (τ 1, τ 2 ) (5.27) 0 σ 2 (τ 1, τ 2 ) i=1 CAR(τ 1, τ 2 ) N (0, σ 2 (τ 1, τ 2 )) (5.28) ˆ σ 2 (τ 1, τ 2 ) = 1 N 2 N ˆσ i 2 (τ 1, τ 2 ) (5.29) 3 τ 1 t 5 τ 2 t 5 t i=1 17

20 J 1 = CAR(τ 1, τ 2 ) [ˆ σ 2 (τ 1, τ 2 )] 1 2 a N (0, 1) (5.30) γ (Large) 100 (Mid) 200 (Small) i t C {,, } M C i,t Bullishness i,t = M i,t M i,t + M i,t (5.3) 1 ln (1 + M + M i,t + M i,t + M i,t ) (5.31) i,t A.16 Bull, Mid, Bear L, M, S Large, Mid, Small All L,M,S Bull, Mid, Bear *, **, *** 5% 1% 0.1% t-1 0.1% t t+1 0.1% t t+5 0.1% All L, M, S t Large 1% t Mid 0.1% t t+1 Small 0.1% t-1 Small 0.1% Small t t+5 0.1% Small 6 Bull, Mid, Bear t t+1 Bull 0.1% t Mid 0.1% t Bear ( )

21 Bull t t+5 0.1% Bull All Small Mid Bull L, M, S t Large Mid t t+1 Small 0.1% t+2 Small 1% Small t t+5 0.1% Bull Small A.17 t (Post-Earnings-Announcement Drift) Ball and Brown 1968) Bull Mid L, M, S t Large 5% Mid 1% t Small 0.1% Small t t+3 5% Bear L, M, S t Large 0.1% Mid 0.1% t Small 0.1% t-1 Small 0.1% Bear Small t t Small t Large, Mid( ) A.17 A.16 Bull A.16 A.17 A.16 19

22 6 6.1 textream 5.2 1( ) 2( ) ( ) Mackinlay(1997) Antweiler and Frank(2004) Sabherwal et al.(2011) 6.2 Support Vector Machine 20

23 [1] Antweiler, W., and Frank, M. Z., (2004). Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards. Journal of Finance 59 (3), [2] Ball, R., and P. Brown, (1968). An Empirical Evaluation of Accounting Numbers. Journal of Accounting Research 6 (2), [3] Das, S. R., Chen, M. Y., (2007). Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web. Management Science 53 (9), [4] Fama, E. and French, K.R. (1992). The cross-section of expected stock returns. Journal of Finance 47, [5] Leung, H., and Ton T., (2015). The impact of internet stock message boards on cross-sectional returns of small-capitalization stocks. Journal of Banking and Finance 55, [6] MacKinlay, A.C., (1997). Event Studies in Economics and Finance. Journal of Economic Literature 35 (1), [7] Sabherwal, S., Sarkar, S.K., and Zhang. Y., (2011). Do Internet Stock Message Boards Influence Trading? Evidence from Heavily Discussed Stocks with No Fundamental News. Journal of Business Finance & Accounting 38 (9-10), [8] Tetlock, P. C., (2007). Giving content to investor sentiment: The role of media in the stock market Journal of Finance 62 (3), [9] Tetlock, P. C., Saar-Tsechansky, M., and Macskassy, S., (2008). More Than Words: Quantifying Language to Measure Firms Fundamentals. Journal of Finance 63 (3), [10] Tirunillai, S., and Tellis, G. J., (2012). Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance, Marketing Science 31 (2), [11] Tumarkin, R. and Whitelaw, R.F., (2001). News or Noise? Internet Postings and Stock Prices. Financial Analysts Journal 57, [12] Wysocki, P., (1999). Cheap Talk on the Web: The Determinants of Postings on Stock Message Boards, Working paper, University of Michigan. [13] Yu, Y., Duan. W., and Can, Q., (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach, Decision Support Systems 55 (4), [14],,, (2013). - -,, 51 (12), [15],, (2012).,, 53 (1),

24 [16],,, (2008).,, 46 (11-12), [17],, (2005)., 20,

25 A A.1: textream ( : ) 23

26 A.2: textream ( ) A.3: textream ( ) 24

27 A.1: insample accuracy(c=buy, Hold or Sell) % Buy Hold Sell Buy Hold Sell A.2: out of sample accuracy(3-fold cross validation, C=Buy, Hold or Sell) % Buy Hold Sell Buy Hold Sell A.3: out of sample accuracy(4-fold cross validation, C=Buy, Hold or Sell) % Buy Hold Sell Buy Hold Sell A.4: insample accuracy(c=buy or Sell) % Buy Sell Buy Sell

28 A.5: out of sample accuracy(3-fold cross validation, C=Buy or Sell) % Buy Sell Buy Sell A.6: out of sample accuracy(4-fold cross validation, C=Buy or Sell) % Buy Sell Buy Sell A.7: ( ): Return t : 3386 Return t : 4661 t t t t Message t Bullishness t MarketRet t Return t AdjustedR

29 A.8: ( : Large) Return M essage Bullishness M Return M arket V olume Mean SD Skewness Kurtosis Max th Median th Min A.9: ( : Mid) Return M essage Bullishness M Return M arket V olume Mean SD Skewness Kurtosis Max th Median th Min A.10: ( : Small) Return M essage Bullishness M Return M arket V olume Mean SD Skewness Kurtosis Max th Median th Min

30 A.11: (Large) Return Message Bullishness MReturn Return 1 Return Message Bullishness MReturn Return A.12: (Mid) Return Message Bullishness MReturn Return 1 Return Message Bullishness MReturn Return A.13: (Small) Return Message Bullishness MReturn Return 1 Return Message Bullishness MReturn Return

31 A.14: ( ): Return t Small Medium Large t t t Message t Bullishness t MarketRet t Return t AdjustedR A.15: ( ): V olume t Small Medium Large t t t Message t Bullishness t Market t V olume t AdjustedR

32 A.16: ( ) Day t t-5 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t+5 Abnormal All Average Returns *** *** *** * * L * ** * M *** S *** *** *** ** * Bull *** *** * L * * M * S *** *** ** Mid *** L * * M ** S *** *** * Bear * L *** M *** S *** *** * * Cumulative All Abnormal Returns *** *** *** *** *** *** L * * M S *** *** *** *** *** *** Bull *** *** *** *** *** *** L * M S *** *** *** *** *** *** Mid L *** M *** S * * * * Bear L *** M * S * ** 30

33 A.17: ( ) Day t t-5 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t+5 Abnormal All Average Returns *** *** *** * * L * ** * M *** S *** *** *** ** * ** *** *** * L * M * S ** *** *** * *** ** L * * M S *** * *** * L ** M *** S *** *** * Cumulative All Abnormal Returns *** *** *** *** *** *** L * * M S *** *** *** *** *** *** *** *** *** *** *** *** L M *** S *** *** *** *** *** *** * ** ** *** ** ** L * *** M *** S ** ** *** *** ** ** L M ** * * * * S

34 B : 69 0,, 1 ( ), 2, 3,, 4,, 5,, 6,, 7,, 8,, 9,, 10,, 11,, 12,, 13,,, 14,,, 15,,, 16,, 17,, 18,, 19,, 20,, 21,, 22,, 23,, 24,, 25, 26, 27,, 28,, 29,, 30,, 31,, 32,, 33,, 34,, 35,, 36,, 37,, 38,, 39,, 40,, 41,,, 42,,,, 43,,,, 44,,,, 45,, ( ), 46,,,, 47,,,, 48,, 49,, 50,,, 51,,, 52,,, 53,,, 54,,, 55,,, 56,,,, 57,,, 58,,, 59,,, 60,,, 61,, 62,,, 63,,, 64,,, 65,,, 66,,, 67,, 68 32

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