Media News and Cross Industry Information Diffusion
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1 Media News and Cross Industry Information Diffusion Li Guo Singapore Management Univeristy June 13, 2017
2 Motivatioin Cross Asset Return Predictability: Information Diffusion: Hong and Stein (1999): Theory Model on Information Diffusion. Cohen and Frazzini (2008): Firm Firm. Hong and Valkavov (2007): Industry Stock Market. Menzly and Ozbas (2010): Industry Industry. Rapach et.al (2015): Core Industries + Technique Spillover Effect. Liquidity: Lo and MacKinlay (1990), Brennan and Swaminathan (1993), Badrinath and Noe (1995), Jegadeesh and Titman (1995) Own-autocorrelation and cross-correlation: Boudoukh and Whitela (1994) Why Information Diffusion? Economic Linkage and Network Complexity Research Question: How News Travels across Different Industries?
3 Hypothesis Assumptions of Information Diffusion (Hong and Valkavov, 2007): Investors Limited Attention: Fang and Peress (2009): # of News Barber and Odeani (2008): Extreme trading volume/return Jacobs and Weber (2012): Regional holidays DellaVigna and Pollet (2009): Friday Dummy... News slowly diffuses across different industries (no formal test). Peress (2015): Media strike drives return predictability. Hong (2009): Negative news travels slowly.
4 Hypothesis I Cross industry news contains valuable information of firm fundamentals. Dyck and Zingales (2002): Media selectively reduce the cost of acquiring and verifying information. Tetlock et.al (2008): Negative words predict future earnings. Bushee et.al (2010): Media serves as an information intermediary. Peress (2015): Media increases the speed with which information diffuses across investors. Predictions: Cross industry news predicts firm fundamentals (SUE). Cross industry news is more valuable to those stocks with poor information environment.
5 Hypothesis II Cross industry news slowly diffuses. Hard interpretation of cross industry news (Information Intermediary) Prediction: Cross industry news has long term effect on future stock returns. Sophisticated investors are able to interpret cross industry news to the market. (Analysts/Institutional investors) Cross industry news should have longer effects on stocks with low analyst coverage or low institutional holdings than others. (Not report yet)
6 Data Sources News Archive: Thomson Reuters News Archive (From 01-Jan-1996 to 31-Dec-2014); Active Institutional Fund Flow: EPFR database. Earnings forecasts and analyst related data from the IBES detailed history; All other accounting variables come from COMPUSTAT, stock returns come from CRSP. Sentiment Index: Policy Uncertainty Index:
7 News Tone Measures News Tone (Tetlock et.al, 2008): Firm Specific News: Tone = # of negative word occurrences Total # of Words in the news. (1) Peer Industry News: D d=1 Firm News i,t = Tone i,d. (2) D K k=1 Peer News i,t = FirmNews k,t, where i k. (3) K Cross Industry News: J j=1 Cross Industry News i,j,t = Firm News j,t, with i, j in different industries. J (4)
8 Cross Industry News Signal (CIS) Step I: Predictive regression N r i,t+1 = α i + b i,j Industry News j,t + ɛ i,t+1, for t = 1,..., T - 1, (5) j=1 Adaptive Lasso Estimation: ˆb i N N = argmin r i,t+1 α i b i,j Industry News j,t 2 + λ i ŵ i b i,j, (6) j=1 j=1 Choose inverse of OLS estimation as weighted parameter as Zou (2006). Use Cross Validation to select the optimal λ i. Require at least 260 weekly observations for each firm and set the initial estimation window as 208. Step II: Out-of-sample Forecast CIS i,t = ˆr i,t +1 = α i + N E t[b i,j,t+1 ]Industry News j,t, (7) j=1 Step III: Repeat step I and step II recursively to have a time series of CIS for each firm.
9 Cross Industry News Signal (CIS) Formation Repeat Table 2 of Rapach, D., Strauss, J., Tu, J., & Zhou, G. (2015)
10 CIS v.s. CIR I: Abnormal Return around CIR Figure: This figure plots firms average abnormal return from 64 trading weeks preceding a top (bottom) decile of out-of-sample forecasted return based on CIR. All news stories come from Thomson Reuters between 1996 and 2014 inclusive. The out-of-sample period is We also compute 95 confidence interval for both positive and negative signals represented by the gray area.
11 CIS v.s. CIR II: Abnormal Return around CIR only Figure: This figure plots firms average abnormal return from 64 trading weeks preceding a top (bottom) decile of out-of-sample forecasted return based on unique CIR signals. All news stories come from Thomson Reuters between 1996 and 2014 inclusive. The out-of-sample period is We also compute 95 confidence interval for both positive and negative signals represented by the gray area.
12 CIS v.s. CIR III: Abnormal Return around CIS only Figure: This figure plots firms average abnormal return from 64 trading weeks preceding a top (bottom) decile of out-of-sample forecasted return based on unique CIS. All news stories come from Thomson Reuters between 1996 and 2014 inclusive. The out-of-sample period is We also compute 95 confidence interval for both positive and negative signals represented by the gray area.
13 Cross Industry News and SUE Adaptive Lasso: No Controls Tetlock 2008 All Controls Coef P value Coef P value Coef P value Food 0.09 < Beer < 0.01 Smoke Games Books Hshld Clths Hlth Chems Txtls < 0.01 Cnstr Steel FabPr ElcEq < < < 0.01 Autos < < < 0.01 Carry Mines Coal 0.06 < 0.01 Oil 0.12 < < < 0.01 Util 0.11 < < < 0.01 Telcm Servs BusEq Paper Trans < < 0.01 Whlsl Rtail < 0.01 Meals 0.05 < 0.01 Fin Other
14 Fama-MacBeth regressions of stock returns on CIS CIS 0.137*** 0.130*** 0.095*** 0.126*** 0.118*** 0.079*** 0.153*** 0.148*** 0.118*** (7.15) (6.48) (5.09) (5.21) (4.51) (3.32) (4.90) (4.74) (3.98) Lagged Return *** *** *** *** *** *** *** *** *** (-21.08) (-22.12) (-23.60) (-17.62) (-18.46) (-19.27) (-11.81) (-12.43) (-13.73) Peer News (-0.60) (-1.53) (-0.56) (-1.14) (-0.28) (-1.02) Firm News ** *** *** *** *** (-2.04) (-5.02) (-0.68) (-2.72) (-2.63) (-5.09) # of Peer News (0.01) (0.24) (0.06) (-0.54) (-0.07) (1.11) # of Firm News 0.001*** * ** 0.001*** 0.001*** (5.17) (0.76) (1.66) (-2.25) (7.29) (6.05) Size 0.001*** 0.001*** 0.001*** (8.39) (6.67) (5.13) B/M 0.000*** 0.000*** 0.000*** (8.19) (6.10) (5.52) Turnover *** *** *** (-4.83) (-3.84) (-3.00) Leverage *** *** *** (-5.90) (-4.58) (-3.78) Volatility *** * *** (-3.41) (-1.73) (-3.44) Intercept ** *** (-0.04) (-0.14) (-2.41) (-0.94) (-1.25) (-2.95) (0.99) (0.82) (-0.64) N 1,401,162 1,401,162 1,401, , , , , , ,070 Average R 2 (%)
15 Long-short Portfolio Log Cumulative Returns of Cross-Industry-News (CIS) portfolio Cumulative Return CIS Portfolio Equal Weight
16 Sensitivity of News Based Trading Returns to Forecast Horizons Week Cross Industry News after News Raw Return (%) T Raw α (%) T α
17 Sensitivity of News Based Trading Returns to Forecast Horizons Week Firm Specific News after News Raw Return (%) T Raw α (%) T α
18 Robustness Test & Further Discussion Robustness Test CIS is more valuable to small stocks, illiquid stocks, high volatility stocks, low analyst coverage stocks and high analyst dispersion stocks. Cross industry news is more valuable during a high uncertainty period proxied by VIX and news dispersion, while it is not sensitive to policy uncertainty. CIS is more valuable during a high sentiment period than the low sentiment periods. Channels of News Traveling Cross Industry News Tone affect analyst forecast revision and improves their forecast accuracy. Cross Industry News Tone affect active institutional fund flows. CIS is incorporated into firm specific news tone by a delayed timer.
19 Conclusions Media News Travels Slowly across Different Industries. Cross industry news contains valuable information about firm fundamentals. A long-short portfolio based on CIS generates an annulized risk adjusted return 10.85% after 10 weeks of the signal. Cross industry news is more valuable to stocks with small size, illiquidity, high volatility, low analyst coverage and high analyst dispersion. Cross industry news is more valuable during a high uncertainty period and high sentiment period. Analyst forecasts, institutional fund flows and media news might be 3 channels that interpret cross industry news to the market.
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