Are Institutions Informed About News? · No consensus on informed institutional trading Empirical...

Are Institutions Informed About News?

Terrence Hendershott1 Dmitry Livdan1 Norman Schürhoff2

1University of California Berkeley2University of Lausanne, Swiss Finance Institute and CEPR

December 2014

Hendershott, Livdan, and Schürhoff (2014) Are Institutions Informed About News? December 2014 1 / 31

Are institutions informed about news?

1 Stock prices aggregate dispersed informationfrom many different news sources: Firm; Media; Macro; Prop Researchthrough trading by many different participants: Institutions, insiders, retail(Grossman and Stiglitz, 1980; Kyle, 1985; Glosten and Milgrom, 1985)

2 Institutions play active governance role + institutional ownership >50%

0"

5"

10"

15"

0%"10%"20%"30%"40%"50%"60%"

1950"1960"1970"1980"1990"1995"2000"2005"2009"

Ins0tu0onal"equity"($"bn)" %"of"total"

This paper studies link between institutional trading, news & stock returns


Are institutions informed? Case of Martha Stewart

Martha Stewart was CEO and chairwoman of Martha Stewart LivingOmnimedia (MSO)She was indicted on charges of securities fraud, trial started on Jan 27,2004.

M. S

tew

art t

rial b

egin

s

Fra

ud c

harg

e di

smis

sed

Cou

rt v

erdi

ct

-30

-20

-10

010

Ret

urn

(%)

01feb2004 01mar2004 01apr2004Date

-20

-15

-10

-50

IOF

(%

)

01feb2004 01mar2004 01apr2004Date


Are institutions informed?

New York Times, “Can settlements actually level the playing field for investors?”, 23 Dec 2002

“For years, Wall Street’s dirty little secret was that its research was devisedexpressly for two key constituencies: its institutional investors and its corporateclients. If the individual investor wanted to join the party, well, caveat emptor.”

1 Better access to informationDirect line of communication with publicly traded firms & brokeragesEmploy buy-side analysts & enjoy relationships with sell-side analysts

2 Better information processingEconomies of scale allow monitoring many sources of informationEmploy professionals & technologies with superior data processing skills


No consensus on informed institutional tradingEmpirical evidence for YES:

1 High lead low institutional ownership stock returns - Badrinath, Kale, Noe (1995)2 Higher institutional holdings associated with more efficient pricing - Sias and

Starks (1997), Boehmer and Kelly (2009)3 Profitable institutional buying beginning 5 days prior to release of analysts’

initial reports with positive recommendations - Irvine, Lipson, Puckett (2007)4 Institutional trading predicts returns at firm, industry & market levels -

Boulatov, Hendershott, and Livdan (2011)

Empirical evidence for NO:1 Institutional not net buyers in target firms prior to acquisitions & trading not

related to future earnings announcement returns - Griffin, Shu, Topaloglu (2011)2 Institutions’ trading strategy around takeover announcements does not yield

significant abnormal returns - Jegadeesh and Tang (2010)3 Institutions are not able to differentiate between good and bad stock

recommendations - Busse, Green, Jegadeesh (2010)4 Short sellers do not anticipate news - Engelberg, Reed, Ringgelberg (2010)


Our paper uses comprehensive news & trading data

Combine daily non-public data on buy & sell volume by all institutionswith Reuters news data feed & news sentiment for 2003–05

1 Comprehensive list of >1,600 NYSE-listed stocks

2 Complete trading records for all institutional investors (aggregate IVol & IOF)

3 All news announcements from Reuters news feed + textual sentimentanalysis

More complete picture and, more importantly, study joint behavior ofinstitutional trading, public news releases & stock returns

1 Occurrence of news announcements

2 Direction & sentiment of news content

3 Stock returns on announcement days

4 What types of news are institutions more/less informed about?

5 How do institutions trade on news?


Our findings

1 Institutional trading predicts:1 Occurrence of news announcements (IVol rises before news)

2 Direction & sentiment of news (Net buying/selling before pos./neg. news)

3 Stock returns on announcement days, but also on non-news days

4 Real performance (IBES earnings announcement surprises)

2 True for specific news types, but less statistically significant

3 Institutions predict company crisis (BKRT, MNGISS, JOB, CRIM, JUDIC)

4 Press releases & sentiment reversals: Do not trade on company hype

5 Trade on firm-specific news more than on industry, market, macro news

6 Significant fraction of price discovery occurs before public news release

→ Consistent with model of strategic trading on long-lived asymmetricinformation with correlated public news releases (a la Kyle)


Data description

1 Daily comprehensive institutional trading data (NYSE CAUD)Institutional volume in each NYSE stock

Institutional order flow imbalance in each NYSE stock

2 Take-by-take news releases on the Reuters Data Feed (RDF)News announcements (un-)scheduled & news generating process

News sentiment from Reuters NewsScope Sentiment Engine (RNSE)

3 CRSP stock returns + IBES accounting dataEarnings actual/surprises (SUE score)

Profitability & performance


Institutional trading data

NYSE Consolidated Equity Audit Trail (CAUD) for 2003-05Comprehensive daily trading data: 756 trading days in 1,667 NYSE stocksNon-public data; part of TORQ by J. Hasbrouck (144 stocks, Nov90–Jan91)Similar to TAQ, but 2 extra files: CD (audit trail) + SOD (system order data)Started after 87 crash; Used in other papers, e.g., Kaniel, Saar, Titman(2008, 2011) for “individuals”; Boehmer & Kelly (2009) and Boulatov,Hendershott, Livdan (2011) for institutions

Each transaction has code that identifies type of buyer & seller(proprietary/agency, program trade, index arbitrage, individual investor)

Institutional order flow imbalance is IOFi,t = IBuysi,t - ISalesi,t with

IBuysi,t =

# of Buysi,t∑n=1

$Buysni,t

MCi,t−250, ISalesi,t =

# of Salesi,t∑n=1

$Salesni,t

MCi,t−250.


News + sentiment data

Reuters Data Feed (RDF) – used by HFT & algo trading programs537K unique stories with 1-45 takes for 1-38 firms (126K story firm-days)Takes: alert, article, append, overwriteData: Unique story/take identifier, item type, time stamp, source, story text

Reuters NewsScope Sentiment Engine (RNSE)Stories classified into >70 topic codes (RES, MRG, HOT, MNGISS, CRIM)Most common topics: RES (Corporate Results, 46K), RESF (Results Forecasts,40K), STX (Stock Markets, 37K), MRG (Mergers & Acquisitions, 36K), DBT (DebtMarkets, 33K), NEWS (Major Breaking News, 30K), BACT (Business Activites, 28K)

RNSE uses 3-stage natural language processing (lexical analysis,linguistic parsing, machine learning) to measure news tone & relevance

Sentiment score ∈ [0, 1]: Pr(positive), Pr(neutral), Pr(negative)Relevance score ∈ [0, 1]: Calculated from firm mentions in headline & textRelevance-weighted news sentiment for each take, story & daily average

Net sentiment = 1No. Takes

∑Takes (Pr(positive) - Pr(negative))*Relevance


RNSE news announcements:

ticker

story_id

seqnofirm

0count

date

time

relevance

sent_pos

sent_neut

sent_neg

sent

item_type

attribtn

size

bcast_text

SLE 1 1 1 25Jan05 15:04:41 1 0.272 0.136 0.593 I1 ARTICLE RTRS 3197 UPDATE03ISara0Lee0cuts0fiscalIyear0forecast;0shares0dropSLE 2 1 1 25Jan05 15:10:56 1 0.218 0.611 0.171 0 ALERT RTRS 0 SARA0LEE0<SLE.N>0CEO0SAYS0CONSIDERING0FURTHER0"STRATEGIC0PORTFOLIO0INITATIVES"SLE 2 2 1 25Jan05 15:12:09 1 0.216 0.611 0.173 0 ALERT RTRS 0 SARA0LEE0CEO0SAYS0COMMODITY0COSTS0NOW0SEEN0$3500MILION0HIGHER0IN0FY05SLE 2 3 1 25Jan05 15:12:53 1 0.112 0.235 0.653 I1 ALERT RTRS 0 SARA0LEE0CEO0SAYS0PRICING0OPTIONS0"VERY0LIMITED"SLE 2 4 1 25Jan05 15:13:55 1 0.852 0.118 0.030 1 ALERT RTRS 0 SARA0LEE0CEO0SAYS0TO0DETAIL0PORTFOLIO0INITIATIVES0BY0FEBRUARY0ANALYSTS0MEETINGSLE 2 5 1 25Jan05 15:25:03 0.707 0.842 0.123 0.035 1 ARTICLE RTRS 866SLE 3 1 1 25Jan05 16:25:39 1 0.078 0.155 0.767 I1 ARTICLE RTRS 4034 UPDATE04ISara0Lee0cuts0fiscalIyear0forecast;0shares0dropSLE 4 1 1 26Jan05 15:23:32 1 0.056 0.125 0.819 I1 ARTICLE RTRS 495 RESEARCH0ALERTIJP0Morgan0lowers0Sara0Lee0Corp.0to0"neutral"SLE 5 1 1 27Jan05 17:30:09 1 0.826 0.130 0.044 1 ARTICLE BSW 1548 Sara0Lee0Corporation0Declares0236th0Consecutive0Quarterly0Dividend0<SLE.N>SLE 6 1 1 27Jan05 17:31:05 1 0.833 0.127 0.040 1 ARTICLE BSW 1511 REGISara0Lee0Corporation<SLEq.L><SLE.N>0Dividend0DeclarationSLE 7 1 1 27Jan05 17:31:51 1 0.596 0.397 0.007 1 ALERT RTRS 0 SARA0LEE0CORP0<SLE.N>0DECLARES0DIVIDENDSLE 7 2 1 27Jan05 17:32:07 1 0.853 0.117 0.030 1 ALERT RTRS 0 SARA0LEE0CORP0<SLE.N>0STOCK0PROVIDES0ANNUALIZED0DIVIDEND0YIELD0OF0MORE0THAN03.5%SLE 7 3 1 27Jan05 17:33:39 1 0.555 0.438 0.007 1 ALERT RTRS 0 SARA0LEE0CORP0<SLE.N>0DECLARES0DIVIDEND0$0.1975/SHR,0UNCHANGEDSLE 7 4 1 27Jan05 17:34:41 1 0.227 0.615 0.158 0 ARTICLE RTRS 183SLE 8 1 1 31Jan05 12:49:04 1 0.056 0.125 0.819 I1 ALERT RTRS 0 WACHOVIA0CUTS0SARA0LEE0<SLE.N>0TO0"UNDERPERFORM"0FROM0"MARKET0PERFORM"SLE 8 2 1 31Jan05 12:55:16 1 0.056 0.125 0.819 I1 ARTICLE RTRS 387 RESEARCH0ALERTIWachovia0cuts0Sara0Lee0to0"underperform"SLE 9 1 1 31Jan05 17:00:00 1 0.056 0.125 0.819 I1 ARTICLE RTRS 387 RESEARCH0ALERTIWachovia0cuts0Sara0Lee0to0"underperform"SLE 10 1 2 1Feb05 16:06:04 0.522 0.289 0.369 0.342 0 ARTICLE RTRS 4307 UPDATE04IAvon0quarterly0profit0climbs,0dividend0raisedSLE 11 1 2 1Feb05 21:41:00 0.500 0.267 0.359 0.374 I1 ARTICLE RTRS 4516 UPDATE05IAvon0quarterly0profit0climbs,0dividend0raisedSLE 12 1 5 3Feb05 20:23:07 0.132 0.057 0.127 0.816 I1 ARTICLE RTRS 4568 Weak0margin0growth0starts0to0worry0Wall0StreetSLE 13 1 6 11Feb05 7:12:17 0.158 0.021 0.197 0.782 I1 ARTICLE RTRS 3047 PRESS0DIGEST0I0Wall0Street0Journal0I0Feb011SLE 14 1 1 16Feb05 20:00:24 1 0.198 0.579 0.223 0 ARTICLE BSW 1493 Sara0Lee0Corporation0to0Webcast0Presentation0at0CAGNY;0Executives0to0<SLE.N>SLE 15 1 1 16Feb05 21:59:19 1 0.248 0.129 0.623 I1 ARTICLE RTRS 1344 Sara0Lee0barred0from0using0Jimmy0Dean0endorsementSLE 16 1 1 17Feb05 11:57:22 1 0.596 0.397 0.007 1 ALERT RTRS 0 PIPER0RAISES0SARA0LEE0<SLE.N>0TO0"OUTPERFORM"0FROM0"MARKET0PERFORM"SLE 16 2 1 17Feb05 12:05:10 1 0.556 0.437 0.007 1 ARTICLE RTRS 417 RESEARCH0ALERTIPiper0upgrades0Sara0Lee0to0"outperform"SLE 17 1 1 17Feb05 17:00:03 1 0.556 0.437 0.007 1 ARTICLE RTRS 417 RESEARCH0ALERTIPiper0upgrades0Sara0Lee0to0"outperform"SLE 18 1 1 17Feb05 20:21:11 0.913 0.095 0.211 0.694 I1 ARTICLE BSW 2206 Nutrition0Expert0to0Discuss0Heart0Health,0Importance0of0Whole0Grains0<SLE.N>SLE 19 1 3 23Feb05 2:45:59 0.667 0.207 0.117 0.676 I1 ARTICLE RTRS 2906 INTERVIEWIFlowers0Foods0sees0organics0in0futureSLE 20 1 1 24Feb05 0:30:02 1 0.513 0.476 0.011 1 ALERT RTRS 0 SARA0LEE0CORPORATION0REVIEWS0TRANSFORMATION0PLAN0AT0CAGNY'S0ANNUAL0CONFERENCESLE 20 2 1 24Feb05 0:31:34 1 0.214 0.610 0.176 0 ALERT RTRS 0 SARA0LEE0<SLE.N>0SAYS0TO0SELL0ITS0EUROPEAN0APPAREL,0EUROPEAN0PACKAGED0MEATS,0U.S.0RETAIL0COFFEE,0DIRECT0SELLING0BUSINESSESSLE 20 3 1 24Feb05 0:32:24 1 0.176 0.810 0.014 0 ALERT RTRS 0 SARA0LEE0<SLE.N>0SAYS0ALSO0PURSING0SPINIOFF0ITS0BRANDED0APPAREL0AMERICAS/ASIA0BUSINESSSLE 20 4 1 24Feb05 0:34:07 1 0.225 0.614 0.161 0 ALERT RTRS 0 SARA0LEE0<SLE.N>0SAYS0TO0SPEND0$2500MLN0A0YEAR0ON0ADVERTISING,0PROMOTION,0R&DSLE 20 5 1 24Feb05 0:35:08 1 0.171 0.811 0.018 0 ALERT RTRS 0 SARA0LEE0<SLE.N>0EXPECTS0CHANGES0TO0SAVE0$5750MLNI$8100MLN0ON0ANNUALIZED0BASISSLE 20 6 1 24Feb05 0:36:36 1 0.535 0.456 0.009 1 ALERT RTRS 0 SARA0LEE0<SLE.N>0SEES0DEBT0DOWN0TO0$1.50BLNI$20BLN0BY0FISCAL020100FROM0$4.80BLN0FISCAL02004SLE 20 7 1 24Feb05 0:44:00 1 0.700 0.279 0.021 1 ARTICLE RTRS 189 TEXTISara0Lee0outlines0transformation0plan


Descriptive statistics

Mean S.D. 5% 50% 95%

Panel A: News releases and sentiment (126,148 observations)News stocks per day (% of firms) 11.506 3.921 5.886 11.187 17.595News days per stock (% of days) 10.610 16.667 0.000 4.636 47.458Stocks per news release 7.197 9.729 1.000 4.000 24.000Sentiment per news release 0.007 0.421 -0.726 0.040 0.738

Panel B: Institutional trading (1,096,514 observations)IVol (% of market cap) 0.832 1.644 0.046 0.432 2.728IOF (% of market cap) 0.004 0.168 -0.176 0.002 0.189|IOF|/IVol 0.153 0.166 0.008 0.102 0.475IBuys (% of market cap) 0.418 0.829 0.020 0.216 1.375ISales (% of market cap) 0.414 0.824 0.019 0.213 1.367

Cross-sectional variation in news coverage (11% of firms) & intermittentnews (1 per 10 days) & news clustering (7 firms)News sentiment not obviously biased on averageInstitutional order flow varies substantially & 15% of institutional volume


Most common topic codesCode Topic Description News0days SentimentRES Corporate+Results All+corporate+financial+results;+accounts,+annual+reports 46,457+++++ 0.015+(0.412)RESF Corporate+Results+Forecasts All+forecasting+of+corporate+financial+results;+tabular+and+textual 40,216+++++ E0.030+(0.462)STX Stock+Markets All+news+about+equity+markets+operations,+regulations+&+structure 37,983+++++ 0.016+(0.376)MRG Mergers+and+Acquisitions All+corporate+stories+about+ownership;+stakes;+mergers;+acquisitions 35,911+++++ 0.248+(0.461)DBT Debt+Markets All+debt+market+news;+including+primary+issuance+&+trading+of+debt 33,660+++++ E0.019+(0.463)NEWS Major+Breaking+News Top+stories+of+major+international+impact 30,550+++++ E0.131+(0.475)BACT Business+Activites News+relating+to+business+activities 28,724+++++ 0.138+(0.283)LAW Legislation Legislation+affecting+budgets,+securities+laws,+lawsuits,+court+rulings 27,633+++++ 0.043+(0.562)CORA Corporate+Analysis Analysis+about+a+company+or+group+of+companies 17,364+++++ 0.039+(0.065)HOT Hot+Stocks News+about+stocks+on+the+move 16,840+++++ E0.097+(0.529)REGS Regulatory+Issues News+about+regulation 16,234+++++ E0.259+(0.433)WASH Washington/US+Govt All+US+Federal+government+politics,+policies+and+economics 14,432+++++ E0.230+(0.432)DIV Dividends Dividend+forecasts,+declarations+and+payments 13,809+++++ 0.398+(0.381)ISU New+Issues All+new+corporate+issues+of+debt+and+corporate+issues+of+equity 13,523+++++ 0.149+(0.393)RCH Broker+Research+&+Recomm. All+news+about+broker+research+and+recommendations 13,014+++++ 0.013+(0.576)AAA Ratings All+news+about+credit+ratings 11,455+++++ 0.027+(0.512)JOB Labour;+(Un)employment All+news+on+labour+issues;+(un)employment;+labour+disputes;+strikes 10,768+++++ E0.131+(0.478)MNGISS Management+issues/policy Management+issues+including+executive+pay,+bonuses,+governance 9,302+++++++ E0.106+(0.466)IPO Initial+Public+Offerings First+public+listing+of+a+company's+stock 4,574+++++++ 0.162+(0.433)CRIM Crime,+Law+Enforcement Civil+and+criminal+law;+law;+crime;+corporate+crime;+fraud;+murder 4,573+++++++ E0.464+(0.339)JUDIC Judicial+processes/cases All+stories+about+judicial+processes/court+cases/court+decisions 3,923+++++++ E0.462+(0.333)ECI Economic+Indicators News,+forecasts+or+analyses+of+such+indicators 2,935+++++++ E0.029+(0.495)MCE MacroEEconomics All+news+and+analysis+on+macroEeconomics 2,474+++++++ E0.083+(0.507)BKRT Bankruptcies Corporate+insolvencies+and+bankruptcies.+ 2,276+++++++ E0.346+(0.419)VIO Civil+unrest Stories+about+riots,+demonstrations+and+other+internal+disturbances 1,285+++++++ E0.360+(0.404)FED Federal+Reserve+Board FED+activities+and+news 1,024+++++++ E0.200+(0.451)


Institutional trading around news releasesEvent Study on Returns and IVol

Return volatility around news days: IVol around news days:

.009

6.0

098

.01

.010

2.0

104

.010

6|R

etur

n|

−10 −5 0 5 10Day

.42

.44

.46

.48

IVol

−10 −5 0 5 10Day

Return volatility & institutional volume rise around news releases

More uncertainty & potential for speculation before news


Institutional trading around news releasesLogit regression with firm fixed effects

(A) (B) (C) (D) (E)

IVolt−1 1.025*** 1.017***|Returnt−1| 1.101*** 1.015***Newsdayt−1 2.474*** 6.261***|Sentimentt−1| 2.697*** 0.471***

Likelihood -2.405 -2.403 -2.343 -2.388 -2.378

Institutional volume predicts news releases, beyond the rise in volatility &clustering in news days


Institutional trading around news daysEvent Study on IOF and Returns

Abnormal returns around news days: Abnormal IOF around news days:

-1-.

50

.51

Ret

urn

(%)

-10 -5 0 5 10Day

-2-1

01

2IO

F

-10 -5 0 5 10Day

Good (Bad) news days = Top (Bottom) quintile of relevance-wt sentimentMean abnormal buy-and-hold return (BHAR) for i ∈ (Good,Bad):

BHAR(t0, t1) =∑

i

wi

t1∏t0

Ri,t −∑

Control

wControl

t1∏t0

Rcontrol,t

Controls = All stocks on news days; Abnormal IOF uses same methodHendershott, Livdan, and Schürhoff (2014) Are Institutions Informed About News? December 2014 16 / 31

Institutional trading predicts news day returnReturns on news day regressed on lagged IOF + controls (firm fixed effects & double-clustered SE)

NewsdayReturnt (A) (B) (C) (D) (E) (F)

IOFt−1 0.652*** 0.693*** 0.541***IBuyst−1 0.632*** 0.668*** 0.518***ISalest−1 -0.693*** -0.731*** -0.575***Returnt−1 -0.015 0.002 -0.015 0.002Sentimentt−1 0.034 0.037* 0.034 0.037*ln(Volume)t−1 -0.024 -0.004 0.001 0.018Industry returnt 0.715*** 0.715***Market returnt 0.266*** 0.266***N 126,148 126,148 126,148 126,148 126,148 126,148

Institutional trading predicts announcement day return

Both institutional buying & selling predict returns (ISales more so)


Institutional trading predicts news sentimentSentiment on news day regressed on lagged IOF + controls (firm fixed effects & double-clust. SE)

Sentimentt (A) (B) (C) (D) (E) (F)

IOFt−1 0.079*** 0.052** 0.052**IBuyst−1 0.071*** 0.046** 0.046**ISalest−1 -0.095*** -0.061*** -0.061***Returnt−1 0.009*** 0.009*** 0.009*** 0.008***Sentimentt−1 0.167*** 0.167*** 0.167*** 0.167***ln(Volume)t−1 -0.013* -0.012* -0.007 -0.007Industry returnt 0.016*** 0.016***Market returnt -0.011*** -0.011***N 126,148 126,148 126,148 126,148 126,148 126,148

Institutional trading predicts news sentiment, controlling forpre-announcement runup & sentiment persistence

ISales more informative than IBuys (upward trend in institutional holdings)


Institutional trading before & on news releasesIOF on news day regressed on lagged IOF + controls (firm fixed effects & double-clustered SE)

IOFt (A) (B) (C) (D) (E) (F)

IOFt−1 0.257*** 0.247*** 0.249***IBuyst−1 0.259*** 0.250*** 0.252***ISalest−1 -0.254*** -0.244*** -0.246***Returnt−1 0.004*** 0.004*** 0.004*** 0.004***Sentimentt−1 -0.001** -0.001** -0.001** -0.001**ln(Volume)t−1 0.002** 0.002** -0.001 -0.001Industry returnt 0.001*** 0.001***Market returnt -0.011*** -0.011***N 126,148 126,148 126,148 126,148 126,148 126,148

Institutional trading persistent (ρ ≈ 25%)

Institutions tend to trade in same direction on news days as before


Returns on news vs. non-news daysReturn on all days regressed on lagged IOF + controls (firm fixed effects & double-clustered SE)

Returnt (A) (B) (C) (D) (E) (F)

IOFt−1 0.393*** 0.285*** 0.288***IOFt−1 ∗ Newsdayt 0.408*** 0.398***IBuyst−1 0.385*** 0.280*** 0.288***ISalest−1 -0.406*** -0.294*** -0.288***IBuyst−1 ∗ Newsdayt 0.401*** 0.375**ISalest−1 ∗ Newsdayt -0.420*** -0.431***Returnt−1 -0.017 -0.017 -0.019 -0.017 -0.017 -0.019Sentimentt−1 0.048*** 0.048*** 0.079*** 0.049*** 0.048*** 0.080***ln(Volume)t−1 -0.007 -0.008 -0.012 0.002 0.001 -0.012Newsdayt 0.015 0.015 0.020 0.035Returnt−1 ∗ Newsdayt 0.005 0.005Sentimentt−1 ∗ Newsdayt -0.046 -0.046ln(Volume)t−1 ∗ Newsdayt 0.008 0.032

Institutional trading predicts returns on news & on non-news days

Institutional trading predicts returns more on news than non-news days


Joint behavior of IOF, return & sentimentPanel VAR & impulse responses

yit = (IOFit, Returnit, Sentimentit)′ jointly & endogenously generated

Panel VAR with unobserved heterogeneity (Holtz-Eakin et al., 1988):

yit = α0 +

L∑l=1

λlyit−l +αi + εit,

where α0 is 3x1 vector of intercepts, λl are 3x3 coefficient matrices, αi isvector of unobserved individual (fixed) effects, and εit are innovations

Coefficient estimates obtained using GMM estimation

Impulse responses:Dependent variables ordered in sequence: sentiment, IOF, return

Correspond to 1 S.D. shock in model with L = 2 lags

Standard error bands at 5% level for impulse responses (dashed lines)generated using Monte-Carlo simulations with 1,000 draws


Panel VAR: Joint behavior of IOF, return & sentiment

IOFt Returnt Sentimentt

IOF t − 1 0.240*** 0.169*** 0.003***(0.008) (0.016) (0.001)

t − 2 0.077*** 0.032** -0.001(0.006) (0.016) (0.001)

Return t − 1 0.003*** -0.005*** 0.001***(0.000) (0.001) (0.000)

t − 2 0.001*** 0.000 0.000(0.000) (0.001) (0.000)

Sentiment t − 1 0.000 0.090*** 0.098***(0.001) (0.014) (0.002)

t − 2 -0.001 0.018 0.030***(0.001) (0.013) (0.002)


News types: Macro news

MACRO categories

MACRO ECI FED GVD MCE WASHN 17,586 1,812 990 3,682 1,918 13,075

Panel A: Returnt

IBuyst−1 0.756** 0.518 0.872 0.736 -0.028 1.069**ISalest−1 -0.810** -0.787 -0.883 -0.999 -0.406 -1.084**Returnt−1 0.018 0.017 -0.052 0.028 0.022 -0.005Sentimentt−1 0.056 0.048 0.221 0.273*** 0.154 -0.031

Panel B: Sentimentt

IBuyst−1 0.121 0.481** -0.224 0.066 -0.061 0.174ISalest−1 -0.122 -0.467** 0.221 -0.005 0.047 -0.192Returnt−1 0.011*** 0.022 0.040*** 0.023*** 0.033** -0.001Sentimentt−1 0.173*** 0.189*** 0.256*** 0.234*** 0.211*** 0.135***

Panel C: IOFt

IBuyst−1 0.210*** 0.140** 0.212*** 0.191*** 0.159*** 0.239***ISalest−1 -0.201*** -0.149*** -0.217*** -0.167*** -0.157*** -0.238***Returnt−1 0.004*** 0.003*** 0.004*** 0.003*** 0.003*** 0.004***Sentimentt−1 -0.002** -0.006 -0.004 -0.003 -0.001 -0.003**


News types: Crises

CRISIS categories

CRISIS BKRT MNGISS JOB CRIM JUDIC REGS CDV WEAN 31,633 2,069 7,338 8,766 3,925 3,407 14,270 2,466 2,234

Panel A: Returnt

IBuyst−1 0.738*** 1.350 0.455 0.788 1.278* 1.047 1.127** -0.162 -0.190ISalest−1 -0.872*** -1.652 -0.621 -0.978* -1.444** -1.303-1.287*** -0.265 0.226Returnt−1 -0.003 0.069 -0.007 -0.008 -0.029 -0.025 0.009 0.070***-0.095**Sentimentt−1 0.047 0.107 0.062 0.106 -0.027 0.036 0.013-0.478*** 0.086

Panel B: Sentimentt

IBuyst−1 0.156** 0.718** 0.224 0.118 0.038 0.236 0.094 0.058 0.382ISalest−1 -0.171*** -0.807** -0.226 -0.117 -0.019 -0.226 -0.107 -0.072 -0.383Returnt−1 0.003 -0.010 0.005 -0.001 -0.002 0.001 0.006 0.008 -0.006Sentimentt−1 0.151*** 0.095** 0.185*** 0.139*** 0.131*** 0.009 0.128*** 0.221***0.197***

Panel C: IOFt

IBuyst−1 0.241*** 0.192*** 0.256*** 0.206*** 0.210*** 0.248*** 0.243*** 0.191*** 0.206**ISalest−1 -0.237***-0.206*** -0.257***-0.207***-0.203***-0.248***-0.243***-0.179***-0.198**Returnt−1 0.004*** 0.004*** 0.004*** 0.004*** 0.004*** 0.004*** 0.004*** 0.003***0.005***Sentimentt−1 -0.001* 0.004* -0.002 -0.000 -0.001 -0.002-0.003*** -0.008* 0.002


News types: Earnings surprisesQuantifiable event related to fundamental real performanceStandardized unanticipated earnings score (SUE):

SUEi =ERi − E[ERi]

σ(ERi)

SUEt (A) (B) (C) (D)

IOFt−1 0.980** 0.838*IBuyst−1 1.058** 0.853*ISalest−1 -0.980** -0.837*Returnt−1 0.059** 0.058**Sentimentt−1 0.104 0.103ln(Volume)t−1 0.066 0.058

Contemp. correlation positive w/ returns & sentiment, negative w/ IOF:

Correlations IOFt Returnt Sentimentt

SUEt -0.038*** 0.338*** 0.211***


News or Hype?

Hype = Predictions in news articles that are ultimately wrong

Measuring hype:1 Press releases by companies from PR Newswire & Business Wire2 Sentiment reversals within x = 5, 10 days

Press releases:PR Newswire: 76,394 obs, +0.290 average sentimentBusiness Wire: 51,147 obs, +0.317 average sentimentContemporaneous correlation between returns & sentiment close to 0

Hypotheses:1 Naive: follow trend by buying during & selling after hype2 Short-run informed/creator: buy before & sell during hype3 Long-run strategic: anticipate reversal & abstain from trading

Institutions do not fall for hype; they do not react significantly


How do institutions respond to hype?PR Newswire (PRN) press releases

0.0

2.0

4.0

6.0

8F

ract

ion

-1 -.5 0 .5 1Sentiment

0.0

1.0

2.0

3.0

4S

entim

ent

-10 -5 0 5 10Day

-.25

-.2

-.15

-.1

-.05

0R

etur

n (%

)

-10 -5 0 5 10Day

-1-.

50

.51

IOF

(%

)

-10 -5 0 5 10Day

PR Newswire (PRN) => IOF does not follow hypeHendershott, Livdan, and Schürhoff (2014) Are Institutions Informed About News? December 2014 27 / 31

How do institutions respond to hype?Business Wire (BSW) press releases & regulatory filings

0.0

2.0

4.0

6.0

8F

ract

ion

-1 -.5 0 .5 1Sentiment

0.0

1.0

2.0

3.0

4.0

5S

entim

ent

-10 -5 0 5 10Day

-.15

-.1

-.05

0.0

5R

etur

n (%

)

-10 -5 0 5 10Day

-1-.

50

.5IO

F (

%)

-10 -5 0 5 10Day

Business Wire (BSW) => IOF does not follow hypeHendershott, Livdan, and Schürhoff (2014) Are Institutions Informed About News? December 2014 28 / 31

How do institutions respond to hype?Sentiment reversals between t and t + 5

Sentiment reversal = Top decile of |Sentimentt − Sentimentt+5|

IOF does not follow hype

Long-run strategic behavior

-.5

0.5

Ret

urn

(%)

-10 -5 0 5 10Day

-2-1

01

2IO

F (

%)

-10 -5 0 5 10Day


How do institutions respond to company hype?Sentiment reversals between t and t + 10

Sentiment reversal = Top decile of |Sentimentt − Sentimentt+10|

IOF does not follow hype

Long-run strategic behavior

-.5

0.5

1R

etur

n (%

)

-10 -5 0 5 10Day

-10

12

3IO

F (

%)

-10 -5 0 5 10Day


Conclusion

NYSE audit trail data on buy & sell volume by institutions (IOF)

Reuters NewsScope Sentiment Engine (News sentiment)

Institutional trading predicts:1 News announcements

2 Sentiment of the news

3 Return on announcement days

4 Earnings announcement surprises + crisis news

Institutions do not trade based on company hype

Institutional trading does not predict industry, market, macro news

→ Institutions are informed about various news sources & contributesignificantly to price discovery


Date post:	15-Aug-2020
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times

Are Institutions Informed About News? · No consensus on informed institutional trading Empirical...

Documents