Talking Numbers: Technical versus Fundamental Recommendations

Transcription

1 Talkng Numbers: Techncal versus Fundamental Recommendatons Doron Avramov *, Guy Kaplansk **, Ham Levy *** Ths verson: August 20, 2015 Abstract: Ths study assesses the economc value of techncal and fundamental recommendatons smultaneously featured on Talkng Numbers, a CNBC and Yahoo jont broadcast. Techncans dsplay stock-pckng sklls, whle fundamentalsts reveal no value. In partcular, techncans overwhelmngly outperform fundamentalsts n predctng returns over horzons of three to nne months and moreover they produce large alpha wth respect to the Fama and French (1993) and momentum benchmarks. Consderng market ndexes, Treasures, commodtes, and varous equty ndexes, both schools of recommendaton generate poor forecasts. Overall, the evdence shows that propretary tradng rules could, at best, enhance nvestments n sngle stocks, whle returns on broader assets are unpredctable. Keywords: fundamental analyss; techncal analyss; market effcency; abnormal returns JEL Codes: G10, G14, G24 * The Hebrew Unversty of Jerusalem, emal: [email protected]. ** Bar-Ilan Unversty, Israel, emal: [email protected]. *** The Hebrew Unversty of Jerusalem, emal: [email protected]. 0

2 1. Introducton Ths paper employs a novel dataset from Talkng Numbers to assess the economc value of techncal and fundamental recommendatons coverng a comprehensve lst of assets. Hosted by CNBC and Yahoo Fnance, Talkng Numbers s a meda broadcast smultaneously featurng fundamental and techncal recommendatons before and durng the market open. Dual recommendatons are made by hghly experenced analysts representng promnent nsttutons. Ths unque setup featurng synchronzed recommendatons, multple assets, and the presence of leadng professonals, offers mportant nsghts n assessng the value of fnancal analyss. For one, we establsh a natural experment to contrast techncal and fundamental analyses and gauge the real tme value of dual recommendatons. Our experments are robust to several bases characterzng analysts forecasts. To wt, as the bar to partcpate n the show s hgh, analysts are less prone to career concerns, and, moreover, the smultaneous broadcast elmnates potental cross-herdng between analysts. Next, analysts recommendatons span ndvdual stocks and broader assets, ncludng Treasures, commodtes, domestc and foregn market ndexes, and varous equty ndexes. Durng the broadcast, both schools of thought are essentally exposed to the same publc nformaton. Thus, comparng performance enables one to assess the extent to whch techncans and fundamentalsts effcently process the flow of publc nformaton. Our analyss s reasonably robust to data mnng concerns. Indeed, to our knowledge, we are the frst to vst the fundamental and techncal recommendatons broadcasted n Talkng Numbers, and moreover, we explctly study techncal recommendatons rather than techncal rules, whch are at the core of the lterature on techncal analyss. Fnally, analyst recommendatons feature the largest stocks (e.g. Apple, Google, Exxon Mobl), lqud 1

3 commodtes (e.g. gold, ol), man exchange rates (e.g. the US dollar), major bonds (e.g. the U.S. ten-year notes), major ndces (e.g. the varous Dow Jones ndexes), and promnent sectors (e.g., Technology, Real Estate, Pharmaceutcal). In addton, our experments are comprehensve employng 1000 dual recommendatons on 262 stocks and 620 dual recommendatons on the other assets. Thus, our fndngs are general enough and are less prone to lqudty concerns. Fgure 1 hghlghts the major emprcal evdence for techncal and fundamental stock recommendatons durng the sample perod from November 2011 to December Plotted are the Cumulatve Abnormal Returns (CARs) startng from the recommendaton broadcast (Panels A and B) and the cumulatve payoffs generated by four spread portfolos (Panel C) undertakng long (short) postons n stocks wth buy (sell) recommendatons. In partcular, we consder buymnus-sell and strong buy-mnus-sell, both techncal and fundamental, spread portfolos. [Please nsert Fgure 1 here] The evdence shows that techncans dsplay rather mpressve stock-pckng sklls, whle fundamentalsts provde no value, whatsoever. To llustrate, observe from Panel A that the nnemonth CARs of the strong sell, sell, hold, buy, and strong buy techncal recommendatons are 8.85%, 2.74%, 0.02%, 1.74%, and 7.92%, respectvely. In contrast, Panel B shows that CARs attrbutable to fundamental analyss do not algn wth the type of recommendaton. If anythng, sell recommendatons generate hgher CAR than the buy recommendatons. Smlarly, observe from Panel C that the value of the fundamental buy-mnus-sell portfolo s non-postve throughout the entre sample perod, and the value of the fundamental strong-buy-mnus-sell portfolo rotates around zero. In contrast, the value of the two correspondng techncal portfolos s postve and t typcally ncreases wth the nvestment horzon. Over the sample perod, the buy-mnus-sell portfolo value s $0.42 per $1 ntal long 2

4 and $1 ntal short postons, recordng annual alpha of 14.6% (t = 2.32). More promnently, the value of the strong buy-mnus-sell portfolo s $2.30, recordng strkngly large annual alpha of 45.3% (t = 3.58). Consderng tradng costs upon enterng and extng a poston, the threshold cost that would set the alpha of the buy-mnus-sell (strong buy-mnus-sell) portfolo to zero s 0.82% (3.08%) per transacton. We fnd that techncal analyss outperforms along two dmensons. Frst, t generates a hgher proporton of correct recommendatons, where a correct recommendaton amounts to buy (sell) recommendatons followed by advancng (dmnshng) stock prces. Second, techncal recommendatons record hgher gans followng correct recommendatons and lower losses followng ncorrect recommendatons. The success of techncans n pckng stocks s robust to controllng for common rsk factors as well as for frm-level sze, book-to-market rato, volatlty, tradng volume, and past trends n stock prces. It s also unaffected by analyst s gender, by the mmedate mpact of the broadcast on stock prce (whch s found to be hghly sgnfcant), and, as shown earler, by reasonable tradng costs. We further demonstrate that the nablty of fundamentalsts to predct future returns s unform across all ndustres and styles consdered. In contrast, techncal stock recommendatons produce robust predctons for all styles and ndustres, excludng mnng. The falure to predct returns on mnng stocks mrrors the nablty of all analysts, partcpatng n Talkng Numbers, to predct future commodty prces. In fact, both schools of thought have been unable to predct returns not only on commodtes but also on the other broader assets, e.g., Treasures, market ndexes, and ndustres. The dfference n performance among ndvdual stocks versus broad ndexes s possbly due to arbtrage captal n that nvestable patterns n broad market ndexes mmedately attract captal and are thus traded away. Moreover, common wsdom suggests that 3

5 the abltes to effcently process publc nformaton or to extract prvate sgnals from prces and volume mostly characterze ndvdual stocks whle they are less appealng to broader assets. Three strands of studes are related to our work. The frst nvestgates the value of fundamental recommendatons. Jegadeesh et al. (2004) fnd that the level of analysts consensus recommendaton provdes lttle value over other nvestment sgnals. Stckel (1995) and Womack (1996) document value n revsons n consensus recommendatons, whle Barber et al. (2001) dsplay the dsappearance of that value n the presence of transacton costs. Lkewse, Metrck (1999) and Jaffe and Mahoney (1999) exhbt the lack of forecastng value focusng on comprehensve samples of nvestment newsletters. Here, we show that even consderng the elte group of analysts, appealng to the large crowd, fundamentalsts provde no nvestment value. The second strand deals wth techncal rules. Theoretcally, Brown and Jennngs (1989) and Blume et al. (1994) show that past prces and tradng volume, respectvely, could reveal the presence of prvate nformaton, and Zhu and Zhou (2009) show that combnng movng average wth other techncal sgnals mproves asset allocatons. Emprcally, the evdence on the strength of techncal analyss s mxed. Brown et al. (1998) show that Dow rules exhbt predctve ablty, yet tradng frctons could consume proftablty. Brock et al. (1992) fnd that techncal rules predct returns on stock ndexes. However, such predctablty becomes nonexstent n the presence of transacton costs, per Bessembnder and Chan (1998). Sullvan et al. (1999) and Allen and Karjalanen (1999) do not fnd substantal value n techncal rules, whle Lo et al. (2000) show that techncal patterns predct ndvdual stock returns. Han et al. (2013) apply movng average to equty portfolos and report proftablty, and Neely et al. (2014) show that techncal ndcators exhbt predctve power for the equty premum. Notably, our paper assesses the value of techncal recommendatons rather than the value of publczed techncal rules. 4

6 The thrd strand examnes the mmedate mpact of meda publczed recommendatons. Mathur and Waheed (1995), Lu et al. (1990), and Barber and Loeffler (1993) document abnormal returns shortly after the publcaton of recommendatons n the newspaper, and Hrschey et al. (2000) report abnormal returns on the day after the recommendatons are posted on the nternet. However, Dewally (2003) detects no market reacton to recommendatons posted by newsgroup on the nternet. Neumann and Pepp (2007) fnd that recommendatons made by Jm Cramer, the host of the CNBC Mad Money program, are followed by abnormal payoffs durng the followng day, and Busse and Green (2002) fnd that recommendatons broadcasted n the CNBC Mornng Call and Mdday Call programs produce abnormal mmedate profts wthn 15 seconds. Relatve to these studes, we examne the value, rather than the mmedate mpact, of recommendatons. Eventually, techncal stock recommendatons provde value not only for an mmedate tradng, but also for a few months followng the broadcast. Indeed, to our knowledge, we are the frst to compare head-to-head the qualty of fundamental and techncal analyses. Our setup s unque n that both schools of thought are exposed to the same publc nformaton, smultaneous recommendatons are made by wellpostoned analysts, and the collecton of assets covered s comprehensve. A remanng task s to shed lght on the economcally large alpha delvered by techncal stock recommendatons. In actve asset management, alpha reflects stock pckng and benchmark tmng sklls, where stockpckng sklls could further be attrbutable to ndustry or style rotaton. Economc theory (e.g., Admat et al. 1986) typcally formulates sklls through manageral ablty to process prvate sgnals. Emprcally, however, one cannot conclude whether a postve-alpha manager does possess prvate nformaton or perhaps that manger has the ablty to process publc nformaton more effectvely. Of course, there has always been the bad-model concern. In partcular, 5

7 performance specfcatons may mproperly account for those factors characterzng the rskreturn tradeoff and further they are lkely to msspecfy the nature of tme varaton n both benchmark loadngs and benchmark rsk premums. Smlar ssues and concerns apply n our context. Essentally, we rule out the possblty of market tmng and ndustry or style rotaton, as techncans fal to predct returns on broad ndexes. Puttng asde bad model concerns, techncans could ndeed use prvate sgnals as prescrbed by theory. Alternatvely and perhaps more convncngly, techncans may process publc nformaton more effectvely through ther nvestment toolkts. Pnnng down the exact source of stock pckng sklls n a general context s a worthy research agenda for future work. The remander of the paper s organzed as follows: Secton 2 presents the data and methodology. Secton 3 reports the emprcal results correspondng to ndvdual stocks. Secton 4 extends the analyss to other asset classes noted earler. Secton 5 concludes. The lst of assets and the recommendaton classfcaton system are gven n the appendces. 2. Methodology and Data Our techncal and fundamental recommendatons are extracted from the meda broadcast enttled Takng Numbers. Pror to May 2013, the program was exclusvely hosted by the CNBC televson network. From May 2013, CNBC and Yahoo Fnance have been jontly hostng the show. Based on Yahoo, the broadcast takes a 360 approach to tradng-hghlghtng the best nvestment opportuntes by analyzng stocks both a techncal and a fundamental pont of vew A typcal broadcast features assets that make headlnes n the fnancal meda. Examples nclude stocks of promnent frms that are about to post fnancals, hot sectors, hot markets, and 6

8 general assets experencng substantal prce changes (e.g., the recent drop n commodty prces and the rse n the U.S. dollar). Fundamental analyss typcally starts wth a macroeconomc outlook, ndustry condtons, and then a recommendaton follows along wth supportng dscussons. Techncal analyss, n most cases, descrbes a chart of hstorcal prces along wth movng averages. The analyst then dscusses the man techncal characterstcs underlyng the recommendaton. Often, there are more supportng charts and even a dscusson lnkng the techncal recommendaton to fundamental factors. It s common that the techncal analyst, the fundamental analyst, and the show hosts debate the nature of the recommendatons. The sample spans November 8, 2011 through December 31, November 8, 2011 featured the frst comparson between techncal and fundamental ponts of vew. Beforehand, Talkng Numbers was a rather dfferent show. It was part of the CNBC broadcast Closng Bell, and usually featured the vew of a sngle analyst who manly dscussed the S&P 500 ndex. In the frst year of the sample, the program was broadcasted once per tradng day, typcally featurng four recommendatons: two dstnct assets each of whch s covered by both techncal and fundamental analysts. More recently, the program has usually been broadcasted several tmes daly whle n most cases each program covers a sngle asset. In a few cases, the program features only one analyst delverng ether techncal or fundamental recommendaton wthout a counter vew. Such sngle recommendatons are excluded from the prmary analyss and are later consdered for examnng the robustness of results. Pror to CNBC s merge wth Yahoo n May 2013, we approached the broadcasts usng two man sources: the CNBC archve at vdeo.cnbc.com and The Internet Archve's TV news research servce at archve.org. For that perod, we employ several net searchng practces to 7

9 detect programs that were mssng from the man data archves. After the merge, the man source s Yahoo Fnance at fnance.yahoo.com. Ths source s organzed chronologcally and contaned all the post-merger programs. Overall, we cover the vast majorty, f not all, of Talkng Numbers shows durng the sample perod. We classfy techncal and fundamental recommendatons nto fve conventonal categores,.e., strong buy, buy, hold, sell, and strong sell. In about 20% 30% (dependng on the asset class) of the cases, the analyst s formal ratng s explctly stated verbally or n a capton. Then, the classfcaton clearly adheres to analysts explct ratngs. In other cases, the recommendaton s not explct. Then we systematcally extract the recommendaton category based on the content of the show, as dscussed n the next paragraph. We vewed each program twce and classfed separately nto each of the fve recommendaton categores. In most cases, the two classfcatons were dentcal. If a msmatch emerged the program was vewed agan and the fnal classfcaton was then delvered. Appendx A provdes the full lst of terms characterzng the fve recommendaton categores, whle Appendx B llustrates how classfcaton s made for specfc program. Below we provde a comprehensve dscusson. The strong buy category features dstnct and enthusastc recommendaton to buy an asset wthout any reservaton. Any expectaton for at least 20% gan durng the comng year (expressed drectly or mpled by the analyst s prce target) falls wthn ths category. The buy category characterzes a buy recommendaton wth reservatons that do not deter from mmedately buyng the asset, a clearly postve busness forecast, and the use of postve terms such as cheap and overweght. For example, f an analyst suggests to start buyng the asset and ncrease buyng as a pullback emerges, such explct recommendaton would be classfed as 8

10 a buy. However, f an analyst recommends to wat for a pullback and only then buy the asset, that contngent recommendaton would be classfed as a hold. The strong sell category conssts of dstnct recommendatons to mmedately sell the asset wthout any reservaton, whch s occasonally even accompaned by a suggeston to sell t short. Any expectaton of at least 20% prce drop durng the comng year falls wthn ths category. The sell category features a sell recommendaton wth reservatons that do not deter from mmedately sellng the asset, a clearly negatve busness outlook, a dstnct do not buy statement, and the use of terms such as underperform and overbought. The hold category conssts of all recommendatons to hold the asset or recommendatons featurng assets as market perform and neutral. To avod subjectve judgment bases and msnterpretaton, we attrbute to the hold category mxed, contngent, ambguous, and contradctng recommendatons. Ths classfcaton guarantees that the buy and sell categores are unambguous and transparent. Whle dfferences between strong buy and buy and between strong sell and sell recommendatons could be subtle, dstnctons between buy and sell groups are clear and well defned. It s unlkely that a postve recommendaton would be classfed as a sell or a negatve recommendaton would be classfed as a buy. Notably, the man results are qualtatvely smlar whether we employ the fve-category scale, a three-category scale (all buy, hold, and all sell), as well as a two-category scale (all buy and all sell, excludng hold). Several addtonal notes are n order. Frst, we consder only recommendatons correspondng to nvestment horzons, rangng from a few months to one year whch are provded n all the programs. Yet, n a few programs, analysts also provde a separate one-day or a few days tme-horzon recommendaton, usually referred as a tradng recommendaton. 9

11 Even less common, n a few cases analysts also provde a long-term forecast for horzons longer than one year (usually three to fve years). Such recommendatons are exceptonal tems. Moreover, they are always provded along wth the recommendaton for the man nvestment horzon and are usually provded by a sngle analyst. We dscard short-term and long-term recommendatons. Second, whle dscussons about the market ndex (S&P500) often nclude both negatve and postve aspects and tones, sngle stock dscussons are more dstnctve and clear wth techncal dscussons typcally beng more transparent and strct than fundamental ones. Table 1 summarzes descrptve statstcs of the broad set of recommendatons for sngle stocks, the market ndex, partcular sectors, bonds, commodtes, and currences. Appendx C descrbes the full lst of all ndvdual stocks featured n Talkng Numbers as well as all other assets. Altogether, we have been able to capture 1620 dual recommendatons, as detaled below. There are 1,000 techncal recommendatons and 1,000 fundamental recommendatons (1,000 dual recommendatons) featurng 262 ndvdual stocks. There are 149 dual recommendatons coverng the S&P500 ndex (the NYSE Composte ndex n one case); 256 dual recommendatons correspondng to 58 ndces and ETFs, such as the NASDAQ 100, the DOW JONES Industral/Utltes/Transportaton, partcular sectors ncludng bankng, retal, homebulders, mners, and botechnology, as well as non-u.s. markets ncludng emergng markets, fronter markets, and the Nkke 225; 50 dual recommendatons featurng bond yelds (mostly ten-year Treasures but also muncpal bonds); 144 dual recommendatons about 17 commodtes (especally gold and crude ol); and 21 dual recommendatons coverng exchange rates between the U.S. dollar and three other currences and one basket of currences. In 370 shows, a sngle recommendaton records no correspondng comparson, because ether there was 10

12 only one analyst partcpatng n the show or one of the analysts dd not ultmately dscuss the relevant asset. As noted earler, such recommendatons are excluded from the man analyss but are later consdered n robustness tests. There are 28 observatons whch are excluded because the underlyng asset s unque (e.g., Btcon). Observe from Table 1 that whle among general asset classes the number of techncal and fundamental analysts s qute smlar, t s markedly dstnct among sngle stocks. There are 34 techncal versus 159 fundamental analysts. The smaller number of techncans coverng stocks could be attrbutable to ther reasonably successful predctons, as shown below, whch would encourage the program drectors to keep them. Also notable s the relatvely small number of fundamental and techncal female analysts about 10% across all the varous asset classes. Whle among the asset classes, recommendatons span all fve categores, there are substantally more buy and sell recommendatons than strong buy, strong sell, and hold recommendatons. The Spearman rank correlaton coeffcent, whch measures the correlaton between the one through fve fgures (e.g., 1 stands for strong sell) correspondng to the fundamental and techncal recommendatons, s typcally small. It s 0.05 for sngle stocks, 0.18 for the market ndex, 0.21 for sectors and non U.S. ndces, 0.29 for bonds, and 0.38 for commodtes. Techncal and fundamental recommendatons are closely related n predctng exchange rates, recordng Spearman correlaton coeffcent of Lkewse, the Person s Ch-squared statstc strongly rejects the hypothess that techncal and fundamental recommendatons for exchange rates dffer to sgnfcant degrees. We next dscuss the sources of fnance, accountng, and economc data used n the emprcal analyss to assess the qualty of recommendatons. Stock return and tradng volume fgures are from the Center of Research n Securty Prces (CRSP). Frm accountng varables 11

13 such as book value are from CUMPUSTAT. Earnngs surprses are based on the Insttutonal Brokers' Estmate System (I/B/E/S). The Fama and French and momentum factors, used to rskadjust nvestment returns, are provded by Kenneth R. French s lbrary. Stock ndces covered by Talkng Numbers are provded by the S&P Dow Jones Indces, NASDAQ OMX Global Indexes, Nkke, Moscow Exchange, Bucharest Stock Exchange, and Internatonal Securtes Exchange (Homebulders Index). Prces of precous metals are provded by The London Bullon Market Assocaton. Natural gas prces are from the U.S. Energy Informaton Admnstraton (EIA). Coper prces are provded by the New York Mercantle Exchange. Agrculture prces are provded by CME and Intercontnental Exchange (ICE). The CRB Index s provded by Thomson Reuters. All other commodty prces are from The Federal Reserve Bank of St. Lous. Exchange rates are also from the Federal Reserve Bank of St. Lous wth the excepton of the ICE Dollar Index whch s provded by ICE. Interest rates are also provded by the Federal Reserve Bank of St. Lous. The 90-day treasury-bll rate serves us as a proxy for the rsk-free rate. To measure performance of ten-year bond recommendatons we employ two methods. Frst, the ten-year Treasury Constant Maturty Rates are used to calculate the prce of a notonal zero-coupon ten-year bond. Second, we employ the prce of the Shares 7-10 Year Treasury Bond ETF. As the emprcal evdence for both methods s smlar, we report fndngs for the frst approach. 3. Indvdual stocks: the emprcal evdence Ths secton exclusvely focuses on sngle stock recommendatons. The other asset classes,.e., commodtes, market-wde ndces, sectors, and currences, wll be analyzed n the next secton. Fgure 2 depcts average stock returns for the fve recommendaton categores. Womack (1996) 12

14 and Jegadeesh and Km (2004) report a drft n prces lastng between one and sx months after a recommendaton revson. Here, we consder smlar nvestment horzons of one, three, sx, and also nne months followng the broadcasts. Left (rght) fgures pertan to fundamental (techncal) analyss. Top fgures exhbt raw average returns whle bottom fgures dsplay returns adjusted for the three Fama-French (1993) and momentum factors. Consstent wth fndngs reported n the ntroducton, t s evdent from Fgure 2 that fundamentalsts have not been successful n predctng stock returns. The mean raw returns durng one, sx, and nne months followng sell recommendatons are actually hgher than mean returns followng buy recommendatons. For the nne-month horzon, mean returns assocated wth sell and buy recommendatons are 18.39% and 13.79%, respectvely. The correspondng rsk adjusted fgures are 2.80% and 0.27%. In contrast, the techncal analyss reveals rather strong return-recommendaton relaton. Focusng on the sx-month horzon, average returns are 3.65% (strong sell), 7.25% (sell), 11.77% (hold), 10.81% (buy), and 16.84% (strong buy). The rsk adjusted fgures are 5.20% (strong sell), 1.78% (sell), 2.57% (hold), 1.76% (buy), and 5.46% (strong buy). [Please nsert Fgure 2 here] Table 2 reports the relaton between nvestment average return, recommendaton category, and the nvestment horzon n more detal. We report average returns for the fve recommendaton types. Moreover, as the classfcaton to all buy (buy and strong buy recommendatons) and all sell recommendatons (sell and strng sell recommendatons) s farly unambguous, we also report returns correspondng to such all categores. Startng from the fundamental analysts, sell recommendatons are followed by hgher average returns than buy recommendatons for one, sx, and nne-month horzons. For nstance, 13

15 for the nne-month horzon, sell (buy) recommendatons record 18.4% (13.8%) average return. Comparng strong buy and strong sell fundamental recommendatons reveals more appealng outlook. Return spreads between the two extreme categores are 1.3%, 5.0%, 7.8%, and 11.2% for the four nvestment horzons. Such spreads may appear nconsstent wth the payoff descrpton (Fgure 1c) of the strong buy-mnus-sell portfolo. Notce, however, that pror to August 2013 there were no records of strong sell recommendatons. For the next few months afterward, there was a sngle such recommendaton followed by a bg loss due to a substantal advance n the correspondng stock prce. The payoff descrpton (Fgure 1c) of the fundamental strong buy-mnus-sell portfolo s largely mpacted by the rare appearance of fundamental strong sell recommendatons, durng the begnnng of the sample. Nevertheless, our overall fndngs are consstent n that the return spread between all buy and all sell fundamental recommendatons are relatvely small gven by 0.1%, 1.4%, and 0.4% and 1.1%, respectvely. Lkewse, for all sell and all buy fundamental recommendatons, the Mann-Whtney test reveals that the return dstrbutons are ndstngushable, mplyng that the fundamental analyss s comparable wth random draws of recommendatons. In contrast, techncal analyss reveals mpressve stock-pckng sklls. Ther buy recommendatons predct unformly hgher average returns, both raw and rsk adjusted, than sell recommendatons. For nstance, for the nne-month horzon, buy and sell recommendatons are assocated wth 17.0% and 13.8% average raw return, respectvely. The correspondng rsk adjusted fgures are 2.5% and 0.6%. Further, return spreads between all buy and all sell recommendatons are equal to 1.9%, 2.4%, 6.2% and 6.1% for the four horzons consdered. Smlar evdence emerges on the bass of rsk adjusted returns. Investment returns followng all 14

16 buy recommendatons are unformly larger than all sell. For example, the correspondng nnemonth returns are 19.4% and 13.3%. All statstcal tests pertanng to the techncal recommendatons are hghly sgnfcant, ndcatng that the success of the techncans s not random. Specfcally, among techncal recommendatons, the Kruskal-Walls statstc (whch s a non-parametrc test for the equalty of the mean return dstrbutons) sgnfcantly rejects the null hypothess of equal mean returns for the varous categores of recommendatons. Smlarly, the Mann-Whtney statstc, whch s a non-parametrc test for the equalty of all buy and all sell dstrbutons, hghly rejects the null hypothess, mplyng that the dstrbuton of returns realzed followng all buy recommendatons s sgnfcantly dfferent (shfted to the rght) from that of all sell recommendatons. [Please nsert Table 2 here] Predctablty s success can be assessed through the average return followng the recommendaton or the relaton between the type of recommendaton and the sgn of future return regardless of ts magntude. Fgure 3 reports the number of correct versus ncorrect recommendatons as well as the average return condtonal on recommendatons for the sxmonth horzon. A correct (ncorrect) recommendaton amounts to postve (negatve) return followng hold, buy, and strong buy recommendatons or negatve (postve) return followng sell and strong sell recommendatons. [Please nsert Fgure 3 here] Startng from raw returns (Fgure 3a) out of 340 techncal buy recommendatons, 250 turn out to be correct whle only 90 turn ncorrect. Correspondng fgures for fundamental analysts are 242 and 102. For both techncal and fundamental analysts, the number of correct sell recommendatons s substantally smaller than that of ncorrect recommendatons, whle the 15

17 numbers of strong sell correct and ncorrect recommendatons are nearly dentcal. Movng to rsk-adjusted returns (Fgure 3b), the number of correct techncal recommendatons s substantally larger than ncorrect across all categores ncludng sell (146 versus 117) and strong sell (46 versus 26). The correspondng fundamental fgures are 150 versus 138 and 34 versus 30, respectvely. A smple Sgn test confrms the superorty of techncal analyss. The null hypothess of equal number of correct and ncorrect techncal recommendatons s sgnfcantly rejected (p < 0.01) for all horzons, regardless of whether hold recommendatons are ncluded or excluded and regardless of whether all buy and all sell recommendatons are consdered separately. For fundamental recommendatons, the null hypothess s not consstently beng rejected. Overall, Fgure 3 shows that techncal recommendatons generate more correct recommendatons as well as hgher nvestment returns. As noted, for buy recommendatons, there are 250 techncal correct recommendatons (74%) versus 90 ncorrect recommendatons (26%), whereas there are 242 (70%) correct fundamental recommendatons versus 102 (30%) ncorrect recommendatons. Moreover, the average return of buy correct recommendatons s 19.6% (techncal) versus 18.3% (fundamental). Smlarly, the average return of ncorrect buy recommendatons s 13.7% (techncal) versus 14.6% (fundamental). Aggregatng fgures, buy recommendatons are followed by average return of ( % %)/340=10.81% (techncal) versus ( % %)/344=8.59% (fundamental). Smlarly, the performance fgures favor techncal recommendatons among all recommendaton categores, both for raw and rsk adjusted returns. The advantage s apparent along two dmensons: the number of correct recommendaton and the qualty of recommendatons manfested through hgher gans followng correct recommendatons and lower losses followng ncorrect ones. 16

18 3.1 Cross-secton analyss Regresson analyss s essental for further studyng the qualty of recommendatons, as t allows one to control for frm attrbutes known to predct the cross secton of future returns. In addton, n the context of analysts recommendatons t has been shown that frm sze (Womack, 1996), past return, volume, the book-to-market rato (Jegadeesh et al., 2004), and ndustry afflaton (Bon and Womack, 2006) are assocated wth performance of recommendatons. In response, we run the cross secton regresson R 7 = γ 0 + γ REC 1 γ VOLUME + γ 8 2 ME + γ VOL + γ ( BE j= 1 γ 9 j / ME ) + γ VOL 4 RECIMPACT j + + γ VOLUME 2 1 =ח γ 5 10 j SURPRISE + j 3 j= 1 γ 6 j + ε, R j + (1) where s the stock-specfc subscrpt, R s the nvestment return; REC descrbes the recommendaton category (1-strong sell, 5-strong buy); ME s the prevous year log of the market captalzaton; BE s the prevous year postve book value and zero otherwse; VOL s return volatlty measured from daly returns over the year pror to the recommendaton broadcast; VOLUME s the log of the average daly tradng volume over the year pror to the broadcast; j=1,2,3 R denote returns durng sx months, one month, and two to four months pror to the recommendaton broadcast; VOL and VOLUME are, respectvely, the changes n volatlty and volume durng the last three months relatve to the whole year pror to the broadcast; j=1,2 RECIMPACT are the return and change n volume over two days followng the recommendaton broadcast, ntended to control for any mmedate mpact of recommendatons; and j=1,2 SURPRISE are the percentage surprses n earnng per share durng the past two quarters. 17

19 Table 3 reports the regresson evdence. We consder 15 dstnct tests. The dependent varable n most tests (unless otherwse noted) s the sx-month return followng the broadcast. Test 1 excludes all control varables. Here, consstent wth prevous analyses, the fundamental recommendatons (REC ) coeffcent s nsgnfcant whle the techncal counterpart s sgnfcantly postve (t = 6.80). Test 2 consders the past sx months return as a control varable. Whle past return enters sgnfcantly, the techncal recommendatons coeffcent s stll sgnfcantly postve (t = 5.48). Lkewse, unreported regressons confrm that techncal recommendatons are sgnfcantly postvely correlated wth past returns correspondng to horzons rangng from one to seven months. Nevertheless, trend followng by tself does not capture the ablty of techncal analysts to delver reasonably robust predctons. Tests 3 controls also for sze, the book-to-market rato, volatlty, and volume. The evdence supportng techncal recommendatons s stll profound. Notce that our sample conssts of large frms mostly belongng to the upper sze decle, wth an average market captalzaton of 39 bllon dollar, and medum book-to-market frms belongng to low-md book-to-market decles (see Appendx C for the lst of stocks). Thus, t may not be surprsng that our sample of stocks does not exhbt effects related to sze, volatlty, or the book-to-market rato. Indeed, all addtonal control varables are nsgnfcant. [Please nsert Table 3 here] Test 4 accounts also for past returns over varous perods, change n volume and volatlty n the last three months, earnngs surprses, as well as controls for the potental mmedate mpact of the recommendaton broadcast on stock return and tradng volume. Agan, fundamental recommendatons do not dsplay economc or statstcal sgnfcance, whereas techncal recommendatons are postvely assocated wth future stock returns (t = 3.48). 18

20 Controllng for varous past stock return varables does not capture the predctve power of techncal recommendatons. The return-recommendaton relaton s also not attrbutable to the drect short-term mpact of the broadcasts on stock prces and tradng volume even when the coeffcents correspondng to these two varables are sgnfcant. Whle there s a sgnfcant mmedate mpact of recommendatons on stock prce and tradng volume, the predctve ablty of techncal recommendatons perssts long afterwards (see n partcular the evoluton of nvestment payoffs dsplayed n Fgure 1). Tests 5 and 6 mrror Tests 3 and 4, respectvely, except that the dependent varable s sxmonth return adjusted to Fama-French and momentum factors. Evdently, the predctve ablty of techncal recommendatons s unexplaned by common rsk factors that could smultaneously affect stock prces and the recommendaton category. Our sample conssts of relatvely homogenous group of elte analysts. Ths mtgates potental systematc bases nvolvng analysts experence and reputaton (Graham, 1999; Sorescu and Subrahmanyam, 2006) as well as career concerns (Hong, et al. 2000; Clement and Tse, 2005). Moreover, Kumar (2010) shows that female analysts dsplay superor forecast ablty due to self-selecton process. Presumably, those females who have superb abltes as analysts pursue a career n a male-domnated ndustry. In our sample there are about 90% male analysts among fundamentalsts and techncans across all asset classes (see Table 1). Thus, gender does not seem to establsh a potental source for systematc bas. Nevertheless, Test 7 mplements a formal test accountng for analyst gender. The fundamental recommendatons coeffcents are near zero and nsgnfcant regardless of the analyst s gender. The techncal recommendatons coeffcents are relatvely larger and hghly sgnfcant (t = 5.10 for male and 3.47 for female). Whle the coeffcent correspondng to female 19

21 analysts s slghtly larger (0.029 versus 0.024), gender effects are altogether nsgnfcant. In sum, the success of techncal recommendatons n predctng returns on ndvdual stocks are not captured by the analyst s gender effect. Moreover, female techncal or fundamental analysts do not outperform male analysts. Whle the dependent varable n Tests 1 through 7 s stock return (raw or rsk adjusted) over sx months followng the recommendaton broadcast, we also examne one, three, and nne month nvestment returns followng the broadcasts. Tests 8 through 10 report the emprcal evdence. For all nvestment horzons, fundamental recommendatons coeffcents are ndstngushable from zero, whle the techncal counterparts are postvely sgnfcant. The remander tests n Table 3 dsplay the robustness of results focusng on sx-month returns. Test 11 excludes the hold category to avod potental msclassfcaton errors. Indeed, the dfference between buy and sell recommendatons s more dstnctve from the dfference between hold and buy or hold and sell recommendatons. Smlarly, the dfference between buy and sell recommendatons s more dstnctve from the dfference between buy and strong buy and between sell and strong sell recommendatons. Test 12 focuses on all buy and all sell categores. As noted earler, all buy s composed of both buy and strong buy recommendatons, whle all sell s composed of both sell and strong sell recommendatons. The evdence agan shows that the fundamental recommendatons coeffcents are nsgnfcant, whle the techncal recommendatons coeffcents are hghly sgnfcant (t = 5.71 and t = 4.12, respectvely). That s, the results are robust to possble classfcaton errors. They persst when the hold category and the dstnctons between strong buy and buy and between strong sell and sell are excluded. 20

22 Tests 13 and 14 examne the senstvty of results to the presence of outlers. The dependent varable n Test 13 s the sx-month return wnsorsed at 2.5%. In Test 14 we employ the quantle regresson around the medan (τ = 0.5) whch s less senstve to extreme observatons than the OLS regresson. In both cases the techncal recommendatons coeffcents are hghly sgnfcant (t = 5.88 and t = 3.45, respectvely) suggestng that stock-pckng sklls of techncal analysts to are not attrbutable to outlers. Fnally, a few programs featured a sngle, ether fundamental or techncal, recommendaton wth no comparson. Whle all reported tests exclude such programs, Test 15 accounts for sngle recommendaton shows. The overall evdence s unchanged. To summarze, cross-secton regressons confrm the strong predctve ablty of techncal recommendatons. That predctve ablty s uncaptured by frm s sze, the book-to-market rato, volatlty, volume, past stock trends, as well as by common rsk factors, analyst s gender, and the potental drect mpact of recommendaton broadcasts on stock prces. The results are also robust to the presence of outlers as well as to potental classfcaton errors. Fundamental recommendatons, n contrast, do not exhbt clear and consstent relaton wth subsequent returns. 3.2 Examnng ndustry and style effects Bon and Womack (2006) argue that the economc value of fnancal analysts relates to analysts beng ndustry specalsts. To explore potental effects of ndustry afflaton and frm attrbutes on recommendatons, we run the cross-secton regresson R = γ0 + γ1 j ( REC )( FIRM j ) ε, (2) + j 21

23 where R s the sx-month stock return (we consder both raw and rsk adjusted return) and REC descrbes the recommendaton category (1-strong sell, 5-strong buy). We consder two specfcatons. In one, FIRM j (j = 1,2,,7) are dummes for seven ndustres: mnng, constructon and manufacturng, utltes, trade, fnancal and admnstraton, and servces. The ndustry dvson s made accordng to the Standard Industral Classfcaton (SIC) code wth the excepton that constructon as well as wholesale trade and admnstraton sectors, for whch we record less than ten observatons, are merged nto ther closest matchng ndustres. In the second specfcaton, FIRM j (j = 1,2,3) are dummes for frms belongng to the bottom 30%, core 40%, and top 30% of ether frm s sze, the book-to-market rato, volatlty, or past return. [Please nsert Table 4 here] Table 4 reports the results. Startng from the fundamental analyss, recommendatons do predct future returns on the servces ndustry. The mnng coeffcent s negatvely sgnfcant whle all other recommendaton coeffcents are generally nsgnfcant. Movng to the techncal front, excludng the mnng ndustry, analyst recommendatons produce robust predctons based on raw and rsk adjusted returns. The falure to predct mnng stock returns s consstent wth the promnent nablty of both techncal and fundamental analysts to predct commodty prces, as we show below. Panel B of Table 4 reports the mpact of frm characterstcs on recommendatons. As the sample s domnated by large frms, we attrbute the 19 frms belongng to the bottom group to the core group. The coeffcents correspondng to sze, the book-to-market rato, volatlty, and past return groups of fundamental recommendatons are, for the most parts, nsgnfcant. Ths s consstent wth the noton that fundamental recommendatons dsplay low power n predctng future returns. In contrast, all coeffcents correspondng to techncal recommendatons are 22

24 hghly sgnfcantly postve. The F statstcs n the bottom of the table show that the predctve power of the techncal analyss s hgher for smaller cap and value frms, t s stronger for the core group of volatlty relatve to the two extreme groups, and t tends to be hgher when returns durng the prevous year are lower. 4. Examnng forecasts among broader asset classes Why are techncal recommendatons successful n predctng returns on ndvdual stocks? One possblty s that techncans trade on prvate sgnals, as prescrbed by Brown and Jennngs (1989), Blume et al. (1994), and Zhu and Zhou (2009). Common wsdom could suggest that prvate sgnals need not apply to broad assets, rather they manly characterze ndvdual stocks. Hence, we hypothesze that the value of propretary nvestment toolkts reles on ther ablty to better refne prvate nformaton from nformed tradng. The second possblty s that techncans could process publc nformaton more effectvely through ther toolkts. Also n that case the success of techncans may be more promnent among ndvdual stocks as arbtrage captal s more nvested n general assets and ndexes thereby rather rapdly elmnatng abnormal profts n those assets. The emprcal evdence provdes support for these two potental explanatons. In partcular, Fgure 4 presents the average returns on varous asset classes for each school of thought. Left (rght) plots feature fundamental (techncal) recommendatons. Asset classes nclude the S&P500 ndex (Fgure 4a), sector/ndustry/non-u.s. ndces (Fgure 4b), U.S. bonds (Fgure 4c), commodtes (Fgure 4d), and the U.S. dollar (Fgure 4e). Further detals of asset 23

25 classes are provded n Appendx C. The four curves n each plot depct average returns over one, three, sx, and nne months followng the recommendaton broadcast. [Please nsert Fgure 4 here] Brefly, Plots 4a through 4d show that both fundamental and techncal analysts have been unable to predct the S&P500 ndex, sector/ndustry/non-u.s. ndces, U.S. bonds, and commodtes. Conversely, Fgure 4e shows that both fundamental and techncal recommendatons mpressvely predct future currency rates, wth the most outstandng postvely monotonc curve correspondng to nne-month horzon. Lkewse, Fgure 5 presents the cumulatve returns relatve to mean returns durng the studed perod for the fve recommendaton categores and for the varous asset classes. Consstent wth the former analyses, there s no clear assocaton between relatve cumulatve returns and recommendatons on the S&P 500 ndex, sector/ndustry/non-u.s. ndces, bonds, and commodtes. In contrast, both analyst groups are able to dentfy future trends n exchange rates. [Please nsert Fgure 5 here] The apparent success n predctng exchange rates should be nterpreted wth cauton. For one, only 21 dual recommendatons on exchange rates have been recorded. Moreover, the transparent monetary polces of central banks to keep nterest rates low and mprove lqudty could enhance the ablty to predct future rates. Indeed, past work supports that hypothess. For example, Szakmary and Mathur (1997) fnd that the proftablty of techncal rules n foregn exchange markets may be explaned by a leanng aganst the wnd polcy of central banks. LeBaron (1999) and Sapp (2004) report an assocaton between techncal rules and central banks nterventons. Here we document the success of techncal and fundamental analysts even when 24

26 both schools of thought mplement very dfferent toolkts, whch typcally lead to very dfferent predctons about the other asset classes. The same lne of reasonng,.e., central bank frm nterventon, does not apply to predctng prces of ten-year bonds, as prces of long term bonds may be exposed to other market factors beyond short-term nterest rates. Indeed, ten-year rsk-free rates exhbt consderable volatlty durng the sample perod amountng to 6.66% n annual terms. Table 5 reports summary statstcs smlar to those exhbted n Table 2 but focusng on all other asset classes. Starng wth the market ndex, consstent wth Fgure 5a, there s no clear assocaton between recommendatons and subsequent returns. Null hypotheses that () the fve recommendaton categores have the same return dstrbutons, () returns correspondng to buy and strong buy fundamental recommendatons and sell and strong sell fundamental recommendatons have the same dstrbuton, and () the same as () but for techncal recommendatons, are generally not rejected. When they are rejected, the dfference goes n the wrong drecton as mean returns correspondng to sell recommendatons are hgher than those correspondng to buy recommendatons. [Please nsert Table 5 here] Smlar results are obtaned for sector/ndustry/non-u.s. ndces (Panel B), bonds (Panel C), and commodtes (Panel D). Fnally, the success of both fundamental and techncal recommendatons to predct exchange rates (Panel E) s statstcally sgnfcant and robust. Here we dsplay monotoncally ncreasng average returns along the recommendaton categores, for all nvestment horzons. The apparent success to predct ndvdual stock returns s the excepton rather the rule. In all the other asset classes, excludng the U.S. dollar, both techncans and fundamentalsts reveal 25

27 no predctve ablty. Our fndngs thus ndcate that the markets correspondng to vrtually all assets are effcent, yet neffcency appears to exst among ndvdual stocks. 5. Concluson Ths study employs a novel database from a leadng meda broadcast to confront head-to-head the performance of fundamental versus techncal analysts and assess ther real tme economc value. The data s composed of dual fundamental and techncal recommendatons on the same underlyng asset. The unque setup of the broadcast, featurng synchronzed dual recommendatons, multple asset classes, and the presence of leadng professonals, offers mportant nsghts n assessng the value of fnancal analyss. Ultmately, both fundamental and techncal analysts are exposed to the same publc nformaton and ther recommendatons could dffer due to dstnct toolkts appled. The smultaneous broadcast equates analyst exposure to herdng, elmnates tme gap bases such as cross-herdng among analysts, and t allows one to control for the mmedate short-term effect of the broadcast tself. The hgh profle of partcpatng analysts levels the playng feld thereby mtgatng bases related to analysts qualty, experence, and career concerns. In addton, the broad focus of the program and the comprehensve lst of assets covered make our fndngs general and mtgate concerns about llqudty bases and exceptonal observatons. Consstent wth the sem-strong market effcency hypothess, the fundamental analyss reveals no ablty, whatsoever, to predct future returns on all the assets examned. Surprsngly, techncal analysts exhbt sgnfcant predctng ablty of ndvdual stock returns whch could pont to market neffcency even among the unverse of the largest and most traded stocks. For 26

28 one, tradng ndvdual stocks based on techncal recommendatons yelds large payoffs even after accountng for reasonable transacton costs. Moreover, such stock-pckng ablty s hghly robust and s unaffected by controllng for common rsk factors, frm s characterstcs, ncludng past returns, ndustry effects, analyst s gender, the potental mmedate mpact of the broadcast, transacton costs, and outlers. However, the predctng ablty of techncans characterzes ndvdual stocks only. In contrast, returns on more general asset classes ncludng equty ndces, sectors, bonds, commodtes, and market ndexes, are unpredctable. Such dfferental results support the noton that the predctve ablty of techncal analysts reles on the possesson of propretary, publcly unknown, nvestment toolkts. Consderng the nature of techncal analyss, one appealng explanaton s that such toolkts enable to extract prvate nformaton from nformed tradng, whch s more relevant to ndvdual stocks and s vrtually un-applcable to broader assets. Alternatvely, techncans could process publc nformaton more effectvely through ther toolkts. In such case the success of techncans s more promnent n ndvdual stocks as arbtrage captal s probably more nvested n general assets and ndexes thereby elmnatng abnormal profts n those assets. References Admat, A.R., Bhattacharya, S., Pflederer, P. and Ross, S.A. (1986), On Tmng and Selectvty. Journal of Fnance 41, Allen, F. and Karjalanen, R. (1999), Usng Genetc Algorthms to Fnd Techncal Tradng Rules. Journal of Fnancal Economcs 51, Barber, B.M. and Loeffler, D. (1993), The Dartboard Column: Second-Hand Informaton and Prce Pressure. Journal of Fnancal and Quanttatve Analyss 28, Barber, B.M., Lehavy, R., McNchols, M. and Trueman, B. (2001), Can Investors Proft from the Prophets? Securty Analyst Recommendatons and Stock Returns. Journal of Fnance 56,

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31 Table 1: Overvew of recommendaton categores for varous asset classes The table dsplays the frequency of recommendaton categores for varous asset classes. The sample perod s November 8, 2011 (the day when a frst smultaneous fundamental-techncal comparson was broadcasted) through December, The Spearman s correlaton coeffcent s between the numercal values of fundamental and techncal recommendatons (e.g., strong sell=1). The Pearson s Ch-square null hypothess asserts that frequences of fundamental and techncal recommendatons across categores are not sgnfcantly dfferent. Fundamental Strong Strong Sell Hold Buy Total Spearman s Pearson's Techncal Sell buy correlaton Ch-Square 262 stocks (159 fundamental analysts 17 females; 34 techncal analysts 3 females) Strong Sell Sell Hold Buy Strong buy Total (p=0.39) The U.S. market Strong Sell (S&P 500) Sell (24 fundamental Hold analysts 4 females; Buy techncal analysts 3 females) Strong buy Total (p=0.16) 58 sector / ndustry / Strong Sell non-u.s. ndex Sell (34 fundamental Hold analysts 4 females; Buy techncal analysts 3 females) Strong buy Total (p=0.01) 3 bond types Strong Sell (14 fundamental analysts 3 females; 13 techncal analysts 2 females) Sell Hold Buy Strong buy Total (p=0.54) 17 commodtes Strong Sell Sell (31 fundamental Hold analysts 3 females; 20 techncal Buy analysts 3 females) Strong buy Total (p=0.33) 4 exchange rates Strong Sell Sell (9 fundamental Hold analysts 3 females; 3 Buy techncal analysts 1 female) Strong buy Total (p=0.88) 30

32 Table 2: Stock returns per recommendaton category The table descrbes the relaton between return and the recommendaton category. Investment horzons are one, three, sx, and nne months followng the recommendatons. Panel A (B) consders raw (rsk-adjusted) average returns, wth rsk adjustment pertanng to the Fama and French and momentum factors. All sell (buy) recommendatons encompass both sell and strong sell (buy and strong buy) recommendatons. The Kruskal-Walls null hypothess asserts that the fve categores delver the same return dstrbuton. The Mann-Whtney null hypothess asserts that all buy and all sell recommendatons exhbt the same return dstrbuton. Fundamental recommendatons Techncal recommendatons Strong. Strong. Kruskal All All Mann- Strong. Strong. Kruskal All All Mann- Sell Sell Hold Buy buy -Walls sell buy Whtney sell Sell Hold Buy Buy -Walls sell buy Whtney A. Raw returns One month Mean Std. dev Skewness Max Mn (0.63) (0.325) (0.02) (0.01) Three months Mean Std. dev Skewness Max Mn (0.13) (0.11) (0.09) (0.01) Sx months Mean Std. dev Skewness Max Mn (0.04) (0.14) (0.00) (0.00) Nne months Mean Std. dev Skewness Max Mn (0.05) (0.21) (0.00) (0.00) B. Rsk adjusted returns One month Mean Std. dev Skewness Max Mn (0.60) (0.23) (0.01) (0.00) Three months Mean Std. dev Skewness Max Mn (0.21) (0.11) (0.01) (0.00) Sx months Mean Std. dev Skewness Max Mn (0.10) (0.11) (0.00) (0.00) Nne months Mean Std. dev Skewness Max Mn (0.07) (0.15) (0.00) (0.00) 31

33 Table 3: Stock recommendatons: cross-secton regressons The table reports the results of the cross secton regresson R = γ + γ REC + γ ME + γ ( BE / ME + γ VOL + γ VOLUME + γ R + γ VOLUME + γ VOL + γ RECIMPACT + γ SURPRISE + ε, ) j= 1 6 j j where s the stock-specfc subscrpt, R s the nvestment return; REC descrbes the recommendaton category (1-strong sell, 5-strong buy); ME s the prevous year log of the market captalzaton; BE s the prevous year postve book value and zero otherwse; VOL s return volatlty measured from daly returns over the year pror to the recommendaton broadcast; VOLUME s the log of the average daly tradng volume over the year pror to the recommendaton broadcast; j=1,2,3 R denote returns durng sx months, one month, and two through four months pror to the recommendaton broadcast; VOL and VOLUME are, respectvely, the changes n volatlty and volume durng the last three months pror to the recommendaton broadcast relatve to prevous year fgures; j=1,2 RECIMPACT are the return and change n volume over three days followng the recommendaton broadcast, ntended to control for any mmedate mpact j=1,2 of recommendatons; and SURPRISE are the percentage surprses n earnng per share durng the past two quarters. The frst lne n each test reports the coeffcent value, whle the second lne reports the t-value (n brackets) correspondng to heteroskedastcty- and autocorrelaton-consstent (HAC) standard errors sorted on analysts. One and two astersks ndcate a sgnfcance level of 5% and 1%, respectvely. Recommendaton Frm characterstcs Potental explanaton Dependent Past return Volume Volatlty Rec. mpact Surprse varable Test Const. Fundamental Techncal ME BE/ME Volatlty Volume 6m 1m 2 4m 3m 3m Return Volume 3m 6m F Sx months returns 7 1a (2.72 ** ) (1.06) 1b (1.11) (6.80 ** ) 2a (2.90 ** ) (0.89) (1.72) 2b (1.08) (5.48 ** ) (2.41 * ) 3a (-0.07) (1.17) (-0.43) (-1.45) (0.63) (0.76) (1.21) 3b (-0.29) (5.51 ** ) (-0.46) (-1.50) (0.85) (0.61) (1.23) 4a (1.23) (0.59) (-0.36) (-1.82) (1.22) (-0.29) (0.15) (-0.34) (0.59) (0.91) (0.39) (4.76 ** ) (-3.83 ** ) (0.83) (-1.73) 4b (0.74) (3.48 ** ) (-0.36) (-2.00 * ) (1.68) (-0.46) (0.20) (-0.63) (0.45) (0.86) (0.40) (5.34 ** ) (-2.89 ** ) (0.93) (-1.67) 8 2 j= 1 9 j j 2 j= 1 10 j j Sx months returns adjusted to four factors 5a (-0.32) (1.14) (-0.54) (-1.97 * ) (0.18) (0.51) (0.97) 5b (-0.43) (5.67 ** ) (-0.64) (-2.08 * ) (0.23) (0.34) (0.93) 6a (0.80) (0.57) (-0.30) (-1.74) (1.21) (-0.59) (-0.43) (-0.30) (0.87) (1.09) (0.61) (4.08 ** ) (-4.40 ** ) (1.05) (-1.81) 6b (0.36) (3.52 ** ) (-0.30) (-2.22 * ) (1.35) (-0.70) (-0.48) (-0.56) (0.66) (1.14) (0.55) (4.96 ** ) (-3.20 ** ) (1.18) (-1.78) 32

34 Recommendaton Frm characterstcs Dependent varable Const. Fundamental Techncal ME BE/ME Volatlty Volume Past return 6m F for gender F Male Female Male Female 7a months returns (-0.01) (1.35) (0.50) (-0.43) (-1.46) (0.61) (0.72) (1.22) (p = 0.17) 7b (-0.28) (5.10 ** )(3.47 ** ) (-0.45) (-1.51) (0.79) (0.80) (1.23) (p = 0.62) 1 month returns 3 months returns 8a (0.58) (0.49) (-1.65) (-0.36) (-0.24) (1.06) (0.55) 8b (0.21) (2.40 * ) (-1.48) (-0.28) (-0.29) (1.00) (0.32) 9a (0.55) (1.009) (-1.43) (-1.66) (-0.15) (0.99) (0.20) 9b (0.56) (2.23 * ) (-1.72) (-1.37) (-0.26) (1.23) (0.19) 10a months returns a (-0.39) (0.98) 1.03 (-0.11) (2.93 ** ) (-0.30) (0.43) 10b (-0.47) (3.29 ** ) (1.01) (0.12) (3.46 ** ) (-0.42) (0.30) 6 months returns, 4 categores (no hold) 6 months returns, 2 categores (buy, sell) 6 months returns, Wnsorsng at 2.5% 6 months returns, Quantle regresson (τ=0.5) 6 months returns, ncludng sngle recommendatons (no comparson) 11a (-0.06) (1.08) (0.07) (-0.45) (0.57) (0.32) (0.92) 11b (-0.16) (5.71 ** ) (-0.74) (-1.15) (0.16) (0.74) (2.20 * ) 12a (0.06) (0.24) (0.11) (-0.32) (0.52) (0.33) (0.96) 12b (-0.23) (4.12 ** ) (-0.75) (-1.16) (0.11) (0.79) (2.32 * ) 13a (-0.17) (1.34) (-0.33) (-1.60) (0.10) (0.92) (1.94) 13b (-0.39) (5.88 ** ) (-0.41) (-1.97 * ) (0.13) (0.76) (2.39 * ) 14a (-2.18 * ) (1.81) (-1.60) (-0.14) (-1.37) (3.81 ** ) (6.16 ** ) 14b (-1.57) (3.45 ** ) (-1.21) (-0.11) (-1.32) (2.91 ** ) (4.05 ** ) 15a (-0.50) (1.57) (-0.31) (-1.30) (0.54) (0.97) (1.44) 15b (-0.37) (5.99 ** ) (-0.64) (-0.72) (0.89) (0.82) (0.97) a The nne-month s results are subject to updates based on future returns. 33

35 Table 4. Industry and style effects The table reports the results of the regresson: R = γ0 + γ1 j ( REC )( FIRMj ) + ε, j where R s stock return or return adjusted for Fama-French and momentum factors (R adj ) over sx months followng the recommendaton broadcast; REC descrbes the recommendaton category (1-strong sell, 5-strong buy); FIRM j are frm characterstcs dummes: Seven ndustry dummes n Panel A and three dummes n Panel B correspondng to bottom 30%, core 40%, and top 30% of ether frm s sze, the book-to-market rato, volatlty, and past return from two to 12 months pror to recommendaton broadcast. The frst lne n each test reports the coeffcent value, whle the second lne reports the t-value (n brackets) correspondng to heteroskedastcty- and autocorrelaton-consstent (HAC) standard errors sorted on analysts. One and two astersks ndcate a sgnfcance level of 5% and 1%, respectvely. A. Industry Manufacturng Transportaton Fnance, Insurance, Recommendatons Constant Mnng & Constructon & Publc utltes Wholesale & Retal trade Real estate & Publc admnstraton Servces Number of observatons F all ndustres equal (p-value) Fundamental Techncal R R adj R R adj (2.90 ** ) (-3.60 ** ) (0.90) (1.64) (-0.31) (0.99) (2.48 * ) (p < 0.001) (-0.47) (-2.37 * ) (0.98) (1.53) (0.18) (0.13) (2.21 * ) (p = 0.002) (0.94) (-1.40) (4.97 ** ) (5.91 ** ) (1.96) (3.66 ** ) (5.24 ** ) (p < 0.001) (-4.61 ** ) (-0.92) (5.20 ** ) (5.20 ** ) (2.63 ** ) (2.63 ** ) (5.21 ** ) (p < 0.001) B. Frm s attrbutes Frm s varable: Sze BE/ME Volatlty Past return Recommendatons: Fundamental Techncal Fundamental Techncal Fundamental Techncal Fundamental Techncal Return type: R R adj R R adj R R adj R R adj R R adj R R adj R R adj R R adj Constant (2.63 ** ) (-0.53) (1.10) (-5.10 ** ) (0.78) (-0.37) (1.07) (-5.30 ** ) (3.31 ** ) (-0.03) (0.98) (-4.51 ** ) (2.99 ** ) (-0.10) (1.08) (-4.26 ** ) Bottom (1.45) (1.52) (7.03 ** ) (6.95 ** ) (0.10) (-0.40) (4.04 ** ) (3.56 ** ) (1.11) (1.15) (7.39 ** ) (7.63 ** ) Core (2.38 * ) (2.35 * ) (5.86 ** ) (5.57 ** ) (-0.09) (0.13) (3.59 ** ) (4.49 ** ) (2.15 * ) (1.92) (5.82 ** ) (5.46 ** ) (1.03) (1.28) (5.98 ** ) (6.22 ** ) Top (0.68) (0.78) (4.99 ** ) (4.93 ** ) (-0.10) (-0.37) (3.22 ** ) (2.92 ** ) (-0.50) (-0.09) (3.65 ** ) (4.93 ** ) (-0.08) (-0.11) (2.96 ** ) (3.02 ** ) F all equal (p-value) F bottom equal top (p-value) (0.05) (0.05) (0.00) (0.01) (0.00) (0.00) (0.00) (0.02) (0.09) (0.10) (0.12) (0.08) (0.14) (0.13) (0.02) (0.03) (0.05) (0.02) (0.11) (0.01) (0.69) (0.70) (0.12) (0.76) (0.15) (0.10) (0.05) (0.03) 34

36 Table 5. Summary statstcs of average returns on varous asset classes The table reports summary statstcs of returns on varous asset classes for each recommendaton category over one, three, sx, and nne months followng the recommendatons broadcast. Asset classes are the S&P500 ndex, sector/ndustry/non-u.s. stock ndces, U.S. bonds, commodtes, and the U.S. dollar. The Kruskal-Walls test null hypothess asserts that nvestment returns based on the fve categores have the same dstrbuton. The Mann- Whtney null hypothess asserts that all buy and all sell recommendatons have the same dstrbuton. When no statstc exsts t s denoted not applcable (na). Fundamental recommendatons Techncal recommendatons Strong Strong Kruskal- Mann- Strong StrongKruskal- Mannsell Sell Hold Buy buy Walls Whtney sell Sell Hold Buy buy Walls Whtney A. Underlyng asset s the U.S. market (S&P 500 ndex) One month Mean Std. dev Skewness na na Max Mn (0.26) (0.07) (0.06) (0.01) Three months Mean Std. dev Skewness na na Max Mn (0.23) (0.27) (0.48) (0.48) Sx months Mean Std. dev Skewness na na Max Mn (0.18) (0.06) (0.47) (0.08) Nne months a Mean Std. dev Skewness na na Max Mn (0.29) (0.12) (0.42) (0.22) B. Underlyng asset s sector/ndustry/non-u.s. ndces One month Mean Std. dev Skewness Max Mn (0.83) (0.28) (0.95) (0.32) Three months Mean Std. dev Skewness Max Mn (0.69) (0.29) (0.35) (0.01) Sx months Mean Std. dev Skewness Max Mn (0.72) (0.19) (0.85) 75 (0.15) Nne months a Mean Std. dev Skewness Max Mn (0.68) (0.24) (0.97) (0.33) C. Underlyng asset s bonds One month Mean Std. dev Skewness na na Max Mn (0.63) (0.06) (0.31) (0.08) 35

37 Fundamental recommendatons Techncal recommendatons Strong Strong Kruskal- Mann- Strong StrongKruskal- Mannsell Sell Hold Buy buy Walls Whtney sell Sell Hold Buy buy Walls Whtney Three months Mean Std. dev Skewness na na Max Mn (0.06) (0.02) (0.02) (0.01) Sx months Mean Std. dev Skewness na na Max Mn (0.07) (0.04) (0.63) (0.05) Nne months a Mean Std. dev Skewness na na Max Mn (0.83) (0.47) (0.44) (0.17) D. Underlyng asset s commodtes One month Mean Std. dev Skewness Max Mn (0.22) (0.26) (0.61) (0.16) Three months Mean Std. dev Skewness Max Mn (0.27) (0.27) (0.39) (0.08) Sx months Mean Std. dev Skewness Max Mn (0.74) (0.11) (0.64) (0.10) Nne months a Mean Std. dev Skewness Max Mn (0.88) (0.32) (0.71) (0.10) E. Underlyng asset s the U.S. dollar One month Mean Std. dev Skewness na na Max na na Mn (0.02) (0.00) (0.51) (0.09) Three months Mean Std. dev Skewness na na na na Max Mn (0.01) (0.00) (0.11) (0.01) Sx months Mean Std. dev Skewness na na Na na Max Mn (0.00) (0.00) (0.07) (0.00) Nne months a Mean Std. dev Skewness na na Na na Max Mn (0.01) (0.00) (0.16) (0.01) The nne-month s results are subject to updates based on future returns. 36

38 1a. CARs per techncal recommendatons CAR (%) H0: Buy equal Sell (t=2.75) 1b. CARs per fundamental recommendatons CAR (%) H0: Buy equal Sell (t=-2.69) Tradng day s from recommendaton broadcast Buy, (t=0.45) Strong buy, 7.92 (t=2.50) Buy, 1.74 (t=0.90) Hold, (t=-0.01) Sell, (t=-1.15) Strong sell, (t=-1.46) Sell, 6.18 (t=2.64) Hold, 0.83 (t=0.29) Strong buy, (t=-1.26) Strong sell, (t=-0.33) 1c. Payoffs for spread portfolos Tradng days from recommendaton broadcast Fgure 1. Cumulatve abnormal returns (CARs) and portfolo payoffs The top two panels depct CARs for techncal and fundamental recommendatons, startng from recommendaton broadcast (day zero) and endng nne months (189 tradng days) afterward. The t statstcs on the rght-hand sde of both panels correspond to the null hypothess that the nne-month CAR s ndstngushable from zero. H0 null hypothess asserts that the CAR correspondng to buy and strong buy s not sgnfcantly dfferent from that correspondng to sell and strong sell. The bottom panel presents cumulatve returns of four zero-cost tradng strateges: () buy mnus sell for fundamental recommendatons () strong buy mnus strong sell for fundamental recommendatons; () buy mnus sell for techncal recommendatons; and (v) strong buy mnus strong sell for techncal recommendatons. Alpha s the annual Jensen s alpha obtaned from regressng portfolo s excess return on the market excess return. 37

39 2a. Raw returns Rate of return (%) b. Adjusted returns Rsk adusted rate of return (%) Nne months, Sx months, Three months, 8.61 One month, Strong sell Sell Hold Buy Strong buy Fundamental reccomendaton Nne months, 4.62 Sx months, 5.01 Three months, 2.48 One month, Strong sell Sell Hold Buy Strong buy Fumdamental reccomendaton Rate of return (%) Rsk adusted rate of return (%) Nne months, Sx months, Three months, One month, Strong sell Sell Hold Buy Strong buy Techncal reccomendaton Nne months, 7.36 Sx months, 5.46 Three months, 1.43 One month, Strong sell Sell Hold Buy Strong buy Techncal reccomendaton Fgure 2. Average stock return per recommendaton category The fgure depcts average returns on stocks for strong sell, sell, hold, buy, and strong buy categores. The four curves n each dagram exhbt average returns over one, three, sx, and nne months followng the recommendatons broadcast. Left (rght) fgures pertan to fundamental (techncal) analyss. Top fgures exhbt raw returns whle bottom fgures dsplay returns adjusted for the three Fama-French (1993) and momentum factors. 38

40 3a. Raw returns 3b. Rsk adjusted returns Fgure 3. The number of correct and ncorrect stock recommendatons The fgure reports the number of correct versus ncorrect recommendatons as well as the average return condtonal on correct versus ncorrect recommendatons for the sx-month nvestment horzon. A correct (ncorrect) recommendaton amounts to postve (negatve) return followng hold, buy, and strong buy recommendatons or negatve (postve) return followng sell and strong sell recommendatons. The total average return s reported on the left whle the condtonal average returns are reported near the correspondng bars. Top fgure exhbts raw returns whle bottom fgure dsplays returns adjusted for the three Fama-French (1993) and momentum factors. 39

41 4a. S&P500 Rate of return (%) Nne months, Sx months, Three months, One month, Strong sell Sell Hold Buy Strong buy Fundamental reccomendaton 4b. Sector/ndustry/non-U.S. ndces Rate of return (%) Nne months, Sx months, Three months, One month, Strong sell Sell Hold Buy Strong 1.63 buy Fundamental reccomendaton 4c. U.S. bonds Rate of return (%) Nne months, Sx months, Three months, One month, - Strong sell Sell Hold Buy Strong 0.57 buy Fundamental reccomendaton 4d. Commodtes Rate of return (%) 1 One month, Three months, Sx months, Nne months, Strong sell Sell Hold Buy Strong buy 4e. U.S. dollar Rate of return (%) Fundamental reccomendaton 18 Nne months, Sx month Three months, One month, Strong sell Sell Hold Buy Strong buy Fundamental reccomendaton 15 Nne months, Sx months, Three months, One month, Strong sell Sell Hold Buy Strong buy Fgure 4. Average returns on varous asset classes The fgures present average returns on varous assets over one, three, sx and nne months after broadcastng fundamental recommendatons (left-hand fgures) and techncal recommendatons (rght-hand fgures). The underlyng assets are the S&P500 ndex, sector/ndustry/non-u.s. ndces, U.S. bonds, commodtes and the U.S. dollar exchange rates. Rate of return (%) Rate of return (%) Rate of return (%) Rate of return (%) Rate of return (%) 15 Techncal reccomendaton Nne months, Sx months, Three months, One 1.63 month, Strong sell Sell Hold Buy Strong buy Techncal reccomendaton 6 Nne months, Sx months, Three months, One month, Strong sell Sell Hold Buy Strong buy Techncal reccomendaton 1-4 One month, Three months, Sx months, Nne months, Strong sell Sell Hold Buy Strong buy Techncal reccomendaton 18 Nne month Sx months, Three months, One month, Strong sell Sell Hold Buy Strong buy Techncal reccomendaton 40

42 5a. S&P500 5b. Sector/ ndustry/non-u.s. ndces 5c. U.S. bonds 5d. Commodtes 5e. U.S. dollar Fgure 5. Relatve cumulatve returns on varous asset classess The fgures present comulatve returns less the mean return on the S&P500 ndex, sector/ndustry/non-u.s. ndces, U.S. bonds, commodtes and the U.S. dollar for fundamental recommendatons (left-hand sde fgures) and techncal recommendatons (rght-hand sde fgures). The t statstcs are n brackets. The null hypothess asserts that the nne-month comulatve return relatve to the mean return s not sgnfcantly dfferent from zero. 41

43 Appendx A. Classfcaton of recommendatons Strong buy strong buy, tme to buy buy buy, great buyng opportunty, I am a bg buyer, keep buyng the stock, brllant buy, you have to buy t, I m absolutely a buyer, you defntely want to hold t, you have to be long, you must own t, love the asset, love the chart, we love t, I lke everythng, very clear bullsh pattern, very strong bullsh pattern, very clear bullsh sgnal, very bullsh ndcaton, very postve, very attractve, very very bullsh setup, very optmstc, looks phenomenal, looks wonderful, looks perfect, ths chart looks lke a wnner, does look very good, now t s a great tme to own the stock, you have to own, a lot of reasons to own the stock, extremely compellng valuaton, extremely compellng buy, extremely strong, fantastc, delcous, exctng, ncredble, fundamentals are phenomenal, great numbers great stock, from strength to strength, ths stock s on fre, I am super-fred on the stock, the stock s a rock, the sky s the lmt, gong to the roof, a great place to be, extreme oversold, brght future, unquely compellng, tremendous opportunty, does not get better, outstandng (techncal) poston, expect hgh returns, gong a lot hgher, contnue to run, the stock s colng for a bg move up, plenty hgher prces, much hgher prces, plenty room for upsde, plenty of more upsde, we re gong to get a bg breakout, and a prce target (f gven) whch at least 20% above the current prce. Buy buy, we buy, t s a buy, I would be a buyer, comfort to buy, buyng opportunty, compellng buy on rsk reward bass, I would buy ths chart, I am a buyer here, you want to buy the sector, buy when there s blood n the streets, a buyng opportunty, buyers are gong to overwhelm sellers, t s a stock to own, you want to be long, chase t, I am long, great name to play, buy on any pullback, the chart says t s a buy, constructve chart, chart s constructve, good chart, I expect the chart to head hgher, I expect the chart to go hgher, bullsh chart, bullsh contnuaton patter, (bullsh) trend s ntact, bullsh flag, farly bullsh, bearsh to bullsh reversal, mldly bullsh, relatvely bullsh, very constructve, very nterestng, very nce uptrend, very nce opportunty, very nce trade, very postve sgn, I see postve sgns, postve forecast, postve on the longer term, the trend s postve, no sgn for a change n (postve) trend, no ndcator for a change n (postve) trend, nce uptrend, well-defned uptrend, good entry pont, compellng entry pont, attractve entry pont, good tme to hold t, looks good, good nvestment, all good, good to be long, stll looks good, good rsk-reward, decent rskreward, I lke t here, I lke t at ths level, you can jump n, I am on board, you want to reman n the sector, set to a breakout, about to break, we are lookng for a breakout, I thnk t wll go up, prce wll go up, expect a rally, move hgher, I expect the stock to move hgher, the next move s hgher, headed n the rght drecton, movng above average, more upsde than downsde, plenty of upsde, there s upsde potental here, strong case for upsde, sentment s n favor, play the momentum, play the momentum from the long sde, I m optmstc on t, optmstc, cheap, overweght qute attractve, great leadershp, sold busness, strong foundatons, healthy, prced for the bad news, oversold, wll bounce back, chance to recover, form a bottom, back on track, a lot of 42

44 reasons to hold the stock, I do see value there, and any prce target (f gven) whch s 10%- 20% above than current prce. Hold hold, weak hold, holdng pattern, mxed, mxed bag, neutral, market performance, market stock, sector perform, farly valued, far value, t s prced farly, prce s far, prce target s equal to current prce, equal-weght, O.K., only O.K., results are only O.K., O.K. shape, lookng O.K., rght prcng, borng, extremely borng, not mpressed, so what?..., pause, flat, I go flat, a range bound, be cautous, I m cautous, be careful, be careful to enter the poston, wat, wat before buy, wat for (some value, e.g. 10%) pullback to buy, wat for a better entry pont, wat untl, wat to, I rather wat, not somethng we would buy today but, I wll not commt more captal, would not commt new captal, would not commt fresh captal, not convnced to buy, I m not sure t s tme to jump on the wagon, not a compellng entry level, not the rght entry pont, lookng for a catalyst, we need more nformaton, need to watch the market response, we need to see confrmaton (for potental trend), no catalyst n sght, could go ether way, nflecton pont, ndecson, I don t know how to trade, anythng s possble, bear and bull tensons, bear and bull battle, rsk reward proposton s symmetrcal, watch from the sdelne, stay on the sdelne, stay on the sdeway, do nothng, a lttle upsde, upsde s lmted, there s no upsde, all the good news n the stock, much of the story s already n prce,, not a great fan of, not a fan, I m not very excted, not that great, It s hard to be enthusastc, a lttle speculatve, market got ahead of tself, close to a buy, I would not chase t and would not short t, there are sgns of hopes, a lttle skeptcal, a lttle concerned, I would be a buyer f (future event, e.g. prce goes to ), I would be a seller f (future event), and recommendaton s not clear, recommendaton s ambguous (e.g. may rally but looks weak, gong to break one sde or another, at a crtcal pont ), recommendaton contngents or depends on future event, and contradctng recommendatons over md and long tme-horzons wthn the range of one month and one year.. Sell sell, wll be a seller, I would be a seller, t s a sell, more sellng pressure, sellng pressure, go for the sell, keep sellng, t s a sellng pont, call t a day, take your money, out of asset, I sold t, I m out of t, I would not touch t, stay away, avod ths stock, I would not buy t, don t buy t, do not buy!, defntely not buy, you are better off buyng other assets, ths s not a chart I m gong to buy, I would not buy the stock, let someone else buy t, no reason to buy, we would defntely not buy t, I would not hold t, take your money and run, take some profts, we avod, stay away, keep away, I watch from the sdelnes, I stay on the sdelnes, leave t alone, tme to take profts, trm your profts, take the money of the table, I am aganst the asset, I do not want to hold t, t s not the place to put your money, dslke, I do not lke the odds, do not lke t, I don t lke the rsk reward, not the space you want to be, do not hold t, keep away, lousy stock to own, not the tme to own ths stock, no reason to be nvolve wth, don t touch t, I m out, further weakness, there s a downsde, t s gong lower, wll go lower, It s gong lower, prce wll not hold, looks bad, sde wnds ahead, bearsh chart, bearsh 43

45 dvergence, bear market, bearsh pattern, bearsh techncally, I m bear on ths stock, I am n the bearsh camp, (bearsh) trend s ntact, mountng evdence of bearsh, more bearsh than bullsh, pretty bearsh, bearsh formaton, a broken chart, unnsprng chart, bull trap, (postve) trend reversal, vulnerable, techncally vulnerable, stock looks vulnerable, gone too far too fast (upward), too far above ts trend lne, ths chart s broken, the (upward) angle s unsustanable, (prce) unsustanable, very expensve. (prce) extremely stretched, expensve, underperform, overbought, not attractve, unjustfed prce, cheap for a reason, does not look rght, prcy, prce devaluaton, prcng does not make sense, (value) much to rch, valuaton s tough, prce far too hgh, (prce) too hgh, prcng does not make sense, very concerned, concerns, serous problems, negatve, negatve forecast, too rsky, sck, I see weakness, I see weakness all the board, the story only gets worse, somethng s wrong, true threat, challengng, a challenge, overdone, game over, comes to ts end, dead cat bounce, catchng a fallng knfe, never try catchng a fallng knfe, negatve momentum, shaky grounds, not nterested n, gong nowhere, wll lag, much better n other names (of companes), no sgn for a change n (negatve) trend, no ndcator for a change n (negatve) trend, hold off, expect a declne, It s gong down, contnue to fall, contnue to go down, gong to pull back, more thngs to downsde, we wll see a break to the downsde, a break to the downsde s more lkely, the trend remans down, rsk-reward tends to be downsde, momentum s for the downsde, wll probably go lower, wll probably fall, I see weakness contnues, more downsde from here, ready to break to downsde, I expect a large pullback, prce gong down ten percent, and prce target (f gven) whch s 10%-20% below than current prce. Strong sell strong sell, I am a seller, I would be a seller rght here, sell and run away, dump the stock, you want to be a seller, you want to be out, step off, dump the stock, I would be aggressve seller, get out of t, sell wth confdence, sell short, compellng short sell, t s tme to bet aganst the stock, massve short, I want to be short, lookng to short t, short sgnal, very bearsh, ultra bearsh, very bearsh setup, the chart s a dsaster, trend s very negatve, terrble, the stock goes straght down, gong down bg tme, prce s gong lower!, goes from bad to worse, gong a lot lower, bg pullback, clearly a sell, the party s over, posed to roundtrp down, massvely overvalued, dead money, a falure, a broken story, unquely vulnerable, streamng to the ext, any name but ths stock, I hate t, wll not buy t under any crcumstances, the stock worth nothng, there s nothng here, you want to avod t, downward spral, crappy, a lot of crap, the show s over, and any prce target (f gven) whch s at least 20% below the current prce. 44

46 Appendx B. Illustraton of recommendatons classfcaton Below, we present a program summary publshed n Yahoo. Based on the strct stay away cte we classfed the fundamental recommendaton as sell. Based on look very good, very bullsh ndcaton, plenty of more upsde and contnue to run the techncal recommendaton s classfed as strong buy. It should be emphaszed that we made the classfcatons accordng to the full dscusson n the program rather than only accordng to the summary n Yahoo, whch s presented here for llustraton purpose, as the full dscusson usually ncludes more classfcaton words and addtonal clarfcatons. Ths hot stock may perk up even more By Lawrence Lewtnn August 22, :31 PM Shares of Keurg Green Mountan were percolatng on Frday thanks to a deal wth Kraft Foods. But whle the stock has been on fre for the last couple of years, could nvestors get roasted n the months ahead? Though Keurg Green Mountan s stock s up over 77 percent year-to-date and has more than quntupled n the last two years Chad Morganlander, portfolo manager at Stfel Ncolaus Washngton Crossng Advsors, s not warm on the stock. We at Stfel have a hold recommendaton on t, Morganlander sad. Stfel Ncolaus makes a market n Keurg Green Mountan s stock. As a value manager, I beleve that ths stock s somewhat frothy, sad Morganlander, notng that the stock trades around 34 tmes ts 2015 expected earnngs. Morganlander s also wary on the company tself. The busness model s somewhat sketchy here when t comes to prcng, he sad. There are compettve ssues that they wll have n the comng years. Keurg Green Mountan may not be the best nvestment dea, accordng Morganlander. You want to be somewhat more pragmatc about nvestng n t, he sad. Ths s bubblcous to me. Stay away. Steven Pytlar, chef equty strategst at Prme Executons, s more optmstc on Keurg Green Mountan based on the techncals. It does look very good on the charts, actually, he sad. Snce about the end of 2013, we ve seen a number of hgher lows develop. And what that means s that the stock s beng revalued hgher. The market s rewardng that value and payng hgher prces. Keurg Green Mountan s breakout above $124 per share on Frday was sgnfcant, accordng to Pytlar. Snce February, people weren t wllng to pay more than $124, In techncal terms, that s usually a very bullsh ndcaton. It usually means there s plenty of more upsde, and we thnk that the stock can contnue to run. 45

47 Appendx C. A comprehensve lst of all assets featured n Talkng Numbers U.S. market S&P 500, NYSE COMPOSITE INDEX; Sector/ndustry/non-U.S. ndces Sector ndex S&P100, DOW INDASTRIAL, DOW UTILITIES, DOW TRANSPORTS, NASDAQ COMPOSITE, NASDAQ 100, RUSSEL2000, Industry ndex GUGGENHEIM SHIPPING ETF (SEA), KBW BANK INDEX (BKX), PHLX HOUSING SECTOR INDEX (HGX), MSCI REIT INDEX (RMZ), ALERIAN MLP (AMLP), GOLD MINERS ETF (GDX), JUNIOR GOLD MINER ETF (GDXJ), BROKER DEALERS ETF (IAI), ISHARE NASDAQ BIOTECH (IBB), RUSSELS 2000 ETF (IWM), ISHARE US REAL ESTATE ETF (IYR), ISHARE DJ TRANSPORTATION AVR (IYT), SPDR KBW REG BANKING (KRE), S&P400 MICAP (MDY), OIL SERVICE HOLDERS (OIH), MARKET VECTORS RETAILS (RTH), ISE HOMEBUILDERS INDEX (RUF), MARKET VECTORS STEAL (SLX), SOCIAL MEDIA ETF (SOCL), VANGUARD REIT (VNQ), NYSE ARCA AIRLINE INDEX (XAL), S&P AEROSPACE DEFENCE (XAR), SPDR S&P HOMEBUILDERSA (XHB), ENERGY SPDR (XLE), SPDR FINANCIAL ETF (XLF), INDASTRIAL SELECT SECTOR SPDR (XLI), TECHNOLOGY SPDR (XLK), CONSUMER STAPLE SPDR (XLP), UTILITIES SPDR ETF (XLU), HEALTH CARE SECTOR SPDR ETF (XLV), CONSUMER DICRTIONARY (XLY), SPDR S&P MTLS&MNG ETF (XME), SPDR S&P RETAIL (XRT), ISHARE DJ US HOME (ITB) Non-U.S. ndex NIKKEI 225, SHANGHAI COMPOSITE, S&P BSE SENSEX, ISHARE MSCI INDIA ETF (INDA), HANG SANG, NIGERIA ETF (NGE), ROMENIA BET, VIETNAM ETF (VNM), WISDOMTREE (DXJ), ISHARES MSCI EMERGING MARKETS (EEM), ISHARES MSCI MEXICO (EWW), ISHARE MSCI BRAZIL (EWZ), ISHARE FTSE CHINA 25 (FXI), MARKET VECTORS RUSSIA (RSX), RTS MOSCOW (RTS), ISHARE MSCI TURKEY ETF (TUR), VANGUARD MSCI EUROPE (VGK) U.S. Stocks ALCOA (AA), AUTO PARTS (AAP), APPLE (AAPL), ABBOT LABRATORIES (ABT), AUTOMATICE DATA PROCESSING (ADP), AMERICAN EAGLE (AEO), AFLAC (AFL), AIG (AIG), ALLSTATE (ALL), ADVANCED MICRO (AMD), AMGEN (AMGN), AMZON (AMZN), AUTONATION (AN), ABERCROMBIE & FITCH (ANF), AOL (AOL), APACH (APA), ANADARKO PETROLEUM (APC), APPOLO GROUP (APOL), AEROPOSTALE (ARO), ATHENAHEALTH (ATHN), ACTIVISION BLIZZARD (ATVI), AMERICAN EXPRESS (AXP), ASTRAZENCA (AZN), AUTOZON (AZO), BOEING (BA), BANK OF AMERICA (BAC), BED BATH & BEYOND (BBBY), BLACKBERRY (BBRY), BEST BUY (BBY), BARCLAS (BCS), SOTHBY'S (BID), BIOGEN IDEC (BIIB), BARNESS & NOBLE (BKS), BURGER KING (BKW), BRISTOL MYERS (BMY), BRITISH PETROLIUM (BP), BUFFALO WILD WINGS (BWLD), CITI GROUP (C), CABELA'S (CAB), CONAGRA (CAG), CHEESECAKE FACTORY (CAKE), CATERPILLAR (CAT), CHUBB (CB), CBS CORP (CBS), CARNIVSAL (CCL), CHESAPEAKE ENERGY (CHK), CLIFF NATURAL (CLF), COLONY FINANCIAL (CLNY), COMCAST (CMCSA), CHIPOTLE (CMG), CABOT OIL AND GAS (COG), COACH (COH), CONOCOPHILLIPS (COP), COSTCO (COST), CAMBELL SOUP (CPB), CARTER'S (CRI), SALESFORCE (CRM), CICO (CSCO), CINTAS (CTAS), CVS CAREMARK (CVS), CHEVRON (CVX), CEASARS (CZR), DOMINION RESOURCES (D), DELTA AIR LINES (DAL), DUPONT (DD), 3D SYSTEMS (DDD), DEERE (DE), DELL (DELL), DIAGEO (DEO), DOLLAR GENERAL (DG), D.R. HORTON (DHI), WALT DISNEY (DIS), DISH NETWORK (DISH), DUNKIN BRANDS (DNKN), DIMOND OFFSHORE (DO), DR PEPPER (DPS), DOMINO'S (DPZ), DARDEN RESTAURANT (DRI), DIRECTTV (DTV), DEVON ENERGY (DVN), DREAMWORKS (DWA), ELECTRONICS ART (EA), EBAY (EBAY), CONSOLIDATED EDISION (ED), ENTERPRISE PRODUCTS (EPD), EQUITY RESIDENTIAL (EQR), EXPEDIA (EXPE), FORD (F), FACEBOOK (FB), FREEPORT MCMORAN (FCX), FAMILY DOLLAR (FDO), FEDEX (FDX), FREDDIE MAC (FMCC), FREDDIE MAC (FNMA), FOSSIL GROUP (FOSL), TWENTY-FIRST CENTURY FOX (FOXA), FIRST SOLAR (FSLR), GENERAL DYNAMIC (GD), GENERAL ELECTRIC (GE), GILEAD 46

48 SCIENCES (GILD), GENERAL MILLS (GIS), GENERAL MOTORS (GM), GREEN MOUNTAIN (GMCR), RANGOLD RESOURCES (GOLD), GOOGLE (GOOG), GOPRO (GPRO), GAP (GPS), GARMIN (GRMN), GROUPON (GRPN), GOLDMAN SACHS (GS), HALLIBURTON (HAL), HOME DEPOT (HD), HEBALIFE (HLF), HRLEY-DAVIDSON (HOG), HOVNANIAN (HOV), HEWLETT PACKARD (HPQ), H&R BLOCK (HRB), HERTZ GLOBAL (HTZ), HUMANA (HUM), IBM (IBM), ICAHN ENTERPRISES (IEP), IMAX (IMAX), INTEL (INTC), INVENSENSE (INVN), INTUITIVE SERGICAL (ISRG), JETBLUE (JBLU), J.C. PENNEY (JCP), JOHNSON & JOHNSON (JNJ), JUNIPER NETWORKS (JNPR), JOS A BANK (JOSB), JPMORGAN (JPM), NORDSTROM (JWN), KB HOME (KBH), KRISPY KREME (KKD), COCA-COLA (KO), MICHAEL KORS (KORS), KANSAS CITY SOUTHERN (KSU), LYBERTY GLOBAL (LBTYA), LENNAR (LEN), LIONS GATE (LGF), LOCKHEED MARTIN (LMT), LINKEDIN (LNKD), LORILLARD INC (LO), LOWE'S (LOW), LUFKIN INDUSTRIES (LUFK), LULULEMON (LULU), SOUTHWEST AIRLINES (LUV), LAS VEGAS SANDS (LVS), MACY'S (M), MASTERCARD (MA), MACERICH (MAC), MATTEL (MAT), MCDONALD'S (MCD), KRAFT (KFT/MDLZ), MGM RESORTS (MGM), MONSTER BEVERAGE (MNST), ALTRIA (MO), MARATHON PETROLIUM (MPC), MERK (MRK), MORGAN STANLEY (MS), MICROSOFT (MSFT), MADISON SQUARE (MSG), MICRON TECHNOLOGY (MU), MURPHY OIL (MUR), NAVISTAR (NAV), NASDAQ OMX (NDAQ), NOODLES (NDLS), NEWMONT MINING (NEM), NETFLIX (NFLX), NICE SYSTEMS (NICE), NIKE (NKE), NOKIA (NOK), NORFOLK SOUTHERN (NSC), NUANCE COMM (NUAN), NYSE EURONEXT (NYX), OLD MOMINION FREIGHT (ODFL), OMNICOM GROUP (OMC), ORACLE (ORCL), OUTERWALL (OUTR), ORBITZ (OWW), OCCIDENTAL PETROLEUM (OXY), PANDORA (P), PRICELINE (PCLN), PEPSICO (PEP), PFRIZER (PFE), PROCTOR & GAMBLE (PG), PULTEGROUP (PHM), PVH (PVH), QUALCOMM (QCOM), ROYAL CARIBBEAN (RCL), ROYAL DUTCH SHELL (RDS-A), REVLON (REV), TRANSOCEAN (RIG), RALPH LAUREN (RL), REALIGY HOLDINGS (RLGY), ROSS STORES (ROST), SPRINT (S), STARBUCKS (SBUX), SOLARCITY (SCTY), SEAWORLD (SEAS), SEARS (SHLD), SHERWIN WILLIAMS (SHW), SIRIUS XM RADIO (SIRI), SIX FLAGS (SIX), SAKS (SKS), SKECHERS (SKX), SCHLUMBERGER (SLB), SANDISK (SNDK), SONY (SNE), SODASTREAM (SODA), SONIC (SONC), STAPLES (SPLS), CONSTELLATION (STZ), AT&T (T), MOLSON COORS (TAP), TASER INTERNATIONAL (TASR), TAUBMAN CENTERS (TCO), TARGET (TGT), TIFANY (TIF), TOYOTA (TM), TOLL BROTHERS (TOL), TRIPADVISOR (TRIP), TRINITY INDUSTRY (TRN), TRAVELERS (TRV), TESLA (TSLA), TESORA (TSO), TAKE TWO INTER (TTWO), TIME WARNER CABLE (TWC), TWITTER (TWTR), TIME WARNER (TWX), TEXAS INSTRUMENTS (TXN), UNDER ARMOUR (UA), UNITED CONTINENTAL (UAL), UBS (UBS), UNITED HEALTHCARE (UNH), ULTRA PETROLEUM (UPL), UNITED PARCEL SERVICE (UPS), URBAN OUTFITTERS (URBN), USB (USB), UNITED TECHNOLOGIES (UTX), VISA (V), VIACOM INC (VIAB), VALERO ENERGY (VLO), VODAPHONE (VOD), VERIZON (VZ), WEBMD HEALTH (WBMD), WENDY'S (WEN), WELLS FARGO (WFC), WHOLE FOODS (WFM), ANTM (WLP), WAL-MART (WMT), WEINGARTEN REALITY INVESTORS (WRI), WORLD WRESTLING (WWE), WYNN RESORTS (WYNN), US STEAL (X), EXXON MOBIL (XOM), YELP (YELP), YAHOO (YHOO), YUM BRANDS (YUM), ZILLOW (Z), ZINGA (ZNGA) U.S. Bonds 10-YR T-NOTE (Shares 7-10 Year Treasury Bond ETF -IEF (94US10Y) ISHARE S&P NATIONAL MUNI (MUB) BARCLAYS MUNI BOND (TFI) Commodtes GOLD COMEX (GCZ4), SILVER COMEX (SIZ4), COPPER (HGZ4), NATURAL GAS (NGV14), PALLADIUM (PAL), BRENT CRUDE OIL (BRENT), RABOB GASILINE (GASOLINE), WTI CRUDE OIL (WTI), CORN (CORN), ORANGE JUICE (ORNG), WHEAT (WHEAT), DEUTCHE BANK COMMODIDITIIES ETF (DBC), SPDR GOLD ETF (GLD), IPATH DJ-UBS COFFEE (JO), SILVER ETF (SLV), NATURAL GAS FUND (UNG), CRP INDEX FORX DOLAR INDEX, YEN-DOLAR, DOLAR-EURO, DOLAR-RUPPY Others VIX, RENAISSANCE IPO ETF (IPO), BITCOIN, NYC REAL ESTATE, LUXORY HOUSES, JUNK BONDS ETF, ALIBABA IPO, MORTGAGE RATES 47