Social Media Content on Financial Markets

Transcription

1 Inernaional Journal of New Technology and Research (IJNTR) ISSN: , Volume-2, Issue-3, March 2016 Pages Social Media Conen on Financial Markes Juheng Zhang Absrac Socks are weeed by invesors and are raded in he markes wih a poenial inerplay beween daily sock price movemens and social media conen. We use four daily ime-series variables: sock reurn, volailiy, liquidiy, and he volume of wees o sudy he inerdependences and comovemens of social media conen and sock performance. We find ha he Granger causaliy relaionship beween he sock liquidiy and he volume of wees over socks. Index Terms sock movemens, vecor auoregression model, wees. I. INTRODUCTION Social media has increasingly gained populariy in recen years. The sudy [2] finds ha 73% people are acive on social media. People use social media sies o share informaion, read news, and exchange heir opinions. The sudy finds ha 84% people rus recommendaions of heir friends and family more han oher sources. Social media play an imporan role in consumers decision making. Companies are also adoping social media when devising heir markeing sraegy or disclosing company news. The influence of social media in consumer world has also been observed in he financial world. Invesors use social media o ge marke informaion, company news, raders opinions, and oher invesors senimen on sock shares [9]. People are influenced by peer recommendaions and posiive or negaive senimen on social media. The sudy [8] finds ha 62% brokers and raders believe social media conen can move sock prices. An infamous case ha how social media influences financial markes was he sock marke crashed in he year 2015 afer he fake wee posed on Twier ha announced ha Presiden Obama was injured in an explosion a he Whie House. The informaion was correced shorly bu he impac was devasaed wih $130 billion in sock value loss and -0.9% S&P500 index drop. This is an exreme example bu i shows he huge impac of social media on financial markes. Sock price changes or any informaion ha may affec sock prices riggers people o wee on Twier. Traders and invesors pos daily price changes of sock picks, and any vial daa abou he socks. Sock performance is discussed on social media and people exchange heir opinion and share heir rading experience on social media. Given 500 million wees every day, Twier has a lo of informaion embedded in i and reaches ou o a large number of audience in a few seconds. In his sudy, we invesigae he inerwined relaionship beween wees and sock performance. We examine he comovemens beween wees and sock performance raher han assuming he wees as a predicor of sock performance no he oher way ha sock price movemens move he volume of wees. We believe ha wees influence sock prices, and sock prices rigger people o wee. We invesigae he dynamic relaionship of he variables using he ime series VAR model. II. LITERATURE Exising sudies [4, 7] use he wees o predic he movemens of sock marke index. They use he OpinionFinder sysem o find he mood of he wees and compare i wih anoher algorihm ha discovers six differen senimens in he wees. They find he mood and senimens are srong predicors of sock marke performance. The focus is he collecive senimen of Twier users and is predicive relaionship wih he sock marke. Oher sudies [14-19] on social media use differen social media merics o predic firm values or company performance. We use he vecor auoregression (VAR) model o sudy he dynamic relaionship beween wees and sock price movemens. The model was proposed by Sims [10] for he ineracions of muliple ime series variables. The VAR model has been widely applied in differen fields [3, 12], and was recenly presened o he IS sociey by Adomavicius e al. [1] and was also adoped in he sudy [6]. I has grea performance in daa descripion/forecasing and srucural inferences for muliple imes series [e.g., 1, 10, 11, 13]. In he VAR model, each variable is modeled as he lagged values of he variable iself and he lagged values of oher endogenous variables in addiion o conrol exogenous variables. This n-variable n-equaion model capures he dynamic inerdependences and comovemens of muliple ime series, comparing o he univariae auoregression ha is a single variable, single equaion model. III. DATA DESCRIPTION We use he 288 socks ha wen public in 2014 as our sample daa. We rerieved he accouning numbers of he companies from he Compusa daabase. Around weny of he companies have missing daa. We provide he basic saisics of he socks in Table 1. We downloaded he wees abou he socks during he period from January 1, 2014, o June 1, We searched he wees wih he socks icker symbols and company names. We added he dollar sign in fron of he ick symbol o idenify a sock wee. We also used varian company names when searching he wees, for example, Alibaba Group Holding Ld., Alibaba Group Holding, or Alibaba for Alibaba company. Juheng Zhang, Deparmen of Operaions and Informaion Sysems, Universiy of Massachuses Lowell, Lowell, MA, Unied Saes, 134

2 Table 1: Descripive Saisics of Sock Prices Variable Mean Sd Dev Min Max N Price Asse Ne Income Liabiliies Inangible Asse We colleced deailed informaion abou he company wier accouns, including he creaed dae, he number of followers, he number of favories, he number of wees, ec. Among he 288 sample companies in our sudy, only 118 had a Twier accoun. In Table 2, we provide he saisics for he 118 companies. As shown in he able, he average wee age was 3.8 years, he maximum was 7.6, and he minimum was On average, he companies had 5, wees, 36,537 followers, 2, friends, and favories. Table 2: Descripive Saisics of Twier Accouns Variable Mean Sd Dev Min Max N Age Sauses Followers Friends Favories IV. EXPERIMENT RESULTS We consruc he variable wees, liquidiy, reurn, and volailiy. The variable wees is he number of daily wees over he socks, he variable liquidiy is he number of daily rading volume, reurn is he raio of close price o open price minus one, and volailiy is he bid-ask spread as he raio of he highes bid price o he lowes ask price minus one. We ake he naural logs of he variables o eliminae disribuion skewness. We aggregae each of he four variables across companies. The aggregaion was done wih he same weigh assigned o each company. Differen weighs based on he firms asse sizes are examined in he aggregaion and we derived he similar resuls as when he same weigh was used. We use hese informaion crierion mehods and he saisics of hese mehods such as he Akaike Informaion Crierion (AIC), he Bayesian Informaion Crierion (BIC), or he Hannan-Quinn Informaion Crierion (HQIC) o selec he number of lags in he VAR model. The mehods consisenly indicae ha he appropriae lag lengh is one. Therefore, he lag lengh used in he following analysis is one, i.e., p 1. Daa saionariy is an assumpion of VAR model. Saionary daa have a propery wih mean, variance, and auocorrelaion srucure unchanged over ime. The Dickey-Fuller uni roo es [5] is generally used o check for saionariy. We esed for a uni roo wih rend and drif, wih he resuls repored in Table 3. Table 3: Augmened Dickey-Fuller Uni Roo Tes Twees Liquidiy Reurn Volailiy Inercep 7.9E E E-08 Lag 1 8.5E E-14 <2e E-09 Time E p-value: 2.2E E E-16 As shown in Table 3, we can rejec he null hypohesis ha here is a uni roo wih a drif or rend in our ime series daa. The p-values are negligible, wih 8.15E-13 on a uni roo, 7.91E-12 on drif, and on rend for he variable wees. Similar resuls are observed for he variable liquidiy and volailiy. The resuls are significan a a 99.9% confidence level. In he VAR model, he sandard pracice is o presen he resuls of a Granger causaliy es, Impulse Response Funcion (IRF) analysis, and Forecas Error Variance Decomposiion (FEVD) analysis. Table 4 provides he p-values for pairwise Granger causaliy ess. The Granger causaliy es deermines if prior values of a ime series variable can help o predic fuure values of anoher variable. I is used o examine he causal relaionships among endogenous variables. There are several significan Granger causaliy relaionships among he four ime series variables. The wees ime series significanly Granger-causes he liqudiy series a a level, and he p-value of he Granger-causal relaionship is We can conclude ha he pas values of wees can predic he fuure values of rading volume. Oher significan Granger causaliy relaionships include volailiy liquidiy and reurn volailiy. Recall ha he seleced lag lengh is one. The es resuls sugges ha he number of wees can predic he rading volumes for he nex day, and sock reurns can predic he nex day s bid/ask spread. Table 4: Granger Causaliy Tes P Values Twees Liquidiy Reurn Volailiy Twees ** Liquidiy Reurn ** Volailiy ** Signif. codes: 0 *** ** 0.01 * The resuls of he IRF analysis are graphically represened in Fig.1. The IRF analysis shows he amoun of shock on a variable over ime due o one uni of change (impulse) in anoher variable. As demonsraed, he responses from wees on liquidiy are significanly differen from zero over he ime periods 0 and 1. The posiive responses diminish and converge o zero over ime as expeced. The resuls graphically presened in Figure 2 are consisen wih he resuls of Granger causaliy ess, where rading volume increases as wees increases, wees liquidiy

3 Inernaional Journal of New Technology and Research (IJNTR) ISSN: , Volume-2, Issue-3, March 2016 Pages Figure 1: Forecas Error Impulse Responses The FEVD analysis deermines he amoun of forecas error variance of a variable accouned for by shocks o anoher variable. I indicaes he conribuion of a variable in explaining anoher variable. The FEVD analysis resuls are demonsraed in Fig.2. Figure 2: Forecas Error Variance Decomposiion (FEVD) 136

4 The variable wees is responsible for around 20% of he forecas error variance of he liquidiy variable, and for abou 10% of ha of he volailiy variable. Each variable iself accouns for he larges porion of is forecas error variance, as expeced. Very lile of he error variance of reurn is accouned for by oher variables. A possible explanaion for he minimal impac of oher variables is ha while a firm s daily reurns are likely o be deermined by inrinsic values of he firm such as earnings, liquidiy and volailiy are likely o be affeced by wees. The resuls of our VAR analysis demonsrae ha wees impacs sock performance and can be used o predic a sock s rading volume (liquidiy) in he fuure. Also, hey also show ha wees accoun for he forecas error variance in he bid/ask spread (volailiy) and in rading volumes (liquidiy). Sock reurns may hen, in urn, cause volailiy. The four ime series: wees, liquidiy, reurn, and volailiy are inerwined, and here are significan inerdependences beween sock performance and wees abou socks. REFERENCES [1]. Adomavicius, G., Bocksed, J., and Gupa, A. Modeling supply-side dynamics of i componens, producs, and infrasrucure: An empirical analysis using vecor auoregression. Informaion Sysems Research, 23, 2 (2012), [2]. Benne, S. 73% of online aduls now use social media [3]. Bernanke, B.S., and Blinder, A.S. The federal funds rae and he channels of moneary ransmission. The American Economic Review, 82, 4 (1992), [4]. Bollen, J., Mao, H., and Zeng, X. Twier mood predics he sock marke. Journal of Compuaional Science, 2, 2011 (2011), 1-9. [5]. Dickey, D.A., and Fuller, W.A. Disribuion of he esimaors for auoregressive ime series wih a uni roo. Journal of he American Saisical Associaion, 74, 366a (1979), [6]. Luo, X., Zhang, J., and Duan, W. Social media and firm equiy value. Informaion Sysems Research, 24, 1 (2013), [7]. Mao, H., Couns, S., and Bollen, J. Predicing financial markes: Comparing survey, news, wier and search engine daa. arxiv preprin arxiv: (2011). [8]. Milnes, P. Fuure of funds: How echnology and social media are disruping and opening new opporuniies for he fund indusry [9]. Shell, A. Wall sree raders mine wees o gain a rading edge. USA TODAY, [10]. Sims, C.A. Macroeconomics and realiy. Economerica: Journal of he Economeric Sociey, 48, 1 (1980), [11]. Sock, J.H., and Wason, M.W. Vecor auoregressions. Journal of Economic perspecives (2001), [12]. Walsh, C.E. Moneary heory and policy. MIT press, [13]. Wason, M.W. Vecor auoregressions and coinegraion. Handbook of economerics, 4 (1994), [14]. Zhang, J. Linear discriminaion wih sraegic missing values. Informaion Sysems and Operaions Managemen, Gainesville, FL, USA: Universiy of Florida, [15]. Zhang, J. Informaion revelaion and social learning. Inernaional Journal of Business and Social Science, 5, 2 (2014), [16]. Zhang, J. Ensuring rus online hrough he wisdom of crowd. Journal of Inerne and e-business Sudies, 2015 (2015). [17]. Zhang, J. Volunary informaion disclosure on social media. Decision Suppor Sysems, 73, 2015 (2015), [18]. Zhang, J., Ayug, H., and Koehler, G.J. Discriminan analysis wih sraegically manipulaed daa. Informaion Sysems Research, 25, 3 (2014), [19]. Zhang, J., Khan, R.M., and Shih, D. The raing deerminans facored in decision-making for hoel selecion. Inernaional Journal of Applied Managemen and Technology, 14, 1 (2015), Juheng Zhang is an assisan professor in he Deparmen of Operaions and Informaion Sysems from he Manning School of Business a Universiy of Massachuses Lowell. She received a Ph.D. in Business Adminisraion from Universiy of Florida. Her research focuses on daa analyics and examines informaion disclosure and manipulaion on decision-makings. Juheng Zhang has published in Informaion Sysems Research, Decision Suppor Sysems, and oher academic journals. V. CONCLUSION People cha and commen on socks in social media. In his sudy, we ask if wees have any impac on socks price movemens and if sock price movemens have any influence on wees. Using daily sock daa and wees over socks, we employ a VAR model o sudy inerdependences beween wees and sock performance. We find ha he volume of wees Granger-causes sock liquidiy can significanly predic he fuure values of he sock liquidiy. The amoun of wees also accouns for he forecas error variance in volailiy. There are srong inerdependences and comovemens beween wees and sock performance