Forecasting Stock Prices using Sentiment Information in Annual Reports A Neural Network and Support Vector Regression Approach

Transcription

1 Per Hájek, Vladmír Olej, Renáa Myšková Forecasng Sock Prces usng Senmen Informaon n Annual Repors A Neural Nework and Suppor Vecor Regresson Approach PETR HÁJEK 1, VLADIMÍR OLEJ 1, RENÁTA MYŠKOVÁ 2 1 Insue of Sysem Engneerng and Informacs 2 Insue of Busness Economcs and Managemen Faculy of Economcs and Admnsraon Unversy of Pardubce Sudenská 84, Pardubce CZECH REPUBLIC [email protected] Absrac: - Sock prce forecasng has been mosly realzed usng quanave nformaon. However, recen sudes have demonsraed ha senmen nformaon hdden n corporae annual repors can be successfully used o predc shor-run sock prce reurns. Sof compung mehods, lke neural neworks and suppor vecor regresson, have shown promsng resuls n he forecasng of sock prce due o her ably o model complex non-lnear sysems. In hs paper, we apply several neural neworks and ε-suppor vecor regresson models o predc he yearly change n he sock prce of U.S. frms. We demonsrae ha neural neworks and ε-suppor vecor regresson perform beer han lnear regresson models especally when usng he senmen nformaon. The change n he senmen of annual repors seems o be an mporan deermnan of long-run sock prce change. Concreely, he negave and uncerany caegores of erms were he key facors of he sock prce reurn. Profably and echncal analyss raos have sgnfcan effec on he long-run reurn, oo. Key-Words: - Sock prce, forecasng, predcon, senmen analyss, annual repor, neural neworks, ε-suppor vecor regresson. 1 Inroducon Prevous leraure has shown ha he problem of sock prce forecasng has o be aken as complex snce sock prce changes n me are hghly nonlnear wh a changng volaly and many mcro and macroeconomc deermnans [1]. To address hese ssues, several arfcal nellgence, sof compung and machne learnng mehods have been used n order o oban more accurae predcons. Especally, neural neworks (NNs) [2] and suppor vecor regresson (SVR) [3] have shown promsng resuls n modellng sock prce me seres due o her good robusness agans nose, capably o model non-lnear relaonshps and generalzaon performance. In hs paper we wll demonsrae ha he longrun behavour of sock prce can be effecvely predced employng NNs and ε-svrs. We furher hypohesse ha he predcon of sock prce reurn can be more accurae when usng qualave exual nformaon hdden n annual repors. Therefore, we develop a model ha combnes quanave npu varables (mosly fundamenal analyss ndcaors) wh qualave senmen from annual repors. Then, NNs and ε-svrs are used o perform a oneyear ahead sock reurn forecas. Ths paper s organzed as follows. Frs, a bref revew of leraure on sock prce forecasng usng quanave and qualave nformaon s presened. Then, he mehodology of our research s nroduced. In hs secon, he appled mehods are nroduced as well. Nex secon descrbes he daa se. The expermenal resuls secon compares he forecasng performance across he NNs and ε- SVRs. 2 Leraure Revew In he sock prce forecasng leraure, has been proven ha non-lnear predcors from he felds of arfcal nellgence, sof compung and machne learnng are more accurae n forecasng sock prces, see e.g. [4]. Yoon and Swales [5] demonsraed he capables of forecasng performance of mullayer percepron NNs (MLPs) compared o E-ISSN: Issue 4, Volume 10, Ocober 2013

2 Per Hájek, Vladmír Olej, Renáa Myšková mulvarae sascal mehods. In a smlar manner, MLPs have been employed o predc shor-erm sock prces or ndexes on varous sock markes, see e.g. [6,7,8]. Excep for MLPs, oher NNs archecures have successfully been appled o sock prce forecasng such as generalzed regresson NNs [9], radal bass funcon (RBF) NNs [10, 11] and relaed SVR [12]. The non-lnear characer of sock prce daa have furher been examned usng oher sof-compung and AI mehods such as chaos heory [13,14], mul-agen sysems [15,16], or fuzzy rule-based sysems [17]. The advanages of ndvdual sof compung mehods have been combned n hybrd sysems [18,19,20]. Fuzzy rule based sysems [21] and NNs [22] have been also successfully appled sock marke rend where he h rao of correcly predced rends s used as a measure of forecasng performance. The problem of sock prce forecasng becomes even more complex when performng long-run forecass. Shor-run forecass are manly based on echncal ndcaors whls long-run forecass are performed usng fundamenal analyss. Campbell and Ammer [23] repor ha long-run sock reurns of US companes are drven largely by news abou fuure excess sock reurns and nflaon, respecvely. Curren and expeced dvdend yelds have shown o be oher mporan drver of longerm sock reurns across sock markes [24]. Campbell and Shller [25] demonsraed ha prceearnngs raos and dvdend-prce raos are mporan drvers of fuure sock prce changes. Prevous reurns seem o affec fuure sock prce reurns, oo (a long memory propery of sock marke) [26]. However, large varaons n sock prces have no been explaned adequaely so far. Bak e al. [27] argue ha he large varaons may be due o a crowd effec (wh agens mang each oher's behavour). The varaons were explaned by he nerplay beween raonal raders and nose raders. The raonal raders behavour s based on fundamenal analyss, whereas he nose raders make decsons based on he behavour of oher raders. Then, fundamenal analyss can be used o forecas fuure sock reurns effecvely only when he number of raonal raders (arbrageurs) s larger. Researchers n behavoural fnance have been workng wh wo basc assumpons [28]: (1) nvesors are subjec o senmen; and (2) beng agans senmenal nvesors s cosly and rsky. Invesor senmen s measured eher boom-up (nvesors under reac or overreac o pas reurns or fundamenals) or op-down (he effec of aggregae senmen on ndvdual socks). Recenly, he effec of marke senmen on sock marke behavour has been nvesgaed n agen-based smulaors [29]. Accordng o [28], a hgh sensvy o aggregae nvesor senmen s assocaed wh low capalzaon, younger, unprofable, hgh volaly, non-dvdend payng, growh companes, or socks of frms n fnancal dsress. Bollen e al. [30] showed ha he aggregae senmen can be exraced from he ex messages on he Twer. They analyzed he ex conen of daly Twer feeds by measurng (1) posve vs. negave mood, and (2) mood n erms of 6 dmensons (Calm, Aler, Sure, Val, Knd, and Happy). The accuracy of DJIA (Dow Jones Indusral Average) daly predcons were sgncanly mproved by he ncluson of specfc publc mood dmensons. Telock [31] fnds ha senmen n news sores deermnes boh sock prce reurn and volaly. Specfcally, hgh meda pessmsm predced downward pressure on marke prces followed by a reverson o fundamenals. In addon, unusually hgh or low pessmsm predced hgh marke radng volumes. These fndngs conform o nose raders models. Demers and Vega [32] nvesgaed he effec of senmen n earnngs announcemens. They conclude ha (1) unancpaed ne opmsm n managers language predcs abnormal sock reurns, and (2) he level of uncerany n he ex s assocaed wh dosyncrac volaly and predcs fuure dosyncrac volaly. Sascal approaches such as Naïve Bayes classfer, vecor dsance classfer, dscrmnan-based classfer, and adjecve-adverb phrase classfer were used by [33] o analyse he senmen of sock message boards. The senmen analyss proves o be a sgnfcan deermnan of sock ndex levels, radng volumes and volaly. Annual repors are an mporan vehcle for organzaons o communcae wh her sakeholders. In addon o quanave daa (accounng and fnancal daa drawn from fnancal saemens), annual repors conans narrave exs,.e. qualave daa. Besdes oher hngs, annual repors descrbe company s manageral prores. Kohu and Segars [34] noced ha communcaon sraeges n annual repors dffer n erms of he subjecs emphaszed when he company s performance worsens. Senmen analyss of ex documens s carred ou usng eher word caegorzaon (bag of words) mehod or sascal mehods. The former mehod requres avalable dconary of erms and her caegorzaon accordng o her senmen. However, such a dconary s conex sensve (doman-specfc dconares have o be developed). E-ISSN: Issue 4, Volume 10, Ocober 2013

3 Per Hájek, Vladmír Olej, Renáa Myšková The laer mehods, on he oher hand, requre he lkelhood raos o be esmaed based on subjecve classfcaon of exs one [31]. The sudy by [35] uses word classfcaon scheme no posve and negave caegores o measure he one change n he managemen dscusson and analyss secon of corporae annual repors. The resuls ndcae ha sock marke reacons are sgnfcanly assocaed wh he one change of he annual repors. A sascal approach was employed also by [36,37] o show ha (1) sock marke reacs o he senmen of annual repors and (2) he predcon of fuure sock reurn can be sgnfcanly mproved usng he senmen. Loughran and McDonald [38] developed a dconary for fnancal doman whch enables capurng he conex specfc one of annual repors. The comparave advanage of he fnancal dconary over a general Harvard dconary was demonsraed on he predcons of reurns, radng volume, reurn volaly, fraud, maeral weakness, and unexpeced earnngs n [38]. Recenly, Hajek and Olej [39] demonsraed ha senmen n annual repors sgnfcanly mproves he accuracy of corporae fnancal dsress forecasng models. I was he frequency of posve, lgous and weak modal erms ha seem o be an early warnng ndcaor of fuure fnancal dsress. To conclude hs secon, prevous fndngs suppor he hypohess ha qualave verbal communcaon by managers s, ogeher wh quanave nformaon, mporan deermnan of fuure corporae fnancal performance and sock reurns. However, only lle aenon has been drawn o her long-run effecs and, n addon, only sascal mehods have been appled o forecas sock prce reurns usng qualave exual nformaon hdden n annual repors. 3 Research Mehodology The research mehodology used n hs paper s presened n Fg. 1. Frs, quanave daa were colleced from fnancal saemens and qualave (exual) daa from annual repors (10-Ks). Tex n annual repors was pre-processed lnguscally (usng okenzaon and lemmazaon) [40]. The se of poenal erm canddaes was hen represened by a sequence of agged lemmas. Ths se was flered usng he fnancal dconary developed by [38]. As a resul, he fnal se of erms was generaed. The erms were labelled by word caegorzaon (negave, posve, ec.). Nex, he f.df erm weghng scheme was appled o he ex corpus n order o ge he mporance of erms. An average wegh (mporance) was hen calculaed for each senmen caegory. Tex nformaon from annual repors Quanave ndcaors Lngusc preprocessng Word caegorzaon from dconary Daa preprocessng Fg. 1: Research mehodology Term fler Term weghng scheme Feaure selecon NNs, ε- SVR The quanave daa were pre-processed usng he mpuaon of mssng daa (wh medan values) and daa sandardzaon (Z-score). Snce such a se of varables was large, he feaure selecon mechansm was appled o oban he fnal se of varables used as npu of NNs and ε-svr. Thus, he forecasng of sock prce reurn was made more effecve and, addonally, he error of he models could be reduced usng he opmzed se of npu varables. 3.1 Senmen Analyss Senmen analyss represens a complex problem due o he ambguy n word caegorzaon. The ambguy can be resolved usng conex knowledge, for example from fnancal doman. The correc caegorzaon of erms no he bags of words (posve, negave, ec.) s dffcul because words may have dfferen meanngs and ones n ndvdual domans. Therefore, here have been aemps o propose a doman-specfc word caegorzaon recenly. In hs sudy we used he word caegorzaon from he fnancal dconary proposed by [38] wh he followng caegores of erms: negave (e.g. abandon, abolsh, abuse, annoy, annul, assaul, bad, loss, bankrupcy, barrer, calamy, cancel, close, corrup, crcal, crucal, danger declne, defaul, depress, dmnsh, dsagree, mbalance, mproper, problem, suffer, unable, weak), wf = 2349, posve (e.g. able, accomplsh, acheve, advance, assure, boos, collaborae, complmen, creave, E-ISSN: Issue 4, Volume 10, Ocober 2013

4 Per Hájek, Vladmír Olej, Renáa Myšková delgh, easy, enable, effecve, enjoyng, excellen, gan, progress, srong, succeed), wf = 354, uncerany (e.g. ambguy, assume, depend, crossroad, devae, flucuae, may, maybe, nexac, probably, random, reconsder, rsk, unknown, varable), wf = 291, lgous (e.g. allege, amend, appeal, arbrae, aes, aorney, bal, codfed, consuon, conrac, crme, cour, defeasance, delegable, ndc, judcal, legal, sue), wf = 871, modal srong (e.g. always, bes, clearly, defnely, hghes, mus, never, srongly, undoubedly), wf = 19, modal weak (e.g. almos, appeared, could, depend, mgh, nearly, possble, seldom, somemes, sugges), wf = 27, where wf s he frequency of erms n he word caegores lsed n he fnancal dconary. The frequency of ne posve words was deermned as he posve erm coun mnus he coun for negaon (posve erms can be easly qualfed or compromsed). The mos common f.df (erm frequency-nverse documen frequency) erm weghng scheme was used n hs sudy. The weghs can be defned as follows n he f.df ( 1 + log( f, j)) N w = log f f,j 1, j (1 + log( a)) df, (1) 0 oherwse where N denoes he oal number of documens n he sample, df sands for he number of documens wh a leas one occurrence of he -h erm, f.j s he frequency of he -h erm n he j-h documen, and a denoes he average erm coun n he documen. 3.2 Feaure Selecon Algorhms ha perform feaure selecon can generally be dvded no wo caegores, he wrappers and he flers [41]. The wrappers use e.g. cross-valdaon n order o esmae he error of feaure subses. Ths approach may be slow snce he predcor s called repeaedly. The flers operae ndependenly of any learnng algorhm. Undesrable feaures are flered ou of he daa before predcon sars. The orgnal se of npu parameers x m, m=48 was opmzed by usng correlaon based fler (CBF) [42]. The CBF opmzes he se of npu parameers so ha evaluaes he worh of a subse of npu parameers (feaures) by consderng he ndvdual predcve ably of each feaure along wh he degree of redundancy beween hem. The objecve funcon f(λ), also known as Pearson s correlaon coeffcen, s based on he heursc ha a good feaure subse wll have hgh correlaon wh he class label bu wll reman uncorrelaed among hemselves. Based on he saes facs s necessary o mnmze he number of x m, m=48 and maxmze funcon f(λ) whch can be expressed as f(λ)= λ ζ cr λ + λ (1 λ) ζ rr, (2) where λ s he subse of feaures, ζ cr s he average feaure o oupu correlaon, and ζ rr s he average feaure o feaure correlaon. A genec algorhm (GA) [43] was used o reduce he orgnal se of npu parameers x m, m=48 and, hus, o selec only sgnfcan parameers. I was used as a search mehod for he menoned feaure selecon mehod,.e. maxmzes he objecve funcon f(λ). I can be expressed for example n he followng way: 1. Inalzaon: Le here be a random nal populaon H. 2. Valuaon: λ Η calculae objecve funcon f(λ), he objecve funcon saes he qualy of ndvdual λ. 3. f max f(λ)< ρ hen repea λ /* ρ s he hreshold value for objecve funcon f(λ)*/ begn selecon: random selecon (1 p s ) P of ndvduals from he nal populaon H o he ransonal populaon H. The selecon sep reproduces genoypes based on her fness funcon. /* p s s he probably of selecon, P s he sze of populaon */ crossng: probably selecon p c P/2 of pars of ndvduals from he nal populaon H. For each par creae wo descendans by crossng and add hem o ransonal populaon H. /* p c s he probably of crossover */ muaon: nver random bs of ndvduals from he ransonal populaon H wh probably of muaon p m. acualzaon: H H. valuaon: λ Η calculae objecve funcon f(λ) end 4. Reurn of ndvdual λ Η, whch akes he hghes value of objecve funcon f(λ). E-ISSN: Issue 4, Volume 10, Ocober 2013

5 Per Hájek, Vladmír Olej, Renáa Myšková Based on he mnmzaon of he number of parameers x m, m=48 usng Pearson s correlaon coeffcen and on he maxmzaon of objecve funcon f(λ) wh defned parameers of he GA, s possble o desgn a general formulaon of he model. 3.3 Neural Neworks The srucure of MLP [44,45,46] s gven by he ask whch execues. The oupu of MLP can be expressed for example as follows y K = v J k( d ( w j,k x j,k )), (3) k= 1 j= 1 where y s he oupu of he MLP, v k s he vecor of synapses weghs among neurons n he hdden layer and oupu neuron, w j,k s he vecor of synapses weghs among npu neurons and neurons n he hdden layer, k s he ndex of neuron n he hdden layer, K s he number of neurons n he hdden layer, d s he acvaon funcon, j s he ndex of he npu neuron, J s he number of he npu neurons per one neuron n he hdden layer, and x j,k s he npu vecor of he MLP. In he process of learnng, he values of synapse weghs among neurons are adjused. The mos ofen used ones are graden mehods. The erm RBF NN [46,47] refers o any knd of feed-forward NN ha uses RBF as her acvaon funcon. RBF NNs are based on supervsed learnng. The oupu f(x,h,w) RBF NN can be defned hs way f ( x, H,w) = w h ( x), (4) q = 1 where H={h 1 (x),h 2 (x),,h (x),,h q (x)} s a se of RBF acvaon funcons of neurons n he hdden layer and w are synapse weghs. Each of he m componens of he vecor x=(x 1,x 2,,x k,,x m ) s an npu value for he q acvaon funcons h (x) of RBF neurons. The oupu f(x,h,w) of RBF NN represens a lnear combnaon of oupus from q RBF neurons and correspondng synapse weghs w. The acvaon funcon h (x) of an RBF NN n he hdden layer belongs o a specal class of mahemacal funcons whose man characersc s a monoonous rsng or fallng a an ncreasng dsance from cenre s of he acvaon funcon h (x) of an RBF. Neurons n he hdden layer can use one of several acvaon funcons h (x) of an RBF NN, for example a Gaussan acvaon funcon (a one-dmensonal acvaon funcon of RBF), a roary Gaussan acvaon funcon (a wodmensonal RBF acvaon funcon), mulsquare and nverse mulsquare acvaon funcons or Cauchy s funcons. Resuls may be presened n hs manner q x s h(x,s,r)= exp( ), (5) = 1 r where x=(x 1,x 2,,x k,,x m ) represens he npu vecor, S={s 1,s 2,,s,,s q } are he cenres of acvaon funcons h (x) of RBF NN and R={r 1,r 2,,r,,r q } are he raduses of acvaon funcons h (x). The neurons n he oupu layer represen only weghed sum of all npus comng from he hdden layer. The acvaon funcon of neurons n he oupu layer can be lnear, wh he un of he oupu evenually beng convered by jump nsrucon o bnary form. The RBF NN learnng process requres a number of cenres s of acvaon funcon h (x) of he RBF NNs neworks o be se as well as for he mos suable posons for RBF cenres s o be found. Oher parameers are raduses of cenres s, rae of acvaon funcons h (x) of RBFs and synapse weghs W(q,n). These are se up beween he hdden and oupu layers. The desgn of an approprae number of RBF neurons n he hdden layer s presened n [46,47]. Possbles of cenres recognon s are menoned n [46,47] as a random choce. The poson of he neurons s chosen randomly from a se of ranng daa. Ths approach presumes ha randomly pcked cenres s wll suffcenly represen daa enerng he RBF NN. Ths mehod s suable only for small ses of npu daa. Use on larger ses ofen resuls n a quck and needless ncrease n he number of RBF neurons n he hdden layer, and herefore unjusfed complexy of he NN. The second approach o locang cenres s of acvaon funcons h (x) of RBF neurons can be realzed by a K-means algorhm. 3.4 Suppor Vecor Regresson In nonlnear regresson ε-svr [46,48,49,50] mnmzes he loss funcon L(d,y) wh nsensve ε [46,48,49]. Loss funcon L(d,y)= d y, where d s he desred response and y s he oupu esmae. The consrucon of he ε-svr for approxmang he desred response d can be used for he exenson of loss funcon L(d,y) as follows 2 E-ISSN: Issue 4, Volume 10, Ocober 2013

6 Per Hájek, Vladmír Olej, Renáa Myšková d y ε L ( d, y) = for d y ε 0 else ε, (6) where ε s a parameer. Loss funcon L ε (d,y) s called a loss funcon wh nsensve ε. Le he nonlnear regresson model n whch he dependence of he scalar d vecor x expressed by d=f(x) + n. Addve nose n s sascally ndependen of he npu vecor x. The funcon f(.), and nose sascs are unknown. Nex, le he sample ranng daa (x,d ), =1,2,...,N, where x and d s he correspondng value of he oupu model d. The problem s o oban an esmae of d, dependng on x. For furher progress s expeced o esmae d, called y, whch s wdespread n he se of nonlnear bass funcons ϕ j (x), j=0,1,...,m 1 hs way y = m1 w ϕ ( x) T j = w ϕ( x), (7) j = 0 j where ϕ(x)=(ϕ 0 (x), ϕ 1 (x),, ϕ m1 (x)) T and w=(w 0,w 1,...,w m1 ) T. I s assumed ha ϕ 0 (x)=1 n order o he wegh w 0 represens bas b. The soluon o he problem s o mnmze he emprcal rsk R emp 1 N = Lε ( d, y ), (8) N = 1 under condons of nequaly w 2 c 0, where c 0 s a consan. The resrced opmzaon problem can be rephrased usng wo complemenary ses of nonnegave varables. Addonal varables ξ and ξ descrbe loss funcon L ε (d,y) wh nsensvy ε. The resrced opmzaon problem can be wren as an equvalen o mnmzng he cos funconal N 1 φ ( w, ξ, ξ ' ) = C( ( ξ ' wt + ξ )) + w, (9) = 1 2 under he consrans of wo complemenary ses of non-negave varables ξ and ξ. The consan C s a user-specfed parameer. Opmzaon problem (9) can be easly solved n he dual form. The basc dea behnd he formulaon of he dual-shaped srucure s he Lagrangan funcon [46], he objecve funcon and resrcons. Can hen be defned Lagrange mulplers wh her funcons and parameers whch ensure opmaly of hese mulplers. The opmzaon of he Lagrangan funcon only descrbes he orgnal regresson problem. To formulae he correspondng dual problem a convex funcon can be obaned (for shorhand) N N = 1 = 1 Q ( α, α' ) = d ( α α' ) ε ( α + α' ) 1 N N ( α ' ' α )( α j α j ) K ( x, x j ), (10) 2 = 1 j = 1 where K(x,x j ) s kernel funcon defned n accordance wh Mercer's heorem [46,48,49]. Solvng opmzaon problem s obaned by maxmzng Q(α,α ) wh respec o Lagrange mulplers α and α and provded a new se of consrans, whch hereby ncorporaed consan C conaned n he funcon defnon φ(w,ξ,ξ ). Daa pons covered by he α α, defne suppor vecors [46,48,49]. 4 Daase The ndcaors of fundamenal analyss represen commonly used predcors of long-run sock marke movemen. Inpu varables used n our sudy for descrbng companes can be dvded no wo groups: (1) fnancal ndcaors and (2) senmen ndcaors, see Table 1. Only U.S. frms from he NASDAQ and New York Sock Exchange were seleced. Quanave fnancal ndcaors were drawn from he Value Lne daabase, and senmen ndcaors were drawn from annual repors avalable a U.S. Secures and Exchange Commsson EDGAR Sysem. All daa were colleced for he year = 2010 and he sock prce reurn was calculaed as a one-year change (365 days afer he release of he frms annual repor). As a resul, we were able o collec 685 daa on U.S. frms. In addon o he presened ndcaors, we also calculaed her change over he las year (growh rae from 2009 o 2010),.e. Δx =(x x -1 )/x -1, where =1,2,,24. Therefore, we used 48 npu varables as he npus of he opmzaon process (feaure selecon process). As ndcaed n Table 1, several subgroups of fnancal ndcaors were used o conrol sze (marke capalzaon), profably, leverage, and marke value raos. In general, he value of shares reflecs he auhenc daa obaned from he analyss of prevous years and he expeced daa based on he esmaes of fuure developmen. Accordng o he effcen marke heory he changes n he marke prce of secures are represened by random varables. The prces are he resul of supply and demand respondng o prce-sensve nformaon [51]. Mehods for assessng he nvesmen crera E-ISSN: Issue 4, Volume 10, Ocober 2013

7 Per Hájek, Vladmír Olej, Renáa Myšková and he fnal nvesmen decson are deermned by he ype of nvesor. Table 1: Inpu and oupu varables n he daase Varable Mean Sd.Dev. x 1 Marke capalzaon (Sze) Prce o earnngs per share x 2 (P/E) Expeced growh n revenues x 3 (nex 5 years) x 4 Earnngs per share x 5 Reurn on equy (ROE) x 6 Reurn on capal (ROC) x 7 Prce o book value (PBV) x 8 Enerprse value / earnngs x 9 Deb-o-capal rao x 10 Hgh o low sock prce x 11 Dvdend yeld x 12 Payou rao Sandard devaon of sock x 13 prce x 14 Bea coeffcen x 15 Shares held by nsders x 16 Shares held by muual funds x 17 Pre-ax operang margn Non-cash workng capal x 18 (NCWC) x 19 Frequency of negave erms x 20 Frequency of posve erms x 21 Frequency of uncerany erms x 22 Frequency of lgous erms x 23 Frequency of srong modal erms x 24 Frequency of weak modal erms y +1 Sock prce reurn = (P +1 P )/P The frs ask n analyzng a sock s a dealed performance evaluaon of he relevan jon-sock company especally from he fnancal pon of vew. The fnancal polcy of he company represens relevan nformaon o he nvesors whch can be consdered from wo perspecves: dsrbuon of prof deermnes he dvdend polcy of he jon-sock company, capal srucure affecs he ndebedness of he jon-sock company The dvdend polcy of he jon-sock company s based on he deermnaon of he proporon beween he pad and reaned prof (reaned for he nex perod). The polcy formulaon s abou deermnng he approprae mx of pad dvdends and reaned earnngs reflecng he fnancal suaon and he needs of he company. Varous economss, nvesors and analyss perceve he mporance of dvdends dfferenly and accordng o her percepon hold dfferen vews on he mpac of dvdend polcy on he share prce movemen. However, he mos common s he clam ha hgher dvdend payou rao leads o an ncrease n he share prce. Creang an opmal capal srucure leads o he maxmzaon of he share prce on he marke and hereby ncreases he marke value of he company. I s an approprae choce n he proporon and srucure of he deb and equy. The deb-o-equyrao s an ndcaor of a company s level of ndebedness. Even n hs area of corporae fnancal polcy he economss have dfferen approaches as o he mpac of capal srucure on share prces. Assumng ha up o a ceran percenage of deb and equy here are posve effecs of he ax sheld and leverage, hen, f he level of ndebedness rses up o hs hreshold, he marke prce of shares ncreases. Marke capalzaon (sze) reflecs he value of he jon sock company on he open marke ( s equal o he number of ssued shares mes he curren share prce on he sock marke). Prce o earnngs per share (prce earnngs rao - P/E) s one of he mos mporan raos of he capal marke (marke value raos). Ths ndcaor s regularly publshed for he raded shares bu mus be approached wh cauon. Is value can be dsored by he appled accounng mehods, one-off busness or fnancal operaons or recency of he appled values. The P/E rao n he range from 8 o 12 s consdered generally accepable [52]. Expeced growh n revenues (nex 5 years) can be consdered as a creron ha does no affec all nvesors equally. The followng rule apples ha he hgher he expeced reurn, he hgher he rsk assocaed wh hese secures. (Noe: low-rsk and hghly lqud nvesmen s less profable agan.) I depends on he nvesor s nclnaon owards rsk. Earnngs per share serve as a key ndcaor of a company s fnancal suaon and reflec he sze of ne ncome (EAT) arbuable o one share. Reurn on equy (ROE) s an ndcaor of he rae of reurn on he money nvesed by shareholders. I should be hgher han he yelds on governmen bonds and also hgher han he profably raos of oal asses (reurn of oal asses - ROA). Reurn on nvesed capal (ROIC) s used exclusvely for he rerospecve assessmen of profably. ROIC uses cash flow and s calculaed as: earnngs afer ax/(oal asses shor-erm fnancal asses - nonneres bearng curren lables). E-ISSN: Issue 4, Volume 10, Ocober 2013

8 Per Hájek, Vladmír Olej, Renáa Myšková Prce o book value P/B (Prce/Book Value Rao) s he rao of he share marke value o s book value. A posve aspec of hs ndcaor s ha akes no accoun equy (no jus profs), bu on he oher hand s nfluenced by accounng pracces, whch can dsor he ndcaor n he ner-company comparson process. The marke prce of shares n a prosperous company exceeds her book value, so he P/B rao should be greaer han 1. Book value per share - he rao of equy o he number of common shares should show a growng endency n case of hrvng busnesses. Deb-o-capal rao s he rao of he company's deb o s oal capal, so by he deb proporon s possble o assess wheher he company s more or less lkely o use deb fnancng. The opmal deb rao s dfferen for dfferen busness secors and depends on he nvesmen demands of he parcular busness % share of deb on oal asses of he company s consdered as an opmal lm. Hgh o low sock prce ('52-Week Hgh / Low ') - he hghes and lowes prces a whch he shares are raded a n he prevous year (n he las 52 weeks). I does no only serve he nvesors o predc he fuure movemen of prces bu also he owners o decde when o buy or sell he shares. Dvdend payou rao expresses he proporon of dsposable (ne) ncome pad o shareholders n dvdends. The rao s amoun should no be he only assessed value f a company operaes n a purely nvesmen-orened ndusry, hs rao decreases, bu n he long run parcularly hese nvesmens lead o he growh of share prces. Dvdend yeld measures he value of he dvdend per share o s curren prce. I s necessary o ake no accoun ha he dvdend s backdaed, whle he share prce s up o dae. The sandard devaon of sock prce - he sascal value s used o express he hsorcal volaly. The greaer he sandard devaon, he greaer he sock prce volaly expeced by he nvesors n he fuure. (e.g. he sandard devaon of a sable blue chp sock wll be low). Bea coeffcen expresses he correlaon beween he secury reurns (or he ndusry) and reurns of he marke porfolo represened by he marke ndex n a specfed me perod. Bea coeffcen reflecs he exen o whch he parcular secury s subjec o he nfluence of he general marke rses or falls, and measures he conrbuon of he secury o he rsk of he porfolo. Socks wh a bea greaer han 1 ndcae rsky secures because hey end o nensfy he general sock marke movemens. In he long run, he bea coeffcen s usually hgher n companes wh a lower marke capalzaon han n hose wh a larger marke capalzaon. Shares held by nsders - percenage of he company s ousandng shares, employee shares The legslaon for he employee share ownershp dffers n each counry. In mos counres, employee shares usually do no consue any vong rghs and are no freely radable (resrced sock un). They are graned o employees, for example as a form of compensaon. Shares held by muual funds - shares n nvesmen funds - hs ndcaor s mporan for assessng he amoun of shares held by nvesmen funds. In he even ha he funds announce he nenon o reduce shares n he company, can cause a sharp ncrease n prof akng on he le o shares (sale of shares). Ths may cause her prces o fall. Oher ndcaors can be also used o assess he fnancal resuls and value of he company. Table 1 also ncludes: Pre-ax operang margn - he prof margn before axes - he rao of profs before neres and ax o revenues, Non-cash workng capal (NCWC) s expressed as he sum of nvenory and recevables. The amoun s deermned wh regard o he naure of he busness on he bass of pas busness developmen and experence of managers. In addon o hese ndcaors, he neres and expecaons of nvesors may also be affeced by he way he company presens self. Gven he large amoun of nformaon conaned n he annual repor oher aspecs are checked n hs mandaory documen. The legslaon usually regulaes he parculars n he annual repor ha have o be saed uncondonally, he oher saed daa depend on he managemen of he company. Ths means ha wh he same fnancal resuls he repor may be wren n dfferen ways - opmsc or pessmsc. A more cauous managemen s raher crcal; a managemen whch s able o accep a hgher level of rsk assesses he resuls more generously. As shown n prevous leraure, senmen ndcaors are srongly relaed o fnancal ndcaors and, addonally, hey also nform on he busness poson (busness dversfcaon, busness rsk, characer, organzaonal problems, managemen evaluaon, accounng qualy, ec.). Snce he number of npu varables was oo large and he mpac of many varables on sock prce reurn s ambguous, respecvely, we performed he E-ISSN: Issue 4, Volume 10, Ocober 2013

9 Per Hájek, Vladmír Olej, Renáa Myšková process of feaure selecon usng he CBF mehod. Ths sep reduces he dmensonaly of he feaure space, makes he operaon of learnng algorhms faser, and may also mprove he forecasng accuracy. A GA was used as a search mehod for he CBF n he sage of feaure selecon. The parameers of he GA were se as follows: he propably of crossover p c =0.8, he probably of muaon p m =0.03, he maxmum number of generaons=40, populaon sze (feaure ses)=20. The found se of varables s presened n Table 2. In he opmum se of varables, here were P/E rao, expeced growh n revenues, ROC, and hgh o low sock prce. In addon o prevous years absolue values of hese ndcaors, dynamc ndcaors such as ΔROE or ΔNCWC played an mporan role n sock prce reurn forecasng. Furhermore, he change of senmen (negave and uncerany) seems o be an mporan deermnan oo. Table 2: Inpu and oupu varables afer he opmzaon Varable x 2 Prce o earnngs per share x 3 Expeced growh n revenues (nex 5 years) Δx 3 Δ Expeced growh n revenues (nex 5 years) Δx 5 Δ Reurn on equy x 6 Reurn on capal x 10 Hgh o low sock prce Δx 17 Δ Pre-ax operang margn Δx 18 Δ Non-cash workng capal Δx 19 Δ Frequency of negave erms Δx 21 Δ Frequency of uncerany erms y +1 Sock prce reurn = (P +1 P )/P 5 Expermenal Resuls For he expermens, daa were dvded no 5 pars of he same sze and hen raned wh 4 pars (80%) and esed wh 1 remanng par (20%). Ths procedure was repeaed 5 mes for all pars. In he resuls, we refer o average errors over he 5 expermens. We compared he resuls obaned usng NNs (MLP and RBF) and ε-svr measurng he MSE (mean squared error) and R 2 (coeffcen of deermnaon). Snce he resuls of NNs and ε-svr are sensve o he seng of learnng parameers we auomacally deermned her values n he followng way. Frs, MLPs and RBF NNs were employed o forecas he sock prce reurn. The MLP model was raned usng conjugae graden mehod wh maxmum eraons. Logsc acvaon funcons were used n he hdden layer, and lnear acvaon funcons n he oupu layer. The number of neurons (showng on he complexy of he MLP model) n one hdden layer was se from he se k={2,3,,20}. The parameers of he RBF model were deermned auomacally usng a GA. The number of neurons n he hdden layer was chosen from he se s {1,2,,100}, he radus 1/r of RBF from <0.01,400> and regularzaon parameer lambda from <0.001,10>. The employed GA worked wh he populaon sze of 200 and he maxmum number of generaons=20. Fnally, we compared he forecasng resuls wh a lnear regresson model (Table 3). Table 3: Forecasng performance of he NNs Mehod MSE R 2 MLP (# neurons=8) RBF NNs (# neurons=8) Lnear regresson We also nvesgaed he mporance of he npu varables n he NNs models. The mporance was obaned by he evaluaon of npu varables effecs on he forecasng resuls. In hs sudy he calculaon of npu varables mporance was performed usng sensvy analyss. The values of each of he npu varables were randomzed and he effec on he MSE of he model was measured. Fnally he conrbuons of npu varables were sandardzed so ha he conrbuon of he mos mporan npu varable was 100%, and he conrbuons of oher npu varables were relaed o he mos mporan varable. Thus, he resulng conrbuons of he npu varables represen relave mporance values. The mporance of he varables n he MLP s model s presened n Fg. 2. The resuls show ha ΔROE, he relaon beween hgh and low sock prce value, and ΔNCWC were he mos mporan deermnans of he sock prce reurn. The resuls also ndcae ha senmen nformaon s a more mporan deermnan compared o he ε-svr model (Fg. 3). The ε-svr algorhm was raned wh lnear, polynomal (degree=3) and RBF kernel funcons (Table 4). The RBF kernel funcon proved o be he mos approprae for he predcon. Complexy parameer C was chosen from he nerval <0.1,50000> and 1/r from <0.01,20> usng paern search algorhm, see e.g. [53]. The followng sengs were found wh he paern search algorhm: E-ISSN: Issue 4, Volume 10, Ocober 2013

10 Per Hájek, Vladmír Olej, Renáa Myšková 1) lnear kernel funcon: C=62.5, ε=0.00, # suppor vecors=322, 2) polynomal kernel funcon: degree=3, C=78.6, 1/r =0.14, coef0=1.05, ε=0.10, # suppor vecors=228, 3) RBF kernel funcon: C=184.8, 1/r =0.15, ε=0.07, # suppor vecors=245. Fg. 2: Imporance of varables n he MLP s model Table 4: Forecasng performance of he ε-svr Kernel funcon MSE R 2 Lnear Polynomal RBF Fg. 3 depcs he mporance of npu varables n he ε-svr model wh RBF kernel funcon. The resuls show ha ΔROE, ROC and he relaon beween hgh and low sock prce value were he mos mporan deermnans of he sock prce reurn when usng he ε-svr model. Consderng he senmen ndcaors, he mos mporan varable was he change of he negave senmen. In he followng expermens we used only he fnancal ndcaors as npu varables,.e. whou senmen ndcaors. Fg. 3: Imporance of varables n he ε-svr s model (RBF kernel funcon) In erms of MSE he MLP and RBF NN seem o provde smlar resuls as n he case wh he senmen ndcaors (Table 5). On he oher hand, when usng he coeffcen of deermnaon R 2 as a measure of forecas qualy, he MLP and RBF NN whou senmen nformaon could no explan varance comparable wh he models wh senmen nformaon. The comparson across he models shows ha he MLP wh senmen nformaon ouperformed he remanng models especally n erms of he coeffcen of deermnaon. Table 5: Forecasng performance of he NNs Mehod MSE R 2 MLP (# neurons=3) RBF NN (# neurons=16) Lnear regresson The followng sengs of he ε-svr were found usng he paern search algorhm: 1) lnear kernel funcon: C=107.2, ε=0.00, # suppor vecors=322, 2) polynomal kernel funcon: degree=3, C=55.5, 1/r =0.12, coef0 = 0.95, ε=0.12, # suppor vecors=218, 3) RBF kernel funcon: C=132.2, 1/r =0.14, ε= , # suppor vecors=338. E-ISSN: Issue 4, Volume 10, Ocober 2013

11 Per Hájek, Vladmír Olej, Renáa Myšková The same facs as for he MLP and RBF NN models hold also rue for he ε-svr models (see Table 6). Table 6: Forecasng performance of he ε-svr Kernel funcon MSE R 2 Lnear Polynomal RBF Concluson The arcle presens a sock prce forecasng model ha combnes fnancal ndcaors wh he ndcaors obaned from he exual annual repors of frms. The senmen ndcaors covered sx caegores of senmen. We hypohessed ha he predcon of sock prce reurn can be more accurae when usng he qualave exual nformaon hdden n he annual repors. The fndngs from hs sudy make several conrbuons o he curren leraure. Frs, we conclude ha larger varance n he nex year s sock prce reurn can be explaned usng he change n annual repors senmen, specfcally n he caegores of negave and unceran erms. Second, surprsngly, was no he senmen (one) of he annual repor self, bu he change n he senmen ha was he mos mporan deermnan n he models. In addon o he change of senmen, he long-run sock prce reurns were parcularly affeced by profably raos and fundamenal analyss ndcaors. ε-svrs and NNs performed beer han lnear regresson models when consderng he senmen nformaon. Ths suggess ha ε-svrs and NNs beer coped wh he growng complexy of he forecasng problem whch s lne wh prevous leraure, e.g. [54,55]. The developmen of share prces s nfluenced no only by he menoned facors bu also by he possbles and nenons of nvesors. The possbly of nvesmen s deermned by he sze of he nal nvesmen, whch s dfferen for each ype of nvesmen. Oher decsons relae o he nenon of he nvesor o choose eher drec or ndrec nvesmens. Drec nvesmens are of sraegc naure and her purpose from he nvesor s pon of vew s o acqure he majory sake n he company and ake over he conrol and hus be able o exercse he rghs assocaed wh an nvesmen n a parcular secury. Indrec nvesmens are porfolo nvesmens desgned o dversfy rsk and hus elmnae losses. The percenage share of an nvesor n he company's asses n case of ndrec nvesmens s sgnfcanly smaller han n case of drec nvesmens (up o 10% of he ssued capal). The decson makng process of nvesors s also affeced by he suaon on he sock marke. Ths suaon s relaed o he developmen of macroeconomc varables, namely o he growh of gross domesc produc, he busness cycle and fscal polcy, nflaon and neres raes, money supply and he volume of foregn capal. The sudy has gone some way owards enhancng our undersandng of sock marke behavour. However, furher mporan lmaons need o be consdered. The curren nvesgaon was lmed o he U.S. sock marke and o a shor me perod. Therefore, he approach proposed n hs sudy may be appled o oher sock markes (and dervave markes [56]) elsewhere n he world and o a longer me perod. Thus, counry-specfc and mespecfc deermnans may be aken no accoun. Anoher lmaon consss n he fac ha he words n he word caegores may have varous mporances for sock prce reurn predcon. Thus, he accuracy mprovemen of he models may be lmed. A furher sudy wh more focus on sascal approach s herefore suggesed. Fnally, The expermens n hs sudy were carred ou n Weka and DTREG n MS Wndows 7 operaon sysem. Acknowledgmen Ths work was suppored by he scenfc research projec of he Czech Scences Foundaon Gran No: S. References: [1] S.J. Grossman, R.J. Shller, The Deermnans of he Varably of Sock Marke Prces, The Amercan Economc Revew, Vol.71, No.2, 1981, pp [2] K. Mchalak, P. Lpnsk, Predcon of Hgh Increases n Sock Prces usng Neural Neworks, Neural Nework World, Vol.15, 2005, pp [3] K.J. Km, Fnancal Tme Seres Forecasng usng Suppor Vecor Machnes, Neurocompung, Vol.55, 2003, pp [4] G. Zhang, B.E. Pauwo, M.Y. Hu, Forecasng wh Arfcal Neural Neworks: The Sae of he Ar, Inernaonal Journal of Forecasng, Vol.14, 1998, pp [5] Y. Yoon, J. Swales, Predcon Sock Prce Performance: A Neural Nework Approach, In: Proc. of he 24 h Annual Hawa In. Conf. on Sysem Scence, 1991, pp E-ISSN: Issue 4, Volume 10, Ocober 2013

12 Per Hájek, Vladmír Olej, Renáa Myšková [6] J. Yao, H.L. Poh, Forecasng he KLSE Index usng Neural Neworks, In: Proc. of he IEEE In. Conf. on Neural Neworks, Vol.2, 1995, pp [7] N. Baba, M. Kozak, An Inellgen Forecasng Sysem of Sock Prce usng Neural Neworks, In: Proc. of he In. Jon Conf. on Neural Neworks, Vol.1, 1992, pp [8] J.H. Cho, M.K. Lee, M.W. Rhee, Tradng S&P 500 Sock Index Fuures usng a Neural Nework, In: Proc. of he Annual In. Conf. on Arfcal Inellgence Applcaons on Wall Sree, New York, 1995, pp [9] D. Enke, S. Thawornwong, The Use of Daa Mnng and Neural Neworks for Forecasng Sock Marke Reurns, Exper Sysems wh Applcaons, Vol.29, No.4, 2005, pp [10] X.B. Yan, Z. Wang, S. Yu, Y. L, Tme Seres Forecasng wh RBF Neural Nework, In: Proc. of he 4 h In. Conf. on Machne Learnng and Cybernecs, Guangzhou, Vol.8, 2005, pp [11] K. Chen, H. Ln, T. Huang, The Predcon of Tawan 10-year Governmen Bond Yeld, WSEAS Transacons on Sysems, Vol.8, No.9, 2009, pp [12] L.J. Cao, F.E.H. Tay, Suppor Vecor Machne wh Adapve Parameers n Fnancal Tme Seres Forecasng, IEEE Transacons on Neural Neworks, Vol.14, No.6, 2003, pp [13] Ch.K. Volos, I.M. Kyprands, S.G. Savrndes, I.N. Souboulos, L. Magafas, A.N. Anagnosopoulos, Nonlnear Fnancal Dynamcs from an Engneer s Pon of Vew, Journal of Engneerng Scence and Technology Revew, Vol.4, 2011, pp [14] Ch.K. Volos, I.M. Kyprands, S.G. Savrndes, I.N. Souboulos, Synchronzaon Phenomena n Coupled Nonlnear Sysems Appled n Economc Cycles, WSEAS Transacons on Sysems, Vol.11, No.12, 2012, pp [15] F. Ner, Learnng Predcve Models for Fnancal Tme Seres by Usng Agen Based Smulaons, Lecure Noes n Compuer Scence, Vol.7190, 2012, pp [16] F. Ner, Agen based Modelng under Paral and Full Knowledge Learnng Sengs o Smulae Fnancal Markes, AI Communcaons, Issue 4, Vol.25, 2012, pp [17] P.Ch. Chang, Ch.H. Lu, A TSK Type Fuzzy Rule based Sysem for Sock Prce Predcon, Exper Sysems wh Applcaons, Vol.34, No.1, 2008, pp [18] R. Tsah, Y. Hsu, C.C. La, Forecasng S&P 500 Sock Index Fuures wh a Hybrd AI Sysem, Decson Suppor Sysems, Vol.23, No.2, 1998, pp [19] G. Armano, M. Marches, A. Murru, A Hybrd Genec-Neural Archecure for Sock Indexes Forecasng, Informaon Scence, Vol.170, No.1, 2005, pp [20] V. Olej, Predcon of he Index Fund by Takag-Sugeno Fuzzy Inference Sysems and Feed-Forward Neural Nework, In: Proc. of he 5 h WSEAS In. Conf. on Arfcal Inellgence, Knowledge Engneerng and Daa Bases, Madrd, Span, 2006, pp [21] R.K. La, Ch.Y. Fan, W.H. Huang, P.Ch. Chang, Evolvng and Cluserng Fuzzy Decson Tree for Fnancal Tme Seres Daa Forecasng, Exper Sysems wh Applcaons, Vol.36, 2009, pp [22] P. Hajek, Forecasng Sock Marke Trend usng Prooype Generaon Classfers, WSEAS Transacons on Sysems, Vol.11, No.11, 2012, pp [23] J.Y. Campbell, J. Ammer, Wha Moves he Sock and Bond Markes? A Varance Decomposon for Long-Term Asse Reurns, The Journal of Fnance, Vol.48, No.1, 1993, pp [24] J.Y. Campbell, Y. Hamao, Predcable Sock Reurns n he Uned Saes and Japan: A Sudy of Long-erm Capal Marke Inegraon, The Journal of Fnance, Vol.47, No.1, 1992, pp [25] J.Y. Campbell, R.J. Shller, Valuaon Raos and he Long-run Sock Marke Oulook: An Updae. No.w8221, Naonal Bureau of Economc Research, [26] Z. Dng, C.W.J. Granger, R.F. Engle, A Long Memory Propery of Sock Marke Reurns and a New Model, Journal of Emprcal Fnance, Vol.1, No.1, 1993, pp [27] P. Bak, M. Paczusk, M. Shubk, Prce Varaons n a Sock Marke wh Many Agens, Physca A: Sascal Mechancs and s Applcaons, Vol.246, No.3, 1997, pp [28] N. Barbers, A. Shlefer, R. Vshny, A Model of Invesor Senmen, Journal of Fnancal Economcs, Vol.49, No.3, 1998, pp [29] F. Ner, Quanave Esmaon of Marke Senmen: A Dscusson of Two Alernaves, WSEAS Transacons on Sysems, Vol.11, No.12, 2012, pp E-ISSN: Issue 4, Volume 10, Ocober 2013

13 Per Hájek, Vladmír Olej, Renáa Myšková [30] J. Bollen, H. Mao, X. Zeng, Twer Mood Predcs he Sock Marke, Journal of Compuaonal Scence, Vol.2, No.1, 2011, pp [31] P.C. Telock, Gvng Conen o Invesor Senmen: The Role of Meda n he Sock Marke, Journal of Fnance, Vol.62, 2007, pp [32] E.A. Demers, C. Vega, Sof Informaon n Earnngs Announcemens: News or Nose? Workng Paper, INSEAD, [33] S. Das, M. Chen, Yahoo! for Amazon: Opnon Exracon from Small Talk on he Web. Workng Paper, Sana Clara Unversy, [34] G.F. Kohu, A.H. Segars, The Presden's Leer o Sockholders: An Examnaon of Corporae Communcaon Sraegy, Journal of Busness Communcaon, Vol.29, No.1, 1992, pp [35] R. Feldman, S. Govndaraj e al., Managemen's Tone Change, Pos Earnngs Announcemen Drf and Accruals, Revew of Accounng Sudes, Vol.15, 2010, pp [36] F. L, The Informaon Conen of Forward- Lookng Saemens n Corporae Flngs - A Naïve Bayesan Machne Learnng Approach, Journal of Accounng Research, Vol.48, No.5, 2010, pp [37] A. Huang, A. Zang e al., Informaveness of Tex n Analys Repors: A Naïve Bayesan Machne Learnng Approach. Workng Paper, The Hong Kong Unversy of Scence and Technology, [38] T. Loughran, B. McDonald, When Is a Lably No a Lably? Texual Analyss, Dconares, and 10-Ks, The Journal of Fnance, Vol.66, No.1, 2011, pp [39] P. Hajek, V. Olej, Evaluang Senmen n Annual Repors for Fnancal Dsress Predcon usng Neural Neworks and Suppor Vecor Machnes. In: Proc. of he 14 h Engneerng Applcaons of Neural Neworks (EANN 13), 2013, (n press). [40] R. Feldman, M. Fresko e al., Tex Mnng a he Term Level. In: Proc. of he 2 nd Europ. Symposum on Prncples of Daa Mnng and Knowledge Dscovery, 1998, pp [41] G.H. John, R. Kohav, K. Pfleger, Irrelevan Feaures and he Subse Selecon Problem. In: Proc. of he 11 h In. Conf. on Machne Learnng, 1994, pp [42] M.A. Hall, Correlaon-Based Feaure Subse Selecon for Machne Learnng. Hamlon, Unversy of Wakao, [43] S. N. Svananda, S. N. Deepa, Inroducon o Genec Algorhms. Berln, Sprnger Verlag, [44] R.P. Lppman, An Inroducon o Compung wh Neural Nes, IEEE ASSP Mag., Vol.4, 1987, pp [45] F. Rosenbla, The Percepron, a Probablsc Model for Informaon Sorage and Organzaon n he Bran, Psychol. Rev., Vol.65, No.6, 1958, pp [46] S. Haykn, Neural Neworks: A Comprehensve Foundaon. New Jersey, Prence-Hall Inc., [47] D.S. Broomhead, D. Lowe, Mulvarae Funconal Inerpolaon and Adapve Neworks, Complex Sysems, Vol.2, 1988, pp [48] N. Crsann, J. Shawe-Taylor, An Inroducon o Suppor Vecor Machnes and oher Kernel-based Learnng Mehods. Cambrdge Unversy Press, [49] A. Smola, J. Scholkopf, A Tuoral on Suppor Vecor Regresson, Sascs and Compung, Vol.14, 2004, pp [50] V.N. Vapnk, The Naure of Sascal Learnng Theory. Sprnger, New York, [51] P. Duspva, J. Novoný, Ulzaon of Quanave Mehods n he Decson Makng Process of a Manager. Scenfc Papers of he Unversy of Pardubce, Vol.17, No.2, 2010, pp [52] O. Rejnuš, Fnanční Trhy. Key Publshng, Osrava, 2011, (n Czech). [53] P. Hajek, V. Olej, Cred Rang Modellng by Kernel-based Approaches wh Supervsed and Sem-Supervsed Learnng, Neural Compung & Applcaons, Vol.20, No.6, 2011, pp [54] H. Ln, K. Chen, Sof Compung Algorhms n Prce of Tawan Real Esaes, WSEAS Transacons on Sysems, Vol.10, No.10, 2011, pp [55] P. Hajek, V. Olej, Muncpal Revenue Predcon by Ensembles of Neural Neworks and Suppor Vecor Machnes, WSEAS Transacons on Compuers, Vol.9, No.11, 2010, pp [56] K. Chang, S.S. Wang, P. Ke, Y.R. Huang, Y. Zhen, The Valuaon of Fuures Opons for Emssons Allowances under he Term Srucure of Sochasc Mul-Facors, WSEAS Transacons on Sysems, Vol.11, No.12, 2012, pp E-ISSN: Issue 4, Volume 10, Ocober 2013