Technical Analysis and Liquidity: the Case of the Budapest Stock Exchange

CEU eTD Collection TECHNICAL ANALYSISA OF THE BUDAPESTOF STOC In fulfi partial lment of the requirements forArtslment ofthe thedegreeofMaster of Supervisor: Central European University Department ofEconomics Budapest, Hungary Adam Farago Submitted to Professor ND LIQUIDITY ND 200 By 9

Peter KondorPeter K EXCHANGE K

: THE CASETHE :

CEU eTD Collection between timingand ofsignals. the liquidity tha find I methods data panel Using rules. trading successful most the by generated signals of timing the and liquidity market between relationship the study movements. price future predicting in useful be can rules (1992). al. et Brock by developed methodology the with indicators average moving of profitability the analyze o stocks 26 for data using analysis technical of aspects two examine I thesis this In decisions. investment analysis Technical

f the Budapest Stock Exchange from the period between 1997 and 2008. First, I First, 2008. and 1997 between period the from Exchange Stock Budapest the f

is a popular technique among financial practitioners financial among technique popular a is My results suggest that for the majority of the stocks, technical tr technical stocks, the of majority the for that suggest results My

Abstract i

ntescn ato h hss I thesis the of part second the In tee s o tog link strong no is there t for supporting their their supporting for ading ading CEU eTD Collection Bibliography Appendices 6 5 4 3 2 1 Abstract Table ofContents

Appendix 4 Appendix 3 Appendix 2 Appendix 1 Appendix 5.2 5.1 4.2 4.1 3.2 3.1 The performance analysis oftechnical Data description Introduction Conclusion Relationship between technical analysis and liquidity LiquidityB onthe

Results Data and methodology Results Methodology Results Methodology

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...... Tables Figures Moving average rules examined during analysis the Listingreq

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...... udapest Stock Exchange Stock udapest

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43 35 34 33 32 32 30 28 25 24 21 19 17 13

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CEU eTD Collection ehia trading. technical technique different periods time ha studies empirical information shown have published that seem challenge to analysis. technical of profitability the about skepticism profits. average than higher achieve to used be cannot price, current the in reflected already is movements price past by carried information hypothesis efficient market the of popularity the is first The reasons. two to due mainly was skepticism this been has research markets 1987) Irwin, & (Brorsen have studies survey Numerous decisions. investment their supporting for tool a as practitioners asset the of prices past price future 1

Introduction shown ehia aayi i a omn em o a ie ag of range wide a for term common a is analysis Technical technical of profitability the with dealing is literature of amount substantial Although prac among analysis technical of acceptance general the Despite (Sehgal &(Sehgal 2005) Gupta, s, so far so s,

is the negative empirical findings in a number of early and widely cited studies cited widely and early of number a in findings empirical negative the is -

(Treynor and Fergusson, 1985; Fergusson, and (Treynor movements of financial assets using past information, usually the time series of series time the usually information, past using assets financial of movements

that this form of investment analysis is widely used in various futures various in used widely is analysis investment of form this that that under certain circumstances technical analysis can provide valuable valuable provide can analysis technical circumstances certain under that

no attempt has been made to analyze the relationship between liquidity and liquidity between relationship the analyze to made been has attempt no traditionally quite skeptical quite traditionally

Taking Fm, 1970) (Fama,

ve found technical analysis to be profitable on different markets over over markets different on profitable be to analysis technical found ve

(e.g. Brock(e.g. &et al.,1992;Bessembinder Chan, 1998) , foreign , s . iudt it account into liquidity T echnical

around the world. the traditional opinion of opinion academics. theoreticthe traditional Some

wih ee i is eks fr, mle ta al the all that implies form, weakest its in even which, , exchange

nlss a be vr pplr mn financ among popular very been has analysis

Brown and Jennings 1989) Jennings and Brown

In recent years, however, years,however, recent In markets 1

about it. According to Park and Irwin and Park to According it. about

s ely motn we aayig the analyzing when important really is

(Gehrig & Menkhoff, 2006) Menkhoff, & (Gehrig h scn reason second The methods many , and a great number of of greatnumber a and , titioners, titioners,

studies have been been have studies

trying to predict predict to trying o te original the for , and stock stock and , academic al papers and thus and markets markets (2007) ial ial , CEU eTD Collection analysis separat two 4. these completing chapter in presented are measurement liquidity of results and methodology The period sample t assets of liquidity the measure to is task second The 3. chapter in found be can analysis technical of performance the of assessment The (1992). al. et Brock by proposed methodology the using certa of performance the evaluate will I period. sample the over BSE the analysis oftechnical on the performance withexamining I start will chapter 2, data in analysis the of beginning the market, ana of thelowliquidity),the profit and opportunity disappears. (because quickly him against move to start prices the strategy, trading the implement to tries op rules trading strategy liquidity between ma the of liquidity the that costs transaction strategy. trading the of trading market.The onthe prov that stra trading a always is analysis technical of result The analysis. technical of performance portunity lysis. this In r i has it ,

dd on aded in chapter 5 ides buying and selling signals. The implementation of this strategy this of implementation The signals. selling and buying ides In

Budapest Stock Exchange (BSE) Exchange Stock Budapest suggested by previous empirical studies empirical previous by suggested thesis I will I thesis re t aaye hs eainhp I will I relationship, this analyze to order generate trading signals more often in times times in often more signals trading generate , I ,

will calculate two calculate will eee mlctos eadn profitability. regarding implications severe throughout the period analyzed. period the throughout

the BSE. BSE. the of the ma the of .

Chapter 6 concludes the results ofthe research. theresults 6concludes Chapter Some

analyze the link between liquidity between link the analyze , two separat two , refore, rket is not constant over time. over constant not is rket In rket

studies e task e re t anal to order

and the and

transactio

liquidity measures liquidity s, I will I s, have e tasks have to be accomplished. After describing the the describing After accomplished. be to have tasks e timing of the trades required by the technical trading technical the by required trades the of timing deal n costs

yze how liquidity changed on the BSE over the the over BSE the on changed liquidity how yze analyze the link between li between link the analyze

from 2 t

with th with However, t However,

haveon theprofitabilityeffect a substantial

the period between 1997 and 2008 and 1997 between period the

cannot be exploited. As soon as someone as soon As exploited. be cannot proposed in previous empirical studies. empirical previous in proposed is

use issue

f there is a is there f and the performance the and he problem with this approach is approach this with problem he f o lqiiy than liquidity, low of aa f data

f f If, byassuming in moving average indicators indicators average moving in r example, or r o

m the Hungarian stock stock Hungarian the m significant quidity and quidity a fixed amount fixed involves involves the

of technical of relationship

h profit the technical technical .

At active After tegy tegy the of

CEU eTD Collection gives long aaboutstocksare different how summary presented inthesample. lea days trading stock 38 contained l stocks in and structure criteria shares. these for pointless i perform would rules trading technical how testing therefore and trade, single a without implemented be can market the on trading regular involves usually analysis technical that is stocks liquid more relatively than structure ownership on a well all structuredform thestockslisted for becauseof point, this be to chosenwas techn between relationship the study Exchange. Stock Budapest 2 se i Category in isted

st approximately four years of trading data. trading of years four approximately st the Data descriptionData h eprcl nlss f the of analysis empirical The 1 In sample, the into stocks selecting When

. Thus, certain li certain Thus, .

Table 1 can be foundAppendix 4. canin be 1 Table S. h sae in shares The BSE.

e b the by set the f the

category were excluded from the sample the from excluded were irst corporate history corporate

step of step

“A” can be seen“A” can . In s.

A for “A”

tc ecag regarding exchange stock at all at quidity of a of quidity

the next step, those papers those step, next the building the database for the analysis the for database the building

. shares listed in Category “B”. The reason for analyzing only only analyzing for reason The “B”. Category in listed shares In the case of Category “B” stocks, there are a lot of trading days days trading of lot a are there stocks, “B” Category of case the In T

hv coe te eid rm uy 97 o eebr 08 to 2008 December to 1997 July from period the chosen have I

b lse in listed be o vr mnh of month every Category

of the company. the of

gi ical analysis and liquidity and analysis ical

Appendix ven stock is needed so that so needed is stock ven

thesis data availability. The BSE reports daily trading data in in data dailytrading reports BSE availability. The data “ A”

. This means that all the remaining stocks have at have stocks remaining the all that means This . category are more liquid in general and have a broader broader a have and general in liquid more are , I was focusing on papers listed in Categor in listed papers on focusing was I

focuses 3 starting from July1997.

the sample period. At this point the sample sample the point this At period. sample the

The final sample contains 26 stocks. 26 contains sample final The

1 As of May 2009 May of As cap .

tlzto o te stock the of italization

that did not have data for at least 1000 1000 least at for data have not did that

A, h cmay a t meet to has company the “A”, on the Hungarian stock market, the the market, stock Hungarian the on . , I collected I , The start of the sample period sample the of start The , the listing the , technical trading strategies strategies trading technical

which stocks were were stocks which , the ownership ownership the ,

requirements for for requirements Table 1 Table

certain certain y “A” “A” y s 1

CEU eTD Collection with analyzing theprofitability oftechnicalBSE. trading onthe strategies stocks inthe sampleusingpublished Statistics by Monthly BSE inevery themonth. BSE the of page web official the In following the frosample the in stocks 26 the for volumes trading andprices closing collecteddaily I 2

The official web page of the BSE can be found at http://www.bet.hu found canat be BSE ofthe web page official The chapter

I will startpresentingempiricalIcarri the research

I lo olce mnhy aiaiain aa o al the all for data capitalization monthly collected also I . 4

ed out. I ed out. start

m CEU eTD Collection published showing that under certain conditions technical analysis can be useful in predicting in useful be can analysis technical conditions certain under that showing published 414) support” in strongly are results the that say to fair seems “it and extensive, (1970) Fama to According useless. is analysis technical that implies it efficient, weakly is market a if Thus, returns. average than higher generate that prices past on based strategies picking e weakly least at be to out turns market a If analysis.    of kind what on depending information reflected is inmarket prices: levels three has efficiency Market information. any at available prices when efficient called is market A markets. efficient Fama hypothesis. skepticism This analysis. technical 3

The performance of technical a .

There exists a traditional skepticism in financial literature towards the usefulness of of usefulness the towards literature financial in skepticism traditional a exists There However, technical regarding role important an plays efficiency market of form weak The andboth public private information. Strong annual etc. earnings, stock splits, of announcements asset, the about statistics other and prices past including information, Semi of the asset al is efficiency of form Weak ,

al eprcl ok n h wa fr tss f h efcet akt oe is model market efficient the of tests form weak the on work empirical early - tog om f efficiency of form strong from of efficiency efficiency of from since the middle of the 80s a number of theoretical papers have been been have papers theoretical of number a 80s the of middle the since (1970) ready inthecurrent reflected price.

provides the first comprehensive review on on review comprehensive first the provides – –

all information that can be gathered by analyzing past prices prices past analyzing by gathered be can that information all the current price reflects all available information, including availableinformation, all reflects price current the

has its roots in the popularity of the efficient market market efficient the of popularity the in roots its has

–

nalysis the current price reflects all publicly available available publicly all reflects price current the 5

fficient, it is impossible to create stock stock create to impossible is it fficient,

ie ul rfet all reflect fully time the (Fama, 1970, p. 1970, (Fama, literature about about literature CEU eTD Collection pp. 805 pp. and analysis, technical of performance the analyze that 1960 after published studies different 137 review They literature. this on survey extensive an provide 2007) (2004, Irwin and Park including first theprice period. ofthe information, public all using investors rational by competitively set is price period second wou individuals model this In alone. price period second the than source information better a is prices period second and first the of average rational a in even that, show They information. asset risky the of T periods. two in trade opportunity, it. only past help toexploit prices authors the However, (knowin it receives he estimat Fergusson and Treynor mar the of majority already information to wants he if decide to has he and event certain a about information receives asset an of movements price future 3 technical of profitability the on literature empirical of body large a is There Brownand Jennings -

810) For detailed introduction on the theoretical background of technical analysis see analysis technical of background theoretical the on introduction detailed For e the probability that the information is not yet reflected in the price at the time when when time the at price the in reflected yet not is information the that probability the e reflected in the current price or not or price current the in reflected hw h eet il fet rcs cn e epu i aheig unus achieving in helpful be can prices) affect will event the how g

and and . The problem of the investor is that he does not know know not does he that is investor the of problem The . Tu, n hs model, this in Thus, . (Brunnermeier, 2001, pp. 98 pp. 2001, (Brunnermeier,

ae i ipsil fr h cret rc t price current the for impossible it makes e participants ket

also point out that in the model the in that out point also he authors argue that the noise the that argue authors he (1985) (1989)

hw ht sn ps prices past using that show

propose amodelinwhichagents expectations rational noisy

h cn xli i b tkn te prpit position appropriate the taking by it exploit can he ,

3 at rcs obnd ih te vlal information valuable other with combined prices past . In Treynor and Fergusson’s model model Fergusson’s and Treynor In . - 146)

. If the investor receives the information before the the before information the receives investor the If

- investor economy like this, the weighted weighted the this, like economy investor ld use technical analysis even though the the though even analysis technical use ld

coming from unobserved cu unobserved from coming non - price information creates the profit profit the creates information price ,

h inv the w o reveal all the private private the all reveal o h e estor is able to better better to able is estor ther the inf the ther (Park & Irwin, 2 Irwin, & (Park (1985)

trade on the on trade rrent supply supply rrent ual profits. profits. ual o r

mation is mation n agent an analysis. analysis. 007, 007, . CEU eTD Collection 2002) &Wang, Day 1998; Chan, & Bessembinder 1996; al., et (Hudson markets stock developed periods time various and markets various to methodology same the on theBudapest Exchange. Stock introduce methodology bootstrap al. methodology bootstrap a tradingusing for testofsignificance rules the returns than signals. selling under generated higher consistently are strategies trading the of signals buy under generated 1986. and 1897 between (DJIA) Average Industrial Jones indic break range trading and average calledmoving method.” for application and DJIA) the for years (90 history price long a of use the rules, trading technical of power forecasting the about results positive and technica consistent strongly on of finding works the to influential traced be most can influence The the studies. modern of among one is al. et Brock by study “The (1992) writt paper the is studies these of One 1990s. early the Irwin and Park explosion, revi they studies the of half About years. recent in literature the in explosion an be to seems there that note also authors The results. mixed with studies 19 are there and results, negative obtain studies 20 strategies, studies modern 95 the among that conclude

(1992) 5 4 al. et Brock of work The al. et Brock IrwinAs Park and have fou have

. Park and Irwin and Park Irwin and Park as modern by studies regard are after1988 Studies published

has an important role role important an has nd significant profits, although these p these although profits, significant nd

(1992) (2004) (2007, p.795) (2007,

test surveys 21 studies using the methodology of Brock et al. al. et ofBrock methodology studies the using surveys21 (2007)

ew were published after 1995. As one of the reasons for this this for reasons the of one As 1995. after published were ew 26 technical trading rules from two families of indicators, the so the indicators, of families two from rules trading technical 26

(1992) The main contri main The d for

identifies the publication of some very influential papers in papers influential very some of publication the identifies

in it to analyze the performance of technical trading rules rules trading technical of performance the analyze to it in

my thesis also, as I am going am I as also, thesis my

was followed by a great numbe great a by followed was claims 4 ,

: 56 have suppor have 56 7

bution of the paper is that the authors develop authors the that is paper the of bution

the first time of the model the of time first the ators. The The ators. rofits en by Brock, Lakonishok and LeBaron and Lakonishok Brock, by en seem to seem ting results for technical trading trading technical for results ting

h atos id ht returns that find authors The data series they use they series data 5 . Studies . to apply the model the apply to diminish r of studies that applied applied that studies of r . The paper by paper . The (2007) (1992)

concentrating on concentrating - after transaction after based bootstrap bootstrap based . l trading rules rules trading l

is the Dow the is Brock et Brock et - based based CEU eTD Collection introducing analysis. themethodology ofthe rules. trading technical of profitability the of picture h I stocks, individual analyzing By indices. market on analysis gap. this fill to attempt market. stock European Eastern and Central any or Hungarian, methodology N markets. emerging outperform strate the buy and hold studies previous the markets to emerging Similarly 2000. to 1990 from period the over Lanka Sri and They markets. stock Asian South Power and Gunasekarage the that found also have Vasquez pre rules trading markets, by examined index U.S. found has He 1996. and 1980 between Taiwan) and Mexico Indonesia, Canada, Japan, (U.S., world the ev Ito period. sample the during markets the all in successful was analysis technical examined have markets. emerging on (1992) al. et Brock time. of methodology over declined have and account into taken costs aluated profitabl trading be rulesto general theseem that The studiesis conclusion above of (2000)

the profita the that technical trading strategies outperform the buy and hold stra hold and buy the outperform strategies trading technical that . Markets in Latin America and Asia over the period from 1982 to 1995 have been been have 1995 to 1982 from period the over Asia and America Latin in Markets Ratner and Leal and Ratner of Brock et al. (1992) al. et Brock of

six different Asian stock marketstock differentAsian six

ee focusing were Gnskrg ad Power and Gunasekarage , bility of technical trading rules trading technical of bility peiu work previous o Moreover, previous studies have tested the performance of technical of performance the tested have studies previous Moreover, sent

(2001) eut poie srn suppo strong a provide results ed (1999)

n h Cien tc mark stock Chilean the on forecasting ability forecasting

gy. examine also to analyze the performance of technical anal technical of performance the analyze to .

The authors have concluded concluded have authors The ple te ehdlg o Bok t l (92 on (1992) al. et Brock of methodology the applied ,

so far, far, so d

stock indices from Bangladesh, India, Pakistan, India, Bangladesh, from indices stock 8 s

(2001) between 1975 and 1990 and between1975

throughout the period analyzed period the throughout a apid h model the applied has

on equity indices from different parts of parts different from indices equity on

ae fou have n h nx scin cniu by continue I section next the In t ewe 18 ad 98 They 1998. and 1987 between et Many studies have applied the the applied have studies Many esmidr n Chan and Bessembinder rt for technical trading rules. rules. trading technical for rt p t gt mr complex more a get to ope d ht hi taig rules trading their that nd

In this thesis, I make an an make I thesis, this In that in all these emerging emerging these all in that , and have found haveand , tegy in all but all in tegy - based bootstrap bootstrap based . Parisi and and Parisi . ysis on the the on ysis (1999) (1995

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a longer movingaverage Technical analysis is a common term for a large variety of techniques trying to fo to trying techniques of variety large a for term common a is analysis Technical 6 to chose also I Methodology

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e ol lk t tk it acut h atcreain n h returns the in autocorrelation the account into take to like would we - value for the null the for value - M processes.

ned as the portion of the generated comparison series for which for series comparison generated the of portion the as ned used, and if we would like to control for the changing volatility changing the for control to like would we if and used, - hypothesis that the profitability of the rule can be explained by explained be can rule the of profitability the that hypothesis - based bootstrap in analyzing the profitability of technical of profitability the analyzing in bootstrap based 11 applied during the testing procedure, different different procedure, testing the during applied (1992)

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a general summary about the overall performance of the 120 technical 120 the of performance overall the about summary general a 26 return s return 26 to apply the bootstrap method of Brock et al. (1992), first I had to fit fit to had I first (1992), al. et Brock of method bootstrap the apply

itional expected daily returns ( returns daily expected itional eries and obtain the estimated coefficients and residuals. The The residuals. and coefficients estimated the obtain and eries

al 2 Table rac o te eun i cagn ad ht the that and changing is returns the of ariance 13

o te R1 mdl n n Table in and model AR(1) the for

del, it can be seen from Table 2 Table from seenbecan it del, BSE. The first column of Table 4 Table of column first The BSE. iaig h peec o changing of presence the dicating f technical analysis on the Bud the on analysis technical f - M model the coefficients in the in coefficients the model M 푚 푏 − 푚 푠 ) averaged across all all across averaged )

3 that therethat eviously eviously 8

for shows shows a p

- the est M CEU eTD Collection r fu sok wih ae hi lws value lowest their have which stocks four are movin the profitabilityof the in role important an plays givenstock a of GARCH(1,1) the of column the in appear GARCH(1,1) highgiven cannotbyexplained thatit nullmodel. be the so is rules trading the of half of profitability the that suggests hand, other the on 60, of value high so generate can the by tested explained be cannot that rules returns conditional expected 120 the in difference the of none that indicates 0 value The rejected. d the that hypotheses the rules these for as interpreted be can This 0.05. than smaller is value p simulated the where rules, trading of number the show columns The (1992). al. et Brock the significanceresults. these of no have I far so However, convincing. quite is indicator average moving the of produced signal buy rule trading the of half than more stocks, the of majority vast ( positive is returns conditional expected the between difference the which for 120) of total aroundThis is yearly 25percent ona is aconsiderable which basis, diffe return. sell daily expected the than higher percent 0.1 is return buy daily the average on that average on rules, generat trading the stocks, the of most for that means This positive. are values 푚 ifference between the expected conditional returns is generated by the given null model is model null given the by generated is returns conditional expected the between ifference 푏 − The second column of column second The to 4 column walk, random the to corresponds 3 Column 5 to 3 Columns 푚 ed higher returns under buy signal than under sell signal. A value of 0.001 indicates 0.001 of value A signal. sell under than signal buy under returns higher ed 푠

higher buy returns is over 90,indicatin over is returns higher buy s >

hn ne sell under than 0 - M model. The first observation to make here, is that the lowest numbers usuallynumbers lowest the that here,is make to observation firstThe model. M ). There are only two stocks where this value is below 60, m 60, below is value this where stocks two only are There ).

summarize the results of the model the of results the summarize

inl. o ms o the of most For signals. Table 4 Table

presents the number of the trading strategies (from the the (from strategies trading the of number the presents - M model. This suggests that the changing volatility volatility changing the that suggests This model. M 14 s

g that for these stocks the overall profitability theoverall profitability that for stocks these g

in the column corresponding to the AR(1) AR(1) the to corresponding column the in

tcs te ubr f indica of number the stocks,

- based bootstrap tests developed by by developed tests bootstrap based s

the the generated higher returns under under returns higher generated AR(1), and column 5 to the to 5 column and AR(1), g average rules. There There rules. average g rence. eaning that for the the for that eaning

null model. A model. null t

talked about talked os that tors - CEU eTD Collection ZKERAMIA and ZWACK. and ZKERAMIA null the of one least at to according level 5% at significant be to seem not does rule best that ones same the stocks, nine are There model. null particular the by explained be can rule trading performing best the by generated returns conditional expected the of difference the that level 5% at reject tests three all stocks p the present considerab a have and positive are differences the shows fi The examined. stocks the of each for period the throughout statistic 5 Table of help the f rule of thecases, are thetradingable rules toselectperiodslessvolatile. marketis when those the under deviation than higher is signal sell under standard conditional the where rules trading of number the shows It strategies. average strategiescan moving the of half than more of profitability the which for stocks are there hand other the On models null the of one least at by explained be cannot that returns expected in difference a the of none where stocks, nine are There markets. if thereorder thereturns first autocorrelation in isapositive stock. of the av moving that confirms result This model. AR(1) the positive significantly have which stocks same the exactly are These model. or A general conclusion can be that moving average rules perform differently on different different on differently perform rules average moving that be can conclusion general A 11 10 In 4 Table of column last The

Table 5can be foundAppendix 4. in be 5can Table FOTEX, DEMASZ, BCHEM, the include stocks These

each stock. Therefore, I Therefore, stock. each chapter 5 chapter

ifrne ewe te xetd odtoa rtrs ( returns conditional expected the between difference - values simulated with the model the with simulated values ,

I will continue the analysis using results from the best performing trading trading performing best the from results using analysis the continue will I 11 . By best By . be bymodels. explained any null ofthe not

strategy briefly summarize the performance of the best str best the of performance the summarize briefly

provides some insight into the riskiness of the trading trading the of riskiness the into insight some provides

buy signal. It can be concluded that in the vast majority vast the in that concluded be can It signal. buy

I mean the one that generated the highest the generated that one the mean I

I mentioned earlier, where the profitability of the the of profitability the where earlier, mentioned I 15 - based bootstrap metho bootstrap based

y ag mgiue Clm 2 Column magnitude. large ly

analyzed trading rules could generate such such generate could rules trading analyzed erage indicators can be more successful successful more be can indicators erage

IEB, LINAMAR, MTELEKOM, RICHTER, RICHTER, MTELEKOM, LINAMAR, IEB,

dology. For most of the of most For dology. 푚 rst co rst 푏

− 푚 lumn of Table of lumn 𝜌 푠

). All of the the of All ).

coefficient in coefficient o oun 4 column to ategies with ategies 푚 푏 − 푚 10

5 푠 .

CEU eTD Collection the research chapter.in the next ispresented relations able be to order In strategies. trading the by generated signals the of timing the and market the of liquidity the between relationship the analyzing by issue this examine that trade) bid higher of form (in high so be can transactioncosts low, is liquidity market when generated are signals trading If market. the on trading regular profitabil of sample s larger a examining by implemented be fea could a probably be analysis to This seem exercise. not does task this sample, stocks. the in the stocks 26 between only differences having However, these determine factors what analyze to interesting be cannot rule average moving successful most the even where stocks, some also are there However, that strategies trading popul the of any by seems tested analysis. technical of usefulness t concluded be can it Exchange, Stock Budapest returns under than under buy sell signal signal. 5 Table of columns deviations standard conditional The models. tocks onamarket. bigger stock After the analyzing when account into taken be to has that factor important another is There generate significantly higher returns under buy signal than under sell signal. sell under than signal buy under returns higher significantly generate hip, first I have to measure to have I first hip, ity of technical analysis, technical of ity

the profit opportunity indicated in this chapter cannot be exploited. I will try to try will I exploited. be cannot chapter this in indicated opportunity profit the analyzing

to produce higher than average returns, and this profitability cannot be explained be cannotprofitability this and returns, average than higherproduce to Fo . r ul oes Fr h mjrt o te tcs hr ae t es some least at are there stocks the of majority the For models. null ar

the profitability of moving average indicators on different stocks of the the of stocks different on indicators average moving of profitability the

rvd o e infcnl profitable significantly be to proved al h sok, h bs promn taig ue ae es volatile less have rule trading performing best the stocks, the all r

The

the eff the

liquidity on the BSE over the sample period. sample the over BSE the on liquidity re are stocks where large where stocks are re ect of transaction costs. transaction of ect

f h returns the of

hat picture is mixed is picture hat - ask spread or larger price impact of the the of priceimpact larger or spread ask

r peetd in presented are proportion of the trading rules rules trading the of proportion d rn te eid analyzed. period the uring Technical analysis requires requires analysis Technical , but mainly supports the the supports mainly but ,

to analyze this this analyze to

h ls two last the This part of of part This

It would would It sible CEU eTD Collection 2008, and are not freely accessible. are and 2008, &Végh Kutas by introduced measure bid average the estimate to try They measures. low of groups big al. et Goyenko to According analysis. my during data volume and return from data level transaction obtain to hard is it markets, data for are toasproxies frequently liquidity. referred high using measures than precisely less costs transaction estimate they that is them of disadvantage the hand, other the On markets. various over and time of periods long over liquidity study to possible it make of because that, is measures these of advantage obvious The liquidity. the in low proposed use been that have literature measures of number a problem, this overcome To markets. available not are in sets However, data frequency data. level transaction contain that sets data frequency – – “tightness” of number a covers Kyle market. capital a of properties transactional it because concept, broad a is Liquidity Exchange. Stock Budapest 4

the speed froms with whichpricesrandom recover a the size of an order flow that changes the market price by a given amount, and “resiliency” and amount, given a by price market the changes that flow order an of size the Liquidity In this chapter this In 12 importance special areof Liquidity proxies of measures research, empirical ideal an In

The Budapest Stock Exchange calculates a highy frequency liquditiy measure, the Budapest Liquiditiy Liquiditiy Budapest the measure, liquditiy frequency highy a calculates Exchange Stock Budapest The –

the cost of turning around a market position over a short period of time, “depth” time, of period short a over position market a around turning of cost the

on the on Budapest Stock Exchange - frequency liquidity measures. measures. liquidity frequency -

frequency data. That data. frequency I will make an attempt to measure measure to attempt an make will I - rqec dt, ht s al pie n vlm sre, o estimate to series, volume and price daily is that data, frequency

(2005)

. However, the values of the measure are available only from March March from only available are measure the of values However,the . ,

r r csl. hs s seily re f true especially is This costly. are or

is why measures using only daily price and volume volume and price daily only using measures why is 17 -

ask spread that an investor faces on a given a on faces investor an that spread ask for (1985, p. 1316) p. (1985, h frt ru involves group first The liquidity

my research. Similarly to other emerging emerging other research.to Similarly my

hock. the BSE the

the

should be calculated using high using calculated be should liquidity of assets traded on the on traded assets of liquidity 12

identifies . Therefore, I Therefore, . data availability, they availability, data (2009)

many these p these

o ca so

will use will

there are two two are there or emerging emerging or ae high cases roperties as as roperties ld spread lled

daily - - CEU eTD Collection nelig liquidity. underlying the estimating in efficacy their determine to order in benchmark a to compared are proxies approach markets. emerging be cannot results the thus markets, stock generalized directly markets. tointernational US on focus solely authors the that is paper proxy best the be to seems measure (2002) Amihud’s measures, impact (1999) al. et Lesmond by developed measure The performance. in nine remaining the dominate consistently that proxies three are there that their and proxies the between correlations respecti the cases the of most In benchmarks. cost highof estimations good dailyprovide on baseddata liquiditymeasures c to 2005 and 1993 between period the from data US use They sets. data level transaction from calculated benchmarks perform proxyeach other? Which beused compared inanempirical to res should been a have measures These measures. impact price Amihud while proxies, spread Roll sets. data frequency measures effect try toestimate the of group second The market. ssumptions about the market the about ssumptions Numerous 15 14 13 Lesmond al. et Goyenko

ve benchmarks are high. high. are benchmarks ve I will use Amihud’s (2002) measure as a price impact measure. impact measure price as a willAmihud’s(2002) use I measurwilluse I the analysis. in their included are inthesis this used measures liquidity Both

s iia t te n apid by applied one the to similar is (2005) s tudies

The anal The

(2009) Lesmond

addresses the question, how different liquidity measures perform in in perform measures liquidity different how question, the addresses e introduced by Lesmond et al. (1999) as a spread proxy in myproxy analysis. in spread as a (1999) al. Lesmondbyet introduced e have (1984) ysis covers 23 emerging markets emerging 23 coversysis

arry out the analysis. The general conclusion of the article is that is article the of conclusion general The analysis. the out arry compare various spread and price impact proxies impact price and spread various compare includes . Some important questions arise: how these liquidity measures liquidity these how arise: questions important Some .

proposed (2005) (2002) Goyenko et al. et Goyenko Hasb ,

trade with a giventrade witha on size the oprs fou compares n Pso & Stambaugh & Pastor and ehd fr esrn mre lqiiy sn low using liquidity market measuring for methods rouck rouck

o called so oek et Goyenko

18 is one of these three proxies three these of one is

(2004) test twelve different spread measures and find and measures spread different twelve test

dfeet iudt maue, turnover, measures, liquidity different r ad emn e al. et Lesmond and , price impact measures. Price impact impact Price measures. impact price

al. derived

(2009)

including Hungary. including the market price.the market using , (2003)

aey ht h liquidity the that namely -

frequency transaction frequencytransaction 15 ifrn ies and ideas different

. One caveat of the of caveat One . 14 nrdc diffe introduce . Within the price the Within 13 (1999)

with liquidity with earch The author’s author’s The

develop develop ?

rent -

CEU eTD Collection model thatdescribesabove theidea: information. new reflect will asset the of t exceededbeforebe to has that threshold a as interpretedbe cancost transaction The which trade, not will investor marginal the of value the if that measure) calculateAmihud suggested the by measure paper the in introduced m spread a As studies. previous in proposed measures 4.1 low lev transaction if However, solution. best the not probably (1984) within capturing high a with Ro measure, (2002) Amihud’s

- frequencymeasures liquidity aalternative. reasonable provide It can be concluded that measuring liquidity using daily return and volume data is is data volume and return daily using liquidity measuring that concluded be can It spread the of point starting The the of liquidity the measure to order In Methodology are also valid proxies for liquidity.are also proxies valid is the incidence of days with zero return. The basic idea behind the behind idea basic The return. zero with days of incidence the is - frequency benchmark. The author concludes that the LOT measure is the best in best the is measure LOT the that concludes author The benchmark. frequency - c ountry liquidity and the measures proposed by Amihud Amihud by proposed measures the and liquidity ountry

new information on a given day does not exceed the cost of trading, then trading, of cost the exceed not does day given a on information new

푟 푗 , 푡 = by

emn e al. et Lesmond 푟 푟 0 푗 푗 ll’s (1984) measure, and the measure by Lesmond et al. (1999) al. et Lesmond by measure the and measure, (1984) ll’s ∗ ∗

, , 푡 푡

− −

푟

푗

∗ 훼 , 훼

푡

2 1

, ,

푗 푗

훽

esr o Lsod t al. et Lesmond of measure 푖 푖 푖 푗 푓 푓 푓

∙

푟

al. et Lesmond

(2002) 푟 푟 훼 will result zero return for the given the for return zero result will 푚 푗 푗 19 (1999) ∗ ∗ 1 , ,

푡 푡 푡 , stocks in my sample, I will I sample, my in stocks 푗 > <

+ ≤ 훼 훼 휀 .

푟 푗 1 2 푗 ad s pie mat esr, will I measure, impact price a as and , , ∗ 푡 , , , 푗 푗 푡

, ≤

푎 aue I easure, 푎 푎 푛 푛 푛 훼 푑 el data sets are not available, these these available, not are sets data el 푑 푑 2 ,

(1999) 푗

훼 훼

1 2

, , 푗 푗

will

< >

propose

0 0

(1999) use the liquidity proxy liquidity the use

use two liquidity two use

a latent variable variable latent a (2002) hratr LOT (hereafter, LOT

trading day. trading measure he return return he n Roll and is

CEU eTD Collection assume that the error term normallydistributed. is error the assume that case is1HUF attrib be can that change price the of magnitude the shows It volume. trading of unit one of impact price daily the as interpreted 푟 average ofthe d day that on volume trading asset frequency.LOT Thespread measureasset for likelihood term, error the about assumption distributional and information the on trade to decide will trader marginal the cost, transaction the than higher are gains expected the If zero. is return observed Incase,the trade. this not will he costs, transaction the exceed not gainsdo expected 훼 푟 where 푗 푚 2 , 푡

is the threshold for trades on positive information.is thethresholdonpositive for trades 푡 is the daily the is

is the observed market return, return, market observed the is . h mria tae wl wih h cs o taig gis expected against trading of cost the weigh will trader marginal The 16 measure) Amihud (hereafter, Amihu I e eie a defines He 푟

푗 The loglikelihood function of the ML estimation can be found in Lesmond et al et Lesmond in found be can estimation ML the of function loglikelihood The will ∗ , 푡

16 is the true, unobservable return of asset of return unobservable true, the is LOT. The ofproportionalround measure d estimate . Thisconcept defines liquidity as the (2002) return of return aily li

al lqiiy esr a the as measure liquidity daily

quidities uses a different approach different a uses the LOT measure for each of the assets in my sample at monthly monthly at sample my in assets the of each for measure LOT the

asset .

in the given month e iudt of liquidity he 푗 , 퐴 and uted to a trade worth one unit of the volume, which in this in which volume, the of unit one worth trade a to uted 푚 훼

where 퐿 1 푖 푕 푣

푂 is the threshold for trades on negative information, and information, negative on trades for threshold the is 푢 표 푇 푑 푙 푗 푗

푗 , = 푡 푇 , 푚

is the respective daily volume. This volume. daily respective the is

is the number o number the is 훼

= the true return will be observable. Given some some Given observable. be will return true the 20 푗 2 asset asset

, in

푗 푇 1

to define a price impact measure for a for measure impact price a define to −

훼 푡 month month 푗 = 푇 1

훼 response the pricetoorder of flow. 1

on day on 푗

ratio of the daily absolute return to the the to return absolute daily the of ratio 1 -

and 푣

in trip transaction costs

, 푗 푟 표

푗

푙 , . month 푗 푡

푚 훼 ,

푡

푡 will bewill denoted by ,

f trading days during the month the during days trading f , can be estimated by maximum maximum by estimated be can 푟 푗

, 푚 푡

is the observed daily return, return, daily observed the is

s ipy acltd s the as calculated simply is for assetfor (1999, p. 1122) p. (1999, gains, and if the the if and gains, 퐿 푂 ratio can be be can ratio 푇 푗 , 푚 푗

will be will .

given . They They .

, CEU eTD Collection induced a 0.00045 percent price change on average during the sample period. This does not does This period. sample the during average on change price percent 0.00045 a induced sample the in stock liquid most the of case the 0.02 to 0.0000045 from ranges measure Amihud the for value average the sample measure) calculated I 4.2 illiquidity ratherthan measures ofliquidity. a they sometimes why is That values. higher with months capitalization of thestocksinsample atofmonth the end factor a by series the scale to suggest They variation. this account into takes it that so measure impact price a construct to reasonable is it time, across varies trade HUF million 1 a of size relative the as that argue authors The interpretedas thee the multiplication, the After magnitudes. convenient more produces rescaling The small. extremely were measures the of values the consequence, a As BSE. the o of effect the show measures impact its all multiplied

The second adjustment of the of adjustment second The I First, measure. Amihud the to adjustments two made I analysis, the continuing Before 17 6 Table Stock Budapest the of liquidity the assess to order In liquid less indicate measures liquidity Both here. note to thing more one is There Results

Table 6 can be foundAppendix 4. canin be 6 Table for every asset every for .

the 17

presents two

values by values ffect of aHUF.ffect tradeworth of 1000000

measures introduced in the previous section section previous the in introduced measures

cross 푗 = 1000000 1 - sectional statistics about the calculated liquidity measures. liquidity calculated the about statistics sectional

… 26

in measure ne unit of trading volume, which is 1 HUF in the case of of case the in HUF 1 is which volume, trading of unit ne . The reason for this is that in their original form, price form, original their in that is this for reason The .

every month month every

푐 21 is suggested by Pastor and Stambaugh Stambaugh and Pastor by suggested is 푎 , a trade worth one million HUF million one worth trade a ,

푝 푖 푡 푎 푚 푙 푡 /

= 푐 푎 1 푝 Exchange between 1997 and 2008 and 1997 between Exchange 푖 … 푡 푡 푎 138 − e eerd o s esrs of measures as to referred re 푙 1

1 , where , (LOT measure (LOT

. when the asset asset the when

Amihud measure Amihud 38. This means that in that means This 38. 푐 푎 푝 푖 푡 푎

푙

and Ami and 푡 would have have would appeared

is the total the is

(2003) can be be can

The hud

in .

CEU eTD Collection upward spikes in the Amihud measure. Pastor and Stambaugh (2003) observe similar drops drops similar observe (2003) Stambaugh and Pastor measure. Amihud the in spikes upward T measures. me when liquidity market of feature important an illustrates figure The stock liquidities. individual the of average weighted capitalization the as month every in calculated is liquidity average bid bid average an facing were investors stock liquid most analyzed. sample the in arestocks there that imply values large These %. 52 roughly priceby the changedhave would its in stock, some for that suggests This 0.52. like values find can we measure, Amihud the of values maximum the consider we If effect. change price percent 2.38 a induced have would stock this of market the in that meaning 0.0238, of value a fewstoc a thereareonly effect,bigabut like look

Amihud level. liquidity high relatively .000 .001 .002 .003 .004 .005 Figure plots 1

Table 6 Table - ask spread throughout the period was 2.98 %LOTask wasaccording spreadthroughout the period tothe measure. i i the is his with which it was nearly impossible to trade to impossible nearly was it which with 1998 Figure Figure

p resent the market liquidity of the BSE throughout the sample period. The market Thethe marketliquidityBSE period. thethroughoutmarket the sample of 1999

cainl ag dos n iudt, hc i ascae wt large with associated is which liquidity, in drops large occasional 1

– s

the same statistics about the LOT measure. On the market of the of market the On measure. LOT the about statistics same the Market liquidity measured by by measured liquidity Market 2000 The most illiquid stock in the sample has an average Amihud Amihud average an has sample the in stock illiquid most The 2001 2002 22

most illiquid month, a one million HUF trade trade HUF million one a month, illiquid most

Y e mar Hungarian the stock on ks 2003 a on average on r - 2004 s sra o 05 % Te largest The %. 0.56 of spread ask in some months during the period period the during months some in ,

the tae ot oe ilo HUF million one worth trade a , which is a quite considerable quite a is which ,

2005 Amihud asured by price impact impact price by asured 2006

measure 2007 ket with suchwith ket

2008

CEU eTD Collection B 0.254. is observations all across them between correlation The level. individual on related positively corre o be can spikes upward However bigger measure. impact price the of case the in as liquidity in drops big such show not measures. LOT individual the of average the beginningc ofthe to corresponds spike recent most The 1987. since markets stock for quarter worst the 2002, of 653) p. 2003, LTCM the of collapse the and crisis debt Russian the with associated h the to corresponds 1997 October in spike big first The markets. US of liquidity the analyzing when

SE, I SE, continue can therelationship by examining between them. LOT lation between the two the between lation .004 .008 .012 .016 .020 .024 analyzing After weighted capitalization the with measured when liquidity market the plots 2 Figure

eight of the Asian financial crisis. The second spike in September 1998 can be be can 1998 September in spike second The crisis. financial Asian the of eight when stock markets around the world experienced serious drops. serious experienced world the around markets stock when .

1998 The biggest drop in liq in drop biggest The Figure Figure urrent crisis. urrent

1999 both the performance of technical analysis and market liquidity on the the on liquidity market and analysis technical of performance the both 2

–

measures of market liquidity is 0.498. is liquidity market of measures 2000

Market liquidity measured by the LOT measure LOT the by measured liquidity Market

bserved at the same places as with as places same the at bserved 2001 uidity during the sample period period sample the during uidity

The changes in the measured bid measured the in changes The 2002 23

Y e 2003 a r 2004 2005

the previous measure. The The measure. previous the The The was

(Pastor & Stambaugh, & (Pastor two measures are also are measures two 2006

Until 2002 Until in

- the third quarter third the ask spread does does spread ask 2007

that was was that 2008

CEU eTD Collection generated by the trading the by generated the by required activity trading the and stock a of analysis. technical by obtained be can that profits the lower substantially can trades, the of priceimpact bid higher a of form the in liquidity, Low profitability. on implications si trading more produce generally rules these that out turns it if because rules, trading technical of profitability the regarding aspect important an is liquidity of variability time The time. over varying is liquidity that fact the stocks analyzed. f costs liquidity separate calculating simply by stocks between differences & comparing break calculating by or 2002), Wang, & Day 1999; Ito, 1996; p the throughout is adjustment The strategies. attempt of impact price the as or spread bid fees, various of form the in occur costs liquidity These rule. trading technical every to connected costs liquidity are there that means This market. the on trading active involves performance 5

hn 1998). Chan, Relationship between technical analysis and l The 18 t In However,

ed Break

his chapter his

to adjust for transaction costs when analyzing the profitability of techn of profitability the analyzing when costs transaction for adjust to

at at of part last them to actual transaction costs (e.g. Bessembinder & Chan, 1995; Bessembinder 1995; Chan, & Bessembinder (e.g. costs transaction actual to them

- even transaction cost is defined as the transaction cost that would totally eliminate the profit profit the eliminate totally would that cost transaction the as defined is cost transaction even f ehia analysis. technical of

the eriod analyzed and deducting it whenever a trade occurs (e.g. Hudson et al., et Hudson (e.g. occurs trade a whenever it deducting and analyzed eriod ih hs prah n cn aiy dut o te cross the for adjust easily can one approach this With

possible

rule. I will will

h tei fcss n h rltosi between relationship the on focuses thesis the make an attempt to analyze analyze to attempt an make

problem with above with problem

us al dn b assuming by done ually h trade. the

olwn te signal the Following nl i tms f o lqiiy i hs severe has it liquidity, low of times in gnals 24 oe f h peiu eprcl studies empirical previous the of Some

approaches is that t that is approaches ehia analysis. technical the relationship between the liquidity the between relationship the

fixed amount of transaction costs costs transaction of amount fixed s of a technical trading strategy strategy trading technical a of s iquidity - vn rnato costs transaction even

hey - ask spread or a larger larger a or spread ask The main question question main The

- do not account for for account not do sectional liquidity liquidity sectional liquidity and the the and liquidity r ah f the of each or ical trading ical 18 have

- and ask CEU eTD Collection introduced in chapter 4. The measure introduced by Amihud (2002) will be denoted by by denoted be 퐴 will (2002) Amihud by introduced measure The 4. chapter in introduced d buy) variable will be to sell from or sell to buy (from changes signal of number variable dependent The months. liquidity that rules the whether in interested 5 Table in summarized by 2688 of number total a with unbalanced be observations. will dataset panel the so period, whole throug sample the in present are stocks the all not 2, chapter in described asHowever, ( 2008 December and 1997 July sam chapter in as thedata 4, used the 5.1 in found opportunities profit chapter the case, the is this If usual. than lower is liquidity market in signals trading more generate rules trading whether is chapter the throughout 푚

the best performing trading rule for each of the stocks. These These stocks. the of each for rule trading performing best the 푖 trading signals generated by the technical rule, I will rely on the results of chapter 3 a 3 chapter of results the on rely will I rule, technical the by generated signals trading ple 푕 푢 In order to analyze the relationship between the liquidity of the market and the timing of timingof the and liquiditymarketbetweenthe of analyzerelationship the to In order of variable The month each in generated signals trading of number the be will variable dependent The cross The data. panel as structured is data The Data and m Data and

푑 ( 3 푖 푖 푡 may not be exploitable. may not be

= and the measure developed by Lesmond et al. (1999) will be denoted by denoted be will (1999) al. et Lesmond by developed measure the and 1

…

denoted as denoted 26 ethodology ), while the time dimension consists of 138 consecutive months between months consecutive 138 of consists dimension time the while ), main . I chose to use the best performing rules in the analysis because I am am I because analysis the in rules performing best the use to chose I 푆 푖

neet s h maue f liudt. wl use will I illiquidity. of measure the is interest the analysiscomes these from chapters. 푔

푛 푎 푡

푙 = 푖 푡 . 1

seem to work well work to seem … 138 is calculated for stock for calculated is ). This would mean a total of 3588 observations. 3588 of total a mean would This ). 25

- sectional units are the 26 stocks in the the in stocks 26 the are units sectional

produce more or less signals in low in signals less or more produce are the trading rule trading the are 푖

in month in

uring the month. The The month. the uring 푚

oh measures both

simply as the as simply times when when times 퐿 are that s hout the the hout 푂 푇 푖 푡 .

nd In CEU eTD Collection individual stocks, similarindividual pictures arise. contain to seem variables 5 to 3 Figures already strate best the stocks other for and best performa fact the reflects This different. 7 Table 6 Table in summarized volatility. high and downturns market with associated usually are liquidity in drops because variables, by denoted is and month the during returns daily the stock on return 퐿 variables the use also will I stocks. the of each for months stocks. the of each for months the is measure st for 1 value take will illiquidity. or liquidity extreme with months in behave rules trading the each for stock. low or extremelyliquidity high th variables dummy use will I specifications the of some 푖 푞 20 20 19 cross Some other two use will I 푖 푡

19 Figures 3 to 5 can be found in Appendix 3. foundAppendix canin be 5 to Figures 3 foundAppendix 4. canin be 7 Table

presented that are definedsimilarly. .

It can be observed that the averages of the dependent variable ( variable dependent the of averages the that observed be can It nce

20 highest for stock for highest 푖

. For some For some . present the monthly (simple) averages of the other other the of averages (simple) monthly the present month in -

sectional descriptive statistics of the liquidity measures liquidity the of statistics descriptive sectional the monthly monthly the . Cross . ock 푚 variables in the regressions. The first measures the average daily daily average the measures first The regressions. the in variables of the of the 푖 rn o saoaiy We te aibe ae lte fr the for plotted are variables the When seasonality. or trend , and is denoted by denoted is and ,

in -

that for different stocks, different kind of trading rules trading of kind different stocks, different for that sectional descriptive sta descriptive sectional (capitalization weighted) (capitalization 퐿 0 ecn o te months, the of percent 10 푖

푖 . So So . stocks 푞 gy generated gy

푖 퐼

푡 푙

푙 , il e h dmy o the for dummy the be will

푖 the best trading rule generated trading rule best the 푞

10 26 푖 I will use these dummy variables to usethese variables I dummy will 푡

푅 is a dummy for the 10 percent most illiquid most percent 10 the for dummy a is signals less frequently. less signals 푒 푡 푖 푡 .The second is the standard deviation of of deviation standard the is second .The 푆 tistics 푑 averages of the of averages 푖 푡 at indicate months with extremely with months indicate at .

hn h value the when

t s motn cnrl f control important is It of other of 퐼 푙 푙 푖 푞 5 푖 푡

0 ecn ms l most percent 10 , variables variables are shown in shown are variables

퐿 퐼

Figures 1 and 2 and 1 Figures trading signals often, signals often, trading 푖 푙 푞 푙 liqui 푖 푆 5 have already been already have 푞 푖 푖 10 푔

푡 of the the of , 푛 dity measures. dity 퐼 푖 . 푎 푡

푙

푙 푙 oe f the of None for example for 푖 푖

푡 assess 푞 ) are quite are ) 20 liquidity or these or had the had 푖 푡

, and and , iquid

have have how

CEU eTD Collection units ( units 푆 month variable The period. same the for index (DJIA) Average Industrial Jones Dow the from calculated counterparts their by thi overcome to try variables. instrumental I month. given the of volatility the and return average the liquidity, the affect will which trading, induce will it generated, is signal trading partic simultaneity. of problem the estimation, for a particularstock. example, for cross the determine with model the estimate expected stockreturn. measur Amihud the introduced me months) liquidity extreme indicating dummies the (not form original their in appear measures liquidity the where specifications where generatedI trading by thetechnical estimate will rules, thefollowing model: 푑 퐷 asures. This transformation is suggested by Amihud (2002) in the paper where he he where paper the in (2002) Amihud by suggested is transformation This asures. 퐽 퐼 ipants really use these technical rules, or at least similar strategies. In this case if a a if case this In strategies. similar least at or rules, technical these use really ipants In order to uncover uncover to order In the during account into take to have I that problem possible more one is There The 푖 퐴 푡 퐿 = 푡 . The . 푖

푞 denotes the standard deviation of the daily returns in month month dailyin the returns of deviation standard the denotes 푎 1 푢 푖 푆 푖 …

푑

term 푖

include factors that de that factors include 푔 variable 푖 26 푡 푛 푦 푎 ) arewith the samecalculated instrumentedseries from the DJIA. 푖 푡 푙 in the above model above the in 푖

푡 eoe te iudt measure liquidity the denotes - sectional differences between between differences sectional

푅 I will try to instrument the variables the instrument to try will I 훽 푒 0

푡 퐷 퐴 cross + 푚 퐽 the relationship between liquidity and the number of signals signals of number the and liquidity between relationship the 퐼 훽 퐴 푖 1 푕 - 푡 ∙ 푢 , and e, sectional fixed effects in order to account for factors that that factors for account to order in effects fixed sectional

is the average daily return of the DJIA during month month during DJIA the of return daily average the is 퐿 푑 푖 퐷 푞 termine what type of rule provides the best performance performance best the provides rule of type what termine 푢 퐽 퐼 푖 corresponds to the the to corresponds 퐴

I il s te oaihi tasomto o the of transformation logarithmic the use will I , 푑 etd h efc oe tm o mre ilqiiy on illiquidity market of time over effect the tested 푡 푖 푡

The problem of simultaneity might arise if market if arise might simultaneity of problem The is the Amihud measure calculated from the DJIA in DJIA the from calculated measure Amihud the is 푦 푖 푡 + 27 훽

2 ∙ the 푅 used in the particular specification. specification. particular the in used 푒 푡 number 푖 푡 cross + 훽 log 3 - s ∙ sectional heterogeneity. I will will I heterogeneity. sectional

⁡ 푆 ( of signals generated. These generated. signals of 퐴 푑 푖 푚 푡 푡 + 푖 . 푕

All the cro All the 푢 푎 polm by problem s 푑 푖 푖 + 푡

) 휀 , 푖 푅 푡

푒

푡 ,

ss 푖 푡 , and , - sectional 푡 using using , and , 푆 푑 In 푖 푡 ,

CEU eTD Collection months aremonths taken account. into becom dummies month extreme the of effect the less 0.24 average, on months, liquid most the in that regression this from concluded also be can It 1.2. around is month per signal acros that considering specifications previous the in than higher bit little a is effect the and significant statistically is coefficient The liquidity. usual with months most be can It considered. is months extreme most percent 5 the of effect the measurethis the inthe restspecifications. of significant. w months in on coefficient economic effect this significance negligible. of still is mea Amihud the of value the the of value the in Amihud increase percent hundred a that suggests It small. very is coefficient The signals. sta is coefficient trading more with associated are value) Amihud (higher months liquid less log section. previous the in introduced 5.2

⁡ ( 퐴 Table 8 Table 21 in than strategies trading the by produced is signal trading more 0.1 months illiquid 5 In regressions 3to the In Results 푚

Table 8 can be foundAppendix 4. canin be 8 Table

푖 measure is associated with 0.035 with associated is measure 푕 푢 Since only the Amihud measure had statistically had measure Amihud the only Since 푑 second it 푖 21

푡 h l )

presents the coefficient estimates for the different specifications of the model the of specifications different the for estimates coefficient the presents

tistically significant at 5% significance level. However, the magnitude of the of magnitude the However, level. significance 5% at significant tistically is used as as used is es iudt, oee te ofiin i nt ttsial nr economically nor statistically not is coefficient the however liquidity, less ⁡ ( 퐿

푂 regression illiquidity is measured with the spread measure (LOT). The The (LOT). measure spread the with measured is illiquidity regression 푇 푖 푡 )

the extreme liquidity and illiquidity dummies are used. are dummies used. extreme liquidity andthe illiquidity is positive is the measure of liquidity of measure the sure increases to a large extent large a to increases sure

signal is produced. In columns 4 and 5 and 4 columns In produced. is signal again, implying again,

n h frt ersin clm 1 te variable the 1) (column regression first the In

more trading signals during the month. Althoug month. the during signals trading more

es less and less significant if more and more and more if significant less and less es . The coefficient is positive suggesting that suggesting positive is coefficient The .

that more trading si trading more that

in times of very low liquidity, the liquidity, veryoflow times in significant coeffi significant al tc te average the stock all s concluded that in the in that concluded gnals are generated generated are gnals cient, I use I cient, we can see that that see can we In column 3

only only h

CEU eTD Collection even thestatistical significance isdisappearing. account, into taken is theproblem simultaneity the if and negligible are effects but the of magnitudes relationship, positive significant statistically a is there specifications the of some of number the and illiquidity coefficient becomes statistically insignificant. coefficient the of size The used. is estimation variable handled. be to trying In sum it can be concluded that I have not found a strong relationship between between relationship strong a found not have I that concluded be can it sum In is problem simultaneity the where regression the of result the shows column last The

The variables are the same as in as same the are variables The

trading signals generated by the technical trading strategies. In strategies. trading technical the by generated signals trading

is similar to that in column 1 column in that to similar is the first column first the , but the instrumental the but , , but the the but ,

CEU eTD Collection f h sml pro t suy h rltosi bten akt iudt ad h tmn of timing the and liquidity market between relationship the study to period sample the of (1999). al. et Lesmond by other the and (2002) Amihud by developed one literature, previous in proposed proxies liquidity opportunit whether analysis t ison the of part this were indicators average moving the of none which for sample, the in stocks nine were there hand, other the my profita be to proved in have tested rules trading the of proportion large stocks which for sample were There stocks. different over varies analysis technical of performance GARCH(1,1 models null popular the of any by stocks the of empirical previous with line (1992). al. et Brock of study cited widely the by proposed methodology bootstrap based technical analysis. market the of liquidity 2008. to 1997 from period the (BSE) Exchange Stock Budapest the of stocks different 26 on indicators average moving performance 6

Conclusion In this thesis I have c have I thesis this In examining by continued I analysis, technical of performance the analyzing After BSE the on rules average moving of profitability analyzingthe When the time variation of market liquidity can be a problem in exploiting the profit profit the exploiting in problem a be can liquidity market of variation time the e poie b tcncl rdn strategies. trading technical by provided ies

) ,

of - technical trading strategies provide profit opportunities that cannot be explained explained be cannot that opportunities profit provide strategies trading technical M). he market of ahe particularstock market

technical analysis technical significantly hss ol b t ass the assess to be would thesis

Moreover, by examining individual stocks, I was able to point out that the that out point to able was I stocks, individual examining by Moreover,

can be an obstacle in exploiting the pr the exploiting in obstacle an be can oncentrated on two issues. In the first part, first the In issues. two on oncentrated

research on other emerging markets, I have found that found have I markets, emerging other on research profitable during the period analy period the during profitable In the second part of the thesis, the of part second the In I used panel data of the same 26 stocks over the 138 the over stocks 26 same the of data panel used I on the Hungarian stock market. I have tested 120 tested have I market. stock Hungarian the on of

asset returns (random walk with a drift, AR(1), AR(1), drift, a with walk (random returns asset .

factors

determini o mauig iudt, ue two used I liquidity, measuring For ofit opportunities opportunities ofit

e.Apsil xeso of extension possible A zed. ng I have analyzed whether the whether analyzed have I o scesu technical successful how

I have examined have I ,

used the model the used provided

different different for most for

ble. On On ble. months over over

the by In - CEU eTD Collection literature, abad isnot solution. prac common a is which analyzed, period the over costs transaction handled, even statistical significance isdisappearing. the ru trading technical magnitude by required trades of number the and market the changes the and of illiquidity trades between relationship positive significant statistically found have I the specifications of timing the between link strong no is there that indicate results My strategies. trading successful most the by generated trades My

eut suggest results of the effect is effect the of

ht dutn te rftblt o tcncl nlss ih constant with analysis technical of profitability the adjusting that

economically negligibl economically

e and when the problem of simultaneity is is simultaneity of problem the when and e

n iudt. n some In liquidity. in ie n h empirical the in tice e. H les. owever ,

the the CEU eTD Collection 2009. May, May, 2009 01 date: Source: 6.3.4 6.3.3.1 6.3.3 6.3.2.3 6.3.2.2 6.3.2.1 6.3.2 6.3.1 6.3 Appendix Appendi

Regulations oft Regulations

Minimum FreeMinimum Float hundred (2,500,000,000) interms of million marketvalue. The value below of may tobe series listed security notbe FurtherListing Requirements for Category Equities “A” shall have three full business years,shall have threefullbusiness auditor.certifiedan by IssuerThe the (taking securities legal consideration its of into predecessor well) as market. and arefound t Issuers listing thatmarket thata applysecurities for arealready regulated at listed The requirement related tothe number need ofshareholders examinedfor notbe evidence ownershipavailable. of The be series held at tobeshall by listed least on evidencedlisting.investors withownership at of the time and 6.3.2.2thanthe security shallbeheld series byatfivehundred(500) least If thesecurity does series the marketbillion. freelybe at valuefloating least of shall securities two(2) HUF To meet the required Float Free minimum freefloat. of25%A minimum ofthe securities inthe t series float are: At thetime oflisting thesecurity series,requirements free for the theminimum

ces 1

L ; isting r isting http://www.bse.hu/data/cms61385/ListingReg_010509_tiszta_v1.1.pdf

he Budapest Stock Exchange for listing, continued trading and disclosure disclosure and trading listing,for continued Exchange Stock heBudapest equirements for o pass the categoryusingo passthat thetrading of data testsperformed

not meet the requirements listed in sub section insub not meet6.3.2.1 listed the requirements e quities in category in quities “A”

–

in case theration shortof25% falls e hundred e with (100) investors, o be shall constitute listed the

HUF two billion five HUF five twobillion

; downloaded: 15 15 downloaded: -

Effective Effective -

CEU eTD Collection 2 Appendix 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 # SMA

10 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 LMA

150 125 100 150 125 100 15 90 80 70 60 50 40 30 25 20 15 10 90 80 70 60 50 40 30 25 20 15 10 Moving averageMoving the rules examined ana during 5 60 59 58 57 56 55 54 53 52 51 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 # SMA

20 20 20 20 20 20 15 15 15 15 15 15 15 15 15 15 15 15 10 10 10 10 10 10 10 10 10 10 10 10 LMA 150 125 100 150 125 100 70 60 50 40 30 25 90 80 70 60 50 40 30 25 20 90 80 70 60 50 40 30 25 20 33

90 89 88 87 86 85 84 83 82 81 80 79 78 77 76 75 74 73 72 71 70 69 68 67 66 65 64 63 62 61 # SMA 20 20 20 20 40 40 40 40 40 40 30 30 30 30 30 30 30 30 30 25 25 25 25 25 25 25 25 25 25 20 LMA 125 100 100 150 125 100 150 125 100 150 90 80 90 80 70 60 50 90 80 70 60 50 40 90 80 70 60 50 40 30 lysis

120 119 118 117 116 115 114 113 112 111 110 109 108 107 106 105 104 103 102 101 100 94 93 92 91 99 98 97 96 95 # SMA 125 100 100 50 50 40 40 90 90 90 80 80 80 80 70 70 70 70 70 60 60 60 60 60 60 50 50 50 50 50 LMA 150 125 150 150 125 150 125 100 150 125 100 150 125 100 150 125 100 150 125 100 70 60 90 90 80 90 80 70 90 80

CEU eTD Collection 3 Appendix

Sd Ret Signal -.025 -.020 -.015 -.010 -.005 0.4 0.8 1.2 1.6 2.0 2.4 2.8 .01 .02 .03 .04 .05 .06 .07 .08 .09 .010 .000 .005

Figures 10 10 10 20 20

20 Figure Figure Figure Figure Figure Figure 30 30 30

3 40 40

4 40 – 5

– Monthly averages of variable variable of averages Monthly –

Monthly averages of variable variable of averages Monthly Monthly averages of variable variable of averages Monthly 50 50 50 60 60 60 34 m m

m o o o 70 70 n n 70 n t t h h t h s s s 80 80 80 90 90 90 100 100 100 Signal Ret Sd 110 110 110

120 120 120 130 130 130

CEU eTD Collection Appendix

SYNERGON RICHTER RABA PRIMAGAZ PICK PANNERGY OTP MTELEKOM MOL LINAMAR IEB HUMET GRAPHI GRABO GLOBUS FOTEX FHB EXBUS EGIS DEMASZ DANUBIUS BCHEM ANTENNA ZWACK ZKERAMIA TVK 4

Tables

01/07/1997 01/07/1997 01/07/1997 05/05/1999 01/07/1997 17/12/1997 01/07/1997 01/07/1997 01/07/1997 01/07/1997 14/11/1997 01/07/1997 02/04/2001 01/07/1997 02/04/2001 16/05/2000 01/07/1997 02/04/2001 01/07/1997 01/12/2003 01/03/2000 01/07/1997 01/04/1998 01/07/1997 01/07/1997 02/04/2001

starting date Table Table 1

–

Stocks in in Stocks 17/05/2007 31/12/2008 14/06/2007 28/12/2001 21/08/2006 31/12/2008 31/12/2008 30/09/2005 31/12/2008 30/11/2006 31/12/2008 29/03/2007 15/02/2006 31/12/2008 30/09/2005 31/12/2008 31/12/2008 31/12/2008 31/12/2008 31/01/2003 29/11/2002 31/12/2008 31/12/2008 31/12/2008 31/12/2008 31/12/2008 35

date end the the sample trading 2873 2063 2873 2418 2873 2755 1390 1349 2873 2873 2778 2873 1937 2470 1937 1774 1119 1351 2873 1275 1399 2873 2173 2873 2438 1222 days

trading months 138 138 115 138 132 138 138 133 138 118 138 138 104 138 116 97 67 64 92 93 84 53 64 61 67 58

CEU eTD Collection

ZKERAMIA TVK SYNERGON RICHTER RABA PRIMAGAZ PICK PANNERGY OTP MTELEKOM MOL LINAMAR IEB HUMET GRAPHI GRABO GLOBUS FOTEX FHB EXBUS EGIS DEMASZ DANUBIUS BCHEM ANTENNA ZWACK The estimationwas implementedusing Table Table 2

–

Estimated coefficients for the AR(1) models AR(1) the for coefficients Estimated -0.00263 -0.00006 -0.00003 -0.00009 -0.00055 -0.00012 -0.00068 -0.00059 -0.00129 -0.00083 -0.00033 -0.00012 -0.00006 -0.00195 -0.00062 -0.00171 -0.00015 0.00018 0.00044 0.00027 0.00038 0.00043 0.00016 0.00056 0.00028 0.00014 α 36 (-1.61) (-0.93) (-1.36) (-3.82) (-0.12) (-0.06) (-0.86) (-0.23) (-1.14) (-1.19) (-1.19) (-0.93) (-0.65) (-0.31) (t-stat) (0.22) (0.29) (0.59) (1.03) (0.32) (1.12) (0.63)

(-0.1) (-0.2) (-0.2) (0.6) (0.5) Eviews6. -0.189 -0.084 -0.079 -0.155 -0.151 -0.003 -0.041 -0.046 -0.030 -0.142 -0.005 -0.024 -0.151 -0.035 -0.043 -0.116 0.075 0.007 0.093 0.077 0.018 0.014 0.003 0.065 0.013 0.065

ρ (-0.27) (-0.84) (-8.19) (-1.61) (-2.31) (-4.34) (-8.39) (-3.33) (-5.23) (-0.14) (-1.46) (-1.72) (-1.42) (-7.66) (t-stat) (4.57) (4.14) (0.95) (0.51) (0.18) (3.51) (0.69) (3.51) (3.31) (-4.2) (-5.6) (0.4)

ZWACK ZKERAMIA TVK SYNERGON RICHTER RABA PRIMAGAZ PICK PANNERGY OTP MTELEKOM MOL LINAMAR IEB HUMET GRAPHI GRABO GLOBUS FOTEX FHB EXBUS EGIS DEMASZ DANUBIUS BCHEM ANTENNA The estimationwas implementedusingEViews6.

-0.0007 -0.0012 -0.0040 -0.0012 -0.0008 -0.0025 -0.0033 -0.0015 -0.0015 -0.0024 -0.0008 -0.0030 -0.0004 -0.0005 0.0002 0.0003 CEU0.0003 eTD0.0003 Collection 0.0004 0.0010 0.0007 0.0024 0.0012 0.0003 0.0011 0.0004 θ θ Table Table (-0.87) (-1.74) (-1.07) (-0.97) (-1.09) (-1.52) (-2.36) (-0.52) (-1.47) (-1.63) (-1.01) (-1.98) (-0.59) (-0.95) (0.58) (0.48) (0.61) (0.46) (0.62) (1.71) (1.13) (2.16) (1.24) (0.33) (1.68) (0.52) t-stat 3 37

–

Estimated coefficients for the GARCH(1,1) the for coefficients Estimated -0.30 -1.83 -0.44 -0.59 -0.42 -2.60

1.39 0.45 3.32 1.50 1.22 0.40 1.22 1.09 0.43 0.38 0.66 0.68 1.95 1.87 1.28 0.40 2.51 0.36 2.52 0.33 λ al (-0.64) (-0.39) (-1.64) (-0.19) (-0.98) (0.47) (0.41) (0.56) (0.61) (1.43) (0.79) (0.38) (1.42) (0.29) (1.48) (0.53) (0.38) (0.52) (1.68) (0.49) (0.33) (0.95) (-0.2) (0.8) (1.7) (1.1) t-stat 2.6E-05 2.9E-05 1.8E-05 2.0E-05 1.9E-05 6.1E-05 8.3E-04 6.7E-05 4.9E-05 2.5E-05 2.0E-05 2.7E-05 3.2E-06 6.0E-04 4.6E-04 3.0E-04 1.4E-04 8.1E-05 4.5E-05 4.6E-05 7.2E-05 3.5E-05 2.4E-05 4.1E-05 8.5E-06 6.0E-05 ω aal (22.39) (11.11) (11.73) (16.51) (12.38) (14.33) (12.48) (10.52) (15.21) (10.34) (11.87) (10.22) (10.43) (11.83) (13.5) (8.61) (8.82) (9.19) (8.91) (4.63) (8.39) (11.2) (5.96) (5.71) (7.9) (8.9) t-stat - M model M 0.19 0.21 0.13 0.07 0.10 0.16 0.14 0.17 0.15 0.14 0.10 0.14 0.02 0.15 0.31 0.09 0.33 0.12 0.13 0.15 0.14 0.13 0.17 0.13 0.14 0.24 α al

(16.09) (25.03) (15.39) (19.02) (12.49) (17.58) (14.35) (16.88) (10.58) (22.21) (12.43) (11.46) (14.07) (17.78) (10.31) (20.12) (15.98) (12.06) (29.77) (11.81) (22.5) (6.25) (9.43) (8.59) (7.28) t-stat (6) 0.77 0.78 0.86 0.90 0.87 0.75 0.37 0.79 0.79 0.83 0.85 0.81 0.97 0.20 0.52 0.54 0.60 0.78 0.84 0.79 0.75 0.83 0.79 0.81 0.88 0.67 β al (114.33) (174.08) (162.42) (137.85) (167.16) (305.87) (74.11) (99.01) (74.82) (84.79) (71.94) (76.24) (449.1) (18.19) (30.16) (43.75) (35.25) (25.01) (92.53) (95.99) (62.23) (31.32) (43.9) (4.59) (3.68) (9.78) t-stat

CEU eTD Collection

(p (p walk random the of model null the with p ZWACK ZKERAMIA TVK SYNERGON RICHTER RABA PRIMAGAZ PICK PANNERGY OTP MTELEKOM MOL LINAMAR IEB HUMET GRAPHI GRABO GLOBUS FOTEX FHB EXBUS EGIS DEMASZ DANUBIUS BCHEM ANTENNA 1 3 , p , ).

and p and Table Table 3

denote the simulated p simulated the denote 4 -0.00023 -0.00009 -0.00002

0.00079 0.00103 0.00264 0.00026 0.00143 0.00108 0.00123 0.00152 0.00015 0.00027 0.00074 0.00027 0.00152 0.00157 0.00309 0.00068 0.00212 0.00251 0.00154 0.00168 0.00002 0.00024 0.00025 –

m Summary of the performance of all trading rules trading all of performance the of Summary b -m

s m b -m 110 102 120 116 107 120 102 118 118 120 116 103 114 - s values from the model the from values >0 74 86 82 67 58 75 99 96 99 68 96 74 47 38 # of rules from the 120, where 1 0.05>p ), the AR(1) (p AR(1) the ),

35 89 42 11 51 12 28 28 30 73 77 31 71 0 5 1 9 4 0 0 7 4 0 1 9 7 1 0.05>p 2 ), and the GARCH(1,1) the and ), - 38 80 41 27 51 53 44 54 14 74 87 34 70 10 based bootstrap procedure bootstrap based 0 8 0 9 3 0 5 0 4 0 1 7 2 0.05>p 22 84 43 50 11 35 27 67 23 67 0 0 1 6 1 4 0 0 0 3 1 0 0 1 0 6 3

s b 115 120 120 109 120 120 119 120 120 118 120 120 120 120 120 119 120 118 120 120 113

~~M s~~

CEU eTD Collection respectively. p simulated (p walk random the of model null the p ZWACK ZKERAMIA TVK SYNERGON RICHTER RABA PRIMAGAZ PICK PANNERGY OTP MTELEKOM MOL LINAMAR IEB HUMET GRAPHI GRABO GLOBUS FOTEX FHB EXBUS EGIS DEMASZ DANUBIUS BCHEM ANTENNA 1 , p , 2

~~and p and Table Table - 3~~

~~value is smaller than 0.1, 0.05, and 0.01, it is denoted by *, **, and *** *** and **, *, by denoted is it 0.01, and 0.05, 0.1, than smaller is value~~

denote the simulated p simulated the denote 0.00115 0.00273 0.00301 0.00523 0.00189 0.00247 0.00460 0.00366 0.00288 0.00255 0.00115 0.00231 0.00311 0.00169 0.00472 0.00340 0.00594 0.00319 0.00434 0.00378 0.00353 0.00313 0.00122 0.00186 0.00319 0.00289 5

~~m –~~

~~b Summary of the performance of the best performing rule performing best the of performance the of Summary -m s~~

0.072 0.014 0.002 0.000 0.020 0.010 0.016 0.016 0.002 0.000 0.078 0.004 0.006 0.076 0.020 0.008 0.020 0.022 0.000 0.004 0.008 0.000 0.090 0.038 0.002 0.014 p 1 * ** *** *** ** ** ** ** *** *** * *** *** * ** *** ** ** *** *** *** *** * ** *** ** - 1 model the from values ), the AR(1) (p AR(1) the ), 39 0.062 0.032 0.002 0.008 0.010 0.000 0.000 0.010 0.004 0.076 0.018 0.002 0.008 0.050 0.012 0.002 0.002 0.132 0.014 0.002 0.018 0.004 0.008 0.100 0.034 0.002

p 2 * ** *** *** ** *** ** *** *** ** *** * ** *** *** ** *** *** ** *** * ** *** *** 2 ), and the GARCH(1,1) the and ), - based bootstrap procedure with procedure bootstrap based 0.080 0.072 0.018 0.000 0.028 0.014 0.022 0.048 0.004 0.006 0.072 0.002 0.078 0.058 0.036 0.010 0.026 0.026 0.052 0.018 0.014 0.006 0.212 0.034 0.256 0.026 p 3 * * ** *** ** ** ** ** *** *** * *** * * ** ** ** ** * ** ** *** ** ** 0.0182 0.0250 0.0253 0.0296 0.0237 0.0219 0.0410 0.0300 0.0227 0.0226 0.0188 0.0214 0.0216 0.0280 0.0507 0.0282 0.0262 0.0227 0.0331 0.0202 0.0183 0.0226 0.0191 0.0228 0.0208 0.0220 -

~~M (p M s b~~

~~3 ). 0.0268 0.0329 0.0307 0.0297 0.0292 0.0290 0.0415 0.0351 0.0296 0.0315 0.0231 0.0261 0.0336 0.0327 0.0519 0.0290 0.0477 0.0317 0.0335 0.0313 0.0266 0.0312 0.0225 0.0290 0.0296 0.0223~~

~~If the the If s s CEU eTD Collection~~

~~GRABO GLOBUS FOTEX FHB EXBUS EGIS DEMASZ DANUBIUS BCHEM ANTENNA ZWACK ZKERAMIA TVK SYNERGON RICHTER RABA PRIMAGAZ PICK PANNERGY OTP MTELEKOM MOL LINAMAR IEB HUMET GRAPHI Table Table 6~~

–

Cross sectional statistics of liquidity measures liquidity of statistics sectional Cross 138 138 133 138 118 138 138 104 138 116 138 138 115 138 132 obs 97 67 64 92 93 84 53 64 61 67 58 1.5E-05 7.1E-06 4.6E-06 4.5E-06 0.0068 0.0039 0.0011 0.0006 0.0005 0.0124 0.0035 0.0027 0.0117 0.0105 0.0238 0.0087 0.0022 0.0109 0.0043 0.0004 0.0084 0.0001 0.0010 0.0074 0.0011 0.0009 mean Amihud 40

9.4E-05 2.1E-05 8.4E-05 0.1531 0.5222 0.4444 0.0229 0.1188 0.1664 0.0094 0.2815 0.0012 0.0106 0.4353 0.0312 0.0081 0.4138 0.1148 0.0328 0.0063 0.0005 0.0034 0.2068 0.0568 0.0749 0.3274 max 0.0204 0.0172 0.0137 0.0149 0.0079 0.0137 0.0252 0.0211 0.0148 0.0056 0.0070 0.0065 0.0298 0.0248 0.0267 0.0146 0.0172 0.0281 0.0195 0.0116 0.0187 0.0115 0.0119 0.0164 0.0109 0.0119 mean LOT

~~0.1794 0.1056 0.0513 0.0882 0.0277 0.0602 0.1049 0.1266 0.0592 0.0325 0.0294 0.0535 0.1430 0.1535 0.0795 0.0678 0.0715 0.1172 0.1463 0.0422 0.0598 0.0899 0.0443 0.0512 0.0412 0.0343 max~~

~~CEU eTD Collection~~

~~All ZWACK ZKERAMIA TVK SYNERGON RICHTER RABA PRIMAGAZ PICK PANNERGY OTP MTELEKOM MOL LINAMAR IEB HUMET GRAPHI GRABO GLOBUS FOTEX FHB EXBUS EGIS DEMASZ DANUBIUS BCHEM ANTENNA~~

~~Table Table Mean 7 1.21 0.82 0.45 0.19 1.35 0.42 1.07 0.70 2.15 5.27 0.36 1.11 1.53 0.25 0.82 1.52 5.32 0.07 0.34 1.52 0.55 0.17 0.22 1.22 0.12 0.16 0.44~~

~~– Signal~~

~~Cross Std. Dev.~~

- sectional descriptive statistics of variables of statistics descriptive sectional 1.81 1.23 0.71 0.43 0.49 1.12 0.33 0.57 0.72 0.81 0.72 0.40 1.03 0.63 0.82 0.90 1.20 1.93 0.69 0.84 1.11 0.66 0.84 1.76 2.08 0.26 0.59 -0.00029 -0.00076 -0.00020 -0.00075 -0.00054 -0.00126 -0.00092 -0.00035 -0.00025 -0.00016 -0.00215 -0.00047 -0.00190 -0.00009 -0.00010 -0.00266 -0.00004 -0.00010 0.00032 0.00011 0.00056 0.00024 0.00005 0.00011 0.00012 0.00028 0.00034 Mean 41

Ret Std. Dev. 0.0062 0.0032 0.0071 0.0057 0.0079 0.0058 0.0052 0.0083 0.0075 0.0060 0.0055 0.0048 0.0051 0.0041 0.0073 0.0103 0.0066 0.0090 0.0049 0.0087 0.0046 0.0048 0.0063 0.0045 0.0042 0.0059 0.0047 Mean 0.025 0.018 0.024 0.025 0.026 0.023 0.023 0.035 0.027 0.024 0.023 0.020 0.021 0.025 0.025 0.047 0.024 0.032 0.025 0.029 0.024 0.024 0.024 0.019 0.024 0.023 0.020 Sd Std. Dev.

~~0.015 0.013 0.016 0.015 0.013 0.013 0.012 0.022 0.019 0.012 0.013 0.008 0.011 0.013 0.018 0.025 0.014 0.028 0.012 0.016 0.012 0.009 0.013 0.009 0.012 0.013 0.010~~

~~CEU eTD Collection~~

Obs. R IV-s used FEcross-section Intercept Sd Ret Liq20 (Amihud) (Amihud)Illiq20 Liq10 (Amihud) (Amihud)Illiq10 Liq5 (Amihud) (Amihud)Illiq5 log(LOT) log(Amihud) Dependent var.: arbitrary and time correlation serial p The 2

~~- values are calculated using White period method of EViews EViews of method period White using calculated are values Table Table 8 -7.3585 (0.000) (0.057) (0.029) (0.000) 0.0346 1.7008 8.2496~~

~~– 0.669 2688~~

~~Estimation results for the the for results Estimation yes~~

~~no -6.6339 (0.000) (0.001) (0.059) (0.265) 0.0003 1.3781 7.5211 - 0.668 varying inthedisturbances. variances 2688 yes no 42 -7.1909 -0.2471~~

~~(0.000) (0.001) (0.047) (0.029) (0.013) 0.1080 1.3967 8.1347 0.669 2688 yes Signal no Signal -7.0223 -0.1538 (0.000) (0.001) (0.047) (0.030) (0.090) 1.3970 8.1714 0.0393 0.668 2688~~

~~yes regressions no -6.9430 -0.1253 (0.000) (0.001) (0.057) (0.090) (0.870) 1.4104 8.1049 0.0085 0.668 2688 ht s out to robust is that yes~~

~~no 10.9536 -7.9911 (0.005) (0.074) (0.338) (0.602) 0.0305 1.6812 0.669 2688 yes yes~~

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