Analysis of Calendar Effects: Day-of-the-Week Effect on the Stock Exchange of Thailand (SET)

Transcription

1 X Analysis of Calendar Effecs: Day-of-he-Week Effec on he Sock Echange of Thailand () Phaisarn Suheebanjard and Wichian Premchaiswadi Absrac According o he Efficien Marke Hypohesis (EMH), a sock s reurn is indifferen o a given rading day. Bu he day of he week effec phenomenon produces a differen reurn for each day in he week. This is an abnormal reurn which can affec invesors in deciding an invesmen sraegy and porfolio managemen. This sudy eamines he day of he week effec on sock marke reurns and he volailiy of he Sock Echange of Thailand () inde in order o know wheher his anomaly eiss or no. An eperimen was conduced by using he daily Inde daa of,040 days from 4 January 2005 o 3 March The daa on each day of he week was esed by applying he same predicion funcion. The predicion funcion incorporaes imporan economic facors such as he Dow Jones inde, he Nikkei inde, he Hang Seng inde and he domesic Minimum Loan Rae (MLR). The uning coefficiens of each facor in his research were calculaed by using he wo-membered Evoluion Sraegies (ES) echnique. The resuls provide empirical evidence ha day-of-he-week has a significan effec on he inde wih he highes percen of predicion error on Monday and he lowes percen of predicion error on Friday. Inde Terms Calendar effecs, Day-of-he week effec, Sock Echange of Thailand, Evoluion Sraegies I. INTRODUCTION An ineresing area of research relaed o sock marke reurns is he presence of calendar anomalies. In recen years he esing for marke anomalies on sock reurns has become an acive field of research in empirical finance. Calendar effecs are cyclical anomalies on reurns in he financial marke, where he cycle is based on he calendar. The mos imporan calendar anomalies are he January effec and he day-of-he-week effec. The January Effec (also known as he urn-of-he-year effec or he January anomaly) was firs observed in 983 by Keim [9]. He noed ha he average risk-adjused reurn for a porfolio of small firms socks is large in January and much smaller for he res of he year. The January effec is said o affec small caps more han mid or large caps. The day-of-he-week effec (also known as he weekend effec) refers o he endency of socks o ehibi relaively large reurns on Fridays compared o hose on Mondays. The sudies on day-of-he-week effec have been ongoing since 930 when Kelly [] revealed he eisence of a Monday effec on he US markes where he reurns urned ou o be negaive. The day of he week paerns have been invesigaed eensively in differen markes [2-7]. The average daily reurn of he marke is no he same for each day of he week. The day of he week effec phenomenon resuled in a differen reurn for each day of a week. This phenomenon of reurn can affec invesors in deciding invesmen sraegy, porfolio selecion, and profi managemen. There are some researches on he day-of-he-week effecs for he Sock Echange of Thailand. Their findings suppor he noion ha sock reurns depend on he day of he week. Bu here are differen day of he week paerns. Some researches resuls indicaed ha he reurns are negaive, on average, on Monday [4-5], and oher indicaed ha Monday has a significan posiive reurn on average [6]. The purpose of his research is o presen evidence on he day-of-he-week effecs for he Sock Echange of Thailand by applying a predicion funcion o each day in a week and evaluaing he percen of error. The imporan facors ha were used o forecas he inde in he predicion funcion [5] include he Dow Jones (New York), he Nikkei (Japan), he Hang Seng (Hong Kong) and he Minimum Loan Rae (MLR) from he Bank of Thailand. The uning coefficiens of each facor in his research were calculaed by using he wo-membered Evoluion Sraegies (ES) echnique. The remainder of his paper is organized as follows. Secion 2 provides a brief descripion of he Sock Echange of Thailand. Secion 3 eplains he daa colleced and he mehodology uilized. The empirical findings are repored in Secion 4, and he final secion provides a summary of he paper. II. BACKGROUND ON THE STOCK EXCHANGE OF THAILAND The Thai sock marke in Thailand officially sared rading on April 30, 975. The number of lised companies on he Sock Echange of Thailand increased from 6 in 975 o 476 in And he oal marke capializaion increased from, million bah a he end of 975 o 3.57 billion bah a he end of The inde of he Sock Echange of Thailand is called he Inde. The Inde is a composie marke capializaion-weighed price inde which compares he curren marke value (CMV) of all lised common socks wih is marke value on he base dae of April 30, 975 (Base Marke Value or BMV), which was when he sock marke was esablished. The iniial value of he inde on he base dae was se a 00 poins. The formula of he inde calculaion as shown in () Graduae School of Informaion TechnologySiam Universiy Bangkok 063, Thailand 57 () The inde movemen is dependen on boh global and

2 X domesic economic facors [8-5]. Since counries are linked ogeher, movemen on one sock marke may have an impac on oher sock markes. Naurally, he Thai sock marke has unique characerisics, so he facors influencing he prices of socks raded in his marke are differen from he facors influencing oher sock markes [4]. An eample of facors ha influence he Thai sock marke are foreign sock indees, he value of he Thai Bah, he price of oil, he price of gold, he Minimum Loan Rae (MLR) and many ohers [8, 9, 0,, 2, 3]. There were some researchers ha used hese facors o forecas he Sock Echange of Thailand () inde such as Taninakom [8] who used rading value, rading volume, inerbank overnigh rae, inflaion, ne rading value of invesmen, value of he Thai Bah, price earning raion, he Dow Jones inde, he Hang Seng inde, he Nikkei inde, he Srais Times Indusrial inde and he Kuala Lumpur Sock Echange Composie inde. In 2000, Khumpoo [9] used he Dow Jones inde, he price of gold, he Hang Seng inde, he echange rae of he Japanese yen and he Thai bah, he Minimum Loan Rae (MLR), he Nikkei inde, he price of oil, he Srais Times Indusrial inde and he Taiwan weighed inde. In 2004, Choasiri [0] used he ineres rae of Thailand and he USA, he echange rae of USD, JPY, HKD and SKD, he sock echange indices of USA, Japan, Hong Kong and Singapore, he consumer price inde, and he Taninakom 996 [8] TABLE I.IMPACT FACTORS TO STOCK EXCHANGE OF THAILAND INDEX Khumyoo Choasiri Chaereonkihuakorn Rimcharoen 2000 [9] 2004 [0] 2005 [] el al [2] price of oil. In 2005, Chaereonkihuakorn [] used Unied Sae sock indices including he Nasdaq inde, he Dow Jones inde and he S&P 500 inde. In 2005, Rimcharoen e al. [2] used he Dow Jones inde, he Nikkei inde, he Hang Seng inde, he price of gold and he Minimum Loan Rae (MLR). In 2007, Worasucheep [3] used he Minimum Loan Rae (MLR), he echange rae of he Thai Bah and he US dollar, daily effecive over-nigh federal fund raes in he USA, he Dow Jones inde and he price of oil. In 2008, Chaigusin e al. [4] used he Dow Jones inde, he Nikkei inde, he Hang Seng inde, he price of gold, he Minimum Loan Rae (MLR) and he echange rae of he Thai Bah and he US dollar. The common facors ha researchers used o predic he inde are summarized in able I. In 2005, Rimcharoen, e al. [2] used Adapive Evoluion Sraegies o predic he Sock Echange of Thailand inde for he period of January 2003 o December The economic facors used in heir eperimen were he Dow Jones inde, he Nikkei inde, he Hang Seng inde, he price of gold and he MLR. The eperimenal resuls show ha heir mehod can be used o forecas he Sock Echange of Thailand inde wih an error less han 3%. The Funcion used by Rimcharoen e al. as shown in (2). Worasucheep 2007 [3] Suheebanjard and Premchaiswadi 2009 [5] Nasdaq Inde Dow Jones Inde S&P 500 Inde Nikkei Inde Hang Seng Inde Srais Times Indusrial Inde USD JPY HKD SKD Gold price Oil price MLR Denoe USD is he echanges raes of he Thai Bah and he US dollar where () is oday and (-) is yeserday. JPY is he echanges raes of he Thai Bah and Japanese Yen HKD is he echanges raes of he Thai Bah and Hong Kong dollar SKD is he echanges raes of he Thai Bah and Singapore dollar () = sin 3 [0.338 (-) ].5430hangseng (-) / mlr (-) cos 2 [ (-) ] * 4.68sin[0.365 (-) ].5380cos 3 [ (-) ]-.68cos 3 [ (-) ] sin 3 [.537 (-) ] cos[ (-) ] * 2.344mlr (-) (2) They have evenually found ha he inde can be adequaely eplained by only wo major facors, he Hang Seng inde and he MLR as shown in (3) and he graph as shown in Fig hangseng ( ) ( ) = (3) mlr( ) In 2008, Chaigusin, e al. presened he graph of he forecased inde ha were calculaed from (3) by using he daa from January 2005 o December 2006 compared o he acual inde as shown in Fig

3 X Fig. : The graph of Inde predicion versus acual inde during agains equaion (3) [2] I can be implied from hese wo figures ha he inde can be reasonably described by he Hang Seng inde and he MLR in he year 2003 and 2004 bu no in 2005 and This is because he daa from he firs 420 days during 2003 o 2004 was used as raining daa [2], herefore he inde is eplained reasonably well by he relaionship (-.5430HangSeng)/( MLR) for ha period. In 2009, Suheebanjard and Premchaiswadi [5] applied (+) Evoluion Sraegies o predic he inde during he periods of January 2003 o December 2004 and January 2005 o December The four facors used in he predicion funcion were he Dow Jones inde, he Nikkei Inde, he Hang Seng inde and he MLR as shown in (4) Forecas Fig. 2: The graph of inde predicion versus acual inde during agains (-.5430hangseng)/( mlr) [4] The eperimenal resuls show ha he predicion funcion of Suheebanjard and Premchaiswadi prediced he inde wih less han 2% error for boh periods. The graph of he inde predicion during he period of January 2003 o December 2004 is shown in Fig. 3, and he inde predicion during he period of January 2005 o December 2006 as shown in Fig. 4. ( ) = 0.86( ) (( DJ ( ) N K ( ) HS( ) ) / (4) MLR( ) ) Forecas inde Inde Day Day Fig. 3:The graph of Inde predicion versus acual inde during agains equaion (4)[5] Fig. 4: The graph of inde predicion versus he acual inde during [5] III. DATA AND METHODOLOGY In predicing he inde, his research used he funcion of Suheebanjard and Premchaiswadi [5] as shown in (5) ha is composed of global and domesic facors, namely; Dow Jones inde (New York), Nikkei inde (Japan), Hang Seng inde (Hong Kong), domesic Minimum Loan Rae (MLR) and inde (Thailand) iself. The eperimenal daa were obained from he Bank of Thailand. Daily daa on he inde from January 2004 o December 2004 were used for he raining phase and daa from January 2005 o March 2009 was used for he esing phase. 59 ( ) = a 0 ( ) a2dj( ) + a3nk( ) + a4hs( + a a5mlr( ) where a 0 -a 5 denoe coefficiens. is inde (Thailand) DJ is Dow Jones inde (New York) NK is Nikkei inde (Japan) HS is Hang Seng inde (Hong Kong) MLR is Minimum Loan Rae (MLR) The proposed mehod applied he (+) Evoluion Sraegies [6, 7] in he coefficien uning process, and raining and esing he daa were done for each individual day of he week. The inenion of he research was o find paerns of he inde movemen ha was possibly influenced by he day of he week. ) (5)

4 X Evoluion Sraegies (ES) was inroduced by Rechenberg [8]. Similar o Geneic Algorihms, Evoluion Sraegies (ES) are algorihms which imiae he principles of naural evoluion as a mehod o solve parameer opimizaion problems. The concep is o use he principles of organic evoluion processes as he rules for opimum seeking procedures. The (+)-ES is a simple muaion-selecion scheme called wo-membered ES. I is based upon a populaion consising of one paren individual (a real valued vecor), and one descendan, creaed by means of adding normally disribued random numbers wih idenical sandard deviaions. The resuling individual was evaluaed by a finess funcion and compared o is paren, and he beer of boh individuals survives o become he paren of he ne generaion, while he oher one is discarded. This research used he Mean Squared Error (MSE) as a finess funcion in order o minimize he error of finess beween he predicion funcion and acual value as shown in (6). n 2 MSE = ( g f ) (6) ( ) ( ) n = where g () is an acual value. f () is a forecased value n is he number of daa poins. In conrolling he search sraegy, an adjusmen of he sandard deviaion was considered and aken from he raio of he beer individual during he evoluion process, referred o as he /5 success rule [8] as shown in (7). 7 σ / 0.87 if ( p > / 5) ' σ = σ 0.87 if ( p < / 5) (7) σ if ( p = / 5) This research used he Mean Absolue Percenage Error (MAPE) o measure he error, which is he difference beween he forecased value and acual value. MAPE epresses he error as a percenage. MAPE is commonly used in quaniaive forecasing mehods because i produces a measure of relaive overall fi. The absolue values of all he percenage errors are summed up and he average is compued. The MAPE funcion as shown in (8). n g ( ) f ( ) = g (8) ( ) MAPE = 00 n where g () is an acual value. f () is a forecased value n is he number of daa poins. IV. EXPERIMENTAL RESULTS In his eperimen, he coefficiens of (5) were compued using Evoluion Sraegies for 34,000 imes on each individual day of he week. The lowes MAPE a each level of a 0 (weigh of inde (-) ) from 0.0 o.06 were seleced as shown in Fig. 5. And a each level of a 0 from 0.9 o.04 were seleced as shown in Fig MAPE Weigh of (-) Mon Tue Wed Thu Fri MAPE Weigh of (-) Mon Tue Wed Thu Fri Fig. 5: The lowes MAPE of each single day from (5) a differen weigh of inde(-) scale from Fig. 6: The lowes MAPE of each single day from (5) a differen weigh of inde(-) scale from In Fig. 5, he MAPE of each individual day is nearly he same for almos every level of a 0 from Bu Fig. 6 shows ha a he finer scale of a 0 from , Monday has he highes MAPE and Friday has he lowes MAPE. This sudy also esed he predicion funcion on all days in a week (Monday hrough Friday denoed as Mon-Fri in he figure). The resul was compared o each individual day of he week as illusraed in Fig. 7. The MAPE from he es on all days in a week is in he middle of he graph, which indicaes he average error. Therefore, i can be concluded ha he predicion error on Monday is higher han average, on Tuesday and Wednesday he predicion errors are almos equal o he average and on Friday and Thursday he predicion errors are lower han he average. The deails of

5 X daa used o plo he graphs in Fig. 5-7 are shown in able II.3 and III..2 MAPE Weigh of (-) Mon Tue Wed Thu Fri Mon-Fri Fig. 7: The lowes MAPE of each single day compare o everyday (denoe as Mon-Fri ) from equaion 5 a differen weigh of inde(-) scale from TABLE II. THE LOWEST MAPE OF EQUATION 5 AT DIFFERENT WEIGHT OF INDEX(T-) SCALE FROM Monday (%) Tuesday (%) Wednesday (%) Thursday (%) Friday (%) Monday-Fri day(%) TABLE III. THE LOWEST MAPE OF EQUATION 5 AT DIFFERENT WEIGHT OF INDEX(T-) SCALE FROM Monday (%) Tuesday (%) Wednesday (%) Thursday (%) Friday (%) Monday-Fri day(%)

6 X V. CONCLUSION The primary objecive of his paper was o invesigae he day-of-he-week effec on sock marke reurns and he volailiy of he Sock Echange of Thailand () inde for he period from 4 January 2005 o 3 March 2009, 040 days in oal. The analysis was conduced by applying Evoluion Sraegies. The resuls provide empirical evidence ha he day-of-he-week effec was presen in he Sock Echange of Thailand () reurns daa during he invesigaed period. In his research, predicion funcions were employed o predic he inde on Monday, Tuesday, Wednesday, Thursday and Friday. The resuls show ha he percen of error is highes on Monday and lowes on Friday. Thus, i can be concluded ha he Day-of-he-Week had a significan effec on he Sock Echange of Thailand inde during he invesigaed period. Since his sudy provides evidence of he day-of-he-week effec on he Thai sock marke, herefore i can be used by invesors, in addiion o oher sock marke analysis ools, o maimize heir epeced reurn by eploiing calendar anomalies in heir porfolios as well as o forecas sock marke rends, which can help hem in heir decision making process. [2] Rimcharoen, S., Suivong, D. & Chongsivaana, P. Predicion of he Sock Echange of Thailand Using Adapive Evoluion Sraegies, Tools wih Arificial Inelligence, ICTAI 05. 7h, 2005 [3] Worasucheep, Chukia. A New Self Adapive Differenial Evoluion: Is Applicaion in Forecasing he Inde of Sock Echange of Thailand, Evoluionary Compuaion, CEC 2007, 2007 [4] Chaigusin, S., Chirahamjaree, C. & Clayden, J. Sof compuing in he forecasing of he sock echange of Thailand (). Managemen of Innovaion and Technology, ICMIT h, 2008 [5] Phaisarn Suheebanjard and Wichian Premchaiswadi, Facors Analysis on Sock Echange of Thailand () Inde Movemen, The 7h Inernaional Conference on ICT and Knowledge Engineering, ICTKE2009, Bangkok, Thailand, December -2, [6] Back, T., Hoffmeiser, F. and Schwefel, H.P. A Survey of Evoluion Sraegies, Proceeding of he Fourh Conference on Geneic Algorihm, 99 [7] Beyer, H.G. and Schwefel, H.P. Evoluion Sraegies-A Comprehensive Inroducion, Naural Compuing. Vol., Issue, 2002 [8] Rechenberg, I. Evoluionssraegie: Opimierung echnischer Syseme nach Prinzipien der biologischen Evoluion. Sugar: Frommann-Holzboog Verlag [9] Keim, Donald B. Size-Relaed Anomalies and Sock Reurn Seasonaliy: Furher Empirical Evidence, Journal of Financial Economics 2, 983 REFERENCES [] Kelly, F., Why You Win or Lose: The Psychology of Speculaion, Boson: Houghon Mifflin, 930. [2] Chrisos S. Savva, Denise R. Osborn and Len Gill, The Day of he Week Effec in Fifeen European Sock Markes, Conference on High Frequency Finance. Universiy of Konsanz paper, available a hp:// papers/savva_osborn_gill.pdf [3] Chiaku Chukwuogor-Ndu, Sock Marke Reurns Analysis, Day-of-he-Week Effec, Volailiy of Reurns: Evidence from European Financial Markes , Inernaional Research Journal of Finance and Economics. 2-24, 2006 [4] Ravindra R. Kamah, Rinjai Chakornpipa and Arjun Charah, Reurn disribuions and he day-of-he-week effecs in he sock echange of Thailand, Journal of Economics and Finance. Volume 22, Numbers 2-3, June, 998 [5] Hooi Hooi Lean, Russell Smyh and Wing-Keung Wong, Revising Calendar Anomalies in ASIAN Sock Markes using A Sochasic Dominance Approach, Journal of Mulinaional Financial Managemen. Volume 7, April, 25-4, 2007 [6] Chris Brooks and Gia Persand, Seasonaliy in Souheas Asian Sock Markes Some New Evidence on day-of-he-week Effecs, Journal Applied Economics Leers, Volume 8, March, 200 [7] Anwar, Yunia & Mulyadi, Marin Surya, The day of he week effecs in Indonesia, Singapore, and Malaysia sock marke, MPRA Paper, Universiy Library of Munich, Germany, 2009 [8] Taninakom, T. Economic Facors Affecing Sock Echange of Thailand Inde, Maser Thesis in Economics, Chiang Mai Universiy, Chiang Mai, Thailand, 996 [9] Khumyoo, C. The Deerminans of Securiies Price in he Sock Echange of Thailand, Maser Thesis in Economics, Ramkhamhaeng Universiy, Bangkok, Thailand, 2000 [0] Choasiri, S. The Economic Facors Affecing he Flucuaion of The Sock Echange of Thailand Inde, Maser Thesis in Economics, Chiang Mai Universiy, Chiang Mai, Thailand, 2004 [] Chaereonkihuakorn, K. The Relaionship beween he Sock Echange of Thailand Inde and he Sock Indees in he Unied Saes of America, Maser Thesis in Economics, Chiang Mai Universiy, Chiang Mai, Thailand,