Calendar trading of Taiwan stock market: A study of holidays on trading detachment and interruptions

EMEMAR-00462; No of Pages 15 Emerging Markets Review xxx (2016) xxx–xxx

Contents lists available at ScienceDirect

Emerging Markets Review journal homepage: www.elsevier.com/locate/emr

Calendar trading of Taiwan stock market: A study of holidays on trading detachment and interruptions Ann Shawing Yang Institute of International Management, National Cheng Kung University, 1, University Road, Tainan, 701, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 25 March 2016 Received in revised form 16 August 2016 Accepted 17 August 2016 Available online xxxx JEL classifications: G1 G2 Keywords: Individual and institutional investors Holiday preferences Theories on time and mood Quantile regression

a b s t r a c t We analyze the influence of the Chinese lunar calendar and superstitions on holiday preferences using theories on time and mood to identify investor sentiment. Trading interruptions caused by Thursday holidays negative significantly influence investor sentiment for trust companies and individual investors. Trading detachment derived from cultural holidays in June positive significantly influences investor sentiment for dealers and individual investors. Trust companies and the market exhibit significantly positive sentiments toward winter holidays. The stock exchange indicates negative and positive sentiments toward winter holidays and holidays in January. Cultural holidays and superstition in Taiwan indicate strong support for holiday preferences in Asia. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Investors often derive sentiments from limited stock market trading time. Seasonality (i.e., weekdays, weekends, turn-of-the year, and holidays) influences decision-making and trading behavior (Urquhart and McGroarty, 2014; Bampinas et al., 2015; Yamamoto, 2012; Silva, 2010; Bollen and Inder, 2002; Thaler, 1987; Smirlock and Starks, 1986; McInish and Wood, 1992). Investors develop significant sentiment changes between holiday and non-holiday periods, which in turn cause market anomalies (Chui and Wei, 1998; Sias and Starks, 1997; Teng and Liu, 2013; Al-Khazali, 2014). Individual investors participate in excessive trading because of overconfidence, which can lead to investment losses (Barber and Odean, 2000; Barber et al., 2009); by contrast, institutional investors withhold information to generate profitable stock returns through passive trading to manage trading time constraints (Covrig and Ng, 2004; Keim and Madhavan, 1997; Yao, 2012).

E-mail address: [email protected].

http://dx.doi.org/10.1016/j.ememar.2016.08.004 1566-0141/© 2016 Elsevier B.V. All rights reserved.

Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

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A.S. Yang / Emerging Markets Review xxx (2016) xxx–xxx

Investors adapt information clustering behavior by gathering information during the weekend and formulating trading strategies during the week to meet investment objectives (Berument and Dogan, 2012; Barber and Odean, 2000; Barber et al., 2009, Covrig and Ng, 2004; Basher and Sadorsky, 2006; Lakonishok and Maberly, 1990). Investors may also participate in important buying activities during pre- and post-holiday periods, resulting in greater pre-holiday stock returns than non-pre-holiday stock returns (Chui and Wei, 1998; Sias and Starks, 1997; Teng and Liu, 2013). Hence, investor sentiments derived from seasonal anomalies significantly influence stock returns (Ho and Huang, 2009; Stambaugh et al., 2012; Baker et al., 2012, Yuan and Gupta, 2014; Zhang and Jacobsen, 2013; Dzhabarov and Ziemba, 2010). Investors may develop trading habits or preferences based on their calendar- or time-dependency (Griffiths and Winters, 1997; Sakakibara et al., 2013). Additionally, cultural influences, such as Chinese superstition for business activities and life, may also play a deterministic role in investor decision making (Chung et al., 2014). Thus, reducing investor sentiments concerning holiday preferences is an important method for managing seasonal anomalies for investors, stock exchange, and listed companies when making decisions and developing trading strategies. However, the literature related to latent seasonality preferences that cause changes in investor sentiments is limited. Current studies identify seasonality by focusing mainly on stock return volatilities and trading patterns at individual calendar times (Białkowski et al., 2012; Pantzalis and Ucar, 2014; Ke et al., 2014). Latent changes in seasonality preferences and investor sentiment relative to trading behavior are rarely discussed. Hence, the current study extends existing literature on identifying latent seasonality preferences in accordance with the trading behavior changes derived from investor sentiments. We identify the changes in investor sentiments using low, medium, and high sentiment quantiles on trading behavior relative to the seasonality preferences of individual and institutional investors and the stock exchange. Despite the well-known trading anomalies in weekdays, month, turn-of-the-year, and holidays (Bampinas et al., 2015; Andrade et al., 2013; Yao, 2012; Al-Khazali, 2014), we sought to incorporate the holiday effect into various trading seasonality anomalies. Thus, the influence of holiday with potential latent sentiment, which is derived from culture through the use of the lunar calendar, may also be analyzed as an extension of literature and as an additional source of trading anomalies and sentiment. Therefore, we are motivated by the use of the lunar calendar and the Gregorian calendar for public holidays as latent investor sentiment sources, as well as Chinese superstitions, which strongly influence business activities in Asia. We use data from Taiwan to investigate the changes in investor sentiments via trading activities and latent seasonality preferences across holidays of various calendar times. Then, to indicate sentiment variation levels, we identify changes in investor sentiment through trading activities and holiday preferences based on the ranking of stock return quantiles. We identify various investor panels, including stock exchange, individual investors, and institutional investors. Institutional investors consist of sub-panels of dealers, investment trust companies, and qualified foreign institutional investors. The methods used in the literature include the average comparison of investor sentiment reaction or the time-series analysis of volatility and stock price patterns. However, we use the quantile regression (QR) method to identify the intensity of investor sentiments, which correspond to the changes in trading behaviors and holiday preferences across individual and institutional investors and in the Taiwan Stock Exchange. QR analysis can be used to identify the different conditional distributions of a linear relationship; QR analysis also considers a broad range of stock return analyses (Pohlman and Ma, 2010; Gowlland et al., 2009). QR analysis avoids the elimination of samples to reduce sample selection bias (Koenker and Hallock, 2001), and establishes robustness by allowing extreme values to remain unaffected in the estimation procedure (Koenker and Bassett, 1982). 2. Literature review Weekday trading is often influenced by investor expectations where Mondays are known for selling activities and the remaining weekdays are used for market observation in decision making (Steeley, 2001; Urquhart and McGroarty, 2014). Thus, Monday's trading has the lowest trading volumes, whereas Tuesday's trading receives the highest stock selling pressure (Lakonishok and Maberly, 1990; Abraham and Ikenberry, 1994; Bampinas et al., 2015). Therefore, investors observe significantly negative stock price returns and demonstrate down-side investor sentiments on Tuesdays (Chueh and Chien, 2000). However, individual investor trading activities gradually decline from Wednesdays through Fridays (Lakonishok and Maberly, 1990). Institutional investors trade actively on Wednesdays, which has the highest trading volumes, whereas the lowest volumes are recorded on Mondays (Lakonishok and Maberly, 1990; Berument and Dogan, 2012). Fridays Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

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(i.e., when holidays and weekends are approaching) show the least trading activity; investors punctuate the weekend effect with increased short sell transactions (Chordia et al., 2001; Chen and Signal, 2003). Individual investors participate in sell-side activities on Fridays and continue trading the following Monday because of the weekend effect (Abraham and Ikenberry, 1994). Despite the general Monday and Friday trading anomalies in most Western countries, the Gulf region has unique Wednesday trading anomalies in trading pattern, which are derived from investor mood (Ariss et al., 2011). Holidays present buy-side sentiments among individual and institutional investors despite the weekday trading activities (Chui and Wei, 1998; Al-Khazali, 2014). Individual investors can cause a turn-of-the year effect with higher stock returns in December and January; by contrast, institutional investors show higher stock returns in the last week of December and negative stock returns in the first week of January (Sias and Starks, 1997; Yao, 2012). Holidays, such as the Chinese New Year, Easter holiday, the holy month of Ramadan, the Jewish Yom Kippur holiday, and the Japanese “Oshogatsu” new year holiday, generally increase stock returns (Teng and Liu, 2013; Pantzalis and Ucar, 2014; Białkowski et al., 2012; Kaplanski and Levy, 2012; Sakakibara et al., 2013; Al-Khazali, 2014). The holiday effect brings positive and negative pre- and post-holiday stock return anomalies (Dzhabarov and Ziemba, 2010; Bhana, 1994). Holiday profit-making opportunities generally attract investors with increased investment participation (Białkowski et al., 2013). Investors can accept data on arrival delays or gather additional information with increased time for decision-making and profitable investment returns before the reopening of the stock market (Pantzalis and Ucar, 2014; Ke et al., 2014). Investors' anticipation of holidays brings significantly positive stock returns (Silva, 2010), and their post-holiday positive reactions continue when the stock market reopens (Picou, 2006). Monthly seasonal effects related to different holidays may differ according to the sample period; for example, the January effect indicates significant positive and negative influences during a 100-year basis turning point in the UK (Zhang and Jacobsen, 2013). Although the holiday effect is significant in different countries, dominant investors and stock market trading mechanisms insignificantly influence such holiday effect (Kim and Park, 1994). However, individual investors exhibit the most significant turn-of-the-year effect in a bear market (Ritter, 1988; Peavy, 1995; Yao, 2012). The Halloween effect also exists in both the bull and bear markets in various countries, which indicate investor sentiment for irrationality (Urquhart and McGroarty, 2014; Andrade et al., 2013). Trading anomalies often evolve around weekday (Bampinas et al., 2015), month (Andrade et al., 2013), turn-of-the year (Yao, 2012), and holidays (Al-Khazali, 2014). In particular, weekday trading anomalies are often found on Mondays and Fridays; significant returns and volatilities are also observed in other weekdays such as Wednesday (Urquhart and McGroarty, 2014; Bampinas et al., 2015; Berument and Dogan, 2012). Monthly trading anomalies, however, indicate investor irrationality in decision making, wherein individual and institutional investors actively seek excess returns (Andrade et al., 2013). Around the turn-of-the year, which is generally referred to as the January effect, investor often profit from investing in small firms where returns are greater than in other periods (Urquhart and McGroarty, 2014; Yao, 2012). Moreover, trading around the holidays encourages investors to seek fast profits because of risk aversion (Al-Khazali, 2014). 2.1. Theories on time and mood The emotional curves of individuals occur in weekly, monthly, and seasonal cycles (Cason, 1931). Individuals are under the influence of the unit of a week, which serves as a timekeeping function on daily mood cycle regulations and structures (Larsen and Kasimatis, 1990). Thus, Mondays bring low emotions (i.e., blue Monday), midweek (i.e., between Tuesdays and Thursdays) brings the least positive moods for individuals, and weekends (i.e., Saturdays and Sundays) bring the most positive moods associated with high levels of satisfactions and emotions (Cason, 1931; Reid et al., 2000). Weekly cyclical mood is the highest on Fridays and Saturdays, whereas mood is lowest on Mondays or Tuesdays (Larsen and Kasimatis, 1990). June has the most positive emotions, whereas December and January have the lowest emotions (Cason, 1931). Individuals predict low mood at the beginning of the work week (i.e., blue Monday) and high mood toward the weekend given the “Thank God it's Friday” or “TGIF” mentality (Areni, 2008; Stone et al., 2012). Individuals who experience negative moods because of constraints in self-interest activities and leisure during the work week nurture positive moods during weekends when social interactions can be fully satisfied (Ryan et al., 2010). Mood variability is also significant for individuals prone to weekly cycles and seasonality, which results in responsive of mood changes (Reid et al., 2000). Therefore, weekends and holidays should be viewed as time Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

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away from daily life and as circumstances detached from work to help individuals regain a positive mood and recover from work stress (Fritz et al., 2010; McCabe, 2009). 2.2. Chinese lunar calendar – international perspective on public holidays Chinese lunar calendar, which is an important source for public holidays, is widely observed in Taiwan, Hong Kong, Singapore, Thailand, South Korea, Malaysia, Japan, and China (Chan et al., 1996; Liu, 2013; Bergsma and Jiang, 2016). The most prominent use of the Chinese lunar calendar is in Taiwan, where the majority of Min-Nan population practices lunar calendar rituals in daily life. Chinese ethnic groups, who represent over 60% of the population and dominate local commerce in Southeast Asia, Korea, and Japan, are strongly influenced by Chinese culture; the Chinese lunar calendar is adopted and integrated into the public holidays of these countries (Chan et al., 1996; Yuan and Gupta, 2014; Bergsma and Jiang, 2016). Public holidays based on the Chinese lunar calendar demonstrate the importance of Chinese cultural influences for business and trading. These cultural holidays include the Chinese New Year (usually in January or February), Dragon Boat festival (usually in June), mid-autumn festival (usually in September), and Qingming (Tomb Sweeping Day; usually in March) (Bergsma and Jiang, 2016; Yuan and Gupta, 2014). Furthermore, the Chinese lunar calendar strongly influences the social perspective toward superstitions where unlucky events, activities, numbers, or symbols relating to funeral activity may negatively affect trading activities and business operations (Liu, 2013). The number 8 represents good luck, whereas the number 4 represents bad luck; the latter significantly influences investment returns from superstition as a form of investor sentiment (Chung et al., 2014). Therefore, our study focuses on the Taiwan stock market to illustrate the important influence of the Chinese lunar calendar for the decision making of investors. By extending the existing holiday trading literature, we identify the effects of cultural holidays with latent investor mood toward decision making. Our empirical results and analysis contribute to the greater China region where the lunar calendar significantly influences investor decision making for participation. Our results assist in identifying holiday trading patterns under the influence of lunar calendar and related rituals, such as superstitions. These findings will facilitate comprehensive knowledge in investor decision making for potential investment opportunities across emerging markets that are influenced by Chinese culture. 3. Method We apply the QR method proposed by Koenker and Bassett (1978) to estimate the conditional distribution of stock returns. This distribution is connected to investor trading activities through seasonality using latent holiday preferences based on different levels of investor sentiments (measured as quantiles). We conduct ordinary least square (OLS) analysis to identify the average sentimental reaction of investors for decisionmaking based on latent holiday preferences and trading behavioral changes. The QR method identifies the linear relationships among different conditional distributions, the central tendency of conditional quantiles, and the least absolute deviations (LAD) from the quantile central tendency (Pohlman and Ma, 2010; Gowlland et al., 2009). This method is appropriate for the robust analysis of stock returns (Park, 2010; Högholm et al., 2011; Chang, 2013; Chiang et al., 2010; Tsai, 2012; Meligkotsidou et al., 2009). We then adopt the asymmetric least squares estimations (i.e., symmetric quantile tests) with slope coefficients proposed by Newey and Powell (1987) as the slope equality tests for quantile homogeneities. Symmetric quantile tests are conducted to identify asymmetry in the QR results and to determine significant differences among conditional distributions. We also use OLS, which provides an average estimation of the linear relationship between dependent and independent variables using conditional mean for results (Badshan, 2013; Daigler et al., 2014; Baur and Schulze, 2009). OLS is the basis for conditional mean function and is formulated in the equation below before extending to QR: min

β ∈ Rρ

n X 2 ðγ i −μ ðxi ; βÞÞ

ð1Þ

i¼1

where the sum of the squared differences is minimized for each nth independent variable, which is denoted as γi. The expected value of linear regression at mean value μ is denoted as μ(xi,β). Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

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Therefore, QR is expressed as min

β ∈ Rρ

n X ρτ ðγi −ξðxi ; βÞÞ

ð2Þ

i¼1

where τϵ (0, 1) is the LAD from ξ, and is represented by the check function ρτ = μ (τ− I(μ b 0); I(·) is an indicator function (Koenker and Bassett, 1978). The ρτ is the check function which asymmetrically weights positive and negative values. Daily stock market trading data are collected from the Taiwan Economic Journal database. The study was conducted from January 2, 2002 to December 31, 2013. Prior to 2013, no separate capital gains tax existed in Taiwan, and hence, the tax loss selling hypothesis does not apply to the Taiwan stock market (Bergsma and Jiang, 2016). Despite the newly established 2013 capital gains tax where the index value reaches above 8500 points (Lo and Chiang, 2013), the index only reached above 8500 points on December 27, 30, and 31, 2013. Therefore, an insignificant effect is observed from the 2013 capital gains tax regulatory policy on security trading for our study period. The 2013 capital gains tax regulation was abolished on January 1, 2016. We categorized investor data into several panels, namely, stock exchange, market, individual investor, and institutional investors. Institutional investors include the sub-panels of qualified foreign institutional investors (Qfii), investment trust companies (Trusts), and dealers. We focus on the changes in the buy and Taiwan stock exchange sell orders, which consist of market trading volume (Vol) and market buy-sell imbalance (BS). We also focus on the investors' trading changes, which consists of margin purchase (Mm) and short sells (Ss), and on the buy and sell orders, which are denoted by Q f iibuy and Q f iisell for qualified foreign institutional investors, Trustbuy and Trustsell for investment trust companies, and Dealbuy and Dealsell for dealers. We further identify holidays on weekdays as holiday on Thursdays (HolThur), two-day holidays before weekends (Hol2day), holidays in June (HolJune), holidays in winter (Holwinter), and the turn-of-the-year effect in January (January). We regress the above independent variables against the dependent variable stock market index return (Rtn) to identify the degree of investor sentiments under latent holiday preferences (i.e., calendar mood) and corresponding trading behavior changes. A total of 2982 observations are collected among 16 variables, four investor type categories, and five holiday types. We assess the relationship between stock returns and investor sentiments under the influences of latent holiday preferences and trading behavior changes. These influences include panels of stock exchange, individual investors, qualified foreign institutional investors, investment trust companies, and dealers. We test for changes in investor sentiments according to low-, mid-, and high-sentiment quantiles. The QR regression function is inclusive of all variables for the stock exchange panel as shown in Eq. (3): Rnt t ¼ β0 þ β1 Volt þ β2 BSt þ β3 Mmt þ β4 Sst þ β5 Q fiibuy;t þ β6 Q fiisell;t þ β7 Trust buy;t þ β8 Trust sell;t þ β9 Dealbuy;t þ β10 Dealsell;t þ β11 Januaryt þ β12 HolThur;t þ β13 Hol2day;t þ β14 HolJune;t þ β15 Holwinter;t þ ε;

ð3Þ

where Rntt represents the index return change at time t; β0,1…15 is the estimation coefficient at various conditional quantile functions; Volt and BSt represent market volume changes and buy/sell imbalance changes for overall market sentiment; Mmt and Sst denote changes in the margin purchase and short sales of individual investors; Q fiibuy,t and Q fiisell,t, Trustbuy,t and Trustsell,t, and Dealerbuy,t and Dealersell,t represent buy-andsell order changes for qualified foreign institutional investors, investment trust companies, and dealers; and HolThur,t denotes holidays that fall on Thursdays as mid-week interruptions on investor sentiment response. In addition, Hol2day,t represents two-day holidays that occur prior to the weekend, which results in a fourday weekend and encourages long-weekend relaxation, on investor sentiment response; HolJune represents holidays in June and serves as a mid-year business working break on investor sentiment response; Holwinter denotes holidays in winter and serves as a late-year break on investor sentiment response; Januaryt represents the month for the turn-of-the-year influence on investor sentiment response; and εt is the error term. We conduct QR analysis on selected variables with stock returns and on all holiday variables for various sub-panels. The individual investor panel includes stock return, margin trading, short sells, and all holiday variables. The institutional investor panel focuses on the buy-side and sell-side trading of qualified foreign institutional investors, which is represented by Q fiibuy and Q fiisell, respectively. Investment trust companies are respectively denoted as Trustbuy and Trustsell and Dealerbuy and Dealersell. The stock exchange panel consists Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

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of stock return, volume, buy-sell, all individual and institutional investor transaction variables, and all holiday variables. 4. Results 4.1. Descriptive statistics Table 1 summarizes the statistics for each variable from 2002 to 2013. Individual investors participate in margin purchases approximately twice higher than short selling, whereas institutional investors participate in buying and selling at approximately the same level. However, qualified foreign institutional investors and dealers participate more in buying than in selling orders. By contrast, investment trust companies participate more in selling than in buying orders. January is the most frequently encountered holiday among the holiday cycles, followed by two-day holidays, holidays on Thursdays, holidays in winter, and holidays in June. 4.2. Stock exchange panel results Table 2 presents the parameter estimates for the stock exchange panel. The selected quantiles consist of low-, mid-, and high-sentiment quantiles. Majority of the variables are significant at the 1% level. Trading volume consistently and positively influences investor sentiments in all models. Buy–sell imbalances exhibit gradual increases in its significantly positive influence on medium sentiment quantiles between the 0.4th and 0.6th quantiles, and significantly and negatively influence low-sentiment quantiles at the 0.1st quantile. The margin purchases and short sells of individual investors indicate strong sentiments at low quantiles, with a gradual sentiment decrease at high quantiles. Margin purchases display strong significant influences with higher coefficient values than those of short sells prior to the mid-sentiment quantile at the 0.5th quantile. Margin purchases show fewer coefficient values at high quantiles from the 0.6th to 0.9th quantiles. However, qualified foreign institutional investors show increased buy- than sell-side activities in all models with significantly positive and significantly negative influences. Investment trust companies demonstrate increased sellside than buy-side activities at low- and mid-sentiment quantiles, with equivalent sell-side and buy-side activities at the 0.7th quantile. However, trust companies adopt more buy-side than sell-side activities at the 0.8th and 0.9th quantiles. Dealers adopt a constant buy-side strategy for most of the quantiles; a strategy that reflects slightly increased sell-side activities. Dealers also adopt more sell-side activities than buy-side activities at high sentiment quantiles. Investors show significantly positive sentiments at high quantiles during

Table 1 Descriptive statistics. Variable

Mean

Median

Max.

Min.

Std. Dev.

Skewness Kurtosis Jarque-Bera (Prob.) Obs.

Index Vol. BS MM Ss QfiiBuy QfiiSell TrustBuy TrustSell DealerBuy DealerSell January HolThur. Hol2day HolJune Holwinter

6917 3,975,885 417,862 15,551,578 705,810 1.81E + 10 1.73E + 10 2.89E + 09 2.93E + 09 3.19E + 09 3.16E + 09 0.077 0.010 0.025 0.005 0.008

7075 3,740,813 160,110 15,526,168 652,138 1.72E + 10 1.61E + 10 2.62E + 09 2.76E + 09 2.98E + 09 2.96E + 09 0.00 0.00 0.00 0.00 0.00

9810 11,631,227 5,132,985 23,215,936 1,975,246 1.35E + 11 9.27E + 10 8.51E + 09 1.19E + 10 1.17E + 10 1.80E + 10 1.00 1.00 1.00 1.00 1.00

3850 1,369,000 -11,744,073 10,464,811 129,506 4.80E + 08 4.02E + 08 3.44E + 08 7.91E + 08 1.90E + 08 3.27E + 08 0.00 0.00 0.00 0.00 0.00

1317 1,354,240 1,014,155 2,910,195 307,439 9.64E + 09 9.50E + 09 1.27E + 09 1.10E + 09 1.52E + 09 1.50E + 09 0.266 0.103 0.157 0.070 0.091

-0.233 1.304 0.254 0.167 0.971 1.774 1.317 1.203 1.036 0.765 1.295 3.169 9.497 6.021 13.993 10.783

2.189 5.661 10.263 2.402 3.871 13.925 6.944 4.795 5.463 4.140 8.331 11.048 91.198 37.262 196.805 117.288

108.689*** 1725.753*** 6587.859*** 58.385*** 563.627*** 16,395.260*** 2795.800*** 1119.933*** 1288.394*** 452.633*** 4366.079*** 13,043*** 1,011,363*** 163,886.3*** 4,764,195*** 1,680,730***

2982 2982 2982 2982 2982 2982 2982 2982 2982 2982 2982 2982 2982 2982 2982 2982

Note: Index is the Taiwan Stock Exchange Index. Vol. is the market trading volume. BS is the market buy-sell imbalance. Mm and Ss are margin purchase and short sales trading volume, respectively, for individual investors. Q fiibuy, Q fiisell, Dealerbuy, Dealersell, Trustbuy, and Trustsell are the buy and sell orders for institutional investors. January is the month of January. HolThur., HolJune, Holwinter, and Hol2day are the holidays on Thursdays, in June, in winter season, and for consecutive two-day holidays, respectively.

Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

Stock exchange

Intercept Vol BS Mm Ss Qfiibuy Qfiisell Trustbuy Trustsell Dealerbuy Dealersell January HolThur. Hol2day HolJune Holwinter Adj./Pseudo. R2

Model 1 Low sentiment

Model 2 Medium sentiment

Model 3 High sentiment

Symmetric test

OLS

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.1–0.9

0.25–0.75

0.019⁎⁎⁎ 0.0003⁎⁎⁎

0.043⁎⁎⁎

0.031⁎⁎⁎ 0.0003⁎⁎

0.020⁎⁎⁎ 0.0003⁎⁎

0.027⁎⁎⁎ 0.0004⁎⁎⁎

0.020⁎⁎⁎ 0.0004⁎⁎⁎

0.019⁎⁎⁎ 0.0003⁎⁎⁎

0.011⁎⁎ 0.0005⁎⁎⁎

−1.73E−07 0.019⁎⁎⁎ 0.013⁎⁎⁎ 0.014⁎⁎⁎ −0.012⁎⁎⁎ 0.002⁎⁎⁎ −0.007⁎⁎⁎ 0.010⁎⁎⁎ −0.011⁎⁎⁎ 0.001⁎⁎⁎ −0.001⁎⁎

0.002 0.0004⁎⁎⁎ −3.17E−06 0.011⁎⁎⁎ 0.017⁎⁎⁎ 0.009⁎⁎⁎ −0.006⁎⁎⁎ 0.006⁎⁎⁎ −0.004⁎⁎⁎ 0.011⁎⁎⁎ −0.016⁎⁎⁎

1.74E−06 0.013⁎⁎⁎ 0.015⁎⁎⁎ 0.010⁎⁎⁎ −0.007⁎⁎⁎ 0.004⁎⁎⁎ −0.004⁎⁎⁎ 0.010⁎⁎⁎ −0.015⁎⁎⁎ 0.0007⁎⁎⁎ 0.0007 −0.001⁎⁎⁎ −0.002⁎⁎⁎ 0.0008 0.0007 0.003⁎⁎⁎ 0.002⁎⁎⁎ −0.001⁎ −0.001 0.333 0.330

−0.005 0.0003⁎⁎⁎ −7.18E−06 0.011⁎⁎⁎ 0.012⁎⁎⁎ 0.009⁎⁎⁎ −0.005⁎⁎⁎ 0.009⁎⁎⁎ −0.005⁎⁎⁎ 0.010⁎⁎⁎ −0.017⁎⁎⁎

−0.002 −0.0002 −1.14E−05 0.0007 −0.002 0.0002 0.0003 0.004⁎⁎⁎ −0.002 0.0005 −0.002 −0.0001 −0.001 0.0006 −0.001 −0.0007

−0.007 −5.85E−05 −1.23E−06 −0.001 −0.0005 0.0002 0.0001 0.0002 0.0008 0.001 −0.002⁎

0.0002 −1.45E−06 −1.25E−06⁎⁎⁎ 0.016⁎⁎⁎ 0.022⁎⁎⁎ 0.015⁎⁎⁎ 0.016⁎⁎⁎ 0.011⁎⁎⁎ 0.014⁎⁎⁎ −0.009⁎⁎⁎ −0.012⁎⁎⁎ 0.005⁎⁎⁎ 0.001⁎⁎ −0.006⁎⁎⁎ −0.008⁎⁎⁎ 0.011⁎⁎⁎ 0.0108⁎⁎⁎ −0.014⁎⁎⁎ −0.0103⁎⁎⁎ 0.0006⁎⁎ 0.001⁎⁎ −0.0015⁎⁎ −0.0006 0.0005 0.0009⁎ 0.001⁎ 0.0015⁎⁎⁎ −0.001⁎ −0.003⁎⁎⁎ 0.571

0.445

0.0003 0.0010 −0.001 0.412

5.82E−07 0.017⁎⁎⁎ 0.015⁎⁎⁎ 0.013⁎⁎⁎ −0.011⁎⁎⁎ 0.002⁎⁎⁎ −0.006⁎⁎⁎ 0.010⁎⁎⁎ −0.012⁎⁎⁎ 0.0008⁎⁎⁎

1.06E−06⁎ 0.016⁎⁎⁎ 0.014⁎⁎⁎ 0.012⁎⁎⁎ −0.010⁎⁎⁎ 0.002⁎⁎⁎ −0.005⁎⁎⁎ 0.010⁎⁎⁎ −0.012⁎⁎⁎ 0.0006⁎⁎

1.49E−06⁎⁎ 0.016⁎⁎⁎ 0.015⁎⁎⁎ 0.011⁎⁎⁎ −0.009⁎⁎⁎ 0.003⁎⁎⁎ −0.005⁎⁎⁎ 0.010⁎⁎⁎ −0.012⁎⁎⁎ 0.0008⁎⁎⁎

1.88E−06⁎⁎⁎ 0.014⁎⁎⁎ 0.016⁎⁎⁎ 0.010⁎⁎⁎ −0.008⁎⁎⁎ 0.004⁎⁎⁎ −0.005⁎⁎⁎ 0.010⁎⁎⁎ −0.014⁎⁎⁎ 0.0009⁎⁎⁎

−0.001 0.0001 0.0005 −0.001⁎ 0.385

8.95E−05 −0.0001 0.0007 −0.001 0.364

−0.0005 0.0002 0.002⁎ −0.001 0.349

−0.001 0.0006 0.002⁎⁎ −0.001 0.339

0.0003 −0.001 0.0002 0.002⁎⁎⁎ −0.0006 0.333

−0.0004 −0.002 0.0007 −0.001 0.001

A.S. Yang / Emerging Markets Review xxx (2016) xxx–xxx

Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

Table 2 OLS and quantile regression results with symmetric quantile tests.

Note: The quantile regression results list the various quantiles from 0.1 quantile as low investor sentiment to 0.9 quantile as high investor sentiment; where 0.5 quantile is the median investor sentiment. Symmetric quantile tests, provided on the most right hand columns, are conducted for stock returns between 0.10th and 0.90th quantiles and between 0.25th and 0.75th quantiles. *, **, and *** denotes significance at 10%, 5%, 1%, respectively.

7

8

Individual investors

Intercept Mm Ss January HolThur. Hol2day HolJune Holwinter Adj./Pseudo. R2

Model 2 Medium sentiment

Model 3 High sentiment

Symmetric Test

OLS

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.1–0.9

0.25–0.75

1.99E−05 0.019*** 0.061*** 0.0004 −0.0015* 0.0012* 0.0027** −2.37E−05 0.263

−0.005⁎⁎⁎ 0.023⁎⁎⁎ 0.068⁎⁎⁎ 0.001⁎⁎⁎ −0.003 0.0004 0.003 0.0001 0.220

−0.003⁎⁎⁎ 0.018⁎⁎⁎ 0.071⁎⁎⁎ 0.0006 −0.002 0.0009 0.002⁎⁎⁎

−0.002⁎⁎⁎ 0.016⁎⁎⁎ 0.069⁎⁎⁎ 0.0009⁎⁎⁎ −0.0007 0.001⁎⁎

−0.001⁎⁎⁎ 0.014⁎⁎⁎ 0.068⁎⁎⁎ 0.0008⁎⁎⁎ −0.001 0.001⁎

−9.52E−05 0.013⁎⁎⁎ 0.066⁎⁎⁎ 0.0005⁎⁎ −0.0004 0.0015⁎⁎ 0.001 0.0004 0.125

0.003⁎⁎⁎ 0.014⁎⁎⁎ 0.059⁎⁎⁎ 0.0004 −0.002 0.001 0.002⁎

0.0003 0.011⁎⁎⁎ −0.008 0.0009 −0.005⁎⁎

0.002 −0.001 0.137

0.001⁎⁎⁎ 0.012⁎⁎⁎ 0.065⁎⁎⁎ 0.0003 −0.001 0.001⁎⁎ 0.002⁎

0.005⁎⁎⁎ 0.015⁎⁎⁎ 0.056⁎⁎⁎ 0.0006 −0.003⁎⁎

0.0021 −0.001 0.156

0.0008⁎⁎⁎ 0.0129⁎⁎⁎ 0.0659⁎⁎⁎ 0.0002 −0.0002 0.001 0.002⁎ 0.001 0.116

0.0006 0.109

0.0006 0.098

0.001 0.002 0.0008 0.092

−0.0008 0.001 0.0002

8.41E−05 0.002 −0.002 0.0002 −0.001 0.0004 0.0009 −0.001

Qualified Foreign Inst. Investors

Intercept Qfiibuy Qfiisell January HolThur. Hol2day HolJune Holwinter Adj./Pseudo. R2

Model 1 Low sentiment

−0.0003 0.181

Model 1 Low sentiment

Model 2 Medium sentiment

Model 3 High sentiment

Symmetric Test

OLS

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.1–0.9

0.25–0.75

−0.014*** 0.018*** −0.016*** −0.0001 −0.0022** 7.44E−05 0.001 0.001 0.261

−0.032⁎⁎⁎ 0.023⁎⁎⁎ −0.020⁎⁎⁎

−0.032⁎⁎⁎ 0.020⁎⁎⁎ −0.017⁎⁎⁎

−0.026⁎⁎⁎ 0.018⁎⁎⁎ −0.016⁎⁎⁎

−0.020⁎⁎⁎ 0.017⁎⁎⁎ −0.015⁎⁎⁎

−0.016⁎⁎⁎ 0.017⁎⁎⁎ −0.015⁎⁎⁎

−0.017⁎⁎⁎ 0.016⁎⁎⁎ −0.014⁎⁎⁎

−0.012⁎⁎⁎ 0.015⁎⁎⁎ −0.014⁎⁎⁎

−0.012⁎⁎ 0.015⁎⁎⁎ −0.014⁎⁎⁎

−0.0001 −0.002 −0.0004 0.002 0.001 0.215

6.68E−05 −0.002⁎⁎

7.98E−06 −0.001 −4.46E−05 0.002⁎⁎⁎

0.0001 −0.001 0.0003 0.001⁎

3.35E−05 −0.001 −8.53E−05 0.001 0.001 0.150

−2.85E−06 −0.001 −0.0005 0.001 0.002 0.144

2.09E−07 −0.002⁎⁎⁎

0.0001 −0.001⁎ 0.0002 0.0008 0.002 0.122

−0.001 0.0015⁎⁎⁎ −0.014⁎⁎⁎ 0.0003 −0.002⁎⁎⁎

1.88E−05 0.004⁎⁎⁎ −0.004⁎⁎⁎ 5.76E−05 −0.002 0.0003 −0.0007 −0.0001

−0.007 0.001 −0.0003 −5.03E−05 −0.002⁎

0.0003 0.0006 −0.0005 0.194

−0.0009 0.172

−0.001⁎ 0.159

0.0002 0.002⁎⁎⁎ 0.003⁎⁎ 0.136

0.0006 −0.0009 0.001 0.102

0.0003 −0.0002 −0.0008

A.S. Yang / Emerging Markets Review xxx (2016) xxx–xxx

Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

Table 3 OLS and quantile regression results with symmetric quantile tests.

Intercept Trustbuy Trustsell January HolThur. Hol2day HolJune Holwinter Adj./Pseudo. R2

Model 2 Medium sentiment

Model 3 High sentiment

Symmetric Test

OLS

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.1–0.9

0.25–0.75

−0.019*** 0.017*** −0.015*** 4.98E−05 −0.0029** 0.0005 0.0034** 0.0024** 0.176

0.007 0.019⁎⁎⁎ −0.020⁎⁎⁎

0.002 0.015⁎⁎⁎ −0.016⁎⁎⁎ 0.0009⁎ −0.004⁎⁎

−0.0008 0.014⁎⁎⁎ −0.014⁎⁎⁎ 0.0005⁎ −0.002⁎

−0.005 0.014⁎⁎⁎ −0.014⁎⁎⁎

−0.011⁎ 0.014⁎⁎⁎ −0.013⁎⁎⁎

−0.024⁎⁎⁎ 0.014⁎⁎⁎ −0.011⁎⁎⁎

−0.034⁎⁎⁎ 0.015⁎⁎⁎ −0.011⁎⁎⁎

−0.045⁎⁎⁎ 0.017⁎⁎⁎ −0.011⁎⁎⁎

−0.07⁎⁎⁎ 0.019⁎⁎⁎ −0.011⁎⁎⁎

−0.039⁎⁎⁎ 0.010⁎⁎⁎ −0.006⁎⁎⁎

−0.018⁎⁎ 0.002⁎⁎

−2.56E−05 −0.0004 0.0002 0.002⁎⁎ 0.002 0.085

−0.0003 −0.001 −6.21E−05 0.002⁎⁎ 0.003⁎⁎⁎ 0.101

−0.0004 −0.003⁎⁎⁎

0.0008 0.002⁎ 0.001 0.083

0.0001 −0.001 0.0004 0.002 0.001 0.076

−0.0004 −0.002⁎⁎⁎

0.0006 0.003⁎⁎⁎ 0.001 0.091

0.0001 −0.002 0.002 0.001 0.001 0.076

0.0009 0.002⁎ 0.004⁎⁎⁎ 0.119

0.001 0.0008 0.002⁎⁎⁎ 0.150

−5.51E−05 −0.005 0.0007 0.002 0.002

Dealer

Intercept Dealerbuy Dealersell January HolThur. Hol2day HolJune Holwinter Adj./Pseudo. R2

Model 1 Low sentiment

0.0006 −0.004 0.0003 0.005⁎⁎⁎ 0.003 0.092

Model 1 Low sentiment

Model 2 Medium sentiment

Model 3 High sentiment

−0.0005 0.0005 −0.001 −0.0001 0.0008 0.0005

Symmetric Test

OLS

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.1–0.9

0.25–0.75

0.003 0.023*** −0.024*** −0.0001 −0.0023*** 0.0011** 0.0026** −0.0008 0.402

−0.026⁎⁎⁎ 0.027⁎⁎⁎ −0.025⁎⁎⁎

−0.013⁎⁎⁎ 0.024⁎⁎⁎ −0.023⁎⁎⁎

−0.010⁎⁎ 0.022⁎⁎⁎ −0.021⁎⁎⁎

−0.003 0.021⁎⁎⁎ −0.021⁎⁎⁎

0.001 0.021⁎⁎⁎ −0.021⁎⁎⁎

0.006 0.021⁎⁎⁎ −0.021⁎⁎⁎

0.015⁎⁎⁎ 0.020⁎⁎⁎ −0.022⁎⁎⁎

0.020⁎⁎⁎ 0.021⁎⁎⁎ −0.023⁎⁎⁎

0.033⁎⁎⁎ 0.022⁎⁎⁎ −0.025⁎⁎⁎

0.004 0.007⁎⁎⁎ −0.007⁎⁎⁎

0.005 0.001⁎

−0.0006 −0.003⁎⁎⁎ 0.001⁎⁎ 0.002⁎ 0.0001 0.264

0.0002 −0.004⁎⁎⁎ 0.0007 0.002⁎ −0.001 0.253

0.0001 −0.003⁎⁎⁎ 0.001⁎ 0.001 −0.001 0.241

0.0002 −0.003⁎⁎ 0.001⁎⁎ 0.0002 0.0001 0.229

0.0001 −0.001 0.0009 0.004⁎⁎⁎ 7.97E−05 0.224

−9.01E−05 −0.001 0.0005 0.003⁎⁎⁎ 3.67E−05 0.223

2.85E−05 −0.001 0.001⁎ 0.003⁎⁎⁎ −4.92E−05 0.227

0.0004 −0.001⁎⁎ 0.001⁎⁎ 0.002⁎⁎⁎ −0.001⁎⁎ 0.229

4.31E−05 −0.002⁎⁎⁎ 0.0009 0.002⁎⁎⁎ −0.001 0.238

−0.0009 −0.002 0.0003 −0.004⁎ −0.001

−0.002⁎⁎ −1.14E−05 0.0005 −0.0001 −0.004⁎⁎ −0.001

Note: The quantile regression results list the various quantiles from 0.1 quantile as low investor sentiment to 0.9 quantile as high investor sentiment; where 0.5 quantile is the median investor sentiment. Symmetric quantile tests, provided on the most right hand columns, are conducted for stock returns between 0.10th and 0.90th quantiles and between 0.25th and 0.75th quantiles. *, **, and *** denotes significance at 10%, 5%, 1%, respectively.

A.S. Yang / Emerging Markets Review xxx (2016) xxx–xxx

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Investment trust company

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A.S. Yang / Emerging Markets Review xxx (2016) xxx–xxx

holidays in June, followed by holidays in January in all models and a two-day holiday at a low quantile. Furthermore, investors display significantly negative sentiments on holidays during Thursdays at high quantiles and on winter holidays at low and high quantiles. Investors present the strongest sentiment at low quantiles and a decreasing sentiment at medium and high quantiles. Buying orders of trust companies and selling orders of dealers are tested for symmetry. The results indicate significant asymmetry. Thus, trust companies and dealers undergo significant sentiment changes at high quantiles at the 0.9th and 0.75th quantiles. 4.3. Individual investors, QFII, investment trust companies, and dealers results Table 3 shows the individual investors' panel with more significantly positive short selling activities than margin purchase activities in all models. Short selling activities are highest at low sentiment quantiles, which gradually decrease toward medium and high quantiles. Margin purchase activities are highest at low quantiles, followed by high and medium quantiles. Holidays in January show positive significant sentiments in low and medium quantiles. Holidays in June and two-day holidays show significantly positive sentiments for low-, mid-, and high-sentiment quantiles. However, holidays on Thursdays indicate a significantly negative influence at the 0.9th quantile. Winter holidays typically indicate insignificant influence on investor sentiment. Symmetric tests on margin purchases and holidays on Thursdays show significant asymmetry between the 0.1st and 0.9th quantiles. Therefore, margin purchases and holidays on Thursdays have significant sentiment changes at the 0.9th quantile. Table 3 presents qualified foreign institutional investors with significant buy- and sell-side activities in all models across various quantiles. Qualified foreign institutional investors adopt considerable buy-side activities at low- and mid-sentiment quantiles and equivalent buy- and sell-side activities at high-sentiment quantiles. Qualified foreign institutional investors display great sentiment at low sentiment quantiles, with gradual sentiment decrease in mid- and high-sentiment quantiles. Furthermore, holidays on Thursdays exhibit significantly negative sentiments at low and high quantiles. By contrast, holidays in June show significantly positive sentiments at low, medium, and high quantiles. However, winter holidays exhibit significantly negative sentiments at a medium quantile and significantly positive sentiments at a higher quantile. Holidays in January and two-day holidays show insignificant sentiments. Symmetric tests indicate that the buy- and sell-side activities of qualified foreign institutional investors have significantly positive and significantly negative asymmetries between the 0.1st and 0.9th quantiles. The asymmetries indicate the presence of qualified foreign institutional investors with significant buy- and sell-side strategic changes at high quantiles (0.9th quantile). Holidays on Thursdays show significantly negative asymmetries at the 0.25th and 0.75th quantiles. This result indicates significantly negative sentiments toward holidays on Thursdays at low symmetric quantiles. Investment trust companies in Table 3 indicate significantly positive and great buy-side activities at low and high sentiment quantiles with constant buy-side activities at a mid-sentiment quantile. Trust companies further display the most significantly negative sell-side activities at low sentiment quantiles with continuous less significantly negative sell-side activities at mid- and high-sentiment quantiles. Trust companies demonstrate significant sell- and buy-side activities at the 0.1st and 0.2nd and at the 0.5th through the 0.9th quantiles. Holidays in June elicit significantly positive sentiments in low and high quantiles. Winter holidays have significantly positive sentiments in high quantiles, whereas holidays on Thursdays elicit significantly negative sentiments at low and high quantiles. However, holidays in January indicate significantly positive sentiments at low quantiles only. Two-day holidays elicit show insignificant sentiments across all models of all quantiles. Symmetric tests indicate that investment trust buy- and sell-side activities have significantly positive and negative asymmetries between the 0.1st and 0.9th quantiles and between the 0.25th and 0.75th quantiles. Therefore, investment trust companies may adopt significant buy- and sell-side activity changes at high quantiles. Table 3 presents the QR results for dealers. Dealers participate more in buy-side than in sell-side activities at low quantiles with equivalent buy-side and sell-side activities in mid quantiles, followed by high sell-side activities in high quantiles. June holidays and two-day holidays generate significantly positive sentiments in most low, mid, and high quantiles. Holidays on Thursday show significant negative sentiments in low, mid, and high quantiles, whereas holidays in winter show significantly negative sentiments at the 0.8th quantile. By contrast, holidays in January elicit insignificant sentiment. Symmetric tests show that the buy- and sellside activities of dealers have significantly positive and significantly negative asymmetries between the Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

A.S. Yang / Emerging Markets Review xxx (2016) xxx–xxx

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0.1st and 0.9th and between the 0.25th and 0.75th quantiles, respectively. For dealers, holidays in June elicit significantly negative asymmetries between the 0.1st and 0.9th quantiles and between the 0.25th and 0.75th quantiles. Therefore, dealers may adopt significant changes in their buy- and sell-side trading strategies and sentiments toward June holidays. 4.4. Quantile slope equality test results Table 4 shows the quantile slope equality test results across various investor panels. The results include the 0.25th, 0.50th, and 0.75th quantile parameters. On the one hand, majority of significant inequalities for the stock exchange panel occurs in the 0.75th quantile estimates, followed by the 0.25th quantile estimates when compared with the median estimate at the 0.50th quantile. On the other hand, among the remaining investor sub-panels, the most significant inequality is observed at the 0.25th quantile compared with the median estimate at the 0.5th quantile. These sub-panels include stock exchange, individual investor, qualified foreign institutional investor, trust panels, and dealer panels. Significant inequality on the market panel is observed for trading volume, buy–sell imbalances, and holidays on Thursdays. Individual investor panels indicate significant inequality on margin purchases, whereas qualified foreign institutional investors display significant inequality on buy-side and sell-side activities and winter holidays. Trust company panels demonstrate significant inequality in sell-side activities and holidays in January, whereas dealers exhibit significant inequality in buy-side activity with holidays in June. Therefore, changes in investor sentiment for trading activities and holiday sentiments occur mostly in low-sentiment quantiles. 5. Conclusion This study examines investor sentiments on trading activities and latent holiday preferences of the stock exchange, institutional investors, and individual investors. We identify changes in investor sentiments by extending the standard conditional mean regression method to explore their development, direction, and duration. A similar method is used to identify the existence of sentiments across stock return quantiles. The QR method provides insights into investor sentiment development by uncovering irrational decision-making resulting from latent holiday preferences and by providing a benchmark for rational decision-making on trading activities. The QR results identify individual investors as having the most significant sentiments in margin purchase and short selling for holiday buy- and sell-side activities in mood changes. Qualified foreign institutional investors and dealers demonstrate similar buy-side sentiments and are followed by investment trust companies. Dealers have the most significant sell-side sentiments, followed by qualified foreign institutional investors, and investment trust companies. On the one hand, qualified foreign institutional investors and investment trust companies focus on buy-side activities. On the other hand, dealers and individual investors focus on sell-side activities. The majority of investors displayed significantly negative sentiments, such as interruption from trading participation for holidays on Thursdays. Holidays in June elicit significantly positive sentiments, including detachment from trading participation. Market trading in terms of trading volume, buy–sell imbalance, and investment trust companies found significantly positive sentiments toward winter holidays because of detachment from trading activities. Individual investors and dealers have significantly positive sentiments toward two-day holidays as a long weekend because of trading detachment. Furthermore, holidays in January and in winter elicit significantly positive and significantly negative sentiments only for the stock exchange panel as detachment and interruption from trading. Majority of significant changes in sentiment are found in low quantiles and in high quantiles, whereas medium quantiles show stable sentiments for most investors. We also identify investors' mood variations across holidays in different calendar days. Investors generally exhibit negative mood toward disruption during weekdays when investment decision and strategies are executed with expectations of future stock earnings. However, investors display a positive mood toward twoday holidays starting from the latter days of the week. Thus, good mood at extended weekends is often observed to cause reorganized investment strategies and decision-making. Investors also enjoy holidays in June1 with optimism for mid-year investment strategy and decision review. This finding corresponds to 1

Holidays in June generally refer to the Dragon Boat festival, which is a cultural holiday for most Asian countries.

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Category

Panels

Stock exchange

Vol BS Mm Ss Qfiibuy Qfiisell Trustbuy Trustsell Dealerbuy Dealersell January HolThur. Hol2day HolJune Holwinter

Individual investors Institutional investors

Holidays

Stock exchange

Individual Investors

Qualified foreign institutional investors

Investment trust companies

Dealers

0.25 vs 0.5

0.5 vs 0.75

0.25 vs 0.5

0.5 vs 0.75

0.25 vs 0.5

0.5 vs 0.75

0.25 vs. 0.5

0.5 vs 0.75

0.25 vs 0.5

0.5 vs 0.75

−0.0001 −1.22E−06** 0.002* −0.002 0.002*** −0.002*** −0.001** −0.0006 0.0003 0.001 −0.0001 −0.0007 0.0002 −0.001* 4.09E−05

−6.31E−05 1.26E−08 0.004*** −0.001 0.002*** −0.002*** −0.001*** −0.001** −0.001 0.003*** 0.0002 0.001* −0.0004 −0.0007 −0.001

0.003** 0.0008 – – – – – – 0.0005 −0.001 0.0001 7.81E−05 −0.001

0.0006 0.003 – – – – – – 0.0002 0.0007 −0.0002 −0.0008 −3.39E−05

– – 0.002*** −0.001*** – – – – −1.09E−05 −0.001 0.0002 −0.0009 −0.002*

– – 0.001* −0.001** – – – – 3.94E−05 0.001 −0.0001 −0.0007 −0.001

– – – – 0.0002 −0.001** – – 0.0007** −0.001 4.04E−05 0.0007 −0.0006

– – – – −0.002*** −0.001 – – 0.0001 0.0005 0.0004 −0.0003 −0.001

– – – – – – 0.001** −0.0005 −8.44E−07 −0.0005 −1.42E−05 −0.003*** −0.0007

– – – – – – −2.41E−05 0.001** 1.05E−05 −0.001 0.0001 0.001 0.0008

Note: The quantile slope tests are conducted between 0.25th and 0.50th, and between 0.50th and 0.75th quantiles. *, **, and *** denotes significance at 10%, 5%, 1%, respectively.

A.S. Yang / Emerging Markets Review xxx (2016) xxx–xxx

Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

Table 4 Quantile slope equality test.

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that of Bergsma and Jiang (2016) and Yuan and Gupta (2014) on their positive sentiment toward cultural holidays such as Dragon Boat festival in June. The majority of investors show insignificant mood reactions toward holidays in January and in winter. This finding corresponds to that of Bergsma and Jiang (2016) on the absence of tax loss selling hypothesis in Asian countries that adopt the lunar calendar for business transactions and activities. By contrast, stock exchange and investment trust companies exhibit significantly positive mood reactions toward holidays in January and in winter. Investors most prefer holidays in June and least prefer holidays on Thursdays,2 whereas only dealers and individual investors prefer two-day holidays. This finding corresponds with that of Chung et al. (2014), who found negative sentiment toward unlucky numbers, such as the number 4, in most Asian countries. The majority of investors display a lack of significantly positive or negative mood toward holidays in January and in winter, except for investment trust companies with positive mood and stock exchange with mixed sentiments. However, qualified foreign institutional investors only indicate negative mood toward holidays on Thursdays among different classifications of holidays. The results also provide solutions to potential investor sentiments, specifically on investor categories, trading activities, and holiday cycles. Such assistance can facilitate related market operation schedules, trading timing, and holiday sentiment prediction and development. Thus, the government can construct flexible holiday trading and offer effective opening hours for stock exchange participants. This approach will reduce the negative effects of trading interruption and increase the positive effects of trading detachment. Stock exchange-listed companies with strategic stock price management can ease the effects of holiday time pressure on trading decisions, whereas individual and institutional investors can increase investment options or channels for rational investing behavior and decision making. This increase of options or channels minimizes or eliminates the negative effects of holidays. Overall, our results could serve as a reference for international emerging markets on the significant influences of cultural holidays, namely, the Chinese lunar calendar adopted in greater China region where the majority of Chinese ethnic groups dominate important commercial activities. Cultural holidays not only significantly influence investor sentiment; they also shed light on the degree of sentiment for various market participants. New insights into the influence of lunar calendar for decision making are provided for regional and international investors interested in investing in Asia. Acknowledgements The author is grateful to the Editor and anonymous referees. The author thanks the Ministry of Science and Technology (MOST) of Taiwan, R.O.C., formally National Science Council (NSC), for financially supporting this research under the grant NSC-102-2410-H-006-023. References Abraham, A., Ikenberry, D.L., 1994. The individual investor and the weekend effect. J. Financ. Quant. Anal. 29, 263–277. Al-Khazali, O., 2014. Revisiting fast profit investor sentiment and stock returns during Ramadan. Int. Rev. Financ. Anal. 33, 158–170. Andrade, S.C., Vidhi, C., Fuerst, M.E., 2013. “Sell in May and go away” just won't go away. Financ. Anal. J. 69, 94–105. Areni, C.S., 2008. (Tell me why) I don't like Mondays: does an overvaluation of future discretionary time underlie reported weekly mood cycles? Cognit. Emot. 22, 1228–1252. Ariss, R.T., Rezvanian, R., Mehdian, S.M., 2011. Calendar anomalies in the Gulf Cooperation Council stock markets. Emerg. Mark. Rev. 12, 293–307. Badshan, I.U., 2013. Quantile regression analysis of the asymmetric return-volatility relation. J. Futur. Mark. 33, 235–265. Baker, M., Wurgler, J., Yuan, Y., 2012. Global, local, and contagious investor sentiment. J. Financ. Econ. 104, 272–287. Bampinas, G., Fountas, S., Panagiotidis, T., 2015. The day-of-the-week effect is weak: evidence from the European real estate sector. J. Econ. Financ. 1–19 (Online first). Barber, B.M., Odean, T., 2000. Trading is hazardous to your wealth: the common stock investment performance of individual investors. J. Financ. 55, 773–806. Barber, B.M., Lee, Y.-T., Liu, Y.-J., Odean, T., 2009. Just how much do individual investors lose by trading? Rev. Financ. Stud. 22, 609–632. Basher, S.A., Sadorsky, P., 2006. Day-of-the-week effect in emerging stock markets. Appl. Econ. Lett. 13, 621–628. Baur, D.G., Schulze, N., 2009. Financial market stability — a test. J. Int. Financ. Mark. Inst. Money 19, 506–519. Bergsma, K., Jiang, D., 2016. Cultural new year holidays and stock returns around the world. Financ. Manag. 45, 3–35. Berument, M.H., Dogan, N., 2012. Stock market return and volatility: day-of-the-week effect. J. Econ. Financ. 36, 282–302. Bhana, N., 1994. Public holiday share price behavior on the Johannesburg stock exchange. Invest. Anal. J. 39, 45–49. 2

Holidays on Thursdays generally indicate a latent Chinese superstition toward the number 4 for negative sentiment.

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Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004

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Please cite this article as: Yang, A.S., Calendar trading of Taiwan stock market: A study of holidays on trading detachment ..., Emerg. Mark. Rev. (2016), http://dx.doi.org/10.1016/j.ememar.2016.08.004