宗教迷信和股市的相關性─以台灣為例 - 政大學術集成

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(1)國立政治大學財務管理學系碩士學位論文. 宗教迷信和股市的相關性─以台灣為例. 治Superstition and Stock Relationship between政 Religious 大. 立. Market—Evidence from Taiwan. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. C. i n U. v. h e n g c冠h i男博士指導教授：周研究生：吳保婷撰. 中華民國 107 年 06 月 DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(2) 摘要本文欲探討宗教迷信對於股市的影響。過去許多宗教相關文獻多以西方宗教為主，由於東方傳統宗教多元且不易尋找替代變數，故較缺乏類似研究。因此，本研究以台灣股市為例，使用鬼月以及鬼門開當天為宗教迷信之替代變數，並研究其與股市交易量及報酬率之關係。由於宗教迷信與投資人情緒已在過去文獻中被證實與股市有關聯，本研究資料包含了兩項情緒相關指標：公司規模及年齡。此外，因不同產業在鬼月期間受到的影響各異，本研究進一步將資料劃分成十種不同產業欲探討何種產業會受到較大衝擊。研究結果發現單以事件日進行迴歸，在鬼月及鬼門開當天均出現顯著的交易. 政治大. 量下跌，其中，由於鬼門開當日更具代表性，下跌幅度更甚，平均下跌幅度為 350. 立. 千股，鬼月平均下跌幅度則為 208 千股。並且，此迷信導致規模較大的公司會有. ‧ 國. 學. 更顯著的下跌，在鬼門開當天及鬼月的下跌幅度分別約為 323 千股及 54 千股；雖研究結果指出越年輕的公司越易受到此迷信影響，然而與其關聯遠小於公司規. ‧. 模。至於產業部份，金融相關行業受到鬼月的影響最為顯著且甚大，鬼月區間跌. y. Nat. 幅平均高達 1150 千股，其次為通訊業，其餘產業則沒有顯著影響。. sit. 然而，不同於交易量，報酬率在鬼門開及鬼月區間並沒有非常顯著的下降。. n. al. er. io. 鬼月區間的報酬率下跌僅有 0.106%，且在考量規模及公司年齡之後並無顯著變. i n U. v. 化。而過去有文獻指出異常交易量會導致在其後一定區間內導致報酬率異常，因. Ch. engchi. 此本研究進一步探討鬼月結束之後五天及一個月之報酬率是否有顯著下跌。研究結果指出，加入公司規模及年齡後，在鬼月結束五天內的報酬率顯著下跌 0.323%，在其後一個月內下跌幅度則為 0.135%，顯示此宗教迷信確實對於股市造成明顯的影響，證實投資人不理性行為的存在，並且，來自宗教的迷信及其可能導致的投資人情緒低迷並不會持續很久。本研究填補過去文獻對於東方宗教迷信較少探討之缺口。. 關鍵字：行為財務學、宗教、迷信、投資人情緒、鬼月. I. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(3) Abstract This study investigates whether religion in Taiwan would lead to superstitious beliefs in seventh month in the lunar calendar, called “ghost month”, and results in trading volume anomaly of investors and anomalies in stock returns. Past studies have proved that religious beliefs and superstition have influence on securities markets. This paper is motivated by the link between religion and superstition but focuses on Taiwan. Since most existing research studies focus on Western religions, this paper aim at filling this gap. Empirical results suggest that stock trading volume significantly drops in the first day of seventh month in Chinese lunar calendar and the whole month, with the amount dropping at the first day larger, at 350 thousand shares and 208 thousand shares. 政治大 superstition of this period, yet firm age shows only slight influence. Larger firms have 立. respectively. Besides, larger and younger companies are influenced more by the astonishing significant decrease in trading volume, especially at the first day with the. ‧ 國. 學. amount of 323 thousand shares, and 54 thousand shares in the whole month. Furthermore, dividing all listed companies into ten industries, financial industry is. ‧. significantly influenced the most with a decline of 1150 thousand shares, followed by. y. Nat. telecommunication. As for returns, there is no remarkable effect by “ghost month”.. sit. Therefore, the research further investigates the returns after the end of ghost month, and. al. er. io. find that there is a significant negative stock return in subsequent 5-day period (-0.323%). v i n C hout that religious beliefs The results of this research point e n g c h i U and superstition in eastern countries do have influence on stock market, confirming that investors’ irrational n. and in subsequent one-month period (-0.135%).. behavior do exist, yet those superstition and possible investors’ low sentiment do not last for a long time.. Keyword : behavior finance, religion, superstition, investor sentiment, ghost month. II. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(4) Contents 1.. Introduction ........................................................................................................ 1. 2.. Literature Review............................................................................................... 8 2.1 Religion ........................................................................................................ 8 2.2 Superstition ................................................................................................ 10 2.3 Sentiment ................................................................................................... 12 2.4 Chinese Lunar Calendar ............................................................................. 15. 3.. 政治大. Research Design............................................................................................... 18. 立. 3.1. Sample Selection....................................................................................... 18. ‧ 國. 學. 3.2 Independent Variables — Trading Volume and Stock Return ................... 18. ‧. 3.3 Dependent Variables .................................................................................. 19. y. Nat. io. al. er. Empirical Results ............................................................................................. 24. v i n 4.1 Descriptive StatisticsC .................................................................................. 24 hengchi U n. 4.. sit. 3.4 Regression Design ..................................................................................... 20. 4.2 Regressions of Trading Volume ................................................................. 26 4.3 Regressions of Stock Return ...................................................................... 27 5.. Conclusion ....................................................................................................... 32. References ................................................................................................................ 34. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(5) Tables Table 1 The Period of Ghost Month ............................................................................ 22 Table 2 Industry Classification .................................................................................... 23 Table 3 Summary Statistics ......................................................................................... 25 Table 4 Regressions on Trading volume ..................................................................... 29 Table 5 Regressions on Stock Return .......................................................................... 30 Table 6 Regressions on Stock Return of Subsequent Time Period ............................. 31. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(6) 1. Introduction This study investigates whether religion in Taiwan would lead to superstitious beliefs in seventh month in the lunar calendar and results in trading volume anomaly of investors and anomalies in stock returns. Past studies have confirmed that human beings have irrational behavior affected by many possible factors such as Friday the Thirteenth, numerical superstition, investor sentiments or even the weather, and then would reflect on the securities market. Kolb and Rodriguez (1987) show that the returns for Friday the Thirteenth have been. 治政 significantly lower than the returns for all other Fridays大 over a long period. Hirshleifer 立 and Shumway (2003) find that from 1982 to 1997, across twenty-six countries, daily ‧ 國. 學. stock returns are affected to a large extent by morning sunshine. Kamstra, Kramer, and. ‧. Levi (2003) suggest that the season affects investors’ risk preferences and investment. y. Nat. behavior. Chung, Darrat and Li (2014) focus on market responses to days that are. io. sit. superstitiously regarded in the Chinese cultural as either lucky or unlucky and suggest. er. that inauspicious days, especially day 4 and Friday the 13th, generally exhibit higher. al. n. v i n C h the stock return stock returns. Haggard (2015) examines impact of days with “lucky” engchi U. numbers in markets predominated by Chinese participants, and demonstrates a “lucky” number date trading strategy for Shenzhen market that generates risk-adjusted returns in excess of the market return. Hirshleifer, Jian, and Zhang (2016) show that the frequency of lucky numerical stock listing codes exceeds what would be expected by probability, due to superstition effects; and that companies with “lucky” number listing codes trade at a premium at beginning that disappears within three years after IPO. In addition, Modern day economic sociologists and religion scholars have explored the role religion plays in the economy (e.g. Barro and McCleary (2003); Guiso, 1. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(7) Sapienza and Zingales (2003); Stulz and Williamson (2003)). Previous research has confirmed a positive correlation between religiosity and an individual’s risk aversion. Take Miller and Hoffmann (1995) for example, they report a negative correlation between religiosity and individuals’ attitudes towards risk and danger. Similarly, Osoba (2003) uses individual-level panel data from the Panel Study of Income Dynamics to show that individuals who are risk-averse attend church more often than risk-seeking individuals. Prior studies also show that religion plays a role in financial market. Hilary and Hui (2009) find that religion has influence on corporate investment decisions and chief executive officers’ decision. Kumar, Page and Spalt (2010) indicate that religion. 治政大investors’ portfolio choices, prompted gambling attitudes impact corporate decisions, 立 and stock returns.. ‧ 國. 學. My idea is motivated by the link between religion and superstition but focuses on. ‧. Taiwan. Most existing research studies focus on Western religions, including. sit. y. Nat. Christianity, Catholicism, Judaism, etc. It is much easy to find alternative variables. io. er. since there are a variety of disclosed data of them, for example, church attendance, churches and church membership files, religious participation in every county in the. al. n. v i n C h in the county Uover the total population of the US, the number of religious adherents engchi county, numbers of church establishments in each state, the collection of taxes dedicated to church uses, or ratios like regions with higher concentrations of Catholics relative to Protestants, cities with higher percentages of Jewish residents (Barro and McCleary (2003), Loughran and Schultz (2003), Hilary and Hui (2009), Kumar, Page and Spalt (2010), Golombik, Kumar and Parwada (2011)). By contrast, there are some characteristics of Chinese religion that is totally different from Western ones. First, the development of Chinese traditional culture has a tendency to blend Taoism and Buddhism together, which two are predominant in Taiwan, and the 2. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(8) phenomenon makes it harder to distinguish them clearly. Second, there are a great number of temples around every city, since that people believe there are many gods and each of them is in charge of a specific business. Therefore, diverse kinds of temples can be seen in countries with some worship a particular god and some worship several gods, and counting the number of them would be a meaningless thing. Third, unlike Christianity, there is no need for people to be baptized, also the donation is anonymous and private. Thus, no membership files can be found. In addition, people tend to go to a temple to pray whenever they have trouble or bad feelings, no matter day and night. It is impossible to calculate attendance or participation in countries sharing same. 治政 Chinese culture. Due to these features, it has not been大 widely studied in the finance 立. literature because of difficulties in finding alternative variables. However, there are. ‧ 國. 學. some other symptoms that can possibly link Chinese culture and financial markets, with. ‧. the confirmation that some superstitions and holidays are affected by religious beliefs.. sit. y. Nat. Lip (1992) points out that Chinese culture is especially relevant to Chinese festival. io. er. date, and Chinese lunar calendar plays a markedly important role when business management make business decisions, as the existence of psychological opinions,. n. al. customs, and superstition.. v i n Ch Furthermore, studies have e n grecent chi U. already proved that. individual investors in Taiwan have irrational behavior lead by superstition. Bhattacha, Kuo, Lin and Zhao (2014) point out that, on the Taiwan Futures Exchange, individual investors submit disproportionately more limit order’s price at “8” than at “4”. The study reveals that the main reason for losses suffered by individual investors is numerological superstition. Moreover, firms with lucky listing codes on the Taiwan Stock market are traded at a premium compared to the firms with unlucky listing codes (Weng and Huang (2017)). Ke, Chen, Lin and Liu (2016) indicate that investors in the Taiwan Stock Exchange tend to avoid number 4. On the contrary, institutional investors 3. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(9) are not affected by numerical superstition. Also, the results support the notion that informed traders buy and sell more (less) actively the stocks with a lower (higher) frequency of prices ending with 4. Besides the evidence of superstition above mentioned, Barber, Lee, Liu and Odean (2008) suggests that individual investors in Taiwan are aggressive that the aggregate portfolios of individuals suffer an annual performance penalty of 3.8 percentage points. Individual investor losses are equivalent to 2.2% of Taiwan’s gross domestic product or 2.8% of the total personal income, which can be contributed to their aggressive orders. Certainly, individual investor accounts for a large proportion in stock market in Taiwan.. 治政大some taboos. “Ghost month” In Taiwan, festivals are based on lunar calendar, so do 立. must be the most famous inauspicious month in Taiwan culture due to religiosity, which. ‧ 國. 學. can be briefly viewed as Friday the Thirteenth, an unlucky day in western superstition. ‧. due to religious belief, but last for a month. The definition of “Ghost month” is the. sit. y. Nat. seventh month in the lunar calendar. In Chinese folk religion, it is believed that the. io. er. Kings of the Underworld, who are in charge of torture and punishment of everyone's afterlife, would give the tortured souls a break for a whole month every year. The spirits. al. n. v i n are given a month-long break to C roam in search of food, money, entertainment h ethenworld gchi U and what not. Therefore, at the Ghost Festival, fifteenth of ghost month, families and businesses need to prepare offerings and burning ghost paper for deceased, and the first day of the month called “Open of the gate to the Underworld”, named as “Kuei Men. Kai” in Chinese. There are many taboos during the whole month, such as avoiding taking pictures and whistling at night, going swimming and leaning against the wall, etc. Further, weddings, trips, business deals and other types of ceremonies are all avoided taking place over the period. Thus, whether individual investors will avoid trading stocks aggressively during this month, especially the first day of the month, is 4. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(10) an interesting topic worth to investigate. Ke, Chen, Lin and Liu (2016) mention that investors appear to be more likely to avoid unlucky number 4 in the following four conditions: when the tick size becomes smaller, when it is one week before Chinese New Year, when it is the seventh month in the lunar calendar, and when it is in a bear market. This paper has already contained the idea briefly. There has hitherto been little evidence about how Chinese religious superstition affects stock market. Existing research studies are scant in the area of the relationship between religious superstition in traditional Chinese culture and stock market, with. 治政 most of studies focus on Western religion and this study大 looks to fill this gap. 立. In this paper, I focus on the irrational influence, if any, of the religious superstition. ‧ 國. 學. of market investors on the trading volume and return of public companies on the Taiwan. ‧. Stock Exchange (TWSE). The null hypothesis is that trading volume and return from. sit. y. Nat. exchanges in Taiwan have not been systematically affected by religious superstition. io. er. against the alternative hypothesis that trading volume also return have been systematically affected by religious superstition. Rejection of the null hypothesis. al. n. v i n Cmarkets supports the view that security influenced by investor h e n garecsystematically hi U. psychology, superstition and sentiment coming from inauspicious day and argues for models of asset-pricing. In addition, rejection of the null hypothesis supports the hypothesis that financial markets are, to some degree, irrational and, thereby tossing doubt on the hypothesis that security markets reflect nothing but economic information. To further analyze this effect, I consider three independent variables—firm size, age and industry—with the first two variables referring to Baker and Wurgler (2006), which discuss the stock performance when market is in low or high investor sentiment, due to the fact that people tend to have low sentiment in inauspicious day. As regards the 5. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(11) industry, I use the Industry Classification Benchmark for the classification referring to Chen, Chen and Lee (2013). The indices are: basic materials (MATS), consumer goods (GDS), consumer services (SVS), financials (FIN), health care (HEA), industrials (INDU), oil & gas (OIL), technology (TECH), telecommunications (TELE), and utilities (UTIL). More specifically, my study consists of the following steps of analysis : 1. Examine whether trading volume would be significantly affected by the religious superstition of “ghost month”, especially the first day of the month and therefore brings a negative stock return during this period. 2.. 治政大 with characteristics of Take account of firm size and age, whether corporation 立 larger size and/or young age would be affected more by this superstition.. ‧ 國. 學. 3. Classify firms into ten different industries and test which one would be influenced. ‧. more by the superstition.. sit. y. Nat. My empirical results are briefly summarized as follows. First, on the TWSE, I find. io. er. that stock trading volume significantly drops in the first day of seventh month in Chinese lunar calendar and the whole month, and the amount dropping at the first day. al. n. v i n is larger, at 350 thousand sharesCand shares respectively. Second, larger h e208nthousand gchi U. and younger companies are influenced more by the superstition of this period, yet firm age shows only slight influence. Larger firms have astonishing significant decrease in trading volume, especially at the first day with the amount of 323 thousand shares, and 54 thousand shares in the whole month. Third, dividing all listed companies into ten industries and then choosing health care as benchmark, due to the fact that this industry is affected least by religious superstition after regressing, to see which industry would be affected more, financial industry was significantly influenced the most with a drop of 1150 thousand shares, followed by telecommunication. As for returns, there is no 6. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(12) remarkable effect by “ghost month”. Therefore, the research further investigates the returns after the end of ghost month, and find that there is a significant negative stock return in subsequent 5-day period (-0.323%) but no significance in subsequent onemonth period (-0.135%). The rest of the paper proceeds as follows. Chapter 2 discusses the literature and hypothesis development. Chapter 3 describes my data sources and sample construction. The evidence for religious superstition and its association with investment performance are presented in Chapter 4. The conclusion is in Chapter 5.. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. 7. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(13) 2. Literature Review 2.1 Religion Culture is often considered to influence economic outcomes by affecting personal behavior and traits, for instance, honesty, thrift, willingness to work hard, and openness to strangers. Religion is one important dimension of culture. Beginning with Weber’s (1905) seminal study, religiosity affects economic growth positively, a large literature has emerged disclosing ties between economic development and religion.. 政治大 link between economic growth 立 and religion. The literature may be traced back to Smith. At the macroeconomic level, a literature has emerged that seeks to understand the. ‧ 國. 學. Anderson (1988), in his analysis of the Wealth of Nations, indicates that Smith attempted to show how the self-interest of the clergy and political leaders interacted. ‧. with economic growth and development. More recently, Stulz and Williamson (2003). sit. y. Nat. find that a country’s principal religion helps to predict the cross-sectional variation in. n. al. er. io. credit or rights better than does a country’s openness to international trade, its language,. i n U. v. its income per capita, or the origin of its legal system. They also demonstrate that. Ch. engchi. religion is an important predictor of how countries enforce rights. Barro and McCleary (2003) show that macroeconomic development has a negative correlation with church attendance across countries, but they report that economic growth responds positively to the extent of religious beliefs, notably beliefs in hell and heaven. Guiso, Sapienza, and Zingales (2003) suggest that, across countries, religious beliefs are related to ‘‘good’’ economic attitudes, where good is defined as conducive to higher per capita income and growth. Religious beliefs also affect finance in firm level. Hilary and Hui (2009) examine. 8. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(14) how corporate culture influences firm behavior and find that firms located in counties with higher levels of religiosity display lower degrees of risk exposure, as measured by variances in equity returns or returns on assets. They exhibit a lower investment rate and less growth, but generate a more positive market reaction, when they announce new investments. Also, chief executive officers are more likely to join a firm with a similar religious environment as in their previous firm when they switch employers. Prior research has shown a positive correlation between risk aversion and individual religiosity. Miller and Hoffmann (1995) show a negative correlation between religiosity (measured by church attendance and a subjective rating of religion’s importance in. 治政大danger. Osoba (2003) uses one’s life) and self-reported attitudes towards risk and 立. individual-level panel regressions to show that risk-averse individuals attend church. ‧ 國. 學. more often than risk-seeking individuals. Specifically, he regresses a self-reported. ‧. measure of church attendance with a panel study of income dynamics (PSID) risk-. sit. y. Nat. avoidance indicator and various control variables. The risk-avoidance indicator is based. io. er. on multiple individual decisions, such as whether an individual fastens his or her seat belt, is covered by medical or car insurance, smokes, or maintains a financial cushion.. n. al. Osoba (2003) reports that. v i n riskCavoidance church attendance h e n g and chi U. are consistently. positively correlated at the 1% level. Religion, as a motivator for investment decision making, has been explored by Kumar, Page and Spalt (2010) who use the gambling channel to validate that religion influences investors’ portfolio choices, corporate decisions, and stock returns. The authors show that institutional investors domiciled in Catholic areas have a stronger preference for lottery type stocks when compared to other areas. The main local religion could influence local cultural values and norms and consequently affect the financial and economic decisions of individuals located in that region, even if they do not personally adhere to the dominant local faith. Further, these 9. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(15) financial and economic decisions could aggregate and generate market-wide forces that can potentially influence aggregate financial market out comes. However, a wide range of researches about the relation between religion and financial development are focus on western countries rather than eastern ones. Natural characteristics of eastern religions make it difficult to investigate the influences on economic growths and securities markets. For example, temples are all around in the cities and people attend them for many reasons and do not restrict in specific day. Furthermore, after centuries of development, it is hard to distinguish folk beliefs, especially Taoism and Buddhism, which are predominant in traditional Chinese culture.. 治政 There are no brief rules in Chinese religious beliefs. 大立 ‧ 國. 學. 2.2 Superstition. ‧. The presence of superstition in stock markets should not be surprising since research on behavioral finance documents the significance of human psychology in determining. y. Nat. er. io. sit. stock returns ( Hirshleifer and Shumway (2003); Kamstra, Kramer, and Levi (2003); Yuan, Zheng, and Zhu (2006)). Superstition are beliefs that run against to rational. al. n. v i n thought or are inconsistent withC known laws of nature. Actually, previous studies had hengchi U already shown some superstitions result in investors’ irrational behavior in securities markets. It is noticeable that negative superstitious beliefs can result in severe economic losses. The long-term cost to the airline industry alone owing to superstitious beliefs is substantial, that estimated 10,000 fewer people in the US fly on Friday the 13th. The lost business due to the fear of flying and declined daily business transactions in general on this“unlucky” day is $800–$900 million every time the date occurs ( Shields (2008) ). This Friday the Thirteenth effect also exhibits in securities market. Kolb and 10. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(16) Rodriguez (1987), examining all trading days between July 1, 1962, and December 31, 1985, which includes thirty-nine occurrences of Friday the Thirteenth, indicate that the returns for Friday the Thirteenth have been significantly lower than the returns for all other Fridays over a long period. Coutts (1999), examining the FT-30 index over the period 1935-1994, find a higher mean return on Friday 13th as compared to all other Fridays in UK. Numerical superstition has influence on individuals’ decisions in a wide range of way. For instance, predominantly featuring number seven to be lucky in Western cultures, the Ritz Carlton Hotel in New York offered a July 07, 2007 wedding package with a. 治政 reception for 77 guests, a seven-tier wedding cake, seven大 Tiffany diamonds for the bride, 立 and a seven-night honeymoon at any Ritz Carlton in the world for $77,777 (Block and. ‧ 國. 學. Kramer 2009). The economic boon of lucky numbers is not limited to the US and. ‧. Western cultures. For example, the opening ceremony of the Beijing 2008 Summer. sit. y. Nat. Olympic Games officially started at 8:08 p.m. on August 8, 2008 in China. The number. io. er. 8 is considered lucky in Chinese cultures; unbelievably, at a government auction of license plates in Guangzhou, China, the most expensive plate (AC6688) went for. al. n. v i n 80,000 yuan, which is roughlyC11 per capita income. For the h times e n gthec country’s hi U. preference of lucky number, consumer product advertisements in China disproportionately include 8 and exclude 4 (Simmons and Schindler 2003), and Taiwanese consumers are more likely to purchase and are willing to pay more money for a product with a smaller but “lucky” number of units contained in the package (Block and Kramer 2009). As for securities market, Brown and Mitchell (2008) study trading on the Shanghai and Shenzhen stock exchanges, which historically have been relatively divided along cultural lines, during 1994 and 2002. They document that for much of the sample period, 11. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(17) the prices of A-shares, mostly held by domestic organizations or individuals, traded on the Shanghai stock exchange were more than twice as likely to end in 8, than 4. Similarly, for A-shares traded on the Shenzhen stock exchange a preference for 8 was found. The preference for 8 was much weaker for B-shares, which are largely held by foreigners. Hirshleifer, Jian, and Zhang (2014) test newly listed firms in China from 1991 through 2013 and document that firms in China going public have an abnormally high proportion of lucky listing codes and an abnormally low proportion of firms with unlucky listing codes, and that lucky listing codes are associated with lower post-IPO abnormal stock returns. Bhattacha, Kuo, Lin and Zhao (2014) point out that, on the. 治政 Taiwan Futures Exchange, individual investors submit大 disproportionately more limit 立. order’s price at “8” than at “4”. Chung, Darrat and Li (2014) examine the potential. ‧ 國. 學. effect of superstitious beliefs on stock trading in four Asian- Pacific countries with deep. ‧. Chinese cultural heritage (China, Hong Kong, Singapore, and Taiwan) by using daily. y. Nat. data over 2 January 1991 to 30 December 2011 and suggest that unlucky days,. n. al. er. io 2.3 Sentiment. sit. particularly day 4 and Friday the 13th, generally exhibit higher stock returns.. Ch. engchi. i n U. v. Sentiment and moods have already been proved to affect investors’ financial decisions, which are irrational. Further, individual investors are more likely to deviate from rational valuation of securities than institutional investors (Cohen, Gompers, and Vuolteenho (2001)). Behavioral economic studies reveal that negative sentiment driven by bad mood and anxiety affects investment decisions and may hence affect asset pricing. Sentiment can be definite by many factors, including weather, holidays, season, sports or disasters, besides, the superstitions come from culture can also be a reason. Numerous studies in psychology demonstrate that those factors above mentioned 12. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(18) have a significant effect on human behavior and moods. Saunders (1993) uses daily returns on the Dow Jones Industrial Average over 1927-1989, and daily returns on value and equal-weighted market indices over 1962-1989. As a proxy for weather conditions, Saunders uses the “percentage of cloud cover from sunrise to sunset” according to the New York weather station closest to Wall Street. He suggests that stock returns are significantly lower on cloudy days than on sunny days. Hirshleifer and Shumway (2003) examines main stock exchanges across 26 countries, investigating the relationship between morning sunshine in the city of stock exchanges and daily market index returns from 1982 to 1997. The result suggests that sunshine is strongly significantly correlated. 治政大 to date that stock prices with stock returns. Their work appears to be a direct evidence 立 are not rational reflections of value, but are instead influenced by investors’ emotional. ‧ 國. 學. states. Goetzmann and Zhu (2005) use trading record from 1991 to 1996 and find that,. ‧. while cloud cover does not affect the propensity of investors to buy or sell, whereas it. sit. y. Nat. seems to be associated with wider bid-ask spreads. They conjecture that mood swings. io. er. by individual specialists may account for this observation. They find that when changes in spreads are incorporated in regressions of returns on weather, the weather effect is. n. al. greatly reduced.. Ch. engchi. i n U. v. Kaplanski and Levy (2010) examine the effect of aviation disasters on stock prices. They find evidence of a significant negative event effect with an average market loss of more than $60 billion per aviation disaster, while the estimated actual loss is no more than $1 billion. And a price reversal occurs subsequently within two days. They find the effect to be greater in small and riskier stocks and in firms in less stable industries. This event effect is also accompanied by an increase in the perceived risk: implied volatility increases after aviation disasters without an increase in actual volatility. Edmans, García and Norli (2007) use international soccer results from January 1973 13. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(19) through December 2004 as a variable of sentiment since the comparison of TV viewing figures, media coverage, and merchandise sales suggest that soccer is particular a “national interest” in many countries in their samples. They find a significant market decline after soccer losses. For instance, a loss in the World Cup elimination stage leads to a next-day abnormal stock return of -49 basis points. This loss effect is stronger in small stocks and in more important games. They also document other types of sports including international cricket, rugby, and basketball, and show a loss effect after competitions. Some specific days also tend to have influence on people’s mood. Ni, Wang and Xue. 治政 (2015) examine Chinese stock market and suggest 大 that the influence of investor 立. sentiment is significant from 1 month to 24 months, while the effect is asymmetric and. ‧ 國. 學. reversal. That is to say, it is positive and large for stocks with high returns in the short. ‧. term while negative and small in the long term. This reversal effect verifies the. sit. y. Nat. existence of a strong overreaction in the Chinese stock market. They also find that. io. er. Chinese investors have notable cognitive bias and speculation tendency. Areni (2008) point out that individuals forecast low mood at the beginning of the work week (i.e.,. al. n. v i n C h the weekend given blue Monday) and high mood toward e n g c h i U the “Thank God it's Friday” or “TGIF” mentality. Positive moods are brought up during weekends when social. interactions can be fully satisfied for those who experience gloomy moods due to constraints in self-interest activities and entertainment during the work week (Ryan et al., 2010). With positive mood, holidays exhibit buy-side sentiments amid individual and institutional investors despite the weekday trading activities (Chui and Wei, 1998; AlKhazali, 2014). Investors may take part in important buying activities during pre- and post-holiday periods, leading to greater pre-holiday stock returns than non-pre-holiday 14. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(20) stock returns (Chui and Wei, 1998; Teng and Liu, 2013). Hence, investor sentiments originated from seasonal anomalies have significant influence on stock returns (Baker et al., 2012; Yuan and Gupta, 2014; Yang, 2016). Investors tend to have different trading habits or preferences based on their calendar- or time-dependency. Yang (2016) analyze the influence of the Chinese lunar calendar and superstitions on holiday preferences using theories on time and mood to designate investor sentiment. Trading interruptions caused by Thursday holidays negative significantly influence investor sentiment for trust companies and individual investors. Trading detachment resulted from cultural holidays in June positive significantly influences investor sentiment for dealers and. 治政大market present significantly individual investors. Besides, trust companies and the 立. positive sentiments toward winter holidays. The stock exchange shows both negative. ‧ 國. 學. and positive sentiments toward winter holidays on holidays in January. Culture-based. sit. y. Nat. Asia.. ‧. holidays and superstition in Taiwan can be a strong evidence for holiday preferences in. io. er. Baker and Wurgler (2006) link sentiment with firm characteristics and show that when investor sentiment is low, subsequent returns are much higher for low capitalized,. al. n. v i n smaller, young, highly volatile,C unprofitable, speculative firms h e n g cnon-dividend-paying, hi U with extreme growth potential or distressed stocks. When sentiment is high, such. categories of stock earn relatively low subsequent returns. On the contrary, “bond-like” and safer stocks are less driven by sentiment. The value of a firm with a long earnings history, tangible assets, and stable dividends is much less sensitive, and thus its stock is likely to be less affected by fluctuations in the propensity to speculate.. 2.4 Chinese Lunar Calendar Chinese lunar calendar, which is an important root for public holidays, is widely 15. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(21) observed in Taiwan, Hong Kong, Singapore, Thailand, South Korea, Malaysia, Japan, and China (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. This lunar calendar remains culturally essential today in Chinese communities, especially on some cultural occasions and festivals, this lunar calendar usually performs more influence than the solar calendar. Public holidays based on the Chinese lunar calendar demonstrate the importance of Chinese cultural influences for business and trading. Moreover, the Chinese lunar calendar has been proved that it strongly influences the social perspective toward. 治政大or symbols relating to funeral superstitions where unlucky events, activities, numbers, 立. activity may negatively affect trading activities and business operations (Liu, 2013).. ‧ 國. 學. This kind of cultural influences such as Chinese superstition for business activities. ‧. and life, may play a deterministic role in investor decision making. For example, the. sit. y. Nat. number 8 represents good luck, while the number 4 represents bad luck, with the latter. io. er. significantly influences investment returns from superstition as a form of investor sentiment (Chung et al., 2014). Furthermore, the Chinese lunar calendar is still widely. al. n. v i n C hsuch as choosingUthe most auspicious date for a used for applications in astrology, engchi. wedding or the opening of a building. For traditional households, this lunar calendar is used to pick good dates for important events, funerals and business deals for instance. In essence, this lunar calendar gives advices for short-term and lifetime investment decision. All these phenomena can be attributed to the favorite belief that the Chinese lunar calendar is a prevalent guide to determine how luck can do specific tasks on a specific date. In addition to being part of the culture, following this lunar calendar is undeniably superstition. The cultural influences brought by the Chinese lunar calendar are pervasive mostly in some East Asian countries, including Taiwan. 16. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(22) Therefore, based on Chinese traditional culture, “Ghost month”, the seventh month in Chinese lunar calendar, is viewed as inauspicious in Chinese communities and some important events are need to be avoided taking place during the period. My study focuses on the Taiwan stock market to illustrate the important influence of the Chinese lunar calendar for the decision making of investors. Whether individuals would tend to avoid trading stocks during “Ghost month”. The results assist in identifying individuals trading patterns under the influence of seventh month in lunar calendar and related rituals, such as religious belied and superstitions.. 立. 政治大. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. 17. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(23) 3. Research Design 3.1 Sample Selection This study uses financial statement data from the database compiled by the Taiwan Economic Journal (TEJ) Data Bank. The sample period consists of all trading dates spans from 1971 to 2017. In order to examine whether stock volume would be affected by religious superstition, which can also derive individuals’ sentiment, I use daily data from 1971, which is the beginning year of daily trading data in TEJ data bank, to 2017. 政治大 2001 oil price spike, the 2003 立 SARS outbreak and Iraq War, and the 2007–2008 U.S.. over a 47-year period. This period contains the 1997–1998 Asian financial crisis, the. ‧ 國. 學. subprime crisis and global financial crisis.. ‧. 3.2 Dependent Variables — Trading Volume and Stock Return. Nat. sit. y. This study focus on all listed company at TWSE due to my main hypothesis assume. n. al. er. io. that individuals are more likely to be affected by religious superstition and sentiment. i n U. v. and then reflect on stock market. And a larger proportion of individuals are clustering. Ch. engchi. in trading public companies rather than those in the OTC market. Restrict to database from TEJ Data Bank, the earliest daily trading volume that can be traced back to was January 5th, 1971, therefore, I use the sample period from 1971 to 2017. There are some earlier trading data omitted due to few companies going publish prior to this date, however, since a large proportion of corporations are listed later by the date, also the sample period is still long enough and contains both recession and thriving in Taiwan stock exchange market, the influence could be ignored. In addition, all transactions before the date of the first listed day of the company would be deleted.. 18. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(24) Another restriction is that trading volume cannot be clearly divided into institutions and individuals since the fact that there is no any detail about transactions made by institutions, directly minus institutions’ trading volume will result in duplicatedcancellation. Nevertheless, a few literatures have suggested that institutional investors are more rational; moreover, the religious superstition discussed in this paper is based on traditional Chinese culture, which has much strong effect on individuals. On the basis of the two reasons, it can be expected that abnormal trading volume should be mainly caused by individuals. As for stock return, the data managing process is almost the same as trading volume,. 治政 which is daily basis. The only difference is the cancel大 of the data at first trading day 立 since there is no return at the day. It is expected that negative returns could be found. ‧ 國. 學. since the effects of ghost month, including possibly low sales, low entertainment. sit. y. Nat. expected result shows that market is less than fully efficient.. ‧. activities, do exist and surprise the market even though it occurs every year. And the. er. io. 3.3 Independent Variables. al. n. v i n The principle in this paper is C to examine whether religious superstition would affect hengchi U. trading volume and return. Hence, the main variable would be an event—the seventh month in Chinese lunar calendar, particularly, I would also check the first day of this month separately due to its distinction for the well-known common term “Kuei Men Kai”, which means the open of the gate to the Underworld. Table 1 lists the corresponding date of every seventh month in lunar calendar from 1971 to 2017. It is noticeable that dates vary year by year without a steady pattern, yet most locating in August and September. Without a fix standard reduce the possibility that the results may come from other events like ex-dividend date. 19. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(25) Additionally, due to this month accompanying with relative low investor sentiment, I also take into account other two factors related to corporate characteristics—company size and age—which are found that they have connection with sentiment (Baker and Wurgler (2006)). Intuitively, a smaller and older corporation should usually accompany with a slighter level of influence driven by superstition since a smaller one is deficient in trading volume originally as well as less well known among investors and thereby could mitigate the influence, and an older one could have a stable reputation among individuals. Firm size, measured as price times shares outstanding, has processed by log and based on daily frequency. Age, however, is based on months, so that daily. 治政 trading data of the same month will have the same age 大 independent variable. 立. In the seventh month, there are usually many taboos such as avoid having weddings,. ‧ 國. 學. taking trips and so on, and could result in some specific industries being influenced. ‧. more than other ones. Thus, industry is another independent variable. The classification. sit. y. Nat. of industries in my sample is referring to Chen, Chen and Lee (2013), which suggests. io. er. that industries should be grouped into a relatively small number of broad categories, and use the Industry Classification Benchmark for the classification. The indices. al. n. v i n C consumer include: basic materials (MATS), (GDS), consumer services (SVS), h e n ggoods chi U. financials (FIN), health care (HEA), industrials (INDU), telecommunications (TELE), technology (TECH), oil & gas (OIL) and utilities (UTIL). Table 2 lists the components of each major sector.. 3.4 Regression Design Regressions are briefly show below. Both return and trading volume share the same regressions, therefore, I just list volume ones. The event contains “Kuei Men Kai” and “Ghost Month”. The industry one to nine correspond with industry classification in table 20. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(26) 2, with health care industry being the benchmark, and test which industry displays a larger negative relationship with the inauspicious event. 𝑉𝑜𝑙𝑢𝑚𝑒',) = 𝛽, 𝐸𝑣𝑒𝑛𝑡). (1). 𝑉𝑜𝑙𝑢𝑚𝑒',) = 𝛽, 𝐸𝑣𝑒𝑛𝑡) + 𝛽2 𝑆𝑖𝑧𝑒',) + 𝛽6 𝐴𝑔𝑒',) + 𝛽9 𝐸𝑣𝑒𝑛𝑡) 𝑆𝑖𝑧𝑒',) +𝛽; 𝐸𝑣𝑒𝑛𝑡) 𝐴𝑔𝑒',). (2). 𝑉𝑜𝑙𝑢𝑚𝑒',) = 𝛽, 𝐸𝑣𝑒𝑛𝑡) + 𝛽2 𝑆𝑖𝑧𝑒',) + 𝛽6 𝐴𝑔𝑒',) + 𝛽9 𝐸𝑣𝑒𝑛𝑡) 𝑆𝑖𝑧𝑒',) +𝛽; 𝐸𝑣𝑒𝑛𝑡) 𝐴𝑔𝑒',) + 𝛽< 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦,,) + ⋯ + 𝛽,9 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦C,) +𝛽,; 𝐸𝑣𝑒𝑛𝑡) 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦,,) + ⋯ + 𝛽26 𝐸𝑣𝑒𝑛𝑡) 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦C,). (3). 政治大. The first regression purely demonstrates the relationship between trading volume and. 立. alternative variable of religious superstition. Next, firm size and age are added into. ‧ 國. 學. consideration, together with their interaction, to see whether this superstition is still. ‧. significant enough and if firm characteristics play a role. As mentioned before, I hypothesize larger and/or younger firms would be affected more by this event. To further. y. Nat. io. sit. discuss whether this superstition brings different degree to different industries due to. n. al. er. some industries having more direct connect with individuals such as traveling, in the. Ch. i n U. v. last regression, I divided all public companies into ten industries by the sorting-standard. engchi. shown in table 2, and use it as another variable, which is processed by dummy. To deserved to be mentioned, I take the industry “health care” as benchmark since it has the least impact caused by the event among all industries, which is easily to figure out and compare the level of influence in other industries.. 21. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(27) Table 1: The first day of the seventh month in lunar calendar and the period of the month Year. Open of the gate to. Seventh month Year. Seventh month. the Underworld. 1971. 08.21. 08.21-09.18. 1995. 07.27. 07.27-08.25. 1972. 08.09. 08.09-09.07. 1996. 08.14. 08.14-09.12. 1973. 07.30. 07.30-08.27. 1997. 08.03. 08.03-09.01. 1974. 08.18. 08.18-09.15. 1998. 08.22. 08.22-09.20. 1975. 08.07. 08.07-09.05. 1999. 08.11. 08.11-09.08. 1976. 07.27. 07.27-08.24. 2000. 07.31. 07.31-08.28. 1977. 08.15. 08.15-09.12. 2001. 08.19. 08.19-09.16. 1978. 08.04. 08.04-09.02. 2002. 08.09. 08.09-09.06. 1979. 08.23. 08.23-09.20. 2003. 07.29. 07.29-08.27. 1980. 08.11. 08.11-09.08. 2004. 08.16. 08.16-09.13. 07.31. 07.31-08.28. 2005. 08.05. 學. 08.05-09.03. 08.19. 08.19-09.16. 2006. 08.24. 08.24-09.21. 08.09. 08.09-09.06. 2007. 08.13. 08.13-09.10. 1984. 07.28. 07.28-08.26. 2008. 08.01. 08.01-08.30. 1985. 08.16. 08.16-09.14. 2009. sit. the Underworld. Open of the gate to. 08.20-09.18. 1986. 08.06. 08.06-09.03. 2010. 1987. 08.24. 1988. y. Nat. io. n. al. ‧. 1983. 08.20. er. 1982. ‧ 國. 1981. 立. 政治大. 08.10. 2011. 08.12. Ch. n U e 2012 chi 08.12-09.10 n g. 1989. 08.02. 08.02-08.30. 1990. 08.20. 1991. 08.24-09.22. i v07.31. 08.10-09.07 07.31-08.28. 08.17. 08.17-09.15. 2013. 08.07. 08.07-09.04. 08.20-09.18. 2014. 07.27. 07.27-08.24. 08.10. 08.10-09.07. 2015. 08.14. 08.14-09.12. 1992. 07.30. 07.30-08.27. 2016. 08.03. 08.03-08.31. 1993. 08.18. 08.18-09.15. 2017. 08.22. 08.22-09.19. 1994. 08.07. 08.07-09.05. 22. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(28) Table 2: Industry classification Basic materials (MATS). Health care (HEA). Chemicals. Pharmaceuticals. Mining. Biotechnology. Industrial materials. Health care equipment and services. Forestry and paper. General retailers. Consumer goods (GDS). Industrials (INDU). Automobiles and parts. General industrials. Personal & household goods. Construction and materials. Food producers. Aerospace. Food & beverage. Engineering and machinery. 立. Tobacco. ‧ 國. Oil & gas (OIL). Integrated and exploration and production. Technology (TECH). Nat. sit. y. Information tech hardware and equipment. Banks Life insurance/assurance Equity investment Real estate. Software and computer services. al. n. Financials (FIN). io. Media and entertainment. er. Travel and leisure. Equipment and support services. ‧. Support services Retailers. Industrial goods and services. 學. Consumer services (SVS) Transport. 政治大. v i n C h Fixed line telecommunication engchi U Telecommunication (TELE). Mobile telecommunications. Utilities (UTIL) Electricity Gas, water and multi-utilities. Sources: Dow Jones US sectors and FTSE classifications in Datastream.. 23. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(29) 4. Empirical Results 4.1 Descriptive Statistics Table 3 presents the results for the descriptive statistics of variables in regression models. The number of trading data is 3,828,200 for all listed companies from 1971 to 2017. Due to data processing, some samples were deleted in return, size and age. As can be seen in the table 3, median of trading volume is far less than mean trading volume, which demonstrates a disproportionate volume distribution among large and small. 政治大 The underneath part of table 3 contains descriptive statistics divided into ten 立. companies.. ‧ 國. 學. industries. It is noticeable that trading volume varies from industry to industry, mainly resulting from the amount and the age of firm. The classification of ‘INDU’, ‘TECH’. ‧. and ‘MATS’ are the industries which have highest trading volume at 1,204,218, 885,638. sit. y. Nat. and 623,939 respectively. A large proportion of companies in Taiwan is included in. io. al. also the foundation of Taiwan economic growth.. er. industrials and technology owing to their predominant position over others, which are. n. v i n C hgas and utilities have On the contrary, category of oil & e n g c h i U the least numbers of sample. at 44,602 and 9,370 respectively, due to they are oligopoly in Taiwan—a small island without the possibility of oil exploration that is easily saturated in need of energy related resource. Furthermore, the category of ‘MATS’, ‘GDS’, ‘SVS’ and ‘INDU’ are relatively older industries with the means of their ages all beyond 165 months, almost 14 years. Specially, except the financial industry, which is rather large in size, other industries display a similar size in means, clustering around 8 after log process.. 24. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(30) Table 3 Summary statistics, 1971-2017 Mean. Median. Full Sample Volume 3,828,200 3655 Return 3,827,703 0.0009 Size(log) 3,827,269 8.6424 Age 3,828,147 152.4749 Number of trading days in “Kuei Men Kai ” Number of trading days in Ghost Month Sample classified into ten industries MATS. 立. io. N. 2,376.88 0.005448 8.4996 173.5261 INDU Mean. Volume Return. 1,204,218 1,204,077. Size Age. 1,204,218 1,204,188. n. al. S.D 7,934.76 0.030157 1.42 138.6232. N. Mean. 254,488. 3,144.15 0.000486. S.D 5,691.73 0.03583 1.4858 135.76 S.D 9,731.04 0.138234. 254,488 N. 156,073 156,045 156,073 156,073 N. 74,322 74,304. iv. n74,322 C8.3246 U h e n g1.3567 c h i 74,322 165.0205 129.6412. Volume Return Size Age. Volume Return Size Age. 885,638 885,500 885,638 885,638. Mean 4,692.41 0.000066 8.8755 103.6129 OIL. N. Mean. 44,602 44,596 44,602 44,602. 799.0394 0.000251 8.3859 140.739. S.D 11,946.79 0.046436 1.5124 72.2892 S.D 2,219.16 0.021998 1.7619 95.3507. Mean. N 130,248 130,235 130,248 130,248 N 9,370 9,369 9,370 9,370. 4,401.77 0.02403 1.3846 134.8091 S.D. 10832 18950.8694 0.000207 0.033139 10.2992 1.41 139.6528 116.8698 HEA Mean. TECH N. 2,095.02 0.000315 8.6256 181.3498 FIN. S.D. ‧. 445,302 445,255 445,302 445,279. 9850.82 0.0842 1.4938 123.6185 12,122 299,093. GDS. 254,459 政治大 254,488. Mean. Nat. Volume Return Size Age. 0 -5.7114 1.7918 0. 學. N. 3,523 0.000317 8.6735 188.0722 SVS. 1,947,673 144.0581 15.6603 680. S.D. y. 623,939 623,863 623,008 623,939. ‧ 國. Volume Return Size Age. Mean. Min. sit. N. 865 0 8.5543 126. Max. er. N. S.D. 990.1258 0.000189. 1,936.32 0.022983. 8.0803 143.3282 TELE. 1.0493 150.3162. Mean 3,446.74 0.00048 8.8666 97.0123 UTIL Mean 1,218.70 0.000556 7.5858 116.4827. S.D 5,668.59 0.072648 1.6879 67.19 S.D 2,450.63 0.042673 1.3933 74.6381. 25. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(31) 4.2 Regressions of Trading Volume Table 4 reports the empirical results of trading volume with several regressions. On the top of the table, I regress firm size and age first in order to confirm that a larger and/or a younger company would have positive relationship with trading volume, with firm age has much less effect on it. The regressions in the table are corresponding with those in the chapter 3.4. In first regression, I check merely the relationship between trading volume and the unlucky event and find that there is a negative and statically significant relationship. 政治大. between stock trading volume and event “ghost month”, also the first day of the. 立. month— “Kuei Men Kai”, at -208 thousand shares and -350 thousand shares. ‧ 國. 學. respectively, which means the first day always brings a spike although it occurs every year. The second regression takes two dependent variables, firm size which is a log-. ‧. processed variable, and monthly-based firm age into account. The results show that at. Nat. sit. y. the first day of the month, larger companies tend to be affected more by this. n. al. er. io. inauspicious event, with a significant drop of 323 thousand shares in trading volume,. i n U. v. yet firm age does not have obvious influence in this day. However, in the ghost month,. Ch. engchi. a significant decline is more likely to be seen in larger and younger firms, with approximate decrease of 54 thousand shares in larger ones, though much less comparing with “Kuei Men Kai”, and drop of 0.288 thousand shares in younger ones, which is much slighter in comparison with firm size. Due to the fact that every industry has its own characteristics that may have different impact on investors, the results of third regression suggest that at the first day of ghost month, there is still a significant drop in those firms with larger size, and volume of industries like financial, technology and telecommunication drop at that day, yet no. 26. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(32) significance. On the contrary, in the ghost month, a reversal can be seen that firm age does be affected by this month firm size, though still slight. Meanwhile, firm size has constantly negative relationship with this inauspicious event. It is noticeable that all industries experience dropping during ghost month, and financial industry has significant the largest drop of 1150 thousand shares, also telecommunication has significant decline at about 308 thousand shares.. 4.3 Regressions of Stock Return Table 5 reports the empirical results of returns with several regressions. Since the. 治政 figures of return are too small, the table shows in percent 大as unit to make the outcome 立 clearer. All three regressions are the same as used in trading volume, except for ‧ 國. 學. changing dependent variable to stock daily return. On the top of the table, I regress firm. ‧. size and age first in order to confirm that a larger and/or an older company would have significantly negative relationship with stock return, though both the figures are small.. y. Nat. er. io. sit. In the first regression, return drops during the ghost month by 0.106%, while there is no significance at the first day. In the second regression with adding variables of logged-. al. n. v i n size and monthly-based age, theC return still shows no impact on size and age both at the hengchi U. first day and the whole month. In the last regression, we still can notice that no. significant relationship with size and age. At the same time, there is no single industry shows noteworthy influence caused by ghost month and the first day of it, yet most industries display a negative relationship with it. The results implicate that though the unlucky superstition let investors lower their trading volume, the return almost does not be affected by this abnormal trading activity. In the meantime, investors would take firm characteristics—firm size and age—into consideration when they make trading decisions, however, those two factors affect nothing in return. 27. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(33) Table 6 presents further check of stock return in next five-day period and the subsequent month after the end of ghost month. The idea comes from Gervais, Kaniel and Mingelgrin (2001), which suggests that individual stocks whose trading volume is unusually large (small) over periods of a day or a week, tend to experience large (small) returns over the subsequent month. To fit this high volume premium theory to my study, since investors sentiment comes from religious superstition should not last too long intuitively, I also run a separate regression using five-day period. However, both the results are still mostly insignificant, except for a slight fall in the first regression, which is 0.0862% for 5-day and 0.0893% for one month rather than 0.108%. What is. 治政大 after considering firm size noteworthy is that there is a statistically significant decrease 立. and age by 0.323% for 5-day, which is more than twice larger than the result for one-. ‧ 國. 學. month (0.135%). The outcome shows that the investors’ irrational behavior originating. ‧. from superstition surely exists for a relatively short time.. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. 28. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(34) Table 4: Regressions of trading volume SIZE. 2856.4842*** (3.2806). AGE. 1.0086*** (0.0390). KueiMenKai. AGE KMK*SIZE KMK*AGE. -350.9233*** 2339.2557*** 1907.6730*** (88.6289) (470.7727) (717.0648) 2857.5323*** (3.2854) 1.00657*** (0.0391) -323.5011*** (54.5655) 0.6486 (0.6583). MATS. TECH. ‧ 國 io. TELE. Nat. INDU. al. n. OIL MATS*KMK GDS*KMK FIN*KMK SVS*KMK UTIL*KMK INDU*KMK TECH*KMK TELE*KMK OIL*KMK. -208.0417*** 226.4413** (18.6282) (98.6544) 2860.6826*** (3.4004) 0.9877*** (0.0405) -54.2884*** (11.4387) 0.2880** (0.1395). SIZE AGE GM*SIZE GM*AGE. 政治大. Ch. MATS GDS FIN SVS. ‧. UTIL. (2). 學. SVS. GhostMonth (GM). 2749.8335*** (3.4427) 2.2628*** (0.0415) -262.5604*** (57.2721) 0.1706 (0.6848) 352.3783*** (34.6039) -943.8021*** (37.1057) 3257.2651*** (40.1969) -66.8166* (35.2653) 1218.1724*** (97.0528) 1089.6578*** (33.5585) 1343.3397*** (34.0114) 200.9507*** (40.8833) -1477.5804*** (53.0657) -141.5858 (592.7484) 479.1456 (639.7415) -921.9573 (689.7105) 216.7109 (608.3038) -49.4563 (1708.5660) 253.7148 (575.2250) -344.9334 (681.8679) -176.0063 (699.7311) -7.3681 (929.9949). 立. GDS FIN. (1). UTIL INDU. y. SIZE. (3). TECH TELE OIL. e n gMATS*GM chi GDS*GM FIN*GM SVS*GM UTIL*GM INDU*GM TECH*GM TELE*GM OIL*GM. sit. KueiMenKai (KMK). (2). er. (1). GhostMonth. i n U. v. (3) 213.65136 (153.0317) 2751.0789*** (3.5623) 2.2457*** (0.0430) -27.8723** (11.9716) 0.2491* (0.1448) 367.8918*** (35.9666) -932.7877*** (38.5790) 3345.9604*** (41.7892) -50.4414 (36.6495) 1252.0554*** (100.9658) 1104.4828*** (34.8807) 1352.9810*** (35.3475) 224.8639*** (42.4955) -1477.3858*** (55.2083) -203.4633 (127.9828) -124.9642 (137.0909) -1150.7507*** (148.9501) -202.6384 (131.3568) -431.1923 (358.7723) -178.5566 (124.7447) -134.2984 (126.6156) -308.5004** (152.0312) -7.9003 (195.8230). 29. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(35) Table 5: Regressions of stock return SIZE. -0.0253*** (0.0031). AGE. -0.0002*** (3.70E-05). KueiMenKai. AGE KMK*SIZE KMK*AGE MATS. 立. INDU. io. TELE. Nat. TECH. al. n. OIL MATS*KMK GDS*KMK FIN*KMK SVS*KMK UTIL*KMK INDU*KMK TECH*KMK TELE*KMK OIL*KMK. (2) -0.1330 (0.0936) -0.0251*** (0.0032) -0.0002*** (3.84E-05) -0.0028 (0.0109) 0.0003 (0.0001). SIZE AGE GM*SIZE GM*AGE. 政治大. Ch. MATS GDS FIN SVS UTIL. ‧. UTIL. GhostMonth (GM). (1) -0.106*** (0.01611). 學. SVS. ‧ 國. GDS FIN. (3) -0.3250 (0.6830) -0.0156*** (0.0033) -0.0004*** (3.95E-05) 0.0097 (0.0546) -0.0004*** (0.0007) 0.0702 (0.0330) 0.0668 (0.0354) 0.0264 (0.0383) 0.5780*** (0.0336) 0.0128 (0.0925) 0.0683 (0.0320) -0.0353 (0.0324) -0.0044 (0.0390) 0.0175 (0.0506) 0.3390 (0.5650) 0.3550 (0.6090) 0.2960 (0.6570) 0.5800 (0.5790) 0.3340 (1.6280) 0.2230 (0.5480) 0.0318 (0.5540) -0.0848 (0.667) 0.5430 (0.8860). INDU. y. SIZE. (2) -0.0510 (0.4470) -0.0253*** (0.0031) -0.0002*** (3.70E-05) -0.0010 (0.0518) -5.39E-05 (0.0006). TECH TELE OIL. e n MATS*GM gchi GDS*GM FIN*GM SVS*GM UTIL*GM INDU*GM TECH*GM TELE*GM OIL*GM. sit. KueiMenKai (KMK). (1) -0.067889 (0.07664). GhostMonth. er. Unit : %. i n U. v. (3) -0.0737 (0.1460) -0.0155*** (0.0034) -0.0004*** (4.09E-05) -0.0019 (0.0114) 0.0003 (0.0001) 0.0735 (0.0343) 0.0751 (0.0368) 0.0288 (0.0398) 0.5840 (0.0349) 0.0424 (0.0962) 0.0742 (0.0332) -0.0284 (0.0337) 0.0026 (0.0405) 0.0146 (0.0526) -0.0277 (0.1220) -0.0888 (0.1310) -0.0167 (0.1420) -0.0403 (0.1250) -0.3580 (0.3420) -0.0653 (0.1190) -0.0860 (0.1210) -0.0902 (0.1450) 0.0587 (0.1870). 30. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(36) Table 6: Regressions of stock returns over the subsequent 5 days and one month SIZE. -0.0253*** (0.0031). AGE. -0.0002*** (3.70E-05). 5 day. AGE GM*SIZE GM*AGE MATS. 立. SVS. INDU. io. TELE. Nat. TECH. al. n. OIL MATS*GM GDS*GM FIN*GM SVS*GM UTIL*GM INDU*GM TECH*GM TELE*GM OIL*GM. Ch. engchi. ‧. UTIL. 學. FIN. (1) (2) -0.0893*** -0.1350 (0.0159) (0.0923) -0.0256*** (0.0032) -0.0002*** (3.85E-05) 0.0029 (0.0107) 0.0001 (0.0001). 政治大. ‧ 國. GDS. (3) -0.1290 (0.2810) -0.0162*** (0.0033) -0.0004*** (3.98E-05) 0.0305 (0.0221) 0.0001 (0.0003) 0.0765** (0.0332) 0.0725** (0.0356) 0.0337 (0.0386) 0.5840*** (0.0339) -0.0004 (0.0933) 0.0740** (0.0322) -0.0306 (0.0327) 0.0007 (0.0393) 0.0241 (0.0510) -0.2600 (0.2340) -0.2240 (0.2510) -0.3180 (0.2720) -0.1970 (0.2400) 0.7080 (0.6550) -0.2500 (0.2270) -0.2330 (0.2300) -0.2700 (0.2760) -0.2420 (0.3620). y. SIZE. (1) (2) -0.0862*** -0.3230* (0.0312) (0.1820) -0.0258*** (0.0031) -0.0002*** (3.73E-05) 0.0254 (0.0211) 0.0001 (0.0003). sit. GhostMonth (GM). one month. er. Unit : %. i n U. v. (3) -0.0689 (0.1440) -0.0158*** (0.0034) -0.0004*** (4.10E-05) 0.0011 (0.0113) 0.0002 (0.0001) 0.0780** (0.0343) 0.0736** (0.0368) 0.0331 (0.0399) 0.5800*** (0.0350) -0.0239 (0.0965) 0.0776** (0.0333) -0.0339 (0.0337) -0.0045 (0.0406) 0.0236 (0.0527) -0.0822 (0.1200) -0.0694 (0.1290) -0.0678 (0.1400) 0.0100 (0.1230) 0.4480 (0.3320) -0.1070 (0.1170) -0.0146 (0.1180) -0.0010 (0.1420) -0.0531 (0.1840). 31. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(37) 5. Conclusion Empirical studies are rare regarding the religious beliefs in eastern countries since the difficulties for searching alternative variables. This paper aims at filling the gap. As a result, the paper uses special event—ghost month—originating from Chinese religious beliefs and Chinese lunar calendar in Taiwan as an alternative variable. The results of regressions reject the null hypothesis, which supports the view that security. 政治大 sentiment coming from inauspicious day and argues for models of asset-pricing. In 立. markets are systematically influenced by investor psychology and superstition and. ‧ 國. 學. addition, rejection of the null hypothesis supports the hypothesis that financial markets are, to some degree, irrational and, thereby tossing doubt on the hypothesis that security. ‧. markets reflect nothing but economic information.. sit. y. Nat. Despite proving that religious beliefs and superstition do have influence on stock. al. er. io. market, the result shows that there are only slight significant changes in return during. v. n. the period, which means that investors’ irrational behavior does not definitely promise. Ch. engchi. i n U. profits through speculation. Nevertheless, it demonstrates that investor should better avoid selling stocks during ghost month since the obvious dropping in trading volume implies it may be harder to sell. Furthermore, the return of five days after the end of ghost month displays a rather larger and significant consequence (-0.323%) and implies the religious beliefs and superstition truly leads to negative effect on stock market. As for the constraint, there is a difficulty in distinguishing institutional investors and individuals. Though past studies suggest that individuals are more irrational than institutions and it would be a good explanation for that trading volume and stock return. 32. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

(38) anomalies mainly come from individuals. However, since they could reflect both directions with institutions usually have larger ability to affect them, it would be better to discuss this issue through a separate empirical analysis. The results might be more representative. This paper uses data and the idea of religious superstition in seventh month in Chinese lunar calendar in Taiwan, a representative area in inheritance of Chinese traditional culture in East Asia, and the result is a part in the puzzle of the relationship between eastern religion and financial markets. Since there are a lot of religions in this region, also the diversified customs within a country, how to find other appropriate and. 治政大to discuss. reliable alternative variables is always a difficult question 立. ‧. ‧ 國. 學. n. er. io. sit. y. Nat. al. Ch. engchi. i n U. v. 33. DOI:10.6814/THE.NCCU.Finance.003.2018.F07.

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