A-SHARE MARKET FINANCIAL PSYCHOLOGY ANALYSIS OF NUMERICAL SUPERSTITIONS AND STOCK PRICE VOLATILITY: EMPIRICAL EVIDENCES FROM CHINA'S

Revista Argentina de Clínica Psicológica
2020, Vol. XXIX, N°1, 279-289 279
DOI: 10.24205/03276716.2020.37

 FINANCIAL PSYCHOLOGY ANALYSIS OF NUMERICAL SUPERSTITIONS
 AND STOCK PRICE VOLATILITY: EMPIRICAL EVIDENCES FROM CHINA’S
 A-SHARE MARKET
 Yun Li1,2, Kun Wang1,3*, XuanMing Ji4, Yingkai Tang1,3

 Abstract
 Many Chinese have numerical superstitions, such as the aversion to the number 4 and affinity for the
 numbers 8 and 6. This paper investigates whether numerical superstitions affect the stock price volatility of
 China’s A-share market. Based on the monthly data of the A-share market from September 2014 to December
 2017, the authors analyzed the relationship between stock price volatility and the code effect of numerical
 superstitions from the perspective of financial psychology. The results show that, the stock price volatility was
 greatly affected by the code effect during the bull market in the early days of the A-share market, but this effect
 has gradually disappeared. In the Small and Medium Enterprise (SME) Board and the Growth Enterprise
 Market (GEM) Board, the lucky code affects stock price volatility in a bull market or slow bull market, while the
 unluck code only affect stock price volatility only in a bear market. This research provides new empirical
 evidence on the relationship between numerical superstitions and stock price volatility.
 Key words: Numerical Superstitions, Stock Code, Financial Psychology, Stock Price Volatility.
 Received: 20-02-19 | Accepted: 29-07-19

 INTRODUCTION and "Yu collected the nine golden herding
 Numerical superstitions are a psychological vessels and cast the nine tripods," and so on. In
 the Ming and Qing dynasties, princes and nobles
phenomenon in which people believe that a
 continued to prefer the number nine. For
particular number or combination of numbers
 example, each floor of the temple of heaven has
will bring them a curse or a blessing. Chinese
superstitions and preferences for certain nine more floors than the previous one from the
 first layer of the nine tablets, and a total of nine
numbers date back to the Shang and Zhou
 layers were laid. We can therefore see that the
dynasties. From Yi jing to Laozi to Huai nan zi,
Ancient Chinese sages used numbers to explain superstition surrounding numbers in China
 existed since ancient times.
their observations and understanding of natural
 Just as people in many European and
things, reflecting the philosophical wisdom of
 American countries believe that the number 13
ancient people. In ancient times, numbers were
also status symbols, especially for emperors. For may bring bad luck, the Chinese prefer the
 numbers 6, 8, and 9. The number 4 sounds
example, the "honor of the ninth five-year plan"
 similar to the Chinese word for death, so people
 have a subjective dislike of it. For example,
 1 Instituteof Finance, Sichuan University, Chengdu 610065,
 China. 2 Chengdu Municipal Xindu District People's
 mobile providers tend to offer discounts for
 Government of Sichuan Province, Chengdu 510100, China. mobile numbers that contain the number 4,
 3 Business School, Sichuan University, Chengdu 610065,
 while serial numbers containing 6 and 8 often
 China. 4Finance and Economics School, Jimei University,
 Xiamen 361021, China. sell for tens of thousands or even millions of
 E-Mail: liam_wang@stu.scu.edu.cn RMB. In addition, license plates with the
 numbers 6, 8, and 9 incur an additional fee in the

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280 YUN LI, KUN WANG, XUANMING JI, YINGKAI TANG

Table 1. Mantissa distribution of securities codes in China’s A-share market

 0 1 2 3 4 5 6 7 8 9
 Shanghai’s main board 132 123 90 128 44 119 167 141 222 178
 Shenzhen’s main board 49 46 43 47 10 42 45 41 55 47
 SME Board & GEM Board 157 170 162 160 129 168 168 161 166 160

 Note: Up to December 31, 2017, excluding delisted stocks. Data source: WIND database

 superstition is a kind of knowledge and
process of taking the license plate, and these experience of objective reality among
license plates often bid high prices. The opening superstitious people involving their own
ceremony of the Beijing Olympic Games was interests acquired through learning, which is an
scheduled for 8 PM on August 8. organic unity of their knowledge, feelings, and
 Thus, the numerical superstition is a actions related to the curse or blessing. Sun &
common, daily phenomenon. Tian (2016) indicate that the production and
 The preference for numbers in daily life may enhancement of superstitious psychology is a
extend to finance. Table 1 shows the mantissa kind of causal illusion. Some superstitious
distribution of the stock codes of the main board behaviors are unconscious and even self-
market of the Shanghai Stock Exchange, selected. Rudski (2003) divides superstitions into
Shenzhen Stock Exchange, SME Board, and the four categories according to cultural
GEM Board in China's A-share market. characteristics: (1) cosmology and a worldview
 The statistics show that 222 stocks with codes with a belief in the existence of heaven and hell;
ending in 8 were listed on the main board of the (2) traditional secular superstitions, such as
Shanghai Stock Exchange, which was four times meeting a magpie in China means good luck; (3)
the number of stocks with codes ending with the mysterious experiences of individuals beyond
number 4 (44) up to December 31, 2017. There common sense, such as ghost possession; and (4)
were also more stocks with codes ending in 6 and personal superstition, such as around lucky
9 (167 and 178) than any other number. Among colors, lucky decorations, and so on. Numerical
the stocks listed on Shenzhen's main board, five superstitions involve the categories of secular
times as many stock codes end with 8 compared superstition and personal superstition. Chen,
to those that end with 4. The phenomenon of the Zhang, & Li (2009) point out that due to cultural
last code concentration of the SME and GEM habits and uncertainty about the unknown,
Boards is not significant, due to the different superstition is not completely eliminated.
stock code determination in these boards. Moreover, it reflects mainly in the subjective
 Based on the above analysis, there is a preferences for external factors such as
significant numerical superstition phenomenon numbers, colors, and dates. The authors also
in the stock code selection of listed companies state that numerical superstitions are often the
due to the influence of traditional culture. result of observation and learning, especially in
However, in the secondary market, it is unclear China. For example, an individual with no
whether this superstition affect investors or if numerical preference may attribute a negative
they prefer certain stocks code when choosing life event to factors such as a house number
stocks. In addition, does this numerical containing the number 4. Thus, when it comes to
superstition have a particular impact on stock personal economic interests, the effect of
volatility? superstitious psychology on behavior and
 subjective cognition is unconscious or
 deliberately chosen. Moreover, the more
 THEORY AND HYPOTHESIS DEVELOPMENT complex the decision is, the stronger the effect
 is. In other words, daily superstition is an
 The psychological basis of numerical
 important factor affecting personal subjective
superstitions
 Superstitious thoughts and behaviors in daily preference.
life have been common since ancient times and
 Explaining excessive stock price volatility
tended to continue into modern society
 from the financial psychology perspective
(Wiseman & Watt, 2004). Luo (2001) states that
 Conventional economics is based on the

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FINANCIAL PSYCHOLOGY ANALYSIS OF NUMERICAL SUPERSTITIONS AND STOCK PRICE VOLATILITY: EMPIRICAL EVIDENCES FROM CHINA’S A-SHARE MARKET 281

rational man assumption in which constraints, phenomenon of daily superstition leads to
preferences, and expectations influence irrational behavior from investors, which causes
investment decisions. Many market phenomena abnormal volatility in the securities market.
in securities markets worldwide, including in Accordingly, we develop the first hypothesis:
China, cannot be explained by classical economic H1: Numerical superstitions will lead to
theories. Since the 1970s, the economic circle abnormal stock price volatility in the A-share
grew to include psychology in the related market.
research, which supplemented and improved
classical economics from the perspectives of Market value effect, difference between bull
social and cultural backgrounds, cognitive and bear markets, and stock price volatility
biases, and investor sentiment. A review of the According to traditional investment theory,
literature shows that social and cultural compared with blue chip stocks, small-cap stocks
backgrounds, including traditional secular generally have less assets, lower industry status,
superstitions, are an important factor affecting poorer competitiveness, lower market attention,
individuals' economic decisions and has certain and inactive trading. However, the SME Board
explanatory power for various irrational and GEM Board is generally more popular and
phenomena in the market (Li & Zhang, 2015). with greater volatility in the A-share market, due
Subsequently, Shefrin & Statman (1994) to the influence of the market value effect
proposed a creative Behavioral Asset Pricing (Zhang & Wu, 2005). Banz (1981) was the first
Model (BAPM) and Behavioral Pricing Theory economist to discover and propose the market
(BPT), which laid the foundation for quantitative value effect. Subsequently, Fama & French
research on behavioral finance and financial (1992) and Johansen (1998) prove the
psychology. Tvede (2002) later adopted financial universality of the market value effect in the US
psychology theory and summarized the stock market, but Schwert (1990) argues that the
characteristics of financial markets as forward- market value effect of the US stock market is
looking, irrational, chaotic, and showing self- shrinking. There are significant differences
actualization in a study of many irrational between theoretical and practical circles in
phenomenon in securities market from these terms of the causes of the market value effect.
four aspects. The author confirms that There are three main points. First, small firms
differences in individual investment behavior have small financial bases and unstable financial
caused by irrational subjective preferences exist, indicators. Second, the attention to and
including personal superstition. According to valuation of small-cap stocks is low. Third, big
traditional finance theory, the stock price is investors can more easily manipulate small-cap
equal to discounted sum of the cash flow of the stocks, with a strong carry effect and linkage
future dividend of the stock. However, in actual effect. No matter how the mechanism of the
stock market trading, stock market volatility is market value effect works, its influence on stock
much higher than dividend volatility is. Shiller market volatility certainly exists. On the other
(1981) was the first economist to examine this hand, due to the poor investment environment
phenomenon and explain it from the perspective in bear markets, the potential risk is greater.
of behavioral finance. Based on the profit- However, bull markets have better investment
seeking of capital, the abnormally high volatility environments, in which investors are more likely
of stock prices is inevitably due to investors' to profit, and the main risk is only the rate of
pursuit of profits through speculation, return volatility. Such differences in investment
behavioral cognitive bias, subjective environments will lead to differences in
preferences, and other market factors. investment behavior and psychology. The
 On the one hand, social superstitious volatility caused by the market value effect and
behaviors exist in various aspects of daily life, the difference between bull and bear markets
and many are unconscious and self-reinforcing. will aggravate the volatility in investor
On the other hand, behavioral finance research sentiment, which increases the impact of
demonstrates that psychological factors, superstition. Therefore, we propose hypothesis
including the irrational subjective preferences 2:
that superstitions represent, can impact H2a: Numerical superstitions will have
investors' investment behaviors. This study different impacts on the volatility of the A-stock
hypothesizes that the psychological market for bull and bear markets.

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282 YUN LI, KUN WANG, XUANMING JI, YINGKAI TANG

 H2b: Numerical superstitions will have period ends in December 2005, when the
different impacts on the volatility of the A-stock reforms to non-tradable shares began, so the
market for the SME Board, the GEM Board, and research results may not be applicable to the
the whole market. current market. Ye (2010) studies the
 relationship between the mantissa distribution
 of stock codes and trading volume and find that
 LITERATURE REVIEW AND RESEARCH stocks with mantissa 8 have higher trading
QUESTIONS volumes, and this has a positive effect on the
 stock price. Sheng, Zhang, & Xie (2011) construct
 Numerical superstitions and the price
clustering effect stock portfolios using stock codes with different
 Most research on the relationship between last numbers and show that the portfolio returns
 of the portfolio ending in 4 in a bull market is
numerical superstition and the A-share market
 below the market’s required return, though this
focuses on the price clustering effect, in which
specific numbers appear more frequently in the phenomenon did not appear in the bear market.
 Additionally, investors have no special
opening and closing prices of stocks. There are
 preference for portfolios ending in 6, 8, and 9.
several representative studies on the price
 However, Cao & Li (2012) use China's SME Board
clustering effect and mantissa distributions of
stock codes in China's stock market. Brown, as a sample and reach different empirical
 conclusions. They find that portfolios ending in
Chua, & Mitchell (2002) study the mantissa
 6, 8, and 9 have a higher long-term average
distributions of closing prices in the Hong Kong
stock market and find that 8 is the most return rate and excess return rate, while the
 portfolio ending in 4 has a relatively lower long-
frequent, and 4 is the least frequent among all
 term average return rate and excess return rate.
closing stock prices, and the differences with
 The reasons for the differences between the two
other numbers is significant. Brown & Mitchell
(2008) study the A-share market using the same results may be the stock market value, the
 development and change in China's stock
method and came to the same conclusions,
 markets, and the change from a bull to a bear
showing that the phenomenon of numerical
 market. Zhang & Tang (2015) study the code
superstition does exist in China's relevant stock
market. Rao, Zhao, & Yue (2008) use daily discount and premium effect by analyzing the
 first-day price-earnings ratio of new stocks.
transaction data of all stocks in the A-share
 Using regression and variance analysis, they find
market for three months and find that the
transaction prices of A-shares contain the that the code effect existed in the early A-share
 market, but that it no longer exists. Zhao
number 8 the most often and the number 4 the
 shaoyang and Wang shen examine the code
least often. Moreover, stocks with higher prices,
 effect of stocks listed before 2004 using
higher uncertainty, and more attention from
institutional investors have a more obvious price descriptive statistics, and indicate that the code
 effect influences the long-term return of stocks.
clustering phenomenon. Liu (2008) uses daily
high-frequency data to confirm that 4 is the least
common number in both bull and bear markets. Literature review and summary
 According to the previous literature,
 numerical superstitions can have a subjective
 Numerical superstitions and the stock code
 influence on the firms and investors in the A-
effect
 Early studies on numerical superstitions share market. Research of the effect of
 numerical superstitions on the stock market can
focused mainly on the mantissa characteristics
 provide some empirical support for financial
of stock prices, and do not address the stock
 psychology, and offer an investment reference
code effect. Zhao& Wu (2009) were the first to
examine the relationship between the for practitioners. Previous research on numerical
 superstitions focused chiefly on two aspects. The
characteristics of stock codes and investors'
 first is the numerical characteristics of stock
stock selection behaviors and stock returns.
They find that the price-earnings ratios of stocks prices, with studies on the price clustering effect
with mantissa 8 are higher on the first day of from various perspectives to analyze the
 possible causes of this phenomenon. The second
listing and one year thereafter, but the long-
 is the focus on the relationship between the
term rate of return on such stocks is lower and
the decline is larger. However, their sample characteristics of the mantissa distributions of

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FINANCIAL PSYCHOLOGY ANALYSIS OF NUMERICAL SUPERSTITIONS AND STOCK PRICE VOLATILITY: EMPIRICAL EVIDENCES FROM CHINA’S A-SHARE MARKET 283

stock codes and stock returns and pricing. For in Chinese is akin to "go to hell," and so on. When
various reasons, they did not find the same telecom companies offer a discount on certain
empirical results. telephone numbers, they do so based on
 However, these scholars did not consider the whether the number contains the number 4, not
following problems. First, the stock code effect whether the mantissa is 4.
considers only mantissa distributions, and did Therefore, we define a stock code as lucky if
not exclude some special samples (e.g., the stock code ends with the numbers 6 or 8 and
600488.SH; the pronunciation of 488 in Chinese does not contain the number 4.
is similar to that of "dead father"), although the In addition, we eliminated firms that
last number is 8, it still has a bad meaning when suspended or terminated their listings, firms
combined with other numbers. Second, there are that experienced asset restructuring during the
few studies on the psychological logic behind the sample period, and those whose main business
phenomenon of number preferences. Third, and equity scale changed considerably. Since the
most of the literature is on price clustering or empirical method in our study does not involve
returns, with no studies on whether the relevant financial indicators, the difference
numerical characteristics of stock codes affect a between accounting standards and statement
stock's volatility. preparation has little impact on the empirical
 results, so we retain all financial firms in the
 sample.
 VARIABLES AND RESEARCH MODEL
 Sample selection Variables
 Volatility
 We use monthly data of the A-share market
 We adopt the two methods below to measure
from September 2014 to December 2017 for the
 the volatility of individual stocks:
empirical analysis for several reasons. First, the
existing research covers only market data before (1) We calculate the standard deviation of the
 daily return rate of a single stock and take its
2015. With the rapid development of China's
 average within a month after taking the
stock market in recent years, these research
 logarithm (Rubin & Smith,2009).
conclusions may not match the existing market.
Second, September 2014 was the starting point (2) We use the price amplitude and individual
 volatility to measure volatility (Alizadeh, Brandt,
of the new bull market in Chinese stocks. The
 & Diebold, 2002; Li & Wang, 2010).
sample period in this study, from the start of this
bull market to the December 2017, contains a
 2
complete market cycle of bull - bear - slow bull √( ℎ ℎ − )
 4 2
in the A-share market. Third, the serious = (1)
 _ 
clustering effect of daily high-frequency data
may result in invalid regression results, and are
 is the volatility of stock i in month
not easy to obtain. Although the monthly data
have a long time interval, they can contain most t, ℎ ℎ and represent the highest and
of the information of high-frequency data lowest price of stock i on trading day d of month
through certain statistical methods. Therefore, t, respectively, and _ represents
we select the monthly frequency for the the number of trading days of stock i in month t.
empirical analysis. Model (1) uses volatility to conduct the main
 We define the bull market period from correlation test, while Model (2) uses the new
September 2014 to May 2015, the bear market stock volatility index ( _ ) for the
as from June 2015 to February 2016, and the robustness test. If a stock is suspended for the
slow bull market as from March 2016 to month, the volatility is equal to 0.
December 2017.
 Prior studies also consider only the last Auspicious stocks and unlucky stocks
number in the stock codes and apply this method There is no reference for the construction of
as the grouping standard when studying the this index. We refer to prior methods of
mantissa distributions of the codes. In reality, constructing financing, surges, and declines in
many numbers end in a lucky number, but do not stock variables. We define two dummy variables,
have positive meanings. For example, for auspicious and unlucky, as follows:
600748.SH, the pronunciation of the number 748 Auspicious: if the last number of the stock

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284 YUN LI, KUN WANG, XUANMING JI, YINGKAI TANG

code is 6 or 8 and does not contain the number outstanding in month t. ℎ ℎ _ is the
4, then the dummy variable equals 1, and 0 rate of return of the CSI 300 index in the month
otherwise. t. is the fixed effect and is random
 Unlucky: if the stock code ends in 4, then the effect.
dummy variable equals 1, and 0 otherwise. The Breusch-Pagan and Durbin-Wu-Hausman
 test statistics show that it is more efficient to
 Control variables select the random effect model in the mixed
 (1) Individual stock turnover rate ( ) regression and random effect models, while the
 The turnover rate of a single stock refers to fixed effect is more suitable for the random
the frequency of the stock’s trades in a certain effect and fixed effect. However, a fixed
period. It reflects the degree of this stock’s regression effects model is not possible due to
activity in the market set equal to trading the time-invariant variables and
volume/circulating share capital. In general, . To reduce endogeneity, we selected
turnover rate is positively related to stock the Maximum Likelihood Estimation (MLE) for
volatility. the regression.
 (2) The log of the total market value of shares We collected the data for this study from the
outstanding of a single stock (l ) Wind database and calculated the regression
 The current market value of a single stock is results using the Stata 14MP software package.
equal to the number of tradable shares We eliminate the influence of outliers by
multiplied by the stock price. This measure winsorizing all continuous variables at the 1%
reflects the scale and competitiveness of a listed level.
firm. Considering the stability of the data and the
relative size of the data, we take the logarithms
before the multiple regression. EMPIRICAL ANALYSIS AND RESULTS
 (3) The CSI 300 index's monthly return rate
 Descriptive statistics: Stock price volatility
(ℎ ℎ _ )
 Table 2 shows the descriptive statistics of
 Compared with other indexes, CSI 300 stocks
 stock price volatility. Whether we look at the
generally have advantages such as outstanding
 whole A-shares market or only the SME Board
profitability, good growth, a valuation below the
 and GEM Board, the standard deviations of the
average market level, and it contains more blue
 auspicious stock and unlucky stock groups are
chip stocks, which are often used as the market
 larger than for all stock groups in both the A-
investment benchmark. Therefore, it is
 shares and SME Board and GEM Board,
reasonable to choose the monthly return rate of
 suggesting that the volatility of the auspicious
the CSI 300 index as the market benchmark.
 and unlucky stock groups is above the average
 level of the market. Compared with the standard
 Model
 deviation of the A-share market, the standard
 This study examines whether the code effect
 deviation of the SME Board and GEM Board is
caused by numerical superstitions affects the
 larger, indicating that the stock volatility of the
volatility of the A-share market. We built a
 SME Board and GEM Board is above the average
multivariate unbalanced panel model of
 level of the market.
auspicious stocks, unlucky stocks, control
variables, and individual stock volatility. The
 Multiple regression using the A-share
model is as follows:
 market sample
 In order to verify hypotheses H1 and H2a, we
 = 0 + 1 +
 run the multivariate regression in model (2) for
 2 + 3 + 4 + the bull market, bear market, and slow bull
 5 ℎ ℎ _ + + (2) samples. Table 3 reports the results, which show
 that the constant and the control variables are
where, is the volatility of stock i in significant at the 1% level in most cases, and the
month t. is the dummy variable for overall statistical quality of the model is good.
auspicious stocks, and is the dummy The main research variables are significant under
variable for unlucky stocks. is the some market cycles, and the model overall has
turnover rate of stock i in month t. is high statistical quality.
the log of stock i's total market value of shares

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Table 2. Descriptive statistics: Stock price volatility

 Group Board N Mean St Variance
 A-share 22679 0.11500 0.18587 0.035
 auspicious
 SME and GEM 7791 0.12963 0.23586 0.056
 A-share 6774 0.12180 0.19597 0.038
 unlucky
 SME and GEM 4543 0.12921 0.21713 0.047
 A-share 111670 0.11747 0.17293 0.03
 all
 SME and GEM 49535 0.12981 0.20488 0.042

Table 3. Multiple regression results based on the A-share market sample

 Dependent variable Volatility
Independent variable Bull market Bear market Slow bull
 (a) (b) (c) (d) (e) (f)
 -2.63595 *** -2.6523 *** 0.1951 *** 0.1950 *** 0.02980 *** 0.02992 ***
 (constant)
 (-50.28) (-50.56) (9.36) (9.36) (3.65) (3.66)
 -0.05997 *** -0.000987 -0.001113 (-
 auspicious —— —— ——
 (-2.97) (-0.32) 0.90)
 0.09922 *** 0.002455 0.000786
 unlucky —— —— ——
 (1.94) (0.49) (0.37)
 0.09998 *** 0.1001 *** 0.04595 *** 0.04594 *** 0.05158 *** 0.0516 ***
 turnover
 (34.74) (34.75) (27.31) (27.31) (78.00) (77.97)
 0.02005 -0.3291 *** 0.3000 *** 0.3001 ***
 hushen_index 0.02009(1.61) -0.3291 *** (-25.15)
 (1.60) (-25.15) (21.84) (21.84)
 0.1786 *** 0.1785 *** -0.00472 *** -0.004735 *** 0.001279 ** 0.00125 **
 lnvalue
 (52.84) (52.80) (-3.66) (-3.68) (2.48) (2.44)
 Log-Likelihood 4496.506 *** 4496.74 *** 6215.209 *** 6215.276 *** 46842.84 *** 46842.5 ***
 observations 22376 22376 23105 23105 65219 65219

 Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. Z statistic in parentheses.

 Columns (a) and (b) of Table 3 show the they tend to hold these auspicious stocks instead
regression results of the bull market period, and of trading them frequently, leading to the small
the main variables and volatility for this group. Second, compared to
are both significant at the 1% level. Thus, during individual investors, institutional investors have
this period, the A-share market has a code effect advantages such as information and funds. In
caused by numerical superstitions? particular, private equity institutions increased
 The coefficient of the variable is their investment after the market warmed up, at
negative, indicating that in the bull market cycle, which time they are more likely to choose
the volatility range of the auspicious stock group unlucky stocks to raise funds, as long as the
is smaller than that in the whole market. The stocks are circulating. They create a huge short-
coefficient of the variable is positive, term money-making effect that attracts
indicating that in the bull market cycle, the individual investors to follow. These institutions
volatility range of the unlucky stock group is then sell the stock at high prices and generate
larger than that of the whole market. Therefore, high volatility. We see this for stock codes
the results support H1 in the bull market. 300144.SZ, 300104.SZ, which increased by more
 The possible reasons for this impact are as than 4 times. Due to the difference between
follows. First, in the bull market cycle, the whole institutional and individual investors, unlucky
market has universal profitability. Most people stocks experience more frequent transactions
in the market make a profit, and most and higher volatility. Third, when there is a short
investments are successful, which thus increases decline in the bull market cycle, considering
investors’ confidence. Because of the special capital security and the high throw bargain-
character of auspicious stocks, investors may hunting, investors sell stocks and control
think that holding these stocks can bring them positions during the decline period in order to
good luck and generate more income. Therefore, reduce risks. We can see this from the trend in

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286 YUN LI, KUN WANG, XUANMING JI, YINGKAI TANG

stocks such as 002008.SZ (SME Board) and effect of numerical preference thereby weakens.
000858.SZ (large-cap stocks). By this period, the Third, in the past two years, China gradually
psychological effects make investors more likely increased its reforms of the securities market,
to hold more auspicious stocks and sell unlucky and the government pays more attention to
stocks. Liu & Chen (2017) also show that the investor education. At the same time, with the
enthusiasm for investment is an important factor advancement of IPO reforms, the market has
affecting volatility. Consequently, the bull more choices for investment, which thus
market's stock selection preference and the contains speculation, and the impact of
investment enthusiasm stimulated by "the numerical superstitions on investment decisions
profound memory of investment success" leads diminishes.
to abnormal volatility in stock prices. Fourth, the
bull market sample period is 2014-2015, when Multiple regression using the SME Board and
the real economy declined, the performance of GEM Board samples
listed firms almost declined, and the market's To test hypothesis H2b, we run the multiple
investment form changed from fundamental regressions using the SME Board and GEM Board
investment to concept speculation, shell market samples for the bull, bear, and slow bull
resource speculation, regional speculation, and markets. Table 4 presents the results.
so on. Irrational and malicious speculation The regression results for the two main
increases psychological effects such as numerical variables and differ
superstitions. partially from the regression results for the A-
 In conclusion, the investor's subjective share market, in which the coefficient of
preferences, market investment form, market is not significant in the bull market and
cognition differences, the imperfect securities the coefficient of is significantly
market, and other factors created the code positive at the 5% level. Therefore, the results
effect in the bull market, as well as the confirm hypothesis H2b. There are a few reasons
differences between auspicious and unlucky for this difference. First, bull markets have
stocks in terms of market performance. better conditions and more frequent
 Columns (c), (d), (e), and (f) in Table 3 show transactions. However, due to the smaller
the regression results for the bear and slow bull circulation market value, greater stock price
markets. The coefficient of the variables volatility, and easier stock price manipulation,
 and are consistent with the SME and GEM Boards are more likely to be
those of the bull market, but are not significant, the target of market speculators. This
suggesting that the code effect caused by speculation eliminates the aversion due to
numerical superstitions in the A-share market numerical superstitions. Second, in a recent slow
disappeared during this period. The empirical bull market, the securities market supervision
results do not verify hypothesis H1 for the bear system improved, and most of the investment
and slow bull market cycles, but they do confirm was in white horse and blue chip stocks; for
hypothesis H2a. example, the SSE 50 index surged in 2017, as did
 The code effect may disappear for several speculative stocks such as Xiong'an New District,
reasons. First, in a bear or slow bull market, the Unicorn, and other concept stocks. Speculators
phenomenon of widespread profitability are more likely to select auspicious stocks (e.g.,
disappears, and even widespread losses occurs. Xiong'an leading stock 000856.SZ, Unicorn
Investor confidence is low and investment tends leading stock 002208.SZ). By this time, there is a
to be cautious. Institutional investors' code effect caused by numerical superstitions.
investment positions are strictly controlled, and
stock selection is more precise. At this time, Robustness test
investors cannot select stocks based on the In order to enhance the reliability of the
auspiciousness of its code. The code effect on empirical results and avoid a pseudo-regression
stock price volatility thus disappears. Second, caused by index construction and other factors,
since the beginning of 2016, the Chinese we construct a new stock price volatility variable
economy began to grow, and the performance of following Alizadeh, Brandt and Diebold (2002),
listed firms gradually improved. The market and conduct a panel OLS regression of random
tends to select stocks with good investment effects.
performance and that are undervalued. The

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FINANCIAL PSYCHOLOGY ANALYSIS OF NUMERICAL SUPERSTITIONS AND STOCK PRICE VOLATILITY: EMPIRICAL EVIDENCES FROM CHINA’S A-SHARE MARKET 287

Table 4. Multiple regression results using the SME Board and GEM Board market samples

 Dependent variable Volatility
 Independent variable Bull market Bear market Slow bull
 (a) (b) (c) (d) (e) (f)
 -3.403 *** -3.4203 *** -0.00973 -0.00933 0.01753 0.0178
 (constant)
 (-41.82) (-42.11) (-0.21) (-0.20) (1.06) (1.08)
 -0.07641 ** 0.00796 0.004837 **
 auspicious —— —— ——
 (-2.13) (1.30) (2.14)
 0.0648 -0.00629 -0.000016
 unlucky —— —— ——
 (1.21) (-0.84) (-0.01)
 0.09261 *** 0 .0926 *** 0.0554 *** 0.0541 *** 0.05388 *** 0.05388 ***
 turnover
 (21.03) (21.05) (9.71) (18.60) (51.31) (51.29)
 -0.02377 -0.02385 -0.3302 *** -0.3254 *** 0.3159 *** 0.3158 ***
 hushen_index
 (-1.14) (-1.14) (-14.83) (-13.74) (12.83) (12.83)
 0.2363 *** 0.2362 *** 0.00877 *** 0.00887 *** 0.002063 ** 0.00209 **
 lnvalue
 (44.53) (44.51) (2.99) (3.03) (1.95) (1.98)
 Log-Likelihood 769.893 *** 769.0931 *** 964.573 *** 964.076 *** 15909.5 *** 15907.2 ***
 observations 9565 9565 10081 10081 29370 29370

 Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels , respectively. Z statistics in parentheses.

Table 5. Robustness test results using the A-share market sample

 Dependent variable Volatility
 Independent variable Bull market Bear market Slow bull
 (a) (b) (c) (d) (e) (f)
 -3.0687 *** -3.0845 *** -0.3178 *** -0.318 *** -0.5636 *** -0.5636 ***
 (constant)
 (-31.07) (-31.22) (-11.85) (-11.24) (-33.42) (-33.35)
 -0.6138 *** -0.0014 -0.00239
 auspicious —— —— ——
 (-3.37) (0.32) (-1.27)
 -0.09486 ** -0.0022 0.00289
 unlucky —— —— ——
 (2.04) (-0.46) (1.1)
 0.1346 *** 0.1346 *** -0.0596 *** -0.0596 *** 0.0623 *** 0.0623 ***
 turnover
 (22.52) (22.52) (15.61) (15.62) (31.47) (31.46)
 -0.3710 *** -0.3711 *** 1.5757 *** 1.5757 *** 1.0517 *** 1.0517 ***
 hushen_index
 (-22.27) (-22.28) (113.58) (113.58) (68.45) (68.44)
 0.2047 *** 0.2045 *** 0.0199 *** 0.1997 *** 0.0334 *** 0.334 ***
 lnvalue
 (31.27) (31.18) (12.5) (12.27) (31.80) (31.67)
 Wald-statistic 2374.87 *** 2342.56 *** 16010.89 *** 15711.91 *** 6444.77 *** 6413.51 ***
 observations 22376 22376 23105 23105 65219 65219

 Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. Robust t-statistic in parentheses.

 Table 5 shows the results of multiple Board market samples. The coefficients of the
regression based on the robustness test of the A- two main variables and 
share market sample. The coefficients of the two are consistent with the main test results in Table
main variables and are 4. In the robustness test, the significance of
consistent with the main results in Table 3, but rises to 1%, but the P value does not
the coefficient of is significant at the increase much. Additionally, as with the
5% level. The significance of the control variable robustness test using the A-share market
ℎ ℎ _ in the robustness test varies. In sample, the significance level of the control
the bull market cycle ℎ ℎ _ has higher variable ℎ ℎ _ improves, for the same
significance, which may be due to the change in reason as above. Thus, the main results pass the
the construction of the variables, and thus the robustness test. In conclusion, the main
synchronization improves. Thus, the results pass empirical results of this paper are robust and
the robustness test. reliable.
 Table 6 shows the multiple regression results
using the robustness test with the SME and GEM

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 2020, Vol. XXIX, N°1, 279-289 DE CLÍNICA PSICOLÓGICA

288 YUN LI, KUN WANG, XUANMING JI, YINGKAI TANG

Table 6. Robustness test results using the SME Board and GEM Board market samples

 Dependent variable Volatility
 Independent variable Bull market Bear market Slow bull
 (a) (b) (c) (d) (e) (f)
 -4.3 *** -4.235 *** -0.5837 *** -0.5842 *** -0.728 *** -0.729 ***
 (constant)
 (-28.91) (-29.13) (-7.95) (-7.98) (-26.29) (-26.22)
 -0.713 *** 0.0012 0.00889 **
 auspicious —— —— ——
 (-2.97) (1.19) (2.23)
 -0.0539 -0.115 * -0.00158(-
 unlucky —— —— ——
 (0.7) (-1.68) 0.43)
 0.1235 *** 0.1236 *** 0.0725 *** 0.0723 *** 0.0666 *** 0.0668 ***
 turnover
 (12.50) (12.51) (11.50) (11.52) (24.64) (24.61)
 -0.6391 *** -0.639 *** 1.641 *** 1.641 *** 1.612 *** 1.6116 ***
 hushen_index
 (-27.96) (-27.96) (62.73) (62.78) (46.46) (46.47)
 0.295 *** 0.295 *** 0.0368 *** 0.037 *** 0.0439 *** 0.0441 ***
 lnvalue
 (29.45) (29.43) (8.17) (8.11) (25.18) (25.26)
 Wald-statistic 1760.50 *** 1736.99 *** 7473.40 *** 7077.75 *** 3115.67 *** 3120.62 ***
 observations 9565 9565 10081 10081 29370 29370

 Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. Robust-t statistic in parentheses.

 Science Foundation of China (No. 71072066),
 CONCLUSION Sichuan University (No. SKGT201602), Innovative
 Spark Project of Sichuan University (Grant No.
 The psychological characteristics and
 2019hhs-15) and the Department of Science and
personal preferences of stock investors and the
impact of culture on investor behavior have Technology of Sichuan Province (No. 2018JY0594).
always been the focus of behavioral finance and
financial psychology research. China's stock
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