MSIN 0112
GROUP ASSIGNMENT
Group C
Do people really exhibit stock name biases?

Introduction

Background and Literature Review
Is a stock name and code number critical to stock investors? More specifically, would
investors buy stocks because of the priority preference to the name or code number of this
stock? Those questions are important for understanding the biases on stock name and code
when investors making investment decisions. To make optimal investments, investors spend
tremendous amount of money and efforts to acquire stock-related information. In 2016, Xing
et al. pointed out that there is a positive relationship between stock ticker symbol and the
firm value. The features of stock names were linking to the reflection of investors’ feelings,
which are indeed affecting the assessment of firm values.

Before conducting a comprehensive research on the financial information of specific stocks,
investor’s first impression to a stock, which is commonly presented by the name and the
trading code, may have substantial influence on their investment choices. This psychological
predisposition could be thought as derived from individual’s preferences for certain numbers
or words. Knewtson and Sias (2010) pointed out that personal preferences comfort people in
a way that generating favourable reaction under certain circumstances, which in turn had
impact on investor’s choices of stock selection.

Furthermore, factors influencing the preferences to numbers are worth to pay attention to.
According to Yang (2011), the use of superstitious numbers in pricing and the exploitation of
superstition in retail sales was more than a cultural phenomenon. People have personal
superstitious numbers around the world. In most of the western countries, number 13 is
considered as a symbol of bad luck. Some Italians are superstitious about Friday the 17th
because rearranging the Roman numeral XVII creates the world VIXI – translated from Latin
meaning “my life is over”. And in China, number 4 sounds like the Chinese word for death,
thus some buildings in China skip number 4 in symbolizing floors.

There are two main contributions to the literature. Firstly, we extended this topic to Chinese
stock market, which is a rapidly emerging and animated market in the world, having growing
impact on the international level of the world economy. Secondly, numbers rather than
letters are used to stand for the symbols of stocks, from where we go further to prove that
the results of Xing et al. (2016) could be applied to symbols with numbers as well.

Research Question
The aim of our research is to test whether people exhibit stock name biases in China's stock
market.

Two Hypotheses
1) In China’s stock market, people would invest less in the stocks with symbols they like.
2) In China’s stock market, people would invest less in the stocks whose symbols contain
special numbers.

Method

Participants
We summarize the statistics of our participants referring to Pickering (2017)’s structure. Our
sample includes 20 observations. The ages of our participants range from 20 to 53 years.
The mean of the ages is 31.95, and the standard deviation is 11.4546. 40% of our
participants are males, and 60% are females.




Table 1
Observations n = 20
Age
Mean 31.95
Standard Deviation 11.4546
Min 20
Max 53

Gender
Male 40%
Female 60%

Materials
To research on the topic, we decide to use survey as our research method. The reasons are
as follows. Firstly, compared to experimental designs, non experimental designs (such as
case studies and correlational studies) are more natural. Additionally, within the non
experimental designs, we cannot apply statistical analysis on case studies, and the results
from case studies cannot be generalized on population because of limited samples (1, 2 or a
few people). However, the aim of our research is to generalize population parameters
through the statistical analysis of sample statistics. Thus, we discuss the topic through
correlational studies.

To be specific, we design a questionnaire and do surveys to collect the data we need for
correlational studies. Our questionnaire strictly follows the principles of survey. For example,
to protect the participants’ personal information, our questionnaire does not collect the
participant’s name, address, telephone number, etc. One example is that all of the
participants are the people we know, including the group members’ friends and relatives,
and the age is between 18 and 65 years old.
The questionnaire contains five questions. Through asking for the minimum questions for
the study, we respect the participants’ time and privacy, and reduce the difficulty of
analysing the data. Even though the number of questions is limited, question types are
diversified. For instance, we design open-ended questions when asking about age, birthday
and lucky number of the participant. Besides, fixed questions are used to divide gender and
mark the how participants like symbols.

Design and procedure
First, ask participants open-ended questions about their background. The aim of this part is
to obtain descriptive statistics of our sample, e.g.
1) Please write down your age. “I am 24 years old.”
Besides the use of describing samples, we can use the results of this question to eliminate
the samples not meeting the requirement (age between 18 and 65) as well.
2) Please choose your gender. “I prefer not to tell you my gender.”
Considering the diversity of personality and the willingness of the participants to state
genders, we add another two options “Others” and “Prefer not to tell” in addition to “Male”
and “Female”.

For hypothesis 1: In China’s stock market, people would invest less in the stocks with
symbols they like.
1) IV: Likeability of symbols. Ask participants questions to measure the IV, e.g.
We randomly choose 50 stock symbols from the stock list of China’s market in Yahoo
Finance. The question is design to measure the scale. The participants are asked to use the
number 1-5 to rate the extent to which they like a certain stock symbol. To be specific, 5 =
strongly like, 4 = like, 3 = neither like nor dislike, 2 = dislike and 1 = strongly dislike.
One possible answer might be “I strongly dislike the symbol 600449”.
2) DV: Prices of stocks.
According to the research method of Xing et al. (2016), the impact was measured by stock’s
Tobin’s Q. Thus, we consider the prices of stocks as our DV. The prices are acquired from
Yahoo Finance.
3) Calculate the total score for each symbol, and the log number of stock price.
4) Calculate the correlation between the total scores and the log prices.

For hypothesis 2: In China’s stock market, people would invest less in the stocks whose
symbols contain special numbers (traditional/personal lucky numbers and birthdays).
1) IV: The extent to which the symbols are special to the participants. Ask participants
questions to measure the IV, e.g.
We ask the participants for their birthday, which is considered as special numbers to them.
Possible answer: “0420”.
In addition, the participants’ personal lucky numbers are asked for. Possible answer: “7”.
Traditionally, Chinese consider “6” and “8” as lucky numbers. We allocate one score to the
symbol when it matches one principle mentioned above.
2) DV: Prices of stocks, which is the same as the DV mentioned in the first hypothesis.
3) Calculate the total score for each symbol, and the log number of stock price.
4) Calculate the correlation between the total scores and the log prices.

Results

Hypothesis 1
Table 2
Variable ln Price
Score for likeability 0.0679***
(6.17)
Observations 20
R-squared 0.4422

Table 2 reports the regression result of the impact of likeability of symbols on the prices of
stocks, and DV is stock price in logarithmic form. The regression result shows the estimated
coefficient of likeability of symbols is statistically significant at the level of 1% (p = 0.000),
thus we reject the null hypothesis. Additionally, the coefficient is positive, indicating the
increase of likeability is conducive to raise stock price. On average, when the likeability
increases 1 score, the stock price would increases 0.06%. Our result is in accordance with
the results from Xing et al. (2016), and goes further to prove the conclusion also applies to
symbols with numbers rather than letters.

Hypothesis 2
Table 3
Variable ln Price
Score for special degree -0.0021
(-0.08)
Observations 20
R-squared 0.0001

Table 3 reports the result of special degree of symbols on prices of stocks, and DV here is
log stock price. The result shows the coefficient of special degree is not statistically
significant (p = 0.933), thus the null hypothesis cannot be rejected. There might be several
reasons. First, the special degree of a symbol has no significant relationship with stock price.
The investment behaviours of the investors in China’s stock market are mostly influenced by
other factors, such as the past prices of the stock, or the performance of the firm.
Additionally, the principles we choose (traditional/personal lucky numbers and birthdays)
might not fully represent the special degree of symbols.

Conclusion

General Discussion
The existence of stock name biases misleads investors’ investment choices on stocks, and
followed by influences on stock values. As the developing impact of China’s stock market,
this study make a survey on 20 participants to test whether investors exhibit stock name
biases in this specific stock market. Two hypotheses are conducted to demonstrate our
research objective. The result for the first hypothesis shows that, in China’s stock market,
people would invest more in the stocks with symbols they prefer. However, the result of the
second hypothesis fails to show a significant relationship between investment choices on
stocks and the superstitious numbers in stock symbols. Therefore, to conclude, Chinese
stock market shows a stock name biases to some extent.

Limitations and Suggestions for Further Research
Only 20 individuals participated in the survey for this topic, and most of the participants are
friends and families of the authors, some of whom even have no experience in entering the
stock market. It is not sufficient to fit in the model and delivery an accurate and convincing
result with small database provided by limited sample size. As Yang (2011) pointed out that
in Chinese market, people pay extra money on products with price tags fitting their
superstitious preferences. Yet our model fails to show a significant relation between stock
price and individual’s preference on superstitious numbers in the second hypothesis.

Additionally, because of the narrow scope, our sample cannot stand for the average
investors in China. Besides limited sample data available, our sample obviously contains
biases, such as geographic bias. It does not include participants from enough provinces and
areas in China, as regional and cultural differences are highly noticeable across the country,
thus the superstitious level on symbols of participants also differs.

Further researchers could conduct survey on large sample to obtain abundant and sufficient
database to support the model. Big data modelling with appropriate statistical method is
believed to deliver a more accurate and authoritative result. Furthermore, regional
differences should be taken into consideration. Moreover, in order to display a more in-depth
research in this topic, whether participants having experiences in stock market impact the
result is worth to investigate in.

Word count: 1778
Reference

Knewtson, H. S., & Sias, R. W. (2010). Why Susie owns Starbucks: The name letter effect in
security selection. Journal of Business Research, 63(12), 1324-1327.

Pickering, R. M. (2017). Describing the participants in a study. Age and Ageing, 46(4), 576–
581.

Xing, X., Anderson, R. I., & Hu, Y. (2016). What׳ sa name worth? The impact of a likeable
stock ticker symbol on firm value. Journal of Financial Markets, 31, 63-80.

Yang, Z. (2011). “Lucky” numbers, unlucky consumers. The Journal of Socio-Economics,
40(5), 692-699.

















Appendix

Questionnaire
1) [Open-ended] Please write down your age. _____
2) [Fixed] Please choose your gender. _____
a. Male
b. Female
c. Others
d. Prefer not to tell
3) [Open-ended] Please write down your birthday (yydd, e.g. 0405). _____
4) [Fixed] What is your lucky number between 0 and 9? _____
5) [Fixed] [Likert scale] There are 50 symbols of stocks in China’s market listed below.
Please use the number 1-5 to mark how you like each symbol. (5 = strongly like, 4 = like, 3 =
neither like nor dislike, 2 = dislike, 1 = strongly dislike)
Symbol Score Symbol Score
000038

002270
601968

601138
603557

600276
002472

000002
300625

601998
002297

603288
002562

600104
300497

600663
002628

601088
000908

000651
600571

000333
002148

600000
600449

601166
002847

000858
000919

600900
600117

601328
603579

600028
002101

601628
600106

600036
002060

601988
603916

600519
000779

601857
603681

601318
300220

601398
002892

601939






Results from Stata (screen shots)
1) Descriptive statistics


2) Regression for hypothesis 1


3) Regression for hypothesis 2