ISSN (Print) 0473-453X

Discussion Paper No. 1156

ISSN (Online) 2435-0982

FINANCIAL FORECASTING
IN THE LAB AND THE FIELD:

Qualified Professionals
vs. Smart Students
Te BaoBrice Corgnet
Nobuyuki Hanaki
Katsuhiko Okada
Yohanes E. Riyanto
Jiahua Zhu

January 2022

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Financial Forecasting in the Lab and the Field:
Qualified Professionals vs. Smart Students*
Te Baoa, Brice Corgnetb, Nobuyuki Hanakic, Katsuhiko Okadad,
Yohanes E. Riyantoe, Jiahua Zhuf
January 5, 2022
Abstract
We compare the performance of financial professionals (CFAs) with university students infour financial forecasting tasks ranging from simple lab prediction tasks to longitudinal fieldtasks. Although students and professionals performed similarly in the artificial forecastingtasks, their performance differed in the more realistic tasks. Yet, increasing the‘representativeness of the situation’ in the lab tasks did not systematically benefit financial
professionals as students outperformed CFAs when forecasting historical series. However,professionals outperformed students in the field task. Our results imply that the expertise offinancial professionals might have been underestimated in previous works that focused on lab
tasks.
Keywords: Financial forecasting, financial professionals, financial literacy, cognitive skills.
JEL Codes: C91, C93, G17, D91, G41.

*

The experiments reported in this paper have been approved by the IRB of the Institute of Social and Economic
Research (ISER), Osaka University. We gratefully acknowledge financial support from grant-in-aid for scientificresearch (KAKENHI, grant number: 18K19954, 20H05631) from the Japan Society for the Promotion of Science(JSPS) as well as support from the Joint Usage/Research Center at ISER, Osaka University. We thank the
Securities Analysts Association of Japan for their support in recruiting participants for our experiment. We also
thank Kazuhito Ogawa and staff members at Research Institute of Socionetwork Strategies at Kansai University
for their support in conducting our additional experiments, and Yuta Shimodaira for his excellent research
assistance. This research has been performed within the framework of LABEX CORTEX (ANR-11-LABX-0042)
of Université de Lyon, within the program Investissements d'Avenir (ANR-11-IDEX-007) run by the French
National Research Agency (ANR).
aDivision of Economics and NTU-WeBank Joint Research Center, Nanyang Technological University. Email: baote@ntu.edu.sgbEmlyon Business School. E-mail: corgnet@em-lyon.com
cCorresponding author. Institute of Social and Economic Research, Osaka University. E-mail:nobuyuki.hanaki@iser.osaka-u.ac.jpdInstitute of Business and Accounting, Kwansei Gakuin University, and Magne-Max Capital Management. Email: katsuokada@magne-max.com
eDivision of Economics, Nanyang Technological University. E-mail: yeriyanto@ntu.edu.sg
f
Ma Yinchu School of Economics, Tianjin University. E-mail: jiahuazhu@tju.edu.cn

11. Introduction
Financial forecasting has occupied the mind of people for centuries, possibly startingwith the use of Babylonian tablets to predict crop prices (Lo and Hasanhodzic, 2010).
Prominent scientists of all fields have also tried their luck in forecasting stock prices,
often with little success.1 Financial forecasting likely appeals to the crowds not only
because success can bring fortune, but also because the roots of success remain
secretive. Any enduring recipe for success must indeed remain hidden.
Finance scholars have long argued that no winning recipe can exist because markets
are informationally efficient so that all the relevant information for predicting stocks
are already incorporated into prices (Samuelson, 1965; Fama, 1965, 1970, 1991;
Batchelor, 1990; Hartzmark, 1991; Barber and Odean, 2000; Qu, Timmermann and Zhu,
2019). They postulate that asset prices follow a random walk, and any successful
prediction can only be due to luck (Malkiel, 1999; Tetlock, 2009b). At the empiricallevel, a series of studies have used survey data to evaluate forecasting accuracy
following the early work of Cowles (1933). These studies use data from the Livingston
Survey, a biannual longitudinal survey of experts’ forecasts that started in 1946 andwas commissioned by the Federal Reserve Bank of Philadelphia. Experts, which
include US economists from the public and private sectors and academics, are asked toprovide stock market forecasts for six-month and twelve-month horizons. The last
survey from December 2021 lists twenty-five experts. Studies have shown that the
average expert does not perform better than the random walk model in predicting the
US stock returns (Lakonishok, 1980; Pearce, 1984; De Bondt, 1991).
Furthermore, forecasts are often, though not always, biased (Brown and Maital, 1981;
Dokko and Edelstein, 1989). Anecdotally, Kahneman (2011) also reports, usingarchival data, that the performance rank of financial advisors is not correlated acrossyears. All these findings echo the work of Tetlock (2009a), who showed the inabilityof experts to consistently predict major political events and likened forecasting toreading tarot (Tetlock, 2009b).
Despite the early survey results and the numerous claims that there is no expertise in1

Isaac Newton is an often-cited example (see Malkiel, 1999, p. 45).

2

financial forecasting, a number of factors spark skepticism. At the theoretical level, theargument of finance scholars relies on the belief in the efficiency of markets, but thisargument has been challenged by the advent of behavioral finance (De Bondt andThaler, 1985, 1987; Lo and MacKinlay, 1988; Bernard and Thomas, 1990; Cutler,
Poterba and Summer, 1991; Chopra, Lakonishok and Ritter, 1992; Jegadeesh andTitman, 1993, 1995; Shleifer, 2000; Lo, 2019). At the empirical level, recent works
have highlighted the existence of forecasting skills related to cognitive ability andpattern recognition (Corgnet et al., 2021) and personality traits such as openness toexperience (Gardner and Tetlock, 2016). 2 Furthermore, a cursory look at financial
media indicates that experts and day traders continue to forecast stock markets, thus
suggesting it might be a potentially profitable activity (Antweiler and Frank, 2004;
Engelberg, Sasseville, and Williams, 2012; Sprenger et al., 2014).
The early empirical analysis using data from the Livingstone Survey should also beinterpreted with care. The survey collects forecasts that are not incentivized for theiraccuracy, thus possibly catering to other motives. For example, Pierdzioch and Rülke(2012) have shown that the Livingston experts tend to make forecasts that are markedly
different from the average forecast, thus showing anti-herding behavior. Thisphenomenon relates to the reputational gains a forecaster can obtain from being rightwhen all others are wrong, the so-called ‘superstar effect’ (Scharfstein and Stein, 1990;
Laster, Bennett and Geoum, 1999). Furthermore, the Livingston survey is based only
on a handful of experts, which may not represent the population of experts. 3
Our goal is thus to reexamine the claim that financial experts cannot forecast any better
than lay people using experiments instead of surveys. Our design is inspired by previous
works using forecasting tasks in the lab to study financial forecasting by non-experts.
This research highlights people’s tendency to extrapolate past trends in prices (DeBondt, 1993; Bloomfield and Hales, 2002; Schwaiger et al., 2020) and their failure to
anticipate mean reversion (Beshears et al., 2013). To assess forecasting expertise, we

2Philip Tetlock and Daniel Kahneman (see p. 267 of Kahneman, Sibony and Sunstein, 2021) who wereprominent supporters of the absence of financial forecasting expertise have revised their earlier beliefs
to acknowledge the existence of superforecasters who consistently outperform their peers as well as
computational models.
3
In contrast to the finding of Pierdzioch and Rülke (2012), Graham (1999) reports that analysts withhigh reputation are more likely to herd based on the data from analysts who publish investment
newsletters.

3

conducted our experiments with financial professionals (Certified Member Analysts of
the Securities Analysts Association of Japan) and compared their performance withstudents often used as a convenience sample in finance experiments (Kirchler et al.,2018).
A few experimental studies have directly compared financial professionals withstudents, often showing that students perform at least as well as professionals. Glaser,
Langer, and Weber (2007) reported no differences in performance in a simple task thatconsisted in identifying the trend of a stochastic variable. In a herding experiment,
Cipriani and Guarino (2009) reported results for financial professionals that were very
similar to earlier results obtained with students (Cipriani and Guarino, 2005).
Comparing their findings with financial professionals to previous results with students,
Mann and Lock (2005) showed that both samples exhibited a disposition effect, as
participants were more likely to continue to hold stocks that had lost value than stocks
that had gained value. In Weitzel et al. (2020), professionals and students achieved
similar earnings in experimental markets in which both pools participated in the samesessions. 4 Schwaiger et al. (2020) also found that both professionals and students
exhibited more optimistic price expectations if the price of an asset fell before
recovering than if the price moved in the opposite scenario.
In the studies in which differences were found across samples, professionals often
exhibited stronger behavioral biases than students. For example, Haigh and List (2005)
showed that Chicago Board of Trade traders exhibited higher levels of myopic loss
aversion than students. Gilad and Kliger (2008) also reported that financial
professionals were more affected by priming in a laboratory investment task thanstudents. In particular, professionals were more likely to invest in the stock when they
were asked to remember a story stimulating risk-seeking behavior before making theirinvestment decision. Relatedly, Cohn, Fehr, and Maréchal (2015) showed that priming
financial professionals with a boom or a bust scenario affected their risk attitudes inline with countercyclical risk aversion. Furthermore, Glaser, Langer, and Weber (2007)
reported higher levels of overconfidence of professionals than students in a laboratory
prediction task.
4This earning comparison is not reported in the paper, but the data is publicly available and one cancalculate that the difference in earnings in mixed sessions was close to zero (0.0658%) (Wilcoxon Rank
Sum Test, p-value = 0.910).

4Interestingly, differences among financial professionals have also been identified. In
particular, professionals with more extensive market experience tend to exhibit fewer
biases associated with the endowment effect in various marketplaces (see Genesove
and Mayer, 2001; List, 2003; Locke and Mann, 2005). In a few studies, financial
professionals have also exhibited fewer biases than students. For example, Schwaiger
et al. (2020) showed that students were more likely to be impacted by the framing of
the graphical information associated with the history of stock prices than professionals.
In a series of herding experiments, Alevy, Haigh, and List (2007) showed that financeprofessionals did better than students in assessing the precision of signals and were alsoless likely to be impacted by the presence of losses. However, the authors did not reportsignificant differences in earnings between financial professionals and students (seeResult 1, p. 161).
Overall, the evidence that financial professionals perform better than students inexperimental tasks is somewhat limited (see Fréchette, 2015, for a review). Yet, thislack of sharp differences across samples might be due to the abstract nature of the task
participants faced in the experimental studies. The studies comparing students andfinancial professionals belong to what Harrison and List (2004) defined as ‘artefactual
field experiments’, which are standard lab experiments conducted with non-student
subject pools. Previous studies have thus focused on the ‘representativeness of the
population’ while abstracting away from the ‘representativeness of the situation’.
However, List (2021, p. VIII) claims that the ‘representativeness of the situation’ might
be the most critical dimension:“Third is the representativeness of the situation. A subtle fact is that the researchand policy communities oftentimes generalize results to both a population ofsituations and a population of people, even though we often only speak of the latter.
This is particularly troubling considering that the data, thus far, suggest that
representativeness of the situation is much more important than the representativeness of the population when it comes to generalizing or scaling (see, e.g.,
List, 2007).”
It is interesting to note that, despite focusing on the ‘representativeness of the
population’ by comparing various samples in standard experimental tasks, Snowberg
and Yariv (2021, p. 690) also recognized the relevance of the often-neglected5‘representativeness of the situation,’ as highlighted in the following quotation:“For example, we do not address worries that individuals may respond differently
to choices that do not mimic the somewhat artificial designs often seen in the lab.
We are sympathetic to this view and certainly believe that framing of decisions
matters for choices.”
It follows that financial professionals might perform better in a setting that more closely
resembles their daily work environment. Thus, it seems vital to study financial
forecasting expertise by comparing professionals and students in various settings,
including ‘artefactual field experiments’ and ‘framed field experiments’. The latter
involves conducting longitudinal experimental tasks that consist of forecasting actual
financial series over a long period of time (Harrison and List, 2004) as in Glaser, Iliewa,
and Weber (2019).5 The latter study asked financial professionals recruited from theZEW Financial Market Survey to forecast the German stock market one month ahead
between September 2012 and June 2015. However, this study was not incentivized anddid not compare professionals’ and students’ performance. Our main contribution isthus to assess financial forecasting expertise by comparing the performance ofprofessionals and students in incentivized experiments using both lab and field tasks.
To our knowledge, ours is the first study comparing professionals and students acrosslab and field tasks.
Our study recruits certified financial analysts (CFAs henceforth) from Japan andstudents from an elite Japanese university. All participants were informed that the study
consisted of 4 experiments. Each experiment involved forecasting tasks that varied thetype of financial series to be predicted and the information available for the forecast.
The first two experiments involved simple tasks that consisted of predicting the next
value of artificially-created series, as in Bloomfield and Hales (2002) and Glaser et al.
(2019) (Tasks RW & SPmonthly, henceforth). The third experiment (Task SPdaily,
henceforth) consisted of forecasting historical stock market series. Participants wereshown graphs of 12 months of end-of-day prices of randomly selected stocks from the
S&P 500 starting from a randomly selected day between January 1st, 2008, and June

5

Beyond forecasting tasks, previous works have also framed their experimental design in a financial
context to study bubbles (Kirchler, Lindner, and Weitzel, 2018), risk (Razen, Kirchler and Weitzel, 2020),
and investments in structured financial products (Hanaki, 2022).

6

30th, 2018. They were not told the name of the stock or the starting date of the graph,
which was randomly selected. Finally, the fourth experiment (Task NKI, henceforth)asks participants to forecast the closing price of the Nikkei three to four weeks ahead.
This task is very similar to the daily jobs of finance professionals in our study and thetask used by Glaser et al. (2019) in their Study 3.6
Thus, the first three experiments involved lab tasks, whereas the last one featured a fieldtask identical to finance professionals’ day-to-day jobs. Although students andprofessionals performed similarly in the most abstract lab tasks that do not require
numeracy skills, their performance differed in the more realistic lab task and in the fieldtask. This shows that increasing the ‘representativeness of the situation’ affects therelative performance of both samples, in line with List’s (2021) claim. However,increasing the ‘representativeness of the situation’ did not necessarily benefit financial
professionals. In the case of our third experiment in which professionals had to forecast
a historical series without any information but the graphs of the series, we found thatstudents performed better than professionals. We interpreted this surprising finding as
resulting from experts’ excess zeal, where zeal is defined as the difference between aparticipant’s forecast and the last observed value of a financial series. Zealous experts
thus made forecasts that sharply differed from the last observed value, which is the best
predictor of a stock price if markets are informationally efficient (Fama, 1970). We findthat zeal increased substantially when experts were asked to predict an actual series
compared to a simulated series, which explains their poor performance in the third
experiment. However, professionals outperformed students in the field forecasting task
in which their expertise and better access to information could justify bold predictions.
Furthermore, differences in performance across samples were largely mediated byfinancial literacy, whereas cognitive ability did not play a substantial role. This findingemphasizes that financial forecasting expertise that goes beyond cognitive skills exists.
CFAs could afford to be zealous in the field task (Task NKI) because they had accessto additional tools, including paid services providing real-time updates on the stock
market and technical analysis indicators. It follows that the relationship between the‘representativeness of the situation’ and the relative performance of experts is not

6This type of forecasting task also captures the essence of Keynes’ “beauty contest” metaphor of
financial markets (Nagel, 1995, Hommes, 2021).

7

monotonic. As a result, it might be difficult to assess the true extent of financial
professionals’ expertise in lab studies. The expertise of financial professionals mightthus have been underestimated in previous works that focused on lab tasks. Yet, in linewith Fréchette (2015), we should acknowledge that the size of the difference inperformance between students and professionals remains small, even in the field task
(Cohen’s d = 0.243).
2. Design
2.1. Participants
We recruited financial professionals who were CFAs and students from Osaka
University. Professionals were certified members of the Securities Analysts
Association of Japan (SAAJ) that were recruited thanks to the association’s
support. We were able to invite more than 16,000 members of SAAJ to participate
in our experiment, of which 255 initially registered for the experiment.
CFA is the highest-level professional certification in the financial industry. To become
a CFA, candidates must have at least 3 years of professional experience in investmentrelated jobs. Candidates need to pass two exams with an overall success rate between
20 and 25% on the two tests. Among Japanese CFAs, a large proportion of applicants
work for the most renowned financial institutions in the country, such as Nomura
Securities and Mitsubishi UFJ. Given the difficulty of the exam, they are very likely tohave acute financial knowledge, as is illustrated by the financial literacy scores of our
CFA sample (91.06% of correct answers on the 12-item financial literacy scale,Fernandes, Lynch, and Netemeyer, 2014). To the extent that we are interested inassessing how financial knowledge and experience can explain differences in
performance between financial professionals and university students, CFAs represent
an ideal pool of participants.
Our university participants were enrolled at Osaka University, widely considered anelite university in Japan. According to entry exams scores, students admitted to Osaka
University are among the top 10% in Japan. According to international rankings suchas

QS

(https://www.topuniversities.com/university-rankings/world-university-

rankings/2022) and Shanghai (https://www.shanghairanking.com/), Osaka University
is in the Top 5 in Japan. Not surprisingly, the scores on the CRT test (Frederick, 2005)8

of our Osaka University students’ sample place them at the top of the worldwide
distribution of cognitive ability along with institutions such as MIT and Princeton.
2.2. Protocol
Participants were involved in a longitudinal study that consisted of 4 forecastingtasks. The tasks were as follows:a) RW: Forecasting random walk time series. We asked participants topredict the pre-generated (artificial) time series.
b) SPmonthly: Forecasting S&P 500 stocks prices or stock returns basedon monthly series. We asked participants to predict either stock prices orstock returns from the S&P 500 index without knowing the actual stock’s
name and the selected historical sample. Participants received graphicalinformation of monthly values of either prices or returns.
c) SPdaily: Forecasting S&P 500 stocks prices based on daily series. Thistask was similar to the SPmonthly task, except that participants received
information about the daily instead of the monthly stock values.
d) NKI: Forecasting the Nikkei index. We asked participants to forecast theclosing price of the Nikkei index three to four weeks ahead.
Participants knew they were recruited for four tasks and were told that each of thefirst three tasks would take between 15 and 30 minutes. While participants were
not given the instructions for each task when recruited, they were told that the last task
consisted of forecasting the Nikkei index. Each task was completed sequentially. For
SPdaily, we recruited people who completed RW. For SPmonthly, we recruited
those who completed SPdaily. Although we encouraged participation in all thetasks in our e-mail invitations, it was not mandatory. RW, SPmonthly, and SPdaily
were all conducted between December 2020 and March 2021. The NKI was conducted
between March and September 2021. Table 1 summarizes the main features of the four
forecasting tasks.
[TABLE 1 AROUND HERE]

9

For each of the first three tasks, CFAs (students) were paid ¥1,000 (¥200) for their
participation. They also had a 10% chance of being selected for an additional reward
based on their earned points in the experiment, where 1 point was worth ¥100 for CFAs
and ¥20 for students. The payment scheme for NKI was based on a tournament
structure where the best CFA (student) performer earned ¥5,000 (¥1,000).
In all tasks, the payments offered to CFAs were five times greater than forstudents to compensate the difference in hourly wages for these two populations,considering the hourly wage of undergraduate students at the university is about¥950 and the average hourly wage of CFAs in Japan being about ¥4500.7 All the
payments were made using Amazon gift cards (e-mail version), and the mode ofpayment was known to participants when signing up for the experiment.
Below, we describe each task in detail.
2.3. Tasks
2.3.1. RW: Predicting Random Walk Time SeriesThis task was based on the experimental design of Bloomfield and Hales (2002).
Participants were shown 16 graphs that were generated using a random walkstochastic process following Bloomfield and Hales (2002, p. 403), and they weretold that:“We have constructed a model of a random process that works much likeflipping a fair coin. Using this model, we have created sequences of outcomes.
An upward movement indicates a “heads” outcome, and a downward
movement indicates a “tails” outcome.”
Each graph consisted of nine prices and captured both upward and downward
sloping dynamics (see Appendix A.1 for graphs and detailed instructions).8

7

According to information available on a Japanese website related to change of occupation(https://mynavi-agent.jp/helpful/income2020/category/finance01/, last checked on Oct 28, 2021), theaverage annual income of financial analysts is ¥8,240,000. With standard working hours (8 hours/day,
20 days/months), it is approximately ¥4300.
8The English translation of the instructions can also be found at:https://bgt.au1.qualtrics.com/jfe/form/SV_9RAxWjjGFww0zSl.

10For each graph, we gave participants one unit of an asset whose value could beeither 0 or 100 points depending on the next movement of the graph (0 if i twent down, and 100 if i t went up). After observing each graph, participants stateda price (an integer between 0 and 100) at which they were willing to sell the asset
back to the experimenter. After participants submitted their prices, one of the 16
series was randomly selected for payment. We used a Becker et al. (1964)
mechanism (BDM, henceforth) to elicit participants’ beliefs about the next
movement of the random walk. That is, the price stated by the participant wascompared to a randomly generated integer [0,100]. If the random number was lessthan or equal to the stated price, participants kept the asset, and the payoff wasdetermined by the subsequent movement of the price chart. If the random number
was strictly greater than the stated price, participants were paid the random number
regardless of the next movement of the price chart.9
212 professionals (CFAs) and 228 students participated in the task, lasting around 20
minutes.10 There were 93.4% male participants among CFAs. These professionals had
an average of more than 10 years of experience and an annual gross income greater than
¥10M ($100,000). There were 53.5% male students in our sample, resulting in a
relatively balanced sample across the two genders among our student participants.
CFAs earned an average of ¥1,362, and the maximum payment was ¥11,000.11 Students
earned an average of ¥316, and the maximum payment was ¥3160.12
In addition, we elicited participants’ risk attitude (using a variant of the multiple pricelist of Holt and Laury, 2002), loss attitudes (using the task employed by Kirchler etal., 2018), cognitive reflection (Frederick, 2005), and basic demographics (seeAppendix B for details on these additional measures). We also administered a financial
literacy quiz (Fernandes, Lynch, and Netemeyer, 2014).

9This incentivization procedure differs from Bloomfield and Hales (2002). In Bloomfield and Hales(2002), participants were told that if they stated a price above 50, they were buying one unit of the asset
at each (integer) price between 51 and the stated price, while if they stated a price below 50, they wereselling one unit of the asset at each (integer) price between the stated price and 49. For example, if aparticipant stated a price of 54, he or she bought 4 units of the asset at the following prices: 51, 52, 53
and 54. If the next movement of the random walk was up, then payments were equal to 400 - (51 + 52
+ 53 + 54) = 190. We chose to implement a standard BDM mechanism for the sake of simplifying the
instructions and facilitating the understanding of the task.
10
We excluded those who accessed from the same IP address or who answered multiple times with thesame participant ID. This led us to drop 13 CFA responses. No responses were discarded for students.
11
The average payment for those who have been selected for the additional reward was ¥6,120.
12
The average payment for those who have been selected for the additional reward was ¥1,524.

11

2.3.2. SPmonthly: Forecasting S&P 500 stocks prices or stock returns based onmonthly series
We used the forecasting task similar to the one in Glaser et al. (2019) that asks
participants to forecast either returns or prices of financial series. In line with their study,
we conducted a 2×2 between-subject factorial design, where the first dimension varied
the type of information shown on the graph, either price or return, and the second
dimension varied the forecasting variable, either price or return. In all treatments, we
added the final price of the corresponding series to the chart. Unlike Glaser et al. (2019),
we conducted all four treatments with the same series for both professionals andstudents.
As in Glaser et al. (2019), we simulated financial series using actual stock market data.
In particular, we used historical S&P 500 stock end-of-day prices between January 1st,
2008, and June 30th, 2018. We selected a day at random and then constructed a
price chart of 12 data points corresponding to end-of-month prices over a year. The
end-of-month prices were rescaled, so that starting prices were equal to 100.
Participants were neither told the name of the stock nor the corresponding time
period that was randomly selected. This procedure was such that the generated line
charts did not resemble the graphical information professionals typically face in their
daily job (see Appendix A.2 for graphs and detailed instructions).13-14 We refer to thesecharts as unnatural-display series.
In the two treatments in which participants had to forecast prices, we asked them topredict the price in the next period given the corresponding chart. Performance (inpoints) on the task was measured by:,-./0 12-0/"34 − -0"5.607 ,-./0!"# {200 − 1000 × ++ , 0}-0"5.607 ,-./0

13

The English translation of the instructions can also be found at:https://bgt.au1.qualtrics.com/jfe/form/SV_74H7IiRiVqUQCzA.
14
Glaser et al., (2019) used bar charts for returns and line charts for prices because they are the standard
way of showing this information. We have opted for using line charts for both variables to limit thedifferences between the two treatments.

12

For the other two treatments in which participants had to forecast returns, we asked
them to predict the next return given the corresponding chart. Performance (in points)on the task was measured by:!"# {200 − 10 × |-04;-< 12-0/"34 − -0"5.607 -04;-< × 100|, 0}
Where:-04;-< =-0"5.607 ,-./0 − 5"34 ,-./0
× 100
5"34 ,-./0

Each participant was given 20 series sequentially, thus producing 20 forecasts,
regardless of the treatment. The order of the series was randomized across participants.
174 professionals (CFAs) and 206 students participated in this task, which lasted about
20 minutes.15 Each professional (student) was assigned at random to one of the four
treatments. CFAs earned an average of ¥1,675, and the maximum payment was¥10,930. 16 Students earned an average of ¥284, and the maximum payment was¥2,170.17
We referred to the treatments in which the information provided on the chart (priceor returns) and the variable to be forecasted the same as congruent. By contract,incongruent treatments were those in which the information provided on the chartdiffered from the variable to be forecasted.
2.3.3. SPdaily: Forecasting S&P 500 stocks prices based on daily series.
We asked participants to make 20 price forecasts of the historical S&P 500 series
in this task. Before making a forecast, participants were presented with a chart
showing the end-of-day prices of an S&P 500 stock during a year. The graph was
created based on the price of randomly selected stocks from the S&P 500 startingfrom a randomly selected day between January 1st, 2008, and June 30th, 2018. The
end-of-day prices were rescaled so that starting prices were equal to 100.
Participants were neither told the name of the stock nor the corresponding time15

We excluded those who accessed from the same IP address or who accessed several times with thesame participant ID. As a result, 25 responses from CFAs and 0 response from students were discarded.
16
The average payment for those who have been selected for the additional reward was ¥8338.
17
The average payment for those who have been selected for the additional reward was ¥1441.

13

period that was randomly selected. Given our procedure, there were 1,916,250
possible charts created so that participants could not practically identify thefinancial series. This task is similar to the SPmonthly task, except that participantsreceived information about the daily instead of the monthly stock price movements.
Unlike SPmonthly, the series used in SPdaily resembled the graphical information
professionals would typically face (see Appendix A.3 for graphs and detailed
instructions), as is shown in Figure 1.18 We refer to these charts as natural-display
series.
[FIGURE 1 AROUND HERE]
For each of the 20 charts, participants were asked to forecast the end-of-day priceof the stock 30 days after the last price displayed on the graph. All participants
made price forecasts based on the same 20 time series, but the order in which thesegraphs were shown was randomized across participants.
The payoff for this task depended on the forecast accuracy for one randomly
selected series. As for SPmonthly, the number of points was calculated as follows:,-./0 12-0/"34 − -0"5.607 ,-./0!"# {200 − 1000 × ++ , 0}-0"5.607 ,-./0
198 professionals (CFAs) and 221 students participated in this task, which lasted about
20 minutes. 19 CFAs earned an average of ¥1,840, and the maximum payment was¥10,980. 20 Students earned an average of ¥315, and the maximum payment was¥2,120.21
2.3.4. NKI: Forecasting the Nikkei index[TABLE 2 AROUND HERE]

18

The English translation of the instructions can also be found at:https://bgt.au1.qualtrics.com/jfe/form/SV_0pmGV8LB9DMRPc9.
19
We excluded 6 CFA responses because of multiple entries from the same IP address or using the same
participant ID. There was no response excluded for students.
20
The average payment for those who were selected for the additional reward was ¥8,233.
21
The average payment for those who were selected for the additional reward was ¥1,694.

14

In this task, we asked participants to predict the Nikkei index once a month forseveral months.22-23 Unlike SPdaily, which relies on historical data, the NKI uses
real-time data. Participants had to forecast the closing price of the Nikkei index
three to four weeks after a given deadline. As is shown in Table 2, participants had
3 or 4 days to submit their forecasts (forecast window) for the closing value of the
Nikkei. During these 3 or 4 days, they can freely appeal to all sorts of resources tohelp enhance the quality of their decisions, e.g., they can visit professional websites
or use professional tools. The number of participants varied across waves, as shownin the last column of Table 2.
As for the previous tasks, the payoff depended on forecasting accuracy. In
particular, the most accurate CFA or student forecaster, i.e., the one whose forecast
was closest to the closing value of the Nikkei, for a given forecasting day eachsession received an Amazon gift card of ¥5,000 (¥1,000), there was no participation
fee for this task. In case of a tie, one winner was chosen randomly among the top
performers (see Appendix A.4 for detailed instructions).24-25
After each forecasting day, participants received graphical feedback, separately forstudents and CFAs, showing the realized closing Nikkei index along with theirforecast and the average forecast. The screen also showed the most accurate and thesecond and third most accurate forecasts.
In the June forecast period, we added a questionnaire to the online platform toassess participants’ prior experience in forecasting the Nikkei index, the effort theyexerted on the forecasting task, and the use of professional services (see Appendix
A.4.2).
3. Results
3.1. Forecasting performance of CFAs and students

22

As for the previous tasks, we excluded those who accessed from the same IP address or who accessed
several times with the same participant ID.
23
We also asked professionals to predict the Nikkei in February (16-19), but we do not report the results
here because we did not elicit students’ forecasts.
24
Ties happened in Wave 1 and 3 for students (2 participants) and in Wave 3 for CFAs (3 participants).
25
The English translation of the instructions can also be found at:https://bgt.au1.qualtrics.com/jfe/form/SV_1MNumMb42kKnpDo

15

We compare the forecasts of financial professionals and students by using a measure of
relative forecasting error (RFE), which is defined as follows: 100 ×|"#$%&'()*+',-% |
+',-%

.

Where value is the actual value of the series to be forecasted for SPmonthly, SPdaily,
and NKI, and the bid value that maximizes forecasting performance in RW (i.e., 50).26
We use the RFE measure because participants were incentivized to minimize
forecasting errors in all tasks. In Figure 2, we show the standardized values of RFE
calculated at the participant’s level.
[FIGURE 2 AROUND HERE][TABLE 3 AROUND HERE]In Table 3, we show that CFAs and students performed similarly in RW (see regression(1)), which is not surprising because this task did not require specific financial skills(Cohen’s d = 0.025). Yet, participants who scored higher on cognitive ability (asmeasured with CRT) performed better in RW. This result is in line with previous
research showing that people with high CRT scores tend to understand better theconcepts of probability and randomness (see, e.g., Toplak, West and Stanovich, 2011,2014).
The SPmonthly task requires financial skills related to numeracy (Glaser, Iliewa, andWeber, 2019) that are especially relevant to financial professionals and a key
component of financial literacy (see, e.g., Fernandes, Lynch, and Netemeyer, 2014).
Thus, it is not surprising that CFAs outperformed students in SPmonthly (seeregression (2)). However, the effect size is relatively small (Cohen’s d = 0.278).
[TABLE 4 AROUND HERE]In Table 4, we show that the higher performance of CFAs in SPmonthly is due to theincongruent treatments in which the variable to be predicted (either returns or prices)
differed from the variable that was shown on the chart (either returns or prices) (seeregression (1)). In that case, CFAs might have performed better than students because
they possess greater numeracy skills (see financial literacy scores in Table 6), which
are required to convert prices to returns and vice versa.
26

Similarly, Corgnet et al., (2021) use price forecasting error as their accuracy measure, which is definedas RFE×value.

16

For the two congruent treatments in which the variable to be predicted and the chart
information coincided, numeracy skills were arguably less important because
participants no longer had to make calculations to convert prices to returns and viceversa. This might explain why we observed no differences between CFAs and students
in that case (see regression (2) in Table 4).
Participants forecasted actual financial series in SPdaily. However, this task was
performed in a controlled setting in which financial professionals could not identify theseries and did not have access to news feeds and their usual analysis tools. In that
context, CFAs could not outperform students by using professional tools or theirknowledge they could exploit better than students. Interestingly, CFAs underperformed
students (see regression (3)), although the size of the effect was small (Cohen’s d =0.156). In contrast, CFAs outperformed students (see regression (4)) (Cohen’s d =0.243) in the field task (the NKI) in which they forecasted financial series in a natural
longitudinal context in which they knew the series (Nikkei) and they could employ theirusual professional tools and knowledge.27
3.2. On zeal and forecasting strategies
If markets are informationally efficient (Fama, 1970), then the forecast that minimizes
the RFE equals the last observed value of a financial series. According to that view, weshould interpret any forecast deviating from the last observed value of the stock asfinancial zeal. We use the term zeal to refer to the urge of financial professionals toshow off their expertise by providing detailed forecasts that purposefully differ fromthe last observed value. 28 Zeal relates to overconfidence (e.g., Hirshleifer andSubrhamanyam, 1998; Barber and Odean, 2001; Gervais and Odean, 2001) because
professionals might state bold forecasts when they believe their private information ismore precise than it is (e.g., Alpert and Raiffa, 1982) or when they hold optimistic
views of the future (Weinstein, 1980). We use the term zeal to refer to a situation inwhich financial professionals might exhibit overconfidence or optimism to demonstrate
their expertise.

27

The median and modal response for students was that they never predicted the Nikkei index whereas
CFAs median and modal response was that they predict the Nikkei index once a month.
28
According to Merriam-Webster, zeal is “a strong feeling of interest and enthusiasm that makes
someone very eager or determined to do something.”

17

Formally, we define zeal as follows: 100 ×|"#$%&'()*,'() +',-%|,'() +',-%. Where the last value isthe last observed stock price. Zeal is thus not defined for the artificial series used in
RW. In SPmonthly, the measure of zeal tends to be larger for incongruent than for
congruent treatments. As a result, any discrepancy between the last observed value ofa stock and a participant’s forecast is likely driven by the type of series used in theforecasting task rather than zealous behavior.29
We will thus focus on zeal as a potential explanation for the difference in forecasting
performance between CFAs and students in SPdaily and NKI. In NKI, the last observed
value of the Nikkei index is the last closing price of the index participants could haveobserved before submitting their forecast.30[TABLE 5 AROUND HERE]In Table 5, we show that CFAs tend to be more zealous than students in SPdaily, while
this is not the case in NKI, thus suggesting that field tasks led them to be more cautious
than students by providing forecasts that were closer to the last observed value of stocks.
Zeal leads to worse forecasting performance (see regressions (3) and (4) in Table 5) inboth SPdaily and NKI for both CFAs and students. However, the negative impact ofzeal is less pronounced for CFAs in NKI. This could be because bold forecasts might
result, unlike the SPdaily task, from specific financial knowledge of the Nikkei index.
This could also have been partly explained by CFAs being more dedicated to the task
and thus more attentive to market data when making their predictions. Although CFAs
did wait longer (0.31 days, on average) than students to make their forecasts, possibly
to observe the latest value of the Nikkei index within the forecasting window (seeregression (1) in Table C3 in Appendix C), controlling for the date at which a forecast
was made did not alter the findings reported in regression (4) of Table 3 (see regression(2) in Table C3 in Appendix C).

29

See Table C1 in Appendix C.
Since the Nikkei closes at 3pm, we set 3pm as the cut-off time in determining the closing price. Namely,
if participants submitted their forecast at 3pm or earlier on a given day, the last observed closing price
was set at the previous business day closing value, while for participants who submitted their forecast
after 3pm on a given day, the last observed closing price was set at the current day closing value.

30

18

In the same vein, we controlled for the level of interest and effort (see Intrinsic
Motivation Inventory scale, Ryan, 1982) participants put into the task to assess whether
the superior performance of CFAs was simply due to their greater dedication to the task.
This effort and dedication index (Cronbach’s α= 0.783) was, however, not significantly
different between CFAs (3.320) and students (3.081) (Rank Sum Test = 0.143).
Importantly, controlling for the dedication index, the CFA Dummy in Table 4(regression (4)) continues to be significant (see regression (3) in Table C3 in AppendixC) although only marginally (p-value = 0.063). The magnitude of the coefficient for
CFA Dummy only decreases by 12.1% when controlling for the dedication index, thus
suggesting that the mediating role of dedication is limited. We also find that themediating role of the variables related to the frequency of paid services in theforecasting task was small (see regression (4) in Table C3) as the coefficient for CFA
Dummy only decreases by 15.70% when controlling for paid services.
3.3. Financial literacy, cognitive skills, and forecasting performanceThis section aims to uncover the mechanisms by which CFAs’ forecasting performancediffers from students’ performance.
3.3.1. Mediating role of CRT and financial literacy
In Tables 3 and 4, we assess the effect of being a CFA on relative forecast error while
controlling for CRT scores. In Tables C4 and C5, we conduct the same regressions
without controlling CRT to assess its role as a mediator. We do not find evidence of amediating role of CRT as the coefficient associated with CFA is not substantially
impacted by the inclusion or exclusion of CRT as a control. This is perhaps not
surprising given that students obtained CRT scores as high as CFAs (Wilcoxon Rank
Sum Test, p-value = 0.396).
Although the two samples do not seem to differ in cognitive skills, they differ infinancial literacy test scores. Indeed, CFAs need to obtain very high scores on thesetests to obtain their accreditation and should thus outperform even smart students. One
month after Wave 6 of NKI, we collected financial literacy scores on financial
professionals. We obtained 110 answers, which corresponds to 81.3% of the19participants in the NKI over all waves. 31 Not surprisingly, professionals scoredsubstantially higher on financial literacy than students (p-value < 0.001). We thenassessed the mediating role of financial literacy in predicting relative forecast error
using the same procedure as for CRT (see Tables C6, C8, and C9).32 We show that
financial literacy plays an important role in understanding the difference in forecasting
performance between CFAs and students. For the two forecasting tasks in which CFAs
outperform students (the incongruent treatments in SPmonthly) and NKI, thecoefficient associated with CFAs dummy becomes statistically non-significant when
we introduce financial literacy in the regression (see regression (3) in Table C9 andregression (4) Table C8, respectively). The magnitude of the coefficient decreases by
27.01% and 62.18% for the incongruent treatments of the SPmonthly and the NKI,
respectively. In sum, although CFAs outperformed students when controlling for CRT,
no differences were found when controlling for financial literacy. This result is
especially remarkable given that CRT has been shown to correlate, although moderately,with numeracy skills and financial literacy (e.g., Oechssler, Roider and Schmitz, 2009;
Pennycook et al., 2012; Shenhav, Rand and Greene, 2012; Weller et al., 2013;
Campitelli and Gerrans, 2014).
3.3.2. Elite and non-elite students
The sample of students from Osaka University is characterized by high levels ofcognitive skills comparable to CFAs. These students scored 88.6% of the CRT
questions correctly, placing them at the top US schools’ level and in the top 20% of astandard US student population (see Frederick, 2005; Corgnet, DeSantis and Porter,2018). As a result, we will refer to Osaka University students as elite students (EStudents, henceforth).
To further study expertise and connect it with cognitive ability and financial literacy,
we collected data from another sample of students from a less selective university (NE31

Unlike students, we did not collect financial literacy for the 212 CFA participants in the RW. The
reason we did not originally elicit CFAs financial literacy was that we strongly believed that they would
obtain almost-perfect scores. We were also concerned about offending CFAs by asking them simple
financial literacy questions, which might have the unintended consequence of discouraging them fromparticipating in other tasks.
32
We conducted the same analysis for CRT using the CFA sample of those who completed the financial
literacy questionnaire to alleviate selection issues (see Tables C6, C7 and C9). It is reassuring that we
obtain the same finding of the absence of a mediation role of CRT for the restricted sample as for thewhole sample.

20

Students, henceforth) for the first three forecasting tasks in September 2021.33 In Table
6, we show that NE-Students score significantly lower than E-Students on cognitive
ability and financial literacy (p-values < 0.001). By contrast, E-Students scored as well
as CFAs on cognitive ability while CFAs outperformed NE-Students (p-value < 0.001).
As expected, CFAs outperformed all students on financial literacy.
[TABLE 6 AROUND HERE]
Figure 3 shows that NE-Students tend to produce larger forecasting errors than EStudents and CFAs.
[FIGURE 3 AROUND HERE]In Table 7, we find that NE-Students performed significantly worse than E-Students
and CFAs in RW and SPmonthly. In SPdaily, the performance of NE-Students and
CFAs did not differ significantly. NE-Students performed worse than E-Students in
SPdaily, but this difference is not statistically significant. We also note that thesefindings are qualitatively similar whether CRT scores are controlled for (regressions(2), (4), and (6)) or not (regressions (1), (3), and (5)). Yet, the magnitude of the NEStudent Dummy is reduced by 16.15%, 26.92%, and 74.89% for RW, SPmonthly andSPdaily when controlling for CRT scores. The corresponding p-values are 0.001, 0.016
and 0.130 in regressions (1), (2), and (3) compared to 0.011, 0.096, and 0.739 inregressions (4), (5), and (6). This implies that a substantial part of the increase inperformance of E-Students compared to NE-Students is mediated by cognitive ability.
Interestingly, the share of forecasting performance mediated by cognitive ability seems
to increase as the task is less abstract and more realistic.
Table 7 focuses on cognitive ability as a mediator of the forecasting performance of
NE-Students, but financial literacy, as we have seen in the previous section, might also
play an important role in understanding the performance of NE-Students. This analysis
is shown in Table 8. To provide a meaningful comparison of the mediating role of CRT
and financial literacy on the forecasting performance of NE-Students, we re-estimatedthe effect of CRT for the sample of participants who answered both the financial

33

Because these experiments were conducted in September 2021, we could not gather data for the NKI
for this additional sample.

21

literacy and CRT questions (see regressions (2), (6) and (8)). We observe that, overall,
the mediating role of financial literacy is similar to the one of cognitive ability in
explaining the forecasting performance of NE-Students.
[TABLE 7 AROUND HERE][TABLE 8 AROUND HERE]
4. Conclusion
The forecasting skills of finance experts have often been mocked (see Kahneman, 2011)
and compared to those of other primates (Tetlock, 2009a,b). Focusing on humanprimates only, our study showed that financial professionals could outperform elite
students on various financial forecasting tasks requiring numeracy skills (SPmonthly,incongruent treatments) or using actual financial series in a field setting (the NKI).
Cognitive skills like CRT scores did not drive the result because the level of cognitive
ability is similar in the two groups. Thus, our findings can serve as evidence of financial
professionals’ special skills that go beyond IQ, luck, and the alleged “bragging” culturein the financial industry.
Increasing the ‘representativeness of the situation’ (List, 2021) impacted ourassessment of the relative forecasting performance of CFAs, but the relationship was
not monotonic. Although the two groups did not differ in their forecasting performanceon the artificial forecasting task (RW), elite students outperformed CFAs in forecasting
historical financial series in a controlled setting.
Our study has three main implications. First, the extent of financial professionals’ skills
cannot be reliably measured using only lab tasks. Second, increasing the‘representativeness of the situation’ will not necessarily favor financial professionals
because applying financial knowledge to a forecasting task that looks superficiallysimilar to professionals’ daily jobs might lead to excess zeal. We found excess zeal andpoor performance when professionals had to forecast historical financial series in acontrolled setting without any information but the graphs of the series. Third, we
showed evidence that CFAs possess forecasting skills to outperform elite students.
Furthermore, the extent to which CFAs outperformed students was mediated byfinancial literacy but not by CRT scores, thus suggesting financial knowledge rather
22

than cognitive skills explains performance in financial forecasting tasks.
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79
77
75
73
71
69
67
65

BasePrice100(a) SPmonthly (Series 1)(b) SPdaily (Series 1)

Figure 1. Series 1 for SPmonthly price time series in panel (a) and SP daily pricetime series in panel (b).

30

Figure 2. Average standardized RFE (computed at the participant level) for each task
across CFAs and students along with 95% confidence intervals.

31

Figure 3. Average standardized RFE (computed at the participant level) for each task
for CFAs and the two student samples along with 95% confidence intervals.

32

Tables
Table 1. Description of forecasting tasks
Tasks
RW

Series

Display

Information

Nature ofthe task

Time
frame

Unnatural

SeriesLab

StaticReal

Unnatural

SeriesLab

StaticReal

Unnatural

Series(incongruent)Lab

StaticGlaser, Iliewa
and Glaser(2019)[Studies 1 & 2]

Real

NaturalSeriesLab

StaticThis paper

LongitudinalGlaser, Iliewa
and Glaser(2019)[Study 3]

SPdailyNKI

RealNaturalComplete

Field

CFAs

Students

212

228

87

105

87

101

198

221

Bloomfield andHales (2012)

Artificial

SPmonthly(congruent)
SPmonthly(incongruent)

Number ofparticipants

Adapted from

Cumulative Total Number of Participants

717
538(Cumulative Total.
See Table 2 for the
breakdown)
1,301

Table 2. Description of Nikkei forecasting task (NKI)
Wave

Forecast window

Forecasting day

CFAs Students

1

March 23-26, 2021

April 23, 2021

125

57*

2

Apr. 27-30, 2021

May 21, 2021

113

113

3

May 25-28, 2021

June 25, 2021

125

109

4

June 28-July 2, 2021

July 30, 2021

124

96

5

August 3-6, 2021

August 27, 2021

111

84

6

August 31-September 4,

September 24, 2021

119

79

717

538

2021
Cumulative Total Number
of Participants* The number of participating students was lower than for previous tasks in March because thiscorresponds to the graduation period in Japan.

33

1,193

Table 3. Relative Forecast Error
Standardized RFE

Dependent variable
RW
(1)
0.002
(0.037)

SPmonthly(2)-0.018**(0.008)SPdaily(3)0.026**(0.013)NKI♠(4)-0.116**(0.050)

CRT score (std)-0.099**(0.045)-0.011(0.009)-0.059*(0.034)0.0006(0.028)Constant

0.253***(0.057)-0.025(0.059)0.065(0.055)-0.020(0.080)R2
Observations

0.030
7,040

0.164
7,400

0.445
8,138

0.053
1,181

Task
CFA Dummy (std)Linear panel regressions with random effects and robust standard errors clustered at the individual levels
in parentheses for regressions, series and order fixed effects included and month for NKI.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1
CFA Dummy takes value one for participants who are CFAs.
♠
In NKI, we face selection issues as the study takes place over six months, and not all students and
CFAs make a forecast in each month. We thus estimated Lee bounds (Lee, 2009) in Table C2 inAppendix C (see regression (1)) to correct for selection issues. Both the lower and upper bounds of the
CFA Dummy coefficient are negative. This might not be surprising given that the level of participationis relatively high on average (64.2%).
(std) stands for standardized.

34

Table 4. Relative Forecast Error (SPmonthly)Standardized RFE

Dependent variable

SPmonthly(Incongruent)(1)-0.029**(0.012)

SPmonthly(Congruent)(2)-0.001(0.011)

CRT score (std)-0.006(0.011)-0.024(0.015)Constant-0.112(0.077)0.007(0.084)

R2
Observations

0.202
3,660

0.147
3,740

Task
CFA Dummy (std)Linear panel regressions with random effects and robust standard errors clustered at the individual levels
in parentheses for regressions, series and order fixed effects included.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1(std) stands for standardized.

35

Table 5. Zeal
Dependent variable

Standardized Zeal

Standardized RFE

SPdaily
(1)
-

NKI♠
(2)
-

SPdaily(3)
1.070***(0.066)NKI(4)
0.923***(0.022)0.029**(0.012)-0.070(0.050)-0.005(0.006)-0.052***(0.0321)

-

-

0.015
(0.12051)-0.055***(0.027)

CRT score (std)-0.051(0.032)-0.009(0.028)-0.003(0.007)-0.006(0.008)Constant-0.116***(0.033)0.138*(0.078)-0.060(0.044)-0.161***(0.009)R2
Observations

0.063
8,138

0.007
1,181

0.759
8,138

0.924
1,181

Task
Standardized Zeal
CFA Dummy (std)
CFA Dummy (std) ×
Standardized Zeal

Linear panel regressions with random effects and robust standard errors clustered at the individual levels
in parentheses for regressions, series and order fixed effects included and month for NKI.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1♠
In NKI, we face selection issues as the study takes place over six months, and not all students and CFAs
make a forecast in each month. We thus estimated Lee bounds (Lee, 2009) in Table C2 in Appendix C(see regression (2)) to correct for selection issues. Both the lower and upper bounds of the CFA Dummycoefficient are negative. This might not be surprising given that the level of participation is relatively highon average (64.2%).
(std) stands for standardized.

36

Table 6. CRT and financial literacy

CFA
Japan (n=212, CRT) & (n=110,
Financial literacy)
E-Student (n=228)
NE-Student (n=149)
Wilcoxon Rank Sum Test (p-value)
CFA vs E-Student
CFA vs NE-Student
CFA vs All students
E-Student vs NE-Student

37

CRT

Financial literacy

5.127(1.227)10.927(1.254)5.316(0.899)4.181(1.586)8.285(2.448)7.040(2.379)0.396<0.001
0.009<0.001<0.001<0.001<0.001<0.001

Table 7. Relative Forecast Error and CRT as a mediator
Depe Dependent variable

Standardized RFE

Task
CFA Dummy (std)
NE Student Dummy (std)
CRT score (std)Constant
CFA vs NE Student (p-value)
R2
Observations

RW

SPmonthly

SPdaily

(1)

(2)

(3)

(4)

(5)

(6)

0.009
(0.036)0.100***(0.029)0.006(0.036)0.084**(0.033)-0.017**(0.008)0.023**(0.010)-0.018**(0.008)0.017*(0.010)0.030**(0.014)0.021(0.014)0.027**(0.013)0.005(0.016)--0.043(0.031)--0.016**(0.008)--0.042*(0.022)0.259***(0.050)
0.014
0.032
9,424

0.259***(0.050)
0.044
0.034
9,424

0.021
(0.055)<0.001
0.196
10,0800.019(0.055)0.0003
0.196
10,0800.040(0.046)0.557
0.434
10,7980.040(0.046)0.255
0.435
10,798Linear panel regressions with random effects and robust standard errors clustered at the individual levels in parentheses for regressions,series and order fixed effects included.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1
NE-Student Dummy takes value one for participants who belong to the non-elite university.
(std) stands for standardized.

38

Table 8. Relative Forecast Error and financial literacy as a mediator
Dependent variable
Task
CFA Dummy (std)
NE Student
Dummy (std)
CRT score (std)Financiala literacy(std)Constant

RW
(1)
0.025
(0.046)0.100***(0.029)
-

(2)
0.022
(0.046)0.089***(0.033)-0.029(0.033)--0.254***(0.058)
CFA vs NE Student 0.105(p-value)
R2
0.033
Observations
7,792

(3)
0.062
(0.049)0.084***(0.031)
-

(4)
0.059
(0.807)0.081***(0.034)-0.011(0.033)Standardized RFE
SPmonthly
(5)
(6)
(7)
**
**
-0.020-0.021-0.016*(0.008) (0.008) (0.009)
0.023**
0.016*
0.022**(0.009) (0.010) (0.010)-0.020**(0.009)--0.259***(0.050)0.164-0.076** -0.073*(0.036) (0.037)
0.266*** 0.266***(0.058) (0.058)
0.679 0.693-0.017(0.054)<0.0001-0.019(0.054)0.0002-0.007(0.010)-0.015(0.054)
0.002

0.034
7,792

0.037
7,792

0.198
8,840

0.198
8,840

0.198
8,840

0.037
7,792

SPdaily
(8)
(9)
0.014
0.020**(0.012) (0.014)
0.017
0.019
(0.014) (0.015)-0.010(0.011)(8)-0.020**(0.009)0.016**(0.011)-0.019**(0.009)(7)
0.015(0.012)0.021(0.014)--0.002(0.010)-0.018(0.053)
0.003

-

-

-0.007(0.042)
0.708

0.198
8,840

0.459
9,238

(10)
0.018**(0.009)0.016**(0.008)-0.008(0.008)-0.007(0.042)0.866-0.010(0.017)-0.006(0.043)0.958-0.007(0.009)-0.006(0.044)
0.922

0.459
9,238

0.459
9,238

0.459
9,238

Linear panel regressions with random effects and robust standard errors clustered at the individual levels in parentheses for regressions, series and order fixed effects included.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1
NE-Student Dummy takes value one for participants who belong to the non-elite university.
(std) stands for standardized.

a Collinearity does not seem to be an issue as the variance inflation factors associated with the three regressors (CFA Dummy and Financial literacy)
are below 1.45, regardless of the regression.

39

Appendix A. Instructions
A.1. RW
A.1.1. Instructions
We have constructed a model of a random process that works much like flipping a faircoin. Using this model, we have created sequences of outcomes. An upward movement
indicates a “heads” outcome, and a downward movement indicates a “tails” outcome.
Since outcomes of coin flips are unpredictable, they result in a sequence known as a“random walk.” That is, statistical models are unable to predict future outcomes frompast ones and, on average, there is no upward or downward trend. Random walksequences almost always contain intervals of recognizable patterns. However, sincethese patterns can change greatly at any time, statistical models are still unable topredict future outcomes.
You will be shown 16 plots we have created as described. You are given one unit of bet
that will generate either 0 or 100 points depending on the next movement (on how theseries move next). Your bet generates 100 points if it moves upward, and 0 point if it
moves downward.
You are asked to name the price (in points) at which you are willing to sell this bet backto us. The price you can set is between 0 and 100 points.
One in ten participants will be selected for financial reward. If you are selected for thefinancial reward, we will calculate your earnings for this experiment as follows.
We will select one of the 16 plots at random and then generate a random number
between 0 and 100 (each number is equally likely). If the randomly generated number
is less than or equal to the price you have set for the bet, then, you will keep the bet andyour payoff will be either 0 or 100 points depending on the next move of the series
represented on the plot. If the randomly generated number is greater than the price you
have set for the bet, then, you will receive the points equal to the randomly generated
number.
Example 1 If you set the price for the bet to be 50 points and the randomly generated
number is 40, you keep the bet, and your payoff will be either 0 or 100 points.
Example 2 If you set the price for the bet to be 50 points and the randomly generated
number is 60, you receive 60 points.
You will be paid for the points you have obtained in the selected plot.1 point will be
converted into 100/20 JPY. The payment will be in the form of Amazon gift card. You
will not be informed about the accuracy of your forecast until the end of the experiment.

1A.1.2. Figures
Series used in RW. As is in Bloomfield and Hales (2002), Series 9 to 16 are the mirror
images of Series 1 to 8, respectively.
Series 1
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A.2. SPmonthly
A.2.1. Instructions
Congruent treatment with price predictions
In this experiment, there are 20 tasks. In each task, you will be shown 20 graphs
showing 12 months of end-of-month prices of a stock, and asked to forecast what willbe the end-of-month price for this stock 1 month after the last price shown on thegraph.
Each graph shows the price movement of a randomly selected stock from the S&P 500
starting from a randomly selected month between January 2008, and June 2018. You
will not be told about the name of the stock or the starting date which was randomly
selected.
Please note that end-of-day prices have been rescaled so that all starting prices
will be equal to 100.
You will be rewarded based on the accuracy of your forecasts as follows.
,-./0 12-0/"34 − -0"5.607 ,-./0!"# {200 − 1000 × ++ , 0}-0"5.607 ,-./0
If your forecast is exactly at the realized price, then you will receive 200 points. For
each percentage point difference between your forecast and the realized price, 10 points
will be subtracted.
If your forecast differs from the realized price by more than 20%, you will receive 0
point.
You will do a similar forecasting task for all the 20 randomly chosen stocks. One in ten
participants will be selected for financial reward. If you are selected for the financial
reward, one of the 20 predictions will be selected randomly, and you will receive thereward according to the points you have earned in the selected task. 1 point will be
converted into 100 / 20 JPY. The payment will be in the form of an Amazon gift card.
You will not be informed about the accuracy of your forecast until the end of theexperiment.

Incongruent treatment with returns predictions
In this experiment, there are 20 tasks. In each task, you will be shown 20 graphs
showing 12 months of end-of-month prices of a stock, and asked to forecast what willbe the return for this stock 1 month after the last price shown on the graph. Here,the return is defined as-04:-; =-0"5.607 ,-./0 − 5"34 ,-./0
× 100
5"34 ,-./0
4

(Only the first month, instead of the closing price of the last month, we use the closing
price of the first day of the month).
Each graph shows the price movement of a randomly selected stock from the S&P 500
starting from a randomly selected month between January 2008, and June 2018. You
will not be told about the name of the stock or the starting date which was randomly
selected. Please note that end-of-day prices have been rescaled so that all starting
prices will be equal to 100.
You will be rewarded based on the accuracy of your forecasts as follows.
!"# {200 − 10 × |-04:-; 12-0/"34 − -0"5.607 -04:-; × 100|, 0}If your forecast is exactly at the realized return, then you will receive 200 points. For
each percentage point difference between your forecast and the realized return, 10
points will be subtracted.
If your forecast differs from the realized return by more than 20%, you will receive 0
point.
You will do a similar forecasting task for all the 20 randomly chosen stocks. One in ten
participants will be selected for financial reward. If you are selected for the financial
reward, one of the 20 predictions will be selected randomly, and you will receive thereward according to the points you have earned in the selected task. 1 point will be
converted into 100/20 JPY. The payment will be in the form of an Amazon gift card.
You will not be informed about the accuracy of your forecast until the end of theexperiment.

Incongruent treatment with price predictions
In this experiment, there are 20 tasks. In each task, you will be shown 20 graphs
showing 12 months of monthly return of a stock, and asked to forecast what will be
the price for this stock 30 days after the last return shown on the graph. Here, themonthly return is defined as-04:-; =-0"5.607 ,-./0 − 5"34 ,-./0
× 100
5"34 ,-./0(Only the first month, instead of the closing price of the last month, we use the closing
price of the first day of the month）．
Each graph shows the return movement of a randomly selected stock from the S&P 500
starting from a randomly selected month between January, 2008, and June, 2018. You
will not be told about the name of the stock or the starting date which was randomly
selected.
You will be rewarded based on the accuracy of your forecasts as follows.

5,-./0 12-0/"34 − -0"5.607 ,-./0!"# {200 − 1000 × ++ , 0}-0"5.607 ,-./0
If your forecast is exactly at the realized price, then you will receive 200 points. For
each percentage point difference between your forecast and the realized price, 10 points
will be subtracted.
If your forecast differs from the realized price by more than 20%, you will receive 0
point.
To ease your forecasting task, the closing price of the final month is also shown Please
note that end-of-day prices have been rescaled so that all starting prices will beequal to 100.
You will do a similar forecasting task for all the 20 randomly chosen stocks. One in ten
participants will be selected for financial reward. If you are selected for the financial
reward, one of the 20 predictions will be selected randomly, and you will receive thereward according to the points you have earned in the selected task. 1 point will be
converted into 100 /20 JPY. The payment will be in the form of an Amazon gift card.
You will not be informed about the accuracy of your forecast until the end of theexperiment.

Congruent treatment with returns predictions
In this experiment, there are 20 tasks. In each task, you will be shown 20 graphs
showing 12 months of monthly return of a stock, and asked to forecast what will be
the return for this stock 1 month after the last return shown on the graph. Here,the monthly return is defined as-04:-; =-0"5.607 ,-./0 − 5"34 ,-./0
× 100
5"34 ,-./0(Only the first month, instead of the closing price of the last month, we use the closing
price of the first day of the month）．
Each graph shows the return movement of a randomly selected stock from the S&P 500
starting from a randomly selected month between January 2008, and June 2018. You
will not be told about the name of the stock or the starting date which was randomly
selected.
You will be rewarded based on the accuracy of your forecasts as follows.
!"# {200 − 10 × |-04:-; 12-0/"34 − -0"5.607 -04:-; × 100|, 0}If your forecast is exactly at the realized return, then you will receive 200 points. For
each percentage point difference between your forecast and the realized return, 10
points will be subtracted.
6

If your forecast differs from the realized return by more than 20%, you will receive 0
point.
To ease your forecasting task, the closing price of the final month is also shown Please
note that end-of-day prices have been rescaled so that all starting prices will beequal to 100.
You will do a similar forecasting task for all the 20 randomly chosen stocks. One in ten
participants will be selected for financial reward. If you are selected for the financial
reward, one of the 20 predictions will be selected randomly, and you will receive thereward according to the points you have earned in the selected task. 1 point will be
converted into 100/20 JPY. The payment will be in the form of an Amazon gift card.
You will not be informed about the accuracy of your forecast until the end of theexperiment.

7A.2.2. Figures
Price series
Series 1

Series 2

Series 3

Series 4

Series 5

Series 6

Series 7

Series 8

Series 9

Series 108Series 11

Series 12

Series 13

Series 14

Series 15

Series 16

Series 17

Series 18

Series 19

Series 20

9Returns series
Series 1

Series 2

Series 3

Series 4

Series 5

Series 6

Series 7

Series 8

Series 9

Series 1010Series 11

Series 12

Series 13

Series 14

Series 15

Series 16

Series 17

Series 18

Series 19

Series 20

11A.3. SPdaily
A.3.1. Instructions
In this experiment, you will be shown 20 graphs showing 12 months of end-of-day
prices of randomly selected stocks from the S&P 500 starting from a randomly selected
day between January 1st, 2008, and June 30th, 2018. You will not be told about the nameof the stock or the starting date which was randomly selected. Please note that end-ofday prices have been rescaled so that all starting prices will be equal to 100.
For each graph, you will be asked to forecast what will be the end-of-day price for thisstock 30 days after the last price shown on the graph.
You will be rewarded based on the accuracy of your forecasts as follows.
,-./0 12-0/"34 − -0"5.607 ,-./0!"# {200 − 1000 × ++ , 0}-0"5.607 ,-./0
If your forecast is exactly at the realized price, then you will receive 200 points. For
each percentage point difference between your forecast and the realized price, 10 points
will be subtracted.
If your forecast differs from the realized price by more than 20%, you will receive 0
point.
You will do a similar forecasting task for all the 20 randomly chosen stocks. One in ten
participants will be selected for financial reward. If you are selected for the financial
reward, one of the 20 predictions will be selected randomly, and you will receive thereward according to the points you have earned in the selected task. 1 point will be
converted into 100 /20 JPY. The payment will be in the form of an Amazon gift card.
You will not be informed about the accuracy of your forecast until the end of theexperiment

12

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365
131
129
127
125
123
121
119
117
115
113
111
109
107
105
103
101
99
97
95
93
91
89
87
85
83
81
79
77
75
73
71
69
67
65

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
9
17
25
36
44
52
60
70
78
86
94
105
113
121
129
137
147
155
163
171
182
190
198
206
217
225
234
242
252
260
269
277
287
295
303
311
319
330
338
346
354
365

A.3.2. Figures

Series used in SPdaily.
Series 1
Series 6

149
147
145
143
141
139
137
135
133
131
129
127
125
123
121
119
117
115
113
111
109
107
105
103
101
99
97
95
93
91
89
87

BasePrice100

BasePrice100

Series 2
141
139
137
135
133
131
129
127
125
123
121
119
117
115
113
111
109
107
105
103
101
99
97
95
93
91
89
87
85
83
81
79
77
75
73

Series 7
136
134
132
130
128
126
124
122
120
118
116
114
112
110
108
106
104
102
100
98
96
94
92
90
88
86
84
82
80
78
76
74
72
70
68

BasePrice100
BasePrice100

Series 3
140
138
136
134
132
130
128
126
124
122
120
118
116
114
112
110
108
106
104
102
100
98
96
94
92
90
88
86
84

Series 8
129
127
125
123
121
119
117
115
113
111
109
107
105
103
101
99
97
95
93
91
89
87
85
83
81
79
77

BasePrice100
BasePrice100

Series 4
146
144
142
140
138
136
134
132
130
128
126
124
122
120
118
116
114
112
110
108
106
104
102
100
98
96
94
92
90
88
86
84

Series 9
119

117

115

113

111

109

107

105

103

101

99

97

95

93

91

BasePrice100
BasePrice100
89

87

Series 5
127
124

125

Series 10
122

123

121

118

119

116

117
120

115

113

114

111

112

109

110

105

107

108

103

106

101

104

99

102

94

97

100

92

95

98

93

96

91

90

BasePrice100

BasePrice100

88

86

13

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

1
10
21
30
42
51
60
71
80
91
101
112
121
130
141
150
161
170
182
191
200
212
221
233
242
253
262
274
283
294
303
312
323
333
344
353
365

Series 11
168
166
164
162
160
158
156
154
152
150
148
146
144
142
140
138
136
134
132
130
128
126
124
122
120
118
116
114
112
110
108
106
104
102
100
98
96
94

Series 16
134
132
130
128
126
124
122
120
118
116
114
112
110
108
106
104
102
100
98
96
94
92
90
88
86
84

BasePrice100
BasePrice100

Series 12124122

Series 17
107

120

118

109

116

114

105

101

112

110

103

108

106

99

104

102

97

100

95

98

93

89

96

94

91

92

90

87

BasePrice100
BasePrice100

85

83

Series 13
114

112

Series 18

110

108

106

104

102

100

98

96

94

92

90

88

86

84
140
138
136
134
132
130
128
126
124
122
120
118
116
114
112
110
108
106
104
102
100
98
96
94
92
90
88

BasePrice100
BasePrice100

Series 14
127
125
123
121
119
117
115
113
111
109
107
105
103
101
99
97
95
93
91
89
87
85
83
81
79
77
75
73
71
69
67
65

Series 19
117
115
113
111
109
107
105
103
101
99
97
95
93
91

89
87
85
83
81
79
77
75
73
71
69
67
65
63
61
59
57
55
53
51
49

47
45
43
41
39
37

BasePrice100
BasePrice100

Series 15
114

Series 20

112

110

108

106

104

102

100

98

96

94

92

90

88

108
106
104
102
100
98
96
94
92
90
88
86
84
82
80
78
76
74
72
70
68
66
64
62

BasePrice100

BasePrice100

14A.4. NKI.
A.4.1. Forecasting task instructions
Example of feedback and instruction screens.

15A.4.2. Questionnaire (implemented in Wave 3)
For each of the following statements, please indicate how true it is for you,using the following scale (1-Not at all true, 4-Somewhat true, 7 -Very true):
I put a lot of effort in the forecasting task
I didn’t try very hard to do well in the forecasting task
It was important for me to do well at the forecasting task
I didn’t put much energy into the forecasting task
I feel this is an important task
How often have you used paid services to help you forecast the Nikkei average?
1-Never, 2-Rarely, 3-Sometimes, 4-Often, 5-Always
In the previous year (2020), how often have you made forecasts about the Nikkei
average? 1-Never, 2-Once a year, 3-Once a month, 4-Several times a month, 5Every day
What is the main strategy you use to forecast the Nikkei average?
1- Use of charts, 2- Use of fundamental information, 3- Trend following, 4Financial and international news, 5- Intuition, 6- Others (specify), 7-None.

16Appendix B. Survey
B.1. Loss attitudes
The loss aversion task is taken from Kirchler et al., (2018). The degree of loss
aversion is measured by the number of lotteries chosen out of 6 (the smaller it is,
the more loss averse a participant is).
B.1.1 Instructions
In the 6 decisions below, you have to decide whether you want to participate ina lottery where you can win or lose money. For this task, you receive an initialendowment of 18 points. If you reject the lottery, you will only receive your initialendowment.
The initial endowment and one of your 6 decisions below will be randomly selected
to calculate your payments. To determine your payment in case you chose the lottery,the program will randomly determine if you receive the loss or the gain. Note thatgains and losses are equally likely. Since you do not know which decisions will beselected for payment, and each decision stands an equal chance of being selected, you
should pay attention to the choice you make in each decision.
Please decide for each of the six rows below.
1. Loss of 3 points with 50% probability or gain of 15 points with 50% probability
2. Loss of 6 points with 50% probability or gain of 15 points with 50% probability
3. Loss of 9 points with 50% probability or gain of 15 points with 50% probability
4. Loss of 12 points with 50% probability or gain of 15 points with 50% probability
5. Loss of 15 points with 50% probability or gain of 15 points with 50% probability
6. Loss of 15 points with 50% probability or gain of 15 points with 50% probability
B.2. Risk attitudes
This task is a variant of the multiple price list of Holt and Laury (2002). The
difference is, in our experiments, we fix the probability of the risky outcomes, andthe other option is the certain sure amount of which is varied. A similar method isused in He and Hong (2018). The degree of risk aversion is measured by the numberof safe options chosen. The higher the number, the more risk averse a participantis.
17B.2.1 Instructions
In the 7 decisions below, you have to decide between two options, A or B. One ofyour 7 decisions below will be randomly selected, and you will be paid out
according to the choice you have made for that selected decision, i.e., either thelottery or the sure payoff. To determine your payment in case you chose the lottery,the program will randomly determine if you receive the lower or the larger amount.
Note that the lower and the larger amounts are always equally likely. Since you do
not know which decisions will be selected for payment, and each decision stands anequal chance of being selected, you should pay attention to the choice you make ineach decision.
1. A: 100% sure amount of 3 points v.s., B: 50% 0 point and 50% 24 points
2. A: 100% sure amount of 6 points v.s., B: 50% 0 point and 50% 24 points
3. A: 100% sure amount of 9 points v.s., B: 50% 0 point and 50% 24 points
4. A: 100% sure amount of 12 points v.s., B: 50% 0 point and 50% 24 points
5. A: 100% sure amount of 15 points v.s., B: 50% 0 point and 50% 24 points
6. A: 100% sure amount of 18 points v.s., B: 50% 0 point and 50% 24 points
7. A: 100% sure amount of 21 points v.s., B: 50% 0 point and 50% 24 points
B.3. Cognitive Reflection Test
We used the 6-question version of Cognitive Reflection Test (CRT, Frederick,2005). In particular, we take questions from Finucane and Gullion (2010) andToplak et al., (2014).
1. If it takes 2 nurses 2 minutes to measure the blood pressure of 2 patients, howlong would it take 200 nurses to measure the blood pressure of 200 patients? (inminutes) (Correct answer:

2 minutes; intuitive answer:

200 minutes).

2. A soup and a salad cost 5.50 euros in total. The soup costs 5 euros more than thesalad. How much does the salad cost? (in euros). (Correct answer: 0.25 euro;
intuitive answer:
0.5 euro)18

3. Sally is making sun tea. Every hour, the concentration of the tea doubles. If it
takes 6 hours for the tea to be ready, how long would it take for the tea to reach halfof the final concentration? (in hours) (correct answer: 5 hours; intuitive answer:
3 hours)
4. If John can drink one barrel of water in 6 days, and Mary can drink one barrel ofwater in 12 days, how long would it take them to drink one barrel of water together?
(in days) (correct answer: 4 days; intuitive answer: 9)
5. A man buys a pig for 60 euros, sells it for 70 euros, buys it back for 80 euros,and sells it finally for 90 euros. How much has he made? (correct answer: 20 euros;
intuitive answer: 10 euros)
6. Simon decided to invest 8,000 euros in the stock market one day early in 2008.
Six months after he invested, on July 17th, the stocks he had purchased were down
50%. Fortunately for Simon, from July 17th to October 17th, the stocks he hadpurchased went up 75%. At this point, Simon has: a. broken even in the stock
market, b. is ahead of where he began, c. has lost money. (correct answer: c,because the value at this point is 7,000 euros; intuitive response b.)
B.4. Demographics for CFAs
Years of experience:
1: 0-4 years, 2: 5-9 years, 3: 10-14 years, 4: 15-19 years, 5: 20-24 years, 6: 25-29
years, 7: 30-34 years, 8: 35-39 years
Academic degree:
1: Bachelor, 2: Master, 3: Doctor, 4: OthersAge:
1: 25-29, 2:30-34, 3:35-39, 4: 40-44, 5: 45-59, 6: 50-54, 7: 55-59, 8: 60 -64
Sex
1: Female, 2: Male
Annual gross income (million ¥)1: less than 1, 2: 1-2, 3: 2-4, 4: 4-6, 5: 6-8, 6: 8-10, 7:10-12, 8: 12-14, 9: 14-16,
19

10: 16-18, 11:18-20, 12: 20 or more, 13: no answer

B.5. Financial literacy
These questions are from Fernandes, Lynch, and Netemeyer (2014).
1) Imagine that the interest rate on your savings account was 1% per year andinflation was 2% per year. After 1 year, would you be able to buy:
• More than today with the money in this account• Exactly the same as today with the money in this account• Less than today with the money in this account• Don’t know
• Refuse to answer

2) Do you think that the following statement is true or false? “Bonds arenormally riskier than stocks.”• True• False• Don’t know
• Refuse to answer

3) Considering a long time period (for example 10 or 20 years), which asset
described below normally gives the highest return?
• Savings accounts• Stocks• Bonds• Don’t know
• Refuse to answer

4) Normally, which asset described below displays the highest fluctuations overtime?
• Savings accounts• Stocks• Bonds• Don’t know
20• Refuse to answer

5) When an investor spreads his money among different assets, does the risk oflosing a lot of money:• Increase• Decrease• Stay the same
• Don’t know
• Refuse to answer

6) Do you think that the following statement is true or false? “If you were toinvest $1000 in a stock mutual fund, it would be possible to have less than $1000
when you withdraw your money.”• True• False• Don’t know
• Refuse to answer

7) Do you think that the following statement is true or false? “A stock mutual fundcombines the money of many investors to buy a variety of stocks.”• True• False• Don’t know
• Refuse to answer

8) Do you think that the following statement is true or false? “A 15-year mortgage
typically requires higher monthly payments than a 30-year mortgage, but the totalinterest paid over the life of the loan will be less.”• True• False• Don’t know
• Refuse to answer

9) Suppose you had $100 in a savings account and the interest rate is 20% per year
and you never withdraw money or interest payments. After 5 years, how much21

would you have on this account in total?
• More than $200
• Exactly $200
• Less than $200
• Don’t know
• Refuse to answer

10)Which of the following statements is correct?

• Once one invests in a mutual fund, one cannot withdraw the money in the first year
• Mutual funds can invest in several assets, for example invest in both stocks and bonds• Mutual funds pay a guaranteed rate of return which depends on their past performance• None of the above• Don’t know
• Refuse to answer

11)

Which of the following statements is correct? If somebody buys a bond of

firm B:• He owns a part of firm B• He has lent money to firm B• He is liable for firm B’s debts• None of the above• Don’t know
• Refuse to answer

12)

Suppose you owe $3,000 on your credit card. You pay a minimum paymentof $30 each month. At an Annual Percentage Rate of 12% (or 1% per month),
how many years would it take to eliminate your credit card debt if you made no
additional new charges?
• Less than 5 years• Between 5 and 10 years• Between 10 and 15 years• Never• Don’t know
• Refuse to answer

22Appendix C (Additional analyses)
Table C1. Zeal (SPmonthly)
Dependent variable

Standardized zeal

Incongruent Dummy (std)0.108***(0.023)

CFA Dummy (std)-0.038*(0.020)

CFA Dummy (std) × Incongruent
Dummy (std)-0.041**(0.020)

CRT score (std)-0.022(0.022)

Constant

0.011(0.028)R2
Observations

0.062
7,400

Linear panel regressions with random effects and robust standard errors clustered at the individuallevels in parentheses for regressions, series and order fixed effects included.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1
Incongruent Dummy takes value one for incongruent treatments in SPmonthly .
(std) stands for standardized.

Table C2. Lee bounds estimations (NKI)
Lower / Upper Lee bounds estimates
Dependent variable

Standardized RFE

CFA Dummy (std)(1)−0.425∗∗∗ −0.122∗/(0.067)(0.069)0.187

Trimming proportion

Bootstrapped (5000 reps) standard errors in parentheses.
*** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1(std) stands for standardized.

23Standardized
Zeal(2)−0.301∗∗∗ −0.065∗/(0.064)(0.067)0.188

Table C3. Forecasting day (NKI)
Dependent variable

ForecastingDay(1)Standardized
RFE
(2)
(3)

(4)

Forecasting Day--0.028(0.030)--

CFA Dummy

0.315***(0.076)-0.223**(0.102)-0.204*(0.110)-0.188*(0.106)

CRT score

0.029(0.039)-0.0004(0.024)-0.029(0.027)-0.028(0.026)-0.035(0.028)

Dedication Index-0.029(0.019)Use of paid services
Constant
R2
Observations

23.167***(0.220)
0.943
1,181

0.747
(0.724)
0.054
1,181

0.354
(0.273)
0.062
980

0.272
(0.230)0.060
980

Linear panel regressions with random effects and robust standard errors clustered at theindividual levels in parentheses for regressions, months fixed effects included.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value <0.1

24Table C4. Relative Forecast Error(not controlling for CRT)
Standardized RFE

Dependent variable
RW
(1)
0.009
(0.036)

SPmonthly(2)-0.018**(0.008)SPdaily(3)0.026**(0.013)NKI♠(4)-0.116**(0.050)Constant

0.231***(0.057)-0.025(0.059)0.065(0.055)-0.020(0.080)R2
Observations

0.034
7,040

0.164
7,400

0.445
8,138

0.053
1,181

Task
CFA Dummy (std)Linear panel regressions with random effects and robust standard errors clustered at the individual levels
in parentheses for regressions, series and order fixed effects included and month for NKI.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1
CFA Dummy takes value one for participants who are CFAs.
♠
In NKI, we face selection issues as the study takes place over six months, and not all students and CFAs
make a forecast in each month. We obtained consistent results when estimating Lee bounds (Lee, 2009)
as in Table C2 in Appendix C to correct for selection issues.
(std) stands for standardized.

Table C5. Relative Forecast Error(SPmonthly treatments, not controlling for CRT)
Standardized RFE

Dependent variable

SPmonthly(Incongruent)(1)-0.029**(0.012)Task
CFA Dummy (std)SPmonthly(Congruent)(2)
0.00001(0.011)

Constant-0.114(0.077)0.003(0.084)

R2
Observations

0.202
3,660

0.147
3,740

Linear panel regressions with random effects and robust standard errors clustered at the individuallevels in parentheses for regressions, series and order fixed effects included.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1(std) stands for standardized.

25

Table C6. Relative Forecast Errorfor participants who answered the financial literacy test
(not controlling for either CRT or financial literacy)
Standardized RFE

Dependent variable
RW
(1)
0.025
(0.046)

SPmonthly(2)-0.021**(0.008)SPdaily(3)
0.015(0.012)NKI♠(4)-0.117**(0.050)Constant

0.227***(0.066)-0.089*(0.054)-0.002(0.051)-0.018(0.091)

R2
Observations

0.023
5,408

0.162
6,160

0.482
6,578

0.053
1,048

Task
CFA Dummy (std)Linear panel regressions with random effects and robust standard errors clustered at the individual levels
in parentheses for regressions, series and order fixed effects included and month for NKI.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1
CFA Dummy takes value one for participants who are CFAs.
♠
In NKI, we face selection issues as the study takes place over six months, and not all students and CFAs
make a forecast in each month. We obtained consistent results when estimating Lee bounds (Lee, 2009)
as in Table C2 in Appendix C to correct for selection issues.
(std) stands for standardized.

26

Table C7. Relative Forecast Error(controlling for CRT)
Standardized RFE

Dependent variable
RW
(1)
0.017
(0.046)

SPmonthly(2)-0.022**(0.008)SPdaily(3)
0.015(0.012)NKI♠(4)-0.119**(0.050)

CRT score (std)-0.091*(0.053)-0.013(0.010)-0.002(0.015)0.014(0.035)Constant

0.246***(0.066)-0.086(0.053)-0.002(0.051)-0.018(0.091)

R2
Observations

0.029
5,408

0.161
6,160

0.482
6,578

0.054
1,048

Task
CFA Dummy (std)Linear panel regressions with random effects and robust standard errors clustered at the individual levels
in parentheses for regressions, series and order fixed effects included and month for NKI. Only data forwhich we have both CRT scores and financial literacy
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1
CFA Dummy takes value one for participants who are CFAs.
♠
In NKI, we face selection issues as the study takes place over six months, and not all students and
CFAs make a forecast in each month. We obtained consistent results when estimating Lee bounds (Lee,2009) as in Table C2 in Appendix C to correct for selection issues.
(std) stands for standardized.

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Table C8. Relative Forecast Errorfor participants who answered the financial literacy test(controlling for financial literacy)
Standardized RFE

Dependent variableRW(1)
0.090*(0.050)

SPmonthly(2)-0.017*(0.009)SPdaily(3)
0.021(0.013)NKI♠(4)-0.044(0.051)

Financial literacy (std)1-0.133***(0.047)-0.009(0.012)-0.013(0.016)-0.014(0.098)Constant

0.264***(0.067)-0.087(0.053)-0.001(0.051)0.010(0.099)R2
Observations

0.035
5,408

0.161
6,160

0.483
6,578

0.059
1,048

Task
CFA Dummy (std)Linear panel regressions with random effects and robust standard errors clustered at the individual levels
in parentheses for regressions, series and order fixed effects included and month for NKI.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1
CFA Dummy takes value one for participants who are CFAs.
♠
In NKI, we face selection issues as the study takes place over six months, and not all students and CFAs
make a forecast in each month. We obtained consistent results when estimating Lee bounds (Lee, 2009) as
in Table C2 in Appendix C to correct for selection issues.
(std) stands for standardized.

1

When putting financial literacy and CFA dummy in the same regression, one could be concerned about
multicollinearity issues given their positive correlation (0.503). However, collinearity does not seem tobe an issue as the variance inflation factors associated with the two regressors (CFA Dummy and
Financial literacy) are below 1.55, regardless of the regression.

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Table C9. Relative Forecast Error(SPmonthly treatments, controlling for financial literacy and CRT)
Standardized RFE

Dependent variable
Task

SPmonthly(Incongruent)(2)-0.026**(0.013)

CFA Dummy (std)(1)-0.025**(0.012)

CRT score (std)-Financial literacy(std)a--0.011(0.015)-Constant-0.158*(0.081)

R2
Observations

0.207
3,080

SPmonthly(Congruent)(5)-0.016**(0.007)(3)-0.019(0.013)(4)-0.016**(0.007)(6)-0.015**(0.008)---0.013(0.018)--0.021*(0.012)--0.156*(0.080)-0.156*(0.081)-0.072***(0.020)-0.068***(0.020)-0.072***(0.020)

0.207
3,080

0.207
3,080

0.155
3,080

0.155
3,080

0.155
3,080

-0.002(0.010)Linear panel regressions with random effects and robust standard errors clustered at the individual levels inparentheses for regressions, series and order fixed effects included.
Robust standard errors in parentheses. *** p-value < 0.01, ** p-value < 0.05, * p-value < 0.1(std) stands for standardized.
a: Collinearity does not seem to be an issue as the variance inflation factors associated with the two regressors(CFA Dummy and Financial literacy) are below 1.40, regardless of the regression.

29