Who Is Successful in Foreign Exchange Margin Trading?
New Survey Evidence from Japan

Bernd Hayo
Kentaro Iwatsubo
July 2019
Discussion Paper No.1909

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANWho Is Successful in Foreign Exchange Margin Trading?
New Survey Evidence from Japan

Bernd Hayo* and Kentaro Iwatsubo†*

MACIE, School of Business & Economics, University of Marburg, Germany† Graduate School of Economics, Kobe University, Japan

This version: 10 July 2019

Corresponding author:
Bernd Hayo
Marburg Centre for Institutional Economics (MACIE)
School of Business and Economics
University of Marburg
D-35032 Marburg
Germany
Phone: +49⎼6421⎼2823091
Email: hayo@wiwi.uni-marburg.de* We are indebted to the Financial Futures Association of Japan for supplying the original survey data
we use. The usual disclaimer applies.

1Who Is Successful in Foreign Exchange Margin Trading?
New Survey Evidence from Japan

Abstract
We use a 2018 survey of FX margin traders in Japan to investigate which key factors influence theirperformance: socio-demographic and economic situation, investment strategy and trading behaviour,
and/or financial literacy. First, the data show that variables from all three groups are significant
predictors of traders’ performance. Second, we find that older traders and those without a specifictrading strategy demonstrate lower performance. Performance is higher for those who trade greater
amounts, rely more on fundamental analysis, and report having profitable FX trade skills. Third,
respondents’ subjectively stated claim of having FX trade skills is based on a more advanced
understanding of FX trading and a reliance on professional advice. Neither objective financial
knowledge nor over/underconfidence play a noteworthy role in the performance of margin traders.

JEL: F31, G11, G28, G40.
Keywords: Foreign exchange margin trading, investor survey, foreign exchange trading profits,financial literacy, Japan2

1. Introduction
What factors determine the performance of ‘Mrs Watanabe’? This probably sounds like a strange
research question for a study on foreign exchange (FX) trading, which is typically associated with large
financial investors. However, ‘Mrs Watanabe’ is a well-known synonym for an individual FX margintrader in Japan, and one highlighted again in the recent Japanese yen flash event. On 3 January 2019,
the Japanese yen appreciated very sharply against the US dollar over a few minutes around theopening of the Asian trading session. This flash event quickly cascaded across a number of othermarkets, with the Australian dollar and Turkish lira depreciating sharply during a period of low liquidity.
The Reserve Bank of Australia (2019) reports three key factors likely to have contributed to this flash
event, one of which is the liquidation of ‘carry trade’ positions, particularly those of Japanese retailtraders. 1 Publicly available data on retail positions suggest that Japanese traders held aggressive longpositions in high-yield currencies (including the US dollar, Australian dollar, and Turkish lira) prior to
the event. Retail brokers will automatically liquidate these positions (vie stop-loss orders) if losses due
to adverse exchange rate movements exceed predetermined levels. This automatic liquidation process
would have contributed to the outsized depreciation in some high-yield currencies relative to the yen.
Thus, the behaviour of Japanese retail traders can trigger world FX market turbulence.
FX margin trading is an important influence on the total Japanese FX market. According to the Triennial
Survey 2013, BIS (2013) reports that Japan has the biggest retail spot FX market in the world andaccounts for 36 per cent of global retail spot FX trading, followed by the United States (16%) and Britain(15%). The estimated volume of retail margin trading has exceeded the interbank spot trading volume
in Tokyo since 2013, with a ratio of 1.5 times in 2017 and that of 1.1 times in 2018 (Financial Futures
Association of Japan 2018a, Tables 2 and 3). Thus, this part of the Japanese FX market is not just aninteresting curiosity.
In this paper, we investigate the core characteristics that distinguish a successful margin trader froman unsuccessful one. There are two possible empirical approaches to such an investigation, each with
their advantages and disadvantages. The first approach is to use transaction data, which are objective
and measure what traders actually did, for instance, with regard to trading volume, investment horizon,
and leverage. The second approach relies on survey data, which are subjective and represent what
traders say they did. As such, survey data are prone to a much larger number of possible errors, ranging
from memory shortcomings to giving socially desirable answers. The advantage of the second
approach, however, consists of being able to study variables that are not observable from thetransaction data, for example, objective/subjective knowledge, preferences, trading preparations, or
adopted investment policy. Here, we follow the second approach.
There is considerable evidence on the stock trading behaviour of individual investors (see, e.g., Barberand Odean 2013). For example, retail investors perform worse than typical benchmarks, are subject tothe disposition effect, i.e., holding losing securities and selling winning ones, are affected by limited
attention and past return performance, repeat patterns that were successful in the past, and tend todiversify insufficiently. In contrast, there is only a small literature studying retail FX traders. Menkhoff
and Schmeling (2010) demonstrate that information about a trade’s counterparty affects individual
traders’ trading decisions in the future. Moreover, better informed traders tend to consider publicly
available information in addition to their own private information, whereas uninformed traders
typically simply copy the trading of informed traders. Using an online currency transactions dataset,
Abbey and Doukas (2015) show that individual traders earn positive excess returns, even after
The other two factors are very low market liquidity at the time of the day and year (close to New Year’s Day
and during the vacation period of many participants) and algorithmic trading strategies, which might amplify
the flash event.

1

3

accounting for transaction costs. It also appears that there is a positive correlation between
performance and trade activity. Hayley and Marsh (2016) analyse the benefit of experience. Using
actual trading data, they discover that retail FX traders show no obvious tendency to learn to tradebetter over time, but they do appear to achieve a more accurate assessment of their own abilities.
Our analysis places special emphasis on analysing the effects of financial literacy on FX margin trader’strading performance. There is a large literature studying various aspects of financial literacy. Lusardi
and Mitchell (2014) provide an extensive survey of the extant literature, from which it becomesapparent that those who do research on financial literacy usually focus on stocks and investment trusts.
An important contribution of that literature is to show that financial knowledge can have significant
effects on financial market performance. For instance, von Gaudecker (2015) provides evidence that
individuals relying on professionals or private contacts for advice or who score high on financial literacy
achieve systematically better investment returns. Krische (2018) reports that individuals more likely to
study financial reporting information as a base for making their investment decisions tend to haverelatively high levels of financial literacy. Wang (2009) argues that financial knowledge is a likely
explanation for investors’ financial behaviour. He shows that gender is a crucial factor differentiating
investors’ objective knowledge, subjective knowledge, and risk taking, which are found to be highly
correlated. Xia et al. (2014) study the influence of overconfidence, based on the difference between
subjective and objective knowledge, for stock market participation. They find that overconfidence(underconfidence) is positively (negatively) correlated with stock market participation. Stock market
participation is also affected by household literacy: low literacy decreases the likelihood ofparticipation (van Rooij et al. 2011). Consistency between self-assessment and actual portfolio
composition is investigated by Nicoletta et al. (2017). Their results suggest that investors’ low literacy,
among other factors, makes inconsistencies more likely. Bellofatto et al. (2018) present evidence that
subjective financial literacy helps explain cross-sectional differences in retail investors’ actions.
Investors reporting higher levels of financial literacy appear to invest more intelligently, even after
controlling for gender, age, portfolio value, trading experience, and education. Hsiao and Tsai (2018)
study the question of whether individuals with higher levels of financial literacy are more likely to be
active participants in derivatives markets. Employing survey data on derivatives trading in Taiwan, they
provide evidence supporting the linkage between literacy and participation.
The literature on financial knowledge covers many aspects, but no one has considered higher-risk
assets such as FX investments, which are the focus of our paper. Using survey data on FX margin traderscollected in 2018 on behalf of the Financial Futures Association of Japan, we study factors that areassociated with three alternative investment performance indicators. For our cross-sectional
regression analysis, explanatory variables are divided into three categories: (i) socio-demographic andeconomic situation, (ii) investment strategy and trading behaviour, and (iii) financial literacy. Thus, in
our analysis, we emphasise those factors that cannot be controlled for when employing actual trading
data. We are especially interested in discovering whether there is a relationship between specificaspects of individual financial literacy and FX trading success.
We draw the following conclusions. First, variables from all three groups are significant predictors of
traders’ performance. Second, we find that older traders and those without a specific trading strategy
exhibit lower performance. Performance is higher for those who trade greater amounts, rely more on
fundamental factors, and report high FX trade skills. Third, respondents’ subjectively stated claim ofhaving FX trade skills is based on a more advanced understanding of FX trading and a reliance on
professional advice. Neither objective financial knowledge nor over/underconfidence appear to play anoteworthy role in the performance of FX margin traders.

4In the next section, we present the dataset and our empirical methodology in more detail. Section 3
contains the estimation results, which are interpreted in Section 4. In Section 5, we provide further
analysis of the role of FX trade skills. Section 6 concludes.

2. Data and Methodology
We utilise survey data collected via the Internet on behalf of the Financial Futures Association of Japanin the period 23 February to 1 March 2018 (see Financial Futures Association of Japan 2018b). Basedon a web panel maintained by Nippon Research Center Ltd., a sample of 1,000 members of the general
population between the ages of 20 and 80 has been drawn from across Japan, with a preference given
to those conducting foreign exchange margin transactions at the time of the survey. In 2017, theFinancial Futures Attitude Survey of Individual Investors was conducted to survey 2,000 individual
members of the general public from across Japan, these being chosen according to the results of the2015 national census (Financial Futures Association of Japan 2017). Using the appearance rate of thosehaving experience with foreign exchange margin transactions obtained from the 2017 survey, the sizeof the relevant age and gender groups of FX margin traders was determined, which was subsequently
employed to collect the current sample. Put differently, with regard to the age and gender compositionof respondents, the current sample is in line with the estimate for the population of Japanese individual
FX traders at large. A summary of descriptive results for the survey can be found in Financial Futures
Association of Japan (2018b).
To estimate the influence of correlates on trading success, we employ three alternative dependent
variables. First, we use respondents’ answers to a question asking whether they made a profit or loss
in yen through FX trading in the previous year, i.e., 2017. This question contains eight answer
categories. See the Appendix for more details about the variables employed in our study. We take the
mid-point of these answer categories and construct our first dependent variable as profits/losses (inyen, mean: 123,150 yen, median: 100,000 yen). A problem with that variable is that we know nothing
about the capital invested in trading. Thus, our second dependent variable standardises profits/losses
in yen by the average trading amount of FX transactions (in per cent, mean: 21%, median: 18%). Thiscan be thought of as a proxy for a return on investment. A third operationalisation of a performancevariable is traders’ profits/losses in yen divided by their income in yen (in per cent, mean: 0.52%,
median: 0.41%). In principle, the available FX investment capital is the margin deposited in the FX
broker’s account that the investor is using. However, it can be the case that the investor transfers
liquidity from another account to the FX margin. Hence, potentially, the margin includes part of theinvestor’s cash deposit. To reflect that, we assume that a trader’s liquidity is related to his/her income
level. Thus, our third dependent variable reflects an alternative version of a return that is based on
dividing profits in yen by income in yen (in per cent). Our dependent variables are skewed to the right,
which is typical for financial market returns.
We group our explanatory variables into three broad categories (for a detailed description of variables,
see the Appendix): (i) socio-demographic and economic situation, (ii) investment strategy and trading
behaviour, and (iii) financial literacy. With regard to (i), we describe the respondent’s sociodemographic and economic position by the variables Age (in years), Female (dummy), Education(ranging from primary to graduate school), Occupation (nine dummies), and Income (in million yen per
year; included when not used for constructing the dependent variable).
Variable group (ii), investment strategy and trading behaviour, is captured by Trade Horizon (average
period of holding new positions), Recent FX Trade (most recent FX margin trading), Leverage (leverage
ratios for FX transactions), Fundamental (preference for fundamental analysis vs technical analysis),5Order Stop-Loss (uses stop-loss order), Investment Policy (seven dummies for using a specificinvestment strategy), FX Transactions (number of FX transactions during a year), and Trade Amount(average trading amount for new FX transactions; included when not used for constructing thedependent variable).
We divide the last group, (iii), relating to financial literacy into four subgroups: Subjective Knowledge,
Objective Knowledge, Experience, and Information Channels. Subjective Knowledge is the outcome ofa factor analysis based on three indicators, Knowledge Terms Index (sum of 16 dummies measuring
whether respondents claim to understand FX terms), Subjective Financial Knowledge (self-evaluationof own level of overall financial knowledge), and Knowledge Index (sum of eight dummies measuring
subjective knowledge about FX trading). For details on the factor analysis, see Table A1 in the Appendix.
Based on the usual criterion of an eigenvalue larger than unity, only one factor can be found. Thisfactor is associated with factor loadings ranging from over 0.4 to over 0.7, a clear rejection of the nofactor model, and an acceptable Kaiser-Meyer-Olkin value of 0.6. The factor is employed to preserve
degrees of freedom. Note, however, that all results hold when individually including the variables
underlying the factor.
Objective Knowledge originates from the simple sum of correct answers to two questions: Objective
Knowledge Interest Rate (dummy measuring a correct interest rate calculation) and Objective
Knowledge Leverage (dummy measuring the correct answer to a question about the leverage ratio in
FX trading). Employing the derived indicators for subjective and objective knowledge, we construct thevariable Under/Overconfidence (proxying for the relative under- or overconfidence of respondents),
which is based on recoding Subjective Knowledge into three percentiles (1 ‘< 33%’, 2 ‘33%–67%’,3 ‘>67’) and then subtracting Objective Knowledge.
Specific FX skills are assessed by two variables, FX Experience (years of experience in FX margin trading)and FX Trade Skills (dummy measuring whether respondent claims to have FX skills that on average
generate returns). Willingness to learn about FX trade is captured by Learning Index (sum of eight
dummies indicating which FX-related topics the respondent wants to learn about). Finally, information
aspects are addressed by including the Major FX Info Index (sum of eight dummies measuring thenumber of sources for FX trading information) and FX Adverts (respondent knows about
advertisements indicating spreads for FX trading).
We employ ordinary least square models to estimate the impact of our various indicators on theperformance of FX margin traders. We use up to 33 explanatory variables, the influence of which weestimate jointly in a general model. Considering all possibly relevant variables takes into account bothomitted variable bias and standard-error-decreasing complementarity (Hayo 2018). We then derive aparsimonious model using a general-to-specific modelling approach (see Hendry 1993). Specifically,
we use the Autometrics reduction algorithm implemented in Oxmetrics (Doornik 2009). This algorithm
employs a tree search to discover and eliminate insignificant variables, thereby improving on the multipath search in Hendry and Krolzig (2005). Autometrics ensures that the final reduced model is acongruent representation of the general model. For all tests conducted here, we apply a nominal levelof significance of 5 per cent.
Testing for heteroscedasticity using the White (1980) test indicates problems in the case ofProfits/Losses in Yen as a dependent variable. We thus employ the heteroscedasticity-robust standard
error estimator developed by MacKinnon and White (1985), which is based on jack-knifing and has
better small-sample properties than the robust standard errors estimator originally put forward by
White (1980).

6

We assess the robustness of the reduced model using impulse-indicator saturation (see Johansen andNielsen 2009; Castle et al. 2012), which systematically assesses the possibility of multiple breaks in thedata. This powerful process is based on the principle of adding an impulse indicator for every
observation to the regressors of interest, which results in the total number of estimated parameters
exceeding the sample size. This approach is based on entering the impulse indicators in blocks andthen retaining the significant ones. The approach does not suffer from collinearity, as impulse
indicators are mutually orthogonal. Note that the systematic study of possible combinations of impulse
indicators in samples like the present one is computationally time intensive, even with fast computers.
The final result of impulse-indicator saturation allows us to study the variables of interest after
accounting for all outliers in the dataset.

3. Estimation Results
The left-hand side of Table A3 in the Appendix sets out estimates from the general models for thethree dependent variables proxying traders’ performance. While each of the models is jointly
significant, most variables are not individually significant. Use of the Autometrics algorithm results in
the reduced models shown in Table 1. Diagnostic information (Row (5)) shows that we cannot reject
the implied testing-down restriction (i.e., that the coefficients are jointly equal to zero). Since theeliminated variables have no significant explanatory power as a group, we find no evidence that theirindividual insignificance is caused by collinearity. Therefore, we concentrate on the reduced models.
Starting with Profits/Losses in Yen as a dependent variable, we obtain a significantly estimated model(see Table A3). Since residuals appear to be subject to heteroscedasticity, we apply jack-knife robust
standard errors. Using Autometrics, we derive the reduced model shown in the first column of Table
1. All the remaining variables are jointly as well as individually significant. Income, Leverage, and FX
Experience turned out not to be robust under impulse-indicator saturation. To facilitate theinterpretation of the coefficient, we study the impact of a one standard deviation change in the
explanatory variables. Doing this for the dummy variables, however, does not make much sense,
where we focus on the change of the dummy from zero to one. We find that several variables have anegative effect on making profits from FX margin trading. On average, a one standard deviation change
in age decreases profits by 47,014 yen. A one standard deviation change in Trade Horizon towards along-term perspective leads to a decrease in profits of 65,545 yen. Traders increasing their leverage by
one standard deviation can be expected to realise a loss of 43,323 yen. A number of variables have apositive effect on profits. All evaluated at a one standard deviation change, we find that higher income
raises profits by 41,340 yen, a higher trade amount increases profits by 109,163 yen, a change towards
more fundamental analysis generates a profit of about 16,284 yen, more FX experience yields anadditional 35,480 yen, and those who report having FX skills are better off by 122,753 yen, which is by
far the greatest effect, followed by Trade Amount and Trade Horizon.
Table 1 also shows the reduced model when using Return on Investment (Profit/Trade Amount * 100)
as a dependent variable. The number of significant variables is notably smaller in this case, as is the fitof the model to the data (see Row (3) at the bottom of the table). The No Specific Strategy variable is
not robust to impulse-indicator saturation. Age again has a negative impact on performance. A one
standard deviation increase in Age reduced Return on Investment by 31 percentage points (pp), a very
noteworthy effect. Also in line with our previous findings, Fundamental and FX Trade Skills have apositive impact of 12 pp and 63 pp, respectively, after a one standard deviation hike. On average,
adopting no specific strategy results in much lower performance, namely, a decline of 233 pp
compared to those who have chosen a specific trading strategy.
7

Table 1: Reduced model: explaining performance of FX margin traders (OLS)
Profits/losses in yen
Variables

CoefficientStandarderror

Profits/losses relative
to trading amount in %Profits/losses relative
to income in %CoefficientStandard
error

Coefficient-2.44**#(0.81)

Standarderror(i) socio-demographic and economic situation
Age-3673**#(1242)

Income0.0013**(0.0005)n.a.

(ii) investment strategy and trading behaviour
Trade Horizon-31212**#(8373)

Leverage-27077**(9731)

Fundamental

45233**#(14150)32.60**#(11.60)1.47*#(0.70)-11.94**#(2.61)

1.26**#(0.48)

4.93**#(1.56)-7.90**#(2.48)

Investment Strategy
Hold positions for

Referencelong periods
No specific-233.38**(52.52)strategy
Trade Amount68227**#(10800)FX Experience

22175*(10910)FX Trade Skills

245505**#(33970)n.a.

(iii) financial literacy
126.01**#(30.27)

Constant(1) No. of
observations(2) Test of joint
significance(3) R-squared(4) Test forheteroscedasticity(5) Testing-down
restriction

962

962

962

F(8,954)=22.9**

F(4,958)=13.1**

F(4,957)=13.1**

0.15

0.05

0.05

F(15,946)=5.47**

F(6,955)=1.65

F(6,955)=2.03

F(26,928)=0.70

F(28,929)=0.58

F(28,929)=1.20

Notes: Estimation method: OLS. For Profits/losses in yen, jack-knife robust standard errors are used.
In the models without a constant, the R-squared is based on the multivariate correlation coefficient. *and ** indicate significance at a 5 per cent and 1 per cent level, respectively. # indicates robustness
after impulse-indicator saturation.

8

The right-hand side of Table 1 shows the reduced model when using Return (Profit/Income * 100) as
the dependent variable. With a similar coefficient of determination as in the case of Return on
Investment, we find three variables that have a positive effect on performance, all of which are robust
under impulse-indicator saturation. On average, a one standard deviation change in Trade Amountincreases Return by 2 pp. The corresponding effects for Fundamental and FX Trade Skills are 1 pp and
2 pp, respectively. Performance is negatively impacted by not having a clearly specified trading
strategy; respondents without such a strategy suffered a 12 pp lower return, a very notable effect.

4. Interpretation of Estimation Results
Based on the estimation results, we show that five variables are especially robust among variablesfrom the three categories: (i) socio-demographic and economic situation, (ii) investment strategy andtrading behaviour, and (iii) financial literacy. We define ‘robust’ as significantly predicting at least twoof the alternative performance indicators and surviving the impulse-indicator saturation. Thesevariables are Age, No Specific Strategy, Trade Amount, Fundamental, and FX Trade Skills. Especiallystrong economic effects were estimated for No Specific Strategy and FX Trade Skills.
The result that older participants perform relatively worse is not due to estimating a linear model, as
including a squared age term does not suggest a nonlinear relationship either. This result holds
unconditionally in the case of profits/losses in yen, but only conditionally in the case of profits/losses
relative to trading amount in per cent. Moreover, there is no notable impact on FX trading performanceof an interaction between age and gender. It is noteworthy that socio-demographic variables otherthan age and a positive influence of income on profits play no role in trading performance. In particular,
we find no difference between men and women, between respondents with different educational
achievements, or between those working in various occupations.
Which trading strategy is most successful is a longstanding question in the field of finance (Siegel, 1998;
Froot and Thaler 1990). We use subjective data here, so we measure what respondents think they do.
Thus, it could very well be that respondents do not use the same mental models about trading
strategies when answering the questions. In this respect, relying on actual trading data is beneficial, as
it allows classifying traders’ strategy based on common definitions (Barber and Odean 2013; Abbey
and Doukas 2015; Hayley and Marsh 2016). However, subjective data have an advantage, too, as they
provide information about what respondents think they are doing. Our result is interesting in that itsuggests that it does not much matter what type of trading strategy a person adopts; what mattersmore is that he or she thought about adopting any specific strategy in the first place. Put differently, itis those traders who have failed to sufficiently structure and focus their trading activity who dorelatively worse.
In light of our estimation results, some comments seem in order. The influence of Trade Amount on
performance can be interpreted along two lines. First, seen from a technical perspective, as long as expost returns are positive, investing more funds will generate higher profits. As shown in Table A1 in
the Appendix, average profits are indeed positive in our sample, so our results can be understood inthat way. However, the second way the influence of Trade Amount can be looked at is to view it as aproxy for trading experience, which would suggest that greater experience has an influence on a
trader’s performance. Inasmuch as the respondent’s income controls for his or her available trading
funds, adopting this view is supported by the finding that Trade Amount is significant when using
profits/losses relative to income as a dependent variable.
Regarding respondents’ preference for fundamental analysis versus technical analysis in FX trading,those who prefer the former show better performance. Again, different interpretations of this result
9are possible. One could view it as an indication of people’s trading approach, where consideration of
fundamental factors is beneficial for performance. Alternatively, it can be viewed as a proxy for the
degree of information acquisition, that is, an indication of how much effort traders put into
understanding the economic environment within which their trades take place.
One of the strongest predictors of FX trading performance in our sample is FX Trade Skills, that is, a
trader’s subjective assessment of his or her own skills. To some extent, this finding using survey datais consistent with the result of Hayley and Marsh (2016) based on actual trading data, where retailtraders learn about their innate abilities as FX traders. The finding is not only highly significant and very
robust, but associated with large economic effects, too. We would interpret it as a subjective form ofspecific financial literacy. Thus, this specific dimension of financial literacy appears to be very
important. Regarding financial literacy more generally, we find few variables to be significant,suggesting that in its general form, financial literacy does not seem to contribute much to improving
profits.

5. Investigating the Role of FX Trade Skills
Given the strength and robustness of the FX Trade Skills variable, it is worth discovering how tradersacquired these profitable trade skills, even though it the variable is only a subjective indicator. Hence,
we further analyse which other financial literacy indicators and socio-demographic variables correlatewith FX trade skills. In particular, we look at the answers to the following items: ‘Knowledge acquiredso far in connection with FX trading’, ‘Do you understand the following terms in the FX glossary?’,‘Major sources of FX trading information’, Objective Knowledge Index, Trade Amount, FX Adverts, FX
Experience, Education, Age, Female, and Income. The general model is estimated with 41 variables,
which is then reduced using the Autometrics algorithm. The results of the reduced model explaining
FX Trade Skills are given in Table 2. 2
Our analysis shows that FX Trade Skills is not associated with socio-economic indicators such as age,sex, education, or income. Neither does it depend on objective knowledge, FX experience, or trading
amount. Instead, other indicators of financial literacy, particularly knowledge acquired so far inconnection with FX trading in the form of mental training, appear to be relevant. This suggests thatthose who report having high FX trade skills have already acquired comprehensive FX knowledge.
Specifically, the data shows that 97 per cent of those who report having knowledge of mental training
have knowledge of trading mechanisms and risk management. Similarly, 83 per cent of those haveknowledge of taxation, 78 per cent of those have knowledge of fundamental analysis techniques, and84 per cent of those have knowledge of technical analysis techniques. These high shares suggest that
extensive knowledge of FX trading, even to the extent of mental training, is required to be confident
about having high trade skills. Arguably, such skills enable traders to be profitable.
Profitable traders are also characterised by understanding the more advanced technical terms ‘OCO’(one-cancels-other order) and ‘cover transaction’. Respondents who report having FX trade skills
obtain their FX trading information chiefly from advice obtained at service counters of financial
institutions, from brochures available at branches of financial institutions, and by relying on the adviceof experts or professional advisors. This is consistent with von Gaudecker (2015), who shows that
individuals relying on professionals or private contacts for advice achieve systematically better
investment returns. Finally, successful traders are aware of FX adverts and are motivated to invest.

To economise on space, we omitted the estimation details for the general model. They are available from theauthors on request.

2

10

However, reflecting the outcome of the impulse-indicator saturation robustness test, we conclude that
under no configuration does FX Trade Skills become insignificant when including these financial literacy
indicators in the reduced model.
Table 2: Reduced model: explaining FX Trade Skills (OLS)
Variables

CoefficientStandard error

Knowledge Index:
Unconnected with specific knowledge

Reference

Mental training

0.14**(0.04)

Knowledge Terms Index:
Cannot understand any of the foregoing terms

Reference

OCO, if done

0.10**(0.03)

Cover transaction

0.15**(0.04)

Major FX Info Index:
Other sources

Reference

Advice obtained at service counters of financial institutions

0.18**(0.06)

Brochures available at branches of financial institutions

0.23**(0.07)

Advice from experts or professional advisors

0.23**(0.06)

0.07**(0.01)FX Adverts(1) No. of
observations(2) Test of joint
significance(3) R-squared

999
F(7,992)=136.4**
0.16(4) Test forF(8,990)=2.53*
heteroscedasticity(5) Testing-down
F(34,957)=0.76
restriction
Notes: Estimation method: OLS. Jack-knife robust standard errors are used. The R-squared is based on
the multivariate correlation coefficient. * and ** indicate significance at a 5 per cent and 1 percent
level, respectively.
In contrast to FX Trade Skills, Objective Knowledge about FX trading is not a significant predictor of
margin traders’ performance. There are two possible interpretations of this conclusion. First, assumingthat our Objective Knowledge indicator works well, our results suggest that performance does notdepend on a trader’s level of factual knowledge. Second, it could also be that the survey questions do
not measure the theoretical concept well enough. It thus would be interesting to conduct a similar
survey in which objective knowledge is based on a larger number of items.
We constructed an indicator for the relative under/overconfidence of traders and discovered that
there are possible combinations of explanatory variables for which this indicator becomes significant
at a 10 per cent level. This implies that those who are overconfident experience relatively worse
trading outcomes. However, this variable is not robust under our definition of the term and thus this11

finding should not be overemphasised. As noted above, the objective knowledge indicator is based on
only a few questions; considering more knowledge dimensions would likely yield a better indicator ofunder/overconfidence. Other psychological factors, such as risk aversion or time preference, may also
play a role in trading outcomes, but, given the variables included in the dataset, there is no way tocontrol for these.
Finally, it should be noted that endogeneity is a potential problem with our findings. Those who wereable to make profits in the previous year are, based on their good performance, likely to report having
trade skills. We re-estimate the models presented in Table 1 using as instrumental variables the ones
from Table 2 (except Mental Training). As can be seen from the F-statistics in Table 2, these
instruments easily pass Staiger and Stock’s (1997) rule-of-thumb threshold of 10 for strong instruments.
The results of the instrumental variable estimations concentrating on the effect of FX Trade Skills are
given in Table 3.
Table 3: Reduced model: explaining performance of FX margin traders (IV)
Profits/losses in yen
Variables

CoefficientProfits/losses relative
to trading amount in %Profits/losses relative
to income in %Standard
error

CoefficientStandard
error

CoefficientStandard
error(89150)

142~

(86)

7.3~

(4.4)

Includes all variables from Table 1
FX Trade Skills(1) No. of
observations(2) Test of joint
significance(3) Sargan test

438298**
962

962

962

Chi2(7)=135**

Chi2(4)=37**

Chi2(4)=45**

Chi2(5)=6.1

Chi2(5)=1.7

Chi2(5)=7.3

Notes: Estimation method: two-stage least squares. ~, *, and ** indicate significance at a 10 per cent,
5 per cent, and 1 per cent level, respectively. # indicates robustness after impulse-indicator saturation.
Row (3) of Table 3 shows that the Sargan orthogonality test cannot be rejected. We find in the case ofProfits/Losses in Yen that the coefficient is highly significant and much larger than the one estimatedwith OLS. In the case of Profits/losses relative to trading amount, the magnitude of the estimatedvalues has risen too, although they are significant only at a 10 per cent level. Thus, even if we try tocontrol for possible endogeneity between FX trade skills and FX trade performance using instrumental
variable estimation, the results shown in Table 1 appear to be robust.

6. Conclusion
Using novel survey data collected in 2018 on behalf of the Financial Futures Association of Japan, weinvestigate the main characteristics that differentiate between successful and unsuccessful FX margintraders. Using cross-sectional regressions, we operationalise trading performance by three indicators:(i) profits in yen, (ii) profits in yen divided by trading amount, and (iii) profits in yen divided by income.
We use three groups of indicators as regressors: (i) socio-demographic and economic situation, (ii)
investment strategy and trading behaviour, and (iii) financial literacy. Using automatic model selection,
we derive reduced models and test their robustness by employing impulse-indicator saturation.
Although other variables may be significant in different model specifications, we opted for focussing
only on variables that are robust.
12

We draw the following conclusions. First, variables from all three groups are significant predictors of
traders’ performance. Second, we find that older traders and those without a specific trading strategy
exhibit lower performance. Performance is higher for those who trade greater amounts, rely more on
fundamental factors, and report having high FX trade skills. Third, respondents’ subjectively statedclaim of having FX trade skills is based on a more advanced understanding of FX trading and a relianceon professional advice. Neither objective financial knowledge nor over/underconfidence appear toplay a noteworthy role in the performance of FX margin traders.
Based on our results, we derive some recommendations for supervisory authorities, advising
institutions, and FX margin investors. First, and possibly most important for successful trading, is thenecessity of having a specific trading strategy. Which strategy is actually chosen is of lesser importance,
as long as trading is guided by a structured approach. Put differently, random trading is not likely toyield positive outcomes. Moreover, we find that respondents who consider fundamental analysis tobe more important than technical analysis achieve better performance. Second, having sufficient FX
trading skills is key to successful trading. One way of interpreting our findings is that, among otherthings, these skills reflect a more advanced understanding of technical terms and an extensive
knowledge of trade techniques. Third, obtaining, and listening to, advice from professional institutions
and FX experts can have a strong influence on successful trading. In our estimations, these aspects
were found to have the greatest economic impact on trading performance.

References
Abbey, B. S. and J. A. Doukas (2015), Do Individual Currency Traders Make Money? Journal ofInternational Money and Finance 56, 158–177.
Bank for International Settlements (BIS) (2013), Retail Trading in the FX Market, BIS Quarterly Review
December, 39.
Barber, B. M. and T. Odean (2013), The Behavior of Individual Investors, in: G. M. Constantinides, M.
Harris, and R. M. Stulz (eds.), Handbook of the Economics of Finance, Vol. 2, 1533–1570.
Bellofatto, A., C. D’Hondt, and R. de Winne (2018), Subjective Financial Literacy and Retail Investors’
Behavior, Journal of Banking and Finance 92, 168–181.
Castle, J. L., J. A. Doornik, and D. F. Hendry (2012), Model Selection When There Are Multiple Breaks,
Journal of Econometrics 169, 239–246.
Doornik, J. A. (2009), Autometrics, in: J. L. Castle and N. Shephard (eds.), The Methodology and Practiceof Econometrics, Oxford: Oxford University Press, 88–121.
Financial Futures Association of Japan (2017), Overview of Financial Futures Attitude Survey of
Individual Investors, Tokyo: Financial Futures Association of Japan.
Financial Futures Association of Japan (2018a), Results of the Actual Conditions Survey of Over-theCounter Retail Foreign Exchange Margin Trading, Tokyo: Financial Futures Association of Japan.
Financial Futures Association of Japan (2018b), Overview of Financial Literacy Survey of Investors in
Foreign Exchange Margin Transactions, Tokyo: Financial Futures Association of Japan.
Froot, K. A. and R. H. Thaler (1990), Anomalies: Foreign Exchange, Journal of Economic Perspectives 4,
179–192.
13

Hayley, S. and I. W. Marsh (2016), What Do Retail FX Traders Learn? Journal of International Moneyand Finance 64, 16–38.
Hayo, B. (2018), On Standard-Error-Decreasing Complementarity: Why Collinearity Is Not the Whole
Story, Journal of Quantitative Economics 16, 289–307.
Hendry, D. F. (1993), Econometrics: Alchemy or Science? Oxford: Blackwell.
Hendry, D. F. and H.-M. Krolzig (2005), The Properties of Automatic Gets Modelling, Economic Journal
115, C32–C61.
Hsiao,Y.-J. and W.-C. Tsai (2018), Financial Literacy and Participation in the Derivatives Markets,
Journal of Banking and Finance 88, 15–29.
Johansen, S. and B. Nielsen (2009), An Analysis of the Indicator Saturation Estimator as a Robust
Regression Estimator, in: J. L. Castle and N. Shephard (eds.), The Methodology and Practice of
Econometrics, Oxford: Oxford University Press, 1–36.
Krische, S. D. (2018), Investment Experience, Financial Literacy, and Investment-Related Judgments,
Working Paper, American University, Washington, DC.
Lusardi, A. and O. S. Mitchell (2014), The Economic Importance of Financial Literacy: Theory andEvidence, Journal of Economic Literature 52, 5–44.
MacKinnon, J. G. and H. White (1985), Some Heteroskedasticity−Consistent Covariance Matrix
Estimators with Improved Finite Sample Properties, Journal of Econometrics 29, 305–325.
Menkhoff, L. and M. Schmeling (2010), Trader See, Trader Do: How Do (Small) FX Traders React toLarge Counterparties’ Trades? Journal of International Money and Finance 29, 1283–1302.
Nicoletta, M., C. Mazzolib, and F. Palmuccic (2017), Mind the Gap: Inconsistencies Between Subjective
and Objective Financial Risk Tolerance, Journal of Behavioral Finance 18, 219–230.
Reserve Bank of Australia (2019), The Recent Japanese Yen Flash Crash, Statement of Monetary Policy,
Box B, February 2019, 24–28.
Siegel, J. (1998), Stocks for the Long Run, McGrow-Hill, 2nd ed.
Staiger, D. and J. H. Stock (1997), Instrumental Variables Regression with Weak Instruments,
Econometrica 65, 557–586.
van Rooij, M., A. Lusardi, and R. Alessie (2011), Financial Literacy and Stock Market Participation,
Journal of Financial Economics 101, 449–472.
von Gaudecker, H.-M. (2015), How Does Household Portfolio Diversification Vary with Financial
Literacy and Financial Advice? Journal of Finance 70, 489–507.
Wang, A. (2009), Interplay of Investors’ Financial Knowledge and Risk Taking, Journal of Behavioral
Finance 10, 204–213.
White, H. (1980), A Heteroskedasticity−Consistent Covariance Matrix Estimator and a Direct Test for
Heteroskedasticity, Econometrica 48, 817–838.
Xia, T., Z. Wang, and K. Li (2014), Financial Literacy Overconfidence and Stock Market Participation,
Social Indicator Research 119, 1233–1245.

14Appendix
See Financial Futures Association of Japan (2018b) for more information about the survey and the questionnaire.

Table A1: Variable definitions and descriptive statistics
Variable

Coding and Comments

Mean

Std. Dev.

Min

Max

Profit= Profit in Yen

123150551544-1500000

1500000

RoI= Profit in Yen/Trade Amount * 100

21.3

491

-3000

3000

Return= Profit in Yen/Income * 100

0.52

25.3

-300

300

(i) Socio-demographic and economic situation
Age

Based on 10-year intervals from 20s to 70s. Continuous variable basedon coding midpoints, i.e., 25 for ‘20s’, 35 for ‘30s’, etc.

43.6

12.8

25

75

Female

Dummy

0.24

0.43

0

1

Education

Continuous variable coded as 1 ‘Elementary, Junior high school’, 2‘High school’, 3 ‘Special (vocational) school’, 4 ‘Junior college,
technical college’, 5 ‘University’, 6 ‘Graduate School’.

4.2

1.4

1

6

Occupation

9 dummies: ‘Self-employed (agricultural/forestry/fishery)’,‘Independent professional (physicians, lawyers, etc.)’, ‘Family
employee (incl. domestic help)’, ‘Full-time employee (officers,
managers)’, ‘Full-time employee (non-managerial)’, ‘Part-time
employee’, ‘Homemaker’, ‘Unemployed, pension beneficiary’,‘Student’.

n.a.

n.a.

0

1

15

IncomeAnnual income in yen. Continuous variable based on coding
midpoints, i.e., 500,000 ‘< 1 million yen’, 150,0000 ‘1 million yen to <2 million’, 2,500,000 ’2 million yen to < 3 million’, 3,500,000 ‘3 million
yen to < 4 million’, 4,500,000 ‘4 million yen to < 5 million’, 6,000,000‘5 million yen to < 7 million’, 85,000,000 ‘7 million yen to < 10 million’,12,500,000 ‘10 million yen to < 15 million’, 17,500,000 ‘15 million yen
to < 20 million’, 25,000,000 ‘> 20 million yen’.

22000000

31800000

50000085000000(ii)‘Investment strategy and trading behaviour
Trade Horizon

Continuous variable coded as 1 ‘< 1 min’, 2 ‘1 min to < 10 min’, 3 ‘10
min to < 1 hour’, 4 ‘1 hour < 1 day’, 5 ‘1 day < 1 week’, 6 ‘1 week < 1month’, 7 ‘1 month < 6 months’, 8 ‘6 months < 1 year’, 9 ‘1 year < 5years’, 10 ‘> 5 years’.

5.5

2.1

1

10

Recent FX Trade

Continuous variable coded as 1 ‘Currently trading’, 2 ‘Traded within
the past one month’, 3 ‘Traded over a month ago, within the past 3
months’, 4 ‘Traded over 3 months ago, within the past 6 months’, 5‘Traded over 6 months ago, within the past 1 year’, 6 ‘Traded over a
year ago, within the past 3 years’, 7 ‘Traded over 3 years ago, within
the past 5 years’, 8 ‘Traded over 5 years ago’.

1.2

0.86

1

8

LeverageContinuous variable coded as 1 ‘< 1:1’, 2 ‘1:1 to < 1:5’, 3 ‘1:5 < 1:10’, 4‘1:10 < 1:15’, 5 ‘1:15 < 1:20’, 6 ‘1:20–1:25’.

3.4

1.6

1

6

Fundamental Analysis

Continuous variable coded as 1 ‘Fundamental 0% vs. technical 100%’,
2 ‘Fundamental 25% vs. technical 75%’, 3 ‘Fundamental 50% vs.
technical 50%’, 4 ‘Fundamental 75% vs. technical 25%’, 5‘Fundamental 100% vs. technical 0%’.

0.15

0.36

0

1

Order Stop-LossContinuous variable coded as 1 ‘Stop-loss: rarely use’ 2 ‘Use
occasionally’ 3 ‘Use frequently’ 4 ‘Use very frequently’.

2.3

1.2

1

4

Investment Strategy

6 dummies: ‘Hold positions for long periods’, ‘Focus on short-term
profits’, ‘Place emphasis on swap points’, ‘Place emphasis on hedging
exchange risks’, ‘Other’, ‘No specific policy’.

n.a.

n.a.

0

1

16

FX Transactions

Continuous variable coded as 1 ‘< once a year’, 2 ‘1 < 10’, 3 ‘10 < 20’, 4‘20 < 50’, 5 ‘50 < 100’, 6 ‘100 < 500’, 7 ‘500 or more’.

4.1

1.9

1

7

Trade AmountContinuous variable coded as 1 ‘Less than ¥100,000’, 2 ‘¥100,000 to <¥500,000’, 3 ‘¥500,000 to < ¥1 million’, 4 ‘¥1 million to < ¥3 million’, 5‘¥3 million to < ¥5 million’, 6 ‘¥5 million to < ¥10 million’, 7 ‘¥10
million to < ¥30 million’, 8 ‘¥30 million to < ¥50 million’, 9 ‘¥50 million
or more’.

2.3

1.6

1

9

Subjective Knowledge

Factor based on Knowledge Terms Index, Subjective Financial
Knowledge, and Knowledge Index (see Table A2).

0

0.81

-1.9

1.5

Knowledge Terms Index

Sum of positive answers to whether respondents understand terms in
the FX glossary: ‘Bid/ask (offer)’, ‘Spread’, ‘Margin’, ‘Limit order’,‘Stop-loss order’, ‘OCO, if done’, ‘Loss-cut’, ‘Swap point’, ‘Leverage
effect’, ‘Cash settlement’, ‘Slippage’, ‘Interbank market’, ‘Covertransaction’, ‘Marry transaction’, ‘Mechanism that allows investors tostart either buying or selling a foreign currency’, ‘Cannot understandany of the foregoing terms’. Multiple answers possible.

8.9

4.2

0

15

Subjective Financial
Knowledge

Continuous variable coded as 1 ‘Poorly informed’, 2 ‘Relatively lessinformed’, 3 ‘Informed at an average level or no idea’, 4 ‘Relatively
well-informed’, 5 ‘Better-informed’.

3.3

0.98

1

5

Knowledge Index

Sum of positive answers about whether respondents acquired specificknowledge in connection with FX trading: ‘Trading mechanism, risk
management’, ‘Taxation’, ‘Fundamental analysis techniques’,‘Technical analysis techniques’, ‘Mental training’, ‘Other’,‘Unconnected with specific knowledge’. Multiple answers possible.

2.7

1.7

0

6

Objective Knowledge

Sum of correct answers to Objective Knowledge Interest Rate andObjective Knowledge Leverage.

1.3

0.66

0

2

(iii) Financial literacy

17

Objective Knowledge Interest
Rate

Dummy, 1 for correct answer.

0.85

0.36

0

1

Objective Knowledge Leverage

Dummy, 1 for correct answer.

0.48

0.50

0

1

Under/Overconfidence

Based on recoding Subjective Knowledge into three categories basedon percentiles (1 ‘< 33%’, 2 ‘33%–67%’,3 > ‘67%’) and subtracting
Objective Knowledge.

-0.35

0.95

-2

2

FX Experience

Continuous variable coded as 1 ‘< 1 month’, 2 ‘1–3 months’, 3 ‘3–6
months’, 4 ‘6 months–1 year’, 5 ‘1–3 years’, 6 ‘3–5 years’, 7 ‘> 5years’.

5.7

1.6

1

7

FX Trade Skills

Dummy, 1 for having skills to generate a return from FX trading on
average.

0.4

0.5

0

1

Learning Index

Sum of positive answers to whether respondents wants to learn
specific aspects of FX trading: ‘Trading mechanism, risk management’,‘Taxation’, ‘Fundamental analysis techniques’, ‘Technical analysis
techniques’, ‘Mental training’, ‘Other’. Multiple answers possible.

2.0

1.6

0

6

Major FX Info Index

Sum of using major sources of information for FX trading: ‘Advice
obtained at service counters of financial institutions’, ‘Brochuresavailable at financial institutions’ branches’, ‘Lecture meetings,
seminars’, ‘Advice from experts or professional advisors’, ‘Mass media(TV, newspapers, etc.)’, ‘Websites’, ‘Conversations with family andfriends’, ‘Coursework or lectures at schools’, ‘Printed books about
financial instruments or investments’, ‘Other sources’. Multiple
answers possible.

1.5

1.0

0

3

FX Adverts

Continuous variable coded as 1 ‘Unaware’, 2 ‘Aware but cannot
understand’, 3 ‘Aware but not motivated to invest’, 4 ‘Aware andmotivated to invest’.

2.9

1.1

1

4

18

Table A2: Principal component analysis for latent variable Subjective Knowledge
Eigenvalues
Factor 1

Factor 2

Factor 3

1.20

-0.04

-0.23

Factor loadings Factor 1
Knowledge Terms Index

Subjective Financial Knowledge

Knowledge Index

0.69

0.43

0.74

LR test: Chi2(3)=611**

Overall Kaiser-Meyer-Olkin=0.6

Observations: 1,000

Table A3: General model: explaining performance of FX margin traders (OLS)
Profits/losses in yen
Variables

CoefficientProfits/losses relative to trading
amount in %Standard error

CoefficientStandard error

Profits/losses relative to incomein %CoefficientStandard error(i) socio-demographic and economic situation
Age-3697*(1498)-2.18(1.45)-0.07(0.07)

Female55226(54359)67.67(44.48)2.42(2.17)

Education-12569(12918)-8.92(11.95)-0.89(0.59)

32.42(12.03)

Occupation
Other
Self-employed

Reference
107982(300518)

101.4419(245.90)

Independent professional

70021(303725)155.59(250.90)

38.19**(12.27)

Family employee-306420(383515)-65.43(311.20)

24.41(15.19)Full-time employee (officers, managers)

9048(301505)

101.61(245.50)

32.19**(11.99)Full-time employee (non-managerial)-1244(296268)86.06(241.90)

33.57**(11.83)Part-time employee

5437(296987)64.55(245.90)31.64**(12.04)

Homemaker

1005(302331)45.40(248.60)

34.10**(12.16)

Unemployed, pension beneficiary

20711(298183)111.30(244.20)32.84**(11.95)Student

77730(307647)209.56(271.50)

34.54**(13.29)0.0014**(0.0005)

0.000001(0.000001)

Trade Horizon-27339*(11036)-8.88(9.60)-0.02(0.47)

Recent FX Trade-17021(16042)-8.33(18.91)-0.23(0.92)

Leverage-23114*(10602)-15.87(10.05)-0.61(0.49)

Fundamental

50575**(16893)43.31**(14.99)1.92**(0.73)

Order Stop-Loss-3723(17577)-3.21(15.73)-0.15(0.77)

Incomen.a.

(ii) investment strategy and trading behaviour

Investment strategy
Hold positions for long periods

Reference

Focus on short-term profits-3814(47172)

8.19(41.94)2.51(2.05)Emphasis on swap points

57310(53601)

48.91(54.08)6.53(2.64)

Hedging exchange risks

21820(117147)-121.95(109.50)

2.09(5.36)

Other

316670(281805)

94.44(243.70)

5.66(11.92)

No specific policy-78361(76635)-217.78**(64.48)-8.91**(3.15)20

FX Transactions

2430(11419)5.67(10.14)0.37(0.50)

Trade Amount68619**(14244)

1.49**(0.52)

Subjective Knowledge

18191(65313)31.15(57.60)2.09(2.82)

Objective Knowledge-21890(62231)-11.50(58.50)0.20(2.86)

Under/Overconfidence-19782(56590)-52.17(52.67)-1.56(2.57)FX Experience

22942(12374)

3.25(11.80)0.12(0.58)FX Trade Skills

223153**(38095)136.20**(34.44)4.60**(1.68)

Learning Index-7405(12480)-1.00(10.84)-0.76(0.53)

Major FX Info Index

20048(21069)-18.16(18.68)-1.33(0.91)FX Adverts

19190(16943)7.88(15.80)0.96(0.77)

Constant-54944(346725)-58.27(288.40)-38.38**(14.10)n.a.

(iii) financial literacy(1) No. of observations(2) Test of joint significance(3) R-squared(4) Test for heteroscedasticity

962

962

962

F(33,928)=5.66**

F(32,929)=2.19

F(32,929)=2.69**

0.17

0.07

0.09

F(50,911)=2.72**

F(48,913)=1.20

F(48,913)=1.09

Notes: Estimation method: OLS. For Profits/Losses in Yen: Jack-knife robust standard errors are used. * and ** indicate significance at a 5 per cent and 1 per centlevel, respectively.

21