Retail Investor E-Trading App Usage - The Impact of the Financial Situation of Consumers and COVID-19 on Retail Investor Usage of E-Trading Apps: A Study from 2017 to 2021

Abstract

This paper investigates the factors influencing the number of monthly active users of E-Trading Apps from January 2017 until June 2021. As digital platforms increasingly facilitate retail investors’ access to financial markets, understanding the drivers behind user engagement is essential. Trading apps, defined as mobile applications that enable the buying and selling of financial instruments, have transformed the financial landscape, enhancing the role of retail investors. The analysis utilises a comprehensive panel dataset containing data from four major economies – the United States, United Kingdom, Germany and France. We find that monthly income is a significant predictor of E-Trading apps usage, suggesting that higher income levels drive greater retail participation in financial markets. However, variables such as unemployment rates, mortgage rates, and stock market returns did not show statistically significant effects. Additionally, we found a significant positive influence of the COVID-19 pandemic on user numbers, highlighting its role in boosting retail investor activity during the studied period. Overall, this paper emphasises the importance of both economic indicators, affecting the financial situation of consumers, and extraordinary events in shaping retail investor usage of E-Trading apps.

Full text

FACULTY OF ECONOMICS AND BUSINESS
Retail Investor E-Trading
App Usage
The Impact of the Financial Situation of Consumers and
COVID-19 on Retail Investor Usage of E-Trading Apps:
A Study from 2017 to 2021
Guus Elsbeek (R0793445)
Promoter: Prof. Dr. G. Wuyts
Supervisor: Drs. B. Choi
Thesis submitted to obtain the degree of
MASTER IN DE ECONOMIE, HET RECHT EN DE BEDRIJFSKUNDE
Academic year 2023-2024
Driss de Deene (R0793718)

FACULTY OF ECONOMICS AND BUSINESS
Retail Investor E-Trading
App Usage
The Impact of the Financial Situation of
Consumers and COVID-19 on Retail Investor
Usage of E-Trading Apps: A Study from 2017 to
2021
This paper investigates the factors influencing the number of monthly active users of E-Trading Apps
from January 2017 until June 2021. As digital platforms increasingly facilitate retail investors’ access to
financial markets, understanding the drivers behind user engagement is essential. Trading apps, defined
as mobile applications that enable the buying and selling of financial instruments, have transformed the
financial landscape, enhancing the role of retail investors. The analysis utilises a comprehensive panel
dataset containing data from four major economies – the United States, United Kingdom, Germany and
France. We find that monthly income is a significant predictor of E-Trading apps usage, suggesting that
higher income levels drive greater retail participation in financial markets. However, variables such as
unemployment rates, mortgage rates, and stock market returns did not show statistically significant
effects. Additionally, we found a significant positive influence of the COVID-19 pandemic on user
numbers, highlighting its role in boosting retail investor activity during the studied period. Overall, this
paper emphasises the importance of both economic indicators, affecting the financial situation of
consumers, and extraordinary events in shaping retail investor usage of E-Trading apps.

I
Acknowledgements
The writing process of our economic paper was undoubtedly a challenging one. The choice
of a topic which related to our legal thesis was challenging and many topics were examined
and considered. Additionally, the data collection process also proved challenging at times.
In this light, we would like to acknowledge the important roles that our promotor, Prof. Dr.
Gunther Wuyts, and our supervisor, Drs. Bok Min Choi, played in the successful completion
of this thesis. The effort and time they invested in our academic career is greatly valued.
We were lucky enough to have been able to have multiple meetings with Prof. Dr. Gunther
Wuyts, which helped us decide on our topic. Additionally, we would like to express our
deepest gratitude to our supervisor, Drs. Bok Min Choi, whose expertise, guidance, and
feedback were invaluable throughout the development of this thesis. She provided
feedback on many occasions, containing specific recommendations which would help
improve our overall research and model. She was available for questions until the very end,
and for this we express our deepest gratitude. Their efforts and time with regard to our
academic careers is greatly valued. Furthermore, we would like to thank our parents for
their unconditional support during our academic careers. Without their guidance, support
and encouragement, we would not be in the position we are today. Finally, we thank our
friends for providing their support and words of encouragement during the writing of this
thesis, as well as to those who helped perform the final checks of the final version.

II
Table of contents
1 General introduction ................................................................................................. 1
2 Literature Review ...................................................................................................... 3
2.1 Determinants of E-Trading App Usage .......................................................... 3
2.2 Economic Factors ........................................................................................... 3
2.2.1 Monthly Income .................................................................................... 4
2.2.2 Consumer Price Index (CPI) ................................................................ 4
2.2.3 Unemployment Rate ............................................................................ 5
2.2.4 Consumer Confidence Index (CCI) ...................................................... 5
2.2.5 Stock Market Index Return ................................................................... 6
2.2.6 Mortgage Rate ..................................................................................... 7
2.3 Non-Economic Factors ................................................................................... 7
2.3.1 COVID-19 ............................................................................................. 7
2.4 Overview ........................................................................................................ 8
3 Data ........................................................................................................................ 10
3.1 Data Collection ............................................................................................. 10
3.2 Data Description ........................................................................................... 11
3.2.1 Monthly Active Users ......................................................................... 11
3.2.2 Monthly Income .................................................................................. 11
3.2.3 Consumer Price Index (CPI) .............................................................. 12
3.2.4 Unemployment Rate .......................................................................... 12
3.2.5 Consumer Confidence Index (CCI) .................................................... 12
3.2.6 Index Return ....................................................................................... 12
3.2.7 Mortgage Rate ................................................................................... 13
3.3 Data Cleaning ............................................................................................... 14
3.4 Summary Statistics ....................................................................................... 15
4 Methodology ........................................................................................................... 17
4.1 Model ............................................................................................................ 18
4.2 Dependent Variable ...................................................................................... 18
4.2.1 Monthly Active Users ......................................................................... 18
4.3 Independent Variables ................................................................................. 19
4.3.1 Monthly Income .................................................................................. 19
4.3.2 Consumer Price Index (CPI) .............................................................. 20
4.3.3 Unemployment Rate .......................................................................... 20
4.3.4 Consumer Confidence Index (CCI) .................................................... 20
4.3.5 Stock Market Index Return ................................................................. 21
4.3.6 Mortgage Rate ................................................................................... 21
4.3.7 COVID-19 ........................................................................................... 22
5 Discussion of the Results ....................................................................................... 23
5.1 Correlation Analysis ..................................................................................... 23
5.2 Regression Analysis ..................................................................................... 24
5.3 Limitations .................................................................................................... 25
6 Conclusion .............................................................................................................. 27
7 Appendices ............................................................................................................. 28

III
8 Table List ................................................................................................................ 31
9 References ............................................................................................................. 32

1
1 General introduction
This study explores the factors that influenced the number of monthly active users on E-
Trading apps in the period from January 2017 to (including) June 2021. In an era where
digital platforms are becoming increasingly popular as a means of facilitating access to
financial markets for retail investors (Chong et al., 2021; Briere, 2023; Fan, 2021),
understanding the drivers behind retail investor engagement is crucial. Trading apps are
defined as mobile applications that facilitate the buying and selling of financial instruments
such as stocks, bonds, cryptocurrencies, and other assets (Smith & Doe, 2020). The
platforms and applications provide users with real-time market data and tools to execute
trades directly with their mobile devices. Many of these trading apps offer zero-commission
trading to users (Mihm, 2020).
The widespread adoption of trading apps has significantly transformed the financial
markets landscape. Subsequently, the retail investor has gained substantial influence in
financial markets. A retail investor is an individual who engages in trading activities in
financial markets for personal investment purposes (Palmer et al., 2023). This
phenomenon has sparked considerable interest among economists, financial analysts,
investors, regulators, and policymakers. Recent studies have highlighted the rise in the
number of retail investors and their increasing influence levels on financial markets (Baig
et al. 2022). As financial information has become increasingly easier to access for the
general public, individuals are in a position where they now have more influence over
markets which were previously controlled by institutions, hence the need for more attention
to the level of retail investor participation and their influence in financial markets
(Annunziata, 2023). This increase in retail investor activity was further spurred by the
COVID-19 pandemic and various studies have observed retail investor activity during the
pandemic (Ortmann et al. 2020; Talwar et al., 2021; Chakraborty et al., 2022).
This study integrates a comprehensive dataset from four major economies - the United
States, United Kingdom, Germany, and France - over a period of 54 months, making use
of the panel data approach so as to have a model that has cross-sectional and time-wise
variation. The dataset includes a range of economic variables such as unemployment data,
consumer confidence indices, price levels, mortgage rates, and average monthly income,
allowing us to explore the multifaceted economic factors that influence the monthly usage
statistics of E-Trading apps as they affect the financial situation of consumers (i.e. retail
investors). Additionally, the model accounts for the impact of the COVID-19 pandemic. The
data and the model allow for a detailed analysis of how economic events and the pandemic
affected user levels during the studied period.
The results of our regression analysis present a nuanced picture of the factors driving E-
Trading app usage. We find strong evidence that economic factors such as monthly income
and consumer price indices are significant predictors of the number of active users. Higher
income levels and inflation appear to encourage greater participation in retail trading, likely
as consumers seek to leverage their financial stability or protect their purchasing power
through investments. The consumer confidence index, while only marginally significant,
also suggests that consumer sentiment plays a role in shaping trading behaviour.
Interestingly, variables such as unemployment rates, mortgage rates, and stock market
returns do not show statistically significant effects within our model. This could indicate that

2
while these factors are important in broader economic contexts, their direct impact on E-
Trading app usage may be more complex or mitigated by other variables.
One of the most significant findings is the strong positive effect of the COVID-19 pandemic
on the number of active users. Our analysis confirms that the pandemic had a substantial
influence on retail investor behaviour, aligning with prior research that observed increased
stock market participation during this period.
While an improvement in (personal) financial well-being (or an improvement of their
economic situation) for consumers, as indicated by higher (monthly) income is a key driver
of the number of users of E-Trading apps, other factors such as unemployment and
mortgage rates may not directly influence retail investor engagement as strongly as
expected. Rising price levels also drove retail investor trading app usage in the studied
period, potentially explained by the increased interest from investors in financial
instruments that would function as a ‘hedge’ against rising price levels. However, the
COVID-19 pandemic stands out as a critical factor, significantly boosting the number of
active users during the studied period.
Overall, the findings in this study contribute to a deeper understanding of the dynamics at
play in digital trading platforms, emphasizing the importance of both economic indicators
and extraordinary events (so-called “black swan events”) in shaping user levels. The
insights discussed provide a foundation for future research to further investigate the
evolving landscape of retail investment in the digital age.

3
2 Literature Review
2.1 Determinants of E-Trading App Usage
The first aspect that forms a potential determinant of the level of E-Trading app usage is
the level of retail investors and retail investment and their capacity to engage in investment
practices. With the aim to include the number of monthly active users of the leading E-
Trading apps in the four different territories (Germany, US, UK and France) as our
dependent variable in the regression, we will look at economic factors, data and indicators
in the different territories (January 2017 to (including) June 2021) in order to understand
the economic situation and background which could provide clarification and explanation
for the number of E-Trading app users during the period.
Various studies have looked at a multitude of possible driving factors for retail trader levels
as well as influencing factors. Nair et al. (2022) found that effort expectancy, performance
expectancy and perceived returns were primary determinants influencing the behavioural
intentions to use mobile applications for e-trading, suggesting that ease of use plays an
important role in the adoption process of trading apps amongst retail investors. It is
important to note that this research is conducted in emerging markets. Moreover, Gupta &
Dey (2023) linked the digitalization of stock trading to an increased accessibility which in
turn led to higher participation of retail investors. Other studies have investigated the
factors that could potentially influence the risk tolerance of investors such as financial
knowledge and income (Hermansson & Jonsson, 2021; Lusardi & Mitchell, 2017;
Aleknonyte et al., 2019; Chavali & Mohanraj; 2016). However, these studies also leave a
knowledge gap with regard to the direct relationship between economic factors and the
number of retail investors (for a given period of time).
Whereas these studies have focused on ease of use and accessibility in order to explain
the increasing levels of retail investor use of trading applications, leaving a gap when it
comes to economic factors, this study looks at the economic drivers behind the surge in
retail investors using trading apps between 2017 through halfway 2021.
In order to identify the variables that will be included in the regression, we turn to the current
literature on the driving factors behind the rise of retail investors and the adaptation of E-
Trading apps by these retail investors. Additionally, we review literature concerning the
effects of the COVID-19 pandemic on retail investor levels.
2.2 Economic Factors
According to the International Monetary Fund (2021), economic factors encompass
macroeconomic variables like (average) monthly income, consumer price indices, and
employment levels, which shape the economic and financial situation of consumers and
impact the individual financial behaviour of these (potential) retail investors.

4
2.2.1 Monthly Income
Monthly income represents the earnings received by an individual or household each
month. It serves as an indicator of financial stability and can differ based on factors like
employment status, job type, and geographic location.
Bui et al (2022) found that retail investors with higher incomes trade more frequently than
those with lower incomes. Their study is in line with the earlier study conducted by Peress
(2004), which showed that wealthier investors (i.e. more financial means) take extra risks
by trading more stocks. Additionally, Kaustia et al. (2023) in their study on the drivers of
stock market participation, refer to income as one of the six traditional variables used when
looking at stock market participation levels (for individual private persons).
Redmon & Howard (2007) showed that wealthier individuals tend to invest more in equities.
Furthermore, Rasyid et al. (2018), Fischer & Jensen (2014) and Bal (2021) found that
income significantly influences individual stock investments, and that financial
expectations, current income, and future income prospects all play a role in investment
decisions.
Moreover, a link can be made between Gross Domestic Product (GDP) and the disposable
income of individuals. GDP is a fundamental macroeconomic indicator reflecting a country's
overall economic activity (Kankal et al., 2011). Changes in GDP can significantly influence
consumer behaviour, including the usage of E-Trading apps. An increase in GDP typically
leads to higher disposable income, potentially resulting in increased usage of these apps.
Conversely, a decrease in GDP may reduce disposable income, leading to lower usage of
E-Trading apps.
The inclusion of the average monthly income in this study model offers a more precise
reflection of individual financial circumstances than the more general Gross Domestic
Product measure (GDP). While GDP provides a broad overview of a country’s economic
performance, it does not capture the specific income levels that directly influence consumer
behaviour. Monthly income, on the other hand, directly affects an individual’s capacity to
save and invest, making it a more relevant variable for understanding how economic
conditions drive the use of E-Trading apps.
2.2.2 Consumer Price Index (CPI)
The Consumer Price Index (CPI) measures inflation by comparing the changes in prices
over time for a basket of consumer goods and services (Baldridge & Silbert, 2024).
Therefore, the CPI, which measures the price levels and inflationary or deflationary
development of these levels, is a crucial macroeconomic indicator reflecting the general
rise for goods and services and directly affecting consumers (i.e. retail investors). When
these prices rise, they erode purchasing power (Mankiw & Reis, 2002). High inflation can
create uncertainty, often leading to increased levels of volatility in stock markets and
potentially slowing down economic activity. The relationship between market volatility and
rising price levels has been widely studied (Geske & Roll, 1983; DeFina, 1991; Geetha et
al., 2011; Chiang & Chen, 2023).
Research has demonstrated that price inflation can have a substantial impact on economic
growth (Impin & Kok, 2021). GDP growth often occurs simultaneously with an increase in

5
inflation (Hall, et al. 2023) and affects economic activity (Sa’idu & Muhammad; 2015;
Benkovskis et al., 2011), underscoring the importance of including inflation in economic
models. Additionally, Braggion et al. (2023) found a negative correlation between local
inflation and the volume of stock purchases by retail traders, further indicating how rising
price levels can shape consumer behaviour and preferences. Therefore, the CPI is an
essential variable in our analysis, offering a deeper understanding of the economic factors
directly affecting consumers and retail investors and therefore influencing E-Trading app
usage.
2.2.3 Unemployment Rate
The unemployment rate profoundly impacts economic activity and consumer behaviour.
Deteriorating labour market conditions can predict decreases in various activities, such as
physical activity (An & Liu, 2012), and influence behaviour in other domains, including
trauma incidence (Arshi et al., 2017). Soylu et al. (2019) found a relationship between
economic growth and the unemployment rate, with higher economic growth leading to
lower unemployment rates, indicating its relevance in economic analyses.
The interplay between inflation and unemployment has also been explored, with studies
showing its complex influence on economic variables (Omran & Bilan, 2021). The
multifaceted impacts of unemployment extend to various aspects of human activity, such
as divorce rates (González-Val & Marcén, 2016), crime rates (Janko & Popli, 2016), and
overall economic growth.
Afonso & Blanco-Arana (2021) concluded in their research that GDP growth and inflation
significantly affect the evolution of the unemployment rate. Moreover, the COVID-19
pandemic significantly impacted employment levels in G20 countries (International Labour
Organization, 2024). Additionally, Chomicz-Grabowska and Orlowski (2021) discuss the
relationship between financial market risk and macroeconomic stability, including the
unemployment rate. They find that heightened financial market risk (measured by the VIX
index) negatively impacts macroeconomic stability and is strongly linked to higher
unemployment rates. This instability and higher unemployment can influence investor
behaviour by increasing risk aversion and altering investment strategies, particularly during
periods of financial distress.
2.2.4 Consumer Confidence Index (CCI)
The Consumer Confidence Index (CCI) is a numerical representation of consumers'
feelings about current and future economic conditions (Chatterjee & Dinda, 2015). It is a
significant indicator of people's financial conditions, their perceptions of the overall
economic situation, and whether they believe it is a good time to make significant
purchases such as cars or houses (Hagerty & Land, 2012; Merkle, et al., 2003). Generally,
higher consumer confidence indicates a higher degree of economic growth, leading to
increased consumption, while lower consumer confidence suggests slowing economic
growth and negatively affects consumer spending (Mazurek & Mielcová, 2017).
The CCI plays a crucial role in shaping consumer behaviour and economic activity.
Research has shown its impact on customer loyalty (Ou et al., 2013), stock market
performance (Ferrer et al., 2014), and economic growth (Cinel & Yolcu, 2020; Guo & He,

6
2020). The index also influences consumers' purchasing decisions and their perceptions
of economic conditions (Ilmiah & Wonoseto, 2021). Ferrer et al. (2014) furthermore also
confirmed that consumer confidence indices have predictive power regarding stock market
performance.
Investor sentiment, closely related to consumer confidence, also impacts market dynamics.
Guo (2023) found that investor sentiment affects the price volatility of the Chinese stock
market, indicating that sentiment influences market movements. Studies by Lahiri et al.
(2015) and Ahmad & Rangaraju (2019) demonstrated the forecasting power of consumer
confidence on consumer spending, offering insights into its predictive ability for
consumption behaviour.
Higher consumer confidence tends to positively influence the stock market due to herding
behaviour and optimism, as found by Akin & Akin (2024). This finding links to Hsieh et al.
(2020), who discovered that the degree of retail investor attention to a particular stock is
positively linked with herding behaviour among retail investors.
During the COVID-19 pandemic, consumer confidence indices reflected significant volatility
in public sentiment and economic confidence. Starr (2010) studied the role of economic
news coverage on consumer confidence and found that sentiment is significantly affected
by news shocks.
2.2.5 Stock Market Index Return
The stock market performance (Index Return) significantly impacts economic activity and
consumer behaviour. Studies have demonstrated a relationship between stock returns and
economic forces, highlighting the stock market's influence on the broader economy
(Elhussein & Warag, 2020).
Research has also explored the relationship between stock market performance and retail
investing. For instance, Schmitz and Weber (2012) found a positive correlation between
retail investor buying and stock returns in Germany. Reyes (2018) noted that retail investors
tend to be net buyers of attention-grabbing stocks, especially after days with negative
returns. This suggests that retail investors often react to market news and trends.
Previous literature has examined the performance of retail investors and found that they
frequently underperform the market (e.g. the bonds bought most heavily by retail investors
underperform the market substantially) (Barber & Odean, 2013; deHaan et al., 2023).
Garay and Pulga (2021) also studied this relationship, reinforcing the finding that retail
investors generally do not achieve market-level returns. Vidani (2024) highlighted that up
to 90% of stock market traders incur losses, emphasizing the challenges faced by individual
investors.
Symons-Hicks (2023) discovered a positive relationship between FTSE returns and
increased retail investor attention, indicating that market movements can attract retail
investors' interest. Mbanga et al. (2019) examined the influence of investor sentiment on
aggregate stock returns, emphasizing the significant role of investor attention in driving
market dynamics.

7
Additionally, Garcia (2016) found that investor decisions are heavily influenced by past
market performance, with higher past returns prompting more buying activity. This
behaviour contributes to momentum effects in the market, where trends persist as investors
chase returns.
2.2.6 Mortgage Rate
The mortgage rate can be considered as an independent variable in a regression analysis
on the number of monthly active users of E-Trading apps due to its potential impact on
consumer behaviour and investment decisions.
Mortgage rates can strongly influence consumer saving and investment behaviour. When
mortgage rates decline, homeowners often have the chance to refinance their mortgages
at more favourable rates, leading to lower monthly payments. These savings from these
reduced payments can provide consumers with extra funds, which they might decide to
allocate towards savings or investments in other areas (Andersen et al., 2021).
Furthermore, Andersen et al. (2021) also showed that consumers, for every dollar freed up
from mortgage payments, tend to increase their savings by a certain proportion. This further
suggests that mortgage rate changes can possibly influence the amount of income that is
disposable and available for saving and investing.
Additionally, Günes and Tunç (2018) found, in their study focusing on US households
between 1999-2015, that a 1% increase in mortgage payments led to a 8.8% decrease in
the saving rate. This further supports the relationship between mortgage rates and the
savings and subsequent investment levels of consumers.
The inclusion of mortgage rates as an independent variable in our regression model on the
number of monthly (retail) users of E-Trading apps provides a more direct measure of the
financial conditions experienced by consumers. Mortgage rates, unlike central bank
interest rates, have a more immediate and tangible impact on household finances,
particularly in terms of disposable income. Lower mortgage rates can lead to reduced
monthly payments, freeing up funds that consumers may then choose to invest in equities
through, for example, trading apps. By using mortgage rates instead of the more general
central bank interest rates (such as those from the FED, BOE, and ECB), our model
captures the specific financial pressures and opportunities faced by households, making it
a more accurate predictor of retail investor usage levels in the context of E-Trading apps.
2.3 Non-Economic Factors
2.3.1 COVID-19
During the COVID-19 pandemic lockdown in spring 2020, retail trading activity significantly
increased. This increase was particularly significant among stocks which were widely
covered in (financial) media (Döttling & Kim, 2022). This surge in trading activity by retail
investors resulted in record high trading volumes and new accounts on trading apps such
as Robinhood (Ozik et al., 2021).

8
Shiller (2020) attributes these and other aspects of recent market dynamics to “crowd
psychology” which was very much present in times of the pandemic. What is also
remarkable is the fact that the stock market behaviour during the COVID-19 pandemic was
very different from previous pandemics, including the Spanish flu, which had modest effects
on the US markets at the time (Baker et al. (2020)).
Studies such as those by Ortmann et al. (2020) and Ma (2023) have investigated the
trading patterns of retail investors during the outbreak of COVID-19, highlighting the shifts
in sentiment and trading activity in response to the pandemic. This study concluded that
retail trading activity increased on a UK-based trading app during COVID-19. Retail traders
also opened more positions. Additionally, research by Talwar et al. (2021) suggests that
retail investors with an optimistic attitude may perceive the COVID-19 stock market crash
as an opportunity to increase their trading activity, indicating the influence of psychological
factors on investor behaviour during the pandemic. Welch (2022) further emphasized that
Robinhood investors traded more actively during the lockdowns, indicating a shift towards
retail investors engaging more with financial markets during the lockdowns (Clancey-
Shang, 2023).
Furthermore, the impact of COVID-19 on investor sentiment and investment decisions has
been explored in studies such as those by Parveen et al. (2021) and Tjandra &
Widoatmodjo (2022), which have assessed the effects of the pandemic on investor’s
sentiments, trading activities and decision-making styles. These studies provide insights
into the nuanced responses of retail investors to the challenges posed by the pandemic.
Additionally, the (previously cited) study by Ozik et al. (2021) addressed the impact of the
COVID-19 lockdown and the staggered implementation of stay-at-home
advisories/measures on retail trading, shedding light on the potential influence of external
economic stimuli on retail investor behaviour.
Moreover, the relationship between COVID-19 and investor behaviour has been examined
in the context of specific financial instruments and markets. For instance, research by
Djalilov & Ülkü (2021) has focused on individual investors’ trading behaviour in the Moscow
Exchange during the COVID-19 crisis, while Zhao (2021) has researched the factors
influencing individual investors’ trading behaviour in the US stock market during the
pandemic.
Overall, these studies collectively contribute to our understanding of the multifaceted
impact of COVID-19 on retail investor behaviour, encompassing changes in sentiment,
trading activity and decision-making processes in response to the challenges posed by the
pandemic.
2.4 Overview
The literature indicates a complex interplay of factors influencing the rise of retail investors,
including (average) monthly income, the consumer price index, unemployment rates,
consumer confidence, stock market performance, the impact of COVID-19, and mortgage
rates. Evaluating the factors that have contributed to the increase in retail investors is
crucial, not only to identify the individual influences but also to understand their relative
importance. By analysing these factors together, we can uncover valuable insights into the

9
dynamics driving retail investor behaviour, offering a comprehensive perspective that has
not been thoroughly explored in existing research. This holistic approach allows us to
determine which factors have had a more significant impact, thereby providing a nuanced
understanding of the phenomenon and contributing to the literature in a novel and
meaningful way.
Additionally, the interplay between the different independent variables also allows for
insightful observations into whether or not variables have the potential to magnify the
effects caused by other variables on the dependent variable. For example, whereas Baker
& Wurgler (2007) found that there was no significant effect of investor sentiment on the
aggregate returns of stocks, Brown & Cliff (2005) did find a significant effect but only for
longer-term horizons (1 to 3 years). Therefore, for the period January 2017 until June 2021,
our model and data will allow us to look at the correlations between these variables for the
different territories and look at the interplay between these (and the other included)
variables in our model.
This thesis seeks to investigate the statistical relationships between the number of active
users of retail trading apps and economic, non-economic and market variables across four
distinct territories: Germany, the United Kingdom, the United States, and France. The
selected territories represent major economies with well-developed financial markets and
substantial retail investor participation. By focusing on these four territories, this study aims
to provide insights that are relevant to both local market dynamics and broader global
trends.
To achieve these objectives, this study employs rigorous statistical techniques, including
correlation analysis and a regression analysis. By leveraging these methodologies, we can
discern and uncover meaningful patterns and relationships, or the lack thereof, within the
data, allowing for robust conclusions regarding the impact of retail trading app usage on
market dynamics.
The findings of this study are expected to yield valuable insights for various stakeholders,
including policymakers, market regulators, financial institutions and retail investors
themselves. By understanding the interplay between retail trading app usage and market
variables, stakeholders can make more informed decisions regarding market regulation,
risk management, and investment strategies.

10
3 Data
3.1 Data Collection
In this section, we discuss the process of data collection, and the assembly of the dataset
used for our regression model. This involved gathering relevant economic indicators and
user activity metrics from various reputable sources to ensure the robustness of our
analysis.
For this study we opted for secondary data collection. This is primarily due to its efficiency
and accessibility. We utilize secondary data from reputable sources such as government
publications and commercial data providers, which allows us to create a high-quality
dataset with data that has been rigorously collected and verified. Moreover, the usage of
secondary data enables us to conduct longitudinal and cross-sectional analyses with a
broader scope. Through the use of secondary data, we are able to maintain a higher level
of comparability and consistency in our research findings, benefiting from standardized
methodologies and well-documented datasets that facilitate more accurate and
generalizable results.
In the context of this study, the decision to use secondary data is made given the nature of
our independent variables, which include the average monthly income, inflation, consumer
confidence, interest rates, unemployment rates and the index returns in the (four)
respective territories. These economic indicators are routinely collected by national and
international agencies.
The papers cited in the literature review were used as inspiration in order to select the
variables and approach for our model and the subsequent collection of the data. Data from
January 2017 until June 2021 was collected from the variables set out in Table 1.
Table 1: Data Source Table
NAME
FORMULA/DEFINITION
SOURCE
Dependent Variable
Users
Number of monthly active users
of E-Trading apps
Statista
Independent Variables
Monthly Income
Average monthly income for
country
Bureau of Labour Statistics,
Trading Economics, and Statista

11
Consumer Price
Index (CPI)
CPI on monthly basis
World Bank Group
Unemployment Rate
Monthly unemployment rate
Trading Economics
Consumer
Confidence index
(CCI)
Monthly consumer confidence
index
Organization of Economic
Cooperation and Development
Index Return
Monthly return of leading national
stock market index
S&P Global, Yahoo Finance
Mortgage Rate
Average mortgage interest rate
Trading Economics and Statista
COVID-19
Presence/Absence of COVID-19
Declaration of Pandemic in
March 2020 by World Health
Organization (WHO)
3.2 Data Description
Following Table 1, we will discuss the data with regard to the used units, in more detail in
this subchapter to provide more insight into the collection and sourcing of the data used for
the regression model.
3.2.1 Monthly Active Users
The variable Users represents the number of monthly active users of E-Trading apps within
the studied territories. This data, sourced from Statista and originally collected by Airnow,
captures the level of engagement and participation of retail investors on these platforms
from January 2017 until June 2021.
For our regression model, to interpret the results, we will take the log of Users. This is
further discussed and elaborated on in section 4.2.1 Monthly Active Users.
3.2.2 Monthly Income
The average monthly income reflects the typical earnings of individuals across the four
territories making up our data model. For the United States, the data was sourced from the
Bureau of Labor Statistics, providing a comprehensive view of income levels within the
country. For France, the data was obtained from Trading Economics, which utilized figures
from INSEE, the national institute of statistics and economic studies. Similarly, for
Germany, Trading Economics supplied the data which was based on information from the

12
German Federal Statistical Office. Lastly, for the United Kingdom, the average monthly
income was retrieved from Statista, which relied on data from the Office for National
Statistics (UK).
The monthly average income will be expressed in EUR (see section 3.3 Data Cleaning for
US and UK data).
3.2.3 Consumer Price Index (CPI)
The CPI reflects the changes in price levels, capturing the impact of rising prices and
inflation for our model. Our data was sourced from a database from the World Bank Group
website (World Bank Group). This database, constructed by the World Bank’s Prospects
Group, contains the CPI for 209 countries on a monthly level. The database is also part of
a research paper by Ha et al. (2023) in which they explain that the database contains
roughly 37,000 annual observations. In the context of our research, we use the “headline
consumer price index monthly” from the database. The base-period used in the dataset is
2010.
3.2.4 Unemployment Rate
The unemployment rate reflects the relative level of unemployment across the territories
that make up our model. It is calculated by dividing the number of unemployed individuals
by the total labour force. The data was collected through Trading Economics. For the US
the data is originally from the Bureau of Labour Statistics, for the United Kingdom from the
Office for National Statistics, for Germany from the Bundesagentur für Arbeit and for France
from INSEE.
3.2.5 Consumer Confidence Index (CCI)
The degree of consumer optimism or pessimism is measured by using the CCI. The CCI
data for the different studied territories was sourced from the Organization of Economic
Cooperation and Development (OECD) website, which used data from the OECD Data
Explores databank. The index is derived from the responses with regard to the anticipation
of households on their financial condition, their outlook on the overall economic
environment, and their ability to save money (OECD). A CCI value higher than 100
suggests increased consumer confidence, reducing their likelihood to save funds and
making it more likely that money will be spent. On the contrary, a value below 100 suggests
a pessimistic view of future economic developments, making higher savings levels more
likely.
The OECD presents this indicator as an amplitude-adjusted index, so as to smooth out
fluctuations and variations in the data, with the long-term average of the index set at 100.
3.2.6 Index Return
The index return in our analysis is constructed using data from the four main national stock
indices representing the territories which make up our data model. These indices are the

13
S&P 500 for the US, the FTSE 100 for the UK, the DAX 30 for Germany and the CAC 40
for France.
United States: S&P 500
The S&P 500 index is widely regarded as the most comprehensive measure of market
performance in the United States. It includes 500 of the largest publicly traded companies
and covers approximately 80% of the available market capitalization, making it a reliable
indicator of the overall health and trends in the U.S. stock market. Its diverse composition
across multiple sectors provides a robust reflection of the economic conditions and investor
sentiment in the country (Kenton, 2024).
United Kingdom: FTSE 100
In the United Kingdom, the FTSE 100 is considered the primary benchmark for market
performance. It comprises the 100 largest companies listed on the London Stock
Exchange, representing a significant portion of the market capitalization. The FTSE 100 is
instrumental in capturing the economic activities and trends within the UK market. Its
prominence in financial analyses and media further underscores its role as a key indicator
of market performance (Young, 2024).
Germany: DAX 30
The DAX index is the premier benchmark for market performance in Germany. It consists
of 30 major companies listed on the Frankfurt Stock Exchange, covering a broad spectrum
of industries. The DAX is particularly valued for its comprehensive representation of the
German market and economy. It provides insights into the financial health and trends of
leading German firms, serving as a vital indicator for investors and policymakers (Chen,
2024).
France: CAC 40
For France, the CAC 40 index serves as the leading indicator of market performance. It
includes 40 of the largest and most actively traded stocks listed on Euronext Paris. The
CAC 40 is a critical tool for assessing the economic climate and investor behaviour in
France. Its composition and methodology ensure it reflects the performance of major
French corporations, making it a reliable benchmark for market analyses (Hayes, 2024).
The calculation of the returns is discussed in section 3.3 Data Cleaning.
3.2.7 Mortgage Rate
The variable for (average) mortgage rates reflects the typical borrowing costs for home
buyers across the studied territories. For the United States, the data was sourced from
Trading Economics, which utilized data from the publicly listed entity Freddie Mac, which
provides financial services to mortgage lenders, on the most popular mortgage product in
the US: the 30-year fixed mortgage rate (70% of total volume; McMillin & De Vita, 2023).
The United Kingdom data was collected from Statista, using information from the Bank of
England. For Germany, Statista provided the data based on a weighted average calculated
by the European Mortgage Federation. Lastly, the French data was also obtained through

14
the Statista database, which in turn obtained the data from a study conducted by Banque
de France, which focused on the average interest rate on new mortgage loans.
The calculation of the monthly mortgage rate used in the data set is discussed in section
3.3 Data Cleaning.
3.3 Data Cleaning
Data cleaning is an essential preliminary step in our research to ensure the accuracy of our
analysis. As we use data from the four different studied territories, it is important to
standardize data formats, enabling us to provide more precise and actionable insights into
the studied phenomena.
As part of the data cleaning process, we converted the (average) monthly income data for
the United States and United Kingdom into euros to ensure consistency and comparability
across our dataset; considering that the other two territories (Germany and France) both
have the same currency. This conversion was necessary as the original data for these
countries was reported in their respective local currencies (i.e. US dollars (USD) and British
pounds (GBP)). To perform this conversion, we based ourselves on the average exchange
rates during the period of our studied period (1 January 2017 to 30 June 2021) using the
data from the European Central Bank (ECB) (Appendix 4). This results in the following
applied exchange rates:
Table 2: Exchange Rate Table
This approach provided a balanced and realistic exchange rate for the period, reflecting
the economic conditions over the entire period of study. By transposing these values into
euros, we eliminated potential currency-drive disparities, allowing for a more accurate
analysis in the income-related variable across the different regions.
Moreover, since the mortgage rates sometimes fluctuated over the course of a month, we
calculated the monthly average by considering all the rate changes (including their weight
during this period) that occurred during that period. This method ensured that the monthly
mortgage interest rate accurately reflected the variations within the month, providing a
reliable measure for our analysis.
Furthermore, to calculate the monthly index return for the stock market, we employed a
straightforward method by taking the opening price on the first trading day of each month
and the closing price on the last trading day of the month. This approach allowed us to
capture the full range of market activity, accurately reflecting the monthly fluctuations in the
market indices for the respective territories, within each month, providing a clear picture of
the market’s performance over that month.
GBP 1 = 1.1355 EUR
USD 1 = 0.8696 EUR

15
Finally, we take the logarithm of our dependent variable, Users, to transform the data in a
way that allows us to interpret the regression results in terms of percentage changes rather
than absolute changes. This log transformation further also helps to normalize the
distribution of the data, reducing the impact of extreme values and making the relationship
between the dependent and independent variables more linear. Using the log of Users, we
are able to have a model that is more robust when analysing growth patterns, as it
effectively captures the proportional differences in user activity.
3.4 Summary Statistics
A brief descriptive overview of the variables will precede the methodology of this study. The
table below contains essential statistical values of the variables. It provides a first
impression of the four territories and their values, observed for a period of 54 months (01-
2017 to 06-2021).
Table 3: Summary Statistics
The used panel data set is balanced as it contains all observations for each of the variables
used for the regression.
The average number of monthly active users across the sample period is around 1.45
million with a standard deviation of 3.09 million which indicates substantial variability in the
number of users and reflects the diverse usage patterns across the different periods and
regions. The highest observed number of monthly active users was 16.8 million.
Additionally, the average monthly income across the dataset is approximately 3.345 EUR
with a standard deviation of 835.8 EUR which shows a, again, substantial variation in
income levels. Moreover, we are able to deduce that the income data ranges from a
minimum of 1.902 EUR to a maximum of 4.457 EUR which shows the diverse economic
conditions across the regions (not accounting for purchasing power).
For unemployment, the average unemployment rate across the observed periods is 5.9%,
with a relatively low standard deviation of 2.1%, suggesting some consistency in
unemployment rates across the different territories. The highest measured unemployment
rate was 14.9%.
The COVID-19 variable has a mean of 0.296, meaning that approximately 29.6% of the
observed periods were impacted by the pandemic. Additionally, we observe that the the
index return variable shows a mean of 0.88%, indicating that, on average, the stock indices
observed experienced a slight positive monthly return. The standard deviation, of about

16
4.81% reflects the variability of the index returns and this is further evidenced by the
substantial decline of -19.73% in the worst-performing month and a substantial rise of
20.12% in the best performing month.
Finally, the range of CPI values from 106.9 to 124.3 suggests that, compared to the
minimum value, there has been an observable increase in the CPI over the period covered
by our model. The maximum value is from one of the very last months of the observed
period, meaning that consumers where paying more towards the end of the studied period
for the same basket of goods and services than they did at the time when the CPI was at
its lowest.

17
4 Methodology
The goal of our main research questions is to empirically test the influence that economic
and other key factors as well as the COVID-19 pandemic had on the number of (monthly)
active users of the leading E-Trading apps in four different territories: the US, Germany,
France and the UK. Based on the research question and the arguments discussed and
elaborated on in the literature review, the following hypotheses can be formulated:
(H1) Improving economic conditions (higher income, absence of high inflation, high
consumer confidence, etc.) for consumers (i.e. retail investors) lead to higher E-Trading
app user levels.
(H2) The COVID-19 pandemic had a significant positive effect on the number of monthly
active users of E-Trading apps.
Based on our dependent and independent variables, we obtain the following regression
formula:
ln#(𝑈𝑠𝑒𝑟𝑠) = 𝛽!+ 𝛽!"#𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑅𝑎𝑡𝑒$!"%& + 𝛽!"'𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑅𝑎𝑡𝑒$!"%& + 𝛽!"(𝐶𝐶𝐼$!"%&
+ 𝛽)"*𝑀𝑜𝑛𝑡ℎ𝑙𝑦𝐼𝑛𝑐𝑜𝑚𝑒$!"%& + 𝛽!"+𝐶𝑃𝐼$!"%& + 𝛽!",𝐼𝑛𝑑𝑒𝑥𝑅𝑒𝑡𝑢𝑟𝑛$!"% &
+ 𝛽!"-𝛿(𝐶𝑜𝑣𝑖𝑑19)+ 𝜀!
The dependent variable is the number of active retail investors using the top five largest
trading apps in the studied territories (US, UK, France and Germany) through the time
period 2017-2021, with monthly data points for 4.5 years. Our regression model is designed
as a log-lin model. The dependent variable is transformed into its logarithmic form to
facilitate the analysis of percentage changes. We decided to retain the independent
variables in their linear form, even for those expressed as percentage in the data model.
This approach makes it easier to understand how an absolute change in the rate (e.g. a
one percentage point increase in mortgage rates or unemployment) influences the
dependent variable, providing clearer insights into the relationship between the variables
and the dependent variable. Additionally, we include one dummy variable, ensuring a
comprehensive and flexible model structure that can account for a variety of influences on
user activity levels.
This will shed light on which factors played a pivotal role in the rise of retail traders. We can
even further specify this in two aspects: firstly, did the variable statistically significantly
influence the activity of retail traders on the trading app? This can be evaluated by
analysing in the p-value of the independent variables.
Secondly, we will check if the variable has a large influence on the number of active users
of E-Trading apps. This can be found in the 𝛽 coefficient for the economic variables.
In section 3.4 we forecast each variable separately based on academic research if the 𝛽
would be positive or negative. The same will be done for the coefficient 𝛽."-𝛿 for our dummy
variable COVID-19.
Both aspects will give us a better view to explain the rise in retail investor activity.

18
The model, data sample and variable specification to test these hypotheses will be
elaborated on below.
4.1 Model
Given that this study aims to analyse the relationship between the number of active monthly
users of E-Trading apps and various economic and non-economic indicators, we opt for
the panel data model. This model allows us to capture both cross-sectional variations and
time-series variations which provides a more comprehensive understanding of the
relationship between the variables.
We constructed four different models, one for each territory, and collected the data using
our independent variables. This results in a model consisting of 1728 combined individual
data points which enable us to make accurate regressions and conclusions from the data.
The usage of panel data enables our study to control for individual and entity-specific (such
as the countries in this study) characteristics that may affect the number of active monthly
users of E-Trading apps, while simultaneously examining how changes in economic
indicators influence user activity over time. Additionally, the panel data method allows for
more efficiency and statistical power compared to cross-sectional analysis, especially when
dealing with correlated observations and unobserved heterogeneity. Therefore, as our
dataset will be declared panel data, we use a Panel ID variable which is a code (ranging 1
to 4) for the specific observed territories and a Time variable (ranging from 1 to 54) for our
monthly observations.
Furthermore, as we are interested in analysing the impact of variables that vary over time,
the fixed effects model is useful. To confirm this, we perform a Hausman test in order to
decide if random or fixed effects is preferred for our model. The result indicates that the
null hypothesis can be rejected on a 5% level of significance (Appendix 1). This serves as
an indication that a fixed effects model is to be preferred for this study.
Moreover, the correlation table (Appendix 2) is used to see whether there is any risk of
potential multicollinearity and the subsequent issues arising from this. We use the 80%
correlation rule (Kennedy, 2008) which shows us that no concerns of multicollinearity can
be found for this model.
As mentioned in the previous section, our regression model is designed as a log-lin model.
4.2 Dependent Variable
4.2.1 Monthly Active Users
For the independent variable we will look at the active users of trading apps. For each
territory, a regression will be made with the number of monthly active retail traders (users)
as the dependent variable. In their paper on global trends and drivers behind retail trading,

19
Gurrola-Perez et al. (2022) also use the monthly level data on the number of retail trading
accounts for their analysis.
In order to measure the active users, we use the monthly active users (MAU) metric
provided by Statista. Statista relies on this data generated by Airnow (Airnow & Statista,
2022). An important side note is that this data set does not define MAU. MAU is a metric
that is differently calculated depending on the industry and the company. We therefore
looked at how the company Robinhood calculates its MAU. Specifically, an individual is
considered an MAU if they fulfil any of the following criteria within the specified month
(Robinhood, 2024):
a) Initiate a debit card or credit card transaction through any account associated
with a Robinhood entity.
b) Navigate between different screens on a mobile device while logged into their
Robinhood account.
c) Load a page within a web browser while logged into their Robinhood account.
Additionally, it's important to clarify the term "E-trading apps", which refers to applications
that facilitate online share trading. Statista offers data on the MAU of the five most utilized
trading apps, based on the MAU metric, for specific countries. For this research, we
focused on the countries USA, UK, France, and Germany, utilizing Statista's data to
examine MAU trends in the context of e-trading app usage.
In our analysis, we apply a logarithmic transformation to the dependent variable. This
transformation enables us to interpret the results in terms of percentage changes rather
than absolute changes, providing a clearer understanding of the relative impact of
economic indicators on user activity levels. By taking the log of the dependent variable, we
can more effectively capture the proportional variations in user engagement, which is
particularly useful when dealing with data that spans a broad range of values.
4.3 Independent Variables
4.3.1 Monthly Income
Incorporating average monthly income as an independent variable in the regression model
allows for the examination of how changes in the personal financial situation of individuals
may impact the number of monthly active users of E-Trading apps. This approach highlights
the role of income levels in driving engagement with E-Trading platforms.
Melcangi & Sterk (2024) document the relation between income and stock market
participation in their study on stock market participation levels. Additionally, the previously
cited study by Bui et al. (2022) showed that retail investors with high incomes trade more,
further demonstrating the positive relationship between income and trading activity.
Therefore, based on previous studies and the literature research we expect 𝛽/01%234.15067
> 0. Meaning that a one unit, meaning a €1 increase, in the average monthly income results

20
in an increase in the number of monthly users and, subsequently, a decrease in the
average monthly income figure would result in lower user levels.
4.3.2 Consumer Price Index (CPI)
The incorporation of the consumer price index of the studied territories as an independent
variable in the regression model allows for the examination of how changes in the general
price level within an economy may impact the number of monthly active users of E-Trading
apps. We use data on a monthly basis, giving us twelve data points for each territory per
full year (54 total per territory). This is in line with the paper from Sathyanarayana &
Gargesa (2018) which focuses on the relationship between stock market returns and
inflation (closely related to CPI) and uses monthly data points.
We expect 𝛽89. < 0, supported by the study conducted by Braggion et. al, 2023 who found
a negative relationship between local inflation and the number of stock purchases by retail
investors.
This means that, as the CPI will be a linear variable in our regression model, a one unit
increase in the CPI translates to a one-point increase or decrease in the CPI index.
4.3.3 Unemployment Rate
Research has shown that the unemployment rate can have a significant impact on
economic activity and consumer behaviour. Therefore, incorporating the unemployment
rate as an independent variable in the regression model allows for and contributes to a
comprehensive examination of its influence on the usage of E-trading apps. The monthly
unemployment data from the different territories will be used to gather these data points.
This approach is in line with research conducted by Falnita & Sipos (2007) who use the
monthly unemployment data in their regression model as dependent variable the CPI.
Increased market risk correlates with higher unemployment, affecting investor behaviour
through heightened risk aversion (Chomicz-Grabowska & Orlowski, 2021).
Therefore, we can expect that as the unemployment rate increases the investor will have
more risk aversion and the influx of new retail investors will decrease on trading apps. This
will ultimately lead us to expect 𝛽:176;304671%<=%7 < 0.
Seeing as the unemployment rate is expressed as a percentage, for our regression model
this means that, as unemployment will be a linear variable, a one unit increase for the
variable unemployment rate translates to a one percentage point change in the
unemployment rate.
4.3.4 Consumer Confidence Index (CCI)
In the context of this study, our model includes consumer confidence as an independent
variable in our regression model as this reflects the morale of consumers in the respective
territories, which means that this also helps to analyse the influence and relationship that
consumer confidence has on the dependent variable meaning the number of retail
investors (using E-Trading apps). As studied by previous literature the consumer

21
confidence index is an important factor for observing the economic environment. More
specifically Chen (2011) discovered that a low consumer confidence index can trigger risk
aversion and can cause a shift away from equities. Therefore, this could also lead to less
use of trading apps by retail investors, when the consumer confidence index decreases.
Therefore, we expect a positive correlation between consumer confidence index and the
active users of trading apps resulting in 𝛽88. > 0.
This means that, as the CCI will be a linear variable in our regression model, a one unit
increase or decrease in the CCI translates to an increase or decrease in the CCI index of
one point.
4.3.5 Stock Market Index Return
In order to obtain the performance of the leading stock market index of the studied
territories, we (as discussed in 3.2.6 Index Return) opted for using a major index specific
to each country, as is commonly done in other research (Fortin et al., 2023).
Garcia (2016) found that investor decisions are heavily influenced by past market
performance, with higher past returns prompting more buying activity. Consequently, as
investors are inclined to buy based on past market performance, we also anticipate higher
retail investor activity on trading apps.
Furthermore, Kaplanski et al. (2016) found that realized returns of the previous month show
a positive correlation with the perceived returns of the next month. Moreover, past
performance tends to create an expectation for future returns to be around the same level
as past returns (Byrne, 2005), which is periods following positive returns would mean that
people would expect more (or higher) positive future returns resulting in higher levels of
investing.
This leads us to suspect that 𝛽.1>7?<7%@A1 > 0.
The variable index return is expressed as a percentage, for our regression model this
means that, as the index return will be a linear variable, a one unit increase for the variable
index return rate translates to a one percentage point change in the index return.
4.3.6 Mortgage Rate
Furthermore, interest rates are closely connected to mortgage rates, as changes in the
central bank’s interest rates directly influence the rates at which mortgages are offered.
When interest rates are low, mortgage rates also tend to decrease, making borrowing more
affordable and freeing up household income that might otherwise be spent on higher
mortgage payments. This increased disposable income can then be redirected towards
investments in (for example) stocks. Research indicates that low mortgage rates, driven by
low interest rates, can influence investor behaviour. Hermans et al. (2023) found that low-
interest rate environments reduce the tendency to save and makes speculative
investments into riskier asset classes, such as equities and bonds, a more interesting
alternative.

22
Similarly, Hau & Lai (2016) observed that during periods of low interest rates, investors
reallocated their portfolios towards more risky assets, including equities. Therefore, it can
be concluded that lower mortgage rates, which are often a result of lower interest rates
(from central banks), likely lead to more active equity purchasing among investors and
therefore higher levels of activity in financial markets. Consequently, an increase in interest
rates, leading to higher mortgage rates, could reduce the shift to equities and investment
activity, therefore decreasing the number of active users of E-Trading apps.
Therefore, we expect 𝛽/0A%B=B7<=%7 < 0.
As the mortgage rate is expressed as a percentage, for our regression model this means
that, as the mortgage rate will be a linear variable, a one unit increase for the variable
mortgage rate translates to a one percentage point change in the mortgage rate.
4.3.7 COVID-19
From March 2020 onward, the variable COVID-19 will be assigned a value of 1, and 0
otherwise. This approach is similar to the method used by Chen et al. (2021), who in their
study on the impact of the COVID-19 pandemic on consumption, incorporated a variable
labelled “threat”. This variable was defined as 1 for observations following the start of the
pandemic and 0 otherwise. We chose March as the starting point because, as reported by
both the WHO (2020) and CDC (2022), this month marked the beginning of lockdown
measures in the EU and USA. The dummy variable will remain at 1 for the rest of the period
under consideration since COVID-19 measures continued to be implemented until the end
of 2021.
Naseem et al. (2021) analysed investor psychology and stock market behaviour during
COVID-19, finding that between January and May 2020, negative emotions and pessimism
caused investors to halt financial investments in the stock market, leading to a decline in
stock market returns. Additionally, Ozik et al. (2021) examined the impact of COVID-19
lockdowns and stay-at-home measures on retail trading. They discovered that these
measures led to a surge in retail trading activity, driven by increased market access through
online trading platforms and more time spent at home. Finally, Niculaescu et al. (2023)
observed that on average, during the first COVID-19 wave, retail investors increased their
investments by 4.7%.
Therefore, we expect 𝛽8CD.E#F > 0.
As the variable for COVID-19 will be a dummy variable in our model, the coefficient will
indicate the increase (see 4.2.1 Monthly Active Users) or decrease percentagewise in the
number of monthly active users.

23
5 Discussion of the Results
5.1 Correlation Analysis
We start the discussion of the results by looking at the correlation table. The interpretation
of the correlations allows us to understand the relationships between the key variables
included in our study. The correlation table provides a preliminary overview of how these
variables interact with each other. By examining the strength and direction of these
correlations, we can identify potential patterns and dependencies that will inform the
subsequent regression analysis. This initial exploration helps us to assess the potential
impact of each independent variable on the dependent variable. For this section, we limit
ourselves to the interpretation of the correlation coefficients between the independent
variables and our dependent variable. Furthermore, the interpretation of the correlation
coefficients shall be limited to our studied time period and the (interim) conclusions drawn
from these coefficients can therefore only be applied to the studied time frame.
We are able to deduct from the table that there is a strong positive correlation between the
CPI and the number of users, suggesting that as price levels increased, the number of
users increased as well. Additionally, there appears to be a moderately positive correlation
with monthly income, suggesting that increases in average monthly income, and
subsequently in the level of disposable income, resulted in higher user levels.
With regard to the COVID-19 pandemic, a moderately positive correlation can be observed.
However, this indicates that the presence of the pandemic may have led to increased
activity on E-Trading apps.
Positive, but rather weak, correlation coefficients can be seen for mortgage rates, index
returns and unemployment. For these variables there is a positive correlation, however,
especially for Index Return and Unemployment, this tends to be rather low.
Finally, we see a strong negative correlation between CCI and the number of users. This
suggests that when consumer confidence declined, likely due to the start of the pandemic
with the subsequent increase in unemployment and the decline of (financial) markets, more
people joined E-Trading apps and started investing. This increase in users could be linked
Table 4: Correlation Matrix

24
to the increase in investment activity observed during the pandemic as we referred to when
citing the study by Niculaescu et al. (2023).
5.2 Regression Analysis
Our regression model aims to explore the factors influencing the number of monthly active
users on E-Trading apps based on data obtained from the US, UK, Germany and France
territories. These results and their meaning will be discussed before we move to answering
the hypotheses from chapter 4 in the conclusion.
The regression table provides us with various insights examining the impact of various
economic indicators, affecting the financial situation of consumers, as well as the COVID-
19 pandemic on the log of our dependent variable: the number of monthly active users of
E-Trading apps. The independent variables Unemployment, MortgageRate and
IndexReturn are not significant (at neither the 1%, 5% or 10% level). The wide confidence
intervals for these variables suggest considerable uncertainty around these estimates.
However, the variables Covid19, CCI, MonthlyIncome and CPI are significant, albeit at
different levels of significance. The positive coefficient for CCI suggests that higher
consumer confidence is associated with an increase in the number of users. However, with
a p-value of 0.089 this effect is marginally significant at the 10% level.
The CPI has a positive and statistically significant effect on the number of users. The
coefficient suggests that rising price levels (inflation measured by the CPI) is associated
with an increase in the number of users. The result is statistically significant with a p-value
of 0.003, indicating a robust relationship. The coefficient suggests that an increase of one
unit in the CPI results in an 𝑒GHG+ increase, which equals an increase of approximately
5,13% in the level of monthly users. The positive coefficient of this independent variable is
suprising, at least compared to our initial expectation as discussed in 4.3.2 Consumer Price
Index. However, the extraordinary nature of, especially the last 1.5 years, of our studied
period might provide an answer here. The high volatility and subsequent inflation
(especially during the recovery) coincided with the increase in users, potentially due to
supply chain shocks during this period, causing prices to rise. Additionally, fiscal and
monteray stimulus packages, rolled out as a response to the (global) economic shock
caused by the pandemic, caused the money supply to rise, contributing to elevated inflation
levels and rising price levels (CPI) (Borio et al., 2024).
Table 5: Regression Table
*** p<0.001, ** p<0.05, * p<0.01

25
Furthermore, MonthlyIncome also has a statistically significant and positive impact on the
number of users. This implies that higher average monthly income is associated with
(statistically) significant increase in the number of users as the p-value of 0.001 confirms.
The coefficient of 0.0019 suggests that an increase of 1 EUR in the average monthly
income, results in an increase of 0.19% in the number of monthly active users. Therefore,
an increase in the average monthly income of 100, would result in an 𝑒GH#F increase which
gives a 20.92% increase in the number of monthly active users.
Finally, the dummy variable Covid19 also shows a positive and highly significant impact on
the number of users. The coefficient of 0.5062 suggests that during the pandemic, the
number of monthly active users increased by about 𝑒GH+G,', which equals a 65.9% increase
in the number of monthly active users. The p-value of 0.000 indicates strong statistical
significance for this variable.
The overall R-squared value of 0.8097 (Appendix 3) reflects the proportion of the total
variation in the dependent variable (across both time and countries) that is explained by
the independent variables in the model. This high value indicates that the model has a
strong overall explanatory power, capturing a substantial portion of the variation in the
number of monthly active users on E-Trading apps.
5.3 Limitations
In the context of our study and research, several limitations should be acknowledged that
may influence the interpretation of our findings. Firstly, while we incorporate the average
monthly income as a key independent variable, we do not differentiate between different
income percentiles or groups. This approach overlooks the potential variations in behaviour
across different income levels, which could be significant in understanding how income
disparities impact the usage levels of E-Trading apps. Individuals in lower income brackets
might respond differently to economic conditions compared to those in higher brackets, and
this nuance is not captured by our model.
Additionally, our analysis does not account for variations in financial literacy across the
population. Financial literacy plays a crucial role in investment decisions, and differences
in understanding of financial markets could lead to varying levels of engagement with E-
Trading apps. Studies show that individuals with higher financial literacy levels are more
inclined to invest in the stock market and those with lower levels of financial literacy invest
less (Rooij et al. 2007; Khan et al. 2018; Bucher-Koenen, 2023). By not including this
variable, we miss the opportunity to explore how knowledge and education in financial
matters could moderate the relationship between economic factors and trading app usage.
Moreover, another limitation of our study is the excursion of different age groups. Age can
significantly influence investment behaviour, with younger individuals possibly being more
inclined to use digital trading platforms compared to older demographics. Nyakurukwa &
Seetharam (2022) show in their study that age is significantly associated with stock market
participation. Additionally, Alessieet et al. (2004) showed that stock market participation
increases with age, especially among those 40 years or older and increases with age
(Arrondel et al., 2010; Faig & Shum, 2006). The omission may result in an incomplete
understanding of how demographic factors interact with economic indicators to drive the
usage of E-Trading apps.

26
Furthermore, our model does not account for the innovation and accessibility of trading
apps as an independent variable. Previous research has identified a significant link
between these factors and the number of active users of trading apps (Nair et al., 2022;
Gupta & Dey, 2023). This research has been done in emerging markets where the fintech
industry and accessibility of trading apps are known to be less developed than in the EU
Countries (Acharya, 2023). A change in innovation would therefore have a bigger impact
in emerging markets. Nevertheless, further innovations in trading apps during the period of
2017-2021 could have significantly impacted the rise of retail investors in Europe as well.
However, due to the lack of data on this factor, we did not include it in our regression
analysis. For a more comprehensive analysis, future studies should incorporate this
variable. Furthermore, our study does not include variables such as gender and investment
experience. These are often considered to be important drivers of investor behaviour
(Barber & Odean, 2001; Seru et al., 2010).
Lastly, our research focuses exclusively on E-Trading apps and while this is highly relevant
in the context of the current (global) investment climate, it does not consider apps offered
by domestic (retail) banks that provide investment opportunities. These banking apps,
which often include investment features, could attract a different segment of the population
or exhibit different usage patterns compared to standalone E-Trading platforms. By not
including these apps, our study may not fully capture the broader landscape of digital
investment platforms and the factors influencing their use.
These limitations highlight areas where further research could deepen our understanding
of the dynamics at play in the adoption and usage of E-Trading apps across different
segments of the population.

27
6 Conclusion
This study aims to demonstrate the impact of various factors influencing the financial and
economic position of consumers (i.e. retail investors) on the level of monthly active E-
Trading app users. Focusing on the period from January 2017 until June 2021, the analysis
was conducted using 216 observations, capturing a range of economic indicators to obtain
the percentage change on the level of monthly users through the use of seven dependent
variables (Unemployment Rate, Mortgage Rate, COVID-19, CCI, Monthly Income, CPI and
Index Return):
ln#(𝑈𝑠𝑒𝑟𝑠) = 𝛽!+ 𝛽!"#𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑅𝑎𝑡𝑒$!"%& + 𝛽!"'𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑅𝑎𝑡𝑒$!"%& + 𝛽!"(𝐶𝐶𝐼$!"%&
+ 𝛽)"*𝑀𝑜𝑛𝑡ℎ𝑙𝑦𝐼𝑛𝑐𝑜𝑚𝑒$!"%& + 𝛽!"+𝐶𝑃𝐼$!"%& + 𝛽!",𝐼𝑛𝑑𝑒𝑥𝑅𝑒𝑡𝑢𝑟𝑛$!"% &
+ 𝛽!"-𝛿(𝐶𝑜𝑣𝑖𝑑19)+ 𝜀!
Concerning the first hypothesis that improving economic conditions for consumers lead to
higher E-Trading app user levels, we find mixed evidence with regard to this hypothesis
based on the regression model used. We find strong evidence to suggest that (average)
monthly income and CPI are significant predictors of the number of monthly active users.
Additionally, the consumer confidence index was marginally significant, indicating some
relationship, though not a very strong one. However, other variables, such as
unemployment, mortgage rates, and index returns, did not show statistically significant
effects within this model.
According to our second hypothesis, the COVID-19 pandemic had a significant positive
effect on the number of monthly active users of E-Trading apps. Therefore, we find that the
pandemic had the most substantial positive impact on the level of users in our model. This
is especially interesting seeing as the COVID-19 pandemic, a so-called “black swan event”,
and its effects on equity markets shows “no significant similarity to any previous event”
(Sharma et al., 2021).
These findings highlight the complexity of factors influencing E-Trading app usage and
suggest that while certain economic indicators like income and price levels (CPI) are key
drivers, other factors may play less of a role than initially expected. The strong impact of
the COVID-19 pandemic underscores the importance of external shocks in shaping
consumer behaviour in financial markets. Overall, this study contributes to a deeper
understanding of the multifaceted nature of retail investor activity, emphasizing the need
for further research to explore the interplay of economic conditions, market dynamics, and
unexpected global events in influencing (digital) trading platform engagement.

28
7 Appendices
Appendix 1: Hausman Test
Appendix 2: Correlation Table

29
Appendix 3: Regression Table
Appendix 4: Conversion Tables ECB

30

31
8 Table List
Table 1: Data Source Table .............................................................................................. 10
Table 2: Exchange Rate Table ......................................................................................... 14
Table 3: Summary Statistics ............................................................................................. 15
Table 4: Correlation Matrix ............................................................................................... 23
Table 5: Regression Table ................................................................................................ 24

32
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