A Work Project, presented as part of the requirements for the Award of a Master’s degree in Finance from the Nova School of Business and Economics.
RETAIL INVESTORS’ PREFERENCE ON ESG FUNDS:
EVIDENCE FROM ROBINHOOD USERS
SIGVALD K. B. AAMODT
Work project carried out under the supervision of:
Virginia Gianinazzi
01/06/2022
Abstract
This paper tried to identify whether investors on the fintech brokerage Robinhood favor investing in sustainable funds. Using data from Robinhood and Morningstar, I find that Robinhood users are more likely to choose funds who are rated higher in sustainability contrary to a fund rater lower even though an ESG estimator, such as the Morningstar globe rating used in this paper, is not readily available on the platform. This result can be explained by a rising concern for climate change, especially among the younger population, its young userbase, and outside influence.
Keywords
Fintech, Robinhood, ESG, Morningstar, Globe rating, Sustainability, Fund, ETF
This work used infrastructure and resources funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2013, UID/ECO/00124/2019 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209).
I. Introduction
Over the past half century, how and what investors trade has changed significantly. In the 1950s, only about 4,2% of the US population was invested in the stock market, and it was both more time consuming and expensive than today to trade. This friction to invest in
common stock was due to the norm of fixed commission and limited competition. Investment options were also quite limited, yet to have the surge of mutual funds happening in the 1970s – 1990s. However, forwarding to today, several significant changes been made, such as in 1975 when the Securities and Exchange Commission (SEC) banned fixed minimum
commission rates. This, among other decisions, and technological advancements like being able to trade on your phone, has outstandingly decreased the time and cost of trading and made it accessible for everyone (The Investopedia Team, 2021). Also, the number of investment opportunities has increased notably, such as the big surge of Exchange Traded Funds (ETFs) (Israel, 2020). All these advancements have led us till today to trading platforms such as Robinhood.
Robinhood was the first platform to offer commission free trading and made it convenient and simple through an engaging phone app. Because of its simplicity, accessibility, zero commission fees except when selling, and design, Robinhood has accumulated 22.8 million users as of March 2022. According to a spokesman from
Robinhood, the app has mainly attracted the younger demographic where its average user’s age is 31 years old and about 50% are first time users (Lam, 2022).
The outflow of greenhouse gas has been increasing exponentially ever since the industrial revolution. In the last 50 years, climate change has been a rising topic gaining more and more traction and concern as consequences like rising temperature resulting in higher sea levels, affecting crop, and animal distinction are becoming reality. This concern has grown significantly in the last 10-20 years. Already, the planet is starting to see consequences from
greenhouse emissions. The current and predicted consequences for the upcoming decades have led to real concern all around the world, especially in the western world which has brought on climate change activists such as Greta Thunberg who is only 19 years old (as of 19th of April 2022). People like Greta and rising concern among the population, especially amongst the younger generation, particularly gen y (born between 1981and 1996) and gen z (born between 1997 and 2012) has led to a shift from a profit driven mindset to an ever evolving environmental, social and governance (ESG) mindset.
The fear for the planet’s future has created an increasing demand for solutions to the problem. Most of the world has been using a linear production and consumption system, meaning we extract raw material, create products, consume it, then lastly, dispose it. This system has been under the assumption of unlimited and cheap natural resources. In the last few decades, it has become clear that natural resources are limited, and at an alarming rate with the current consumption rate. This has led to some companies and governments
switching to a circular economy where resources are recycled and reused rather than disposed.
This has yielded a rise of existing companies changing their ways to become more sustainable and greener, and a rise of new companies solely focused on solutions and making alternatives to existing products people are using. The biggest example of this is Tesla
creating climate friendly cars that runs on electricity instead of oil. Because of Tesla and the demand of its products, it has pushed existing car companies such as Volvo, Toyota,
Mercedes, etc., to invest heavily into creating and producing electric vehicles (EVs).
Companies such as Tesla are rising in many fields.
The rising concern amongst the population, especially among the younger
demographic, the new wave of companies with a more ESG driven mindset, and a close to frictionless stock market through platforms like Robinhood has led to people wanting help their way, such as investing in companies that are ESG driven. With the rise of ESG focus
among the population, specialized mutual funds and ETFs focusing on ESG has been made.
As most investors, especially on Robinhood due to its young userbase and many newcomers, do not know what companies to invest in that is ESG friendly because of financial illiteracy or simply laziness. Funds, however, provide the easiest way for investors to invest in a broad spectrum of companies which purposes are aligned with yours.
The importance on how a company treats its employees and its impact on the world and its people are also rising. There are a variety of reasons why many investors utilize ESG investment strategies. A couple of examples are to uncover ethical issues and pushing companies to incorporate sustainable practices. In fact, studies have found that many ESG funds outperformed equivalent non-ESG funds (Lauricella and Liu, 2020). The reasons for the outperformance can likely be attributed to the fact that these ESG investment strategies
consider companies’ impact on climate change, customer data privacy, human right
violations, etc., and not only its financial returns. Additionally, studies also found that during the covid-19 shock, ESG funds not only outperformed, but were more resilient in terms of investment risk, more shock resistant, and offered better downside protection. These findings were done by Morningstar comparing a high sustainability rating (globe rating) against funds with lower sustainability rating. In other words, the more a fund included companies that mitigated ESG risks, the better its risk profile. The globe rating is a measure of ESG risk rated from 1 to 5 where 1 is high ESG risk and 5 is low. This measure will be explained in more detail on page 9 to 10.
One important note is that the overall number of ESG funds are still relatively small.
Money held in mutual funds and ETFs in 2021 was about $2.7 trillion compared to $63.1 trillion in open-ended funds in 2020 alone worldwide. The capital invested in ESG funds has, however, increased globally by 53% from 2020 to 2021 showing promise it is overall
increasing as a strategy. However, new strategies are still being implementing to account for
ESG as new definitions and methods are being created. For example, in 2021, the Sustainable Finance Disclosure Regulation (SFDR) changed the way you rank funds on sustainability in the EU, with the objective of improving transparency in the market for sustainable investment products and preventing greenwashing.
In 2020, Morningstar found that for the trailing one year, the median ESG fund only lost 98,3% of its category benchmarks decline. This comparably is much better than the average fund which lost 104,8% against its category benchmark. This means that the median ESG fund outperformed the average fund in its Morningstar category against their category benchmark by 6,5%. Trailing 3 years was a 4,95% difference and trailing 5 years was a 10,09%.
As discussed above, it appears that already, people are favoring investing in funds with a higher globe rating. However, is this the case for Robinhood investors, or is this rather unique userbase indifferent whether a fund is rated high or low on ESG risk?
An important thing to highlight is how the platform is built in terms of user
interaction. When you check out a stock, ETF etc. on Robinhood, you are given the evolution of the price, some basic stats like high and low, price to equity ratio, volume and so on. You are further given news about the security, analyst ratings, and lastly earnings per share.
Robinhood does not give you any indication to what extent the fund or stock are subject to ESG risk. To have access to an ESG risk measure, namely the Morningstar globe rating, and other ratings such as the Morningstar star rating along with other in-depth research from Morningstar and other perks on the platform, you must subscribe for Robinhood Gold. The star rating is rated from 1 to 5 in terms of risk adjusted return. Robinhood Gold is a paid version on the platform to use extra features and access more information for the price of 5 US dollars. This means that the globe rating is not readily available for investors on
Robinhood. As explored further in the literature review, this makes it less likely for
Robinhood investors, who are subjects to attention-induced trading, to make decisions about a trade depending on a security’s globe rating or any other form of ESG measurement.
II. Literature Review
As there are only few recent papers that examines the Robintrack dataset (e.g., Ivo Welch, 2020), while a larger literature covering ESG funds and ETFs (e.g., Ben-David et al., 2020), there is little research existing on Robinhood investors preference for funds. However, there is some. It has been discovered, using the Robintrack data, that ESG disclosures are irrelevant to retail investors’ portfolio allocation decisions by analyzing adjustments in
portfolios on ESG press release (event days), versus non-ESG press releases (non-event days) (Moss et al., 2020, p. 20). This contrasts with evidence from experimental studies which suggests that retail investors’ respond positively to ESG disclosures (Martin and Moser, 2016, p. 14). In their experiment, they find that investors respond more positively to voluntary disclosure of green investments rather than no report.
A Prior study present causal evidence that investors collectively value sustainability.
Additionally, funds who receive a Morningstar globe rating of 5 receive more than $24 billion greater funds, and those that receive a Morningstar globe rating of 1 experience a reduction in fund flows of more than $12 billion (Hartzmark and Sussman, 2019, p. 1). This suggests the market views sustainability as a positive attribute of a company and that they place their money accordingly. They also found, after Morningstar published the sustainability ratings, that a globe rating of 2, 3, and 4 do not significantly affect neither the inflow nor the outflow of a fund, only if it has a globe rating of 1 or 5. Before the publication of the ratings, funds received similar level of inflow and outflow. To be more specific, over an eleven-month period subsequent of the publication of the globe ratings, funds with a globe of 1 experienced a decrease of about 6% of fund size (12 to 15 billion outflow), while funds with a 5-globe
rating experienced a 4% increase of fund size (24 to 32 billion inflow). These finding suggests investors ignore more detailed sustainability information and are rather easily influenced by simple ratings which is easy to understand and quick to assess. However, this was an experimental finding using MBA students and Amazon Mechanical Turk participants.
This paper addresses a similar question – whether investors adjust their holding in funds depending on the funds ESG rating – but does so using an empirical archive rather than experimental research design. I believe continued examination of this topic is important and vindicate, as the mindset of the population on this topic has changed a lot over years, with the share of Americans that sees global climate change as major threat increasing from 44% to 60% from 2009 to 2020. This will likely continue to change with rising concern for the future and continued growth and demand for ESG focused companies. The literature can best progress using up to date data that better reflects the current environment of investors’
mindset. Also, trying new methods both experimental and empirical is important to explore this phenomenon, but can, however, lead to different results.
Likely one of the most fundamental questions from financial economics is whether findings from a laboratory can provide well-grounded inferences outside the walls of an experiment. Levitt and List (2007) argues that individuals peoples choices depend not only on financial implications, but also the extent and nature of others scrutiny, the context of a decision is embedded, and how the selection process for participants is done. Lab
environments systematically differs from your natural environment outside the lab on these points, which is why experiments cannot always provide results that is generalizable and can reflect the population or a group of people. Hartzmark and Sussman (2019) researched the same topic as in this paper with subtle but important differences. It was done in the context of a laboratory; therefore, it is plausible that the participants had a social-desirability bias as they knew they were being observed which may have resulted in participants valuing ESG rating
to a higher degree when they normally favor financial returns when they adjust their portfolios from your everyday setting. They also did an event study, the event being the publication of the Morningstar globe rating. It was found that funds rated 1 experienced roughly -0.44% greater outflow per month than 3 globe funds, while 5 globe funds experienced 0.30% increased inflow per month than 3 globe funds.
Investment decisions incorporating ESG has seen substantial growth, but this positive trend might affect market efficiency (Cui and Docherty, 2020). Cao et al. (2018) argues that ESG investors possibly overweight ESG impact relative to firms’ financial performance which could decrease market efficiency. Furthermore, Starks et al. (2017) reported that socially responsible funds are less likely to divest in stocks with high ESG rating, even after negative returns are reported.
The platform in which is being used can also heavily influence how one invest. Noted by Barber et al. (2021), Robinhood has features on its app that gamifies investing arguably putting too much emphasis on the fun of investing. Also, half of Robinhood users are first- time investors which makes them more subject to be influenced by attention. It is further noted that Robinhood users trades 9 times as much as E-trade users and 40 times as much as Charles Schwab customers because Robinhood investors trade more speculative contrary to trading for non-speculative reasons like saving for retirement.
There is certainty other aspect to keep in mind when analyzing the data, but these highlighted ones are some that will be important which will add to the view on how Robinhood investors invest in funds.
III. Data and Methods
In this section I describe the Robintrack dataset which keeps track of how many users on Robinhood hold a particular security over time, the Morningstar globe rating which serves as
the main explanatory variable, and two additional control variables. Furthermore, I showcase summary statistics and explain the methods used to identify whether Robinhood users favor funds who are ESG driven.
A. Robintrack Data – dependent variable
The primary dataset used for the analysis is downloaded from the Robintrack website (https://robintrack.net/), which collected popularity data on stocks, ETFs, etc., from the Robinhood app from May 2nd, 2018, to August 13th, 2020. Popularity data in this context, is the number of users holding a certain security, for example Microsoft, on Robinhood. The dataset contains repeated cross-sectional snapshots of user counts for individual securities (e.g., on 03-05-2018 at 17:43:27, 113242 Robinhood users held Microsoft stock). The main result uses only funds, because the main objective with this paper is to find whether
Robinhood users favor investing in a higher rated fund on sustainability contrary to a lower rated one. Furthermore, the dataset mainly includes ETFs because Robinhood financials currently does not support assets such as mutual funds and close-end funds.
The Robintrack data is changed from hourly data to daily data by taking the last value of each day. From here it is filtered by using the tickers from the Morningstar sustainability rating dataset so that the Robintrack dataset include only funds.
B. Morningstar Sustainability Rating – main independent variable
“The Morningstar Sustainability Rating for funds helps investors make comparisons across industries and better understand and manage total ESG risk in their investments”
(Morningstar.com, 2019). A fund with high ESG risk relative to its Morningstar Global Category would receive 1 globe, while a fund with low ESG risk would receive 5 globes (Morningstar.com, 2019), hence the variable is categorical. The rating “is calculated using
Sustainalytics’ ESG Risk Ratings for corporate issuers and Sustainalytics’ Country Risk Ratings for sovereign issuers and is based on historical holdings” (Barr et al., 2021). To elaborate a bit more on what this encapsulates, the first input, ESG Risk Ratings, measures to which degree a company’s economic value is at risk because of ESG factors “or, more technically speaking, the magnitude of a company’s unmanaged ESG risks” (Barr et al., 2021). The second input, Country Risk Ratings, “assess the risks to a sovereign entity’s socioeconomic well-being by combining an assessment of the government entity’s current stock of capital with an assessment of its ability to manage the wealth in a sustainable
manner” (Barr et al., 2021). This is reflected through two dimensions. First wealth, reflecting how vulnerable a country is to ESG risks (calculated by the World Bank). Second ESG performance, assessed by how well a country manages its key ESG factors. Important to note is that each category has its own definition of what is relatively high and relatively low ESG risk. The funds are compared against other funds in its category, meaning that a fund could have higher ESG risk than other funds and still receive a better rating if those other funds are in different global categories. The Morningstar sustainability rating is updated every month and currently has ratings from August 2018 to January 2021. The data was provided from my work project advisor.
Because of the limited time Robinhood’s API was publicly available and the
Morningstar globe rating dataset only starting from August 2018, the final dataset will only include data from 1st of August 2018 to 13th of August 2020. It is further filtered excluding all funds that does not have a single rating in the timeframe. If a fund contains even one rating in any month from August 2018 to August 2020, it is included along with its missing data to not loose data. Additionally, all zeroes are classified as “NoneType” or “NaN” so that when performing regressions, it ignores these rows. Lastly, I merged the Robintrack data with the monthly Morningstar sustainability rating.
C. Additional independent variables (control variables)
First, I included Morningstar ratingTM, often called the star rating which again was collected through my advisor and is a categorical variable like the globe rating. “It is a purely quantitative, backward-looking measure of a fund’s past performance, measured from one to five stars” (Morningstar, 2021). It was filtered the same way as the main independent
variable, including changing all zeros to Nans’. It “rates funds within the same Morningstar Category based on an enhanced Morningstar Risk-Adjusted Return measure” (Morningstar, 2021). A rating of 1 means a funds risk-adjusted return falls within the bottom 10%, while a rating 2, 3, 4, and 5 puts the funds in the top 90%, 67.5%, 32.5%, and 10%, respectively (Morningstar, 2022). The ratings are calculated at the end of each month. The rating is added in case the main independent variable, the globe rating, is endogenous as these rating are provided by the same company, Morningstar.
Secondly, I added a binary variable using the names of the funds to control whether funds that include key words such as “sustainable”, “climate”, “green”, “water”, “clean”,
“esg” etc. attract more Robinhood users than funds who do not. This data was gathered through the yfinance package using python. yfinance, is a package that allows you to
“download market data from Yahoo! Finance API” (Ran Aroussi, 2022). From the package, I collected the long name of each fund. If a fund contains one of these key words, it got
assigned 1, if it did not contain any key words, it got assigned 0. Hence, the variable is binary.
The star rating and ESG keywords was then merged with the Robintrack dataset.
D. Summary statistics
Before looking at the summary statistic, I want to note that the panel data created is unbalanced, meaning total observations for each ticker is not equal. This can be due to a fund
being made or becoming available at some point in time after 1st of August 2018 or seizing to exist or becoming unavailable to invest in before 13th of August 2020.
From table 1 and table 2, the data, after being filtered as specified earlier in the paper yields, 754 638 observations across all tickers having 1233 unique tickers. Interestingly, the average user holding in a fund at any given time from 1st of August 2018 to 13th of August 2020 was 495 users. However, if we look at the last value of each ticker, the average is 964.
This is to be expected as the number of users on Robinhood increased from about 6 million to 13 million from 2018 to 2020. There are big discrepancies between the user holding in each fund with the lowest number being 0 users and the max being 118 862 users. The median is even more surprising being as low as 50. This can imply investors on Robinhood do not invest much in funds, or not in a great amount of funds, except from a few popular ones such as SPDR S&P 500 ETF Trust (SPY) which is the fund with 118 862 users shown in table 3.
Even the top 25% fund still only have 193 investors which is very low compared to SPY.
Table 1: Summary statistics
Table 1 Showcases the number of observations, the average, median, minimum, and maximum value, and the top 25% and bottom 25% of each variable, after being filtered as explained in section B under Data and Methods.
Table 2: Summary statistics using the last value for each ticker
Table 2 Showcases the same statistics as table 1, however, only using the last value of each ticker.
The reason the number of observations is different, is because globe rating and star rating contains Nans’ which is not included in these calculations due to irrelevance and would most importantly change the average and median to be lower than what they likely would be if these funds was rated by Sustainalytics.
The sustainability rating has 734 347 covering 1211 out of 1233 funds, and the average rating remains the same with a rating of 2.8. An average of 2.8 implies that the distribution of
obs average min 25% median 75% max
Users holding 1 233 964.2 0 20 74 297 118 862
Globe rating 1 211 2.8 1 2 3 4 5
Star rating 799 3.0 1 2 3 4 5
obs average min 25% median 75% max
Users holding 754 638 495.2 0 15 50 193 118 862
Globe rating 734 347 2.8 1 2 3 4 5
Star rating 446 561 3.2 1 2 3 4 5
rating only has 446 561 observations out of the total 754 638 users holding dataset across 799 funds. The average is 3.2 looking at the whole dataset meaning more funds are rated 4 and 5 than 2 and 1, visualized in figure 2. Looking at only the last value of each fund yields an average of 3.0. The reason for this can be that funds with lower rating has more missing observations across time than those with higher rating, or several funds with low ratings only got made or became available on Robinhood later in the timeframe 2018 to 2020.
Figure 1: Number of Morningstar ratings from August 2018 to August 2020
Showcases the number of observations from 1st of August 2018 to 13th of August 2020 for both the globe (sustainability) rating and the star (risk-adjusted return) rating for all funds.
Figure 2: Number of Morningstar ratings using the last value of each fund
Showcases the number of observations for globe rating and star rating for each rating using the last value of each ticker.
Figure 3 below, showcases the percentage of funds including ESG glossary in each Morningstar sustainability rating. It appears that from rating 1 to 5, the amount of funds in each sustainability rating with ESG glossary is growing exponentially with the ones rated 1 containing only 1.8% ESG glossary while those rated 5 containing 21.9% ESG glossary.
Interestingly, the funds which are not rated have a percentage of ESG glossary of 6.3%. This can imply that the funds in this group would on average have a rating between 3 and 4, however, they remain “not rated” for future analysis in this paper. The reason it is important to look at both globes and ESG glossary as a proxy for sustainable funds is to see if they provide the same information. From Figure 3, it is clear the higher rated a fund is, the higher likelihood they will include ESG glossary in their name.
Figure 3: Percentage of funds in each Morningstar globe rating that contains ESG glossary
Showcases the percentage of funds that contains an ESG glossary compared to total funds that is rated a certain globe rating. The glossary includes the words: 'sustainable', 'climate', 'vegan', 'water', 'esg', 'governance', 'environmental', 'social', 'sustainability', 'net zero', 'renewable', 'scope', 'zero waste', 'csr', 'responsibility', 'green', and 'clean'. To be noted, “Not Rated” highlights the funds that do not have a rating. To make this plot, I took the last value of each ticker so that one name is not included twice. This also means that some Nans’ or 0 are included as not all funds in this dataset have a rating on its last date.
Figure 4 further showcases the number of funds in figure 3 that includes ESG glossary. It indicates, as expected, an increase in the number of funds the higher the rating, however, drops slightly when the rating is globe 5 due to the relatively small number of funds that is rated 5, that is 74 funds, shown in figure 2. It appears that funds who is rated high on sustainability also promotes it and make it obvious for the investor that this is a fund catered with ESG and sustainability in mind. Of course, the list of words used to make this plot is subjective, but it encapsulates the trend on how companies name their funds even if some glossaries were missing or should be dropped.
Figure 4: Number of funds containing ESG glossary for each globe rating using the last value of each ticker
Showcases the number of globe ratings for each rating using only the last rating for each fund.
The reason a fund does not have a Morningstar sustainability rating is because one must be eligible for it. Sustainalytics, which is a Morningstar company providing ESG research, ratings, and data, first identifies the qualified holdings, which is the assessment of potential ESG risk in equities, fixed-income instruments, commodities, real estate etc. Then they identify eligible holdings which is where they check whether a risk ratings framework exists, hence can contribute to as a measure of risk in the Morningstar sustainability rating.
Once qualified holding and eligible holding is identified, eligible holding is divided by qualified holding. If a fund does not have at least 67% of its qualified holdings that is eligible to be rated, it will be considered unsuitable for a Morningstar sustainability rating.
Table 3 below showcases the funds most users on Robinhood invest in. The top fund is the ticker SPY, briefly mentioned before. It is a fund that replicates the S&P 500. There are several reasons for why that has the most investors. It follows the most well know index in the US, has been around since 1993, and has yielded an average annual return of roughly 8%
adjusted for inflation. This is the case for several of these funds. A closer look at the names of the other funds, we can see that four out of 10 funds follow the S&P 500, although with different focus such as high dividend, low volatility, and growth. There is one fund following
traded companies on the New York Stock Exchange (NYSE), and one fund, Invesco QQQ, which tracks the Nasdaq-100 index largest non-financial companies listed on Nasdaq based on market cap. The four other funds, namely, MJ, JETS, BOTZ, and ARKK, follows an investment theme, cannabis, airlines, robotics and artificial intelligence, and disruptive innovation, respectively. Six out of the top ten funds follow a well know index while the remaining four are themed funds which gives an idea of the type of investments Robinhood users are attracted too. For the themed funds, what attracts is not its return or longevity, but the theme itself, its future potential growth and possibly its pleasing ticker name. JETS for example has been around for only 7 years and has yielded a negative return over its lifespan but is still the fifth most popular fund to invest in on Robinhood surprisingly.
Table 3: Top ten funds ranked by users holding
This table showcases the top ten funds ranked by user holding filtered using the last value of each fund. Note that these are the top funds after filtering the Robintrack data by excluding all funds that do not have a single globe ranking in the time frame 1st of August 2018 to 13th of August 2020.
E. Regression analysis
What I am testing and want to answer in this section is whether the degree of sustainability of a fund significantly affects which funds Robinhood users invest in. I used clustered standard errors, specifically White’s estimation which is the equivalent used in Stata, that accounts for heteroscedasticity across clusters or entities of observations, which in this case is tickers, for all regressions showcased in table 4 and table 5. Clustered standard errors do not really affect the coefficient estimate, but it affects the t-statistic to be more accurate. This account for non-independence between periods within each ticker. As a result
Ticker Timestamp Fund name Globe rating Star rating ESG glossary Users holding
SPY 13/08/2020 SPDR S&P 500 ETF Trust 3 4 0 118862
SPHD 13/08/2020 Invesco S&P 500 High Dividend Low Volatility ETF 4 2 0 90627
QQQ 13/08/2020 Invesco QQQ Trust 3 5 0 49318
MJ 13/08/2020 ETFMG Alternative Harvest ETF 1 0 45692
JETS 13/08/2020 U.S. Global Jets ETF 2 0 38415
SPYD 13/08/2020 SPDR Series Trust - SPDR Portfolio S&P 500 High Dividend ETF 4 1 0 31007
BOTZ 13/08/2020 Global X Robotics & Artificial Intelligence ETF 3 0 27907
ARKK 13/08/2020 ARK Innovation ETF 1 5 0 24792
DIA 13/08/2020 SPDR Dow Jones Industrial Average ETF Trust 3 5 0 24060
SPYG 13/08/2020 SPDR Series Trust - SPDR Portfolio S&P 500 Growth ETF 3 4 0 20787
observations drop to 439 981 observations. To be able to compare coefficients, all regressions will be done using 439 981 observations.
I started off by doing a simple OLS regression with the Robintrack data as my dependent variable and the Morningstar globe rating as my independent variable. This result is denoted as (1) in table 4. The alpha or constant in the regression is 309, meaning a fund which is not rated can expect to have about 309 investors from Robinhood. If you increase the globe rating by 1, the result suggests the fund would see an increase of about 86 investors for each increase in its rating. (1) suggest each rating yields on average the same increase in investors. Therefore, an additional regression was performed to see the effect of each globe rating on user holding denoted (2) in table 4. I find that a globe rating of 1 yield on average 411 investors, while a globe of 2 yield a decrease of 61 investors. A rating of 3,4 and 5 increases user holding on average 285, 235, and 124, respectively. For the entity fixed effect models, (3) and (4), there is little difference in the result from (1) and (2). Entity fixed effect controls for any individual-specific attributes that do not vary across time. This suggests there are little ticker specific attributes that significantly affects the coefficients. Lastly, there is time fixed effect denoted (5) and (6) in table 4. Time-fixed effect accounts for the increase in investors on the platform over time. The categorial globe variable in (5) about halved to 42 from 86 in (1). This implies that in (1), increase in users over time was a significant driver to why the coefficient is as high as it is. From 2018 to 2020, the number of users doubled which explains why the coefficient halved in (1). This has an interesting effect on (6) where globe 2 increases to -32 from -61 in (1), but globe 3 turns from having a significant positive effect of 285 to have a negative effect of -60. Globe 4 decreases to an effect of 181 and globe 5 only yields 12 users compared to 124 in (1). Overall, the result in (5) and (6) reveals a significant decrease in effect from the globe rating compared to what (1), (2), (3), and (4) suggests. The
r-squared remains extremely low for all regressions saying that the globe rating explain very little of the variation of the user holding data.
Table 4: Regression output using globe rating as the only independent variable
Dependent variable:
Robintrack users holding
OLS panel
linear
(1) (2) (3) (4) (5) (6)
Globe rating (categorical) 85.583*** 84.861*** 42.176***
t = 29.580 t = 43.480 t = 19.306
Globe 2 dummy -61.100*** -71.060*** -32.313***
t = -11.811 t = -16.946 t = -12.307
Globe 3 dummy 285.499*** 276.735*** -60.676***
t = 25.815 t = 81.767 t = -20.608
Globe 4 dummy 234.830*** 226.540*** 181.038***
t = 17.824 t = 29.807 t = 21.111
Globe 5 dummy 124.310*** 121.128*** 11.901**
t = 12.019 t = 18.612 t = 2.294
Constant 309.276*** 411.479***
t = 47.429 t = 93.856
Observations 439,981 439,981 439,981 439,981 439,981 439,981
R2 0.001 0.003 0.001 0.003 0.0003 0.002
Adjusted R2 0.001 0.003 -0.001 0.001 -0.002 -0.0002
Note: *p<0.1; **p<0.05; ***p<0.01
The regressions exclude all rows that contains NaNs in all dependent and independent variables. It is done to match the number of observations to compare coefficients between regressions both in table 4 and table 5. All regression uses the Robintrack data as its dependent variable. (1) is a simple OLS regression with globe rating, which is a categorical variable, as its independent variable. (2) uses the globe rating as the regressor, now however treating each globe as a dummy variable to see the effect of each globe. Globe 1 is captured in the constant. (3) and (4) uses the same regressor as (1) and (2) respectively, but with entity fixed effect. (5) and (6) perform the same regression as (1) and (2) respectively, but with time fixed effect.
To expand upon this analysis, I added 2 additional independent variables, or more precisely control variables, namely the Morningstar star rating and ESG glossary dummy variable. I add these to check whether they influence the results from table 4 and to potentially account for an endogeneity problem due to omission of variables.
Table 5: Multiple regression output
Dependent variable:
Robintrack users holding
OLS panel
linear
(1) (2) (3) (4) (5) (6) (7)
Globe rating (categorical) 67.609*** 64.301*** 43.095***
t = 21.646 t = 49.262 t = 18.900
Globe 2 dummy -121.213*** -141.495*** -48.762***
t = -22.049 t = -27.766 t = -14.568
Globe 3 dummy 196.052*** 173.356*** -77.530***
t = 20.583 t = 88.331 t = -20.478
Globe 4 dummy 147.480*** 125.506*** 176.012***
t = 10.236 t = 22.920 t = 20.493
Globe 5 dummy 67.847*** 56.630*** 5.833
t = 6.400 t = 8.360 t = 1.238
Star rating 162.550*** 160.657*** 172.116*** 182.927*** 181.596*** -145.248*** -150.132***
t = 31.999 t = 32.428 t = 34.758 t = 66.874 t = 67.790 t = -17.734 t = -17.868 ESG glossary -492.382*** -450.050*** -438.877*** -563.806*** -525.753***
t = -66.727 t = -69.079 t = -72.691 t = -41.506 t = -38.759 Constant -155.710*** -31.448** 2.739
t = -11.940 t = -2.188 t = 0.190
Observations 439,981 439,981 439,981 439,981 439,981 439,981 439,981
R2 0.005 0.007 0.005 0.006 0.008 0.003 0.004
Adjusted R2 0.005 0.007 0.005 0.005 0.006 0.001 0.002
Note: *p<0.1; **p<0.05; ***p<0.01
The regressions are done excluding all rows that contains a NaN in all variables. (1) is a multiple regression including all independent variables, namely globe rating, star rating, and ESG glossary dummy variable. (2) performs the same regression as (1) using dummy variables for each globe rating. (3) Uses only star rating and ESG glossary as their independent variables. (4) and (5) replicates (1) and (2), respectively, using entity-fixed effect. (6) and (7) replicates (1) and (2), respectively, using time-fixed effect.
I first did a multiple OLS regression denoted (1) in table 5. From the simple OLS regressions denoted (1) in table 4, the globe rating decreased from 86 to 68. Star rating appears to have a higher significant effect on funds user holding of about 163. ESG glossary, surprisingly, appear to have a negative effect on a fund’s user holding with a significant effect of -492. I conducted the same regression now using dummy variables for each globe category,
of 1 now affect a fund’s user holding negatively rather than positively which was the case in regression (2) in table 4. Furthermore, a globe of 2 yields a negative effect on a fund’s user holding while a globe of 3, 4, and 5 yields a positive effect, however, a globe of 3 appears to provide the biggest positive change in user holding while a globe of 5 yields the lowest positive effect.
As I expected ESG glossary to have a positive effect, I performed an additional regression excluding globe rating to check whether the two variables suffer from
multicollinearity because they are trying give an indicator for the same hypothesis, whether Robinhood investors invest in sustainable funds. The coefficient is, however, still negative, and not far from its coefficient in (1) and (2) indicating multicollinearity is not significantly present. The entity fixed effect regression performed in (4) and (5) yields no big difference from (1) and (2). Lastly, I performed regression (1) and (2) with time fixed effect denoted (6) and (7) respectively. Accounting for time and the increase in user holding over the two-year period, 2018 to 2020, (6) suggest an increase in globe rating yields an increase of 43 investors on average, however, (7) reveals a globe of 2 and 3 yields a negative effect on user holding while only a globe rating of 4 and 5 significantly increases number of investors holding a fund. ESG glossary gets lost when performing the regression.
To be more confidence the result, I checked for imperfect multicollinearity for all independent variables. Multicollinearity happens when two or more regressors are highly correlated. High correlation between two or more regressors could lead to coefficients being estimated imprecisely, meaning a bigger standard error would be present than the normal distribution would assume. The check was done using the volatility inflation factor (VIF).
VIF indicates how much larger the standard error is compared to what it would be if the one of the variables would be uncorrelated to the other variables. The presence of
multicollinearity does not bias the estimated coefficient; however, it affects the t-statistic due
to larger standard error which would make it less likely the coefficient will be statistically significant.
Table 6: VIF analysis
A general rule of thumb is if VIF is higher or equal to 10, multicollinearity is present. In this case, no multicollinearity is present.
The VIF results from table 6 suggests no multicollinearity is present. According to the result from table 5, all independent variables appear to be significant at the 1 percent level except the globe 5 coefficient from time-fixed effect regression (7).
An important observation about all the regressions done, is that the r-squared, and more importantly for the multiple regressions the adjusted r-squared, is extremely low. What the r-squared tells you is how well the predictors explain the variation of the outcome
variable. A low r-squared therefore tells you that the independent variables included in the regression do not explain much of the variation in the dependent variable. This implies that there are other predictors than globe rating, star rating, ESG glossary and todays return that explain the variation in the user holding among funds. Usually, a low r-squared is seen as bad since the outcome variable was not explained fully. However, the purpose of this paper is not to explain the variation of users holding in funds on Robinhood but to whether investors on Robinhood invest in sustainable funds.
IV. Discussion
From the result in table 4, an increase in globe ratings has a significant positive effect on user holding of 86, and after controlling for time an increase of 42. A lower globe,
specifically 1 and 2, seems to on average yield a decrease in investors while a higher globe, globe of 4 and 5, on average increases user holding in a fund. This is relatable to what Hartzmark and Sussman (2019) found which was funds with a globe rating of 5 received
Globe rating Star rating ESG glossary
1.03 1.02 1.01
billion decrease in funds. This is also aligned with Morningstar’s findings that the median ESG fund lost 98,3% of its category benchmark while the average fund lost 104,8% against its category benchmark. A globe of 3 also appears to increase a funds user holding but after accounting for time it is revealed it has a slight negative effect.
In table 5, after adding star rating and ESG glossary dummy variable as control variables, the globe rating seems to have a lower average effect than revealed in table 4 with an average increase of 68 investors. However, after adjusting for time with time-fixed effect, the coefficient is 43, almost the same as the one found in table 4 with time-fixed effect. The specific effect of each globe changed as well. Hartzmark and Sussman (2019) also stated that a globe rating of 2, 3, and 5 do not significantly affect the inflow or outflow of funds in a fund, only the extreme rating of 1 and 5. My result, however, suggests that a rating of 2 has the greatest negative implication on a fund while a rating of 4 has the highest positive implication on a funds inflow of investors.
ESG glossary is highly negative coefficient implying a that including an ESG glossary affects the inflow of new investors negatively. This is surprising as one would expect the oppositive, that is a positive effect. As the result suggests a higher globe rating attracts or at least is correlated to a positive increase in users, and funds with ESG glossary increases exponentially the higher globe rating it have as showcased in figure 3, the coefficients might be subject to multicollinearity. This was checked both by omitting globe rating as an
explanatory variable to see if the coefficient of the ESG glossary changed significantly and by doing a VIF analysis. Both tests confirm no multicollinearity is affecting the result.
As mentioned in the introduction, Robinhood does not showcase the globe rating on their platform but is only readily available through a subscription which likely is only bought by the more active and wealthy investors on the platform. I wanted to add the number of Robinhood gold users in my analysis, but they do not publicly disclose this number. However,
unless a significant amount is in fact gold users, this alone should not yield a significant positive relation no matter the wealth of an investor, as most users on the platform are not wealthy given their average age of 31 years old and the crowd Robinhood attracts. Investors might be incentivized to invest in funds rated high globe in a more indirect which is hard to quantify and might be impossible to add as an explanatory variable. The result might be driven because of outliers, namely funds that has a significant number of investors as most funds included in this analysis has less than 1 000 investors. In fact, looking at table 1, the bottom 75% of funds in terms of users has less than 193 users. This is a huge difference from the top fund, SPY, with almost 119 thousand investors. One possible adjustment to the data one can do is to delete outliers or winsorize the data. However, I decided not to do that since the outliers are not extreme enough and dropping a few tickers or winsorizing the data would change the result significantly. Not to mention, those huge funds are important representations of what people hoards towards and do not serve as extreme events that I believe justifies deleting or winsorizing them in the context of this paper.
An explanation for the positive significant relation between star rating and user holding is that Robinhood investors are attention driven as found by Barber et al. (2021) that Robinhood users engage in more attention driven trading. For example, it is quite normal to look at the graph for instance and if they see a graph which have yielded high return in the past years, they are more likely to invest in it. The risk-adjusted return will be high unless extreme risk was present which correlates with a high star rating.
The negative ESG glossary is the most surprising one as I expected a fund that includes these glossaries to attract more investors contrary to funds that do not include them.
The naming likely attracts some investors due to attention induced trading (Barber et al., 2021) mentioned in the literature review, however, the result from the regression may be, again, affected by the few funds at the top. In table 3, it is shown that none of the top 10 funds
include ESG glossary. The funds which are popular and general, such as an index fund, likely attracts more investors as these funds are usually cheap because they are more passively managed, broad, has good return long-term, are consistent, and have existed for a long time.
For example, the funds SPY and QQQ which follows the S&P 500 and Dow Jones Industrial average, respectively. This result is not aligned with Barber et al. (2021) and could subject to omitted variable bias do too few additional control variables. While the name seems to have the opposite effect of what I was expecting and is significant, it could mean that the name is not a significant enough driver to convince one to invest, meaning, it is not the biggest attention driver. At least not significantly more than another fund who is not promoting sustainability such as JETS with a globe rating of 2 shown in figure 3.
Although my question was answered, it is subject to limitations. Due to the lack of other independent variables or control variables, and to the low explanation of the Robintrack variation, there are other drivers that might affect a Robinhood user’s decision making both inside and outside of the platform such as attention-grabbing top-movers (Barber et al., 2021), and for example influencers. Additionally, google top recommendations and the company owner of the funds such as SPDR, a subsidiary of S&P Global and creator of SPY, and Invesco, creator of QQQ, can be interesting control variables for future research. Therefore, the analysis is not clear from omitted variable bias which would lead to the variable being endogenous, resulting in the coefficient potentially being wrong. The analysis should not be subject to sample selection bias as the variables lost were due to the lack of data on the independent variables, namely the globe rating and the star rating, not the absence of data on the dependent variable, the Robintrack dataset. Although data was lost, it was randomly lost on my end, however, not completely randomly lost as the funds excluded from the regression is excluded due to not meeting the criteria to be rated by Morningstar.
V. Conclusion
This paper studied whether Robinhood users, who are mostly young and
inexperienced, favor investing in sustainability when investing in funds, or more specifically, exchange traded funds (ETFs) as Robinhood do not offer mutual funds on its platform. The concern for climate change and other social and governance aspects has been rising more and more over the last few decades which has built the demand for solutions and an ever-growing mindset of sustainability amongst the population, especially in the US.
I argue that investors on Robinhood favor investing in funds rated higher on
sustainability contrary to investing in lower ones. My analysis shows that users on Robinhood prefers investing in funds that is more sustainable. However, that is not the only factor that goes into the decision of an investor on the platform as they also care about the risk-adjusted return and the name of the fund.
For the future, to further the research on this topic, more explanatory variables should be added to check whether there are other drivers to Robinhood investors’ decisions. About 50% of investors on the platform are newcomers and the average age is 31 years, suggesting most investors on the platform are financial illiterate. Likely, many of them recognizes that, and seek help elsewhere, such as influencers on YouTube, TikTok, Instagram, etc., top google search recommending for what to invest in, the company name of fund creators.
For practical implications this insight can be used to predict future investor behavior on Robinhood specifically due to its rather unique investors, however, more research is needed to implement it practically and be sure of its result. This can also be useful insight for customer service workers working at Robinhood. To have a better understanding of the population, one would need data from other brokerages such as Charles Schwab, Fidelity, and Interactive Broker to capture a broader spectrum of people.
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