# The Effect of Product Recommendations on Online Investor Behaviors

Ruiqi Rich Zhu111Scheller College of Business, Georgia Institute of
Technology, Atlanta, Georgia 30308. E-mail:
<EMAIL_ADDRESS>Cheng He222Wisconsin School of
Business, University of Wisconsin-Madison, Madison, Wisconsin 53706. E-mail:
<EMAIL_ADDRESS>Yu Jeffrey Hu333Mitchell E. Daniels, Jr. School
of Business, Purdue University, West Lafayette, Indiana 47907. E-mail:
<EMAIL_ADDRESS>
Despite the popularity of product recommendations on online investment
platforms, few studies have explored their impact on investor behaviors. Using
data from a global e-commerce platform, we apply regression discontinuity
design to causally examine the effects of product recommendations on online
investors’ mutual fund investments. Our findings indicate that recommended
funds experience a significant rise in purchases, especially among low
socioeconomic status investors who are most influenced by these
recommendations. However, investors tend to suffer significantly worse
investment returns after purchasing recommended funds, and this negative
impact is also most significant for investors with low socioeconomic status.
To explain this disparity, we find investors tend to gather less information
and expend reduced effort in fund research when buying recommended funds.
Furthermore, investors’ redemption timing of recommended funds is less optimal
than non-recommended funds. We also find that recommended funds experience a
larger return reversal than non-recommended funds. In conclusion, product
recommendations make investors behave more irrationally and these negative
consequences are most significant for investors with low socioeconomic status,
which can amplify wealth inequality among investors in financial markets.

Keywords: Fintech, Product Recommendations, Behavioral Finance, Mutual Fund

##  1 Introduction

The number of online retail investors has surged. In 2020, U.S. online
investors bought an average of $1 billion in listed equities daily, marking a
fivefold rise from pre-2020 figures (Chiapetta et al. 2021). By Q3 2021,
equity market retail flow had nearly doubled since 2010 (Faery et al. 2022).
Online investment platforms like Robinhood and SoFi have also witnessed
tremendous growth (Fitzgerald 2021). These platforms, emulating e-commerce
sites, use features like product recommendations to help investors discover
good financial products and achieve high investment returns. However, in
countries like China and the U.S., regulations prevent them from offering
algorithm-driven personalized stock or fund recommendations unless provided by
a registered financial advisor. Therefore, many platforms adopt general
recommendations by highlighting products with strong past performance for all
investors, akin to e-commerce sites showcasing popular and highly-rated
products.

For platforms selling consumer goods, literature (De et al. 2010, Brynjolfsson
et al. 2011, Hosanagar et al. 2014, Goldfarb and Tucker 2019) has shown that
product recommendations can boost sales for the platform by highlighting
products’ ratings and sales (Adomavicius and Tuzhilin 2005), therefore
offering strong quality signals to consumers (Pathak et al. 2010). However,
for financial products, the efficacy of recommendations is uncertain. Studies
have demonstrated that historical returns do not reliably predict future
returns, so rational investors should not treat them as reliable quality
signals when purchasing financial products (Kahn and Rudd 1995, Clifford et
al. 2021, Barber et al. 2022). Thus, while product recommendations effectively
drive consumer goods sales by providing reliable quality signals, their
influence on investor behaviors and financial product sales, when based on
unreliable quality signals, remains unclear.

Furthermore, consumers typically have a similar perception of quality signals
for consumer goods (Zhao et al. 2013). In contrast, financial products elicit
varied perceptions of quality signals, depending on an investor’s knowledge
and experience (Havakhor et al. 2021). Unsophisticated investors, for
instance, often chase financial products with high historical returns and tend
to suffer losses due to trend-chasing (Barber et al. 2022). With this study,
we investigate specifically whether unsophisticated investors might be more
likely to adopt an online investment platform’s recommendations, as well as
whether they suffer diminished outcomes after purchasing the recommended
products. Recommender systems ideally should help reduce knowledge gaps among
consumers, by providing a reliable quality signal to less-educated consumers
(Oestreicher-Singer and Sundararajan 2012); we consider instead whether, in
financial markets, recommender systems actually might amplify wealth
inequality among investors, as a dark-side implication.

To address these possibilities, we collect a novel data set from a leading
global e-commerce platform that sells financial products directly to
investors.444The platform ranks among the top five global e-commerce
companies, according to GMV (Fabric 2021). This unique data set includes
investor-level mutual fund purchases, redemption, and investor profiles, rare
in prior literature.555Most studies of mutual funds focus on market-level
mutual fund inflows and outflows, without considering information about
individual investors who purchase the fund. The platform recommends top-
performing funds based on annual returns, a practice adopted by other
prominent platforms like Alipay, Tenpay, and TD Ameritrade. When investors
view a fund, the platform recommends the top five funds in the same fund type
as the viewed fund with the highest annual returns on the viewing date. Using
a sharp regression discontinuity design (RDD), we compare sales of funds that
just reach the annual return cutoff to be recommended with non-recommended
funds that narrowly miss this cutoff. Using this approach, we can look for
discontinuous jumps in fund purchases that follow discontinuous changes in the
fund’s recommendation status. Our findings show that on average, being
featured as a recommended fund increases purchases by 5.6%. Therefore, product
recommendations affect investors’ behaviors, even though they are not reliable
quality signals from a rational investor’s perspective.

Next, we investigate how investor characteristics impact the effect of product
recommendations. Notably, investors with lower socioeconomic status, (lower
education level, lower income level, and higher liabilities) are more
influenced by recommendations. In follow-up analyses, we calculate profits
(losses) experienced by investors when they purchase recommended versus non-
recommended funds using their purchase and redemption histories. Our analysis
reveals that investors tend to suffer significantly worse performance after
purchasing recommended funds. This worse performance is also most salient for
investors with low socioeconomic status. Thus, investors of low socioeconomic
status, who arguably should be better protected in financial markets, instead
tend to have worse investment outcomes when they follow product
recommendations provided by online investment platforms. This might amplify
wealth inequality between investors with varying socioeconomic statuses.

Finally, we investigate why purchasing recommended funds versus non-
recommended ones is associated with worse investment returns. First, we
observe that while investors’ fund consideration set size does not change,
there is a significant reduction in search time spent on researching funds
when purchasing recommended funds, leading to less informed investment
decisions. This phenomenon is most pronounced among low socioeconomic status
investors, who are also most responsive to product recommendations. Probing
further into the kind of funds that receive diminished search time by
investors, we find when investors purchase recommended funds (versus non-
recommended ones), they significantly reduce the time allocated to exploring
similar alternative funds that share the same fund type as the purchased
funds. However, this reduction does not extend to funds with different fund
types from the purchased fund. Additionally, our simulation analysis further
indicates that investors who purchase recommended funds tend to redeem at a
worse time than those who purchase non-recommended funds. Surprisingly, this
lapse in redemption timing is predominantly observed among low socioeconomic
status investors. Lastly, upon accessing fund future performance, data
indicates that recommended funds generally underperform their non-recommended
counterparts. Potential explanations for this underperformance may include
regression to the mean and initial price overreaction (De Bondt and Thaler
1985, Atkins and Dyl 1990, Huang et al. 2010).

Our study makes several contributions. First, we expand the existing body of
literature on the economic impacts of product recommendations. While prior
research has emphasized that product recommendations can boost sales by
offering reliable quality signals (Pathak et al. 2010), our study highlights
their influence on investor behaviors even in situations where the quality
signals are not reliable. Second, we contribute to the literature on the
potential dark side of recommendations by demonstrating how purchasing
recommended funds (versus non-recommended funds) is associated with worse
investment returns. This disparity in returns is particularly pronounced among
investors with low socioeconomic status who also tend to rely the most on
these recommendations. This exposes a disconcerting facet of product
recommendations in the realm of finance, indicating their potential to
encourage irrational behaviors and exacerbate economic inequality (Adomavicius
et al. 2013, Walsh 2020, Zhang et al. 2021).

Third, our study enriches the field of behavioral finance by utilizing
extensive individual-level data to investigate the influence of features like
product recommendations on investors’ purchasing decisions and investment
returns. Furthermore, we delve into subsequent behavioral changes and
potential explanations for worse performance when investors follow
recommendations by examining their behaviors in the stages of purchase and
redemption. When following product recommendations, investors spend
significantly less search time researching funds and also make less favorable
choices when it comes to redeeming their investments. These findings
contribute significantly to the behavioral finance literature, shedding light
on the adverse effects on investor behavior that can arise when technology
makes financial information more accessible and prominent to investors.
(Barber and Odean 2002, Clifford et al. 2021, Havakhor et al. 2021).

##  2 Literature Review

In reviewing existing literature related to the study of product
recommendations for financial products, we consider two main streams,
pertaining to (1) the economic effects of product recommendations and (2)
behavioral finance.

###  2.1 Economic Effects of Product Recommendations

Existing literature on the economic effects of product recommendations
suggests that they increase firm sales and revenues (De et al. 2010, Pathak et
al. 2010, Brynjolfsson et al. 2011, Hosanagar et al. 2014, Kawaguchi et al.
2019, Lee and Hosanagar 2021). De et al. (2010) show that product
recommendations can increase sales of both promoted and non-promoted products.
Pathak et al. (2010) point out that the strength of the recommendation is
relevant to the positive effect on sales. Product recommendations also have a
volume effect that encourages consumer consumption and increases both views
and final conversion rates (Hosanagar et al. 2014, Lee and Hosanagar 2021).
Ursu (2018) find recommendations to decrease search costs and increase the
probability of a match with a seller. Paper by Lee and Hosanagar (2021)
provide a detailed literature review on the economic effects of product
recommendations.

Whereas prior work focuses on the effect of product recommendations for
consumer goods such as books and clothes, we extend prior research to
financial products, which are distinct from consumer goods. Though product
recommendations serve as reliable quality signals for consumer goods using
past ratings and sales, their reliability is questionable for financial
products due to their reliance on historical returns. The value of financial
products lies in unobserved future returns, which cannot be reliably predicted
using historical returns (Kahn and Rudd 1995, Jain and Wu 2000, Choi and Zhao
2020). Thus, the relationship between recommendations and increased sales is
uncertain for financial products. Our study aims to fill this gap by analyzing
how product recommendations influence online investor behaviors.

Furthermore, product recommendations can unintentionally skew consumer
judgment and choices when purchasing consumer goods. Adomavicius et al. (2013)
suggest that product recommendation ratings can shape consumers’ preferences,
often causing them to place undue importance of these suggestions. Even when
presented with random recommendations, consumers’ perceived value of a product
can significantly shift (Adomavicius et al. 2018). Moreover, consumers are
found to be less sensitive to price for prominently recommended products,
which might reduce their overall benefits from the purchase (Zhang et al.
2021). Additionally, there’s an over-reliance on algorithm-generated
recommendations, even if they are inferior (Banker and Khetani 2019). Our
study extends this literature on the potential pitfalls of product
recommendations, specifically in the context of financial product investments.
We also identify several negative impacts of product recommendations that make
investors behave more irrationally and realize worse investment outcomes. As
our research centers on the economic effect of product recommendations, we do
not discuss the rich literature that studies the technical design of
personalized recommendations (Shen and Miguel Villas-Boas 2018, Tong et al.
2020, Ke et al. 2022). For a detailed literature review on the taxonomy and
technical details of recommender systems, please refer to the literature
review by Adomavicius and Tuzhilin (2005) and Roy and Dutta (2022).

###  2.2 Behavioral Finance

Behavioral finance literature highlights factors that shape investment
decisions. Investors often opt for financial products that stand out due to
exceptional performance (Barber and Odean 2008), marketing efforts (Barber et
al. 2005), customer satisfaction rating (Sorescu and Sorescu 2016), product
promotion (Jiang et al. 2021), herding by peers (Zhang and Liu 2012), or
visible fees (Barber et al. 2005). For instance, studies show that investors
gravitate toward stocks or mutual funds that are currently in the media
spotlight, indicating that media presence reduces the effort investors have to
make when choosing where to invest (Sirri and Tufano 1998, Tetlock 2007).
Additionally, investments with notably volatile returns tend to attract more
attention (Clifford et al. 2021). Our research delves deeper, exploring the
impact of automated product recommendations on online platforms. These
recommendations not only shift investor attention but also continuously update
what products are recommended. We utilize detailed investor-level data,
allowing us to analyze recommendations’ effects on individual investors. This
in-depth approach helps us understand how different types of investors respond
and offers insights into their decision-making processes and investment
outcomes, areas not extensively covered in previous studies.

Furthermore, behavioral finance studies explore how investor characteristics
impact their investment decisions. Studies by Barber and Odean (2001) suggest
men, driven by overconfidence, trade more often than women and reduce their
net gains. Cognitive abilities also play a role: high-IQ investors tend to
avoid funds with high management fees (Grinblatt et al. 2016). Furthermore,
investors who are younger, less educated, or have lower incomes often allocate
a bigger chunk of their wealth to individual stocks, leading to frequent
trading and suboptimal results (Anderson 2013). Utilizing our rich data, we
categorize investors by socioeconomic status through their demographic
information. Our findings build upon previous research, highlighting that
investors of low socioeconomic status are most influenced by product
recommendations, often to their detriment. These investors often invest based
on recommendations without comprehensive research, resulting in significant
investment losses.

Our work is also related to research that studies how expert recommendations
influence fund flows. A study by Cookson et al. (2021) examines expert-curated
mutual fund suggestions on investment platforms. Given that these expert
recommendations are often viewed as reliable quality signals, they noticeably
boost sales. In contrast, the recommendations in our study are purely based on
past performance and lack human intervention, making them less reliable
signals for investors. Thus, the impact of such automated product
recommendations is unclear. While Cookson et al. (2021) illustrate the link
between expert advice and sales, our method employs a regression discontinuity
design to understand the direct causal effect of these automated
recommendations on investor behaviors in investment platforms.

Another relevant work by Barber et al. (2022) highlights how Robinhood app’s
unique “Top Mover” list drives investors to herd and buy attention-grabbing
stocks. As the “Top Mover” list displays stocks with notable price
fluctuations, it sends a different signal from the product recommendations in
our study, which rely on historical returns. While this paper emphasizes the
impact of app features on sales using broad order flow data, our research
delves deeper, examining the granular effects of recommendations on individual
investors. Our rich dataset, encompassing both purchase and redemption
histories, allows us to study both purchasing behaviors and individual
performance outcomes. This granularity facilitates exploration into how
product recommendations might differentially influence investor return
outcomes across socioeconomic statuses. Additionally, by leveraging click
stream data, we scrutinize the intricacies of investors’ fund-searching and
decision-making processes when purchasing funds.

##  3 Institutional Background and Data

###  3.1 Recommendation in Online Investment Platforms

The surge in online retail investor participation has led investment platforms
to borrow features such as product recommendations from traditional
e-commerce. They employ these features to improve investors’ experience with
the goal of simplifying investors’ decision-making process, helping investors
discover promising financial products, and empowering them to achieve
attractive investment returns.666https://www.investopedia.com/best-online-
brokers-4587872 However, investment platforms cannot provide personalized
recommendations to investors the same way as e-commerce platforms
do.777https://srinstitute.utoronto.ca/news/the-art-and-science-of-recommender-
systems-insights-from-spotify Personalized recommendations for financial
products, like stocks or mutual funds, face stringent regulations on
investment platforms. In the U.S., personalized stock and financial
recommendations are generally restricted to professional investment advisers
who have met the requirements set forth by regulations and laws. For instance,
the Investment Advisers Act of 1940 requires that investment advisers register
with the SEC and adhere to regulations designed to protect
investors.888https://www.investopedia.com/terms/i/investadvact.asp Several
anti-fraud provisions also restrict the provision of personalized investment
advice unless the provider is properly qualified and registered. This includes
Sections 206(1) and 206(2) of the Investment Advisers Act and Section 10(b)
and Rule 10b-5 of the Securities Exchange Act of
1934.999https://www.sec.gov/about/about-securities-laws

Like the U.S., China enforces strict regulations concerning financial product
recommendations. The financial markets in China are regulated by various
bodies, including the China Securities Regulatory Commission (CSRC), the
People’s Bank of China (PBOC), and the State Administration of Foreign
Exchange (SAFE), among others. As stipulated by the Securities Law of the
People’s Republic of China, providing investment advice in relation to
securities without proper qualifications or outside of approved institutional
frameworks could lead to penalties.101010http://www.npc.gov.cn/englishnpc/
Online platforms aren’t exempt; unauthorized individuals providing financial
advice can be held accountable for disseminating investment advice without a
proper license. Similarly, regions like the European Union maintain stringent
controls on financial product
recommendations,111111https://finance.ec.europa.eu/regulation-and-
supervision/financial-services-legislation/implementing-and-delegated-
acts/markets-financial-instruments-directive-ii_en mirroring the regulatory
standards set in both the U.S. and China.

Investment platforms, due to those regulatory constraints, often resort to
ranking-based product recommendation designs that comply with financial
regulations. Such designs showcase financial products, like mutual funds, with
the highest historical returns to all investors. While not directly advising
investors, highlighting products with high historical returns implicitly sends
quality signals that those are “good” products. This indirect method
effectively serves as product recommendations. Major investment platforms,
including TD Ameritrade in the U.S. and Alipay and East Money in China, use
this approach, recommending products with notable historical returns to
investors.

###  3.2 Data and Empirical Context

We collect a unique data set from a leading global e-commerce platform that
operates on a business-to-consumer (B2C) model. The financial firms on the
platform sell various products, such as mutual funds and insurance, as well as
provide services like crowdfunding and loans directly to investors. In this
study, we focus on mutual funds, one of the most popular financial products,
to examine the impact of recommendations.

####  3.2.1 Product Recommendations Design

Similar to many online investment platforms, this platform highlights mutual
funds that have yielded high historical returns. When investors view a fund
(referred to as “the focal fund” in our study), the platform suggests five
other funds on the right side of the focal fund detail page. All these
recommended funds belong to the same fund type as the focal fund and are
listed in descending order based on their annual returns on the focal fund
view date. Our data includes five types of funds: hybrid, stock, bond, money
market, and QDII.121212QDII (Qualified Domestic Institutional Investor) funds
enable domestic investors to invest in securities in foreign markets. More
details at https://www.investopedia.com/terms/q/qdii.asp As depicted in Figure
1, funds that rank fifth or higher in terms of annual returns are recommended.
For example, if the focal fund is a hybrid fund, then the fourth fund
displayed would represent the hybrid fund with the fourth highest annual
return among all hybrid funds on the focal fund view date.

Figure 1: Product Recommendation Illustration

####  3.2.2 Investor Transactions

Our data set comprises a random sample of 12,441 individual investors, each
identified by a unique encrypted ID. We have their fund transaction histories
spanning from January 2015 to December 2016. For each transaction, we note the
date, fund code, investor ID, transaction type (purchase or redemption), and
order amount.

####  3.2.3 Mutual Fund Attributes

Our dataset encompasses comprehensive information on 2,999 funds available on
the platform from January 2015 to December 2016. Details include the fund
code, name, fund type, fund establish date, asset size, management fees,
purchase fees, custodial fees, fund risk levels, and a dummy variable
indicating valuable fund brand.131313Determined by Easymoney, the most
prominent fund research website in China We also construct the variable fund
tenure, as the difference between the fund transaction date and fund establish
date, measured in days. The data also provides fund tenure and performance
metrics including weekly, monthly, three-month, annual, and lifetime
returns141414Measure of how much the value of the fund has increased since the
fund is established on each day.

####  3.2.4 Investor Characteristics

We also obtain demographic information for each investor from a mandatory
questionnaire when the investor creates the account, as summarized in Table A1
in Appendix A. This information includes investors’ levels of income,
education, and liabilities. In the following sections, we use this information
to segment investors into different socioeconomic status groups. An in-depth
discussion on this segmentation can be found in Section 5. Our investor sample
closely resembles the mutual fund investor demographics in
China151515https://edu.efunds.com.cn/c/2022-01-14/497432.shtml though with a
slightly higher percentage of low socioeconomic status investors.

##  4 Effect of Product Recommendations on Fund Purchases

In this section, we examine the effect of product recommendations on fund
sales. A critical challenge in identifying the causal effects of product
recommendations stems from the potential intrinsic differences between
recommended and non-recommended funds. A direct comparison between the two
might yield biased estimation results. To address this, we employ a regression
discontinuity design (RDD) that leverages the specific design in which the
platform recommended funds (details in Section 3.2.1). We further validate our
findings through a series of robustness checks.

###  4.1 Empirical Strategy: Regression Discontinuity Design

Figure 2: RDD Illustration

In our study, the top five performing funds based on annual returns are
recommended to investors. We exploit this product recommendation design and
narrow our focus to funds that rank third to fifth by annual return, i.e.,
funds that barely make the recommendation cutoff, and funds that rank sixth to
eighth, i.e., funds that just fall short of this threshold. This bandwidth is
optimal in mean squared error and funds within this window exhibit better
covariate balance and overlap (Cattaneo and Titiunik 2022). To strengthen our
findings, we also conduct a robustness check with a narrower bandwidth and
find consistent results. As illustrated in Figure 2, funds ranking third to
fifth get recommended to the investor; funds ranking sixth to eighth do not.

To formally evaluate the influence of product recommendations on fund
purchases, we employ the following log-linear model for our analysis:

$\displaystyle\begin{aligned} \log{(NumberOfPurchases_{it})}=&\beta\ast
Recommended_{it}+f(AnnualReturn_{it})+\\\ &\theta\ast
X_{it}+\alpha_{i}+\gamma_{t}+\xi_{t}+\epsilon_{it}\end{aligned}$ (1)

Our unit of analysis is fund ($i$) – day ($t$) level. The variable
$Recommended_{it}$ is a binary indicator set to 1 if fund $i$ ranks third,
fourth, or fifth in annual returns among funds with the same type as fund $i$
on day $t$. Conversely, $Recommended_{it}$ is 0 if fund $i$ ranks sixth,
seventh, or eighth in annual returns among funds with the same type as fund
$i$ on day $t$. log($NumberOfPurchases_{it}$) denotes the logarithm of the
number of purchase transactions for fund $i$ on day $t$. The function
$f(AnnualReturn_{it})$ represents a local polynomial function of the fund’s
annual return, up to the degree of 2,161616The choice of this degree is based
on obtaining the highest BIC. that allows for a smooth relationship between
annual return and fund purchases around the cutoff. $\alpha_{i}$ corresponds
to fund type fixed effects, which capture variations in sales among funds
sharing the same fund type. $\gamma_{t}$ are month fixed effects, capturing
seasonal trends and market fluctuations that impact sales of all funds.
$\xi_{t}$ are day-of-week fixed effects that incorporate variations in fund
sales on different days in the week for all funds. Through $X_{it}$, we
control fund attributes including management fees, purchase fees, custodial
fees, tenure, a dummy for valuable fund brand, asset size, and one-week,
three-month, six-month, and lifetime returns. These control variables capture
potential impacts of fund attributes on the number of fund purchases. Finally,
$\beta$ is our parameter of interest. Given the identifying assumption that
other determinants of fund purchases are continuous at the cutoff and the
running variable annual return cannot be manipulated, we use $\beta$ to
capture product recommendations effects on fund purchases (Thistlethwaite and
Campbell 1960, Lee and Lemieux 2010, Goldfarb et al. 2022, Cattaneo and
Titiunik 2022).

###  4.2 Validity of Identification Assumptions

Before we present the estimation results, we first test the validity of our
RDD design by examining two key identification assumptions: the continuity of
observable variables and the manipulation test.

Continuity of Observable Variables One of the foundational assumptions
underpinning RDD is that while factors like annual returns, management fees,
purchase fees, or asset size, vary continuously around the cutoff, becoming a
recommended fund to investors is discontinuous at the fifth rank in annual
returns. We test this continuity assumption by plotting the average fund
annual return, lifetime return, asset size, management fees, purchase fees,
custodial fees, fund risk levels, and fund tenure by fund ranking from third
to eighth during our sample period. Figure 3 presents a visual representation
of our results. We observe that annual returns decrease continuously and all
the fund attributes also vary smoothly around the cutoff, thus supporting the
continuity assumption.

Figure 3: Identification Assumption Check Figure 4: Manipulation Test

Manipulation Test Another challenge of RDD design is the manipulation of the
running variable. If the running variable, i.e. annual return, is manipulated
in our RDD design, the quasi-random assignment of a fund’s recommendation
status would be compromised and the running variable will change
discontinuously at the cutoff, thus violating the continuity assumption and
undermining the validity of our RDD-derived estimates. Given that annual
returns, as opposed to other variables like management fees, are outside the
direct control of the funds, they are less susceptible to manipulation.
Consequently, it’s reasonable to assume that funds cannot manipulate their
recommendation status. To more rigorously test for manipulation, we perform
the manipulation test on the annual return using the local polynomial density
estimators proposed by Cattaneo et al. (2020). Results are presented in Figure
4, with the middle line being the cutoff point. Given the cutoff value that
determines a fund’s recommendation status changes daily, we use the average
daily value of annual returns for funds ranked in fifth and sixth place to
approximate the cutoff value. In Figure 4, we observe a large overlap of
annual return density estimation in the middle. Results also show the annual
return density difference around the cutoff to be insignificant ($p=0.398$)
and the change in density is smooth and continuous. This suggests there is no
statistical evidence of systematic manipulation of annual returns.

###  4.3 Estimation Results

Table 1 contains the estimated effect of recommendations on online investors’
purchase behaviors. The estimates in Column (1) suggest that funds featured by
product recommendations prompt more purchases and the effect is statistically
significant (coefficient = 0.055; $p<0.01$). The log-linear model indicates a
5.6%171717According to the log-linear model, the coefficient translates to
100*(exp(0.055) - 1)% change. increase in the number of purchases of
recommended funds, compared with non-recommended funds. As shown in Columns
(2) and (3), this result remains consistent when we gradually relax our model
specification by removing control variables and fixed effects. This finding
suggests that product recommendations that use historical returns exert
influence in financial product domains. However, as prior literature has
established, historical returns are not reliable quality signals in financial
markets (Kahn and Rudd 1995, Jain and Wu 2000, Choi and Zhao 2020). As a
result, investors still follow recommendations even though such
recommendations based on historical return don’t provide reliable quality
signals from the perspective of rational investors.

Table 1: Effect of Recommendation on Fund Purchases Dependent Variable: | (1) | (2) | (3)
---|---|---|---
log(NumberOfPurchases) |  |  |
Recommended | 0.055*** | 0.058*** | 0.044***
| (0.006) | (0.006) | (0.007)
Polynomial Degree | 2 | 2 | 2
Fund Type Dummies | Yes | Yes | No
Month Dummies | Yes | Yes | No
Day of Week Dummies | Yes | Yes | No
Control Variables | Yes | No | No
Number of Observations | 21,834 | 21,834 | 21,834
$R^{2}$ | 0.153 | 0.081 | 0.010
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The dependent variable in Columns
(1) - (3) is log(NumberOfPurchases). Control variables include management
fees, purchase fees, custodial fees, asset size, dummy for valuable fund
brand, fund past returns, fund risk level, and fund tenure. Robust standard
errors are in parentheses.

###  4.4 Robustness Checks

To strengthen the causal interpretation of our results, we conduct several
robustness checks, including (1) a falsification exercise, (2) alternative
bandwidths, (3) alternative measurements, and (4) a different model
specification. Robustness check results are presented in Appendix B and they
align with our main findings, as we detail next.

####  4.4.1 Falsification Exercise

To rule out an alternative explanation that any funds with higher rankings
prompt more purchases, we perform a falsification exercise in which funds that
rank fourth are the recommended funds and funds that rank fifth represent the
“placebo” non-recommended funds. Since both funds are recommended, funds that
rank fourth should not account for significantly more purchases than funds
that rank fifth. The results, as presented in Column (1) of Table B1 in
Appendix B, indicate that this placebo recommendation effect is not
statistically significant. We perform a similar placebo assessment of funds
that rank in sixth and seventh place. As the results in Column (2) of Table B1
in Appendix B show, the effect of “placebo” product recommendations remains
insignificant. The falsification test thus strengthens our causal
interpretation of the main effect estimation.

####  4.4.2 Alternative Bandwidths

Because funds that are closer in rank might be more similar than funds that
are farther in rank, we check the robustness of our results with a narrower
bandwidth. That is, we include only the funds that rank in fifth place as
recommended and those that rank in sixth place as non-recommended. The
estimation result, provided in Column (3) of Table B1 in Appendix B, shows
that the recommendation effect on the number of purchases remains positive and
statistically significant. We also perform similar analyses by including funds
that rank in fourth and fifth place as recommended funds and funds that rank
in sixth and seventh place as non-recommended funds. Results are provided in
Column (4) of Table B1 in Appendix B and the results are positive and
statistically significant. Thus, our results are robust to the choice of
narrower bandwidths.

####  4.4.3 Alternative Measurements

Besides the number of purchases, there are alternative measures to represent
the sales of funds such as the number of unique buyers, the total units of the
fund sold, and the cash amount of the fund sold. To test the sensitivity of
our dependent variable, we use these three alternative measurements to examine
the effect of product recommendations on fund sales.

Specifically, $\log{(NumberOfInvestors_{it})}$ is the logarithm of the number
of unique investors who purchase fund $i$ on day $t$;
$\log(PurchaseAmount_{it})$ is the logarithm of the total units of fund $i$
purchased on day $t$; $MoneyAmount_{it}$ is the inflow of funds in cash amount
for fund $i$ on day $t$. Our regression model is the same as in Equation 1 but
we replace the dependent variable with these three alternative measurements.
The estimation results are presented in Table B2 in Appendix B. The
coefficients of $Recommended_{it}$ are all positive and significant in these
three models, in line with our primary results.

####  4.4.4 Different Model Specification

Since the dependent variable log($NumberOfPurchases_{it}$) in our study is
count data, to test the sensitivity of model specification, we use the Poisson
regression model instead of the linear model to check whether our results
remain consistent (Consul and Famoye 1992). We present the results in Column
(1) of Table B3 in Appendix B where we use the same formula as in Equation 1.
The coefficient for $Recommended_{it}$ remains positive and statistically
significant.

##  5 Effect of Product Recommendations by Investor Socioeconomic Status

The results thus far show that on average, product recommendations affect
investors’ purchase behaviors. However, different investors might perceive
quality signals provided by product recommendations differently. Therefore, in
this section, we examine the heterogeneous effects of product recommendations
across investors with different socioeconomic statuses (SES).

###  5.1 Investor Segmentation

In our study, we collect investors’ information on income, education, and
liability,181818The summary statistics of these variables are presented in
Table A1 in Appendix A which are the three most essential characteristics to
determine investors’ SES level (Calvet et al. 2009). In detail, each investor
has a score for income, education, and liability respectively. These scores
range from 0 to 2, with a higher value indicating a higher level for variables
of income and education. To make it consistent (i.e. a higher value means
better wellness), a higher value indicates a lower level for the variable of
liability. We sum up the scores of these three variables for each investor to
represent the overall SES score. Then we divide investors into low, medium,
and high SES levels by using cutoffs at 1/3 and 2/3 of the highest sum of the
scores. A summary of the three investor groups, characterized by varying
levels of SES, appears in Table 2.

Table 2: Investor Segmentation Investors Group | # of Investors | Income | Education | Liability
---|---|---|---|---
Low SES | 9,657 | 0.035 | 0.022 | 0.978
Medium SES | 1,855 | 1.075 | 1.009 | 1.380
High SES | 929 | 1.904 | 1.273 | 2.000
Notes: Investor segmentation is based on the combined scores of investors’
income, education, and liability levels. Specific values for the scores are
presented in Table A1. Investors are segmented into three groups using cutoffs
at 1/3 and 2/3 of the highest sum of the scores. Columns (3) - (5) presents
the average income, education, and liability scores by investor SES group.

###  5.2 Heterogeneous Effects of Product Recommendations by Investor
Socioeconomic Status

Since investors with diverse SES might perceive the quality signal from
product recommendations differently, here we examine how the effect of product
recommendations varies across investor SES. We perform the analyses similar to
Equation 1 but replace our dependent variable with
log($NumberOfPurchases_{ijt}$), the logarithm of the total number of purchases
for fund $i$ on the day $t$ from investor group $j$. Group $j$ refers to
investors of low, medium, and high SES respectively. The estimation results
are presented in Table 3. Columns (1) - (3) show the estimation results by
using sub-samples of investors with low, medium, and high SES respectively.
Results suggest that the purchase behaviors of all three investor groups are
significantly influenced by recommendations provided by the platform despite
varying degrees of magnitude. The recommendation effect is strongest for
investors with low SES, as indicated in Column (1), suggesting that they are
more likely to purchase recommended funds. On average, low SES investors
engage in 4.1% more recommended fund purchases than non-recommended funds.
With the Chow test, we find that this number is significantly larger than
investors with medium-level ($F=5.04;p<0.05$) and high-level SES
($F=8.68;p<0.01$). Low SES investors are least likely to possess enough
knowledge to choose funds and make rational investment decisions. As a result,
compared with medium and high SES investors, investors of low SES are more
likely to perceive recommendations by the platform as a strong quality signal
that should inform their purchase decision.

Table 3: Heterogeneous Effects of Recommendation by Investor SES Dependent Variable: | (1) | (2) | (3)
---|---|---|---
log(NumberOfPurchases) | Low SES | Med SES | High SES
Recommended | 0.040*** | 0.020*** | 0.016***
| (0.005) | (0.003) | (0.003)
Polynomial Degree | 2 | 2 | 2
Fund Type Dummies | Yes | Yes | Yes
Month Dummies | Yes | Yes | Yes
Day of Week Dummies | Yes | Yes | Yes
Control Variables | Yes | Yes | Yes
Number of Observations | 21,834 | 21,834 | 21,834
$R^{2}$ | 0.147 | 0.079 | 0.070
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The dependent variable in Columns
(1) - (3) is log(NumberOfPurchases). Control variables include management
fees, purchase fees, custodial fees, asset size, dummy for valuable fund
brand, fund past returns, fund risk level, and fund tenure. Columns (1) - (3)
include analysis results using sub-samples of investors with low, medium, and
high SES respectively. Robust standard errors are in parentheses.

##  6 Effect of Following Product Recommendations on Investment Return

Our study indicates that investors follow product recommendations using
historical returns. However, existing literature establishes that historical
returns cannot reliably predict future returns (Kahn and Rudd 1995, Clifford
et al. 2021, Barber et al. 2022). Therefore, we question whether purchasing
recommended funds leads to better or worse investment return outcomes for
investors. In this section, we examine the effect of following product
recommendations on investment returns.

###  6.1 Investment Return Outcome after Purchasing Recommended versus Non-
Recommended Funds

To compare the investment return outcomes after investors purchase recommended
versus non-recommended funds, we construct our dependent variable
$InvestReturn_{ijt}$ as follows. Since an investment consists of multiple
purchases and redemption, we cannot measure return using a single transaction
and we instead define an investment session to calculate the investment
return. Each investment session starts with an investor purchasing a fund.
After that purchase, we monitor the investors’ follow-up purchases,
redemption, and the entire net holding history of the purchased fund in the
investment session. The investment session concludes when the net holdings of
the purchased fund hit zero, with the final investment return calculated using
all the transactions in the session.191919For investment sessions that do not
end on the last day of the sample period, we presume all remaining funds are
sold on December 31, 2016, our sample’s final date. That gives us an
investor’s paper loss for that investment session. In the following models, we
also include an indicator to show whether the investment session ends before
the last day of the sample period. In summary, $InvestReturn_{ijt}$ is
calculated as

$\displaystyle\begin{aligned}
InvestReturn_{ijt}=\frac{\sum_{T}NAV_{T}*Amt_{T}*Redemption_{T}-\sum_{T}NAV_{T}*Amt_{T}*Purchase_{T}}{\sum_{T}NAV_{T}*Amount_{T}*Purhcase_{T}}\end{aligned}$
(2)

The subscript T covers all transactions within the investment session.
$NAV_{T}$ is the net asset value of the fund and $Amt_{T}$ is the units of
funds involved in the transaction. $Redemption_{T}$ and $Purchase_{T}$ are
dummies denoting whether the transaction is a fund purchase or redemption. The
final investment returns are presented in basis points. Our unit of analysis
is fund ($i$) - investor ($j$) - day ($t$). $InvestReturn_{ijt}$ denotes the
investment return for an investment session starting with fund $i$ being
purchased by investor $j$ on day $t$. If the fund is recommended on the day it
is first purchased, then we define the fund in this investment session as a
recommended fund.

To investigate the investment return difference between recommended and non-
recommended funds, we adopt a similar RDD approach in Section 4 by comparing
the funds ranked third to fifth with those ranked sixth to eighth. We use the
following model to formally estimate the effect of following product
recommendations on the investment return:

$\displaystyle\begin{aligned} InvestReturn_{ijt}=&\beta\ast
Recommended_{it}+Group_{j}+f(AnnualReturn_{it})+\\\ &\theta\ast
X_{it}+\alpha_{i}+\gamma_{t}+\xi_{t}+\epsilon_{ijt}\end{aligned}$ (3)

$Group_{j}$ are the investor SES group dummies. The fixed effects and control
variables are the same as in Equation 1.

The estimation results, presented in Column (1) of Table 4, suggest that
purchasing recommended funds is associated with 0.89 lower basis points in
returns. Such a difference is also statistically significant ($p<0.05$). On
average, investors appear to perform worse when they purchase recommended
funds (versus non-recommended funds). This finding is in direct conflict with
the investment platforms’ goals of using recommendations to help investors
discover good financial products and achieve higher investment
returns.202020https://www.investopedia.com/best-online-brokers-4587872 Instead
of helping investors, investors actually perform worse when following product
recommendations.

Table 4: Effect of Recommendation on Investment Return Dependent Variable: | (1) | (2) | (3) | (4)
---|---|---|---|---
InvestReturn | All Investors | Low SES | Med SES | High SES
Recommended | -0.895** | -1.127** | -1.105 | 1.597
| (0.452) | (0.463) | (0.793) | (1.979)
Polynomial Degree | 2 | 2 | 2 | 2
Investor Group Dummies | Yes | No | No | No
Fund Type Dummies | Yes | Yes | Yes | Yes
Month Dummies | Yes | Yes | Yes | Yes
Day of Week Dummies | Yes | Yes | Yes | Yes
Control Variables | Yes | Yes | Yes | Yes
Number of Observations | 4,618 | 3,221 | 886 | 511
$R^{2}$ | 0.135 | 0.118 | 0.193 | 0.491
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The dependent variable in Columns
(1) - (4) is InvestReturn. Control variables include management fees, purchase
fees, custodial fees, asset size, dummy for valuable fund brand, fund past
returns, fund risk level, and fund tenure. Columns (2)-(4) show the estimation
results for the sub-samples of investors with low, medium, and high SES
respectively. Robust standard errors are in parentheses.

###  6.2 Heterogeneous Effect of Product Recommendations on Investment Return
by Investor SES

Our previous results consistently indicate that the product recommendations
effect is heterogeneous across investors with different SES. These
heterogeneous effects in turn might lead to heterogeneous investment returns
across investors with different SES. We examine such heterogeneous effects
using Equation 3 and perform sub-sample analysis, only considering investment
sessions conducted by investors with low, medium, and high SES respectively.

The estimation results are presented in Columns (2) - (4) of Table 4. The
results show that only investors with low SES suffer significantly worse
performance when purchasing recommended funds. For investors of medium and
high SES, purchasing recommended funds is not associated with significantly
worse returns than purchasing non-recommended funds. Along with our prior
results that investors with low SES are more likely to purchase recommended
funds, these findings imply that low SES investors rely most on product
recommendations and are also hurt the most by recommendations. This potential
dark side of product recommendations is worrisome, in that investors who need
recommendations the most also suffer the most, which could amplify the
problematic wealth inequality among investors in financial markets.

##  7 Subsequent Behavioral Changes and Potential Explanations on Worse
Performance When Following Recommendations

In this section, we delve into investors’ subsequent behavioral changes after
following product recommendations and provide three potential explanations,
supported by empirical data, as to why purchasing recommended funds often
results in sub-optimal performance, especially for low SES investors. First,
we examine search behavior differences in the purchase stage, comparing search
efforts when investors purchase recommended versus non-recommended funds. Then
we investigate the difference in fund redemption timing between investors who
purchase recommended funds and those who purchase non-recommended funds.
Finally, we evaluate and compare the future performance between recommended
and non-recommended funds as another possibility.

###  7.1 Behavioral Differences in the Purchase Stage

When investors apply greater effort, they are more likely to make informed and
rational investment choices (Barberis and Thaler 2003). However, the presence
of product recommendations might lead investors to diminish their search
efforts, potentially culminating in less optimal purchasing decisions. Thus,
it is critical to compare differences in search behaviors when investors
purchase recommended and non-recommended funds. Using investors’ individual-
level click stream data, we examine the impact of following product
recommendations on fund search efforts.

####  7.1.1 Overall Search Efforts in the Purchase Stage

We use the total search time and size of the fund consideration set to
represent investors’ search efforts respectively. To calculate the total
search time, we monitor the fund click history of investors and use the
temporal gap between successive clicks to estimate the time devoted to each
individual fund measured in seconds. Then we calculate the total search time
as the cumulative duration an investor devotes to all funds within the three
days preceding any fund purchases. To examine the impact of following fund
recommendations on investors’ total search time, we adopt the model similar to
Equation 3 but substitute the dependent variable with the total search time.
The results presented in Column (1) of Table 5 reveal that investors tend to
spend significantly less total search time when purchasing recommended funds
as opposed to non-recommended ones. This suggests that product recommendations
may inadvertently curtail the thoroughness of investors’ overall search
efforts.

Next, we examine how investors’ fund consideration set size changes when
following fund recommendations. Specifically, we measure consideration set
size by counting the number of unique funds investors engage with three days
prior to making a purchase. We adopt the model similar to Equation 3 and the
findings in Column (2) of Table 5 indicate that, on average, there’s no
statistically significant variation in the number of distinct funds investors
consider when purchasing recommended funds compared with non-recommended ones.
This implies that while product recommendations tend to diminish the time
investors spend on research, they don’t notably influence the size of funds
within an investor’s consideration set.

As investors decrease their total search time but do not change the
consideration set size when following product recommendations, it is expected
that the average time investors dedicate to each individual fund also
decreases. To formally test this conjecture, we construct the average search
time per fund by dividing the total search time by the fund consideration set
size. We adopt the model similar to Equation 3 and our findings in Column (3)
of Table 5 reveal that, when following fund recommendations, investors spend
around one minute less average search time on each fund.

Table 5: Effect of Recommendation on Fund Search Effort Dependent Variable: | (1) | (2) | (3)
---|---|---|---
| Total Time | Unique # of Funds | Avg Time Per Fund
Recommended | -1332.860* | -0.048 | -65.471**
| (755.645) | (0.376) | (30.841)
Polynomial Degree | 2 | 2 | 2
Investor Group Dummies | Yes | Yes | Yes
Fund Type Dummies | Yes | Yes | Yes
Month Dummies | Yes | Yes | Yes
Day of Week Dummies | Yes | Yes | Yes
Control Variables | Yes | Yes | Yes
Number of Observations | 5,339 | 5,339 | 5,339
$R^{2}$ | 0.055 | 0.050 | 0.067
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The dependent variables in
Columns (1) - (3) are the search time spent on all funds, the average search
time per fund, and the unique number of funds viewed. Control variables
include management fees, purchase fees, custodial fees, asset size, dummy for
valuable fund brand, fund past returns, fund risk level, and fund tenure.
Robust standard errors are in parentheses.

####  7.1.2 Variations of Search Efforts Reduction by SES

Our earlier results indicate that investors of different SES interpret product
recommendation quality signals differently. Consequently, the effects of
product recommendations on fund search efforts could also differ across these
groups. We explore this differential impact using sub-sample analysis by
investor SES groups and use the same approach in Section 7.1.1, with the
dependent variable being the average search time per fund. Results are
summarized In Table 6. Column (1) indicates that investors of low SES spend
significantly less time for each fund when following product recommendations,
with the coefficient $Recommended_{it}$ being negative and statistically
significant. However, as shown in Columns (2) and (3), the effect is
insignificant for investors with medium and high SES, suggesting that only low
SES investors’ search efforts are negatively impacted by product
recommendations. This could potentially explain our results in Section 6.2
which finds that only investors of low SES realize significantly worse
investment returns when following product recommendations.

Employing the Chow test, we further discern that the influence of
recommendations is significantly more pronounced for low SES investors
compared to both medium SES ($F=10.30;p<0.01$) and high SES investors
($F=5.21;p<0.05$). This distinction reveals that low SES investors exhibit a
considerably steeper decline in search effort when choosing recommended over
non-recommended funds.

This observation is in line with Havakhor et al. (2021), which posits that
increased access to tech-enabled financial performance data can induce
gambling-like behaviors among less sophisticated investors. Such accessibility
to vast performance data might amplify overconfidence and trading frequency
among these investors, primarily because they often struggle to effectively
process relevant financial details. Our study supports this conclusion with
novel empirical evidence that when presented with recommendations spotlighting
historically high-performing funds, less adept investors might be unduly
swayed, manifesting increased overconfidence and impulsivity in trading. This
behavioral pattern resonates with our earlier conclusions that low SES
investors are more likely to follow product recommendations and realize
significantly worse investment returns.

Table 6: Effect of Recommendation on Fund Search Effort by Investor SES Dependent Variable: | (1) | (2) | (3)
---|---|---|---
Avg Time Per Fund | Low SES | Med SES | High SES
Recommended | -81.831** | 14.068 | 11.396
| (38.918) | (53.648) | (61.256)
Polynomial Degree | 2 | 2 | 2
Investor Group Dummies | Yes | Yes | Yes
Fund Type Dummies | Yes | Yes | Yes
Month Dummies | Yes | Yes | Yes
Day of Week Dummies | Yes | Yes | Yes
Control Variables | Yes | Yes | Yes
Number of Observations | 4,143 | 609 | 587
$R^{2}$ | 0.074 | 0.197 | 0.088
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The dependent variable in Columns
(1) - (3) is the average search time per fund. Columns (1) - (3) include
analysis results using sub-samples of investors with low, medium, and high SES
respectively. Control variables include management fees, purchase fees,
custodial fees, asset size, dummy for valuable fund brand, fund past returns,
fund risk level, and fund tenure. Robust standard errors are in parentheses.

####  7.1.3 Search Efforts Allocation in the Purchase Stage

Building on our analysis that overall search efforts decrease preceding the
purchase of recommended funds compared with non-recommended ones, we want to
understand what kind of funds get less search time by investors. To address
this, we use the model analogous to Table 5 with the dependent variables
replaced by the average search time allocated to funds that investors
eventually purchase and to the funds that they do not purchase. Results are
presented in Table 7. In Column (1), our findings indicate preceding the fund
purchase, there is no significant difference between the average search time
allocated to the purchased recommended and the purchased non-recommended fund.
Transitioning our focus to the average time dedicated to funds investors
eventually bypass, results from Column (2) reveal that when investors follow
fund recommendations, they markedly reduce the time spent considering
alternative fund options. This suggests that recommendations play a
significant role in diminishing investors’ exploration of non-purchased funds.

As the search efforts in alternative funds reduce when following product
recommendations, we further probe into the average time investors allocate to
non-purchased funds. Specifically, we examine the time devoted to non-
purchased funds within the same fund type and different fund types as the
purchased fund respectively. We use the model similar to Table 5, and
interestingly, our results in Column (3) reveal that when following fund
recommendations, investors significantly curtail their research time for other
funds within the same fund type. This finding is consistent with the design of
product recommendations that highlight top-performing funds within each fund
type. Hence, when a recommended fund is purchased, investors tend to allocate
less time to investigating its similar counterparts. In a contrasting
analysis, detailed in Column (4), we don’t find a significant change in search
time dedicated to funds in different fund types when purchasing recommended
(versus non-recommended funds).

Table 7: Effect of Recommendation on Fund Search Time by Fund Category Dependent Variable: | (1) | (2) | (3) | (4)
---|---|---|---|---
|  Purchased Fund |  Non-Purchased Funds |  Same Type Funds |  Different Type Funds
Recommended | -115.246 | -48.259* | -45.434** | -68.528
| (70.726) | (28.197) | (21.924) | (44.735)
Polynomial Degree | 2 | 2 | 2 | 2
Investor Group Dummies | Yes | Yes | Yes | Yes
Fund Type Dummies | Yes | Yes | Yes | Yes
Month Dummies | Yes | Yes | Yes | Yes
Day of Week Dummies | Yes | Yes | Yes | Yes
Control Variables | Yes | Yes | Yes | Yes
Number of Observations | 5,339 | 5,339 | 5,339 | 5,339
$R^{2}$ | 0.042 | 0.064 | 0.042 | 0.059
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The dependent variables in
Columns (1) - (4) are the average search time spent on the purchased fund,
non-purchased funds, funds of the same type as the purchased fund, and funds
of different types as the purchased fund. Control variables include management
fees, purchase fees, custodial fees, asset size, the dummy for valuable fund
brand, fund past returns, fund risk level, and fund tenure. Robust standard
errors are in parentheses.

###  7.2 Behavioral Differences in Fund Redemption Timing

The timing of fund redemption significantly influences investment returns.
Typically, selling funds during price peaks yields superior returns. We
investigate the possibility that investors who choose recommended funds,
potentially due to lack of research as we show in Section 7.1, possess
weakened confidence in their decisions. Such weakened confidence might enable
them to cash out their investments at sub-optimal times, thus worsening their
returns.

To evaluate this possibility empirically, we undertake a simulation analyzing
the effects of varying fund redemption timings on investment returns. In
particular, we simulate scenarios where investors redeem their funds three
months after their actual final redemption date in the investment session.
Subsequently, we compute the disparity in fund return when redeeming on the
simulated and actual final redemption date, which we term “hypothetical excess
return.” A larger value in this metric signifies a worse redemption timing.

We perform a comparative analysis of the average hypothetical excess return
between purchases of recommended and non-recommended funds. Results are
presented in Table 8. Row (1) includes the average simulated hypothetical
excess return for all investors in our sample and results indicate that, on
average, those who purchase recommended funds would achieve higher
hypothetical excess returns than those opting for non-recommended ones. This
implies that investors who choose recommended funds tend to redeem at a worse
time than those who purchase non-recommended funds.

Since the effects of product recommendations are shown to be affected by
investor SES levels, here we aim to further compare hypothetical excess return
differences by investor SES levels. We present results at Rows (2) - (4) in
Table 8. Intriguingly, our analysis reveals that the hypothetical excess
returns for purchasing recommended funds are significantly positive only among
investors from low SES backgrounds (0.036 vs 0.010; difference =
$0.026;p<0.01$), indicating that only low SES investors tend to redeem at a
worse time when purchasing recommended funds (versus non-recommended funds).
This marked discrepancy suggests that sub-optimal timing during the redemption
phase could be a consequential factor that exacerbates poorer investment
returns specifically for low SES investors who purchase recommended funds.
This segment of investors is disproportionately impacted by less-than-ideal
redemption timing and corresponds with our prior results that only investors
of low SES realize significantly worse investment returns when following fund
recommendations. The finding contributes to our understanding of how SES
factors may interact with investment choices, elevating the discussion
surrounding the potential dark side implications of product recommendations on
investor behaviors.

Table 8: Hypothetical Excess Return Comparison Between Recommended and Non-Recommended Funds Investor Group | Non-Recommended Funds | Recommended Funds | Difference
---|---|---|---
All Investors | -0.033 | -0.016 | 0.017**
SES: Low | -0.036 | -0.010 | 0.026***
SES: Medium | -0.026 | -0.031 | -0.005
SES: High | -0.030 | -0.027 | -0.003
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The significance level is based
on an independent sample t-test.

###  7.3 Fund Future Performance Reversal

Besides investors’ behavioral changes after following product recommendations,
we also explore potential differences between recommended and non-recommended
funds. Specifically, we examine the difference in future performance of
recommended and non-recommended funds as potential explanations for worse
performance when investors follow recommendations. Since our study
intentionally narrows its focus to funds that marginally meet or miss the
recommendation threshold, this design choice posits an intriguing question:
Given that the annual returns of these recommended and non-recommended funds
are closely aligned at the time of purchase, could their future performance
diverge significantly? To explore this, we examine the correlation between
current and future annual returns for both recommended and non-recommended
funds in our data set.

Specifically, we calculate the mean value of each fund’s current annual return
and its annual return three months into the future. This three-month future
return metric aligns well with the average holding period observed in our data
set, which stands at approximately 3.3 months.

As revealed in Table 9, recommended funds—those rank third, fourth, and
fifth—exhibit higher annual returns than their non-recommended counterparts
(funds rank sixth, seventh, and eighth) at the time of the recommendation.
Intriguingly, this disparity narrows markedly after a three-month span. Put
differently, on average, investors who opt for recommended funds experience
less favorable returns after three months compared to those who select non-
recommended funds (-0.230 vs. -0.144). This phenomenon of price reversals,
which is also observed in broader financial markets, could be attributed to
market overreactions or herd behavior, as highlighted in existing literature
(De Bondt and Thaler 1985, Atkins and Dyl 1990, Huang et al. 2010).

Consequently, apart from variations in investor behaviors, the observed
reversal in future performance between recommended and non-recommended funds
may offer another explanation as to why investors often fare worse when
purchasing recommended funds. This insight provides another perspective on how
fund recommendations negatively influence investment outcomes.

Table 9: Relation Between Current and Future Annual Return Variable | CurrentAnnualReturn | ThreeMonthAnnualReturn | Gain
---|---|---|---
Recommended Funds | 0.569 | 0.339 | -0.230***
Non-Recommended Funds | 0.455 | 0.310 | -0.144***
Difference | 0.114*** | 0.029** |
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. CurrentAnnualReturn is the
current annual return of the fund. ThreeMonthAnnualReturn is the annual return
of the fund after three months. Gain is the difference between
ThreeMonthAnnualReturn and CurrentAnnualReturn. The significance level is
based on Paired t-test.

##  8 Conclusion

As the number of retail investors trading on online investment platforms
continues to soar, their increasing influences are propelling shifts in
financial markets. These investors rely on online investment platforms, which
adopt product recommendations to help investors find good financial products
and realize higher investment returns. Whereas product recommendations can
provide reliable quality signals for consumer goods using past ratings and
sales, their quality signals appear unreliable for financial products when
they rely on historical returns. In this paper, we examine the impact of
product recommendations within the realm of financial products. We delve into
how product recommendations affect investors’ adoption, subsequent behavioral
changes, and investment performance.

First, we reveal that investors follow product recommendations to make
investment decisions even though recommendations based on historical returns
cannot offer reliable quality signals. Further analyses reveal investors
realize significantly worse investment returns when purchasing recommended
funds (versus non-recommended ones). These findings suggest that platforms
should be cautious when employing unreliable quality signals such as
historical performance to design recommender systems. They should also clearly
inform investors that historical returns cannot predict future returns and
investors should not solely rely on product recommendations in their
decisions. Platforms might also consider simultaneously providing
recommendations based on other metrics such as fund risk and diversity so
investors can obtain a more comprehensive picture of recommended funds.

Second, our observations reveal a notable disparity: investors with lower SES
exhibit a significantly higher tendency to follow product recommendations, in
contrast to investors with medium and high SES. Alarmingly, low SES investors
are the sole group experiencing significantly diminished investment returns
after purchasing recommended funds. These findings raise concerns about the
unintended consequences of product recommendations on investment platforms, as
they appear to exacerbate wealth inequality. It is troubling that those who
should benefit the most from financial protection, namely low SES investors,
end up being the most adversely affected by these recommendations. In light of
these findings, investment platforms should consider prioritizing the
provision of educational resources to all investors, with a particular focus
on those with low SES backgrounds. By equipping investors with fundamental
knowledge of investments, platforms can empower them to make more informed
decisions, better comprehend historical returns, and interpret financial data
effectively, ultimately mitigating the negative impacts of product
recommendations. Platforms can further enhance their commitment to
safeguarding low SES investors by implementing additional protective measures.
These measures may include offering guidance on diversifying investment
portfolios and strategies for minimizing risks, thereby promoting a more
secure and inclusive investment environment for all.

Third, our findings also reveal that investors significantly reduce search
time when purchasing recommended funds versus non-recommended ones. This
reduction in research time can result in less-informed investment decisions, a
trend particularly pronounced among low SES investors, who also heavily rely
on product recommendations. To address this issue and encourage more informed
decision-making, investment platforms could consider implementing gamification
elements that motivate investors to dedicate more effort. This might involve
incentivizing investors to explore a certain number of funds or different fund
types before finalizing their purchases. Fourth, our simulation analyses
indicate that investors who opt for recommended funds often redeem them at
less opportune times compared to those who invest in non-recommended funds.
While platforms cannot offer specific guidance on the perfect redemption
timing, they can enhance the investor experience by furnishing more
comprehensive data on historical trends and market cycles. This additional
information can assist investors in making more considered and judicious
redemption decisions.

Finally, our findings also yield crucial implications for policymakers
concerning the utilization of recommendations within financial platforms. Even
in the case of non-personalized recommendations, without appropriate design,
they may still yield adverse and unintended consequences for investors. Most
significantly, this could potentially give rise to systemic risks within the
financial market, as numerous investors may make similar investment choices by
following these recommendations, leading to concurrent losses that could
propagate throughout the financial system (Benoit et al. 2017). To avert such
detrimental outcomes for investors and the broader financial market,
investment platforms, in collaboration with policymakers, should devise
effective strategies for monitoring the deployment of product recommendations.
Furthermore, they should advocate best practices to support the decision-
making processes of investors, thereby enhancing the overall resilience and
stability of the financial ecosystem.

We have sought to pinpoint the specific impact of product recommendations
within the context of financial products, using the example of mutual funds.
As an avenue for future research, we advocate expanding the scope to include
an array of other prevalent financial instruments like futures, options, and
insurance products. Our study is chiefly concerned with the mechanism of
product recommendations—a design ubiquitously employed across e-commerce and
financial platforms alike. Moving forward, it would be illuminating to examine
the influence of other common platform designs such as portfolio comparison or
custom portfolios. By broadening the range of financial products and platform
designs studied, future research could offer a more comprehensive
understanding of how various platforms shape investor behaviors and financial
outcomes.

##  9 Funding and Competing Interests

All authors certify that they have no affiliations with or involvement in any
organization or entity with any financial or non-financial interest in the
subject matter or materials discussed in this manuscript. The authors have no
funding to report.

## References

  * Adomavicius et al. (2013) Adomavicius G, Bockstedt JC, Curley SP, Zhang J (2013) Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research 24(4):956–975.
  * Adomavicius et al. (2018) Adomavicius G, Bockstedt JC, Curley SP, Zhang J (2018) Effects of online recommendations on consumers’ willingness to pay. Information Systems Research 29(1):84–102.
  * Adomavicius and Tuzhilin (2005) Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6):734–749.
  * Anderson (2013) Anderson A (2013) Trading and under-diversification. Review of Finance 17(5):1699–1741.
  * Atkins and Dyl (1990) Atkins AB, Dyl EA (1990) Price reversals, bid-ask spreads, and market efficiency. Journal of Financial and Quantitative Analysis 25(4):535–547.
  * Banker and Khetani (2019) Banker S, Khetani S (2019) Algorithm overdependence: How the use of algorithmic recommendation systems can increase risks to consumer well-being. Journal of Public Policy & Marketing 38(4):500–515.
  * Barber et al. (2022) Barber BM, Huang X, Odean T, Schwarz C (2022) Attention induced trading and returns: Evidence from Robinhood users. The Journal of Finance 77(6):3141–3190.
  * Barber and Odean (2001) Barber BM, Odean T (2001) Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics 116(1):261–292.
  * Barber and Odean (2002) Barber BM, Odean T (2002) Online investors: Do the slow die first? The Review of Financial Studies 15(2):455–488.
  * Barber and Odean (2008) Barber BM, Odean T (2008) All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies 21(2):785–818.
  * Barber et al. (2005) Barber BM, Odean T, Zheng L (2005) Out of sight, out of mind: The effects of expenses on mutual fund flows. The Journal of Business 78(6):2095–2120.
  * Barberis and Thaler (2003) Barberis N, Thaler R (2003) A survey of behavioral finance. Handbook of the Economics of Finance (Elsevier, Amsterdam).
  * Benoit et al. (2017) Benoit S, Colliard JE, Hurlin C, Pérignon C (2017) Where the risks lie: A survey on systemic risk. Review of Finance 21(1):109–152.
  * Brynjolfsson et al. (2011) Brynjolfsson E, Hu YJ, Simester D (2011) Goodbye pareto principle, hello long tail: The effect of search costs on the concentration of product sales. Management Science 57(8):1373–1386.
  * Calvet et al. (2009) Calvet LE, Campbell JY, Sodini P (2009) Measuring the financial sophistication of households. American Economic Review 99(2):393–98.
  * Cattaneo et al. (2020) Cattaneo MD, Jansson M, Ma X (2020) Simple local polynomial density estimators. Journal of the American Statistical Association 115(531):1449–1455.
  * Cattaneo and Titiunik (2022) Cattaneo MD, Titiunik R (2022) Regression discontinuity designs. Annual Review of Economics 14(1):821–851.
  * Chiapetta et al. (2021) Chiapetta J, Fontes A, Liu E, Serednesky G (2021) The evolving landscape of U.S. retail investing. BNY Mellon, New York, NY.
  * Choi and Zhao (2020) Choi JJ, Zhao K (2020) Carhart (1997) mutual fund performance persistence disappears out of sample, Working paper, Yale University, New Haven, CT.
  * Clifford et al. (2021) Clifford CP, Fulkerson JA, Jame R, Jordan BD (2021) Salience and mutual fund investor demand for idiosyncratic volatility. Management Science 67(8):5234–5254.
  * Consul and Famoye (1992) Consul P, Famoye F (1992) Generalized poisson regression model. Communications in Statistics-Theory and Methods 21(1):89–109.
  * Cookson et al. (2021) Cookson G, Jenkinson T, Jones H, Martinez JV (2021) Best buys and own brands: Investment platforms’ recommendations of mutual funds. The Review of Financial Studies 34(1):227–263.
  * De et al. (2010) De P, Hu YJ, Rahman MS (2010) Technology usage and online sales: An empirical study. Management Science 56(11):1930–1945.
  * De Bondt and Thaler (1985) De Bondt WF, Thaler R (1985) Does the stock market overreact? The Journal of Finance 40(3):793–805.
  * Fabric (2021) Fabric (2021) The world’s largest e-commerce companies by GMV. Fabric Blog. Accessed December 5, 2022, https://fabric.inc/blog/ecommerce-companies/.
  * Faery et al. (2022) Faery R, Sharma C, Franco M, Thrasher C (2022) The investor landscape. Credit Suisse, Zürich, Switzerland.
  * Fitzgerald (2021) Fitzgerald M (2021) Robinhood has 18 million accounts with $80 billion in assets after rapid growth, IPO filing shows. CNBC (July 2), https://www.cnbc.com/2021/07/01/robinhood-has-18-million-accounts-managing-80-billion-after-rapid-one-year-growth-ipo-filing-shows.html.
  * Goldfarb and Tucker (2019) Goldfarb A, Tucker C (2019) Digital economics. Journal of economic literature 57(1):3–43.
  * Goldfarb et al. (2022) Goldfarb A, Tucker C, Wang Y (2022) Conducting research in marketing with quasi-experiments. Journal of Marketing 86(3):1–20.
  * Grinblatt et al. (2016) Grinblatt M, Ikäheimo S, Keloharju M, Knüpfer S (2016) IQ and mutual fund choice. Management Science 62(4):924–944.
  * Havakhor et al. (2021) Havakhor T, Rahman MS, Zhang T, Zhu C (2021) Tech-enabled financial data access, retail investors, and gambling-like behavior in the stock market: Evidence from a natural experiment, Working paper, Temple University, Philadelphia, PA.
  * Hosanagar et al. (2014) Hosanagar K, Fleder D, Lee D, Buja A (2014) Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation. Management Science 60(4):805–823.
  * Huang et al. (2010) Huang W, Liu Q, Rhee SG, Zhang L (2010) Return reversals, idiosyncratic risk, and expected returns. The Review of Financial Studies 23(1):147–168.
  * Jain and Wu (2000) Jain PC, Wu JS (2000) Truth in mutual fund advertising: Evidence on future performance and fund flows. The Journal of Finance 55(2):937–958.
  * Jiang et al. (2021) Jiang Z, Chan T, Che H, Wang Y (2021) Consumer search and purchase: An empirical investigation of retargeting based on consumer online behaviors. Marketing Science 40(2):219–240.
  * Kahn and Rudd (1995) Kahn RN, Rudd A (1995) Does historical performance predict future performance? Financial Analysts Journal 51(6):43–52.
  * Kawaguchi et al. (2019) Kawaguchi K, Uetake K, Watanabe Y (2019) Effectiveness of product recommendations under time and crowd pressures. Marketing Science 38(2):253–273.
  * Ke et al. (2022) Ke TT, Lin S, Lu MY (2022) Information design of online platforms. Working paper .
  * Lee and Hosanagar (2021) Lee D, Hosanagar K (2021) How do product attributes and reviews moderate the impact of recommender systems through purchase stages? Management Science 67(1):524–546.
  * Lee and Lemieux (2010) Lee DS, Lemieux T (2010) Regression discontinuity designs in economics. Journal of Economic Literature 48(2):281–355.
  * Oestreicher-Singer and Sundararajan (2012) Oestreicher-Singer G, Sundararajan A (2012) Recommendation networks and the long tail of electronic commerce. MIS Quarterly 36(1):65–83.
  * Pathak et al. (2010) Pathak B, Garfinkel R, Gopal RD, Venkatesan R, Yin F (2010) Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems 27(2):159–188.
  * Roy and Dutta (2022) Roy D, Dutta M (2022) A systematic review and research perspective on recommender systems. Journal of Big Data 9(1):59.
  * Shen and Miguel Villas-Boas (2018) Shen Q, Miguel Villas-Boas J (2018) Behavior-based advertising. Management Science 64(5):2047–2064.
  * Sirri and Tufano (1998) Sirri ER, Tufano P (1998) Costly search and mutual fund flows. The Journal of Finance 53(5):1589–1622.
  * Sorescu and Sorescu (2016) Sorescu A, Sorescu SM (2016) Customer satisfaction and long-term stock returns. Journal of Marketing 80(5):110–115.
  * Tetlock (2007) Tetlock PC (2007) Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance 62(3):1139–1168.
  * Thistlethwaite and Campbell (1960) Thistlethwaite DL, Campbell DT (1960) Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology 51(6):309–317.
  * Tong et al. (2020) Tong S, Luo X, Xu B (2020) Personalized mobile marketing strategies. Journal of the Academy of Marketing Science 48:64–78.
  * Ursu (2018) Ursu RM (2018) The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions. Marketing Science 37(4):530–552.
  * Walsh (2020) Walsh M (2020) Algorithms are making economic inequality worse. Harvard Business Review (October 22), https://hbr.org/2020/10/algorithms-are-makingeconomic-inequality-worse.
  * Zhang and Liu (2012) Zhang J, Liu P (2012) Rational herding in microloan markets. Management Science 58(5):892–912.
  * Zhang et al. (2021) Zhang X, Ferreira P, Godinho de Matos M, Belo R (2021) Welfare properties of profit maximizing recommender systems: Theory and results from a randomized experiment. MIS Quarterly 45(1):1–43.
  * Zhao et al. (2013) Zhao Y, Yang S, Narayan V, Zhao Y (2013) Modeling consumer learning from online product reviews. Marketing Science 32(1):153–169.

##  Appendix A  Investor Characteristics

Table A1: Summary Statistics of Investor Characteristics Characteristics | Description | # of Investors | Value | Percentage
---|---|---|---|---
Income | Not Stable | 9,328 | 0 | 74.98%
Stable | 2,125 | 1 | 17.08%
Stable and High | 988 | 2 | 7.94%
Liability | High | 246 | 0 | 1.98%
Medium | 10,531 | 1 | 84.65%
Low | 1,664 | 2 | 13.38%
Education | High School or Below | 9,696 | 0 | 77.94%
Bachelor | 2,223 | 1 | 17.87%
Graduate | 522 | 2 | 4.20%
Notes: Investor characteristics are encoded into ordinal values for investor
segmentation by socioeconomic status. Higher values represent higher income,
education, and fewer liabilities.

##  Appendix B  Robustness Checks

Table B1: Robustness Check: Falsification Exercise & Alternative Bandwidth Dependent Variable: | (1) | (2) | (3) | (4)
---|---|---|---|---
log(NumberOfPurchases) |  |  |  |
Recommended | -0.012 | 0.008 | 0.029*** | 0.028***
| (0.011) | (0.010) | (0.010) | (0.007)
Fund Type Dummies | Yes | Yes | Yes | Yes
Month Dummies | Yes | Yes | Yes | Yes
Day of Week Dummies | Yes | Yes | Yes | Yes
Control Variables | Yes | Yes | Yes | Yes
Number of Observations | 7,278 | 7,278 | 7,278 | 14,556
$R^{2}$ | 0.173 | 0.182 | 0.197 | 0.171
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The dependent variable in Columns
(1) - (4) is log(NumberOfPurchases). Control variables include management
fees, purchase fees, custodial fees, asset size, dummy for valuable fund
brand, fund past returns, fund risk level, and fund tenure. Columns (1) and
(2) show the results of the falsification exercise. Column (1) compares funds
in fourth and fifth places (both are recommended). Column (2) compares funds
in the sixth and seventh places (both are non-recommended). Column (3) uses a
smaller bandwidth that includes only funds in the fifth (recommended) and
sixth (non-recommended) places in the sample. Column (4) uses a smaller
bandwidth that includes only funds in the fourth to seventh places in the
sample. Robust standard errors are in parentheses.
Table B2: Robustness Check: Alternative Measurements Dependent Variable: | (1) | (2) | (3)
---|---|---|---
Recommended | 0.052*** | 0.163*** | 20.440***
| (0.006) | (0.018) | (2.787)
Fund Type Dummies | Yes | Yes | Yes
Month Dummies | Yes | Yes | Yes
Day of Week Dummies | Yes | Yes | Yes
Control Variables | Yes | Yes | Yes
Number of Observations | 21,834 | 21,834 | 21,834
$R^{2}$ | 0.156 | 0.144 | 0.066
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. The dependent variable in Column
(1) is log(NumberOfInvestors), log(PurchaseAmount) in Column (2), and
MoneyInflow in Column (3). Columns (1) - (3) use alternative measurements to
operationalize the effect of recommendation on purchase behavior. Control
variables include management fees, purchase fees, custodial fees, asset size,
dummy for valuable fund brand, fund past returns, fund risk level, and fund
tenure. Robust standard errors are in parentheses.
Table B3: Robustness Check: Alternative Model Specification Dependent Variable: | (1)
---|---
NumOfPurchases |
Recommended | 0.401**
| (0.061)
Fund Type Dummies | Yes
Month Dummies | Yes
Day of Week Dummies | Yes
Control Variables | Yes
Number of Observations | 21,834
Pseudo $R^{2}$ | 0.302
Notes: *** $p<0.01$, ** $p<0.05$, * $p<0.1$. NumberOfPurchases is the
dependent variable in Column (1). Column (1) displays the result using a
Poisson regression model. Control variables include management fees, purchase
fees, custodial fees, asset size, dummy for valuable fund brand, fund past
returns, fund risk level, and fund tenure. Robust standard errors are in
parentheses.