# The Impact of Customer Online Satisfaction on Stock Returns: Evidence from
the E-commerce Reviews in China

Zhi Sua, Danni Wua, Zhenkun Zhoub,c, Junran Wud and Libo Yine,* *Corresponding
author<EMAIL_ADDRESS>aSchool of Statistics and Mathematics, Central
University of Finance and Economics, Beijing, China
bSchool of Statistics, Capital University of Economics and Business, Beijing,
China
cChina Consumption Big Data Academy, Capital University of Economics and
Business, Beijing, China
dState Key Lab of Software Development Environment, Beihang University,
Beijing, China
eSchool of Finance, Central University of Finance and Economics, Beijing,
China

###### Abstract

This paper investigates the significance of consumer opinions in relation to
value in China’s A-share market. By analyzing a large dataset comprising over
18 million product reviews by customers on JD.com, we demonstrate that
sentiments expressed in consumer reviews can influence stock returns,
indicating that consumer opinions contain valuable information that can impact
the stock market. Our findings show that Customer Negative Sentiment Tendency
(CNST) and One-Star Tendency (OST) have a negative effect on expected stock
returns, even after controlling for firm characteristics such as market risk,
illiquidity, idiosyncratic volatility, and asset growth. Further analysis
reveals that the predictive power of CNST is stronger in firms with high
sentiment conditions, growth companies, and firms with lower accounting
transparency. We also find that CNST negatively predicts revenue surprises,
earnings surprises, and cash flow shocks. These results suggest that online
satisfaction derived from big data analysis of customer reviews contains novel
information about firms’ fundamentals.

###### keywords:

Consumer opinions; Stock return; China’s A-share market; Cash flow surprises

††articletype: ARTICLE

##  1 Introduction

Customer satisfaction is a critical factor in a company’s success, as cash
flow generation largely depends on the value provided to customers (Agarwal,
Qian, and Zou 2021). Despite this, there is limited research on the extent to
which consumer opinions can offer insights into a company’s future cash flows
and stock returns (Huang 2018). With the growth of e-commerce platforms,
investors have access to vast amounts of data on consumer online behavior.
However, few studies have examined the impact of consumer information on stock
return in the Chinese market due to data availability limitations. This study
aims to close this research gap by examining the effect of online reviews and
consumer attitudes on the Chinese stock market.

By analyzing and mining alternative data, this paper examines the information
content and investment value of such data, enriching relevant research.
Alternative data refers to real-time and detailed information that differs
from conventional financial reports and filings obtained from sources such as
investor presentations, analyst reports, and company filings (Monk, Prins, and
Rook 2019). The ability of alternative data to provide excess returns and
forward-looking information is a concern for both investors and researchers.
Past research has shown that online customer activity data can provide insight
into a firm’s fundamentals, enabling anticipation of standardized unexpected
earnings, revenue, and announcement returns (Froot et al. 2017; Zhu 2019). Our
work provides empirical evidence that alternative data, such as consumer
reviews data, can be used to investigate the impact of consumer satisfaction
on stock returns. Furthermore, we demonstrate that consumer reviews affecting
stock returns are not entirely consistent with traditional financial
information and contain additional information beyond financial data.

This paper explores the investment value of consumer opinions using
alternative data and measuring such opinions through customer product reviews
posted on JD.com, a prominent online retailer in China. By analyzing over 18
million consumer product reviews between 2008 and 2017, we have gathered
compelling evidence supporting the notion that consumer opinions hold
investment value. We construct static and dynamic panel models with a vector
of firm characteristics, and results show that future stock returns are
negatively correlated with both Customer Negative Sentiment Tendency (CNST)
and One-Star Tendency (OST). The conclusions investigate the impact of
consumer online satisfaction stock returns in China’s A-share market.

By using the Consumer Confidence Index (CCIs) to evaluate sentiment in the
Chinese stock market, we can analyze the impact of future stock returns under
various economic conditions and present our research findings. CNST can
significantly and negatively predict future stock returns during high
sentiment, while low sentiment has little predictive potential for future
stock returns. This negative impact appears to be concentrated for different
types of companies, and we use return on assets(ROA) as a measure of company
profitability, book-to-market(B/M) as a measure of growth or value companies,
and asset growth(AG) as a measure of company investment. CNST can
significantly negatively predict future stock returns during high ROA, high
asset growth, and low book-to-market. Additionally, CNST can significantly
negatively predict future stock returns during high earnings aggressiveness
(EA) and earnings smoothing (ES). Based on these heterogeneity tests, we also
analyze the reasons why e-commerce sales data can be used for prediction. CNST
can also negatively predict revenue surprises, earnings surprises, and cash
flow shocks. The results indicate that consumer online reviews and opinions
contain valuable insights regarding future cash flows that were previously
unrecognized and future stock returns.

Our study makes the following contributions to the literature. First, our
research contributes to understanding the influence of alternative data in
financial markets by examining the effects of online review information on
stock returns. Despite the recent surge in research exploring the impact of
consumer behavior data on financial markets, previous findings have overlooked
the influence of consumer opinions regarding product emotions. We present the
first formal empirical evidence confirming that consumer opinions have a
negative impact on expected stock returns and that these opinions are composed
of features extracted using a tendentious lexicon. Second, we consider a range
of established predictors in the panel analysis of cash flow, as well as
factors likely to be associated with online consumer opinions. Our results
indicate that aggregated customer reviews provide valuable new insights not
obtainable from traditional sources such as financial statements.
Additionally, results also indicate that customer online opinions may offer
new insights into future revenue cash flows. We pay attention to a novel
perspective of consumer companies, that is, the consumer online opinion of
consumer companies. Given the substantial increase in data generated by large
crowds, it would be worthwhile for future research to explore how this
information can be leveraged in other corporate scenarios. Third, this study
sheds light on the factors contributing to the predictability of stock returns
based on consumer opinions. In particular, it examines whether predictability
is especially prominent for stocks experiencing periods of high sentiment,
lower accounting transparency of corporate information, and growth companies.

The remainder of this paper is organized as follows. Section 2 discusses the
related literature. Section 3 describes the data and variable definitions.
Section 4 presents the empirical results, and Section 5 concludes.

##  2 Literature review

As far as we know, this study is relevant to the fourth research direction of
literature: consumer-related information, product-related information, the
wisdom of crowds, and customer capital.

First, due to the lack of data related to consumer information in early
studies, most literature has mainly studied the influence of consumer
satisfaction on firm performance and stock valuation through customer survey
data (Anderson, Fornell, and Mazvancheryl 2004; Ittner, Larcker, and Taylor
2009; Jacobson and Mizik 2009; Luo, Homburg, and Wieseke 2010; Tuli and
Bharadwaj 2009) Previous research has generally established a positive
correlation between higher consumer satisfaction111The customer satisfaction
data comes from the American Customer Satisfaction Index (www.theacsi.org).,
improved future operating performance, and higher firm valuation. The
emergence of technologies such as big data and cloud computing has enabled the
utilization of consumer behavior data in academic research (Da, Engelberg, and
Gao 2011a; Froot et al. 2017; Huang 2018; Zhu 2019; Agarwal, Qian, and Zou
2021; Li and Liu 2022) For example, Huang (2018) have shown that customer
ratings on Amazon.com can be used to predict a company’s future performance
and stock returns but did not combine consumers’ opinions on product emotions.
Li and Liu (2022) examines the impact of online sales disclosure on earnings
management for companies in consumer industries. The results indicate that
earnings management decreases when third-party online sales data is publicly
disclosed in a well-known financial database.

Second, previous empirical studies have revealed that product recalls are
linked to a negative abnormal return on the stock price. This suggests that
negative impacts on brand equity, company reputation, inferior product
quality, and poor customer perception can lead to a negative impact on stock
returns (Hoffer, Pruitt, and Reilly 1988; Barber and Darrough 1996; Zhao, Li,
and Flynn 2013) Recent research by Da, Engelberg, and Gao (2011b) demonstrates
that the Search Volume Index (SVI) of a company’s products on Google can
provide insight into the company’s future fundamentals. This information can
be used to predict revenue and earnings surprises, as well as earnings
announcement returns. Additionally, the SVI is a valuable leading indicator
for a firm’s future cash flow and is relevant for determining the firm’s
value. Tang (2018) find that information about products created by third
parties and shared on Twitter can forecast a company’s fundamentals and
performance. Our study contributes to this body of research by focusing on a
method that provides a direct measurement of how customers feel about the
quality of a product.

Third, with the rise of online platforms, users have accumulated large groups
of digital records of historical behaviors on various platforms, such as
comments and sharing opinions on investment and social media platforms.
Therefore, aggregating the behavior records of a large number of users may
reflect some behavioral patterns of the group. There is an increasing body of
literature that delves into the influence of behavioral information
aggregation and the wisdom of crowds on stock prices. For example, previous
research shows that a significant number of financial market participants
conveyed information about future stock returns and cash flows through their
collective actions (Kelley and Tetlock 2013; Chen et al. 2014; Lee, Ma, and
Wang 2015). Green et al. (2019), Sheng (2022), Chi and Chen (2021), and
Symitsi and Stamolampros (2021) used millions of employee online reviews data
from the Glassdoor website, the largest website for employee reviews, and
found that the summarized company evaluation value can generate investment-
relevant signals and significantly predict stock market reactions. Different
from the above literature, our paper uses the aggregation of consumers’ online
shopping information. This is due to the fact that customers are among the
most significant nonfinancial stakeholders of companies.

Fourth, there is a growing body of literature on the relationship between
customer capital and finance, which refers to customers’ brand loyalty toward
a firm. The interplay between a firm’s financial and product market attributes
was first explored by Titman (1984) and Titman and Wessels (1988), both
theoretically and empirically. Since then, several studies have investigated
the impact of financial attributes on a firm’s decisions and performance in
the product market (Fresard 2010; Phillips and Sertsios 2013; Gourio and
Rudanko 2014; Gilchrist et al. 2017). Likewise, various studies have examined
the effect of product market attributes on corporate policies and
valuation(Belo, Lin, and Vitorino 2014; Vitorino 2014; Gourio and Rudanko
2014; Dou and Ji 2021). For example, Dou et al. (2021) proposed the
Inalienability of Customer Capital (ICC) model, which explores the
relationship between customer capital and key talents’ contributions and pure
brand recognition. Their findings suggest that firms with higher ICC have
greater average returns, higher talent turnover, and more cautious financial
policies. Our study contributes to this literature by analyzing customer
product reviews and deriving two categories of features to measure customer
satisfaction with products and brands.

##  3 Data and variable definitions

This section provides an overview of the key variables and describes the data
sources used in our analysis.

###  3.1 Online consumer review data

JD.com is one of the top online retailers in China, with sales reaching a
staggering 672 billion dollars in 2018. The company was established in June
1998, but it did not begin allowing consumers to publish reviews of its
products until 2008 when it finished developing its 3C (Computer,
Communication, and Consumer Electronics) division. Since its inception, over
305 million customers have made purchases on the website and left over one
billion reviews, establishing it as the foremost provider of consumer reviews
on the Internet. According to the guidelines for creating reviews provided by
JD.com, anyone who has purchased a product through JD.com is eligible to write
a review of that product. Reviews should concentrate on particular aspects of
the product, the quality of the service received, and consumer (or merchant)
reply content in transaction orders. Thus, it is likely that consumers’
judgments of a product’s quality and value are reflected in the reviews
provided by customers on JD.com. The criteria forbid unreasonable reviews,
hostile reviews, and useless reviews in an effort to reduce the number of
potential conflicts of interest. In addition, JD.com uses complex technical
measures, such as a captcha, to protect its website from being hacked by spam
bots and other malicious software. As a result, the customer reviews that can
be seen on JD.com are both trustworthy and of high quality. The reviews
encompass not only the product but also the seller’s performance, such as the
shipping experience, packaging, and product availability. Additionally, any
factors that could potentially impact an investor’s decision, including a
customer’s impression of the brand, are also taken into consideration. The
feedback provided by customers typically consists of a rating system ranging
from one to five stars, with one representing the lowest rating. Additionally,
customers are given the opportunity to include a text review. The posting time
of each review is recorded, enabling the tracking of consumer opinions over a
period of time.

To ensure a comprehensive selection of consumer reviews, we source the list of
companies from Sina Finance, one of the largest financial news portals in
China. Sina Finance categorizes the entire stock market into various sectors
based on stock characteristics. Following our previous work (Wu, Xu, and Zhao
2020), we select four sectors most relevant to consumer products: home
appliances, garments, wine, and food industries. Within these four sectors,
there are a total of 177 companies with market values ranging from billions to
hundreds of billions, representing diverse market capitalization firms. To
obtain online customer review data, we used stock names and codes as seeds. We
manually searched for company names on JD.com to identify public firms with
customer product reviews and checked whether these firms sell products on the
website. We retrieved the list of brands from JD.com and identified the firms
that own these brands. In total, we ultimately found 106 businesses with
JD.com customer product reviews.

We created a web crawler to collect reviews for a sample of public firms,
using each brand as a search term on JD.com and searching all reviews for
products. We then gathered information about each product, including the
product’s name, the brand’s name, the date of review, star rating in numbers,
review text, and the number of days between the order and review dates. The
time period covered by the sample of reviews ranges from 2008 to 2017, and we
also deleted duplicate reviews made on the same day for the same product by
the same reviewer account ID. We created a panel dataset consisting of firm-
weeks that include customer reviews. To ensure data reliability, we set a
threshold of at least 1000 online reviews and a minimum time span of 12
months. for each firm. The final sample has 21,703 firm-weeks. Table 1
provides information on the number of reviews, products, and firms in the
entire sample, as well as for each of the four industry sectors. There are
almost 18 million reviews available online, covering 164,715 different goods
created by the sample companies. The home appliance sector and the garment
industry have the highest number of product reviews, with more than 7.2
million and 5.3 million reviews respectively. Together, these two industries
account for 70 percent of the reviews we have acquired.

[Insert Table 1 here]

###  3.2 Variables

####  3.2.1 Main Variables

According to the existing literature (Luo 2009; Tirunillai and Tellis 2012;
Luo, Zhang, and Duan 2013; Huang 2018; Zhou, Xu, and Zhao 2018), we derived
two categories of features. The first category consists of characteristics
extracted from a review based on tendentious words in the review text. These
words reflect specific attitudes towards the purchasing experience, such as
positive or negative sentiment, and their correlation with stock returns
varies based on the type of attitude expressed. The second category is based
on the review rating, ranging from one to five, which indicates the level of
customer satisfaction with the purchase experience. This category complements
the first by providing an additional measure of satisfaction.

Customer (Positive/Negative) Sentiment Tendency

The Customer Sentiment Tendency category is based on tendentious words
extracted from review text on JD.com, a platform where consumers share their
experiences with products and services, generating a large amount of
unstructured data that reflects their emotions and attitudes. Furthermore, the
relationship between various tendency reviews and stock return varies (Luo
2009; Tirunillai and Tellis 2012; Zhou, Xu, and Zhao 2018). Therefore, we
construct the indicators of negative and positive emotional trends. We define
the Customer (Positive/Negative) Sentiment Tendency of firm $i$ in week $t$ as

$N(\mathrm{rev})^{neg}_{i,t}=\sum_{r}^{N}\\{1\ if\ NW_{i,t,r}\ >\ PW_{i,t,r}\
else\ 0\\}$ (1)

$N(\mathrm{rev})^{pos}_{i,t}=\sum_{r}^{N}\\{1\ if\ PW_{i,t,r}\ >\ NW_{i,t,r}\
else\ 0\\}$ (2)

$Diff(\mathrm{rev})^{neg}_{i,t}=N(rev)^{neg}_{i,t}-N(\mathrm{rev})^{neg}_{i,t-1}$
(3)

$Diff(\mathrm{rev})^{pos}_{i,t}=N(\mathrm{rev})^{pos}_{i,t}-N(\mathrm{rev})^{pos}_{i,t-1}$
(4)

where $N$ represents the number of reviews published in $n$ weeks, with $n$
=1,2…,12. $NW_{i,t,r}$ and $PW_{i,t,r}$represent the counts of negative and
positive words, respectively, in review $r$ for firm $i$ in week $t$. If the
count of negative words in a review is higher than that of positive words,
then $N(\mathrm{rev})^{neg}_{i,t}$ is equal to 1. Similarly, if the count of
positive words is higher than that of negative words, then
$N(\mathrm{rev})^{pos}_{i,t}$ is equal to 1. $Diff(\mathrm{rev})^{neg}_{i,t}$
and $Diff(\mathrm{rev})^{pos}_{i,t}$ represent the differences between week
$t$ and week $t-1$. Customer Positive Sentiment Tendency (CPST) typically
involves favorable emotions and suggestions to purchase particular products.
In contrast, Customer Negative Sentiment Tendency (CNST) relates to negative
emotions and recommendations not to purchase particular items. This includes
derogatory comments about the brand, criticism of the product itself, and
complaints regarding instances of product malfunctions or poor customer
service that led to unsatisfactory experiences. Table A shows that the mean of
$Diff(\mathrm{rev})^{neg}_{i,t}$ is approximately zero, while the mean of
$Diff(\mathrm{rev})^{pos}_{i,t}$ is 0.01.

Star Tendency (Five-Star Tendency / One-Star Tendency)

The Star Tendency category is derived from the numerical review ratings that
customers have given the product. These ratings provide a clear indication of
how customers feel about the product’s quality, value, and overall experience.
Earlier studies have shown that customer review ratings have a significant
relationship with stock returns (Huang 2018; Luo, Zhang, and Duan 2013). To
account for potential biases in product ratings on JD.com, we have created two
distinct categories for Star Tendency: Five-Star Tendency (FST) and One-Star
Tendency (OST). Products on JD.com are evaluated on a scale of one to five
stars, where one corresponds to the lowest rating and five represents the
highest. However, there is a possibility of selection bias on JD.com, where
products sold on the platform may be perceived to have higher quality.
Additionally, products that receive low ratings may face a decrease in sales
and, in certain cases, may even become unavailable for purchase. This can
result in a smaller customer base and lead to negative reviews. Conversely,
positive reviews and product sales could reinforce each other, attracting more
buyers who provide favorable reviews and creating a greater number of five-
star ratings. The Star Tendency categories help mitigate any potential bias in
the rating system. We define the number of reviews with ${Star}^{s}_{i,t}$ of
firm $i$ in week $t$ as

${Diff({Star}^{s})}_{i,t}=\frac{{{Star}^{s}}_{i,t}}{N_{i,t}}-\frac{{{Star}^{s}}_{i,t-1}}{N_{i,t-1}}$
(5)

where $N$ represents the number of reviews published in $n$ weeks, with
$n$=1,2…,12. ${Star}^{s}_{i,t}$ represents the rating score equal to $s$,
where $s$=1 or 5 for firm $i$. $N_{i,t}$ represents the number of reviews for
firm $i$ in week $t$. $Diff({Star}^{s})_{i,t}$ represents the difference
between the ${Star}^{s}$ Ratio in week $t$ and week $t-1$. FST and OST
variables are complements the explanation of the CNST and CPST variables based
on the numerical way. Table A shows that the mean of $Diff({Star}^{1})_{i,t}$
is approximately zero and the interquartile range is 0.02. The mean of
$Diff({Star}^{5})_{i,t}$ is also approximately zero, with an interquartile
range of 0.05.

####  3.2.2 Control Variables

To generate additional firm-level variables, we acquired financial statement
data and stock return data from the China Stock Market and Accounting Research
(CSMAR) database. In addition to commonly used firm characteristics such as
return on assets(ROA), book-to-market (B/M), and size (Size), we also
considered several characteristics likely to be connected to consumer opinions
or stock return predictability. To avoid using consumer reviews and ratings as
a proxy for a firm’s operating performance inferred from financial statements,
we constructed other variables such as idiosyncratic volatility (Ivol, Ang et
al. 2006; Boyer, Mitton, and Vorkink 2010), asset growth (AG, Cooper, Gulen,
and Schill 2008), illiquidity (Illiq, Amihud 2002), market risk (Beta,
Frazzini and Pedersen 2014), gross profitability (GP, Novy-Marx 2013), and
turnover rate (Turn, Jiang, Qi, and Tang 2018). Ivol is the weekly standard
deviation of daily residuals estimated from the Fama–French three-factor
model. AG is the quarterly growth rate of total assets. Illiq is calculated as
the absolute price change scaled by volume. Beta is determined as the
coefficient obtained by regressing monthly stock returns against market
returns using a rolling window of fifty days. We define GPas the proportion of
pre-extraordinary income to the book value of assets. Turn is calculated as
the mean value of weekly turnover, representing the ratio of shares traded to
shares outstanding, over the previous week. Additionally, we took into account
expenses on advertising (Ad) and research and development (R&D) as these can
influence a company’s perception among consumers. The China Asset Management
Research Centre (CAMRC)222Source: http://sf.cufe.edu.cn/info/1198/12041.htm
provides the excess market returns (MKT) as well as the following factors:
high-minus-low (HML), small-minus-big (SMB) and winners-minus-losers (UMD).

####  3.2.3 Other Variables

Measuring Sentiment

Following (Wang, Su, and Duxbury 2021), we utilize the consumer confidence
index (CCIs) as a proxy for sentiment in China’s A-share market, recognizing
that there is a positive relationship between consumer confidence and investor
sentiment (Lemmon and Portniaguina 2006; Qiu and Welch 2004). The CCIs is an
indicator that measures consumer confidence and captures their assessments of
the state of the economy today, as well as their subjective perceptions of
future prospects, income levels, income expectations, and psychological states
related to consumption. It serves as a leading indicator for forecasting
consumption and economic developments. CCIs is often seen as an essential
addition to total consumption. The CCIs comprises two components: the consumer
expectation index and consumer satisfaction index. Moreover, we convert the
monthly CCIs into weekly ones to ensure frequency consistency. Table A
presents the mean and standard deviation of the CCIs, which are 107.93 and
7.24, respectively.

Measuring Accounting transparency

The level of accounting transparency in a firm is determined by the extent to
which its reported accounting earnings accurately reflect its genuine economic
earnings. To measure accounting transparency, we commonly use proxies such as
earnings aggressiveness (EA) and earnings smoothing (ES) (Bhattacharya, Daouk,
and Welker 2003). Therefore, we use EA and ES measures for the firm’s
accounting transparency.

We deﬁne the EA of ﬁrm $i$ in year $y$ as

${EA}_{i,y}=\frac{{ACC}_{i,y}}{{TA}_{i,y-1}}$ (6)

We deﬁne the ES of ﬁrm $i$ in year $y$ as

${ES}_{i,y}=\frac{Std\left(\
\frac{{CFO}_{i,y-3}}{A_{i,y-4}},\frac{{CFO}_{i,y-2}}{A_{i,y-3}},\frac{{CFO}_{i,y-1}}{A_{i,y-2}},\frac{{CFO}_{i,y}}{A_{i,y-1}}\right)}{Std\left(\
\frac{{NI}_{i,y-3}}{A_{i,y-4}},\frac{{NI}_{i,y-2}}{A_{i,y-3}},\frac{{NI}_{i,y-1}}{A_{i,y-2}},\frac{{NI}_{i,y}}{A_{i,y-1}}\right)}$
(7)

where${ACC}_{i,y}$ is the accruals of ﬁrm $i$ in year $y$; ${TA}_{i,y-1}$ is
defined as the total assets of firm $i$ in year $y-1$; $Std$ represents the
standard deviation in parentheses; ${CFO}_{i,y-k}$ represents the variance in
cash flow from operations of firm $i$ in year $y-k$, where $k$=0,1,2,3;
${NI}_{i,y-k}$ defined as net profit for ﬁrm $i$ in year $y-k$, where
$k$=0,1,2,3. Moreover, we convert the quarterly EA and ES into weekly ones to
ensure frequency consistency. Table A displays the summary statistics for EA
and ES, indicating a mean of 0.13 and 4.13, respectively.

Measuring Cash flow

Numerous studies suggest that when customers perceive a superior level of
quality, firms can benefit from increased future cash flows. This can be
achieved by improving their standing with customers, lowering price elasticity
and reducing marketing expenses, increasing the range of available products,
and lowering informational costs for consumers (e.g. Allen 1984; Choi 1998;
Cabral 2000). The proliferation of internet-based consumer opinion platforms
has the potential to amplify these effects, as consumers now play an active
role in producing and accessing product-related information. Consumer opinions
can also reveal novel information about financial flows. To capture cash flow
surprises, we construct two measures.

First, we measure revenue surprises (SUR) using a standardized unexpected
revenue growth estimator (Jegadeesh and Livnat 2006). Specifically, we define
the SUR of firm $i$ in quarter $q$ as

${SUR}_{i,q}=\frac{{REV}_{i,q}-{REV}_{i,q-4}}{\sigma_{i,q}}$ (8)

where ${REV}_{i,q}$ represents the revenue per share for ﬁrm $i$ in quarter
$q$, ${REV}_{i,q-4}$ represents the revenue per share for ﬁrm $i$ in quarter
$q-4$, and $\sigma_{i,q}$ represents the standard deviation of the growth in
revenue for ﬁrm $i$ in quarter $q$.

The second measure is earnings surprises (SUE), which we quantify using the
standardized unexpected earnings estimator (Jegadeesh and Livnat 2006). We
deﬁne the SUE of ﬁrm $i$ in quarter $q$ as

${SUE}_{i,q}=\frac{{Earnings}_{i,q}-{Earnings}_{i,q-4}}{\sigma_{i,q}}$ (9)

where ${Earnings}_{i,q}$ represents the operating profit for ﬁrm $i$ in
quarter $q$, ${Earnings}_{i,q-4}$ represents the operating profit for ﬁrm $i$
in quarter $q-4$, and $\sigma_{i,q}$ represents the standard deviation of the
quarterly operating profit for ﬁrm $i$ in quarter $q$. Additionally, we
convert the quarterly SUE and SUR into weekly ones to ensure frequency
consistency. The mean values for SUR and SUE are presented in Table A and are
0.24 and 0.47, respectively.

To better capture cash flow shocks for firms, we use measures of profitability
shock. Previous research has shown that there is a positive and significant
correlation between profitability shocks and stock returns (Yin and Liao
2021). To construct our measures, we follow the methodologies of Hou and Van
Dijk (2019) and Yin and Liao (2021), which involve using both the HVZ and VOL
models. In contrast to the HVZ model, the VOL model has been designed to
incorporate earnings volatility for the purpose of conducting a more robust
analysis. Additionally, we convert the quarterly profitability shocks into
weekly ones to ensure frequency consistency.

The HVZ model and the VOL model are defined as follows:

(1) HVZ model

${Prof}_{i,q+1}=\alpha_{0,q}+\alpha_{1,q}\frac{V_{i,q}}{A_{i,q}}+\alpha_{2,q}{DD}_{i,q}+\alpha_{3,q}\frac{D_{i,q}}{B_{i,q}}+\alpha_{4,q}{Prof}_{i,q}+\varepsilon_{i,q+1}$
(10)

where ${Prof}_{i,q}$ is the profitability for firm $i$ in quarter $q$;
$\frac{V_{i,q}}{A_{i,q}}$ represents the ratio of an asset’s market value to
its book value, and ${DD}_{i,q}$ is a dummy variable with a value of 1
indicating that firm $i$ does not pay dividends in quarter $q$, and a value of
0 indicating that the firm pays dividends in quarter $q$;
$\frac{D_{i,q}}{B_{i,q}}$ defined as the dividend payments of firm $i$ in
quarter $q$. Profitability in quarter $q$ is measured using ROA.

(2) VOL model

${Prof}_{i,q+1}=\alpha_{0,q}+\alpha_{1,q}\frac{V_{i,q}}{A_{i,q}}+\alpha_{2,q}{DD}_{i,q}+\alpha_{3,q}\frac{D_{i,q}}{B_{i,q}}+\alpha_{4,q}{Prof}_{i,q}+\alpha_{5,q}{Vol}_{i,q}+\varepsilon_{i,q+1}$
(11)

where ${Vol}_{i,q}$ is defined as the earnings volatility of firm $i$ in
quarter $q$. he calculations for the remaining variables align with those
utilized in the HVZ model. We then compute the expected profitability for
quarter $q+1$ by multiplying the independent variables from quarter $q$ by the
coefficients derived from the regressions for quarter $q-1$. Subsequently, we
define the profitability shock (a proxy for cash flow shock) in quarter $q+1$
as:

${ProfShock}_{q+1}={Realized\ Prof}_{q+1}-{Expected\ Prof}_{q+1}$ (12)

Moreover, we convert the quarterly profitability shock into weekly ones to
ensure frequency consistency.

Table A presents the summary statistics of these main variables in this
sample, and we report the mean, standard deviation, minimum, 25th percentile,
median, 75th percentile, and maximum. Detailed definitions for fall variables
are reported in Appendix A1.

[Insert Table A here]

##  4 Results

In this section, we present our empirical findings on the informational value
of customer reviews.

###  4.1 Customer Online Satisfaction and Stock returns

Our objective is to investigate the impact of the Customer Sentiment Tendency
category on future stock returns in China’s A-share market. To achieve this,
we begin by examining the effect using a static panel regression that accounts
for fixed effects. Specifically, we run the following panel regression to
analyze the effect:

${R}_{i,t+1}=\alpha+\tau_{i}+\lambda_{i}+\beta_{1}Diff(\mathrm{rev})^{neg}_{i,t}+\gamma\mathrm{X}_{i,t}+\varepsilon_{i,t+1}$
(13)
${R}_{i,t+1}=\alpha+\tau_{i}+\lambda_{i}+\beta_{1}Diff(\mathrm{rev})^{pos}_{i,t}+\gamma\mathrm{X}_{i,t}+\varepsilon_{i,t+1}$
(14)

To address endogeneity problems, we run a dynamic panel model using GMM
estimators (Arellano and Bond 1991) and run the following regression:

${R}_{i,t+1}=\alpha+\beta_{1}R_{i,t}+\beta_{2}Diff(\mathrm{rev})^{neg}_{i,t}+\gamma\mathrm{X}_{i,t}+\varepsilon_{i,t+1}$
(15)

${R}_{i,t+1}=\alpha+\beta_{1}R_{i,t}+\beta_{2}Diff(\mathrm{rev})^{pos}_{i,t}+\gamma\mathrm{X}_{i,t}+\varepsilon_{i,t+1}$
(16)

where ${R}_{i,t}$ and ${R}_{i,t+1}$ are defined as the weekly returns for firm
$i$ in week $t$ and $t+1$, respectively; $Diff(\mathrm{rev})^{neg}_{i,t}$ and
$Diff(\mathrm{rev})^{pos}_{i,t}$ represent the Customer Negative Sentiment
Tendency (CNST) and Customer Positive Sentiment Tendency (CPST) for firm $i$
in week $t$; $\mathrm{X}_{i,t}$ is defined as the vector of control variables
for firm $i$ in week $t$, including ﬁrm size (Size), book-to-market ratio
(B/M), return on assets (ROA), idiosyncratic volatility (Ivol, Ang et al.
2006; Boyer, Mitton, and Vorkink 2010), asset growth (AG, Cooper, Gulen, and
Schill 2008), illiquid (Illiq,Amihud 2002), market risk (Beta, Frazzini and
Pedersen 2014), gross profitability (GP, Novy-Marx 2013), turnover rate
(Turn,Jiang, Qi, and Tang 2018), and expenses on advertising (Ad)and research
and development (R$\&$D); $\tau_{i}$ represents the time fixed effects, and
$\lambda_{i}$ represents the firm fixed effects.

The findings in Table 3 reveal that both static and dynamic models demonstrate
a significant negative relationship between CNST and future stock returns. The
coefficient for CNST is -0.0008, with t-statistics of -2.7093 and -2.7653 for
the static and dynamic models, respectively. The results also suggest a
significant economic impact. For example, Model 1 shows that a one standard
deviation increase in CNST would result in a significant drop of 0.0008% in
average weekly returns. However, Models 3 and 4 show that CPST is a non-
significant positive predictor of future stock returns for both static and
dynamic models. This result directly reflects the inverse relationship between
the CNST and stock return. Table 3 also reveals several other interesting
findings. Consistent with the evidence in Tirunillai and Tellis (2012), based
on consumer-generated content, positive sentiment words did not predict stock
returns, but negative sentiment words negatively predicted stock returns.
Moreover, various studies have validated that product recalls have a
substantial negative impact on the stock market, indicating that negative
consumer feedback on the quality of inferior products can lead to a negative
impact on stock returns (Hoffer, Pruitt, and Reilly 1988; Barber and Darrough
1996).

[Insert Table 3 here]

Turning to One-Star Tendency (OST) and Five-Star Tendency (FST), we run the
following static and dynamic panel regressions:

${R}_{i,t+1}=\alpha+\tau_{i}+\lambda_{i}+\beta_{1}Diff(Star^{s})_{i,t}+\gamma\mathrm{X}_{i,t}+\varepsilon_{i,t+1}$
(17)

${R}_{i,t+1}=\alpha+\beta_{1}R_{i,t}+\beta_{2}Diff(Star^{s})_{i,t}+\gamma\mathrm{X}_{i,t}+\varepsilon_{i,t+1}$
(18)

where ${R}_{i,t}$ and ${R}_{i,t+1}$ are the weekly returns for firm $i$ in
week $t$ and $t+1$, respectively; $Diff(Star^{s})_{i,t}$ represents the One-
Star Tendency (OST) and Five-Star Tendency (FST) for firm $i$ in week $t$;
$\mathrm{X}_{i,t}$ is defined as the vector of control variables for firm $i$
in week $t$, including GP, Size, B/M, ROA, Illiq, Beta, Ivol, Turn, AG, Ad and
R&D; $\tau_{i}$ represents the time fixed effects, and $\lambda_{i}$
represents the firm fixed effects.

Table 4 presents the regression results for OST and FST, showing a significant
negative relationship between OST and future stock returns. In the static
model, the coefficient for OST is -0.0072 with a t-statistic of -2.1665, while
in the dynamic model, the coefficient for OST is -0.0120 with a t-statistic of
-2.6777. In terms of economic magnitude, Model 1 indicates that a one standard
deviation increase in OST would result in a statistically significant decline
of 0.0072% (t = -2.1665) in average weekly returns. Consistent with prior
work, Table 4 demonstrates that consumers are becoming increasingly
significant stakeholders in firms and customers of their products and services
due to the rising popularity of internet-based consumer opinion platforms.
Customers have access to information about product value and quality and can
generate new information about products (Huang et al. 2015; Fornell, Morgeson
III, and Hult 2016). Low ratings indicate that consumers are less satisfied
with the product and have more negative attitudes and emotions towards it.
However, Models 3 and 4 indicate that FST is not a statistically significant
positive predictor of future stock returns for both static and dynamic models.
This result also implies that the conclusions of Table 3 and Table 4 ’s
conclusions are mutually validated. For example, the non-significance of FST
in both static and dynamic models supports the conclusion from Table 3 that
CPST is not a significant predictor of future stock returns.

[Insert Table 4 here]

###  4.2 Sentiment

Investors have been observed to behave differently under various market
conditions (Gervais and Odean 2001; Nofsinger 2005; Li and Luo 2017).
Moreover, we analyze the impact of CNST on future stock returns in different
economic settings and use the consumer confidence index (CCIs) as a sentiment
proxy in the Chinese stock market (Wang, Su, and Duxbury 2021). To investigate
the relationship between sentiment and expected returns, we split the entire
sample into two subsamples using the median of CCIs as a cutoff point.
Specifically, periods with CCIs larger or smaller than the median of the whole
sample are identified as high or low sentiment periods, respectively. We then
conduct subsample analyses to examine the effects of different sentiment
levels on expected returns. Table 5 presents the regression results for high
and low sentiment periods. The results show that CNST can significantly
negatively predict future stock returns during high sentiment periods.
However, low sentiment does not significantly predict future stock returns.

Consistent with the prior work, investors tend to buy stocks when they are
optimistic and sell them when they are pessimistic (Baker and Wurgler 2006;
Chung, Hung, and Yeh 2012). Therefore, this implies that during periods of
high sentiment, investors are more likely to engage in trading, thereby
exerting a stronger impact on stock markets. Our findings are consistent with
this pattern, as CNST during high-sentiment periods can negatively predict
future stock returns.

[Insert Table 5 here]

###  4.3 Growth Companies and Value Companies

Consumer review data is likely to contain information related to a company’s
future business prospects, especially regarding consumer demand for the
company’s products or purchase intentions. Investors may react differently to
such information for different types of companies. For growth companies,
investors may react more strongly to information about the company’s
prospects, as the market valuation of growth companies is more sensitive to
their future growth prospects. Moreover, such companies have great development
potential, but their future development may be unstable and face more
uncertainty and risk in their future prospects than value companies.
Therefore, consumer reviews and company reputation become more important for
the future development of growth companies. Companies experiencing rapid
growth may exhibit high levels of profitability and investment. To analyze the
impact of company profitability and investment on our results, we use return
on assets (ROA) as a measure of profitability and asset growth (AG) as a
measure of investment. We then split the entire sample into two subsamples
based on the median of ROA and AG. Specifically, periods with ROA and AG
larger or smaller than the median of the whole sample are identified as high
or low company profitability and high or low company investment, respectively.
To further explore the impact of company characteristics on our results, we
split the entire sample into two subsamples based on the median of book-to-
market (B/M). Specifically, periods with B/M larger or smaller than the median
of the whole sample are identified as value or growth companies, respectively.

The regression results for high or low ROA, AG, and B/M are presented in Table
6. The results in Table 6 show that CNST can significantly negatively predict
future stock returns during periods of high ROA, high AG, and low B/M. By
contrast, low ROA, low AG, and high B/M do not significantly predict future
stock returns. This also confirms that the effect of consumer evaluation data
on stock return prediction is indeed caused by investors’ reactions to
information about growth and value companies.

[Insert Table 6 here]

###  4.4 Firm’s Accounting Transparency

Consumer review data is a type of non-financial information. In accounting and
auditing practice, non-financial information is an important tool for judging
whether a company engages in earnings management behavior. For example, Chiu
et al. (2020) obtained information about a firm’s sales and potential
manipulation of operating income by utilizing Google searches of the firm’s
products, specifically through the Search Volume Index (SVI). Additionally, a
firm’s accounting transparency will also directly affect investors’ behavior
in using information. In this subsection, we show whether a firm’s accounting
transparency affects investors’ behavior and further affects the predictive
power of consumer reviews. If a firm’s accounting transparency is low, market
investors and analysts will rely more on non-financial information to make
investment decisions. We use earnings aggressiveness (EA) and earnings
smoothness (ES) as proxies for accounting transparency (as discussed in
Section 3.2). A high value of EA indicates that a company is likely to engage
in earnings management practices to hide its actual economic earnings, which
can reduce accounting transparency. Conversely, a more negative value of ES
suggests a greater likelihood of earnings smoothing, which could conceal the
correlation between earnings and economic performance and result in lower
levels of accounting transparency (Bhattacharya, Daouk, and Welker 2003; Liu
et al. 2017). Therefore, higher EA and ES values indicate lower accounting
transparency of the company and lower quality of accounting information. To
analyze the impact of EA and ES on expected returns, we split the entire
sample into two subsamples using the median of EA and ES as the threshold. The
high and low EA/ES groups represent firms with different tendencies towards
earnings management and smoothing, respectively. We then conduct subsample
analyses to investigate the effects of EA and ES on expected returns.

Table 7 presents the regression results for high or low EA and ES. The results
in Table 7 show that CNST can significantly negatively predict future stock
returns during periods of high EA and ES. By contrast, low EA and ES do not
significantly predict future stock returns. This finding suggests that
consumer opinions will be affected by the earnings opacity of the company.
This indicates that the predictive power of consumer reviews, a type of non-
financial information, is influenced by the degree to which the company
manipulates its financial information. These findings suggest that consumer
reviews, as a type of non-financial information, have greater predictive power
for companies with lower accounting transparency.

[Insert Table 7 here]

###  4.5 Cash Flow

To influence stock returns, consumer online opinions need to bring in new
information regarding a company’s cash flows. Thus, to capture new information
in cash flows, we utilize SUR and SUE. Following Tetlock, Saar-Tsechansky, and
Macskassy (2008); Da, Engelberg, and Gao (2011b); Froot et al. (2017); Chen et
al. (2014), we run the following dynamic panel regressions to resolve
endogeneity issues:

${SUR}_{i,t}=\alpha+\beta_{1}SUR_{i,t-1}+\beta_{2}Diff(\mathrm{rev})^{neg}_{i,t}+\gamma\mathrm{X}_{i,t-1}+\varepsilon_{i,t+1}$
(19)

${SUE}_{i,t}=\alpha+\beta_{1}SUE_{i,t-1}+\beta_{2}Diff(\mathrm{rev})^{neg}_{i,t}+\gamma\mathrm{X}_{i,t-1}+\varepsilon_{i,t+1}$
(20)

where ${SUR}_{i,t}$ is revenue surprises of ﬁrm $i$ in week $t$ (as discussed
in Section 3.2); ${SUE}_{i,t}$ is earnings surprises of ﬁrm $i$ in week $t$
(as discussed in Section 3.2); $Diff(\mathrm{rev})^{neg}_{i,t}$ represents the
CNST of firm $i$ in week $t$ (as discussed in Section 3.2);
$\mathrm{X}_{i,t-1}$ is defined as the vector of control variables for firm
$i$ in week $t-1$, including GP, Size, B/M, ROA, Illiq, Beta, Ivol, Turn, AG,
Ad,and R&D; and $\tau_{i}$ represents the time fixed effects, and
$\lambda_{i}$ represents the firm fixed effects. We also estimate
${SUR}_{i,t-1}$ and ${SUE}_{i,t-1}$ which are the lagged dependent variables.
Since the predictability of CNST is to predict the weekly financial index and
fundamental information when the financial statement is not published, the
prediction depends on the information of CNST of week $t$ and the financial
information before week $t$. Therefore, it is necessary to use $t$ and a
control variable of week $t-1$.

We also consider profitability shocks (a proxy for cash flow shocks) (Hou and
Van Dijk 2019; Yin and Liao 2021). We run the following dynamic panel
regression:

${ProfShock}_{i,t}=\alpha+\beta_{1}ProfShock_{i,t-1}+\beta_{2}Diff(\mathrm{rev})^{neg}_{i,t}+\gamma\mathrm{X}_{i,t-1}+\varepsilon_{i,t+1}$
(21)

Table 8 demonstrates that an increase of one standard deviation in CNST leads
to a statistically significant decrease of 0.0020% (t = -2.0593) in average
weekly returns, and the results show that CNST significantly negatively
predicts revenue surprises. This result implies that consumer opinions contain
tendentious words that carry new information about cash flows. Table 8 also
indicates that CNST is a significant negative predictor of earnings surprises.
The results also demonstrate that a one standard deviation increase in the
CNST variable leads to a statistically significant decrease of 0.0703% (t =
-2.3187) in average weekly returns. After controlling for other determinants
of revenue and earnings surprises, the predictability of CNST is obtained. For
instance, lagged revenue For instance, lagged revenue surprises strongly and
positively predict current revenue and earnings surprises. This result implies
that the average analyst does not properly incorporate the information from
consumer opinions into her estimates.

[Insert Table 8 here]

Table 9 shows the regression results with profitability shocks as the
dependent variable. CNST is a significant negative predictor of profitability
shocks with different models. Table 9 demonstrates that a one standard
deviation increase in CNST is associated with a statistically significant
decrease of 0.0002% (t = -1.8839) and 0.0002% (t = -1.9999) in average weekly
returns, respectively. Consistent with prior results, consumer opinions
contain tendentious words that contain novel information about cash flow
shocks.

[Insert Table 9 here]

##  5 Conclusion

This study investigates the investment value of consumer opinions in China’s
A-share market. We collect a comprehensive dataset of customer reviews from
JD.com and construct two variables, Customer Negative Sentiment Tendency
(CNST) and One-Star Tendency (OST), to quantify customer online satisfaction.
To assess the impact of consumer online satisfaction on stock returns, we
construct both static and dynamic panel models and control for a vector of
control variables. The results show that CNST and OST are negatively
associated with subsequent stock returns.

Our findings indicate that expected stock returns are negatively impacted by
CNST and OST, and this effect remains consistent across static and dynamic
panel data regressions. This negative impact appears to be concentrated among
growth companies. Additionally, CNST can significantly negatively predict
future stock returns during high sentiment periods and for firms with low
accounting transparency. Our findings support the view that consumer online
reviews provide new and unique information regarding cash flows, as
demonstrated by the negative relationship between CNST and revenue surprises,
earnings surprises, and cash flow shocks.

Our empirical results have some worthwhile implications. The findings
presented in this paper underscore the significance of consumers as
information generators in China’s A-share market. In contrast to conventional
information intermediaries such as financial statements or stock analysts,
consumer crowds can offer high-frequency information on a firm’s products and
cash flows. Consumer online satisfaction can provide forward-looking
information for investors and help market participants understand the current
and future development of companies from the perspective of their online
consumers.

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## Appendix A

Table A1

Table 1: Summary statistics on JD.com reviews for 106 public firms. This table reports the summary statistics for a sample of JD.com customer reviews for products of public firms from November 2008 through December 2017. To be included in the sample, a firm must have at least 1000 online reviews and a timespan of more than 12 months.We report the number of reviews, products, and public firms for the full sample.  | Number of reviews | Number of products | Number of firms
---|---|---|---
Final Sample | 18,008,415 | 164,715 | 106
Home Appliance Industry | 7,245,982 | 44,059 | 25
Garment Industry | 5,365,380 | 75,774 | 27
Food Industry | 4,021,617 | 22,964 | 29
Wine Industry | 1,375,436 | 21,918 | 25
Table 2: Summary Statistics of Main Variables. This table presents the summary
statistics for a sample of firms with customer reviews from November 2008 to
December 2017. Descriptive statistics for control variables are also included.
All variable definitions are provided in Appendix A1.

Variable | Mean | Std. Dev. | Min | 25% | Median | 75% | Max
---|---|---|---|---|---|---|---
Customer Reviews
$Diff(Star^{1})_{i,t}$ | 0.00 | 0.11 | -1.00 | -0.01 | 0.00 | 0.01 | 1.00
$Diff(Star^{5})_{i,t}$ | 0.00 | 0.18 | -1.00 | -0.03 | 0.00 | 0.03 | 1.00
$Diff(\mathrm{rev})^{neg}_{i,t}$ | 0.00 | 1.08 | -41.00 | 0.00 | 0.00 | 0.00 | 37.00
$Diff(\mathrm{rev})^{pos}_{i,t}$ | 0.01 | 2.10 | -65.00 | 0.00 | 0.00 | 0.00 | 56.00
Firm-level characteristics
Stock Return(R) | 0.00 | 0.06 | -0.19 | -0.03 | 0.00 | 0.03 | 0.23
Advertising(Ad) | 0.15 | 0.64 | -27.73 | 0.09 | 0.15 | 0.23 | 1.23
Book-to-market (B/M) | 0.53 | 0.22 | 0.04 | 0.36 | 0.50 | 0.68 | 1.24
R&D | 1.99 | 1.67 | 0.02 | 0.59 | 1.95 | 3.14 | 31.98
Return On Assets (ROA) | 0.02 | 0.02 | -0.44 | 0.01 | 0.01 | 0.03 | 0.21
LnSize (Size) | 22.55 | 1.18 | 20.12 | 21.73 | 22.35 | 23.25 | 26.30
Asset Growth (AG) | 0.08 | 0.21 | -0.46 | -0.02 | 0.04 | 0.11 | 3.78
Illiquid (Illiq) | 0.04 | 0.31 | 0.00 | 0.01 | 0.02 | 0.03 | 37.32
Market risk (Beta) | 1.04 | 0.41 | -0.95 | 0.77 | 1.04 | 1.30 | 3.15
Idiosyncratic volatility (Ivol) | 0.04 | 0.03 | 0.00 | 0.03 | 0.04 | 0.05 | 1.73
Gross profitability (GP) | 0.34 | 0.19 | -0.01 | 0.20 | 0.32 | 0.44 | 1.57
Turnover rate (Turn) | 8.91 | 11.88 | 0.01 | 2.78 | 5.26 | 10.42 | 244.62
Earnings Aggressiveness (ES) | -0.12 | 0.13 | -0.61 | -0.20 | -0.11 | -0.02 | 0.30
Earning Smoothing (EA) | 4.13 | 7.93 | 0.20 | 1.13 | 2.11 | 3.92 | 89.54
Cash ﬂow surprises and Sentiment
Consumer Confidence Index (CCIs) | 107.93 | 7.24 | 97.00 | 103.40 | 105.50 | 112.00 | 124.00
Earnings surprise (SUE) | 0.47 | 0.99 | -3.07 | -0.08 | 0.48 | 1.12 | 3.07
Revenue surprise (SUR) | 0.24 | 1.08 | -3.47 | -0.28 | 0.25 | 0.85 | 3.41
Cash flow shock
HVZ model | -0.01 | 0.06 | -1.03 | -0.01 | 0.00 | 0.01 | 0.67
VOL model | -0.01 | 0.04 | -0.52 | -0.01 | 0.00 | 0.01 | 0.65

Table 3: Static and Dynamic Models and CPST/CNST. This table presents the results of static and dynamic panel data regressions of stock returns on Customer Negative Sentiment Tendency (CNST) and Customer Positive Sentiment Tendency (CPST), as well as control variables. All other variables are defined in the Appendix A1. The sample period is from November 2008 to December 2017. The t-statistic is presented in parentheses (* at the 10% level; ** at the 5% level; *** at the 1% level). |  | Static model
---
(1)
$R_{t+1}$
| Dynamic model
---
(2)
$R_{t+1}$
| Static model
---
(3)
$R_{t+1}$
| Dynamic model
---
(4)
$R_{t+1}$
$R_{t}$ |  | 0.0815** |  | 0.0811**
|  | (2.3976) |  | (2.3911)
$Diff(\mathrm{rev})^{neg}_{i,t}$ | -0.0008*** | -0.0008*** |  |
| (-2.7093) | (-2.7653) |  |
$Diff(\mathrm{rev})^{pos}_{i,t}$ |  |  | -0.0000 | -0.0001
|  |  | (-0.3207) | (-0.8108)
$Ad_{t}$ | 0.0007 | 0.0010*** | 0.0007 | 0.0010***
| (1.4115) | (4.0572) | (1.4118) | (4.0539)
$B/M_{t}$ | -0.0313*** | -0.0193*** | -0.0313*** | -0.0193***
| (-8.9579) | (-5.3224) | (-8.9539) | (-5.3196)
$R\&D_{t}$ | 0.0003 | 0.0007 | 0.0003 | 0.0007
| (0.7894) | (1.4339) | (0.7859) | (1.4299)
$ROA_{t}$ | -0.0220 | -0.0365 | -0.0219 | -0.0366
| (-1.0234) | (-1.1803) | (-1.0218) | (-1.1810)
$Size_{t}$ | 0.0023 | -0.0010 | 0.0023 | -0.0010
| (1.1620) | (-1.5882) | (1.1626) | (-1.5908)
$Ivol_{t}$ | 0.0046 | 0.0066 | 0.0043 | 0.0060
| (0.4273) | (0.8057) | (0.4052) | (0.7311)
$GP_{t}$ | 0.0035 | -0.0092* | 0.0035 | -0.0092*
| (0.7246) | (-1.8587) | (0.7237) | (-1.8592)
$Turn_{t}$ | -0.0003*** | -0.0010*** | -0.0003*** | -0.0010***
| (-8.4255) | (-5.6579) | (-8.4250) | (-5.6614)
$Beta_{t}$ | 0.0007 | 0.0008 | 0.0007 | 0.0008
| (0.7182) | (0.6926) | (0.6956) | (0.6614)
$Illiq_{t}$ | 0.0033*** | 0.0022 | 0.0033*** | 0.0022
| (3.1723) | (0.6968) | (3.1754) | (0.7017)
$AG_{t}$ | 0.0037* | 0.0056 | 0.0037* | 0.0056
| (1.7464) | (1.2810) | (1.7484) | (1.2822)
Constant | -0.0341 | 0.0440*** | -0.0341 | 0.0441***
| (-0.7644) | (3.0063) | (-0.7644) | (3.0159)
Number of Code | 106 | 106 | 106 | 106
Year FE | YES |  | YES |
Code FE | YES |  | YES |
AR (1) test p-value |  | 0.1630 |  | 0.2630
AR (2) test p-value |  | 0.2190 |  | 0.2220

Table 4: Static and Dynamic Models and OST/FST. This table presents the results of static and dynamic panel data regressions of stock returns on One-Star Tendency (OST) and Five-Star Tendency (FST), as well as control variables. All other variables are defined in the Appendix A1. The sample period is from November 2008 to December 2017. The t-statistic is presented in parentheses (* at the 10% level; ** at the 5% level; *** at the 1% level). | Static model | Dynamic model | Static model | Dynamic model
---|---|---|---|---
| (1) | (2) | (3) | (4)
| $R_{t+1}$ | $R_{t+1}$ | $R_{t+1}$ | $R_{t+1}$
$R_{t}$ |  | 0.0826** |  | 0.0763*
|  | (2.4323) |  | (1.9122)
$Diff(Star^{1})_{i,t}$ | -0.0072** | -0.0120*** |  |
| (-2.1665) | (-2.6777) |  |
$Diff(Star^{5})_{i,t}$ |  |  | 0.0037* | 0.0063
|  |  | (1.6736) | (1.6114)
$Ad_{t}$ | 0.0007 | 0.0010*** | 0.0007 | 0.0010***
| (1.4136) | (4.0760) | (1.4102) | (3.6401)
$B/M_{t}$ | -0.0313*** | -0.0192*** | -0.0313*** | -0.0190***
| (-8.9570) | (-5.3099) | (-8.9558) | (-4.5878)
$R\&D_{t}$ | 0.0003 | 0.0007 | 0.0003 | 0.0006
| (0.7768) | (1.4275) | (0.7949) | (0.8783)
$ROA_{t}$ | -0.0219 | -0.0369 | -0.0217 | -0.0423
| (-1.0213) | (-1.1885) | (-1.0136) | (-1.1096)
$Size_{t}$ | 0.0023 | -0.0010 | 0.0023 | -0.0009
| (1.1578) | (-1.6051) | (1.1581) | (-1.2916)
$Ivol_{t}$ | 0.0045 | 0.0063 | 0.0045 | 0.0084
| (0.4202) | (0.7778) | (0.4209) | (0.8618)
$GP_{t}$ | 0.0035 | -0.0092* | 0.0035 | -0.0059
| (0.7131) | (-1.8641) | (0.7262) | (-1.0885)
$Turn_{t}$ | -0.0003*** | -0.0010*** | -0.0003*** | -0.0009***
| (-8.4316) | (-5.6656) | (-8.4314) | (-5.1209)
$Beta_{t}$ | 0.0007 | 0.0008 | 0.0007 | 0.0010
| (0.6973) | (0.6835) | (0.6876) | (0.7954)
$Illiq_{t}$ | 0.0033*** | 0.0021 | 0.0033*** | 0.0023
| (3.1715) | (0.6877) | (3.1441) | (0.6702)
$AG_{t}$ | 0.0037* | 0.0056 | 0.0037* | 0.0042
| (1.7623) | (1.2871) | (1.7531) | (0.8831)
Constant | -0.0338 | 0.0443*** | -0.0339 | 0.0386**
| (-0.7591) | (3.0268) | (-0.7602) | (2.5103)
Number of Code | 106 | 106 | 106 | 106
Year FE | YES |  | YES |
Code FE | YES |  | YES |
AR (1) test p-value |  | 0.0000 |  | 0.0000
AR (2) test p-value |  | 0.1530 |  | 0.2750

Table 5: CNST and Sentiment. This table presents the results of fixed effects panel data regressions of stock returns on CNST and control variables after controlling for the sentiment proxy, which is the consumer confidence index (CCIs). All other variables are defined in the Appendix A1. The sample period is from November 2008 to December 2017. The t-statistic is presented in parentheses (* at the 10% level; ** at the 5% level; *** at the 1% level). | High sentiment | Low sentiment
---|---|---
| (1) | (2)
| $R_{t+1}$ | $R_{t+1}$
$Diff(\mathrm{rev})^{neg}_{i,t}$ | -0.0009*** | 0.0000
| (-3.0358) | (0.0005)
$Ad_{t}$ | 0.0081 | 0.0005
| (0.8915) | (0.8880)
$B/M_{t}$ | -0.0408*** | -0.0215***
| (-8.5786) | (-3.3856)
$R\&D_{t}$ | 0.0011** | -0.0013
| (1.9947) | (-1.0324)
$ROA_{t}$ | -0.0308 | -0.0011
| (-1.0679) | (-0.0293)
$Size_{t}$ | 0.0039 | 0.0007
| (1.3532) | (0.1671)
$Ivol_{t}$ | -0.0091 | 0.0158
| (-0.5988) | (1.0472)
$GP_{t}$ | 0.0137* | -0.0009
| (1.9274) | (-0.1016)
$Turn_{t}$ | -0.0003*** | -0.0005***
| (-5.4054) | (-7.3912)
$Beta_{t}$ | 0.0005 | -0.0013
| (0.4256) | (-0.6778)
$Illiq_{t}$ | 0.0034*** | 0.0004
| (3.0964) | (0.1720)
$AG_{t}$ | 0.0004 | 0.0080*
| (0.1418) | (1.8608)
Constant | -0.0702 | 0.0066
| (-1.0928) | (0.0755)
Number of Code | 106 | 106
Year FE | YES | YES
Code FE | YES | YES

Table 6: CNST with Growth Companies and Value Companies. This table presents the results of fixed effects panel data regressions of stock returns on CNST and control variables after controlling for ROA, AG, and B/M. All other variables are defined in the Appendix A1. The sample period is from November 2008 to December 2017. The t-statistic is presented in parentheses (* at the 10% level; ** at the 5% level; *** at the 1% level). | High B/M | Low B/M | High ROA | Low ROA | High AG | Low AG
---|---|---|---|---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
| $R_{t+1}$ | $R_{t+1}$ | $R_{t+1}$ | $R_{t+1}$ | $R_{t+1}$ | $R_{t+1}$
$Diff(\mathrm{rev})^{neg}_{i,t}$ | -0.0008 | -0.0012** | -0.0011*** | -0.0003 | -0.0011*** | -0.0001
| (-1.3116) | (-2.0561) | (-2.8556) | (-0.4733) | (-3.0857) | (-0.2362)
$Ad_{t}$ | 0.0142 | -0.0261 | -0.0172 | 0.0009 | 0.0091 | 0.0005
| (0.6847) | (-1.5720) | (-1.1016) | (0.0635) | (0.7256) | (1.0135)
$B/M_{t}$ | -0.0493*** | -0.0498*** | -0.0278*** | -0.0356*** | -0.0328*** | -0.0391***
| (-4.7662) | (-4.1255) | (-4.3244) | (-5.3741) | (-6.0919) | (-7.3784)
$R\&D_{t}$ | 0.0002 | 0.0008 | -0.0002 | 0.0001 | 0.0009 | -0.0014
| (0.1324) | (0.5473) | (-0.1688) | (0.1429) | (0.8332) | (-1.1382)
$ROA_{t}$ | 0.0031 | -0.0990* | -0.0366 | 0.0012 | -0.0505 | 0.0032
| (0.0591) | (-1.8956) | (-0.8333) | (0.0157) | (-1.3625) | (0.0992)
$Size_{t}$ | 0.0017 | 0.0027 | -0.0042 | 0.0086*** | 0.0011 | -0.0010
| (0.3234) | (0.5886) | (-1.0495) | (2.6136) | (0.3237) | (-0.2901)
$Ivol_{t}$ | -0.0198 | 0.0083 | -0.0142 | 0.0122 | -0.0097 | 0.0076
| (-0.4611) | (0.3988) | (-0.3605) | (0.9992) | (-0.4732) | (0.6017)
$GP_{t}$ | -0.0295 | 0.0171* | -0.0079 | 0.0116 | 0.0024 | -0.0252**
| (-1.2766) | (1.6857) | (-0.8543) | (1.1972) | (0.3164) | (-2.5665)
$Turn_{t}$ | -0.0007*** | -0.0005*** | -0.0003*** | -0.0004*** | -0.0004*** | -0.0004***
| (-7.1194) | (-6.5724) | (-3.4652) | (-6.4090) | (-4.9481) | (-7.4335)
$Beta_{t}$ | -0.0007 | 0.0016 | 0.0006 | 0.0012 | -0.0001 | -0.0004
| (-0.3200) | (0.6979) | (0.3301) | (0.7199) | (-0.0620) | (-0.2592)
$Illiq_{t}$ | -0.0082** | 0.0032** | 0.0347*** | 0.0019* | -0.0097 | 0.0007
| (-2.3907) | (2.4248) | (5.7965) | (1.7212) | (-0.4732) | (0.6344)
$AG_{t}$ | 0.0046 | 0.0090 | 0.0047 | 0.0029 | 0.0024 | 0.0242**
| (1.1734) | (1.5414) | (0.9021) | (0.9813) | (0.3164) | (2.5157)
Constant | 0.0122 | -0.0359 | 0.1195 | -0.1716** | -0.0004*** | 0.0583
| (0.0993) | (-0.3549) | (1.3267) | (-2.3861) | (-4.9481) | (0.7661)
Number of Code | 106 | 106 | 106 | 106 | 106 | 106
Year FE | YES | YES | YES | YES | YES | YES
Code FE | YES | YES | YES | YES | YES | YES

Table 7: CNST and EA/ES. This table presents the results of fixed effects panel data regressions of stock returns on CNST and control variables after controlling for earnings aggressiveness (EA) and earnings smoothing (ES). All other variables are defined in the Appendix A1. The sample period is from November 2008 to December 2017. The t-statistic is presented in parentheses (* at the 10% level; ** at the 5% level; *** at the 1% level). | High EA | Low EA | High ES | Low ES
---|---|---|---|---
| (1) | (2) | (3) | (4)
| $R_{t+1}$ | $R_{t+1}$ | $R_{t+1}$ | $R_{t+1}$
$Diff(\mathrm{rev})^{neg}_{i,t}$ | -0.0015* | -0.0008 | -0.0013*** | -0.0002
| (-1.9359) | (-1.4606) | (-2.5970) | (-0.2685)
$Ad_{t}$ | -0.0225 | -0.0091 | -0.0117 | 0.0006
| (-1.2588) | (-0.4608) | (-0.7747) | (1.0942)
$B/M_{t}$ | -0.0344*** | -0.0471*** | -0.0485*** | -0.0592***
| (-3.4547) | (-6.2619) | (-5.5438) | (-6.1868)
$R\&D_{t}$ | 0.0004 | 0.0009 | -0.0024* | 0.0080***
| (0.1948) | (0.6283) | (-1.8229) | (2.9805)
$ROA_{t}$ | -0.0139 | -0.1094* | -0.0516 | -0.0350
| (-0.2729) | (-1.8595) | (-0.8581) | (-0.8750)
$Size_{t}$ | -0.0002 | 0.0088* | 0.0042 | 0.0039
| (-0.0340) | (1.8137) | (0.8561) | (0.5941)
$Ivol_{t}$ | -0.0078 | 0.0172 | -0.0294 | 0.0092
| (-0.2976) | (0.6875) | (-0.6692) | (0.4795)
$GP_{t}$ | -0.0013 | 0.0364*** | 0.0022 | 0.0093
| (-0.0946) | (2.8141) | (0.1950) | (0.8098)
$Turn_{t}$ | -0.0004*** | -0.0007*** | -0.0008*** | -0.0008***
| (-4.1137) | (-8.6479) | (-8.3922) | (-6.7661)
$Beta_{t}$ | 0.0015 | -0.0008 | 0.0022 | 0.0015
| (0.6018) | (-0.3882) | (1.0124) | (0.7016)
$Illiq_{t}$ | 0.0214*** | 0.0011 | -0.0094*** | 0.0031**
| (2.9556) | (0.9167) | (-2.6392) | (2.5165)
$AG_{t}$ | 0.0004 | 0.0023 | 0.0068* | -0.0053
| (0.0577) | (0.5395) | (1.9266) | (-0.8419)
Constant | 0.0314 | -0.1734 | -0.0524 | -0.0701
| (0.2664) | (-1.5975) | (-0.4857) | (-0.4694)
Number of Code | 106 | 106 | 106 | 106
Year FE | YES | YES | YES | YES
Code FE | YES | YES | YES | YES

Table 8: CNST and Cash Flow Surprises. This table presents the results of dynamic panel data regressions of revenue and earning surprises on CNST and controls described in Eq.19 and Eq.20. The dependent variable is the standardized unexpected revenue growth estimator (SUR) or the standardized unexpected earnings (SUE). All other variables are defined in the Appendix A1. The sample period is from November 2008 to December 2017. The t-statistic is presented in parentheses (* at the 10% level; ** at the 5% level; *** at the 1% level). | $SUR_{i,t}$ | $SUE_{i,t}$
---|---|---
$SUR_{i,t-1}$ | 0.9562*** |
| (180.6413) |
$SUE_{i,t-1}$ |  | 0.9310***
|  | (79.8426)
$Diff(\mathrm{rev})^{neg}_{i,t}$ | -0.0020** | -0.0703**
| (-2.0593) | (-2.3187)
$Ad_{t-1}$ | 0.0021** | 0.0069***
| (2.2882) | (2.9164)
$B/M_{t-1}$ | -0.0273* | -0.0658***
| (-1.6854) | (-3.4444)
$R\&D_{t-1}$ | 0.0046** | 0.0028
| (2.2725) | (1.2853)
$ROA_{t-1}$ | -0.3091** | -1.0120***
| (-2.1165) | (-3.1458)
$Size_{t-1}$ | 0.0046* | 0.0076**
| (1.9725) | (2.3892)
$Ivol_{t-1}$ | 0.0033 | -0.0266
| (0.0683) | (-0.2084)
$GP_{t-1}$ | 0.0387* | 0.0927***
| (1.9268) | (4.0935)
$Turn_{t-1}$ | -0.0004 | -0.0000
| (-0.8996) | (-0.0944)
$Beta_{t-1}$ | 0.0025 | 0.0088
| (0.3403) | (1.1196)
$Illiq_{t-1}$ | 0.0009 | -0.0041
| (0.0936) | (-1.0683)
$AG_{t-1}$ | -0.0197 | 0.0235
| (-0.8551) | (1.0247)
Constant | -0.0837* | -0.1545**
| (-1.6889) | (-2.2830)
AR (1) test p-value | 0.0000 | 0.0000
AR (2) test p-value | 0.7120 | 0.8960

Table 9: CNST and Profitability Shocks. This table presents the results of a dynamic panel data regression of profitability shocks on CNST and controls described in Eq.21. The dependent variable is profitability shocks (HVZ model and VOL model). All other variables are defined in the Appendix A1. The sample period is from November 2008 to December 2017. The t-statistic is presented in parentheses (* at the 10% level; ** at the 5% level; *** at the 1% level). | $Prof\ Shock_{i,t}$ |  $Prof\ Shock_{i,t}$)
---|---|---
| (HVZ Model) | (VOL Model)
$Prof\ Shock_{i,t-1}$ (HVZ Model) | 0.6496*** |
| (15.2823) |
$Prof\ Shock_{i,t-1}$ (VOL Model) |  | 0.6237***
|  | (28.2644)
$Diff(\mathrm{rev})^{neg}_{i,t}$ | -0.0002* | -0.0002**
| (-1.8839) | (-1.9999)
$Ad_{t}$ | -0.0000 | -0.0002
| (-0.0361) | (-0.9744)
$B/Mt$ | 0.0098 | 0.0120**
| (1.5754) | (2.5026)
$R\&D_{t}$ | -0.0000 | -0.0000
| (-0.1402) | (-0.0454)
$ROA_{t}$ | 0.0285 | 0.0368
| (0.7597) | (0.9377)
$Size_{t}$ | -0.0008 | -0.0008
| (-1.1603) | (-1.4661)
$Ivol_{t}$ | -0.0105 | -0.0044
| (-0.6100) | (-0.3174)
$GP_{t}$ | -0.0098** | -0.0073
| (-2.1079) | (-1.1111)
$Turn_{t}$ | 0.0001 | 0.0000
| (1.4105) | (1.1997)
$Beta_{t}$ | 0.0011 | 0.0012
| (1.0764) | (1.1674)
$Illiq_{t}$ | -0.0055 | -0.0074*
| (-1.3352) | (-1.6916)
$AG_{t}$ | -0.0022 | -0.0021
| (-0.8767) | (-0.8200)
Constant | 0.0137 | 0.0121
| (0.9668) | (1.0718)
AR (1) test p-value | 0.0185 | 0.0033
AR (2) test p-value | 0.3380 | 0.3460

Table A1: Variable definitions. Variable |  Description
---|---
$Diff(Star^{1})_{i,t}$ |  The One-Star Tendency (OST) is derived from the customer review rating score equal to one, as described in Eq.3.
$Diff(Star^{5})_{i,t}$ |  The Five-Star Tendency (FST) is derived from the customer review rating score equal to five, as described in Eq.4.
$Diff(\mathrm{rev})^{neg}_{i,t}$ |  The Customer Negative Sentiment Tendency (CNST) represents unfavorable emotions and lack of recommendations for buying certain products, as described in Eq.1.
$Diff(\mathrm{rev})^{pos}_{i,t}$ |  The Customer Positive Sentiment Tendency (CPST) represents favorable emotions and recommendations for buying certain products, as described in Eq.2.
Stock Return (R) |  Stock Return is the weekly individual stock return without considering cash dividend reinvestment.
Advertising(Ad) |  Advertising is the ratio of sales expenses to operating revenue.
Book-to-market (B/M) |  Book-to-market is the book value of common equity divided by the market value of common equity.
R&D |  Research and development is the ratio of R&D expenses to operating revenue.
Return On Assets (ROA) |  Return On Assets is the ratio of net profit to total assets.
LnSize (Size) |  LnSize is the log value of the market cap of tradable shares.
Asset Growth (AG) |  The quarterly growth rate of total assets, following Cooper, Gulen, and Schill (2008).
Illiquid (Illiq) |  The Amihud (2002) illiquidity measure, calculated as the absolute price change scaled by the volume.
Market risk (Beta) |  Market risk is determined as the coefficient obtained by regressing monthly stock returns against market returns using a rolling window of fifty days.
Idiosyncratic volatility (Ivol) |  Idiosyncratic volatility is the weekly standard deviation of daily residuals estimated from the Fama–French three-factor model.
Gross profitability (GP) |  Gross profitability is the proportion of pre-extraordinary income to the book value of assets.
Turnover rate (Turn) |  Turnover rate is calculated as the mean value of weekly turnover, which represents the ratio of shares traded to shares outstanding, over the previous week.
Earnings Aggressiveness (ES) |  Earnings Aggressiveness measures a firm’s accounting transparency, as described in Eq.7..
Earning Smoothing (EA) |  Earning Smoothing measures a firm’s accounting transparency, as described in Eq.6.
Consumer Confidence Index (CCIs) |  Consumer Confidence Index is the sentiment proxy in China’s A-share market.
Earnings surprise (SUE) |  Earnings surprise is the difference between quarterly operating profit and the previous four quarter operating profit scaled by the standard deviation of quarterly operating profit, as described in Eq.9.
Revenue surprise (SUR) |  Revenue surprise is the difference between actual quarterly revenue per share and the previous four-quarter revenue per share scaled by the standard deviation of quarterly revenue growth, as described in Eq.8.
HVZ model and VOL model |  These models refer to two different regression model settings for profitability shock (a proxy for cash flow shock), as described in Eq.10 and Eq.11.


# Fine-tune the Entire RAG Architecture (including DPR retriever) for
Question-Answering

Shamane Siriwardhana
Auckland Bioengineering Instutute
The University of Auckland
Auckland, New Zealand
<EMAIL_ADDRESS>
Rivindu Weerasekera
The University of Auckland
Auckland, New Zealand
<EMAIL_ADDRESS>
Elliott Wen
Auckland Bioengineering Instutute
The University of Auckland
Auckland, New Zealand
<EMAIL_ADDRESS>
Suranga Nanayakkara
Auckland Bioengineering Instutute
The University of Auckland
Auckland, New Zealand
<EMAIL_ADDRESS>

###### Abstract

In this paper, we illustrate how to fine-tune the entire Retrieval Augment
Generation (RAG) architecture (Lewis et al., 2020) in an end-to-end manner. We
highlighted main engineering challenges that needed to be addressed to achieve
this objective. We also compare how end-to-end RAG architecture outperforms
the original RAG architecture for the task of question answering. We have
open-sourced our implementation in the HuggingFace Transformers library (Wolf
et al., 2019).

https://github.com/huggingface/transformers/tree/master/examples/research_projects/rag-
end2end-retriever.

_Keywords_ NLP $\cdot$ Question Answering $\cdot$ Information Retrieval
$\cdot$ Transformers

##  1 Introduction

In September 2020, Facebook open-sourced a new NLP model called Retrieval
Augmented Generation (RAG) on the Hugging Face Transformer library. RAG is
capable to use a set of support documents from an external knowledge base as a
latent variable to generate the final output. The RAG model consists of an
Input Encoder, a Neural Retriever, and an Output Generator. All three
components are initialized with pre-trained transformers. However, the
original Hugging Face implementation only allowed fine-tuning the Input
Encoder and the Output Generator in an end-to-end manner, while the Neural
Retriever needs to be trained seperately. To the best of our knowledge, an
end-to-end RAG implementation that trains all three components does not exist.
In this paper, we introduce a novel approach to extending the RAG
implementation which fulfills the task of training all three components end-
to-end. Although this appears straightforward, there are many engineering
challenges that need to be addressed.

As the name suggests, RAG adds an intermediate information retrieval step
before the final generation process. It combines the ideas of neural
information retrieval with seq2seq generation in an end-to-end manner. During
the training phase, RAG takes an input and encodes it to a high-dimensional
vector. This vector then gets used as a query to select the most relevant
passages from an external database using Maximum Inner Product Search (MIPS)
(Johnson et al., 2017; Guo et al., 2020). Afterward, The input and the
selected set of documents get fed into a generator that produces the final
answer. It is important to emphasize that components of RAG are being
initialized with pre-trained BERT and BART transformers. Finally, The
probability of selecting documents given an input context ($p(z|x)$) is used
to propagate gradients back to the question encoder. During the process
111Please refer to our medium tutorial by clicking this link. of making the
RAG end-to-end trainable, we mainly work on the Retriever part, which uses a
neural retriever model called Dense Passage Retrieval (DPR) (Karpukhin et al.,
2020).

Our main contributions can be summarized as follows:

  * •

Highlighting the importance of making entire RAG architecture end-to-end
trainable.

  * •

Exploration of engineering challenges regarding the extension of original RAG.

  * •

Comparing the original RAG with end-to-end RAG on question answering to
highlight the effectiveness of making entire RAG end-to-end trainable.

  * •

Open source implementation

##  2 Rag Retriever

In our extension, we mainly work with the RAG retriever. The retriever
basically consists of a pre-trained DPR model (Karpukhin et al., 2020). The
DPR model consists of two BERT models. One model encodes questions and the
other model encodes the documents using the CLS token outputs. The DPR model
used in RAG has already been trained with passages and questions extracted
from open domain Wikipedia-based datasets. RAG has a neural retriever and a
reader combined in an end-to-end manner. In practice, however, we freeze the
passage encoder and only train the question encoder. In our approach, the
passage encoder is trainable as well. In RAG, prior to fine-tuning, a frozen
Passage Encoder is used to encode your external Knowledge Base (KB) and save
it to disk. Then we do the dataset indexing. Imagine there are millions of
documents that you need to search and retrieve during the training time. Usual
vector similarity can be too slow. So we use some approximation methods by
clustering the vector space into sub-regions. There are several libraries for
this, such as ScANN (Guo et al., 2020) and FAISS (Johnson et al., 2017). In
the RAG implementation Huggingface uses FAISS to make the retrieval phase
faster. After this step, we start the training process with the indexed
dataset where we only update the model parameters of the Question Encoder and
Generator networks. We do not use the Passage Encoder or change the encoded
embeddings for documents during the training.

###  2.1 Why is it important to fine-tune the entire retriever?

RAG authors illustrated that it is acceptable not to fine-tune the Passage
Encoder for tasks like question answering and fact-checking. But the authors
have conducted their experiments mainly on open domain Wikipedia-like datasets
(Kwiatkowski et al., 2019). Since DPR is also initially trained on Wiki-data,
this really makes sense. Our exploration is mainly motivated by the following
factors.

  * •

Does training of the entire RAG-retriever help with domain adaptation?

  * •

The authors of similar retrieval augmented models like REALM (Guu et al.,
2020; Sachan et al., 2021) mention it would be advantageous to train the
entire retriever.

##  3 Why is it hard to make the entire RAG end-to-end trainable?

Theoretically, there are no problems in propagating gradients to both the
passage encoder and question encoder BERT model. As described in the RAG loss
function (Lewis et al., 2020), we can compute the document scores for latent
documents (p(z|x)) by computing question encoder embeddings and document
encoder embeddings during the training time. This has the following two steps,

  * •

Combined a pre-trained Passage Encoder model to the RAG model prior to the
training.

  * •

During the training, first, retrieve relevant passages given a question by
using the indexed dataset.

  * •

Calculate the document scores by re-computing CLS embeddings for retrieved
documents using the initialized passage BERT model and do the same thing for
input using the question encoder.

Although the propagation of the gradients to the passage encoder is
straightforward, we need to make sure the updated passage encoder gets used in
the overall training architecture. So we have to update the indexed dataset
during the training process.

###  3.1 Why updating the indexed KB during the training is time-consuming and
computationally expensive?

The main two engineering challenges can be illustrated as follows.

  * •

During training, we first need to send each passage in the external KB through
the updated passage encoder to compute the CLS token. Let’s call this step re-
encoding the KB.

  * •

Secondly, with the updated embeddings for each passage, we need to re-index
the external KB with FAISS. Let’s call this part re-indexing of the KB.

So, similar to the REALM training process, we can execute external KB re-
encoding and re-indexing commands every N-training steps. The authors in REALM
have mentioned that if we improve the frequency of KB updates, we can get
better results (check section 3.3 in REALM paper (Kwiatkowski et al., 2019)).
Since the re-encoding and re-indexing processes can take large amounts of
execution time, we should not practically hold the training process for hours
until we finish the above two processes (read about the effectiveness of stale
gradients in REALM paper in section 3.3).

##  4 Making the re-encoding and re-indexing practical during the training
loop

We achieve this task by using HuggingFace Datasets, Pytorch-Lightning Falcon
(2019), and Python Multiprocessing libraries. First, we need to make sure the
re-encoding step is fast and does not make the entire training loop wait until
it finishes the computation of embeddings for every passage in the KB. To
achieve this we executed the re-encoding and re-indexing processes parallel to
the main training loop.

To have a detailed explanation about the implementation details and a code
walk-through, please refer our medium
article222https://shamanesiri.medium.com/how-to-finetune-the-entire-rag-
architecture-including-dpr-retriever-4b4385322552.

##  5 Results

We conducted a simple experiment to investigate the effectiveness of this
end2end training extension using the SQuAD dataset. First we Created a
knowledge-base using all the context passages in the SQuAD dataset with their
respective titles. It is important to note that we chunk each context in to
maximum of 100 words, which creates around 20 000 passages. Then we used the
standard training and validation splits to train and evaluate our model. As
illustrated in Table 1, our results suggest that finetuning the RAG with the
entire-DPR can outperform the original RAG by 12%.

Model Name | Exact Match
---|---
RAG-Original | 28.12
RAG - (Ours) | 40.02
Table 1: Comparison of two RAG models on SQUAD training data, when using
contexts as the knowledge base.

## References

  * Lewis et al. [2020] Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al.  Retrieval-augmented generation for knowledge-intensive nlp tasks.  _arXiv preprint arXiv:2005.11401_ , 2020.
  * Wolf et al. [2019] Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al.  Huggingface’s transformers: State-of-the-art natural language processing.  _arXiv preprint arXiv:1910.03771_ , 2019.
  * Johnson et al. [2017] Jeff Johnson, Matthijs Douze, and Hervé Jégou.  Billion-scale similarity search with gpus.  _arXiv preprint arXiv:1702.08734_ , 2017.
  * Guo et al. [2020] Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar.  Accelerating large-scale inference with anisotropic vector quantization.  In _International Conference on Machine Learning_ , pages 3887–3896. PMLR, 2020.
  * Karpukhin et al. [2020] Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.  Dense passage retrieval for open-domain question answering.  _arXiv preprint arXiv:2004.04906_ , 2020.
  * Kwiatkowski et al. [2019] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al.  Natural questions: a benchmark for question answering research.  _Transactions of the Association for Computational Linguistics_ , 7:453–466, 2019.
  * Guu et al. [2020] Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang.  Realm: Retrieval-augmented language model pre-training.  _arXiv preprint arXiv:2002.08909_ , 2020.
  * Sachan et al. [2021] Devendra Singh Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L Hamilton, and Bryan Catanzaro.  End-to-end training of neural retrievers for open-domain question answering.  _arXiv preprint arXiv:2101.00408_ , 2021.
  * Falcon [2019] et al. Falcon, WA.  Pytorch lightning.  _GitHub. Note: https://github.com/PyTorchLightning/pytorch-lightning_ , 3, 2019.