The Effectiveness of Fama-French 5 Factor Models Under COVID-19
Condition in the Gaming Industry
Chenchen He
1,*
and Yixuan Guo
2
1
Department of International Business Management, University of Nottingham Ningbo China, Ningbo, China
2
Department of Finance, University of Toronto Scarborough, Toronto, Canada
Keywords: Fama-French 5 Factor, Gaming Industry, COVID-19.
Abstract: Contemporarily, it is crucial to investigate the effectiveness of assets valuation approaches in some special
conditions. This article is aimed at assessing the effectiveness of the Fama and French 5 factor model under
COVID-19 pandemic using data from the gaming industry. The empirical evidence has been presented in
stable stock markets; its manifestations during the periods of increased volatility and instability of the markets
remain poorly explored. This research intends to discover how Five-Factor model reacted to the impact from
the pandemic in such a way that the market abnormalities can be analysed via data from the years 2019 and
2020. The R² value in five-factor model has shown more capability in explaining the excess returns during
the pandemic with 0.8482+, compared to 0.6245, which was indicated before. In addition, all five predictors,
which included factors related to profitability and investment strategy, proved to be significant in the
pandemic period, as opposed to only three predictors in the period before the pandemic. Nevertheless, the
predictive power of the model tends to deteriorate, which can be verified by the increase of RMSE. While this
work establishes the strength of the model in a volatile environment, it should also be noted that forecasting
inaccuracies are uncovered. These experiences, therefore, bring copious lessons for the investors and financial
analysts as they analyse portfolios in such crisis scenarios as the one during the COVID-19.
1 INTRODUCTION
Asset portfolio has undergone several significant
developments since the 1950s until now. Initially,
Harry Markowitz introduced the Modern Portfolio
Theory (MPT) in 1952, in which he mentioned
diversifying a portfolio can effectively reduce
unsystematic risk (Markowitz, 1952). Subsequently,
in 1964, William Sharpe proposed the Capital Asset
Pricing Model (CAPM), which was based on MPT.
In CAPM, Sharpe measured the expected return of an
asset by its volatility in the market, which was the
Beta coefficient (Sharpe, 1964). He assumed that
borrowing and lending can be done at a risk-free rate
and that all investors have the same expectation of
future returns (Elbannan, 2014). As the theory
continued to evolve, more factors were considered to
predict the asset return more accurately. Fama and
French argued that there were limitations to the
CAPM, where a single Beta coefficient was
insufficient to represent the complexity of the market
and explain the average returns it presented (Fama &
French, 1992). For this reason, Fama and French
proposed a three-factor model (FF3), where they
added market risk (RM-Rf), size risk (SMB) and
book-to-market ratio risk (HML) to explain in more
detail the variations in the excess returns of stocks
(Eraslan, 2013). Following this, to improve FF3,
Fama and French added two additional factors,
profitability (robust minus weak, RMW) and
investment (conservative minus aggressive, CMA)
factors. RMW measures the excess returns of more
profitable firms relative to less profitable firms,
whereas CMA reflects the outperformance of firms
with conservative investment strategies over firms
with aggressive investment strategies (Fama &
French, 2015).
CAPM, FF3 and FF5 are widely used in empirical
asset pricing studies. Throughout the ages, many
scholars have devoted themselves to studying the
validity of these models in different economic
situations or industries, and their studies have led to
different results. Several scholars have studied some
of the industries in the current pandemic: some of
them believe that the limitations of the FF5 have led
to a reduction in its validity. In contrast, others
170
He, C. and Guo, Y.
The Effectiveness of Fama-French 5 Factor Models Under COVID-19 Condition in the Gaming Industry.
DOI: 10.5220/0013208600004568
In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence (ECAI 2024), pages 170-174
ISBN: 978-989-758-726-9
Copyright © 2025 by Paper published under CC license (CC BY-NC-ND 4.0)
believe that the validity of the model has not been
affected.
The academics believe that there are several main
reasons for the reduced validity of the model, firstly
because the factors used in the model are based on
normal market conditions and economic situations, so
these factors are unable to capture the state of the
market in a situation as volatile as an epidemic.
According to Kostin et al., who studied the
performance of selected companies in the energy
sector as well as in emerging sectors during the
COVID-19 pandemic through the FF3 and the FF5,
the traditionally efficient as well as self-regulating
market was severely disrupted so that the
performance of the market was less dependent on
conventional financial indicators and more
influenced by short-term factors influence, and the
traditional five factors do not capture such unusual
market movements (Kostin et al., 2022). Secondly,
scholars have also argued that the effectiveness of the
model is reduced in emerging markets that are prone
to anomalies and in markets that have been severely
hit by the crisis. According to the study, the FF5 has
a significantly lower R² value during the crisis, which
measures the model's capacity to clarify the
anomalies in the data, which represents the near-zero
ability of the five-factors to explain stock returns in
emerging and energy sectors (sectors severely
affected by the pandemic), which are different from
the normal market (Kostin et al., 2022). Third,
regional differentiation in an epidemic can also lead
to a reduction in the validity of the FF5. According to
the research, countries like China and Russia, which
have adopted both free markets orientated and
economically planned policies, deviate from the
efficient market assumption on which the multifactor
model is based, and the inadequacy of the model is
evident during the pandemic as it is unable to adapt to
the external macroeconomic disruptions and
governmental policies affecting these markets
(Kostin et al., 2022). Finally, scholars argue that the
complexity of the model also reduces its validity. In
previous studies (Kostin et al., 2022; Zhou, 2024),
they all argue that the FF5 is less effective than FF3
in the context of pandemics and that a multi-factor
model would be more firm-focused and therefore of
limited applicability. The RMW and CMA factors in
the FF5 do not enhance the model’s explanatory
power. For example, the FF5 is unable to explain the
market returns of the Chinese pharmaceutical
industry during an epidemic (Zhou, 2024) because its
rigid assumptions rely on traditional risk, which
would limit the model's ability to capture market
changes under high uncertainty.
Conversely, several academics contend that the
reliability of the five-factor model remains robust in
an epidemic scenario. Alqadhib et al., who
incorporated the five-factor model in their research to
measure the risk-managed performance of active
mutual funds in Tehran, put forth compelling
evidence that attests to the model's durability during
the pandemic (Alqadhib et al., 2022). It was found to
elucidate approximately 75% of the fluctuations in
the returns of equity mutual funds. Regardless of the
prevailing pandemic, enterprises with sizable profits
and those pursuing conservative investment strategies
were present. For such enterprises, the five-factor
model capably accounted for the deviation, yielding
an accurate depiction of returns investors garnered
after risk adjustments. Substantially positive returns
were accomplished in the study by adjusting for the
recognized risks.
The study of other scholars also studies exhibit
mixed results. According to Zhang et al. on the excess
returns of real estate investment trusts (REITs), there
conclusion indicates two of the factors in FF5, RM-
Rf and SMB, show a relatively stable impact in
explaining the returns and the model also
demonstrates a certain level of validity after adding
the momentum factor which explains the returns
during the pandemic (Zhang et al., 2023). However,
the applicability and validity of the model is limited
by the fact that the skewness and kurtosis factors are
not consistent across regions.
The research aims to investigate the effectiveness
of the FF5 in the gaming industry before and during
the pandemic. The pandemic provided this study with
a market context of economic disruption and high
volatility, which helped to test the robustness of the
model. Although the FF5 has been widely validated
in stable markets, it lacks performance in the gaming
industry during a crisis. By exploring the gaming
industry, this study aims to gain insights into how the
five-factor model works when an unprecedented
market crisis erupts and to assess the model's
robustness. In the following, this will be done through
an explanation of the methodology used in this study,
presentation of the empirical results, analysis of the
empirical results, conclusions, limitations of the
model and outlook.
2 DATA AND METHOD
The data selected in this article are from daily data of
the game industry in the United States. In this case,
we have intercepted two time periods, the first
starting from 2 January 2019 to 31 December, and the
The Effectiveness of Fama-French 5 Factor Models Under COVID-19 Condition in the Gaming Industry
171
second starting from 2 January 2020 to 31 December.
The purpose of intercepting the data from the two
time periods is that we want to divide the study into
two segments: before the pandemic and during the
pandemic, so that we can do a comparative study
about testing the effectiveness of the model under the
pandemic. Data comes from the Kenneth R. French's
data librarys.
In this study, we use linear regression as the main
statistical technique, which typically employs OLS as
the main parameter estimation method. Specifically,
we aim to model the excess returns of the portfolio
through the risk-free rate and regress residual profits
on a set of dependent variables. The non-dependent
variables included in the regression represent various
characteristics of the market and the portfolio.
The validity and interpretability of linear
regression is why we chose it as our primary
statistical technique, and by assuming a linear
relationship between the independent and dependent
variables, the impact of each factor on the portfolio's
excess returns can be clearly inferred through the
resulting coefficients. Specifically, the following
models will be regressed:
𝑅𝑝
−𝑅𝑓= 𝛽
+ 𝛽
𝑅𝑀 𝑅𝑓
+ 𝛽
𝑆𝑀𝐵
+
𝛽
𝐻𝑀𝐿
+ 𝛽
𝐶𝑀𝐴
+ 𝛽
𝑅𝑀𝑊
+ 𝜖
(1)
Here, there are five explanatory variables, which are
RM-Rf, SMB, HML, CMA and RMW. As described
above, RM-Rf stands for market risk; SMB is size
risk; HML is book-to-market ratio risk; RMW is
excess return from profitability; and CMA is a
comparison of the performance of companies with
different investment strategies. The result of whether
the validity of the five-factor model weakened during
the pandemic was obtained by comparing the beta
coefficient significance, sign, and p-value of the five
factors for the game industry under the two time
periods.
3 EMPIRICAL ANALYSES
3.1 Correlation Analysis
Based on the regression results in Table 1 and Table
2, before COVID-19, the RM-Rf, SMB, and RMW
factors are statistically significant, whereas the HML
and CMA factors do not demonstrate significance.
These suggest that the RM-Rf, SMB, and RMW
factors have the greatest impact on the extra return.
During COVID-19 pandemic, all five factors show
significance. The following is a detailed correlation
analysis for each factor.
The RM-RF factor shows how the stock has moved
in relation to the market. The RM-RF coefficient is
0.698 before the pandemic and almost 1 during
pandemic, demonstrating that the sensitivity of
gaming industry is roughly same as the market
movement during COVID-19 pandemic and less
sensitive to market movements before that. The SMB
factor shows how the stock has moved in relation to
the size premium. In both periods, SMB factor and
excess returns are strongly correlated. The positive
correlation suggesting that small-cap stocks tend to
have better performance than large-cap stocks have.
Before COVID-19, the SMB coefficient of 0.765
indicates a moderate positive exposure to small
market value stocks, while during the COVID-19
outbreak, the SMB coefficient increases to 1.301,
reflecting a much better performance of smaller firms
Table 1: Coefficient of FF5 of gaming industry before COVID-19.
Coefficient Std. err. t-Stat P-value
RMW 0.300 0.139 2.16 0.032
SMB 0.765 0.098 7.81 0.000
RM-RF 0.698 0.056 12.49 0.000
HML -0.137 0.110 -1.25 0.213
CMA -0.233 0.205 -1.14 0.257
Table 2: Coefficient of FF5 of gaming industry during COVID-19.
Coefficient Std. err. t-Stat P-value
RMW 0.657 0.202 3.26 0.001
SMB 1.301 0.117 11.14 0.000
RM-Rf 0.985 0.392 25.12 0.000
HML 0.536 0.243 4.44 0.000
CMA -1.550 0.079 -6.37 0.000
ECAI 2024 - International Conference on E-commerce and Artificial Intelligence
172
over larger ones. The RMW factor shows how the
stock has moved in relation to the profitability
premium. The coefficient of RMW factor doubles
from 0.300 to 0.657 as affected by the pandemic. This
suggests that firms with higher profitability
continuously increase returns, but due to the
pandemic their influence doubles. The HML factor
shows how the stock has moved in relation to the
book-to-market ratio premium. It is not significant to
explain the stock price changes in the game industry
before COVID-19, but after the outbreak, the HML
factor becomes significant, the coefficient of which is
0.536. This shift suggests that firms with a high
book-to-market ratio typically have higher returns.
The CMA factor shows how the stock has moved in
relation to the investment premium. Based on the
result, CMA also changed from a redundant variable
of FF5 before COVID-19 to a valid factor after the
outbreak, with a coefficient of -1.550. This negative
coefficient smaller than -1 reveals that during this
period, firms that pursued more aggressive expansion
strategies generates higher returns than those
pursuing conservative expansion strategies.
3.2 Model Performances
To assess how well the FF5 performed in the gaming
sector prior to and following the COVID-19 outbreak,
Coefficient of determination (R² and Adjusted R²),
and Root Mean Squared Error (RMSE) are the key
measurement statistics. R² measures the proportion of
the excess return's overall anomalies explained by the
five factors. Adjusted R² accounts for the number of
predictors and sample size in the regression model,
serving as a more accurate measure to explain the
variation. RMSE measures the average magnitude of
the errors in predictions made by a regression model.
Table 3: Comparison between R², Adjusted R², and RMSE
statistics before and during COVID-19.
Before COVID-19 Durin
g
COVID-19
F-Stat 81.84 276.03
P-value 0.0000 0.0000
R² 0.6245 0.8482
Ad
j
usted R² 0.6169 0.8451
RMSE 0.5937 1.2458
According to Table 3, the regression results had an R²
value of 0.6245 before COVID-19 pandemic. This
means that the model can explains 62.42 percent of
the variation in excess return. Comparatively, the
second sample shows a marked increase in the value
of R² of 0.8482, which indicates a marked
improvement in the model's ability to explain
variation of excess return during the pandemic. As
adjusted R² varied little from R² in both periods, this
indicates that the model is a good fit for data before
and during COVID-19 pandemic. However, RMSE
rose from 0. 594 to 1246 during the COVID-19
pandemic, which means that the predicted values
were further away from the actual values. The
increase shows that the model was less reliable during
the pandemic.
3.3 Discussion
The results of this study show important information
about how the stock prices of the gaming industry
changed before and during the COVID-19 pandemic.
Before the pandemic, the model shows that RM-RF,
SMB, and RMW are significant for understanding
excess returns. This model explains 62.45 percent of
the differences in returns. The sensitivity of the
gaming industry to market movements is relatively
lower, and smaller, profitable firms shows a positive
impact on returns. During the pandemic, all five
factors, including the HML (value premium) and
CMA (investment premium), are statistically
significant. The rise in R² to 84.82 percent during the
pandemic indicates that the model acquired greater
efficacy in capturing the determinants of surplus
returns in the gaming sector.
From the investment perspective, these results
show that during crises like the COVID-19 pandemic,
smaller gaming firms do better than larger ones, and
firms that make more profit continue to offer
significant returns. Additionally, the HML and CMA
factors that show significance during the pandemic
suggests that firms having higher book-to-market
ratio and invest aggressively may generate better
returns during economic condition like COVID-19.
However, even though the regression model had a
better performance during the pandemic, the results
became less reliable. Therefore, investors should take
the model performance into consideration when
making portfolio decisions based on the five factors.
4 CONCLUSIONS
To sum up, the purpose of this study is to assess the
efficacy of FF5 under the under the COVID-19
through analysis of data selected from the gaming
industry. The analysis revealed that FF5 is able to
explain more of the anomalies in returns at this time
during the pandemic, as seen by a significant increase
in both the R² and Adjusted R² values. As RMSE also
increased during the pandemic, prediction accuracy
of the model decreased during the pandemic. Factors
The Effectiveness of Fama-French 5 Factor Models Under COVID-19 Condition in the Gaming Industry
173
valid to explain excess return changed from RM-RF,
SMB, RMW to all five factors across Pre-COVID and
COVID periods. The results suggests that firms that
are smaller, more profitable, high book-to-market
ratio, and invest aggressively are likely to have higher
returns. Nonetheless, this study is limited due to the
unrepresentative data selected from single industry
and single time. Future research could analyse more
samples from different industries and under different
conditions for more precise and representative results.
The finding of this paper help to better understand
how the FF5 works during COVID-19, providing
useful information for investors and financial analysts.
There exists some limitation to the study. First, a key
issue of this study is that data selected only come from
the gaming industry that may not be representative to
the entire market as different industry may react
differently to the COVID-19 outbreak. Evaluating
effectiveness of FF5 based on regression results using
unrepresentative data can be inaccurate. Second, the
study period is constrained to the time before and
during the COVID-19 pandemic. This limits the
applicability of the results to time after the recovery
from pandemic and other economic conditions. Third,
unreliability of the model during the pandemic creates
large difficulty to make accurate conclusion.
Considering these limitations, future research could
examine data from a wider range of industries and see
how well the Farmer-French 5-Factor model works in
different economic periods or crises. Adding more
factors or trying different models could make
predictions more accurate in unstable markets. The
result of this study helps to understand factors affect
stock returns in the gaming industry. It also gives
important information for investors during economic
condition like COVID-19 pandemic.
AUTHOR CONTRIBUTION
All the authors contributed equally and their names
were listed in alphabetical order.
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