Pastor and Vorsatz (2020) found that growth funds (growth tilt) outshone value funds during the COVID–19 health crisis which contradicted the fundamental finance belief that advocates value investing. Value premium is taken as compensation for investors to undertake additional distress risk fuelled by low profitability offered by value firms (Fama & French, 1996) and the potentially poor performance amid market downturns due to the business inflexibility of value firms compared to its glamour counterpart (Petkova & Zhang, 2005). No doubt, the opposing team also stated that value equities outcompeted glamour equities due to investors’ overconfidence in future growth rate, profitability, and cash flow of growth (glamour) stocks (Lakonishok et al., 1994). In the beginning of 2020, S&P 500 lost almost 40 percent of its value in five weeks due to the fear and anxiety of investors stoked by the rapid spread of the COVID-19 virus. However, in 2021, the U.S. market triumphed with the S&P 500 notching around 70 ‘all-time highs’, the second-highest annual tally in history. Previous studies are limited to time horizons. As far as we are concerned, few studies attempted to examine the determinants of fund performance during the boom period of the COVID-19 crisis. Given that active fund managers are well equipped with sophisticated equities selection skills coupled with perfect knowledge of the intrinsic value of assets, the active fund managers were expected to reap the alpha from the acute market crash in February 2020 and thus generated better performance during the market boom. Hence, we are interested in learning about mutual fund performance during the market boom between April 2020 and December 2021. This horizon is up to date, and therefore, the results would supersede the results from previous studies. By combining our sampling period with Pastor and Vorsatz’s time horizon, investors will secure a deeper insight into the fund performance as this study consists of a complete cycle (precrisis-crash-recovery-boom) analysis on the fund performance during the COVID-19 pandemic. This paper aims to further investigate the fund performance and its determinants, following a previous study by Pastor and Vorsatz (2020). By looking into how rapidly the market had recovered from the tremendous chaos stoked by investors’ fear, we attempt to answer the following three questions. First, could active funds exploit the price dislocation advantage during the crash period to outclass the benchmarks in the midst of the market boom? Second, do sustainability ratings and Morningstar ratings hold the same explanatory power as they do during the market crash? Last, could growth funds deliver an excess return compared to its value counterpart during the market boom? We think that these questions have a significant influence on retail investors’ investment decisions. We began first by estimating the funds’ alpha using the multifactor models with a sample of approximately 3,430 equity funds and 2 benchmarks. Our results were somewhat similar to the results of, Pastor and Vorsatz (2020) with the active funds underperforming the benchmarks in most cases. To further examine the relationship between funds’ return and sustainability, risk rating, and growth tilt (growth fund), we employed Pastor and Vorsatz’s model for both benchmark-adjusted and factor-adjusted returns. In addition to the original indicators, we introduced the Lipper preservation rating to assess funds’ ability to preserve their capital in the unpredictable market and of course to test the explanatory power of ratings awarded by different rating agencies. The evidence highlights the potential role of ratings awarded by rating agencies and growth tilt in investment decisions as the results hold after incorporating three levels of controls and are robust after replacing different determinants. The contribution of our study can be distilled into three points. Firstly, we proved that part of Pastor and Vorsatz’s (2020) results can be generalized to other time horizons during the health crisis. Nevertheless, our results indicated that the sustainability rating does not assert strong predictability power on fund performance. Moreover, fund ratings’ explanatory power remained strong during the market boom period, which indicated the importance of ratings in predicting funds’ return during the health crisis. Lastly, we offer an alternative perspective on the capability of growth investing to generate excess benchmark-adjusted return during the COVID-19 health crisis bullish market. The follow part of this paper consists of the second section— the literature review while the third section examines the data and methodology. The fourth section discusses the results and the last section states our conclusion. LITERATURE REVIEW Mutual fund performance has been extensively studied since the 1950s but evaluated primarily on net value change before the 1960s which fails to consider whether taken market risk is well compensated with desirable returns. The Modern Portfolio Theory (MPT) by Markowitz (1952) played a prominent role in developing modern investment theory, whereby Tobin (1958) included risk-free assets into the framework and proposed a two-fund separation theorem to further enhance the MPT. Later, Treynor (1961), Sharpe (1964), Lintner (1965) and Mossin (1966) introduced Capital Asset Pricing Model (CAPM) by building on the earlier work of Markowitz on MPT to gauge the relationship between systematic risk and returns on assets. The risk-adjusted performance evaluation method has been widely applied ever since. Treynor (1965) first introduced the MPT into the field of fund performance evaluation. Thus, the Treynor index has been utilized to measure risk-adjusted performance of an investment portfolio by analyzing a portfolio’s excess return per unit of systematic risk. Specifically, the Treynor index holds that idiosyncratic risks can be eliminated through diversifying portfolio investment. Numerous theories on fund performance evaluation have been proposed after that and are still in use today, namely the Sharpe ratio (1966), Jensen’s Alpha (1968) and other measurements. In view of the limitations of the CAPM-based one-factor model, some researchers have proposed different multi-factor models to include multiple risk factors. For example, Fama and French (1993) added the size of firms (SMB) and book-to-market values (HML) to the traditional CAPM model, formally introducing the well-known three-factor model. Carhart (1997) then supplemented the momentum factor to the three-factor, which further improved the explanatory power by a large magnitude compared to the CAPM model. A much-debated question is whether active funds outperform passive funds since management fees or other costs are incurred on active funds. Jensen (1968) evaluated the performance of 115 open-end mutual funds between the period, 1945 and 1964 using Jensen’s alpha, and found that active funds underperformed the buy-the-market-andhold policy, either, individually or in aggregate. Additionally, Carhart (1997) summarized that load funds substantially underperformed no-load funds by approximately 80 basis points per year when examining fund performance using the CAPM, 3-factor, and 4-factor models. Pastor and Stambaugh 2000 determined that most of the funds underperformed the CAPM and Fama-French benchmarks by applying a sample of 2,609 U.S. equity mutual funds from 1963 to 1998. Besides, Bogle (2002) empirically demonstrated that the average returns of the active sample funds were significantly overstated after studying the performance of funds in the same style box from 1991 to 2001. Furthermore, Pastor and Vorsatz (2020) concluded that active funds underperformed passive benchmarks during the COVID-19 crisis after analyzing a sample of 3,626 U.S. active equity mutual funds by applying the CAPM and other multi-factor models. With the various performance measures, these studies arrived at the same conclusion whereby actively managed funds often underperform passively managed funds in many aspects. Investors’ appetite consistently grows for active funds despite the underperformance of mutual funds. Such a phenomenon is somehow puzzling as investors are willing to tolerate this underperformance because active funds will outperform passive funds in certain aspects. A study by Grinblatt et al. (1995) has revealed that funds invested in momentum have significantly better performance than other funds. Grinblatt and Titman (1989) concluded that mutual fund managers can pick stocks that outperform their benchmarks before deducting any expenses. The evidence is strong among growth funds, which hold stocks that outperform their benchmarks by around 3 percent per year before deducting expenses. However, investors cannot achieve abnormal returns by purchasing talented managers’ funds because these funds typically incur the highest expenses. A paper published by Wermers (2000) has also shown that fund managers’ superior ability in stock selection does have a significant contribution towards beating the market index by documenting that mutual funds consist of stock portfolios that outperform a broad market index by 1.3 percent. Notwithstanding the above, sustainable investing, which involves the E (environmental), S (social), and G (governance-related), has also gained traction in the asset management industry despite traditional investing that only emphasizes risk-adjusted returns. Lins et al. (2017) presented a model to examine the performance of non-financial U.S. firms during the financial crisis of 2008–2009. The firms with high corporate social responsibility tended to outperform the others during the crisis period. Furthermore, Hartzmark and Sussman (2019) concluded that investors in the U.S. value sustainability ratings when making investment decisions but sustainable investing is often associated with environmental-related issues. Moreover, Pastor and Vorsatz (2020) revealed that mutual funds with higher E ratings seemed to excel during the COVID-19 crisis. In contrast, both S and G did not seem to have much noticeable impact on mutual fund performance. Ferriani and Natoli (2021) also found that sustainable investing could be considered a perfect hedge during the crisis, especially the COVID-19 crisis. Investors with average or higher ESG preferences are inclined to allocate more funds to green firms instead of the market portfolio (Pastor et al., 2021). This could be because investors with high ESG preferences are more resilient and loyal to holding green portfolios than other investors (Albuquerque et al., 2019). Nonetheless, it is assured that sustainable investing breeds positive social impact by directing more capital towards green firms and pushing brown firms to be greener (Pastor et al., 2021). Although mutual funds with high sustainability ratings outperformed the others during the economic turmoil, such outperformance is at the cost of inferior performance during bullish times (Nofsinger & Varma, 2014). Lesser et al. (2016) further elaborated that the outperformance of mutual funds with high sustainability ratings is merely driven by the skills of active fund managers and not solely due to sustainability ratings. It is also observed that there is no outperformance in terms of mean returns and volatility for sustainable funds when applying the Fama-French three-factor model. However, the top quintile sustainability portfolio in Europe seems to yield a better alpha in comparison with the rest mainly due to the higher social and governance scores (Steen et al., 2020). In contrast to the Morningstar sustainability globe rating, the Morningstar rating is one of the commonly used risk-adjusted return measurements. It is considered an essential indicator for fund performance in a prospective way. Blake and Morey (2000) examined the predictive power of the Morningstar rating system over time including fund investment styles, and fund ages. They found that the predictive power is most robust for low rating funds (less than 3 stars), while statistical evidence for highest-rated funds outperforming the four-rated and three-rated funds is weak. Such funding is aligned with previous studies for mutual fund performance persistence—poorer performance is easy to predict, while superior performance is rugged. The value anomaly is a much-debated topic in the financial industry where the outperformance of value portfolios is considered as compensation for an investor to bear the distress risk since the high book-to-market (value) often features persistent low profitability and vice versa for the low book-to-market (Fama & French, 1996). Additionally, value portfolios underperform growth portfolios in a shorter investment horizon coupled with higher risk during bad times or recession (Lettau & Ludvigson, 2001; Petkova & Zhang, 2005). Zhang (2005) further elaborated that higher risk captured by value stocks during bad times is possibly due to business inflexibility. For instance, value firms find it more challenging to disinvest and redistribute their capital for efficient productivity allotment during bad times since most of their capital are tangible assets. On the other hand, Lakonishok et al. (1994) argued that value stocks outperform glamour stocks because the market often overestimates the future growth rate and cash flow of glamour stocks. In other words, the outperformance of value firms is attributed to their outstanding cash flow and actual growth rate but not the additional fundamental risks involved. RESEARCH METHODOLOGY Data We sourced our data primarily from the Center for Research in Security Prices (CRSP) – Mutual Funds, Eikon Refinitive and Yahoo Finance. Our aim was to investigate mutual fund performance during the market boom from April 2020 to December 2021 especially when the S&P 500 reached approximately 70 times ‘all-time high’ in 2021 in the midst of the massive surge in COVID–19 cases due to the emergence of the Delta variant. Thus, our primary sampling period was from 03 April 2020 to 31 December 2021 whereby there was a bounce-back in the S&P 500 index in early April 2020 after the market crash in February 2020. First, we extracted all funds with a fund summary from the CRSP. All non–equity funds were excluded from our sampling based on the Lipper Asset Code available in the ‘Fund Summary’ of CRSP. Furthermore, we removed funds that did not have an ‘X’ at the end of their NASDAQ tickers since NASDAQ tickers ending with an ‘X’ are categorized as mutual funds. In other words, all funds that did not have NASDAQ tickers were excluded from our data sampling given that NASDAQ tickers were vital in our data collection process. Next, we proceeded to remove all funds with names consisting of ‘bond’, ‘money market’, ‘retirement’, ‘index’ and ‘fixed income’. Besides, index funds were excluded from our sample based on the index fund indicator in the ‘fund summary’ of CRSP. A fund size filter was also applied in our sampling process with funds that had at least $15 million in total net asset (TNA) included in our primary sample data. In addition to that, all funds from different classes were excluded from our sample data according to the identical CRSP class group code (CRSP CL Group) in the ‘Fund Summary’ of CRSP. We opted to remove funds with smaller TNA when dealing with funds that contained the same CRSP class group code. Eventually, there were approximately 3,400 mutual funds and 2 benchmarks in our primary data. This paper also attempts to examine the impact of growth funds (growth tilt) or funds with high Morningstar sustainability globe, Lipper preservation rating, and Morningstar risk rating on the benchmark adjusted performance or even alpha during market boom in the midst of the COVID–19 crisis. The fund-level control embodied in the model consisted of the institutional fund indicator, value fund indicator, small-cap fund indicator, large-cap fund indicator, funds’ age as of March 2020, expense ratio, funds’ TNA as of March 2020, number of dividend payments, dividend payments, equity positions and funds’ turnover ratio. In addition, the country-level control augmented represented the funds’ percentage of the TNA in North America, the United Kingdom, Continental Europe and Asia, coupled with the international funds identified in the ‘Lipper Objective’ of CRSP. Finally, the industry-level control encompassed TNA funds percentage in basic materials, communication services, consumer cyclical, consumer defensive, energy, financial services, health care, industrials, real estate, technology and utilities along with industry funds spotted in the ‘Lipper Objective’ of CRSP. The Morningstar sustainability rating is a rating using environmental, social, and governance (ESG) factors whereby 1 globe indicates that the fund has a low ESG rating while 5 globes denote that the fund has a high ESG rating. The E, S, and G risk scores are acquired to measure funds’ exposure to ESG risks in the sense that the higher the score, the higher the risk. Based on ESG risk scores, we transformed them into ESG sustainability, following the formula by Pastor and Vorsatz (2020). We replaced the original risk scores by taking the natural logarithm and multiplying it by the negative one. The following formula indicates that the higher the fund sustainability (transformed risk score), the more sustainable the fund.

E -> -1*log(E); S -> -1*log(S); G -> -1*log(G)

Other than that, the Morningstar risk rating is awarded to mutual funds based on downside losses/variations compared to its peers in the same category, which is also a key component of Morningstar’s risk-adjusted returns (MRAR) at the same time. A 1-star rating signals the lowest risk while a 5-star rating indicates the highest downside volatility of the fund over the past 3, 5, and 10 years. The Morningstar sustainability globe, Morningstar risk rating and funds’ turnover ratio, environmental scores, social scores, and governance scores of mutual funds were obtained from Yahoo Finance by executing automated webscraping. Additionally, the growth or value fund, small-cap or largecap fund indicators were retrieved from the ‘Lipper Classification’ of ‘Fund Summary’ in CRSP. Finally, the expense ratio, number of dividend payments, dividend payments, Lipper preservation rating, and percentage of TNAs in numerous sectors and countries of mutual funds were extracted from the Eikon Refinitive. The correlation and descriptive summary for independent variables are as follows.

Note: Values in square brackets indicate the 95% confidence interval for each correlation. The confidence interval is a plausible range of population correlations that could have caused the sample correlation. * indicates p < .05. ** indicates p < .01

We performed return normalization by employing the following formula to present the differences between average funds’ returns and benchmark returns where Ft is the fund’s price index, Bt is the benchmark’s price index, rFt is the fund’s weekly return on week t and rBt is the benchmark’s weekly returnS on t. G -> -1*log(G) E -> -1*log(E); -> week -1*log(S);

E -1*log(E); -> -1*log(E); -> -1*log(S); G -1*log(G) -> -1*log(G) E -> S ->S -1*log(S); G ->

Ft = 100(1+rF1)(1+rF2)...(1+rFt)

Ft Ft Ft

B S -> -1*log(S); -> -1*log(E); G B->) -1*log(G) Bt =EE100(1+r )(1+rB2)...(1+r 1 S -> -1*log(S); t -1*log(G) -> -1*log(E); G ->

Bt Bt Bt On the other hand, the benchmark-adjusted performance is achieved

Ft rFt log (Ft) – log (Bt) where Ft and Bt are the cumulative compounded t rrFFt t Fby returns of the Bweekly t B t Given that we intended average fund and the benchmark indexes. to measure the alpha of the mutual funds via rBBt F rBt r t rregression, the Capital Asset Pricing Model (CAPM), Fama – French t r3Ft Factors Model, Carhart 4 Factors Model, Fama – French 5 Factors FF FF FF FFt t==100(1+r (2) 11)(1+r 22)...(1+r t)t) B F F F 100(1+r )(1+r )...(1+r (2) rModel t and Six Factors Model that incorporate Fama –(2)French F = 100(1+r )(1+r )...(1+r ) t t rBt B B B Factors Model + MOMBBwere employed to attain the alphas in our B B B = 100(1+r )(1+r )...(1+r ) (3) t t = 100(1+r 11)(1+r 22)...(1+r t t) B F B )...(1+r F F 100(1+rFB1)(1+r (2) (3) (3) t= 1)(1+r t) t =100(1+r 2)...(1+r t) paper. The alphas are Bthe intercepts F F from the regression of Ft =estimated 100(1+rF1)(1+r (2) 2)...(1+r t) B B B the funds’ returns and factors’ returns. rate and Bt = 100(1+r (3) 1)(1+r Besides, 2)...(1+r t) the risk-free Bt B = t100(1+rB1)(1+rB2)...(1+rBt) (3) log (F ) – log (B ) where F and t t t (F – log (Bt) where Ft and Bt MOM etc.) were obtained from Kenneth (HML, SMB, log log (Ft)factors’ – t)log (Breturns t) where Ft and Bt French’s data library. The log (Ft) – log (Bt) where Ft and Bt formulas for the mentioned models are log (Ft) – log (Bt) where Ft and Bt CAPM: CAPM: CAPM:indicated as follows: CAPM: CAPM: CAPM:

FF3: FF3:FF3:

FF3: FF3:

E[Ri-R ] = α + β MarketE[R E[RMM-R -Rf]f] i-Rff] = α + βMarket E[RE[R i-Rf] = α + βMarket E[RM-Rf] E[Ri-Rf] = α + βMarket E[RM-Rf] E[Ri-Rf] = α + βMarket E[RM-Rf]

E[R α + ββMarket E[R ββHML HML ++ββSMB SMB i-Rf]f] = M-Rf]f] + FF3: E[R E[R E[R + HML Market HML SMBSMB βMarket E[R -RMff]]-R ++ββHML HML + β+SMB -Rfi]-R =f]α==+ααβ++ E[R HML βSMB i-R M-R iE[R Market M HML SMBSMB E[Ri-Rf] = α + βMarket E[RM-Rf] + βHMLHML + βSMBSMB

Carhart 4:

Carhart 4: Carhart Carhart Carhart 4: 4:4: Carhart 4: E[R ] =αα++βββMarket -R HML + βSMB +SMB βMOM+ MOM E[R E[R -R ++HML ββHML HML + SMB ββSMB ββMOM MOM (6) i-R Market E[R MM f] + i-R f]f]f= f]f]β E[R HML SMB + i-R M]-R HML SMB MOMMOM E[RE[R αf]=+=αβα+ E[R HML β+ SMB + βMOM i-R f] = Market M-R f f]++ββHML SMB MOMMOM E[R + βMarket E[R ++βSMB SMB + βMOM i-R Market M-R HMLHML

FF5:

FF5: FF5: FF5: FF5: FF5: E[Ri-Rf] = α + βMarket E[RM-Rf] + βHMLHML + βSMBSMB + βRMWRMW + βCMACMA (7) E[R ]f] == ααα+++βββMarket -R ββHML HML ++βSMB SMB ++RMW ββRMW ++ββCMA CMA i-R E[R HML + βSMB βRMW +RMW βCMACMA f] = Market E[R f]f] E[R E[RMMM-R -R ] +β+HML HML βSMB SMB RMW i-Rfi-R Market f+ HML SMB+ RMW CMACMA E[Ri-RE[R f] = α + βMarket E[RM-Rf] + βHMLHML + βSMBSMB + βRMWRMW + βCMACMA

FF5+MOM: FF5+MOM:

FF5+MOM: FF5+MOM: E[Ri-Rf] = α + βMarket E[RM-Rf] + βHMLHML + βSMBSMB +βMOMMOM + βRMWRMW + (8) FF5+MOM: (8) FF5+MOM: i-R f] = α + βMarket E[RM-Rf] + βHMLHML + βSMBSMB +βMOMMOM + βRMWRMW + CMA CMAf] E[RβE[R == αα ++ ββMarket E[RMM-R HML + β SMBSMB MOM + β RMW(8) RMW i-R -Rf]f] ++ ββHML SMB +β +βMOM RMW i-Rf] α Market HMLHML + βSMB MOMMOM + βRMW E[RE[R + βMarket E[RE[R i-Rβf]CMA=CMA M-Rf] + βHMLHML + βSMBSMB +βMOMMOM + βRMWRMW + ββCMA CMA CMA βCMACMA CMA The benchmark adjusted regression model is given by:

Thebenchmark benchmark adjusted regression model The adjusted regression model is given by: is given by:

The benchmark adjusted regression model isisgiven by: benchmark adjusted regression model given Benchmark Adjusted Return = β model HighSustainabilityGlobesIndicator + TheThe benchmark adjusted regression is given by: by: Benchmark Adjusted Return 1= β HighSustainabilityGlobesIndicator +

Adjusted Return = β1HighSustainabilityGlobesIndicator + βBenchmark 2HighMorningstarRiskRatingIndicator + β3HighLipperPreservationRatingIndicator + HighMorningstarRiskRatingIndicator β3HighLipperPreservationRat ββ422GrowthFundIndicator HighMorningstarRiskRatingIndicator + β3+ HighLipperPreservationRatingIndicator + β +FundLevelControl + CountryLevelControl+IndustryLevelControl Benchmark Adjusted Return = β HighSustainabilityGlobesIndicator + Benchmark Adjusted Return β11HighSustainabilityGlobesIndicator + β4Return GrowthFundIndicator +FundLevelControl + CountryL Benchmark Adjusted = β= + + 1HighSustainabilityGlobesIndicator βingIndicator +FundLevelControl + CountryLevelControl+IndustryLevelControl 4GrowthFundIndicator ββ22HighMorningstarRiskRatingIndicator + ββ33HighLipperPreservationRatingIndicator evelControl+IndustryLevelControl HighMorningstarRiskRatingIndicator HighLipperPreservationRatingIndicator ++ β2HighMorningstarRiskRatingIndicator + β+ 3HighLipperPreservationRatingIndicator + The factor adjusted regression model is given by: ββ44GrowthFundIndicator +FundLevelControl ++ CountryLevelControl+IndustryLevelControl GrowthFundIndicator +FundLevelControl CountryLevelControl+IndustryLevelControl The factor adjusted regression model is given by: β4GrowthFundIndicator +FundLevelControl + CountryLevelControl+IndustryLevelControl 92 α= β HighSustainabilityGlobesIndicator + (10) α= βadjusted + by: (10) 1HighSustainabilityGlobesIndicator The factor regression model isisgiven β + The factor adjusted regression model given by: 2HighMorningstarRiskRatingIndicator The factor βadjusted regression model is given by: 2HighMorningstarRiskRatingIndicator + β3HighLipperPreservationRatingIndicator+β4GrowthFundIndicator β HighLipperPreservationRatingIndicator+β GrowthFundIndicator