Inflation hedging via tracking portfolios in the BRICS markets

Meshram, Vedprakash; Lalwani, Vaibhav Article Inflation hedging via tracking portfolios in the BRICS markets Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Meshram, Vedprakash; Lalwani, Vaibhav (2024) : Inflation hedging via tracking portfolios in the BRICS markets, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-16, https://doi.org/10.1080/23322039.2024.2431525 This Version is available at: https://hdl.handle.net/10419/321679 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Inflation hedging via tracking portfolios in the BRICS markets Vedprakash Meshram & Vaibhav Lalwani To cite this article: Vedprakash Meshram & Vaibhav Lalwani (2024) Inflation hedging via tracking portfolios in the BRICS markets, Cogent Economics & Finance, 12:1, 2431525, DOI: 10.1080/23322039.2024.2431525 To link to this article: https://doi.org/10.1080/23322039.2024.2431525 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 01 Dec 2024. Submit your article to this journal Article views: 416 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 FINANCIAL ECONOMICS | RESEARCH ARTICLE Inflation hedging via tracking portfolios in the BRICS markets Vedprakash Meshram a and Vaibhav Lalwani b a Goa Institute of Management, Goa, India; b XLRI Xavier School of Management Delhi-NCR, Jhajjar, India ABSTRACT This study investigates the creation of portfolios that effectively hedge against inflation in the context of the stock markets of Brazil, Russia, India, China, and South Africa (BRICS). Utilizing Ordinary Least Squares (OLS), Ridge, MM, and Quantile regressions, we construct portfolios that closely track the unexpected changes in Consumer Price Index (CPI) inflation. Our empirical analysis, based on data from spanning 2005 to 2023, demonstrates that statistical tracking portfolios outperform benchmark portfolios in tracking inflation, particularly during periods of low inflation uncertainty. Among all the methods, Quantile regression generally shows good tracking performance, though not universally. The findings suggest that these tracking portfolios can assist investors who are seeking to mitigate inflationary risks in volatile emerging markets. This research contributes to the literature by demonstrating out-of-sample performance of tracking portfolios and their application in less stable economic environments. IMPACT STATEMENT This study provides a critical analysis of constructing inflation-hedging portfolios tailored for the BRICS stock markets, utilizing advanced regression techniques such as OLS, Ridge, MM, and Quantile regressions. The results highlight the superior performance of statistical tracking portfolios in mitigating inflation risks compared to benchmark portfolios, particularly under low inflation uncertainty conditions. By addressing the challenge of inflation hedging in less stable economic environments, this study offers valuable insights for investors and contributes significantly to the field of portfolio management in emerging economies. ARTICLE HISTORY Received 27 July 2024 Revised 7 October 2024 Accepted 14 November 2024 KEYWORDS Inflation hedging; tracking portfolios; inflation; out-ofsample; BRICS; emerging markets; inflation uncertainty SUBJECTS Economics; Finance; Investment & Securities JEL CODES G11; G12; G17 1. Introduction Since the COVID pandemic, Inflation around the world has exceeded its highest levels since 1995 (Ha et al., 2023). While increased prices affect everyone, they also pose a serious challenge to savers, as inflation shocks threaten to erode the real values of their investments. Even a modest inflation rate of 4% can erode the purchasing power of cash by about half in 17 years. Uncontrolled inflation can rapidly erode the purchasing power of money, lead to macroeconomic instability, and create an uncertain business environment. Given such extreme consequences, protection from inflationary risks is crucial for investors (Bekaert & Wang, 2010). Therefore, investors need to understand how to invest their money strategically so as to outpace the rate of inflation. There is no universal approach by which investors can protect themselves from inflation (Atti e& Roache, 2009). However, the core principle of inflation hedging is to invest in assets whose returns move in tandem with inflation. In other words, investments with returns (cash flows) linked to real assets can act as inflation hedges. Other assets that tend to rise during bad economic times, such as gold and real estate, have also been pitted as inflation hedges (Shahzad et al., 2019; Salisu et al., 2020). Academics and practitioners have been studying inflation hedging for a long time. Research on asset allocation strategies proposes the inclusion of Inflation-protection bonds (e.g. Kothari & Shanken, 2004) and other alternative asset classes such as real estate, commodities, and hedge funds (e.g. Hoevenaars et al., 2008). Regardless of the number and type of asset classes, the weights of constituents in CONTACT Vaibhav Lalwani [email protected] XLRI Xavier School of Management Delhi-NCR Campus, Jhajjar, Haryana, India ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2431525 https://doi.org/10.1080/23322039.2024.2431525 an inflation-hedging portfolio need to be dynamically adjusted due to changing market conditions. The tracking portfolio approach as in Engle et al. (2020) can be used to achieve this objective. In this study, we use the tracking portfolio approach to explore investment strategies that can be used to hedge inflationary risks. Tracking portfolios closely tracks a particular indicator. Therefore, a tracking portfolio that invests in assets highly correlated with inflation can help investors hedge inflation. In simple terms, if a portfolio is correlated with inflation, its returns increase when inflation is high, thus providing an appropriate hedge against inflationary risk. Although the concept is simple to understand, creating portfolios that are correlated with inflation can be quite challenging. Economic tracking portfolios are popular in the literature ((Lamont, 2001; Vassalou, 2003), etc.); however, much of the existing literature uses in-sample tracking portfolios. Creating tracking portfolios that work outside the original sample is an even more daunting task. We address this challenge in our study. As discussed, the concept of an economic-tracking portfolio is not novel. However, its application in hedging inflationary risks in emerging markets is relatively unchartered. Emerging markets are characterized by their high growth potential, which often comes hand-in-hand with heightened volatility and susceptibility to inflationary pressure. While effective in developed economies, traditional investment strategies may not suffice in such dynamic markets. Thus, there is a paramount need for a specialized approach that accounts for the unique challenges faced by these economies. The average inflation rate in advanced economies from 2000 to 2023 is 2.15%. During the same period, the average monthly inflation in emerging markets and developing economies was 6.27% or around three times the inflation in developed markets. 1 Therefore, the current state of the literature does not tell us whether tracking portfolios can successfully hedge inflationary risks in emerging markets out-of-sample. This study attempts to fill this research gap. We do so by testing whether out-of-sample tracking portfolios do a better job of tracking inflation than an equally weighted benchmark. Based on the data of Brazil, Russia, India, China, and South Africa (BRICS countries) from 2005 to 2023, our results show that it is possible to develop dynamic asset allocation strategies that result in portfolios that efficiently track inflation. The BRICS countries account for more than 40% of the world’s population and the four BRIC countries account for a similar proportion of the world stock-market capitalization (Mensi et al., 2016). We concur with (McIver & Kang, 2020) in suggesting that global investors must better understand how the BRICS markets work. Therefore, our study adds to the literature by showing whether and how investors in emerging markets like BRICS can hedge inflationary risks by creating portfolios with the explicit objective of tracking inflation. The remainder of this paper is organized as follows. We discuss the existing literature in Section 2. Section 3 describes the statistical methodology used to track the portfolio creation. Section 4 explains the data and Section 5 discusses the empirical findings. We conclude our study in Section 6. 2. Literature review The relationship between inflation and asset prices has interested financial economists as far back as (Fisher, 1912). Later, Bodie (1976), (Fama, 1981), and (Bernard & Frecka, 1983) explored how inflation and asset prices interact. The famous (Fisher, 1930) expresses a formal baseline hypothesis of the relationship between price rise (inflation) and asset prices. Fama and Schwert (1977) show that in the following regression of asset returns on the change in the inflation rate, the estimate of the beta of expected inflation shows the relationship between nominal asset returns and inflation: Rjt ¼ajþbjEt−1pt ðÞ þejt (1) An asset whose bjis statistically indistinguishable from zero acts as a perfect hedge for the expected inflation (Arnold & Auer, 2015). Fama and Schwert (1977) augment Equation 1 with unexpected inflation, leading to the following expression: Rjt ¼ajþbjEt−1pt ðÞ þcjpt−Et−1pt ðÞ  þejt (2) In this augmented version of Fischer’s hypothesis, an asset for which the cjis statistically indistinguishable from 1.0, is a perfect hedge against unexpected inflation. A coefficient between 0 and 1 2 V. MESHRAM AND V. LALWANI signifies a partial hedge, whereas a negative coefficient indicates that an asset can be shorted to hedge inflation. The versions of Equations 1 and 2have been extensively used to test whether a particular asset serves as an inflation hedge. A priori, some assets are expected to work as better inflation hedges than others. In Fisher’s model, assets such as stocks and real estate, which represent claims against real assets, are more likely to move in tandem with inflation. A nominal income asset such as a treasury bill cannot provide a hedge for any unexpected inflation until its maturity. As discussed, common stocks, backed by the real assets of the underlying corporations, are expected to act as effective hedges against inflation. However, initial studies ((Bodie, 1976; Nelson, 1976; Fama & Schwert, 1977), among others) observed that there is often a negative or negligible relationship between stock returns and inflation. Fama and Schwert (1977) rightly call this "the most anomalous result" in their study. However, subsequent research rationalizes the counterintuitive relationship between stock returns and inflation. Fama (1981) notes that high inflation is also correlated with lower real activity. In other words, the negative relationship between stock returns and inflation is due to the positive relationship between stock prices and real economic activities. Once real activity is controlled for, the relationship between inflation and stock returns becomes positive. Geske and Roll (1983) provided a similar explanation. They refer to an empirical illusion due to the spurious causality induced by variables linked to debt monetization. Another set of studies provided a different line of thought. They argue that the hypothesized relationship is for the long run, whereas the empirical tests in previous studies use short-run data. Boudoukh and Richardson (1993) tested the relationship between inflation and stock returns in the U.S. and the U.K. using almost 190 years of data from 1802 to 1990 to provide a clearer picture of the long-run dynamics between stock returns and inflation. They reported a significantly positive long-term relationship between inflation and stock returns. Evans and Lewis (1995), Engsted and Tanggaard (2002), and Anari and Kolari (2001) confirm that the long-run Fisher elasticities of stock prices exceed 1. Therefore, stocks are expected to be good hedges against inflation in the long run. More recent analyses by Illeditsch (2018), Boons et al. (2020), and Campbell et al. (2020) also confirm that risks related to inflation are prices in stocks; that is, equity is positively correlated with inflation. While our focus is predominantly on equities, we also discuss some studies that explore the inflationhedging potential of other assets. The list of studies is, by no means, comprehensive. We refer the reader to Arnold and Auer (2015) for a broader list of relevant studies. Beginning with (Fama, 1981), real estate has been shown as a hedge against inflation, both expected and unexpected. While (Bond & Seiler, 1998; Salisu et al., 2020) also show that real estate provides security against inflationary risk, (Fang et al., 2008) show that it does not work, at least in Taiwan, in their sample. Gold is another prominent asset in discussions of inflation hedging. While contradictory evidence exists for all assets, the evidence of the inflation-hedging properties of gold appears to be quite divided. The initial results from Mahdavi and Zhou (1997) and Taylor (1998) do not support the inflation hedging hypothesis for gold. However, Lucey et al. (2017) and Aye et al. (2016) state that gold can act as an inflation hedge only in certain countries and during certain periods. In summary, there appears to be a time-varying relationship between asset prices and inflation, and the same asset mix cannot be expected to work at all times. As the nature of the relationship between assets and inflation changes over time, asset allocation strategies should also be sufficiently dynamic to reflect the changing market conditions. How can we develop a dynamic asset allocation strategy that hedges inflationary risk over time? In this study, we use a tracking portfolio approach to develop such a strategy. While we discuss the statistical methodology of tracking portfolios in the next section, we also discuss the core ideas in order. Simply defined, a tracking portfolio closely tracks an underlying variable of interest. For example, an inflation-tracking portfolio should be composed so that the resulting portfolio returns are highly correlated with some specified inflation proxies. These portfolios can be used to test asset pricing models, such as those in Breeden et al. (1989), Vassalou (2003), and Kapadia (2011). However, if used in an out-of-sample setting, the application of tracking portfolios extends far beyond model testing. Intuitively, a portfolio correlated with an economic indicator can be used to hedge risks arising from changes in the aforementioned indicators. For example, an investor concerned about the erosion of purchasing power due to a rise in inflation can invest in an inflation-tracking portfolio. Therefore, if inflation increases, the value of the investor’s portfolio also increases, offsetting the impact COGENT ECONOMICS & FINANCE 3 of the rise in inflation. The extent to which the impact of inflation is compensated for depends on the extent of the correlation with the economic indicator. A portfolio perfectly correlated with inflation would fully hedge risk. However, developing such portfolios is challenging, especially for out-of-sample portfolios. Chan et al. (1998) and others later show that portfolios that mimic economic factors perform poorly and are indistinguishable from randomly formed portfolios. To the best of our knowledge, Lamont (2001) is one of the first studies to empirically explore the creation of tracking portfolios. Using vector autoregressions (VAR), he creates economic tracking portfolios to track various economic variables such as industrial production, real consumption growth, and inflation. This study shows that tracking portfolios track the underlying economic variables. However, the tracking performance deteriorates considerably out of the sample. Portfolios that track an indicator insample can be constructed efficiently. The jury is still out of question whether in-sample success can be carried out in out-of-sample testing. However, the recent advances in this area are promising. Jurczenko and Teiletche (2022) showed that using machine learning estimators improves the performance of tracking portfolios. Luo et al. (2022) use a regularization approach to improve the out-of-sample performance of forecasts and thus create portfolios with lower tracking errors. Similarly, De Nard et al. (2023) propose a new mimicking portfolio approach that takes into account recent developments in the estimation of large-dimensional covariance matrices in short samples (Engle et al., 2019). To summarize, the literature can be categorized into two strands: the first strand explores whether selected assets (e.g. precious metals, stocks, bonds, real estate, etc.) can hedge inflation. The second strand, which is of our interest, relates to systematic portfolio strategies to generate portfolios that track an indicator. There are two key takeaways from the literature survey. First, it is difficult to create tracking portfolios that work out of samples. Second, much of the literature focuses on the developed markets. Even in developed markets, much of the evidence is from the U.S., with limited research coming from other countries (see (Neville et al., 2021) and (Nakagawa & Suimon, 2022) for exceptions). However, as previously argued, there is substantial heterogeneity in the dynamics of inflation in developing and developed countries. First, the average inflation in emerging markets is much higher than in advanced economies (Ha, Ivanova, Ohnsorge, et al., 2019). Second, emerging and low-income countries are more susceptive to external inflationary shocks and have much higher inflation volatility than developed economies (Ha, Ivanova, Montiel, et al., 2019). Therefore, tracking portfolio methods have been tested under more stable regimes. We need to know if the same methodologies hold in smaller economies, where economic conditions are relatively unstable. This study aims to fill this gap in the literature. We used the BRICS markets as the laboratory for our experiments. Using 13 broad diversified portfolios as our base assets, we test whether it is possible to develop portfolios that track expected and unexpected inflation. We find it possible to create portfolios that do a good job of tracking inflation out of sample. Detailed methods, data sources, and results are presented in the following sections. 3. Tracking portfolio methodology To fix the ideas, a tracking portfolio can be expressed in the form of a linear regression of the following form: Yt¼aþbXþet(3) Where Ytis inflation at time t, and X is the matrix of asset returns over time. The choice of X is determined by the investor and depends on the type of asset they would like to invest in. The estimates of b are the weights of the maximum correlational portfolio, that is, the portfolio with the maximum (in-sample) correlation with inflation. The ordinary least squares (OLS) solution is the most popular estimation technique for tracking portfolios: ^ bOLS ¼ðXTXÞ−1XTy(4) However, the regularization/shrinkage of beta coefficients has been shown to yield better out-of-sample forecasts (Luo et al., 2022). Because our goal is out-of-sample forecasting, we use an alternative estimator that uses the ridge penalty to shrink the estimates. The solution for the ridge regression is given by: 4 V. MESHRAM AND V. LALWANI ^ bridge ¼ðXTXþkIÞ−1XTy(5) where kis a shrinkage parameter provided by the user. We use 10 −4 as the value for the shrinkage parameter. Further, we also use the robust MM-regression of Yohai (1987) to estimate the tracking portfolio weights. The MM-regressions use the following objective function: Minimize X n i¼1 qyi−xT ib sMM  (6) Where parameter sMM is the scale estimate obtained from the initial S-estimation. The MM regression gives less weight to outliers compared to the OLS, a property that can be very useful when dealing with financial market data. We also used the quantile regression of Koenker and Bassett (1978) to estimate the weights of the tracking portfolio. Instead of estimating the conditional mean as in OLS, Quantile regressions estimate the conditional quantiles of the dependent variable. Quantile regression uses the following objective function: Minimize X n i¼1 qsðyi−xT ibÞ(7) where parameter qs(Tau) represents the quantile of interest to be specified by the user. A value of 0.5 denotes the median, whereas 0.75 would indicate the 75 th percentile value. Quantile regression-based tracking offers various benefits compared with other estimators. First, Quantile regression is more robust to outliers and can accommodate heavy-tailed distributions (Koenker & Hallock, 2001; Billah Dar et al., 2014). As asset returns are inherently asymmetric and fat-tailed, quantile regression tracking can yield weights robust to extreme values. Second, Quantile regression allows users to focus on specific parts of the distribution. Therefore, assigning weights to higher inflation levels can help create portfolios that do a better job of tracking an economic indicator, particularly when its value is high. Therefore, using a tau greater than 0.5, we can create portfolios that perform better when inflation is high. In our study, we used tau values of 0.5 and 0.75. Furthermore, tracking portfolio regression can impose additional constraints on the estimated parameters. We used the following three constraints in our study: 1. a¼0:This was imposed to ensure that there was no intercept in the regression. 2. Pk i¼1bk¼1:This constraint is imposed to ensure that the sum of all the weights is equal to one. 3. All bi>0:This long-only constraint ensures that all assets have non-negative weights in the portfolio. In our study, we assume that shorting of the factors used as base assets is not allowed. The first two constraints jointly ensure that the betas obtained from the regressions can be interpreted as weights of assets in a fully invested tracking portfolio. The last constraint ensures that all weights are greater than 0. Equation (3) estimates the maximum correlation weights using the in-sample data. We use a rolling window of five years (60 months) of data for the out-of-sample tracking portfolios. We estimate the initial set of portfolio weights using the first five years of inflation and return data. The resultant tracking portfolio is held for one month and is rebalanced every month using updated weights from the rolling window estimation. While our baseline results use monthly rebalancing, we also create additional portfolios that are rebalanced every six months. As previously discussed, the out-of-sample performance of tracking portfolios is often much worse than that of in-sample tracking portfolios. For better performance, we incorporate some improvements in the portfolio creation process using the following insights from the literature: First, unexpected inflation is used as a measure of inflation. As asset prices already incorporate the expected component of inflation, subsequent price movements are expected to be due to unexpected inflation. Thus, the literature uses unexpected inflation as a proxy for inflation news in the market. The estimation of unexpected inflation involves subtracting the inflation forecast from the actual inflation. COGENT ECONOMICS & FINANCE 5 However, Neville et al. (2021) show that the difference between the current and previous months’inflation works, as well as other sophisticated measures of unexpected inflation. Therefore, we use the monthly difference in inflation as the metric we want to track. Further, Downing et al. (2012) show that using one-month-ahead inflation instead of the current month’s inflation as a dependent variable enhances the performance of the tracking portfolio. Therefore, we create tracking portfolios using one-month-ahead unexpected inflation instead of contemporaneous inflation. We skip the last three months of data in our rolling window to ensure that we use only the information available to an investor at the time of portfolio creation. For example, suppose our rolling window spans five years from January 2000 to December 2004. In this case, we do not consider the data from October 2004 to December 2004, assuming that the inflation data for these months will not be available until January 2005. This step enhances the practical feasibility of our analysis. 4. Data This study was conducted using data from January 2005 to July 2023. We use Data for monthly CPI Inflation of the BRICS countries from the FRED Database. We have used 13 diversified anomaly portfolios from Jensen et al. (2023) as our base assets to construct an inflation-tracking portfolio. These assets constitute a diverse set of portfolios (factors) that are created from multiple cross-sectional indicators, and therefore, these assets demonstrate substantial cross-sectional heterogeneity. The data for these factors can be obtained from the website www.jkpfactors.com. Our choice of assets is similar to Lamont (2001) and Downing et al. (2012), who use a broad set of diversified portfolios for their study. We do not consider other asset classes (gold and real estate) in our tracking portfolio. Our main objective is to test whether an equity-only investment is sufficient to create a workable tracking portfolio. Our results show that equities are sufficient for creating a portfolio that closely tracks inflation. We leave the inclusion of other asset classes to future studies. The details of the base assets and their summary statistics are in Table 1. As shown in the table, our choice of base assets consists of a diverse set of assets composed of broad-based portfolios that reflect various investment themes such as value, accruals, momentum, profitability, quality, etc. The mean, volatility, skewness, and kurtosis of assets vary from each other. We emphasize diversity because an ideal asset base for portfolio creation should consist of assets with different statistical behaviors. Our asset choice seems to fulfill this criterion. We now discuss the creation of the tracking portfolios discussed above. In the next section, we report the results and the overall performance of our tracking portfolios. 5. Results We created ten types of inflation-tracking portfolios. The portfolios correspond to the four choices of estimators –OLS, Ridge, MM, and Quantile Regression (with Tau of 0.5 and 0.75) and two rebalancing frequencies –monthly and half-annually. As a naïve benchmark, we create an equally weighted portfolio for all the base assets. We report the performance of each portfolio on four metrics: Sharpe Ratio (S.R.), average monthly portfolio turnover (ATO), and cumulative tracking error (T.E.). The calculation of these metrics is as follows: The Sharpe ratio is calculated as the average monthly return of the portfolio divided by the standard deviation of the monthly returns. The average monthly turnover of a portfolio is calculated using the following formula: 100 N−1X N t¼2X S i¼1 jwi,t−wi,t−1j Here, Nrefers to the number of months for which the portfolio returns are estimated, Sis the number of assets in the portfolio, and wi,tis the weight for the asset’i’in the month ‘t’in the tracking portfolio. A lower turnover is generally preferred because it signifies that the portfolio strategy does not involve 6 V. MESHRAM AND V. LALWANI Table 1. Descriptive statistics of inflation and base asset returns. Brazil Indicator Mean Std. Dev. Skewness Kurtosis CPI inflation 0.47 0.17 0.70 0.05 Accruals 0.02 3.34 −0.05 3.59 Debt issuance 0.07 2.25 0.06 2.65 Investment 0.20 2.69 0.25 5.08 Low leverage −0.18 2.39 −0.04 3.06 Low risk 0.15 3.45 −0.40 1.29 Momentum 0.71 4.11 −0.89 3.59 Profit growth 0.11 1.67 −0.59 1.23 Profitability 0.22 2.92 −0.19 1.64 Quality 0.27 2.77 −0.30 0.36 Seasonality 0.07 1.62 0.12 2.58 Short term reversal −0.14 2.33 1.54 8.58 Size 0.10 3.26 1.08 5.59 Value 0.35 2.89 0.56 1.06 Russia Indicator Mean Std. Dev. Skewness Kurtosis CPI inflation 0.64 0.30 0.46 −0.71 Accruals 0.19 3.24 0.21 3.55 Debt issuance 0.14 2.19 0.00 1.78 Investment 0.12 2.42 0.24 3.96 Low leverage 0.05 2.26 −1.09 8.10 Low risk −0.08 2.61 −0.56 4.47 Momentum 0.83 4.16 −0.54 3.06 Profit growth 0.42 2.08 0.21 2.80 Profitability 0.36 2.88 −0.37 1.94 Quality 0.56 3.00 0.01 1.60 Seasonality −0.14 1.83 −1.18 6.86 Short term reversal −0.03 3.04 0.21 3.56 Size 0.01 3.62 0.53 4.29 Value 0.57 2.46 0.40 1.51 India Indicator Mean Std. Dev. Skewness Kurtosis CPI inflation 0.56 0.22 0.64 0.31 Accruals 0.03 1.90 0.44 3.28 Debt issuance 0.18 1.35 0.01 0.93 Investment 0.24 1.84 −0.63 5.74 Low leverage 0.03 2.11 −0.25 1.45 Low risk 0.17 3.49 −2.06 12.69 Momentum 0.46 4.18 −1.98 16.00 Profit growth 0.17 1.39 −0.73 3.77 Profitability 0.12 2.65 −0.58 1.59 Quality 0.28 3.45 −0.73 1.73 Seasonality 0.05 0.86 0.30 0.61 Short term reversal −0.19 1.93 −2.06 11.45 Size 0.27 2.77 0.23 1.56 Value 0.19 2.34 −0.13 1.64 China Indicator Mean Std. Dev. Skewness Kurtosis CPI inflation 0.20 0.15 0.82 1.24 Accruals 0.14 1.67 −0.23 0.23 Debt issuance 0.07 0.95 −0.46 1.05 Investment 0.08 2.06 −0.18 1.19 Low leverage −0.03 2.36 −0.42 1.66 Low risk 0.44 3.04 −0.24 0.22 Momentum −0.06 3.52 −0.16 0.46 Profit growth 0.27 1.36 −0.03 1.05 Profitability 0.07 2.94 0.15 2.76 Quality 0.15 2.68 −0.15 1.72 Seasonality 0.11 0.88 0.62 3.01 Short term reversal 0.34 1.91 −0.70 4.90 Size 0.80 4.09 −0.36 2.18 Value 0.36 3.23 0.21 1.90 South Africa Indicator Mean Std. Dev. Skewness Kurtosis CPI inflation 0.00 0.03 0.02 0.55 Accruals 0.03 2.96 0.40 2.53 (continued) COGENT ECONOMICS & FINANCE 7 Note 1. Source: Authors’calculations based on Data from IMF’s World Economic Outlook Database. Author contributions Vaibhav Lalwani: conceptualization, formal analysis, investigation, methodology, supervision, writing –original draft. Vedprakash Meshram: software, validation, writing –review & editing. Disclosure statement No potential competing interest was reported by the author(s). Funding No funding was received About the authors Vedprakash Meshram is an Assistant Professor at the Goa Institute of Management. He holds a PhD from the Indian Institute of Management Lucknow. His research work has been published in several journals, including the Journal of International Accounting, Auditing and Taxation, Applied Economics, and the Journal of Prediction Markets, among others. He has presented his research at several leading international and national conferences. Vaibhav Lalwani is an Assistant Professor at the at the Xavier Labour Relations Institute’s (XLRI) Delhi NCR Campus. He holds a PhD from the Indian Institute of Management Lucknow. His research work has been published in several journals, including Economics Letters, Finance Research Letters, Applied Economics, Managerial Finance, Applied Finance Letters, and Journal of Prediction Markets, among others. 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