Value-weighted Returns Research Articles

The Linkage between Corporate Research and Development Intensity and Stock Returns: Empirical Evidence

Research and development (R&D) is a significant driver of innovation that leads to superior performance. The present study attempts to examine the relationship between R&D intensity and a firm’s performance at both aggregate and industry levels in the emerging market of India using a battery of R&D intensity measures and stock market returns as a measure of a firm’s performance. The study was conducted on 1097 companies from six R&D-intensive industries. The Fama-French portfolio formation method was used to evaluate the stock market performance of R&D-intensive firms for both equal-weighted (EW) and value-weighted (VW) returns. The findings suggest that R&D intensity and stock returns show a positive relationship. A long–short strategy in R&D-intense firms has yielded 1.43% (t = 4.22) per month in the sample. In general, the results suggest an undervaluation of highly R&D-intensive firms that investors can exploit for above-average returns. The effect is not homogeneous across return schemes (equal-weighted and value-weighted) or across industries. R&D growth measures and R&D capital are not found to have significant impacts on stock returns. Both the market firm size and age are included as control variables, and the results reveal that the relationship is robust to these control variables. The sub-periods ranging from 2000 to 2007 and 2008 to 2019 have been considered in the present study and the results are consistent with the overall sample. The study fills the existing empirical void for R&D intensity and stock returns in relation to the emerging market of India.

Bayesian Reconciliation of Return Predictability

Abstract This article considers a stable vector autoregressive (VAR) model and investigates return predictability in a Bayesian context. The bivariate VAR system comprises asset returns and a further prediction variable, such as the dividend-price ratio, and allows pinning down the question of return predictability to the value of one particular model parameter. We develop a new shrinkage type prior for this parameter and compare our Bayesian approach to ordinary least squares estimation and to the reduced-bias estimator proposed in Amihud and Hurvich (2004. “Predictive Regressions: A Reduced-Bias Estimation Method.” Journal of Financial and Quantitative Analysis 39: 813–41). A simulation study shows that the Bayesian approach dominates the reduced-bias estimator in terms of observed size (false positive) and power (false negative). We apply our methodology to a system comprising annual CRSP value-weighted returns running, respectively, from 1926 to 2004 and from 1953 to 2021, and the logarithmic dividend-price ratio. For the first sample, the Bayesian approach supports the hypothesis of no return predictability, while for the second data set weak evidence for predictability is observed. Then, instead of the dividend-price ratio, some prediction variables proposed in Welch and Goyal (2008. “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction.” Review of Financial Studies 21: 1455–508) are used. Also with these prediction variables, only weak evidence for return predictability is supported by Bayesian testing. These results are corroborated with an out-of-sample forecasting analysis.

Replicating and Digesting Anomalies in the Chinese A-Share Market

We replicate 469 anomaly variables similar to those studied by Hou et al. (2020) using Chinese A-share data and a reliable testing procedure with mainboard breakpoints and value-weighted returns. We find that 83.37% of the anomaly variables do not generate significant high-minus-low quintile raw return spreads. Further adjusting risk increases the failure rate slightly to 84.22% based on CAPM alphas and 86.99% based on Fama–French three-factor alphas. We show that the conventional procedure using all A-share breakpoints with equal-weighted returns for the anomaly test is indeed problematic as it assigns too much weight to microcaps and has a very limited investment capacity. The CH3-factor, CH4-factor, and q-factor models show the best performance over the whole sample period. The q-factor model is the best performer in the post-2007 subsample period after significant improvements occurred in China’s financial market environment, such as the completion of the split-share structure reform and the implementation of new accounting standards conforming to the International Financial Reporting Standards. The non–state-owned enterprise subsample in the post-2007 period is a cleaner sample in which the CH4-factor and q-factor models are the best performers. This paper was accepted by Lukas Schmid, finance. Funding: Z. Li acknowledges financial support from the National Natural Science Foundation of China [Grant 72103043] and the Fundamental Research Funds for the Central Universities in UIBE [Grants 19QN01 and 22PY053-72103043]. L. X. Liu acknowledges financial support from the National Natural Science Foundation of China [Grants 71872006 and 72273006]. L. X. Liu and X. Liu acknowledge financial support from the Guanghua Thought Leadership Platform of Peking University. K. C. J. Wei acknowledges partial financial support from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant 15507320]. The authors acknowledge financial support from the Guanghua School of Management, Peking University; the School of Banking and Finance, University of International Business and Economics; and the Non-PAIR Research Centre “Research Centre for Quantitative Finance” at Hong Kong Polytechnic University. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4904 .

A two-dimensional innovation activity factor and stock pricing: Evidence from the Chinese stock market

This paper confirms that innovation activities can bring about excess stock returns in the Chinese stock market. Using monthly data on A-share stocks from May 2007 to December 2022, we find a significant innovation premium in the Chinese stock market by constructing a composite indicator that includes both innovation input and output. Then, we propose a six-factor model by incorporating the innovation factor into the Fama-French five-factor model. Time-series regressions show that the proposed model works well in explaining the variation of excess stock returns, and the innovation factor makes a significant marginal contribution to the explanatory power. Further tests show that the results remain robust for value-weighted returns, individual stocks, and different boards. The proposed model effectively reduces the root mean square errors compared to traditional pricing models. Our study adds significant evidence verifying the value of innovation in financial markets, and also provides practical implication for better enterprise innovation managment and public services.

Stock market anomalies and machine learning across the globe

We identify the characteristics and specifications that drive the out-of-sample performance of machine-learning models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant monthly value-weighted (long-short) returns of around 1.8–2.2%, and a vast majority of tested models outperform a linear combination of predictors (our baseline factor benchmark) by a substantial margin. Composite predictors based on machine learning have long-short portfolio returns that remain significant even with transaction costs up to 300 basis points. By comparing 46 variations of machine-learning models, we find that the models with the highest return predictability apply a feed-forward neural network or composite predictors, with extending rolling windows, including elastic net as a feature reduction, and using percent ranked returns as a target. The results of our nonlinear models are significant across several classical asset pricing models and uncover market inefficiencies that challenge current asset pricing theories in international markets.

Performance of Private to Public MBOs: The Role of Venture Capital

Using a unique dataset, we examine financial performance, and venture capital involvement in 167 MBOs exiting through IPOs (MBO-IPOs) on the London Stock Exchange, during the period 1964-1997. VC backed MBOs seem to be more underpriced than MBOs without venture capital backing, based on average value-weighted returns. MBOs backed by highly reputable VCs tend to be older companies, and exit earlier than MBOs backed by less reputable VCs. The results contradict 'certification' and 'grandstanding' hypotheses supported by US data (Megginson and Weiss, 1991; and Gompers, 1996, respectively). We found no evidence of either significant underperformance, or that VC backed MBOs perform better than their non-VC backed counterparts in the long run. However, MBOs backed by highly reputable venture capital firms seem to be better long-term investments as compared to those backed by less prestigious venture capitalist firms. The results remain robust after using different methods to measure performance, and after controlling for sample selectivity bias.

Portfolio: A command for conducting portfolio analysis in Stata

Portfolio analysis is widely used in empirical asset pricing to explore the cross-sectional relation between two or more variables. In this article, we introduce the methodology of portfolio analysis and describe a new command, portfolio, that provides a one-step solution for portfolio analysis. portfolio calculates the equal- or value-weighted returns with a t statistic for the portfolio and tests the significance of a long-short strategy in portfolios. portfolio also provides the Newey–West standard-error adjustment option for alleviating the impact of potential autocorrelation and heteroskedasticity in financial time series.

A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited

Many researchers seek factors that predict the cross-section of stock returns. In finance, the key is to replicate anomalies by long–short portfolios based on their firm characteristics, with microcap biases alleviated via New York Stock Exchange (NYSE) breakpoints and value-weighted returns. In econometrics, the key is to include a covariance matrix estimator of stock returns for (mimicking) the portfolio construction. This paper marries these two strands of literature in order to test the zoo of cross-sectional anomalies by injecting size controls, basically NYSE breakpoints and value-weighted returns, into efficient sorting. We propose to use a covariance matrix estimator for ultra-high dimensions (up to 5,000) taking into account large, small and microcap stocks. We demonstrate that using a nonlinear shrinkage estimator of the covariance matrix substantially enhances the power of tests for cross-sectional anomalies: On average, t-statistics more than double. Furthermore, the proposed revisited efficient sorting method computes even highly significant factor portfolios net of transaction costs.

Asymmetry and Leverage with News Impact Curve Perspective in Australian Stock Returns’ Volatility during COVID-19

This paper studies the effect of COVID-19 on the volatility of Australian stock returns and the effect of negative and positive news (shocks) by investigating the asymmetric nature of the shocks and leverage impact on volatility. We employ a generalised autoregressive conditional heteroskedasticity (GARCH) model and extend the analysis using the exponential GARCH (EGARCH) model to capture asymmetry and allegedly leverage. We proxy the news related to the negative effect of COVID-19 on the Australian health system and its economy as bad news, and on the other hand, measures taken by government economic stimulus packages through their monetary and fiscal policies as good news. The S&P ASX200 (ASX-200) index is used as a proxy to the Australian stock market, and we use value-weighted returns of the stocks listed on ASX-200 for the period 27 January 2020 to 29 December 2020. The empirical results suggest the EGARCH model fits better in capturing asymmetry and leverage than the GARCH model in estimating the volatility of the Australian stock returns. However, another interesting finding is that the EGARCH model with volatility equation without news demonstrates a larger (smaller) leverage effect of the negative (positive) shocks on the conditional volatility compared to its variant with the news.

R&D information quality and stock returns

Investors demand higher premiums from firms whose future R&D performance is difficult to evaluate. We construct a measure of R&D information quality (RDIQ) by linking a firm's historical innovation input (R&D expenditures) and innovation outcome (sales) and find significant evidence that expected excess returns decrease with RDIQ. We find that the high-minus-low RDIQ hedge portfolio earns excess returns of −23 (−25) bps per month in value-weighted (equal-weighted) returns. We also find that the RDIQ effect is weakly correlated with commonly used risk factors, is stronger for firms with greater uncertain business environment, and exhibits incremental pricing power.

United States Stock Market Response to FDA Approval of New Dermatologic Drugs

The Food and Drug Administration (FDA) has approved several new dermatologic drugs in the last decade. The public response to their approval has not yet been evaluated. To analyze the United States stock market response surrounding FDA approval of new dermatologic drugs between 2008 and 2018. A list of 34 FDA approved dermatologic drugs for publicly traded companies was compiled from the CenterWatch New Dermatology Drugs List and the FDA Annual Reports on New Drugs. Company and stock market data was acquired from the Center for Research in Security Prices (CRSP) United States Stock database. Cumulative abnormal returns (CAR) were calculated as the difference between raw returns and expected value-weighted returns. Data analyses were performed using SAS 9.4 (Cary, NC). The average CAR for the 21-day window period surrounding FDA approval of new dermatologic drugs was +1.71%. Drugs approved for the treatment of hyperhidrosis (+17.7%), bacterial skin infections (+7.18%), and rosacea (+6.83%) added the most market value. The market value added to private or internationally traded companies could not be assessed. FDA approval of dermatologic drugs generally has a positive market response. Information on market reaction may provide important insights for investors, pharmaceutical companies, and researchers. J Drugs Dermatol. 2020;19(6): doi:10.36849/JDD.2020.5033.

New Evidence of the Marginal Predictive Content of Small and Large Jumps in the Cross-Section

We investigate the marginal predictive content of small versus large jump variation, when forecasting one-week-ahead cross-sectional equity returns, building on Bollerslev et al. (2020). We find that sorting on signed small jump variation leads to greater value-weighted return differentials between stocks in our highest- and lowest-quintile portfolios (i.e., high–low spreads) than when either signed total jump or signed large jump variation is sorted on. It is shown that the benefit of signed small jump variation investing is driven by stock selection within an industry, rather than industry bets. Investors prefer stocks with a high probability of having positive jumps, but they also tend to overweight safer industries. Also, consistent with the findings in Scaillet et al. (2018), upside (downside) jump variation negatively (positively) predicts future returns. However, signed (large/small/total) jump variation has stronger predictive power than both upside and downside jump variation. One reason large and small (signed) jump variation have differing marginal predictive contents is that the predictive content of signed large jump variation is negligible when controlling for either signed total jump variation or realized skewness. By contrast, signed small jump variation has unique information for predicting future returns, even when controlling for these variables. By analyzing earnings announcement surprises, we find that large jumps are closely associated with “big” news. However, while such news-related information is embedded in large jump variation, the information is generally short-lived, and dissipates too quickly to provide marginal predictive content for subsequent weekly returns. Finally, we find that small jumps are more likely to be diversified away than large jumps and tend to be more closely associated with idiosyncratic risks. This indicates that small jumps are more likely to be driven by liquidity conditions and trading activity.

A Large-Dimensional Test for Cross-Sectional Anomalies: Efficient Sorting Revisited

Many researchers seek factors that predict the cross-section of stock returns. In finance, the key is to replicate anomalies by long-short portfolios based on their factor scores, with microcaps alleviated via New York Stock Exchange (NYSE) breakpoints and value-weighted returns. In econometrics, the key is to include a covariance matrix estimator of stock returns for the (mimicking) portfolio construction. This paper marries these two strands of literature in order to test the zoo of cross-sectional anomalies by injecting size controls, basically NYSE breakpoints and value-weighted returns, into efficient sorting. Thus, we propose to use a covariance matrix estimator for ultra-high dimensions (up to 5,000) taking into account large, small and microcap stocks. We demonstrate that using a nonlinear shrinkage estimator of the covariance matrix substantially enhances the power of tests for cross-sectional anomalies: On average, ‘Student’ t-statistics more than double.

Art as an Asset and Keynes the Collector

If art indexes are exposed to transaction-channel selectivity, suffer from survivorship bias, or weight individual assets inappropriately, they may be unrepresentative of art investors’ performance. To investigate these issues, we study an actual invested portfolio — the art collection of economist John Maynard Keynes — using valuations that span up to 96 years. We document substantial long-term real returns and show that auction purchases were followed by significantly higher initial returns than primary market acquisitions. We argue that, because of the concentration of art portfolios, indexes based on auction prices for established artists can successfully capture the value-weighted average returns realized by art investors buying in the secondary market. Positive skewness in returns can nevertheless cause most investors to underperform the asset class.

Capitalizing on Sustainability: The Value of Going Green

This paper investigates how corporate Environmental, Social, and Governance (ESG) actions affect firm value. By employing an event study around two close 5-to-4 Supreme Court rulings and using portfolio-based approaches, we document that sustainability is capitalized into the value of firms. When the Court awards broader regulatory authority to the EPA, polluting firms with the largest discrepancies vis-a-vis the new rules heralded by the Court exhibit the largest price responses. On the other hand, when the Court narrows the regulatory limit of an existing environmental law, polluting firms lose value. The announcement returns are more sensitive to the Court rulings for firms in regions with a higher level of trust. More generally, a portfolio of firms with cleaner production practices earns alphas (4.43% annually for value-weighted returns) compared to firms with toxic production practices that have similar exposure to well-known factor models. Firms with greener production practices show positive earnings surprises, higher revenue and profitability, and more capital inflow from institutional investors with longer horizons. The abnormal returns are more pronounced among firms in regions with a high level of trust. In sum, we show that firms gain value when they go green.

Sarbanes-Oxley, agency conflicts and the marginal value of capital expenditure

PurposeThe purpose of this paper is to examine the relationship between the strength of corporate governance and the value of firm-level investment policies following the passage of the Sarbanes–Oxley (SOX) Act of 2002 and the associated changes to the listing requirements of major stock exchanges. In particular the authors seek to examine potential changes in the market’s assessed value of capital expenditures after the passage of the SOX Act relative to before.Design/methodology/approachThe authors employ a difference-in-difference methodology, centred on the year of the passage of the SOX Act to test for the role of governance on the marginal value of capital expenditures. Excess stock returns are calculated by subtracting Fama and French (1993) size and book-to-market portfolio value-weighted returns from the firms’ annual stock returns. Each firm is grouped into one of 25 size and book-to-market portfolios for each year in the sample, with size and the book-to-market ratio proxying for sensitivity to common risk factors in stock returns (Fama and French, 1993).FindingsThe authors find that markets responded to the change in governance brought about by the new regulation by altering the value of firm-level capital expenditures in a way that is generally consistent with predictions of agency theory. While the overall findings imply a reduction in agency conflicts post-SOX, there is some evidence that certain firms may have suffered excessive costs of compliance, while still others saw managers become excessively risk averse.Research limitations/implicationsThe study has implications related to the efficacy of legislation. Cross-sectional variation in the effect of SOX on the marginal value of capital expenditures suggests that one-size-fits-all legislative approach can have both expected as well as unintended consequences. The study limits its analysis to examining the impact of three significant provisions of the Act. While, the value implications of the Act are largely captured by the selected three, a more comprehensive study could expand on the set of provisions studies to obtain a more granular level impact.Practical implicationsThis research should add to the growing body of the literature examining the effect of SOX on firms’ real activities and decisions, as well as contribute to the debate on whether the Act was beneficial or costly to firms. With particular reference to the impact of capital expenditure on firm value, the research contributes to the sparse literature examining the contribution of capital expenditures to firm value and the role that agency conflicts play in this relationship. Additionally, this research adds to the growing body of the literature that examines the costs and benefits of the sweeping new regulations brought on by the adoption of SOX.Social implicationsGiven the importance of investment policy for economic productivity and growth, the insights provided by findings in this research should benefit lawmakers both within the USA as well as in countries where corporate misconduct and fraud is a concern.Originality/valueThis is the first study that examines the impact of the SOX Act on the way capital markets value firm-level investment in capital expenditures. Since use of corporate resources by managers is fraught with agency conflicts, the role of SOX in potentially alleviating this conflict as revealed by the tests in this study are very valuable.

New Evidence of the Marginal Predictive Content of Small and Large Jumps

In an important paper, Bollerslev, Li, and Zhao (2019) find that there is significant predictive content in signed total jump variation. In this paper, we further decompose jumps into small and large (signed) variation. Our primary purpose is to investigate the marginal predictive content of small versus large jump variation, when forecasting oneweek ahead cross-sectional equity returns. However, we also examine earnings announcements, in order to shed new light on the linkages between (small and large) jumps and news. One of our key findings is that sorting on signed small jump variation leads to greater value-weighted return differentials between stocks in our highest and lowest quintile portfolios (i.e., high-low spreads) than when either signed total jump or signed large jump variation is sorted on. Moreover, in a key case, the high-low spread is not significantly different from zero when signed large jump variation is sorted on. Indeed, including large jump variation can actually decrease predictive accuracy, in the sense that average returns and alphas for high-low portfolios are lower when total jump variation is utilized in our prediction experiments rather than small jump variation. These results suggest that there may be a threshold, beyond which “large” jump variation contains no marginal predictive ability, relative to that contained in small jump variation. Analysis of returns and alphas based on industry double-sorts indicates that the benefit of small signed jump variation investing is driven by stock selection within an industry, rather than industry bets. Investors prefer stocks with a high probability of large positive jump variation, but they also tend to overweight safer industries. Additionally, the fact that large and small (signed) jump variation have differing marginal predictive content is explained at least in part by our observation that in double-sorted portfolios, the content of signed large jump variation is negligible when controlling for either signed total jump variation or realized skewness. By contrast, signed small jump variation has unique information for predicting future returns, even when controlling for total jump variation or realized skewness. Further, we find that large jumps are closely associated with “big” news, as might be expected. In particular, large earning announcement surprises increase both the magnitude and occurrence of large jumps. While such news related information is embedded in large jump variation, the information is generally short-lived, and dissipates too quickly to provide marginal predictive content for subsequent weekly returns. Finally, we find that while large jump variation is closely associated with large earnings surprises (“big” news), small jumps tend to be more closely associated with idiosyncratic risks, and can be diversified away.

The risks of old capital age: Asset pricing implications of technology adoption

A dynamic model featuring a stochastic technology frontier shows significant impact of technology adoption for asset prices. In equilibrium, firms operating with old capital are riskier because costly technology adoption restricts their flexibility in upgrading to the latest technology, making them more exposed to technology frontier shocks. Consistent with the model predictions, a long-short portfolio sorted on firm-level capital age earns an average value-weighted return of 9% per year among U.S. public companies. A proxy for technology frontier shocks captures the variation of the capital age portfolios with a positive risk price, corroborating the model mechanism.

Replicating Anomalies

AbstractMost anomalies fail to hold up to currently acceptable standards for empirical finance. With microcaps mitigated via NYSE breakpoints and value-weighted returns, 65% of the 452 anomalies in our extensive data library, including 96% of the trading frictions category, cannot clear the single test hurdle of the absolute $t$-value of 1.96. Imposing the higher multiple test hurdle of 2.78 at the 5% significance level raises the failure rate to 82%. Even for replicated anomalies, their economic magnitudes are much smaller than originally reported. In all, capital markets are more efficient than previously recognized.Received June 12, 2017; editorial decision October 29, 2018 by Editor Stijn Van Nieuwerburgh. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Aggregate Information in Different Anomalies: International Evidence

This paper conducts a comprehensive empirical test of 20 prominent anomalies using the data from 32 countries/regions during the period 1999 to 2017. My results show that most of the anomalies become weaker or even diminish using data in recent years, both in the US and in other markets. This paper also compares five different information aggregation approaches to evaluate their performance of anomaly returns. The results show that although different aggregation methods may perform better in a specific country, on average the latent variable based approach and the Fama-MacBeth regression based approach outperform other approaches in capturing the return-related information. Moreover, results based on equal-weighted returns provide much more significant anomalies than those based on value-weighted returns.