Cross-sectional Variation In Returns Research Articles

The Lost Decade: Have Macro Factor Risk Premia Become Irrelevant?

The role of factors in macro investing has come into question after mediocre performance during the past decade. In this article, the authors confirm this decline in profitability and examine the importance and relevance of macro factors via three different approaches, analyzing their explaining power for asset risks and cross-sectional return variations. They find no evidence of declining importance over time. They discuss a few possible explanations for the apparently unreliable risk premia associated with these factors in the recent decade.

Smart money in China's A-share market: Evidence from big data

In this study, we propose a bar-level tracker of smart money trades by detecting the trading aggressiveness of informed traders. Accordingly, we define monthly smart money measures to identify the direction of informed trades. By using the 1-min transaction data in China’s A-share market from 1999 to 2019, we find a negative cross-sectional relationship between the smart money measures and stock future returns. We construct smart money strategies based on the fuzzy c-means (FCM) clustering algorithm, wherein the optimal strategy produces an annual Sharpe ratio of 1.169. Our findings indicate that the FCM-based portfolio formation could outperform the conventional sorting-based strategies. This study may be useful for research on smart money trades and the analysis of cross-sectional variation in returns.

What Do We Know About Corporate Bond Returns?

Recently, there has been a fast-growing literature on the determinants of corporate bond returns, in particular, the driving force of cross-sectional return variation. In this review, we first survey recent empirical studies on this important topic. We discuss cross-sectional evidence as well as time-series evidence. We then present a model-based analysis of individual corporate bond returns using the structural approach for credit risk modeling. We show, among other things, that the expected corporate bond return implied by the Merton model predicts 1-month-ahead corporate bond returns in the cross section.

Asset Pricing with Panel Trees Under Global Split Criteria

We introduce a class of interpretable tree-based models (P-Trees) for analyzing panel data, with iterative and global (instead of recursive and local) splitting criteria to avoid overfitting and improve model performance. We apply P-Tree to generate a stochastic discount factor model and test assets for cross-sectional asset pricing. Unlike other tree algorithms, P-Trees accommodate imbalanced panels of asset returns and grow under the no-arbitrage condition. P-Trees also graphically capture nonlinearity and interaction effects and accommodate regime-switching and interactions between macroeconomic states and firm characteristics. For example, P-Tree identifies inflation as the most important macro predictor with regime-switching in U.S. equity data. Based on multiple pricing, prediction, and investment metrics, we find that (boosted or time-series) P-Trees outperform standard factor models and PCA latent factor models. An equally-weighted portfolio for five factors generated by P-Trees delivers an excess alpha of 1.09% against the Fama-French 3-factor benchmark, producing an annualized Sharpe ratio of 1.98 out-of-sample. Data-driven cutpoints in P-Trees reveal that long-run reversal, volume volatility, and industry-adjusted market equity drive cross-sectional return variations, consistent with variable importance analysis using random forests.

An alternative nonparametric tail risk measure

The proposition of tail risk as a new asset pricing factor has gained traction in recent years. Recent work by Almeida, Ardison, Garcia, and Vicente (Nonparametric tail risk, stock returns, and the macroeconomy. J. Financ. Economet., 2017, 15(3), 333–376) proxies the cross-sectional variation in returns by Fama-French portfolios, which are further summarized into a few basis assets via principal component analysis. The number of states of nature is set higher than that of the basis assets to estimate the stochastic discount factors, which in turn risk-neutralize the excess expected shortfall as a tail risk measure. As an alternative approach to this means of dimension reduction, we propose tackling the problem directly by forming portfolios that minimize the excess expected shortfall. Our direct measure exhibits greater explanatory power when applied to more liquid, nonlottery-style stock returns. More importantly, our proposed approach reveals direct exposure to downside systematic risk without the need for an additional risk-neutralization step.

The R&D anomaly: Risk or mispricing?

We offer new evidence on the risk versus mispricing explanations for the R&D anomaly. Return covariance with a characteristic-based factor captures the cross-sectional return variation on R&D portfolios not explained by asset pricing models. This is consistent with both covariance risk and mispricing. Under the framework of the ICAPM, we find little economic justification that an R&D factor is a proxy for innovations to a state variable. The characteristic subsumes the factor loading in direct tests, providing support to the mispricing hypothesis. Investigating the mispricing explanation further, we reject the assertion that the R&D anomaly arises from the correction of stocks mispriced by investor sentiment. A natural experiment exploiting the pilot program under Regulation SHO shows no evidence that the anomaly persists due to limits to arbitrage in the form of short sale constraints.

Cryptocurrency Investing Examined

In this work we examine the largest 100 cryptocurrency return series ranging from 2015 to early 2018. We concentrate our analysis on daily returns and find several interesting stylized facts. First, principal components analysis reveals a complex return generating process. As we examine our data in the most recent year, we find that surprisingly more than one principal component appears to explain the cross-sectional variation in returns. Second, similar to hedge fund returns, cryptocurrency returns suffer from the “beta-in-the-tails” hidden risk. Third, we find that predicting cryptocurrency movements with machine learning and artificial intelligence algorithms is marginally attractive with variation in predictability power per cryptocurrency. Fourth, lower volatile cryptocurrencies are slightly more predictable than more volatile ones. Fifth, evidence exists that efficacy of distinct information sets varies across machine learning algorithms, showing that predictability may be much more complex given a set of machine learning algorithms. Finally, short-term predictability is very tenuous, which suggests that near-term cryptocurrency markets are semi-strong form efficient and therefore, day trading cryptocurrencies may be very challenging. Keywords: cryptocurrency, blockchain, machine learning, bitcoin, beta-in-the-tails, risks

Asset Pricing and Extreme Event Risk: Common Factors in ILS Fund Returns

The returns of investment funds specializing in insurance-linked securities (ILS) exhibit a unique behavior. We introduce a new peril-based factor model, which explains the time-series and cross-sectional return variation. Despite a strong overall fit, we are left with significantly positive alphas for about one quarter of the funds, some of which can be attributed to beta exposures associated with non-cat-bond ILS. In addition, they are related to fund size, fund age, and performance fees. Although we do not find evidence for market timing abilities, we can rule out pure luck as the source of out-performance by controlling for false discoveries.

Is ESG Risk Priced?

Factor investing entails exposure to ESG risk. How big is the exposure to this risk ? Is this exposure rewarded by the market ? We investigate the relevance of ESG risk for a cross section (15) of market anomalies long-short portfolios. The Environmental dimension of ESG is consistently relevant for the cross sectional variation in returns (alphas) and its beta risk is priced. This result is extremely robust including under shrinkage of the cross section. The market price of risk is shown to be negative and close to -0.200% monthly over the period 1992-2017. It is associated with a loading which varies between -0.450 and 1.093. Defensive equity (i.e., low volatility and/or low beta stocks) load positively on the E Factor. Since the market price of risk is negative, E exposure can potentially explain the low volatility and low beta anomalies.

Impact of disputed tax litigation risk on firm performance: evidence from India

PurposeThe purpose of this study is to investigate the impact of disputed tax litigation risk on firm performance and stock return behavior using a sample of Indian listed firms.Design/methodology/approachThe authors use disputed tax liability, reported as a contingent liability by the listed firms, as a proxy for the disputed tax litigation risk. To examine the impact of disputed tax litigation risk on firm performance (measured by accounting and market-based measures), the empirical approach used in this study focusses on the panel estimation technique. A portfolio-based approach using alternative asset pricing models examines the cross-sectional return variation because of the influence of disputed tax litigation risk.FindingsThe results of this study show a negative relationship between firm performance measures and disputed tax litigation risk. Cross-sectional test results reveal that higher disputed tax litigation risk is associated with higher expected returns.Research limitations/implicationsThis study focusses on disputed tax reported under the heading of contingent liability as a proxy for litigation risk. The study will help investors and portfolio managers to consider disputed tax litigation risk as an important parameter in the evaluation of firm performance. This study will also help regulators to get feedback on tax related policies and improve the dispute resolution process.Originality/valueThis study adds to the existing literature on the relationship between litigation risk and firm performance. In the context of emerging market, this study is the first-of-its-kind study, which focusses on disputed tax as a litigation risk proxy and examines its possible impact on firm performance and stock return behavior.

Performance Evaluation with Latent Factors

It is common to evaluate mutual fund (and in general, security) returns by linear factor models. However, performance measures from these models are misleading if there are some omitted factors that explain cross-sectional variation in returns. We propose to use a latent-factor approach, Confounder Adjusted Testing and Estimation (CATE), for performance evaluation. Under reasonable economic assumptions, we show that CATE can consistently separate from the return components that are due to common factor exposures, without forcing any particular ex-ante specification of the factors. We demonstrate that CATE outperforms widely used factor models in identifying common variation in mutual fund returns and that CATE alpha positively predicts future fund performance. When ranked by the difference between CATE alpha and CAPM alpha, the most favorable measure used by mutual fund investors, we find that the top decile of funds outperforms the bottom decile by as large as 5% per year. We also find that mutual fund flows become less responsive to returns due to the size, value, and momentum factors over time, yet respond persistently to other factor-related variation.

Currency Carry, Momentum, and US Monetary Policy Uncertainty

Returns to currency carry and momentum are compensations for the risk of global interest rate uncertainty (IRU), with risk exposures explaining 92% of cross-sectional return variations. Higher carry and momentum signal increase investor's expectations for future excess returns and stimulate more risk-taking, yet the global IRU adversely affects investor's risk-bearing capacity and triggers losses of carry and momentum strategies. Expected returns estimated from those risk exposures predict carry and momentum returns, with large economic and statistical significance. Consistently, I find the risk of global IRU predicts contemporaneous and subsequent worsening of intermediary's financial constraints, and it is also priced in momentum returns across other asset classes. The explanatory power is robust to using currency-level tests and is not driven by existing measures of uncertainty or risk aversion.

Firm characteristics, consumption risk, and firm-level risk exposures

We propose a novel approach to measuring firm-level risk exposures and costs of equity. Using a simple consumption-based asset pricing model that explains nearly two-thirds of the variation in average returns across 55 anomaly portfolios, we map the relation between exposures to consumption risk and portfolio-level characteristics. We use this relation to calculate exposures to consumption risk at the firm level and show that the calculated consumption risk exposures yield portfolios with large differences in average returns and ex post consumption risk exposures consistent with those predicted by our calculated betas. Further, industry betas and risk premia implied by our procedure display economically intuitive variation over time. Finally, Fama-MacBeth regressions suggest that risk exposures calculated using our procedure dominate those from alternative factor models at explaining cross-sectional variation in returns.

Determinants of idiosyncratic volatility: Evidence from the Indian stock market

This paper investigates whether firm-specific characteristics explain idiosyncratic volatility in the stocks of non-financial firms traded in the Indian stock market. It employs the linear time series five-factor model, augmented with a liquidity factor and the conditional EGARCH model, to extract yearly idiosyncratic volatility. We estimate a panel data regression to quantify the relationship between firm-specific characteristics and the volatility of individual securities. The results show that idiosyncratic volatility is significant in emerging markets such as India, and that cross-sectional return variations of firms are associated with firm-specific characteristics such as firm size, book-to-market ratio, momentum, liquidity, cash flow-to-price ratio, and returns on assets. We find that the idiosyncratic risk documented in this study is associated with smaller size of company, higher liquidity, low momentum, high book-to-market ratio, and low cash flow-to-price ratio. The findings suggest need to develop alternative tools to make investment decisions in emerging markets.

Creative Destruction and Asset Prices

We relate Schumpeter’s notion of creative destruction to asset pricing, thereby offering a novel explanation of size and value premia. We argue that small-value firms must offer higher expected returns to compensate for the risk posed by serendipitous invention activity, whereas large-growth stocks provide protection against creative destruction and receive expected return discounts. A 2-factor model that accounts for creative-destruction risk effectively explains the cross-sectional return variation of size- and book-to-market-sorted portfolios. The estimated risk compensations associated with creative destruction are substantial and statistically significant, indicating their relevance for asset pricing.

Transaction costs, liquidity risk, and the CCAPM

In this paper, we make a liquidity adjustment to the consumption-based capital asset pricing model (CCAPM) and show that the liquidity-adjusted CCAPM is a generalized model of Acharya and Pedersen (2005). Using different proxies for transaction costs such as the effective trading costs measure of Hasbrouck (2009) and the bid-ask spread estimates of Corwin and Schultz (2012), we find that the liquidity-adjusted CCAPM explains a larger fraction of the cross-sectional return variations.

Earnings Smoothing, Momentum and Statistical Arbitrage: Global Evidence

<p class="ber"><span lang="EN-GB">We employ the notion of statistical arbitrage to investigate the relationship between earnings smoothing and returns from momentum trading of stocks and explore the role that the level of investor sophistication may play in the smoothing-return calculus. To do so, we exploit the observation that both earnings smoothing and momentum profits are related to the cross-sectional variation in returns. We analyze the relevant data of 25 developed and emerging economies. Our results confirm the proposition that momentum profits as indicated by statistical arbitrage measures are inversely related to earnings smoothing but only in those markets where investors are more sophisticated and are able to take advantage of liquidity traders, who are often uninformed. </span></p>

Rare Disaster Concerns Everywhere

We propose an empirical framework of disaster concerns to explain cross-sectional return variation both within and across asset classes. Using a large set of out-of-the-money options on international equity indices, foreign currencies, and global government bonds, we measure the global …nancial market’s rare disaster concerns under only no-arbitrage conditions. Assets that have low return covariations with such concerns earn high excess returns in the future. The return predictability driven by rare disaster concerns is distinct from that driven by exposures to realized disaster shocks such as macroeconomic downturns and liquidity crunches, and is not

Tail Risk Concerns Everywhere

We show that the beta with respect to an index of global ex ante tail risk concerns (𝔾ℝ𝕀𝕏), which we construct using out-of-the-money options on multiple global assets, negatively drives cross-sectional return variations across asset classes, including international equity indices, foreign currencies, and government bond futures. The pricing power of 𝔾ℝ𝕀𝕏 becomes stronger when more asset-class-level tail risk concerns are incorporated in the index construction. 𝔾ℝ𝕀𝕏 also dominates asset-class-level tail risk concerns in pricing assets within each asset class. These evidences imply that the pricing effect of tail risk concerns works predominantly as a global channel. The 𝔾ℝ𝕀𝕏 pricing effect is distinct from that of tail risk factors based on historical realizations, consistent with the interpretation that tail risk concerns likely reflect investors’ ex ante subjective belief about tail risk. This paper was accepted by Neng Wang, finance.

Return-based classification of absolute return funds

We apply a return-based classification approach on a sample of absolute return funds registered for sale in Europe. The classification process results in eight groups with specific risk and return profiles. Each group can be characterized by two dimensions of an underlying investment style: asset allocation and trading strategy. While the returns of one group are largely determined by the asset allocation, the returns of the seven other groups are driven by different trading strategies. Our estimated classification explains 20 per cent of the in-sample and 13 per cent of the out-of-sample cross-sectional return variation, which is superior to existing approaches.