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We here analyze the factor loadings given by the CAPM, the Fama–French three (FF3), and the five-factor model (FF5), and test the performance and the validity of adding two more factors (volatility and dispersion of returns) to the FF5 factor model of European index-based ESG leaders' portfolios. Our ESG leaders' portfolios generated significant negative alphas during 2012–2022, corroborating the literature's negative argument. The negative abnormal returns of ESG leaders' portfolios are homogeneous across the three ESG pillars. We conclude that European ESG leaders' portfolios are biased toward large cap and value stocks with robust operating profitability and against aggressive investments. As robustness tests, we examine Global ESG leaders' index-based portfolios, producing the same results but with reduced importance in some loading factors like profitability and investment strategy. Furthermore, we deduced that European and Global ESG leaders' portfolios tilt towards volatility and herding bias. [ABSTRACT FROM AUTHOR]

Because of hydrogen's low energy density, hydrogen storage is a critical component of the hydrogen economy, particularly when large-scale and flexible hydrogen utilization is required. There is a sense of urgency to develop hydrogen geological storage projects to support large-scale yet flexible hydrogen utilization. This study aims to answer questions not yet resolved in the research literature discussing the valuation of hydrogen geological storage options for commercial development. This study establishes a net present value (NPV) evaluation framework for geological hydrogen storage that integrates the updated techno-economic analysis and market-based operations. The capital asset pricing model (CAPM) and the related finance theories are applied to determine the risk-adjusted discount rate in building the NPV evaluation framework. The NPV framework has been applied to two geological hydrogen storage projects, a single-turn storage serving downstream transportation seasonal demand versus a multiturn storage as part of an integrated renewables-based hydrogen energy system providing peak electric load. From the NPV framework, both projects have positive NPVs, $46, 560, 632 and $12, 457, 546, respectively, and International Rate of Return (IRR) values, which are higher than the costs of capital. The NPV framework is also applied to the sensitivity analysis and shows that the hydrogen price spread between withdrawal and injection prices, site development, and well costs are the top three factors that impact both NPV and IRR the most for both projects. The established NPV framework can be used for project risk management by discovering the key cost drivers for the storage assets. • Establish a Net Present Value (NPV) framework for evaluating geological hydrogen storage assets. • Techno-economic analysis of hydrogen storage and renewable hydrogen production for peak power support. • Comparison of single-cycle and multi-cycle storage scenarios for hydrogen geological storage. • Insights into the impact of site development and well costs on project valuation. • Application of the NPV framework for risk management and identification of key cost drivers. [ABSTRACT FROM AUTHOR]

This article discusses the concept of the "factor zoo" in asset pricing models, which refers to the large number of factors that have emerged in the literature over the past 25 years. The study aims to determine the minimum number of factors needed to explain the factor zoo and compress it into a more manageable set. The researchers use an iterative factor selection approach and find that a factor model consisting of 15 factors is sufficient to explain the available alpha in the factor zoo. They also find that different factor models can outperform common academic models, and that new factors often supersede older ones within clusters. The study contributes to the literature by proposing a method to identify important alpha contributors in the factor zoo and by providing insights into the ideal size of factor models and the regional impact on factor selection. The article discusses the GRS test statistic and its application in evaluating factor models. The study uses global factor data to analyze the performance of different factors in explaining asset returns. The results show that the cash-based operating profits-to-book assets factor is the strongest factor in the factor zoo, and its inclusion in the model significantly reduces the number of significant alphas. The iterative factor selection process identifies a total of 15 factors that provide additional explanatory power beyond traditional academic factor models. The analysis also examines the persistence of factor relevance over time, highlighting the factors that consistently appear in the models. The authors propose an iterative factor selection strategy that reduces the number of factors without losing important information. They find that a set of 10 [Extracted from the article]

This article discusses the importance of incorporating market conditions and macroeconomic factors in factor investing strategies. It explores the potential benefits of factor timing, which involves shifting exposure to different factors based on their expected performance. The authors propose a two-stage machine learning approach that incorporates market risk regimes and other macro variables to enhance the accuracy of factor timing decisions. The preliminary results of their research show promising improvements in factor timing compared to traditional approaches. The article presents a two-stage machine learning framework for factor timing in investment strategies, using data from the FRED database and Factset to analyze the performance of six common equity factors. The authors demonstrate the effectiveness of their approach through historical out-of-sample testing and compare it to benchmark strategies. They conclude that their framework offers a robust and alternative solution for factor timing in asset pricing and investment strategies. The document also provides contact information for three individuals at The Vanguard Group, Inc. in Malvern, PA. [Extracted from the article]

This article analyzes the returns of equity factors and provides insights into their long-term performance, risk characteristics, and behavior. The study reveals that most factors have a positive premium and a negative market beta over the long run. Factors with positive betas do not generate alpha, while factors with negative betas perform well in bear markets. The article also discusses the cyclicality of factor performance and identifies low-beta, seasonal, and momentum effects across factors. These findings can be used to design more effective factor-based investment strategies and contribute to future research in this area. The study is based on data from Jensen, Kelly, and Pedersen (2021) and offers valuable insights into the behavior of equity factor returns. It shows that most factors in the sample have a positive premium and a negative long-term market beta. Factors tend to perform better in bear markets, and the decline in factor performance after 2004 can be attributed to the lower frequency of bear markets during that period. The study also explores the quant cycle, low-beta effect, and seasonal and momentum effects in factor returns. These findings enhance our understanding of factor behavior and can guide the development of more robust factor-based investment strategies. [Extracted from the article]

Exploring the safe-haven characteristics of bitcoin from novel perspectives is crucial to diversify the investment and reap the benefits. This investigation employs bootstrap full-and sub-sample techniques to probe time-varying interrelation between global supply chain pressure (GSCP) and bitcoin price (BP), and further answer if "digital gold" could resist the strains of global supply chain. The empirical outcomes suggest that GSCP positively and negatively affects BP. The positive influence points out that high GSCP might boost the international bitcoin market, driving BP to rise, which indicates that "digital gold" could resist the pressures of global supply chain. But the negative effect of GSCP on BP could not support the above view, mainly affected by the weak purchasing power and more valuable assets, which is not consistent with the assumption of the inter-temporal capital asset pricing model (ICAPM). In turn, GSCP is adversely affected by BP, highlighting that the international bitcoin market may be viewed as a stress reliever for the global supply chain. Against a backdrop of the deteriorative Russia-Ukraine war and the intensifying global supply chain crisis, the above conclusions could bring significative lessons to the public, enterprises and related economies. [ABSTRACT FROM AUTHOR]

Policymakers or prospective investors recognize a continuing need to find target industry sectors to expand or contract for a sustainably healthy economy or investment portfolios. This paper discusses how to apply an optimal portfolio theory to selecting target industry sectors. With the information technology and the analytical approach in mind, this paper provides an operational and workable framework for selecting target industry sectors for an economy. The application incorporates the capital asset pricing model, ways to find excess return to risk ratios, unsystematic risk measures, etc. First, this paper shows a practical approach to finding the optimal weights for target industry sectors. Second, this paper develops a heuristic framework for analyzing the performance of industry sectors to construct realistic mixes of industry sectors. [ABSTRACT FROM AUTHOR]

Although the superiority of Fama-French (FF) five-factor model in capturing the United States (US) equity returns, this model performs poorly in other stock markets (Fama & French, 2017). Using the monthly data of nearly 600 Vietnamese published firms from 2008 to 2022, the primary purpose of this paper is to analyze and examine the performance of four famous multifactor asset pricing models: the capital asset pricing model (CAPM), the Carhart four-factor model, and the FF three-factor and five-factor models. We document the preference for the Carhart four-factor model over other models in producing a precise description to Vietnamese stock returns. The CAPM cannot give a reasonable explanation to the variation of Vietnamese stock returns, implying that market risk only accounts for a small proportion of the risk of holding Vietnamese stocks. Furthermore, adding the profitability and investment factors does not improve the explanatory power of asset pricing models in Vietnam, inconsistent with the result reported in the US stock market (Fama & French, 2015, 2020). [ABSTRACT FROM AUTHOR]

The study focuses on the selected aspects of investment measurement and management for the support of financial and economic decision-making of investors in wood-processing sectors. The aim of the study was to analyze the indicators for the structure and cost of capital of furniture and paper/forest branches in Slovakia, quantify the actual expected return on investment based on the selected methodology, and consequently find out the fundamental differences resulting from the specific conditions of given sectors. Methodologically, the study uses procedures for the weighted average cost of capital (WACC), capital asset pricing model (CAPM) for determining the cost of equity, and calculation of the beta coefficient considering the risk premium. The results of the study demonstrated a similar levered beta in both analyzed sectors (1.17 in furniture, 1.20 in paper/forest), but in each sector for a different reason. The expected rate of return is higher in furniture (7.84%) compared to paper/forest products at the level of 6.04%. The findings provide the possibility of comparing the required and expected rate of return on invested capital and making the appropriate long-term investment decisions. [ABSTRACT FROM AUTHOR]

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This study explores the inclusion of sentiment measures as a risk factor in asset pricing. Using UK market data for the period January 1993 to December 2020, we create a new sentiment variable, and construct both raw and clean sentiment indices from a principal component analysis of a variety of literature-acknowledged sentiment proxies. Essentially, the model estimations are categorized into two: first, the study documents the performance of the traditional pricing models on portfolios formed on different characteristics. Second, the study augments the first category by iterating sentiment variables into the model specification. The findings reveal that sentiment-augmented asset pricing models outperform the traditional models in explaining the excess returns of the portfolios. Furthermore, using Hansen & Jagannathan (1997) non-parametric model performance technique, we observe that the sentiment-induced models produce a small distance error compared to the traditional models, thus validating the use of sentiment measures in our pricing mechanism. It is therefore opined that extant asset pricing models may not be sufficient to explain market or pricing anomalies. Investors' sentiment is an important systematic risk factor that possesses useful information, and by implication, market analysts and stakeholders must take serious cognizance of its propensities when forecasting risk-adjusted returns. [ABSTRACT FROM AUTHOR]

Considering the particularities of economic development, countries should cooperate and participate in joint projects within regional and international groupings. The paper aims to develop a scenario approach to the investment policy of countries in the context of globalization. Defines how important the investment component of a country's economic development is and justifies why countries are heterogeneous regarding investment attractiveness. To fulfil the main goal the author applies the capital asset pricing model, which is a basic concept in the field of finance. In addition to the main goal, the text of the article systematizes the stages of development of the organizational project scenario, justifies the role of foreign investments in economic development, and analyses world trends regarding changes in the attractiveness of investments. The paper estimates how attractive national economies are for foreign investments as well. [ABSTRACT FROM AUTHOR]

Despite a broad consensus on the response of US stock market volatility to the coronavirus outbreak, our micro-level understanding of its variation across industries still needs to be improved. This study contributes to the existing literature by providing an industry-level analysis of the COVID-19 pandemic with two different states. Evidence from the MS-CAPM model indicates the role of portfolio diversification. Specifically, the results reveal that some industries, such as materials, real estate, communication, and utilities, have much higher expected returns. On the other hand, other sectors, including consumer discretionary, industrials, and information technology, become less volatile than the market during the lockdown period. [ABSTRACT FROM AUTHOR]

This paper studies intertemporal asset pricing in network economies when distress shocks can propagate through the network, similarly to epidemic outbreaks. Two classes of equilibria exist. In the first, idiosyncratic shocks are diversifiable and do not affect valuations; the consumption capital asset pricing model applies. In the second, idiosyncratic shocks generate nondiversifiable long-run cascades of shocks (financial pandemics) that introduce a new risk premium component unexplained by traditional systematic factors. We derive closed solutions for asset prices as a function of the network properties and discuss their properties. After a structural break (1984), we find evidence of a network risk premium that is statistically and economically significant. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by Ministero dell'Istruzione, dell'Università e della Ricerca [Grant PRIN-2017TA7TYC] and Baffi CAREFIN. C. Tebaldi is a fellow of Baffi CAREFIN and Innocenzo Gasaprini Institute for Economic Research Centers. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4687. [ABSTRACT FROM AUTHOR]

The investment capital asset pricing model, in which expected returns vary cross-sectionally with investment, profitability, and expected growth, provides an equilibrium foundation for Graham and Dodd's security analysis. The q5 model is a good start to explaining prominent security analysis strategies, such as Abarbanell and Bushee's fundamental signals, Frankel and Lee's intrinsic to market, Greenblatt's "magic formula," Asness et al.'s quality minus junk, Bartram and Grinblatt's agnostic analysis, operating cash flow to market, and Penman and Zhu's expected-return strategy as well as best performing active discretionary funds, such as Buffett's Berkshire Hathaway. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2022.4640. [ABSTRACT FROM AUTHOR]

In this article, we outline problems with the standard test for herding developed by Chang, Cheng and Kohrana (2000), subsequently called the CCK test, which is based on the proposition that the cross‐sectional absolute deviation of stock returns (CSAD) should be linearly related to overall market returns. We show that the test is highly biased against finding herding. The bias arises because the test assumes that, in the absence of herding, stock prices follow the Capital Asset Pricing Model (CAPM) but does not account for the implications of the CAPM not being a perfect asset pricing model. We suggest several simple alternative tests for herding. Finally, we show that the new tests give radically different results to the CCK test finding herding in many of the world's major financial markets when the CCK test rejects herding. [ABSTRACT FROM AUTHOR]

With the economy experiencing rapid growth in recent years, more individuals have started venturing into the stock market. Precisely forecasting the rate of return can mitigate investment risks for stock investors and significantly enhance their investment returns. The Capital Asset Pricing Model (CAPM) and the 3-factor Fama-French model (FF3) are widely recognized in academic and practical settings. This model comparison provides frameworks to analyze the relationship between portfolio risk and return in inefficient markets, contributing to applied data science in finance behavior. This research utilized the Support Vector Regression (SVR) algorithm to forecast the returns of a diversified portfolio in the Hanoi stock market (HNX) from 2010 to 2022. Initially, this study calculated the factors and subsequently constructed diversified portfolios. Subsequently, the explanatory power of the CAPM and FF3 models were compared using the Ordinary Least Squares (OLS) algorithm. Finally, this research incorporated the SVR algorithm within the FF3 framework to develop a predictive model. The research findings demonstrate that the FF3 model provides a superior explanation to the CAPM model. Additionally, the study reveals that the SVR algorithm outperforms the OLS algorithm in terms of efficiency, as it yields lower Root Mean Square Error (RMSE) values. Nevertheless, despite its advantages, the FF3 model still falls short regarding explanatory factors. Consequently, the next research direction entails replacing the FF3 model with a more comprehensive multi-factor model, anticipating obtaining an enhanced predictive model. [ABSTRACT FROM AUTHOR]

COVID-19 has slowed the growth of, the global economy, which has certain practical significance. Consequently, this study seeks to analyze the investment opportunities in the medical sector before and after the COVID-19 outbreak. In this study, the Markowitz mean–variance (MV) model, capital asset pricing model (CAPM), and correlation models are constructed based on the principle of Markowitz MV and correlation analysis. Simultaneously, statistical analysis is used to verify the analysis, and the MATLAB statistical tool is used to build the model. The results show that the actual expected yield of China's medical sector is significantly higher than that calculated by the CAPM before and after the pandemic, and that the investment value of the medical sector is undervalued by the market. From the perspective of risk, China's medical sector has a stable systemic risk premium. Based on the above analysis, when building investment portfolios in the post-pandemic era, investors should appropriately allocate stocks in the medical and pharmaceutical sectors to improve the portfolio income and diversify the investment risk. [ABSTRACT FROM AUTHOR]

The validity of asset pricing models implies white‐noise pricing errors (PEs). However, we find that the PEs of six well‐known factor models all exhibit a significant reversal pattern and are predictable by their lagged values up to 12 months. Moreover, the predictability of the PEs can produce substantial economic profits. Similar conclusions hold for recently developed machine learning models too. Additional analysis reveals that the significant PE profits cannot be explained by common behavioral biases. Our results imply that much remains to be done and there is a great need to develop new asset pricing models. [ABSTRACT FROM AUTHOR]

We show theoretically that variable production costs reduce systematic risk of firms' cash flows if capital and variable inputs are complementary in firms' production and input prices are procyclical. In our dynamic model, this operating hedge effect is weaker for more profitable firms, giving rise to a gross profitability premium. Moreover, gross profitability and value factors are distinct and negatively correlated, and their premia are not captured by the capital asset pricing model (CAPM). We estimate the model by simulated method of moments, and find that its main implications for stock returns and cash flow dynamics are quantitatively consistent with the data. [ABSTRACT FROM AUTHOR]

This work utilizes the fractional Black–Scholes model to estimate the option-implied Hurst exponents, interpreted as forward-looking expectations of return persistence. The focus of the paper is on how corresponding believes enter into factor based asset pricing models. Empirical analyses are carried out for the cross-section of S &P 500 stocks. We make the important observations that (i) stock returns show significant patterns of time-varying persistence and (ii) corresponding believes are reflected within option prices. Incorporating the Hurst exponents allows us to split up CAPM betas into pure market correlation risk (around 70–80%) and into excess persistence believes (about 20–30% of the risk loading). A direct comparison to standard CAPM shows that incorporating persistence believes significantly improves the predictability of future realized returns, and partially releases the beta anomaly. The effects become even stronger the greater the prediction horizon. Hence, the concept of fractal motions enables a deeper understanding of risk structures without the need of additional risk factors. [ABSTRACT FROM AUTHOR]

We introduce aggregate transmission shocks to an epidemic model and link firm valuations to infections via an asset pricing framework with vaccines. Infections lower earnings growth but firms can mitigate damages. We estimate a large reproduction number |${\mathcal R}_0$| and transmission volatility for COVID-19. Using these estimates, we quantify the bias of deterministic approximations based on |${\mathcal R}_0$|⁠. Our model generates predictions consistent with the data: unexpected infection resurgence, nonmonotonic mitigation policies, and higher price-to-earnings ratios during a pandemic. Valuations would be significantly lower absent mitigation and a high vaccine arrival rate. [ABSTRACT FROM AUTHOR]

We develop a new global asset pricing model to study how illiquidity interacts with market segmentation and investability constraints in 42 markets. Noninvestable stocks that can only be held by foreign investors earn higher expected returns compared to freely investable stocks due to limited risk sharing and higher illiquidity. In addition to the world market premium, on average, developed and emerging market noninvestables earn an annual unspanned local market risk premium of |$1.17\%$| and |$9.04\%$|⁠ , and a liquidity level premium of |$1.06\%$| and |$2.39\%$|⁠ , respectively. These results obtained in a conditional setup are robust to the choice of liquidity measure. [ABSTRACT FROM AUTHOR]

Data snooping is a major concern in empirical asset pricing. We develop a new framework to rigorously perform multiple hypothesis testing in linear asset pricing models, while limiting the occurrence of false positive results typically associated with data snooping. By exploiting a variety of machine learning techniques, our multiple-testing procedure is robust to omitted factors and missing data. We also prove its asymptotic validity when the number of tests is large relative to the sample size, as in many finance applications. To improve the finite sample performance, we also provide a wild-bootstrap procedure for inference and prove its validity in this setting. Finally, we illustrate the empirical relevance in the context of hedge fund performance evaluation. [ABSTRACT FROM AUTHOR]

We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics' predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U.S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66. [ABSTRACT FROM AUTHOR]

This article shows that the beta anomaly in the capital asset pricing model arises when investors' attention is drawn to salient payoffs owing to limited attention. Controlling for the salience effect renders the alphas of beta-sorted portfolios and betting-against-beta insignificant. Empirically, we find that stocks' market beta is strongly related to salience measure, indicating that saliencedemand price pressure falls predominantly on high-beta stocks. Furthermore, the low-beta anomaly occurs mainly during periods when the correlation between stocks' salience and beta is high, indicating that the disproportionate salience-demand price pressure on high-beta stocks is an important driver of the beta anomaly. [ABSTRACT FROM AUTHOR]

The recent home bias is primarily related to the equity bias as measured by the international capital asset pricing model (CAPM); this measure has declined over the past two decades amid financial globalization, but remains high in most developed countries. The key to understanding this puzzling phenomenon lies in how accurately this indicator can be predicted. In this paper, we propose to use the savings retention coefficient estimated by the Feldstein–Horioka (F–H) regression as a cross-country measure of home bias. We re-estimate the savings retention coefficient based on an estimation model in which the FH regression is embedded in the model derived from Tobin's q-theory of saddle-road dynamics of investment under convex adjustment costs. We then use dynamic panel estimation to estimate the new measure in OECD countries. The main empirical results are as follows. The new measure of home country bias, such as the equity bias measure, declined steadily until 2008, but recovered to the level of the 60 s and 70 s after the 2008 financial crisis. Interestingly, people expected home country bias to be very high after the financial crisis, but in fact it simply returned to its previous level. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic is an extreme event that created turmoil in stock markets around the world. This unexpected circumstance poses a critical question of whether the prevailing models can help predict the plummets of indices, hence the returns. In this study, we aim to analyse and forecast the daily stock returns using various generalized autoregressive conditional heteroscedastic (GARCH) models with intertemporal capital asset pricing structure and innovation following (1) a mixture of generalized Pareto and Gaussian distributions and (2) generalized error distribution that can capture extreme events. We also employ the parallel griddy Gibbs (GG) sampling, which is a Markov chain Monte Carlo method, to facilitate parameter estimation. Our simulation study shows that the GG estimation method outperforms the benchmark quasi-maximum likelihood estimation method. We then proceed to the empirical study of seven stock markets where the results from the in-sample period before the COVID-19 pandemic justify the use of the proposed GARCH models. The out-of-sample forecasts during the early COVID-19 period also show satisfactory results. [ABSTRACT FROM AUTHOR]

This paper explores the mispricing relative to the capital asset pricing model through an equilibrium model. We find that both the strong risk preference dependent on good market states and strong risk aversion dependent on bad market states can produce high mispricing. Choosing the China stock market, the largest emerging market dominated by individual investors and known for its volatile nature in a short history as our sample, the empirical results also support our theoretical findings. Overall, our paper sheds light on the mispricing caused by the investor's risk preference reference-dependent on market states. [ABSTRACT FROM AUTHOR]

Timor-Leste is a new country still in the process of economic development and does not yet have a capital market for stock and bond investments. These two asset classes have been invested in international capital markets such as the US, the UK, Japan, and Europe. We examine the performance of the capital asset pricing model (CAPM) and the Fama–French three-factor and five-factor models on the excess returns of Timor-Leste's equity and bond investments in the international market over the period 2006 to 2019. Our empirical results show that the market factor (MKT) is positively and significantly associated with the excess returns of the CAPM and the Fama–French three-factor and five-factor models. Moreover, the two variables Small Minus Big (SMB) as a size factor and High Minus Low (HML) as a value factor have a negative and significant effect on the excess returns in the Fama–French three-factor model and five-factor model. Further analysis revealed that the explanatory power of the Fama–French five-factor model is that the Robust Minus Weak (RMW) factor as a profitability factor is positively and significantly associated with excess returns, while the Conservative Minus Aggressive (CMA) factor as an investment factor is insignificant. [ABSTRACT FROM AUTHOR]

We provide a novel explanation for the empirical failure of the capital asset pricing model (CAPM) despite its widespread practical use. In a rational-expectations economy in which information is dispersed, variation in expected returns over time and across investors creates an informational gap between investors and the empiricist. The CAPM holds for investors, but the securities market line appears flat to the empiricist. Variation in expected returns across investors accounts for the larger part of this distortion, which is empirically substantial; it offers a new interpretation of why "betting against beta" (BAB) works: BAB really bets on true beta. The empiricist retrieves a stronger CAPM on days when public information reduces disagreement among investors. [ABSTRACT FROM AUTHOR]

The Capital Asset Pricing Model (CAPM) is a widely used and tractable model relating financial risk and return. However, it has been less successful when taken to the data. In their seminal paper, Frazzini and Pedersen propose a Betting Against Beta (BAB) factor to take advantage of this fact. In their construction, the BAB factor is a portfolio which longs low-beta stocks and shorts high-beta stocks. Since its construction, the BAB factor has been widely cited and used both in financial academics and in industry. This paper proposes a novel betting on and against beta (BOAB) strategy, which bets on beta on days with macroeconomic announcements and bets against beta on days without. Here, macroeconomic announcement days are defined as days when inflation, unemployment, or interest rate decisions are released. Our findings confirm the persistent positive beta-return relationship observed on macroeconomic announcement days. We show that compared to the BAB strategy, BOAB delivers higher average daily excess returns over the time period 1964 to 2021 when applied to the U.S. stock market. This outperformance of the BOAB strategy is robust to different constructions of the BAB factor and is economically and statistically significant when compared with the usual asset pricing factors such as value, size, and momentum. The outperformance of the BOAB strategy could be used to inform agents' portfolio allocation choices. Limitations are also discussed. Plain Language Summary: Macroeconomic announcements and trading strategies Trading strategies are important because they impact how financial market participants invest and manage risk-return tradeoffs. This paper proposes a betting on and against beta (BOAB) strategy, which takes long positions on beta on a subset of trading days and short positions on other days. We determined which days to take long beta positions by testing the prediction of asset pricing models on days with macroeconomic announcements. We carefully constructed macroeconomic announcement days and used the Fama-MacBeth two-step testing procedure to evaluate predictions of the CAPM model. We conclude that the BOAB strategy outperforms the BAB factor, especially due to its outperformance on days with macroeconomic announcements. In the process, we consider varying ways of constructing the betting against beta factor to address critiques of the original BAB factor. We also demonstrate that the large, positive returns of the BAB factor has diminished since the publication of the original BAB paper. [ABSTRACT FROM AUTHOR]

The goal of this paper is to estimate the percentage structure of electricity generation by type of technology in Mexico for the period 1992-2016. Modern portfolio theory and the capital asset pricing model of Sharpe and Litner were used. Results show that using only portfolio theory is not possible to find non-negative percentages in all the technologies. When a risk-free asset is included, there is a region where all technologies have a positive participation in electricity generation. The efficient frontier is found at 20% for wind technology share and for the rest of technologies, shares are almost equally - distributed among them. [ABSTRACT FROM AUTHOR]

Because the risk of falling stock prices has a significant impact on investment decisions and the optimal allocation of resources, it is essential to understand the factors affecting this phenomenon and its underlying factors. Based on the rational stock pricing structure in the Iranian capital market, this study measures the risk of falling stock prices and its alignment with price bubbles. To analyze and test the research hypothesis, data related to 30 companies admitted to the Tehran Stock Exchange for the period 1390-1401 were extracted. For this study, the stock price fall measurement model was estimated for the sample companies, and then its efficiency was measured and evaluated with the Kupiec test, and it was confirmed at a 95% confidence level. After estimating the capital assets pricing models, this study used Bartlett, Lunn, and Brown-Forside tests to assess the alignment between falling stock prices and bubbles. The results showed that there is no alignment between the variance of the risk of falling stock prices and bubbles based on the rational stock pricing structure in our country's context. Therefore, in our country, falling stock prices in line with price bubbles are not based on rational stock pricing. [ABSTRACT FROM AUTHOR]

The exact conditions under which volatility timing strategies yield value are documented. These conditions include: the ability to correctly forecast next period stochastic variance, and violation of a strict version of Merton's intertemporal capital asset pricing model (ICAPM). While the empirical evidence supports the first of these conditions, the latter remains open to debate. Our empirical results confirm the former, but demonstrate a significant violation of the (strict) ICAPM. It follows that volatility timing strategies appear to have value. However, using reasonable parameter values plugged into the derived formulae, the results also show that extreme leverage is often required for success. A method of tempering leverage is proposed, which is somewhat able to loosen the requirement of high leverage while still maintaining a good performance level. Given the likely variation in (strict) ICAPM violations across time and assets, it follows that volatility timing success (or failure) is very much sample dependent. [ABSTRACT FROM AUTHOR]

This paper introduces an instrumental variable Bayesian shrinkage approach specifically designed for estimating the capital asset pricing model (CAPM) while utilizing a large number of instruments. Our methodology incorporates horseshoe, Laplace, and factor-based shrinkage priors to construct Bayesian estimators for CAPM, accounting for the presence of measurement errors. Through the use of simulated data, we illustrate the potential of our approach in mitigating the bias arising from errors-in-variables. Importantly, the conventional two-stage least squares estimation of the CAPM beta is shown to experience bias escalation as the number of instruments increases. In contrast, our approach effectively counters this bias, particularly in scenarios with a substantial number of instruments. In an empirical application using real-world data, our proposed methodology generates subtly distinct estimated CAPM beta values compared with both the ordinary least squares and the two-stage least squares approaches. This disparity in estimations carries notable economic implications. Furthermore, when applied to average cross-sectional asset returns, our approach significantly enhances the explanatory power of the CAPM framework. [ABSTRACT FROM AUTHOR]

Using the capital asset pricing model, this article critically assesses the relative importance of computing 'realized' betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the 'pure' systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include 'realized' versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails. [ABSTRACT FROM AUTHOR]

We develop a dynamic asset pricing model of cryptocurrencies/tokens that allow users to conduct peer-to-peer transactions on digital platforms. The equilibrium price of tokens is determined by aggregating heterogeneous users' transactional demand, rather than discounting cash flows as is done in standard valuations models. Endogenous platform adoption builds on user network externality and exhibits an |$S$| -curve: it starts slow, becomes volatile, and eventually tapers off. The introduction of tokens lowers users' transaction costs on the platform by allowing users to capitalize on platform growth. The resultant intertemporal feedback between user adoption and token price accelerates adoption and dampens user-base volatility. [ABSTRACT FROM AUTHOR]

Higher moments of long-horizon returns are important for asset pricing but are hard to measure accurately using standard techniques. We provide theory showing that short-horizon (e.g. daily) returns can be used to construct precise estimates of long-horizon (e.g. annual) moments without making strong assumptions about the data-generating process. Skewness comprises two components: skewness of short-horizon returns and a leverage effect, that is, covariance between variance and lagged returns. We provide similar results for kurtosis. An application to U.S. stock index returns shows that skew is large and negative and attenuates only slowly as one moves from monthly to multiyear horizons. [ABSTRACT FROM AUTHOR]

We develop an asset pricing model with stochastic transaction costs and investors with heterogeneous horizons. Depending on their horizon, investors hold different sets of assets in equilibrium. This generates segmentation and spillover effects for expected returns, where the liquidity (risk) premium of illiquid assets is determined by investor horizons and the correlation between liquid and illiquid asset returns. We estimate our model for the cross-section of U.S. stock returns and find that it generates a good fit, mainly due to a combination of a substantial expected liquidity premium and segmentation effects, while the liquidity risk premium is small. [ABSTRACT FROM AUTHOR]

There is a discrepancy between CAPM-implied and realized returns. Using the CAPM in capital budgeting—as recommended in textbooks—should thus have real effects. For instance, low beta projects should be valued more by CAPM users than by the market. We test this hypothesis using M&A data and show that bids for low-beta private targets entail lower bidder returns. We provide further support by testing several ancillary predictions. Our analyses suggest that using the CAPM when valuing targets leads to valuation errors (relative to the market's view) corresponding on average to 12% to 33% of the deal values. [ABSTRACT FROM AUTHOR]

Recent evidence indicates that market model alphas are stronger predictors of mutual fund flows than alphas with other models. Some recent papers have interpreted this evidence to mean that CAPM is the best asset pricing model, but some others have interpreted it as evidence against investor sophistication. We evaluate the merits of these mutually exclusive interpretations. We show that no tenable inference about the validity of any asset pricing model can be drawn from this evidence. Rejecting the investor sophistication hypothesis is tenable, but the appropriate benchmark to judge sophistication is different from that used in this literature. [ABSTRACT FROM AUTHOR]

We examine the role of factor models and simple performance heuristics in investor decision-making using Morningstar's 2002 rating methodology change. Before the change, flows strongly correlated with CAPM alphas. After, when funds are ranked by size and book-to-market groups, flows become more sensitive to 3-factor alphas (FF3). Flows to a matched institutional sample (same managers/strategies) follow FF3 before and after the change but are unrelated to the CAPM. Placebo tests with sector funds and other factor loadings show no effects. Our results imply that improvements in simple performance heuristics can result in more sophisticated risk adjustment by retail investors. [ABSTRACT FROM AUTHOR]

We study the effects of market incompleteness on speculation, investor survival, and asset pricing moments when investors disagree about the likelihood of jumps and have recursive preferences. We consider two models. In a model with jumps in aggregate consumption, incompleteness barely matters because the consumption claim resembles an insurance product against jump risk and effectively reproduces approximate spanning. In a long-run risk model with jumps in the long-run growth rate, market incompleteness affects speculation and investor survival. Jump and diffusive risks are more balanced regarding their importance, and therefore the consumption claim cannot reproduce approximate spanning. [ABSTRACT FROM AUTHOR]

We develop a conditional capital asset pricing model in continuous time that allows for stochastic beta exposure. When beta comoves with market variance and the stochastic discount factor (SDF), beta risk is priced, and the expected return on a stock deviates from the security market line. The model predicts that low-beta stocks earn high returns, because their beta positively comoves with market variance and the SDF. The opposite is true for high-beta stocks. Estimating the model on equity and option data, we find that beta risk explains expected returns on low- and high-beta stocks, resolving the "betting against beta" anomaly. Authors have furnished code and an Internet Appendix , which are available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

The well-known weak empirical relationship between beta risk and the cost of equity (the beta anomaly) generates a simple tradeoff theory: As firms lever up, the overall cost of capital falls as leverage increases equity beta, but as debt becomes riskier the marginal benefit of increasing equity beta declines. As a simple theoretical framework predicts, we find that leverage is inversely related to asset beta, including upside asset beta, which is hard to explain by the traditional leverage tradeoff with financial distress that emphasizes downside risk. The results are robust to a variety of specification choices and control variables. [ABSTRACT FROM AUTHOR]