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Foreign investors' transaction volume in the Tokyo Stock Exchange has grown steadily in the past three decades, and Japanese stocks have become a familiar instrument for international diversification. Rebalancing trades in a local national market by foreign institutional investors holding multi-country asset portfolios, often heavily represented by U.S. stocks, amplify the national market's stock index co-movements with the U.S. This paper examines the extent to which greater participation of foreign investors is associated with increases in the unconditional correlation of daily returns between the U.S. (S&P 500) and Japan (TOPIX). A Gaussian copula marginal regression model is applied to the monthly series of unconditional correlations. We find that the growth in foreign investors' transaction share of the Tokyo market's turnover approximately accounts for the rise in U.S.-Japan correlations for 1990–2019. Foreign investors' trades improve the informational efficiency of local national markets. Increased correlations lead to smaller potential gains from international diversification, however. [ABSTRACT FROM AUTHOR]

We generate new evidence on disagreement among traders in the S&P 500 options market from high-frequency intraday price and volume data. Inference on disagreement is based on a model where investors observe public information but agree to disagree on its interpretation; disagreement among investors is captured by the volume–volatility elasticity. For options, there are two natural variables related to disagreement: moneyness and tenor, which we relate to disagreement about the distribution of the market index at different quantiles and times. The estimated volume–volatility elasticity equals unity for options near the money and close to expiration, which is consistent with the case of no disagreement among investors. In contrast, the elasticity estimates decrease with increases in the absolute value of moneyness, indicating investors have a higher disagreement about rare events. Likewise, the elasticity decreases with increases in tenor, implying higher investors' disagreement about more distant events. [ABSTRACT FROM AUTHOR]

This article considers predictive regressions in which a structural break is allowed on an unknown date. We establish novel testing procedures for asset return predictability using empirical likelihood (EL) methods based on weighted score equations. The theoretical results are useful in practice because our unified framework does not require distinguishing whether the predictor variables are stationary or non-stationary. Monte Carlo simulation studies show that the EL-based tests perform well in terms of size and power in finite samples. Finally, as an empirical analysis, we test the predictability of the monthly S&P 500 value-weighted log excess return using various predictor variables. [ABSTRACT FROM AUTHOR]

Purpose: The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders. Design/methodology/approach: This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further. Findings: The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy. Research limitations/implications: One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics. Practical implications: The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools. Originality/value: Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision. [ABSTRACT FROM AUTHOR]

The authors estimate the tax alpha of an S&P 500 direct index strategy with tax loss harvesting over a 60-year horizon including the effects of dividend reinvestment, unqualified dividends, corporate actions, and management fees. The authors estimate the impact of relaxing the key assumptions of unlimited short-term gains, reinvestment of tax savings, and maximum tax rates. The authors find that realized gains from cash acquisitions offset realizable losses at long horizons and that management fees can render tax loss harvesting inferior to an index ETF even under the most favorable assumptions. [ABSTRACT FROM AUTHOR]

In this article, the author discusses the evolution of his thinking about the expected return of equities over three decades, pointing out what has stayed constant and what has changed, and provides his current estimates of the 10-year expected return of the S&P 500 Index. He also presents a simple universal portfolio that reflects his views and can be implemented at low cost by investors worldwide. Finally, he describes the evolution of his investment perspective from an asset-only perspective to an asset/liability/surplus perspective. His central results can be replicated using the S&P 500 dataset in the open-source R package PCRA and are relevant to CIOs and asset allocators as well as to individual investors. [ABSTRACT FROM AUTHOR]

Fit is an essential consideration for organizations, and extensive research has explored its various types. We build on and extend fit research by advancing an important form of fit—organization‐investor (O‐I) fit, which reflects the compatibility between an organization and its investors. We argue that investors tend to be attracted to organizations whose preferences already "fit" their own and, in so doing, provide a relational perspective to the corporate governance literature that often views the relationship between investors and organizational managers as purely transactional. We focus on the fit of one of the most important factors shaping both organizational and investor behavior––temporal preferences. Specifically, we argue that investors are attracted to firms that fit with their temporal preferences and that high O‐I fit leads to better organizational performance. We tested our hypotheses in a longitudinal archival study of S&P 500 firms and supplemented these findings with two experiments. Together, we find strong support for our hypotheses. Our study showcases the critical role of O‐I fit in shaping the makeup of an organization's investors and its subsequent performance, suggesting the value for scholars, managers, and investors to consider the mutual benefits offered by fit in these relationships. [ABSTRACT FROM AUTHOR]

This paper proposes a new index of forward looking absolute deviation extracted from option prices. The new index, named absolute deviation index (ADIX), is model‐free and easy to compute using at‐the‐money call and put option prices. It is shown that the spread between volatility index (VIX) and ADIX captures departures from normality in the risk‐neutral distribution and an empirical analysis using S&P 500 options data for the time period 1996–2021 reveals that the spread carries significant forecasting ability with respect to future equity returns at short to medium horizons. Portfolio strategies that use the spread as a predictor of S&P 500 returns outperform buy‐and‐hold strategies in an out‐of‐sample mean‐variance asset allocation exercise. [ABSTRACT FROM AUTHOR]

This research examines the correlation between corporate green innovation and its positive impacts by analyzing firm-level data from the S&P 500 spanning the years 2001 to 2022. Drawing on previous studies, this research employs rigorous methodologies and measures to assess the influence of green innovation. The study investigates the drivers of green innovation and assesses its potential effects on firm performance and risk reduction. Firstly, it explores the factors that contribute to the adoption of green innovation. Secondly, it examines how green innovation affects firm performance and mitigates risks. The findings of the study demonstrate that implementing green innovation leads to a reduction in firm volatility and credit risk, while also enhancing firm value and emission performance. The robustness tests conducted further reinforce these conclusions, thereby contributing to the existing body of literature that highlights the crucial role of corporate characteristics in promoting sustainable business practices through green innovation. These insights hold significant value for investors, policymakers, and regulators, providing them with valuable information for making informed decisions regarding green investments and the formulation of policies that foster sustainability through green innovation. [ABSTRACT FROM AUTHOR]

To improve the dynamic assessment of risks of speculative assets, we apply a Markov switching MGARCH approach to portfolio risk forecasting. More specifically, we take advantage of the flexible Markov switching copula multivariate GARCH (MS‐C‐MGARCH) model of Fülle and Herwartz (2022). As an empirical illustration, we take the perspective of a risk‐averse agent and employ the suggested model for assessments of future risks of portfolios composed of a high‐yield equity index (S&P 500) and two safe‐haven investment instruments (i.e., Gold and US Treasury Bond Futures). We follow recent suggestions to employ the expected shortfall as a prime assessment of tail risks. To accurately evaluate the merits of the new model, we back‐test the risk forecasting for daily returns over 10 years for heterogeneous market environments including, for example, the COVID‐19 pandemic. We find that the MS‐C‐MGARCH model outperforms benchmark volatility models (MGARCH, C‐MGARCH) in predicting both value‐at‐risk and expected shortfall. The superiority of the MS‐C‐MGARCH model becomes stronger, when the share of comparably risky assets in the portfolio is relatively large. [ABSTRACT FROM AUTHOR]

In order to save transaction costs, a sparse portfolio that consists of a small subset of the constituent stocks is commonly requested especially when the target index has a large number of assets. To the best of our knowledge, a large body of literature on sparse index tracking are mainly focus on the penalized least squares estimation under the no-short selling constraint. Accordingly, these procedures are highly sensitive to outliers and their efficiency may be significantly decreased when the noise distribution is unknown and possibly heavy-tailed. As promising alternatives, robust loss functions such as L1 loss and Huber's loss have been well characterized in penalized M-estimation techniques. This paper considers robust sparse portfolios to simultaneously perform stock selection and capital allocation for high-dimensional index tracking. Through the simulation, we show that newly proposed approach is resistant to heavy-tailed errors or outliers in the response. Finally, we illustrate the superior performance of the proposed method through tracking the Standard & Poor's 500 Index. [ABSTRACT FROM AUTHOR]

The period from January 2020 to December 2022 was marked by a confluence of major events, including the COVID-19 pandemic, the Russia–Ukraine war, the energy crisis, surging inflation, Federal Reserve policy shifts, and banking turmoil, which collectively fueled heightened market volatility, risk management needs, and speculative trading opportunities, leading to volatile swings in trading volume concentration across financial markets, with periods of significant increases followed by rapid declines. This paper examines the variation in the concentration of trading volume across the full spectrum of S&P 500 companies, with a focus on explaining the reasons behind the stochastic changes in trading volume concentration. We analyze different concentration measurement methods, including the power law exponent, the Herfindahl–Hirschman Index, and the Gini-based Trading Concentration Index (TCI). The research employs a novel experimental design, comparing a concentration-driven portfolio, rebalanced daily based on the top 30 stocks by trading volume, against the S&P 500 benchmark. Our findings reveal that the Gini-based TCI fluctuated between 55.98% and 77.35% during the study period, with significant variations coinciding with major market events. The concentration-driven portfolio outperformed the S&P 500, achieving an annualized return of 10.66% compared to 5.89% for the index, with a superior Sharpe ratio of 0.325 versus 0.19. This performance suggests that following trading volume concentration can yield above-average results. However, this study also highlights the importance of understanding and managing the risks associated with concentrated portfolios. This study contributes to the literature on market dynamics and offers practical insights for investors and fund managers on optimizing portfolio strategies in response to evolving concentration patterns in financial markets. [ABSTRACT FROM AUTHOR]

Delta‐hedged option selling strategies typically yield positive returns, owing to the volatility risk premium embedded in the option price. Recent research based on S&P 500 options has found a day–night asymmetry in option returns. We find a similar disparity in the returns for short Nifty option strategies. Positive and significant overnight option returns are accompanied by negative intraday returns. The day–night asymmetry is robust across option categories and subsamples but weaker on days with significant jumps in the underlying. We confirm that the variance risk premium earned by option sellers is mainly a reward for overnight risk. [ABSTRACT FROM AUTHOR]

Between March and August 2020, S&P and Moody's downgraded approximately 25 |$\%$| of collateral feeding into CLOs and only 2 |$\%$| of tranche values, with rating actions concentrating in junior tranches. Both S&P and Moody's modeling indicate that the impacts should have been considerably larger, especially for higher-rated tranches. Neither changes in correlation nor the accumulation of pre-COVID-19 protective cushions can explain the downgrade asymmetry on upper tranches. Instead, CLO managers repositioned their collateral pools to dampen the negative credit shock and rating agencies incorporated qualitative adjustments in their CLO ratings. Important potential policy and market implications from these findings are discussed. 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. [ABSTRACT FROM AUTHOR]

We investigate the implications of recovering real-world conditional expectation of return functions using options on the S&P 500 index and Treasury bond futures. First, we construct estimates of the probability of disasters, defined as higher than 6%, 5%, or 4% equity market declines over option expiration cycles. This measure of disaster probability forecasts realized disasters. Second, we employ options on the futures of the 10- and 30-year Treasury bonds to construct estimates for the expected return of bond futures. These measures display forecasting ability for subsequent futures returns beyond the level, slope, and curvature variables extracted from the yield curve. [ABSTRACT FROM AUTHOR]

With users increasingly spending time on social media platforms, firms are expanding their activities to cover more than one platform. Each has a unique vernacular—its popular communication style—which increases the need for firms to use platform-specific content optimization. This study distinguishes between textual and visual content intentions, depending on the degree of informative and affective appeals used. We examine how congruency between visual content and platform type and textual content affects users' sharing. We distinguish between hedonic platforms, such as Facebook, primarily used for entertainment and social interaction, and utilitarian platforms, such as Twitter, primarily used for receiving timely information. We develop a new approach to examine how textual and visual content composition affects users' sharing behavior across platforms. Based on this new approach, we analyze posts by S&P 500 members operating on Facebook and Twitter. Our results show that posts with visual content congruent with the primary user intent of the platforms are more likely to be shared. Furthermore, Facebook users prefer affective textual and visual content, while Twitter users are more inclined toward a combination of informative visual and affective textual content. [ABSTRACT FROM AUTHOR]

We study how the market return depends on the time of the day using E-mini S&P 500 futures actively traded around the clock. Strikingly, 4 hours around European open account for the entire average market return. This period's returns have a 1.6 Sharpe ratio and remain high after transaction costs. Average returns are a noisy zero during the remaining 20 hours. High returns are consistent with European investors processing information accumulated overnight and thus resolving uncertainty. Indeed, uncertainty reflected by VIX futures prices rises overnight and falls around European open. The results are stronger during the 2020 COVID crisis. [ABSTRACT FROM AUTHOR]

We estimate a model-free term structure of the ex ante dividend risk premium by combining two data sets with different information about future dividends. We aggregate survey forecasts about future dividends for single companies over multiple horizons to construct a term structure of expected S&P 500 dividend growth rates. We use European call and put option prices on the S&P 500 to estimate the term structures of options-implied dividend growth rates and risk-free rates. Applying the method to 2004–2021 data offers a new, ex ante perspective on the conditional time variation of the term structure of the dividend risk premium. [ABSTRACT FROM AUTHOR]

This research is important for both the academic and business environments due to the extraordinary results obtained. Additionally, the significance of the study is also attributed to the addressed topic, which is intensively studied in the world of corporate finance. The primary aim of this research is to scrutinize a cohort of 66 information and technology (IT) companies, all of which are constituents of the American Standard and Poor's 500 Index (S&P 500). The study period spans two decades, covering the years 2003–2022. To summarize the outcomes, the analytical framework incorporated linear models with both fixed (fe) and random effects (re), as well as quantile regression models. This study's key outcomes highlight that firm size, sales growth, current ratio, long-term debt to capital, free cash flow, asset turnover and receivable turnover, board meeting frequency, female board representation, chief executive officer age, audit committee independence, and the presence of compensation and nomination committees, alongside a pandemic indicator, positively impact firm performance. Conversely, firm age, dividend payout ratio, effective tax rate, board size, chief executive officer duality, and corporate social responsibility committee presence have negative effects on performance. Also, regarding quantile regressions, CEO duality significantly influences companies with high profitability rates, and companies with low to medium profitability rates are more strongly and negatively influenced by board size. The implications of the core policy in this research focusing on corporate governance will consider certain rules and guidelines regarding financial transparency and protecting shareholders' interests. Additionally, it will take into account the independence of the board of directors and the presence of its committees, as well as ethical leadership practices. [ABSTRACT FROM AUTHOR]

For systemic risk, the impact of the financial network's characteristics remains imperfectly understood at best, even if the view that network structure is closely related to systemic risk has become a broad consensus. By choosing S&P 500 constituents as the research sample, we investigate the structural characteristics of the Engle-Granger networks and explore the impact of network centrality on one-quarter-ahead systemic risk. We find that a firm's network centrality is positively related to both dimensions of its systemic risk (i.e. the firm's vulnerability to, and contribution to, system-wide downturns). The results remain robust after we consider the potential endogeneity and various sensitivity checks. An examination of potential channels reveals that centrally located firms in the network have a high extent of co-movement with the market, and are likely to trigger systemic market failures caused by stock price crashes in clusters once they fall into a downturn. We further show that the positive relation between network centrality and future systemic risk is more salient for financial firms and more pronounced during recessions. [ABSTRACT FROM AUTHOR]

Aiming to shed light on the interplay of board‐related mechanisms and their influence on sustainable development, this study examines configurations of corporate governance conditions that are associated with high and low ESG performance. Fuzzy‐set qualitative comparative analysis was used, resorting to a sample of S&P 500 manufacturing companies that are committed to contributing to solve the climate change societal challenge (i.e., SDG13). The configurational analysis was also extended to the all sample of S&P 500 companies to assure the generalizability of the findings and provide additional insights. The findings support the functional view of the role of the board, emphasising the notion that configurations (i.e., combinations of conditions) are more important to sustainable development than any single condition. Different from past research, this study emphasises the importance of defining and implementing a CSR strategy to achieve high ESG performance, which is always present in configurations leading to the outcome of interest. Furthermore, the results also suggest that a monitoring mechanism should always be present, while an incentive alignment mechanism is only required in some circumstances. Moreover, the results show that ESG‐related executive compensation should be complemented with other mechanisms, such as board monitoring, to be effective. This study contributes to the debate on whether sustainability‐related performance alignment incentives and monitoring mechanisms act as complements or substitutes. The findings show that linear approaches might not capture the all picture, suggesting that a more nuanced view should be used in future studies, and can inform companies' strategic decisions regarding sustainability. [ABSTRACT FROM AUTHOR]

This study delves into the impacts of the 2008 global financial crisis (GFC) and the COVID-19 health crisis on U.S. financial indices, exploring the intricate relationship between economic shocks and these indices during downturns. Using Markov switching regression models and control variables, including GDP, consumer sentiment, industrial production, and the ratio of inventories-to-sale, it quantifies the effects of these crises on the CBOE Volatility Index (VIX), Standard & Poor's 500 (S&P 500), and the Dow Jones Industrial Average (DJIA) from Q1 2000 to Q2 2023, covering crucial moments of both crises and stable periods (dichotomous variables). Results reveal that the 2008 crisis significantly heightened financial volatility and depreciated the valuation of S&P 500 and DJIA indicators, while the COVID-19 crisis had a diverse impact on market dynamics, particularly negatively affecting specific sectors. This study underscores the importance of consumer confidence and inventory management in mitigating financial volatility and emphasises the need for robust policy measures to address economic shocks, enhance financial stability, and alleviate future crises, especially during endogenous crises such as financial downturns. This research sheds light on the nuanced impact of crises on financial markets and the broader economy, revealing the intricate dynamics shaping market behaviour during turbulent times. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of the characteristics of sustainable boards and NGO directors on biodiversity disclosures. This research uses data from 481 US companies that are taken from Standard & Poor's 500 (S&P), ASSET4, for the period 2009–2020 (5616 company‐year observations). Data on NGO directors is provided by MSCI's GMI Rankings database. A panel data analysis with fixed effects models is used to estimate the results. The results also indicate that a sustainable board structure (proxied by Chief Sustainability Officers (CSOs) and Sustainability‐related executive compensation) is positively correlated with biodiversity disclosure. Our empirical results also show that the moderating role of NGO directors has a greater interaction effect with the characteristics of sustainable boards and biodiversity information disclosure initiatives. The results are also consistent for exploitative and nonexploitative industries, as well as for the pre‐ and post SDG (14–15) of the agenda 2030. In addition, the framework of SDG (14‐15) and the strategic plan of partnership with the Agenda of 2030 show a positive relationship with biodiversity disclosure. We then develop a causal relationship through the positive impact of NGO directors serving on the board of directors on a strategy of partnerships, onboarding, and increase/decrease of NGO directors—possible mechanisms by which NGO directors can influence biodiversity disclosure. Overall, our findings suggest that while NGO directors can potentially be appointed to a company's board of directors for legitimization reasons, these directors are associated with better biodiversity disclosure. The study has valuable implications for enduring board members, practitioners, and scholars. The results are supported by theories of legitimacy, stakeholders and resource dependence. However, evidence on this research question is still unknown and critical, especially in the context of stakeholder‐emphasized governance systems (continental Europe, Japan) and developing countries where there is a lack of knowledge application of regulations related to the disclosure of biodiversity. [ABSTRACT FROM AUTHOR]

This study explores an important yet understudied relationship between CEO narcissism and green innovation, both separately and when moderated by CEO demographic traits (international experience and age). We analyzed a sample of 206 U.S.‐listed firms on the S&P 500 over a 10‐year period. Our initial findings indicate a negative association between CEO narcissism and green innovation. However, further analysis reveals that this relationship is influenced by two CEO demographic traits. Specifically, CEOs' international experience and age mitigate the initially negative link between CEO narcissism and green innovation. These results align with insights from the upper echelon theory, shedding light on the dark side of narcissism. The implications of our findings extend to firms' team management, directors, policy‐makers, and regulators. [ABSTRACT FROM AUTHOR]

We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g. size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selection operator. Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios. [ABSTRACT FROM AUTHOR]

In the literature on score-driven models, alternative choices of the scaling parameters of the conditional score terms are used, but the optimal choice of those parameters is an open question. Although there are score-driven models for which the choice of the scaling parameters is irrelevant, there are important score-driven models for which score-driven scale filters appear in the information matrix, and the choice of the scaling parameters is relevant. We focus on the quasi-autoregressive (QAR) plus Beta- $t$ t -EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, by using data on the Vanguard Standard & Poor's 500 (S&P 500) exchange-traded fund (VOO) and all available S&P 500 stocks for the period of 2013–2023. For QAR plus Beta- $t$ t -EGARCH, each updating term is the product of a scaling parameter and a conditional score, and we use specific alternative scaling parameters from the literature. For different scaling parameters in the scale filter (volatility), alternative location and scale filters coincide. For different scaling parameters in the location filter (expected return), alternative location and scale filters differ significantly. For the statistical and volatility forecasting performances of VOO and most of the S&P 500 stocks, the best-performing scaling parameter for the score-driven location is the conditional inverse information matrix. [ABSTRACT FROM AUTHOR]

We used daily return series for three pairs of datasets from the crude oil markets (WTI and Brent), stock indices (the Dow Jones Industrial Average and S&P 500), and benchmark cryptocurrencies (Bitcoin and Ethereum) to examine the connections between various data during the COVID-19 pandemic. We consider two characteristics: time and frequency. Based on Diebold and Yilmaz's (Int J Forecast 28:57–66, 2012) technique, our findings indicate that comparable data have a substantially stronger correlation (regarding return) than volatility. Per Baruník and Křehlík' (J Financ Econ 16:271–296, 2018) approach, interconnectedness among returns (volatilities) reduces (increases) as one moves from the short to the long term. A moving window analysis reveals a sudden increase in correlation, both in volatility and return, during the COVID-19 pandemic. In the context of wavelet coherence analysis, we observe a strong interconnection between data corresponding to the COVID-19 outbreak. The only exceptions are the behavior of Bitcoin and Ethereum. Specifically, Bitcoin combinations with other data exhibit a distinct behavior. The period precisely coincides with the COVID-19 pandemic. Evidently, volatility spillover has a long-lasting impact; policymakers should thus employ the appropriate tools to mitigate the severity of the relevant shocks (e.g., the COVID-19 pandemic) and simultaneously reduce its side effects. [ABSTRACT FROM AUTHOR]

The asymptotic behavior of quantile regression inference becomes dramatically different when it involves a persistent predictor with zero or nonzero intercept. Distinguishing various properties of a predictor is empirically challenging. In this article, we develop a unified predictability test for quantile regression regardless of the presence of intercept and persistence of a predictor. The developed test is a novel combination of data splitting, weighted inference, and a random weighted bootstrap method. Monte Carlo simulations show that the new approach displays significantly better size and power performance than other competing methods in various scenarios, particularly when the predictive regressor contains a nonzero intercept. In an empirical application, we revisit the quantile predictability of the monthly S&P 500 returns between 1980 and 2019. for this article are available online. [ABSTRACT FROM AUTHOR]

Climate change poses manifold consequences to the world's ecosystems and human well-being. Greenhouse gas emissions reduction and climate-friendly technological innovations at the corporate level are considered effective measures to mitigate climate change. Using the Bayesian Mindsponge Framework (BMF) to analyze 178 enterprises listed in the Standard and Poor's 500 companies from 2016 to 2021, this paper examines how companies' climate risk-mitigating efforts can affect their market value. We found that emitted carbon dioxide negatively affects the stock price. Meanwhile, companies' income and climate risk-mitigating efforts, including producing eco-friendly products, using renewable energy, and environmental investments, are positively associated with their share value. However, the effects of these efforts are conditional on the companies' income. Based on these findings, we suggest that building an eco-surplus culture among investors and improving their climate change knowledge can be a promising approach to promoting a corporation's mitigation efforts. [ABSTRACT FROM AUTHOR]

Reconstructing the risk-neutral density (RND) of an underlying asset has been extensively studied using market-observed European option prices. However, the literature on reconstructing the corresponding implied stochastic process remains limited. To address this challenge, the authors propose an innovative approach that incorporates an implied willow tree (IWT) into market-observable options data across various maturities. Rather than solely focusing on recovering the RNDs, their method aims to reconstruct the risk-neutral process without relying on any prior parametric models. This unique feature enables data-driven model-free valuation of potentially path-dependent options, eliminating the need for specific parametric stochastic models. Through numerical experiments, the authors demonstrate the effectiveness of their approach in pricing American and Asian options, as well as computing Greeks. Furthermore, they demonstrate the resilience of their approach by assessing its performance when subjected to noise introduced into the original data. To conclude, the authors provide empirical evidence of the efficacy of the IWT method using data from S&P 500 index options. [ABSTRACT FROM AUTHOR]

We propose a novel factor model for option returns. Option exposures are estimated nonparametrically, and factor risk premia can vary nonlinearly with states. The model is estimated using regressions with minimal assumptions on factor and option return dynamics. We estimate the model using index options to characterize the conditional risk premia for factors of interest, such as the market return, market variance, tail and intermediary risk factors, higher moments, and the VIX term structure slope. Together, market return and variance explain more than 90% of option return variation. Unconditionally, the magnitude of the variance risk premium is plausible. It displays pronounced time variation, spikes during crises, and always has the expected sign. [ABSTRACT FROM AUTHOR]

The S&P 500 Index is considered the most popular trading instrument in financial markets. With the rise of cryptocurrencies over the past few years, Bitcoin has grown in popularity and adoption. This study analyzes the daily return distribution of Bitcoin and the S&P 500 Index and assesses their tail probabilities using two financial risk measures. As a methodology, we use Bitcoin and S&P 500 Index daily return data to fit the seven-parameter General Tempered Stable (GTS) distribution using the advanced fast fractional Fourier transform (FRFT) scheme developed by combining the fast fractional Fourier transform algorithm and the 12-point composite Newton–Cotes rule. The findings show that peakedness is the main characteristic of the S&P 500 Index return distribution, whereas heavy-tailedness is the main characteristic of Bitcoin return distribution. The GTS distribution shows that 80.05 % of S&P 500 returns are within − 1.06 % and 1.23 % against only 40.32 % of Bitcoin returns. At a risk level (α), the severity of the loss ( A V a R α (X) ) on the left side of the distribution is larger than the severity of the profit ( A V a R 1 − α (X) ) on the right side of the distribution. Compared to the S&P 500 Index, Bitcoin has 39.73 % more prevalence to produce high daily returns (more than 1.23 % or less than − 1.06 % ). The severity analysis shows that, at α risk level, the average value-at-risk ( A V a R (X) ) of Bitcoin returns at one significant figure is four times larger than that of the S&P 500 Index returns at the same risk. [ABSTRACT FROM AUTHOR]

This study introduces a novel Quantum Wasserstein Generative Adversarial Network approach with a Gradient Penalty (QWGAN-GP) model that leverages a quantum generator alongside a classical discriminator to synthetically generate time series data. This approach aims to accurately replicate the statistical properties of the S&P 500 index. The synthetic data generated by this model were compared to the original series using various metrics, including Wasserstein distance, Dynamic Time Warping (DTW) distance, and entropy measures, among others. The outcomes demonstrate the model's robustness, with the generated data exhibiting a high degree of fidelity to the statistical characteristics of the original data. Additionally, this study explores the applicability of the synthetic time series in enhancing prediction models. An LSTM (Long-Short Term Memory)-based model was developed to evaluate the impact of incorporating synthetic data on forecasting accuracy, particularly focusing on general trends and extreme market events. The findings reveal that models trained on a mix of synthetic and real data significantly outperform those trained solely on historical data, improving predictive performance. [ABSTRACT FROM AUTHOR]

This article proposes a vector linear double autoregressive (VLDAR) model with the constant conditional correlation specification, which can capture the co‐movement of multiple series and jointly model their conditional means and volatilities. The strict stationarity of the new model is discussed, and a self‐weighted Gaussian quasi‐maximum likelihood estimator (SQMLE) is proposed for estimation. To reduce the computational cost, especially when the series dimension is large, a block coordinate descent (BCD) algorithm is provided to calculate the SQMLE. Moreover, a Bayesian information criterion is introduced for order selection, and a multi‐variate mixed portmanteau test is constructed for checking the adequacy of fitted models. All asymptotic properties for estimation, model selection, and portmanteau test are established without any moment restrictions imposed on the data process, which makes the new model and its inference tools applicable for heavy‐tailed data. Simulation experiments are conducted to evaluate the finite‐sample performance of the proposed methodology, and an empirical example on analyzing S&P 500 sector indices is presented to illustrate the usefulness of the new model in contrast with competitors. [ABSTRACT FROM AUTHOR]

This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility can be observed. In particular, their high value is often praised in Value-at-Risk. However, the lack of nonlinear structure in most approaches means that conditional variance is not adequately represented in the model. On the contrary, the recent rapid development of deep learning methods is able to describe any nonlinear relationship in a clear way. We propose GARCHNet, a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators in GARCH. The variance distributions considered in the paper are normal, t and skewed t, but the approach allows extension to other distributions. To evaluate our model, we conducted an empirical study on the logarithmic returns of the WIG 20 (Warsaw Stock Exchange Index), S&P 500 (Standard & Poor's 500) and FTSE 100 (Financial Times Stock Exchange) indices over four different time periods from 2005 to 2021 with different levels of observed volatility. Our results confirm the validity of the solution, but we provide some directions for its further development. [ABSTRACT FROM AUTHOR]

We construct an index of long-term expected earnings growth for S&P 500 firms and show that it has remarkable power to jointly predict future errors in expectations and stock returns, in both the aggregate market and the cross section. The evidence supports a mechanism whereby good news causes investors to become too optimistic about long-term earnings growth. This leads to inflated stock prices and, as beliefs are systematically disappointed, subsequent low returns in the aggregate market. Overreaction of long-term expectations helps resolve major asset-pricing puzzles without time-series or cross-sectional variation in required returns. [ABSTRACT FROM AUTHOR]

This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks. [ABSTRACT FROM AUTHOR]

Holding volatility as part of an institutional portfolio is often found not to benefit the overall characteristics of the resulting portfolio. This applies to both simple buy and hold but also to short-selling VIX futures to harvest the volatility risk premium. We show that the latter generates positive returns but is unlikely to benefit an existing equity portfolio due to the high correlation with the returns of the S&P 500. Instead, we propose to harvest the volatility risk premium using the full term structure of the VIX in a robust Markowitz (J Financ 7(1):77–91, 1952. https://doi.org/10.2307/2975974)-framework based on Pedersen et al. (Financ Anal J 77(2):124–151, 2021. https://doi.org/10.1080/0015198X.2020.1854543). We show that VIX carry forecasts have predictive power for the futures returns and consequently use these as a market return expectations. In a number of out-of-sample tests, we find that such ex ante Sharpe-optimal portfolios not only yield statistically significant positive performances but also add significant Alpha over typical equity and fixed income factor returns. Several robustness tests confirm that these findings are insensitive to the specific parameter choices. Overall, we conclude that the volatility risk premium can be harvested profitably with a simple dynamic framework using the full term structure of VIX futures—both stand-alone and in the context of an existing institutional portfolio. [ABSTRACT FROM AUTHOR]

Firms increasingly follow an "always on" philosophy, publishing multiple pieces of firm-generated content (FGC) every day. Current methodologies used in marketing are unfit to unbiasedly capture the impact of FGC disseminated intermittently throughout the day on stock markets characterized by ultra-high-frequency trading. They also neither distinguish between the permanent (i.e., long-term) and temporary (i.e., short-term) price impacts nor identify FGC attributes capable of generating these price impacts. In this study, the authors define price impact as the impact on the variance of stock price. Employing a market microstructure approach to exploit the variance of high-frequency changes in stock price, the authors estimate the permanent and temporary price impacts of the firm-generated Twitter content of S&P 500 information technology firms. The authors find that firm-generated tweets induce both permanent and temporary price impacts, which are linked to tweet attributes of valence and subject matter. Tweets reflecting only valence or subject matter concerning consumer or competitor orientation result in temporary price impacts, whereas those embodying both attributes generate permanent price impacts. Negative-valence tweets about competitors generate the largest permanent price impacts. Building on these findings, the authors offer suggestions to marketing managers regarding the design of intraday FGC. [ABSTRACT FROM AUTHOR]

Existing studies relate the puzzling low average returns on out-of-the-money (OTM) index call and put options to nonstandard preferences. We argue the low option returns are primarily due to the pricing of market volatility risk. When volatility risk is priced, expected option returns match the realized average option returns. Moreover, consistent with its theoretical effect on expected option returns, the volatility risk premium is positively related to future index option returns and this relationship is stronger for OTM options and at-the-money straddles. Finally, we find the jump risk premium contributes to some portion of OTM put option returns. [ABSTRACT FROM AUTHOR]

Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate. The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically, reliably predict future stock prices or forecast changes in the stock market overall. Nonetheless, machine learning (ML) techniques that use historical data have been applied to make such predictions. Previous studies focused on a small number of stocks and claimed success with limited statistical confidence. In this study, we construct feature vectors composed of multiple previous relative returns and apply the random forest (RF), support vector machine (SVM), and long short-term memory (LSTM) ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days. We apply this approach to all S&P 500 companies for the period 2017–2022. We assess performance using accuracy, precision, and recall and compare our results with a random choice strategy. We observe that the LSTM classifier outperforms RF and SVM, and the data-driven ML methods outperform the random choice classifier (p = 8.46e−17 for accuracy of LSTM). Thus, we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small. [ABSTRACT FROM AUTHOR]

Analyzing market states of the S&P 500 components on a time horizon January 3, 2006 to August 10, 2023, we found the appearance of a new market state not previously seen and we shall discuss its possible implications as an isolated state or as a beginning of a new general market condition. We study this in terms of the Pearson correlation matrix and relative correlation with respect to the S&P 500 index. In both cases the anomaly shows strongly. [ABSTRACT FROM AUTHOR]

We propose a new time series model called Rational Gaussian Noise (rGn) to describe a pattern of long-range dependence. The rGn model is shown to be an extension of the traditional fractional Gaussian noise (fGn). Theoretical formulas such as autocorrelation function, and some properties of rGn are derived and compared to that of fGn. Transformed S&P500 daily excessive return data is used as a case study where parameters for both the rGn and fGn models are estimated. [ABSTRACT FROM AUTHOR]

In this paper, we study mean–variance–Conditional Value-at-Risk (CVaR) portfolio optimization problem with short selling, cardinality constraint and transaction costs. To tackle its mixed-integer quadratic optimization model for large number of scenarios, we take advantage of the penalty decomposition method (PDM). It needs solving a quadratic optimization problem and a mixed-integer linear program at each iteration, where the later one has explicit optimal solution. The convergence of PDM to a partial minimum of original problem is proved. Finally, numerical experiments using the S&P index for 2020 are conducted to evaluate efficiency of the proposed algorithm in terms of return, variance and CVaR gaps and CPU times. [ABSTRACT FROM AUTHOR]

We investigate the impact of COVID-19 on commodity return volatility. We find that the impact of COVID-19 on return volatility is different across different markets. Unlike S&P 500 sector indices, commodity return volatility is less sensitive to the impact of COVID-19. The impact of vaccination programs on return volatility is weak for both commodity and financial markets. We employ Fama-French 3 Factor Model and APARCH (1,1) for return volatility estimation. The variation in COVID-19's impact across different markets has an important implication for return volatility hedging. [ABSTRACT FROM AUTHOR]