Search

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractHenri Poincaré is well known for his work in mathematics, physics, and the philosophy of science. From 1900 onwards, Poincaré had the opportunity to become a forerunner of the Efficient Market Hypothesis (EMH), a concept that was formally developed 65 years later, but he never seised it. We show that Poincaré’s view on randomness was incompatible with the EMH framework. Although he did not work directly on financial topics, his work has helped to understand the limits of random walks in asset pricing; a way to reconcile the mathematician with the field of finance. [ABSTRACT FROM AUTHOR]

The United States had one of the worst outcomes in the management of COVID-19 risk, with a death rate in the 94th percentile of all countries. Setting aside the obvious politicized nature of COVID-19 public health recommendations and mandates, we argue that best practices in financial risk management provide parallels that could have served as valuable guidance. We demonstrate here that considerable signals were missed that would have required very little effort and would have been consistent with sound risk management. We also identify examples of misleading information such as that COVID-19 was particularly hard on the elderly. The data actually show that it had a much greater marginal impact on those not elderly. We show here that financial economists and risk managers have a strong knowledge base of how to process vast quantities of data to distinguish signals from noise and have much to teach the public health establishment. [ABSTRACT FROM AUTHOR]

This article, titled "Editors' Introduction: The Birth of Portfolio Theory," discusses the impact of Harry M. Markowitz on the field of investing. Markowitz's 1952 paper and subsequent book, "Portfolio Selection," introduced the concept of portfolio theory and emphasized the importance of diversification. His work revolutionized the field and laid the foundation for modern portfolio management. The article also highlights Markowitz's contributions to other disciplines, such as simulation methods and sparse matrix methods. The special issue of The Journal of Portfolio Management pays tribute to Markowitz's legacy and includes personal reflections as well as scholarly contributions that build upon his work. [Extracted from the article]

This essay summarizes the author's professional and personal relationship with Harry Markowitz. The relationship, which spanned more than 60 years, began in 1962 when the author read Harry's first book, Portfolio Analysis (1959), and only ended with Harry's death in 2023. Harry's 1959 book inspired the author's PhD thesis and led to the author introducing the first course on modern investment analysis at New York University. Harry's work is not only the basis of financial economics, but it also has inspired much of investment practice today. In addition, it has provided motivation for numerous publications, including most of the author's. In this essay, the author highlights some of the many accolades that Harry earned over the course of his lifetime. [ABSTRACT FROM AUTHOR]

The optimization of investment portfolios represents a pivotal task within the field of financial economics. Its objective is to identify asset combinations that meet specified criteria for return and risk. Traditionally, the maximization of the Sharpe Ratio, often achieved through quadratic programming, has constituted a popular approach for this purpose. However, real-world scenarios frequently necessitate more complex considerations, particularly in relation to portfolio diversification with a view to mitigating sector-specific risks and enhancing stability. The incorporation of diversification alongside the Sharpe Ratio into the optimization model creates a joint optimization task, which can be formulated as Quadratic Unconstrained Binary Optimization (QUBO) and addressed using quantum annealing or hybrid computing techniques. These techniques offer promising solutions. We present a novel QUBO formulation for this optimization, detailing its mathematical formulation and demonstrating its advantages over classical methods, particularly in handling diversification objectives. By leveraging available QUBO solvers and hybrid approaches, we explore the feasibility of handling large-scale problems while highlighting the importance of diversification in achieving robust portfolio performance. We finally elaborate on the results showing the trade-off between the observed values of the portfolio's Sharpe Ratio and diversification, as a natural consequence of solving a multi-objective optimization problem. [ABSTRACT FROM AUTHOR]

Harry Markowitz was the father of portfolio selection. His emphasis of the need to integrate utility theory and its corresponding risk-aversion coefficients, Bayesian and robust statistics for covariance and expected return forecasting, transactions costs, and data mining corrections testing educated many investors, asset managers, plan sponsors, and consultants to more objectively measure and assess portfolio performance. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper argues that firms in certain life cycle stages may be more subjectively valued by individual investors, leading to an optimistic bias in stock prices that is subsequently corrected upon the release of earnings news. Using a cash flow-based life cycle stage classification, introduction and decline stage companies exhibit three-day cumulative abnormal returns (CARs) around earnings announcements that are at least 112 bps lower than firms in growth, maturity, and shake-out stages. Specifically, introduction and decline stage stocks exhibit less positive reactions to positive earnings surprises and more negative reactions to negative earnings surprises relative to companies in other life cycle stages. Lottery stocks' excess returns around earnings announcements (Liu et al. in Journal of Financial Economics 138: 789–817, 2020) also vary based on firm life cycle stage. Our findings suggest that individual investors' optimistic expectations for introduction and decline stage stocks are met with disappointment when value-relevant earnings news is released. This study demonstrates that firm life cycle stage has real implications for stock price reactions to earnings announcements. [ABSTRACT FROM AUTHOR]

Building, home contents and comprehensive car insurance promise protection against loss or damage from fire, flooding, accident and theft. In Australia, young people aged 18–24 are among the groups most likely to forego these insurance products. Yet research on the reasons for this remains limited, as noninsurance among young people is attributed to their dependent or 'fledgling' life stage, with minimal income and assets warranting protection. In this article, we argue that noninsurance may have serious consequences for young people, particularly if they have limited savings and cannot count on financial assistance from their families. Drawing upon survey findings, we undertake an in‐depth investigation into the role of asset levels, affordability and attitudes in driving young people to forego insurance. Our findings suggest that young people are not especially predisposed to distrust insurers, to consider insurance inessential or to oppose insurance on principle. However, other attitudes—including lesser risk aversion, higher confidence in their capacity to mitigate risks and perceptions of insurance as irrelevant to their circumstances or 'not for them'—may be more prevalent in this age group, driving them to remain uninsured even when they have assets warranting protection and sufficient income to offset affordability concerns. [ABSTRACT FROM AUTHOR]

This study conducts a citation-based comprehensive systematic literature review (SLR) of the syndicated loan market by identifying and recognizing the sources of knowledge-producing leading articles, journals and authors in this area. In total, we present a citation analysis of 374 articles from the Scopus database using a comprehensive list of keyword searches. We find that the importance of research using syndicated loan data has increased after the global financial crisis (GFC). It is perhaps due to the reason that the GFC was mainly a credit crisis that sparked the interest of research in this area. We identify the Journal of Financial Economics as the leading journal in terms of citation and the Journal of Banking and Finance in terms of publication count. Victoria Ivashina comes out as the leading author in terms of the citation count and Anthony Saunders in terms of publication count. We also perform a content analysis of the top 100 most cited papers and identify data characteristics, major themes, estimation techniques and empirical approaches. Our SLR provides a macro picture of research on syndicated loan studies. Finally, we provide an overview of the direction for future research. [ABSTRACT FROM AUTHOR]

Fishing portfolio diversification has been identified as a mechanism to mitigate risk exposure for harvesters operating in wild-capture fisheries. As in financial markets, however, the benefits from diversification in fisheries will be a function of whether individuals adopt active or passive investment strategies. There is a long history of evidence of the superior performance of passive investment strategies in the financial economics literature, but little is known about the relative performance of these two strategies in fisheries. Here we use a case study of an observed and high-profile climate shock in the California Dungeness crab fishery to estimate the impact of a relatively active investment strategy, in the form of an extreme decision to exit the fishery for the season, in response to the shock. We find robust evidence of negative revenue impacts from an active investment strategy in this context. [ABSTRACT FROM AUTHOR]

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it. [ABSTRACT FROM AUTHOR]

Why do firms take excessive risks that result in failure? Moral hazard theorists argue that the answer lies in the risk-boosting effects of the government safety net, which insulates firms from market discipline. We revisit this conventional wisdom by examining how exposure to government protection has contributed to recent trends in bank risk-taking in the USA. Drawing from insights from economic sociology, we highlight an additional way that exposure to government protection can shape organizational behavior: by reducing resource-based profitability pressures that can spur risky behavior. Using panel data analysis of risky US bank behavior between 1994 and 2015, we find that bank exposure to government protection was more often associated with less risk-taking than more of it. This pattern contradicts the predictions of moral hazard theory but aligns with the predictions of our own institutional-resource theory. We discuss implications for economic sociology and financial economics. [ABSTRACT FROM AUTHOR]

Much of the work on the valuation of levered (and unlevered) warrants assumes that the volatility of the underlying state variable is constant. This paper extends the literature on warrant pricing to a more general assumption for the state variable process, the so-called constant elasticity of variance (CEV) process. The CEV model is well-known for its ability to capture some empirical observations found in the financial economics literature, namely the asymmetry between equity returns and volatility and the implied volatility skew. Using the CEV process, we are able to reduce pricing bias as the volatility becomes a function of the underlying state variable. We price European-style call warrants without restrictions on the debt maturity. When warrants have the same maturity as debt, it is possible to obtain closed-form solutions for warrants prices. When the maturity of warrants is different from the maturity of debt, prices can be computed numerically through very efficient and simple to implement valuation methodologies. [ABSTRACT FROM AUTHOR]

Cryptocurrencies have emerged as an important investment avenue in the past few years. Investors are increasingly interested in these currencies amid surging financial returns. In this context, understanding market efficiency of cryptocurrency has become very crucial for investors and academicians. The price–volume framework is a popular approach in financial economics to understand the market efficiency of stocks in the stock markets. Therefore, this article examines the market efficiency of cryptocurrencies through price–volume framework to understand whether crypto market is predictable. Towards this objective, data on both return and trading volume (TV) of the top eight cryptocurrencies are used for the period 8 August 2015–20 October 2022. As an empirical method, both linear and non‐linear causality models are used to validate the hypothesis. Our results confirm that TV cannot predict the cryptocurrencies' return, thereby validating the market efficiency hypothesis. Furthermore, we divide the sample according to the structural break period. The result from the post‐break period analysis also confirms the presence of market efficiency in the recent period for all currencies, barring XRP, XMR and DASH. [ABSTRACT FROM AUTHOR]

The article outlines the work of economist Ravi Jagannathan, who it notes is the Fellow of the American Finance Association for 2024. It mentions his academic research on topics including the market risk premium, asset pricing, and investor behavior. It says his is a professor at the Kellogg School of Management.

This paper aims to investigate the determinants of different types of market risk faced by Spanish firms from 2012 to 2019. Using Fama and French's (Journal of Financial Economics, 1993, 33, 3) three‐factor model, we estimate total risk, diversifiable risk, and systematic or non‐diversifiable risk in the three dimensions proposed by these authors: market risk, size risk, and valuation risk. Risk determinants are derived from a series of economic and financial variables obtained from the information contained in financial statements. This information is summarised using a factor analysis that aims to resolve the correlation issues between the proposed measures. The study demonstrates that the systematic risk factors proposed by Fama and French in their 1993 three‐factor model incorporate dimensions of systematic risk that are relevant to investors and that the set of economic and financial variables proposed can explain these risks. Among these variables, profitability and the market to book ratio have the greatest impact in explaining company risk, while factors such as operating and financial leverage, growth, or company insolvency have a much smaller effect as explanatory factors for risk. [ABSTRACT FROM AUTHOR]

This paper summarizes recent research in performance-based microfinance, and outlines future directions for research. We use the term 'microequity' to refer to any small-enterprise financing contract in which the repayment owed in each instalment depends positively upon the variable performance of the enterprise. We summarize research from financial economics and development economics to highlight the importance of implicit insurance in microequity contracts, and present a theoretical framework to illustrate the potential of microequity in supporting capital investment and incentivizing asset use. We conclude by highlighting directions for future research in this emerging field of microfinance. [ABSTRACT FROM AUTHOR]

The current study presents a systematic review of macroeconomic risk factors and stock market performance under the broader theme of multi-factor asset pricing or the Arbitrage Pricing Theory (APT). Based on the review of relevant literature, the study summarizes key findings of different research works and identifies significant research gaps that need to be addressed by future research. It assesses the state of published work in the domain of asset pricing by systematically analyzing 116 research articles. The research works whose focal point has been asset pricing have been retrieved from leading databases and journals related to financial economics for the period 1976–2019. The study provides a comprehensive look at the conceptual, theoretical, and methodological (or empirical) developments and patterns prevalent at the aggregate level. The implications of the study hold key importance for academia or future researchers, policy-makers and most importantly the investors for guiding their investment decisions efficiently. [ABSTRACT FROM AUTHOR]

This article discusses the intersection of sustainability, climate change, and financial innovation. It highlights the importance of technology and financial innovation in achieving the Sustainable Development Goals (SDGs) and transitioning to a green, sustainable economy. The article also explores the dominance of green bonds in global thematic debt issuance and the impact of tax incentives on corporate environmental engagement. Additionally, it addresses the role of institutional investors in shaping corporate environmental impact and the performance of Special Purpose Acquisition Companies (SPACs). The article concludes by suggesting future research directions, including the need for interdisciplinary research, the exploration of innovative financial instruments and technologies, and the development of regulatory frameworks for sustainable financial innovation. [Extracted from the article]

This paper addresses the notion of electronic economics by examining financial technology (fintech) firms' performance and corporate governance quality, using data from the United States. The findings support our maintained assumption that due to the economics of electronic platforms perused by financial technology firms, these firms outperform firms from other industries (non-Fintech). The final sample comprises 1,712 company-year observations between 2010 and 2019 (pre-COVID-19). The evidence suggests that our corporate governance quality index, developed from Organisation of Economic Cooperation and Development (www.oecd.org/corporate/corporate-governance-factbook.htm, 2019) guidelines, accurately captures corporate governance quality in the United States, principally due to the inclusion of an anti-bribery policy indicator, in the index. The results suggest that this evidence is not merely an artefact due to the corporate governance quality index potentially capturing priced risk factors. Our findings reveal that fintech firms have superior corporate governance quality than non-fintech and that fintech firms place more reliance on internal versus external corporate governance mechanisms, vis-a-vis companies in other industries. [ABSTRACT FROM AUTHOR]

While volatility spillover is a vital research area in financial economics (due to its importance for risk valuation and portfolio diversification strategies), the volatility linkage between Bitcoin and electricity/energy markets has not received adequate attention. As the Bitcoin mining cost comes mainly from electricity (which is highly dependent on natural gas), we hypothesize that natural gas is a non-trivial Bitcoin price volatility driver and aim to test if this is the case. Specifically, we employ a widely used model called the HAR-RV model to assess volatility spillover across Bitcoin and natural gas using high-frequency data. We find a spillover effect from natural gas to Bitcoin, and the positive (negative) component of natural gas volatility stabilizes (destabilizes) Bitcoin volatility. The spillover effect is further examined and confirmed using an out-of-sample approach. [ABSTRACT FROM AUTHOR]

Motivated by Ahmadi-Javid (Journal of Optimization Theory Applications, 155(3), 2012, 1105-1123) and Ahmadi-Javid and Pichler (Mathematics and Financial Economics, 11, 2017, 527-550), the concept of Tsallis Value-at-Risk (TsVaR) based on Tsallis entropy is introduced in this paper. TsVaR corresponds to the tightest possible upper bound obtained from the Chernoff inequality for the Value-at-Risk. The main properties and analogous dual representation of TsVaR are investigated. These results partially generalize the Entropic Value-at-Risk by involving Tsallis entropies. Three spaces, called the primal, dual, and bidual Tsallis spaces, corresponding to TsVaR are fully studied. It is shown that these spaces equipped with the norm induced by TsVaR are Banach spaces. The Tsallis spaces are related to the Lp spaces, as well as specific Orlicz hearts and Orlicz spaces. Finally, we derive explicit formula for the dual TsVaR norm. [ABSTRACT FROM AUTHOR]

Since different racial and ethnic groups experience different entrepreneurial outcomes, we need to develop an explanation for these differences as a foundation for overcoming them. Early analyses of entrepreneurship by Cantillon, Turgot, Smith, Say are applied to problems of minority entrepreneurship. Menger, Schumpeter, and Kirzner provided the most modern and complete treatments, but entrepreneurship's multifaceted nature makes it difficult to incorporate into formal models. Segmented markets and preferred habitat theories drawn from financial economics are applied to address special issues confronting minority entrepreneurs and suggest how they might inform public policy. Because policy implications are suggested, it is hoped that this paper may contribute to a constructive conversation on opportunities to encourage entrepreneurship and advance minority communities. [ABSTRACT FROM AUTHOR]

This study employs information economics and the financial intermediary theory to explore the influences of private information in virtual communities and financial technology (fintech) derived from virtual currency on financial stocks. The paper conducts robust analyses on 67,166 data observations of the stock markets in China and Taiwan and finds that virtual currency development causes a structural change in the financial industry. The financial stocks in Taiwan are obviously driven by virtual factors, whereas those in China are subject to both pull from substantial factors and push from virtual factors. The research findings also suggest that the non-fundamental herding behavior driven by private information interferes with the value of financial stocks. However, financial innovations boost the competitiveness of the financial industry. It is advised to establish a policy to closely monitor the diffusion of private information and the exchange rate volatility between cryptocurrencies and home currencies to facilitate proactive financial risk management. [ABSTRACT FROM AUTHOR]

The Ethereum blockchain network enables transaction processing and smart-contract execution through levies of transaction fees, commonly known as gas fees. This framework mediates economic participation via a market-based mechanism for gas fees, permitting users to offer higher gas fees to expedite processing. Historically, the ensuing gas fee volatility led to critical disequilibria between supply and demand for block space, presenting stakeholder challenges. This study examines the dynamic causal interplay between transaction fees and economic subsystems leveraging the network. By utilizing data related to unique active wallets and transaction volume of each subsystem and applying time-varying Granger causality analysis, we reveal temporal heterogeneity in causal relationships between economic activity and transaction fees across all subsystems. This includes (a) a bidirectional causal feedback loop between cross-blockchain bridge user activity and transaction fees, which diminishes over time, potentially signaling user migration; (b) a bidirectional relationship between centralized cryptocurrency exchange deposit and withdrawal transaction volume and fees, indicative of increased competition for block space; (c) decentralized exchange volumes causally influence fees, while fees causally influence user activity, although this relationship is weakening, potentially due to the diminished significance of decentralized finance; (d) intermittent causal relationships with maximal extractable value bots; (e) fees causally influence non-fungible token transaction volumes; and (f) a highly significant and growing causal influence of transaction fees on stablecoin activity and transaction volumes highlight its prominence. These results inform strategic considerations for stakeholders to more effectively plan, utilize, and advocate for economic activities on Ethereum, enhancing the understanding and optimization of within the rapidly evolving economy. [ABSTRACT FROM AUTHOR]

We present a novel digital twin model that implements advanced artificial intelligence techniques to robustly link news and stock market uncertainty. On the basis of central results in financial economics, our model effciently identifies, quantifies, and forecasts the uncertainty encapsulated in the news by mirroring the human mind's information processing mechanisms. After obtaining full statistical descriptions of the timeline and contextual patterns of the appearances of specific words, the applied data mining techniques lead to the definition of regions of homogeneous knowledge. The absence of a clear assignment of informative elements to specific knowledge regions is regarded as uncertainty, which is then measured and quantified using Shannon Entropy. As compared with standard models, the empirical analyses demonstrate the effectiveness of this approach in anticipating stock market uncertainty, thus showcasing a meaningful integration of natural language processing, artificial intelligence, and information theory to comprehend the perception of uncertainty encapsulated in the news by market agents and its subsequent impact on stock markets. [ABSTRACT FROM AUTHOR]

Copyright of Ruch Prawniczy, Ekonomiczny i Socjologiczny (0035-9629) is the property of Adam Mickiewicz University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we propose a methodology to identify relationships between web data and social/economic variables, such as inflation. Our method enables the selection of relevant time series from a large data sample by employing a criterion based on a few hypotheses regarding their dynamics. Specifically, we examine the correlation between web activities and the dynamics of two macroeconomic variables: the unemployment rate and US automotive sales. We demonstrate how changes in the search volume of specific keywords, as measured by corresponding Google Trends data, are reflected in the underlying dynamics of these variables. The findings presented in this paper, along with the versatility of our approach, suggest the potential extension of this study to other economic variables. [ABSTRACT FROM AUTHOR]

Objective The optimization of asset portfolios, taking into account market advancements, has emerged as a pivotal subject in financial economics. Constructing asset portfolios is acknowledged as a critical decision for investors. Consequently, researchers focus on identifying factors that influence the selection of portfolios with high returns and controlled risk. Portfolio optimization, a cornerstone of financial economics, has gained prominence in the face of ever-evolving market dynamics. Investors' decision-making plays a pivotal role in portfolio construction, prompting researchers to explore factors that influence the selection of portfolios with high returns and controlled risk. Numerous models have addressed the optimization problem of stock portfolio management, each tailored to specific conditions and constraints. This research focuses on developing a multi-objective optimization model that incorporates investor memory and behavioral preferences. The literature on portfolio optimization is vast and diverse, encompassing various approaches and methodologies. Traditional optimization models, such as Markowitz's Mean-Variance model, aim to maximize expected returns while minimizing risk. However, these models often fail to capture the complexities of real-world investment decisions, which are often influenced by behavioral factors. Investor memory refers to the tendency of investors to base their current investment decisions on past experiences. This can lead to biases and suboptimal outcomes. Behavioral preferences, on the other hand, encompass a range of psychological factors that influence investor behavior, such as risk aversion, overconfidence, and herding. Methods We perceive stock portfolio optimization as a multi-objective challenge, considering two primary criteria. The first criterion involves investor memory, which encompasses utilizing historical price data and market trends to anticipate future performance. The second criterion pertains to behavioral preferences, which involves integrating investor risk aversion, overconfidence, and herding behavior into the model. We employ a Genetic Algorithm (GA) to optimize portfolios under both criteria. GA is a robust optimization technique that can effectively handle complex problems with multiple constraints. The study population comprises companies listed on the Tehran Stock Exchange for the year 2021. Results The achieved results suggest that investor memory serves as a more suitable criterion for optimal portfolio construction compared to behavioral preferences because investor memory incorporates market data and trends, providing a more objective basis for decision-making. Additionally, incorporating market return alongside investor memory data yielded superior results than using behavioral preferences with market return. This indicates that the combination of investor memory and market data can lead to more efficient and profitable portfolios. Pairwise comparisons of portfolios created using investor memory and behavioral preference criteria revealed that investor memory consistently outperformed behavioral preferences across different risk levels. This finding highlights the importance of considering investor memory when constructing optimal stock portfolios. Conclusion This study contributes to the literature on portfolio optimization by demonstrating the effectiveness of incorporating investor memory and behavioral preferences into the decision-making process. The findings suggest that investor memory is a more suitable criterion for portfolio optimization than behavioral preferences. Moreover, the combination of investor memory and market return data can lead to more efficient and profitable portfolios. The findings of this study have important implications for investors and portfolio managers. Investors should consider incorporating investor memory into their decision-making process when constructing stock portfolios. Additionally, portfolio managers can use the proposed multi-objective optimization model to create more efficient and profitable portfolios for their clients. This study provides a foundation for future research on portfolio optimization. Future studies can explore other factors that influence investor behavior, such as social media sentiment and news sentiment. Additionally, researchers can investigate the application of other optimization techniques, such as machine learning algorithms, to portfolio optimization. [ABSTRACT FROM AUTHOR]

This article explores the factors that drive risk and active risk in asset manager model portfolios. The study reveals that model portfolios with different allocations to index and active funds have similar levels of total and active risk. It also finds that a significant portion of the active risk in model portfolios is due to style factors, although the average model has limited exposure to commonly rewarded style factors. The article suggests that there is room for improvement in model performance by adjusting factor exposures. The data used in the analysis comes from Morningstar and BlackRock's Aladdin Risk system. The article discusses the impact of asset allocation on active risk calculations, including the potential for double counting certain exposures in fixed income indexes. It also notes that the Aladdin Risk Model used in the study does not capture the impact of asset class timing. The results show that there is little variation in total risk between models using active funds and index funds. The article further examines active risk relative to peer and custom benchmarks, as well as asset class risk decompositions. It concludes that style factors contribute the most to risk. This document focuses on the factor and risk exposures of model portfolios, which are increasingly popular among individual investors. The analysis primarily looks at equity and fixed income exposures and finds that most model portfolios have minimal exposure to rewarded style factors such as size, value, momentum, profitability, and volatility. The study suggests that model builders could potentially enhance returns by adjusting the magnitude of their style factor exposures. Additionally, the document highlights the potential for future research on performance [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]

Patents and citations are powerful tools increasingly used in financial economics (and management research more broadly) to understand innovation. Biases may result, however, from the interactions between the truncation of patents and citations and the changing composition of inventors. When aggregated at the firm level, these patent and citation biases can survive popular adjustment methods and are correlated with firm characteristics. These issues can lead to problematic inferences. We provide an actionable checklist to avoid biased inferences and also suggest machine learning as a potential new way to address these problems. 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 summarize and synthesize the results of the articles in this symposium issue on research in financial economics related to the COVID-19 pandemic. We argue that the articles, taken together, present evidence that the pandemic resulted in a distributional shock to capital allocation. The underlying mechanisms include accelerating technological shifts, government stimulus programs, and heterogeneous responses of investors and firms. We augment these articles with evidence on the heterogeneous effects of the pandemic on profitability, payout, investment, employment, and productivity across sectors. [ABSTRACT FROM AUTHOR]

The article explores the integration of Illinois English Language Arts (ELA) and Social Studies Standards to create a differentiated and collaborative learning experience for advanced young learners, emphasizing economic and financial literacy. Topics include the integration of ELA and Social Studies Standards, emphasis on economic and financial literacy, and the use of narrative mentor texts to enhance student learning.

This paper aims to investigate the impact of board characteristics on the risk-taking and performance of Indonesian and Malaysian Islamic banks. It proposes an idea of ideal board governance in an Islamic bank institution. Based on hand-collected data of board characteristics in 27 Islamic banks in Indonesia and Malaysia from 2006 to 2016, this study utilises panel data analysis for its empirical approach. The authors used random effect Generalized Least Square (GLS) regression and Generalised Method of Moments (GMM) to ensure the robustness of the results. The regression results indicate that the Sharia Supervisory Boards (SSB) and Board of Commissioners (BoC) play a crucial role in Islamic banks' risk-taking and financial performance. The paper highlights several significant findings: (1) the presence of female directors in SSB and BoC tend to improve performance, and the presence of female BoC tend to reduce risk-taking; (2) the presence of foreign BoC members induce performance and reduce bank's risk-taking; (3) SSB members with not only Islamic but also finance/economics type of education background tend to reduce risk-taking, while BoC members with Islamic education is likely to decrease the four type of risks: insolvency risk, funding risk, credit risk, and Sharia non-compliance risk; (4) SSB and BoC members with financial or economics educational background are likely to increase Islamic bank's performance; (5) SSB and BoC members that are older tend to improve performance, and SSB that are busy tend to reduce risk-taking of Islamic banks. These results are robust to different estimation techniques. [ABSTRACT FROM AUTHOR]

Inspired by Austin's conceptualisation of utterances as performative, that is, they do things rather than merely represent, research has shown how scientific theories can become performative in financial markets. Research also shows that brokerage and investment work is as much about using everyday knowledge of markets as it is about performing scientific theories. We investigate whether and how this knowledge or what Swedberg calls 'folk economics' can also be performative. We focus on Borsa Istanbul, an emerging market where market actors perform what we call 'the American Spirit' – a ubiquitous folk theory that frames and plots the Turkish market as one that moves in tandem with American and other developed markets – and in the process become better market forecasters. Our findings have implications for the study of folk economics and performativity in global economy and finance. [ABSTRACT FROM AUTHOR]

We study how organizations use team selection and delegation of authority jointly to navigate uncertain environments. To do so, we model a managerial decision environment in which a manager both determines the skill heterogeneity of the workers and determines whether to retain or delegate the ability to allocate tasks. Delegation enables better-informed workers to allocate tasks more efficiently when uncertainty is high relative to the incentive conflict between manager and worker. Our novel approach allows us to illustrate that this conflict is endogenously determined by the team selection decision. Experimental data support—although not globally—the direction of our theoretical hypotheses and offer insight into how and why choices deviate from expected behavior. Notably, we identify behavioral characteristics that aid decisions along each dimension. Deliberative thinking improves all decisions under low uncertainty and improves team selection regardless of the level of uncertainty. Risk tolerance improves all decisions in highly uncertain situations and helps managers optimally delegate decision rights in all settings. The results highlight potentially costly ways in which managers seek to simplify their decisions but show how deliberative thinking and risk tolerance can improve performance in a complementary manner. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: The authors thank Florida State University and the Barcelona Graduate School of Economics for financial support. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/orsc.2021.1545. [ABSTRACT FROM AUTHOR]

We employ extreme value theory to identify stock price crashes, featuring low-probability events that produce large, idiosyncratic negative outliers in the conditional distribution. Traditional methods employ approximations under Gaussian assumptions and central moments. This is inherently imprecise and susceptible to misspecifications, especially for tail events. We instead propose new definitions and measures for crash risk based on conditional extremal quantiles (CEQ) of idiosyncratic stock returns. CEQ provide information on quantile-specific impact of covariates, and shed light on prior empirical puzzles and shortcomings in identifying crashes. Additionally, to capture the magnitude of crashes, we provide an expected shortfall analysis of the losses due to crash. Our findings have important implications for a burgeoning literature in financial economics that relies on traditional approximations. [ABSTRACT FROM AUTHOR]

In this paper, we examine the optimal investment-consumption-insurance policies for a wage earner with time-varying risk preferences. The wage earner's objective is to find the optimal investment-consumption-insurance strategies that maximise the expected discounted utilities from intertemporal consumption, legacy and terminal wealth over the uncertain lifetime horizon. Similar to Lichtenstern et al. [Optimal life-cycle consumption and investment decisions under age-dependent risk preferences. Mathematics and Financial Economics, 15, 275–313], by using a separation approach, the problem is divided into two sub-problems, including the consumption-legacy problem and the terminal wealth-only problem. For each sub-problem, the analytical expressions for the optimal strategies and value functions are derived by using the martingale method. In such a way, we obtain the optimal strategies for the original problem by merging the solutions of the two individual problems. Finally, we conduct some numerical experiments to illustrate the effects of some parameters on the optimal strategies and obtain some economic insights. [ABSTRACT FROM AUTHOR]

This article aims to investigate the presence of herding behavior in artificial intelligence (AI)--themed cryptocurrencies following the launch of ChatGPT. The authors analyze daily data from major AI-themed cryptocurrencies between November 2022 and February 2023. This study finds evidence of irrationality among investors in this market segment who tend to imitate others' decisions regardless of their own beliefs during down events. The authors connect this finding to the herding theory in financial economics and highlight the implications for investors and policymakers. This article contributes to the literature on the impact of AI on financial markets and suggests the need for further research in this area. Finally, this study provides important policy implications, as it could help investors better understand the risks associated with this emerging asset class. [ABSTRACT FROM AUTHOR]