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The effect of securities litigation risk on firm value and disclosure

AbstractCritics assert that securities class actions are economically burdensome and yield minimal recoveries, whereas proponents claim they deter wrongdoing. We examine key events in the recent Goldman Sachs Supreme Court case to test the net effect of securities litigation risk on shareholder value. We find that investors view securities class actions as value‐increasing. However, the strength of this effect varies based on external monitoring. Investors view securities class actions as more value‐enhancing when institutional ownership is low. We also use this setting to examine the effect of securities litigation risk on mandatory disclosure because the Goldman Sachs case focuses on mandatory disclosure properties. Using a difference‐in‐differences design, we find firm risk factor disclosures become shorter and less similar to industry peers, and they contain more uncertain and weak terms. Overall, our results show nuanced effects of securities litigation risk on shareholder value and firm disclosure.

Multifractal Behavior of Cryptocurrencies During Periods of Economic Uncertainty

Background: In recent years, investors' interest in cryptocurrencies has increased due to their notable price volatility and rapid price increases. These investors view cryptocurrencies as suitable financial assets for portfolio rebalancing strategies. Purpose: The main objective of this study is to examine the multifractality of the cryptocurrencies Bitcoin (BTC), Lisk (LSK), Quantum (QUA), Litecoin (LTC), Ripple (XRP), Augur (REP), Darkcoin (DASH), EOS, IOTA (MIOTA). Methods: The Detrended Fluctuation Analysis (DFA) econophysics model supports the methodology. Results: The results suggest that during the 2020 pandemic period, the digital currencies LSK, QUA, MIOTA, XRP, REP, BTC, ETH, LTC and DASH showed very significant persistence, indicating that price formation is not random. However, validating that cryptocurrency prices are predictable based on historical time series was impossible. On the other hand, the digital currency EOS proved to be in equilibrium; in other words, price formation follows the random walk pattern, suggesting that prices are not autocorrelated over time. During the 2022 geopolitical conflict, long-term memory patterns shifted significantly towards short-term memories, i.e. anti-persistence. The digital currencies ETH, MIOTA, EOS, LTC, REP, LSK and DASH showed anti-persistence slopes, indicating that prices were less influenced by past events and more by recent events. On the other hand, the cryptocurrencies BTC (0.50), QUA (0.50), and XRP (0.50) demonstrate that prices contain a significant random component and that the residuals are independent and identically distributed (i.i.d.), supporting the idea that white noise might be present. Conclusion: From a risk management perspective, these findings are highly relevant to investors, traders and market participants.

ESG performance on the value of family firms: international evidence during Covid-19

The link between the financial success of family companies during COVID-19 and their environmental, social, and governance (ESG) performance is examined for the first time in this research. We have a natural setting in the COVID-19 era to see if the market rewards family-run firms that integrate social and environmental concerns into their goals during uncertain times. Since they can enhance their image and reputation, which the market values, these companies are likely to pursue broader social objectives, such as environmental improvement (socioemotional wealth perspective); alternatively, managers can act as stewards of the family’s interests by using these initiatives to increase the company’s value (stewardship perspective). However, it is also possible that in this type of companies economic interests prevail over social wellness (“amoral familism”). Therefore, family-owned firms could be reluctant to implement ESG practices unless they yield certain socioemotional benefits, including enhancing or maintaining their reputation in the public eye. In light of the above, we use an international display of the 500 biggest family firms in the world from 2015 to 2021. Taking into account an endogenous relationship between ESG performance and family business value, the study uses generalized moments to construct a dynamic panel (GMM). The primary conclusion is that there is a positive correlation between corporate valuation and ESG performance. Nonetheless, it has been noted that the performance of the companies is negatively impacted during the COVID-19 period. However, for firms with superior ESG performance, this negative impact did not exist over this period, supporting the idea that investors view better ESG performance as a prediction of future good stock performance. The results have a variety of implications. To begin with, this study adds to the body of knowledge on the environmentally friendly and sustainable expansion of family companies by providing recommendations for investors and businesses to better understand the influence of ESG on the profitability of family businesses. Furthermore, managers have to concentrate on enhancing the ESG performance of their organizations as it has the potential to increase value, draw in investments, encourage sustainability, control risks, affect earnings, and interact with the ownership structure. Additionally, managers need to consider how important it is to have a strong ESG performance in order to mitigate the negative effects of external crises like the COVID-19 epidemic. It’s crucial to remember that the precise impact might change based on the sector and other aspects unique to each company.

Investor value orientation and environmental and social implications: a case of New Zealand agribusinesses

PurposeThis study aims to examine the value orientations of New Zealand agribusiness investors and how these orientations influence their reactions to the environmental and social implications of agribusinesses.Design/methodology/approachIn the context of the New Zealand agricultural sector, the views of investors as published in print and broadcast media between 2018 and 2022 are gathered. The study uses qualitative content analysis to analyse the data. The study is based on the value-belief-norm theory.FindingsThe study reveals that New Zealand agribusiness investors express concern about the environmental (biospheric) and social (altruistic) impacts of the agribusiness sector, prompting calls for greater transparency, climate adaptation and ethical investment options. Additionally, they actively support local businesses to benefit their communities and preserve cultural heritage. Despite these biospheric and altruistic tendencies, investors also prioritise financial and non-financial interests (egoistic). This highlights a nuanced perspective guiding their investment choices – a balance between self-interest and contributing to the greater good. This signals a shift towards socially and environmentally responsible investment practices driven by multifaceted values.Research limitations/implicationsThe findings of this study highlight the role of non-pecuniary motives, like values, in determining the relevance of environmental and social information.Practical implicationsThe study’s findings offer insight to agribusinesses on how investors’ value orientations shape their investment decisions. This understanding can guide businesses in framing a reporting strategy that enhances the likelihood of investors perceiving reporting as relevant and persuasive, thereby attracting more investments. In turn, this tailored reporting approach assists investors in making well-informed decisions in assessing the environmental and societal risks of agribusinesses.Originality/valueThe study offers a framework explaining how agribusinesses can increase the likelihood of investors finding firms reporting relevant and persuasive, leading to increased investments in environmentally and socially sustainable practices.

Global investor responses to the International Sustainability Standards Board draft sustainability and climate-change standards: sites of dissonance or consensus

Purpose This paper aims to examine sites of dissonance or consensus between global investor responses to the draft standards, International Financial Reporting Standards S1 (IFRS) (General Requirements for Disclosure of Sustainability-related Financial Information) and IFRS S2 (Climate-related Disclosures), issued by the International Sustainability Standards Board (ISSB). Design/methodology/approach A thematic content analysis was used to capture investor views expressed in their comment letters submitted in the consultation period (March to July 2022) in comparison to the ex ante position (issue of draft standards, March 2022) and ex post summary feedback (ISSB staff papers, September 2022) of the ISSB. Findings There was investor consensus in support of the ISSB and the development of the draft standards. However, there were sites of dissonance between investors and the ISSB, notably regarding the basis and focus of reporting (double or single/financial materiality and enterprise value); definitional clarity; emissions reporting; and assurance. Incrementally, the research further highlights that investors display heterogeneity of opinion. Practical and Social implications The ISSB standards will provide a framework for future sustainability reporting. This research highlights the significance of such reporting to investors through their responses to the draft standards. The findings reveal sites of dissonance in the development and alignment of draft standards to user needs. The views of investors, as primary users, should help inform the development of sustainability-related standards by a global standard-setting body apposite to current policy and future reporting requirements, and their usefulness to users in practice. Originality/value To the best of the authors’ knowledge, this paper makes an original contribution to the comment letter literature, hitherto focused on financial reporting with a relative lack of investor engagement. Using thematic analysis, sites of dissonance are examined between the views of investors and the ISSB on their development of sustainability reporting standards.

The Power of the Default: Investors’ Reactions to Default Amortization Periods and Deviations Therefrom

As standard setters deliberate the accounting for intangibles, the idea of establishing a default useful life with an option to deviate could reduce the costs and complexities inherent in estimating the useful lives of these assets. Across multiple experiments, we investigate investors’ reactions to companies that utilize versus deviate from a default useful life set by standard setters. We find that investors believe (1) a mandated default life is more of an endorsement of that life than are deviations from it, and (2) companies that deviate will have more difficulty determining the useful life and will face increased scrutiny and justification demands. These insights generally hold whether the deviation results in a shorter or longer expected life, and they become more pronounced as the magnitude of deviation increases. Despite this finding, we observe that investors do not generally view a company’s useful life choice as informative about their true beliefs and judged future economic benefits of the intangible. Collectively, the results suggest that investors view deviations from a default as imposing costs on firms, but not conveying credible information about managers’ true beliefs regarding the economic benefits of the asset. Our findings are informative to standard setters and firm managers.

Corporate Governance Dynamics in Saudi Arabia: Audit Committee Composition, Family Ownership, and Financial Performance

The purpose of this research is to examine how the audit committee composition, focusing on independence and the presence of external financial experts, impacts a firm's performance metrics (Tobin’s-Q and Return on Equity-ROE) in the Kingdom of Saudi Arabia (KSA). Additionally, the study aims to understand how family ownership influences this relationship. The research undertook ordinary least squares (OLS) regression analysis on a dataset comprising 485 firm-year observations. The results show a positive association between having independent directors on the audit committee (AudC) and both Tobin’s-Q and ROE. When the audit committee includes external financial experts, it is linked to a higher ROE but a lower Tobin’s-Q. However, the presence of family ownership acts as a negative moderator in these associations, counteracting the positive effects of the audit committee's composition on firm performance. This indicates that, in certain institutional settings, investors view audit committee independence as detrimental, impacting their assessment of the firm's value. This study sheds light on the nuanced understanding of how the composition and utilization of audit committees play out in a specific institutional context, especially within public family-owned firms. It emphasizes the need for a careful consideration of audit committee composition, tailored to the unique business environment of the firm. Regulatory requirements aimed at safeguarding non-family investors may not align well with the dynamics of family businesses. This research significantly contributes to the understanding of corporate governance and its application in family-owned enterprises.

Forecasting Method for Optimal Diversification

Forecasting is a technique that uses historical data as inputs to make estimates that are predictive in determining the direction of future trends. The goal of investors is to make optimal choice that leads to minimization of risk and maximization of returns, but the method that leads to these objectives has been a challenge for investor. In this study, Black-Litterman model (BLM) is adopted and two forecasting methods; EGARCH and GARCH methods are used for two parameters of BLM; investor views and level of uncertainty. The aim of this paper is to investigate the best forecasting method to estimate BLM that would lead to minimum risk and maximum returns. The analysis of this paper shows that EGARCH method gives maximum expected returns and minimum risk.

Views of Investors on Various Portfolios Advisory services provided by Asset management business entities

Views of Investors on Various Portfolios Advisory services provided by Asset management business entities

A Geometric Approach to Asset Allocation with Investor Views

A Geometric Approach to Asset Allocation with Investor Views

Understanding investor views and their impact on satisfaction levels in the Sukanya Samriddhi scheme

Understanding investor views and their impact on satisfaction levels in the Sukanya Samriddhi scheme

Risk-Adjusted Portfolio Optimization: Monte Carlo Simulation and Rebalancing

This study evaluates the risk-adjusted performance of a diversified portfolio in the Indian financial market from 2011 to 2021, incorporating Nifty 50 stocks and new-age assets. Leveraging Monte Carlo simulations and mathematical optimization, the research identifies an optimal portfolio on the efficient frontier. Integration of the Black-Litterman model provides a comparative analysis, emphasizing the impact of investor views. Despite transaction costs, optimized portfolios outperform the Nifty 50 index, with the rebalanced portfolio demonstrating higher cumulative returns. Key findings include TCS. NS is a leader in share price, HDFCBANK.NS showcasing stability and alternative assets exhibit higher volatility but have the potential for amplified returns. This research offers valuable insights for investors seeking resilient strategies in the Indian financial landscape.

Artificial Intelligence: Perspectives of Entrepreneurs and Investors

The paper analyzes the views of technology entrepreneurs and investors on artificial intelligence (AI). The study uses two data collection methods: interviews and questionnaires. Based on the results of the analysis, respondents' views on the main risks associated with the use of AI were revealed, as well as the expected effects from its use in emerging companies. Using ordinal logistic regression, it was revealed that the idea that AI is “hype” is negatively influenced by the experience of founding a company, as well as the use of AI in everyday life. Using the method of hierarchical clustering, groups of respondents with similar views on AI were identified and described.

Enhancing investment performance of Black-Litterman model with AI hybrid system: Can it be done?

The B-L (Black-Litterman) model combines the subjective views of investors with the market's historical equilibrium return through the Bayesian theorem. This approach somewhat compensates for the limitations of the Mean-Variance model, making the asset allocation model attract significant attention. However, due to many investors lacking ample investment experience, they might struggle to provide appropriate subjective views. To address this issue, an Iaah (Industry Asset Allocation Hybrid) system based on the B-L model is proposed. First, a combined model is constructed, which simulates view return vectors and solves the problem of inaccurate view quantification. Then, the Iaah system introduces a dynamic condition correlation algorithm to optimize inherent parameters and explores asset allocation results in the time-varying effects. Next, it expands the types of objective functions and improves the deficiency of only considering the return and variance while ignoring the downside risk. Finally, based on a variety of performance metrics, the Iaah system is evaluated from multiple perspectives to test its practical value. The experiment results with the daily data of 10 SSE (Shanghai Stock Exchange) industry indexes show that the proposed system not only realizes the diversified allocation of industry assets and enhances the investment performance, but also exhibits strong robustness against changes in subjective parameters. It is conducive to managing personal wealth and achieving preservation and appreciation.

Modeling crude oil volatility using economic sentiment analysis and opinion mining of investors via deep learning and machine learning models

Investor emotions can significantly impact the perceived value of financial assets, and sentiment analysis technology can accurately model daily responses and views of investors. In this study, we analyze investor remarks from one of the most popular web economic areas to investigate the effects of investor emotions on the price of crude oil in China. We construct investor emotions indices for China's crude oil futures based on specific economic factors and employ five machine learning technologies, including the AR approach, SVR approach, MLP method, neural networks with a recurrent units model, a recurrent unit with a gate approach, and LSTM model, to predict one-day-ahead prices. Our research offers insights into the effectiveness of different machine learning technologies and how specific economic factors contribute to sentiment analysis accuracy. We discuss the natural language processing techniques and tools used to extract sentiment from financial forum comments, along with the measures taken to ensure the accuracy and reliability of sentiment scoring. Our study highlights the advantages of the LSTM model, paired with the compound sentiment index, in the context of crude oil price forecasting and specifies the accuracy levels achieved by this model in comparison to others. We also discuss potential limitations and challenges in the long-term application of sentiment analysis for price forecasting and propose possible solutions. Our findings have practical implications for investors, financial analysts, and policymakers involved in China's crude oil market. We explore patterns or trends in investor emotions in our analysis and discuss their correlation with major economic or geopolitical events in China and globally. Our study contributes to a deeper understanding of investor emotions' impact on crude oil prices in China and provides valuable insights for various stakeholders in the dynamic and speculative energy oil industry.

Extracurricular Activities for Entrepreneurial Learning: A Typology Based on Learning Theories

Extracurricular activities are increasingly being recognized for developing practical skills among entrepreneurial learners and connecting entrepreneurship curricula with real life. They offer socially situated learning experiences that can be cognitively stimulating and elicit reflective practices. However, the theoretical and pedagogical underpinnings of extracurricular activities in entrepreneurship are still in early stages, with their contribution towards entrepreneurship education requiring more empirical support. Moreover, current entrepreneurship pedagogies lack a much-needed integration of ecosystem actors’ inputs, who posses specific expertise with regards to extracurricular entrepreneurial activities. To address these issues, this study gathered the views of entrepreneurship mentors, consultants, and investors on the extracurricular activities that can be deployed to improve the skills of entrepreneurial learners, through conducting 22 in-depth interviews with experts from 13 countries across the world. We analyzed the results through a hybrid, inductive and deductive, approach. The experts recommended 34 extracurricular activities, that were discursively mapped against relevant learning theories: cognitive, experiential, social, situated, and existential. The study adds to the limited theoretical discussion on the origins of extracurricular activities and paves the way for theoretical evaluations in entrepreneurship education. It can aid educators in effectively integrating extracurricular activities in their curricula to better develop students’ entrepreneurial competences.

Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach

We introduce a new dimension in constructing relative investor views for the Black-Litterman model by incorporating fear/greed technical indicator predictions as a proxy for investor sentiment in the portfolio construction process. We apply a hybrid CEEMDAN-GRU deep learning model to forecast this indicator and the XGBoost ensemble learning algorithm to forecast returns for ten country ETFs and create relative views for the Black-Litterman model. These models beat several benchmark forecasting models. Our empirical results show that the proposed approach outperforms the Markowitz, minimum-variance, equally-weighted and risk-parity strategies along with four other Black-Litterman approaches from the literature for six investment periods.

Exploring the Existence of Efficiency in the Financial Markets

Since the market efficiency hypothesis theory was introduced by Fama in 1970, many investors and researchers argued about the efficiency form of the real financial market. In the real financial market, there are solid pieces of evidence that the price of securities could reflect related public information. However, as real human beings, the investors in the financial market are not always rational and act according to the assumptions stated in the market efficiency theory, which results in a reduction in the efficiency of the real financial market. In this paper, our group tends to use two different views (investors and companies) to prove that all of the market efficiency hypothesis theory assumptions are violated in the real financial market, and the current market efficiency is weak. From an investors view, our group tends to analyze the effect of psychological factors from investors on market efficiency. Furthermore, from the companies side, our group tends to use case analysis to provide solid evidence for the market efficiency analysis.

Accounting Quality of U.S. Listed Chinese Enterprises: Disclosure Level of State-Owned and Non-State-Owned Enterprises

This proposal is commissioned to discuss U.S.s the differences in the disclosure level of U.S. listed Chinese state-owned enterprises (SOEs) and the U.S.-listed Chinese Non-state-owned (Non-SOEs) enterprises. Topics include introduction, objective, importance, test process and conclusion. During the past two decades, the economy of China went through rapid growth, providing Chinses enterprises a solid basement to develop. With the goal of obtaining funds for further expansion and staying competitive in the international market, more Chinese enterprises choose to go public in the U.S. The disclosure level of Chinese enterprises plays an essential role in the decision-making of foreign investors. Whether owned by the Chinese government makes investors view disclosure levels differently since Chinese SOE exists as a special type of business. From the test and analysis, no obvious differences exist between the disclosure levels of SOE and Non-SOE.

Crowded out from the beginning: Impact of government debt on corporate financing

Using hand-collected data on corporate bond and stock offerings, we identify the impact of government debt on corporate financing during World War I. The early twentieth century provides a unique opportunity to identify the impact of government debt on private financing because during this period (1) firms announced the amount they wanted to raise before each security offering and (2) the Treasury issued debt in discrete intervals. We identify the impact of Treasury issues by comparing differences in the amount firms offered to the amount they actually raised when the Treasury was borrowing to when the Treasury was not in the market. We find that long-term government bond offerings negatively affect both amount of long-term corporate bonds and dividend paying stocks issued. In contrast, we find no effect on short-term debt issue. Our findings suggest that investors view stable dividend paying stocks a close substitute for relatively safe long-term bonds.