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The authors consider a variety of cryptocurrency and equity risk factors as potential forces that drive cryptocurrency returns and carry risk premiums. In a cross-section of 2,000 biggest cryptocurrencies during 2014–2020, only downside market risk, cryptocurrency size and cryptocurrency policy uncertainty factors are systematically priced with significant premiums. Cryptocurrencies, which have greater exposures to these factors, yield higher returns subsequently. Equity market risk, particularly equity downside market risk, appears to be more important than cryptocurrency market risk, suggesting greater linkages between cryptocurrency and equity markets than we used to think. Global and the US equity factors are more relevant for the cryptocurrency market than local factors from other markets. However, there is no evidence that exposure to momentum, volatility and Fama–French factors is compensated by higher returns.

We examine the incremental cross-sectional role of several common risk factors related to size, book-to-market, and momentum in size-and-momentum-sorted portfolios. Unlike the existing literature, which focuses on the conditional mean specification only, we evaluate the common risk factors’ incremental explanatory power in the cross-sectional characterization of both average return and conditional volatility. We also investigate the role of ex-ante market risk in the cross-section. The empirical results demonstrate that the size-and-momentum-based risk factors explain a significant portion of the cross-sectional average returns and cross-sectional conditional volatility of the benchmark equity portfolios. We find that the Fama–French (1993) factors and the ex-ante market risk are priced in the cross-sectional conditional volatility. We conclude that the size-and-momentum-based factors provide a source of risk that is independent of the Fama–French factors as well as ex-post and ex-ante market risk. Our results bolster the risk-based explanation of the size and momentum effects.

Purpose: Risk is a multifaceted concept, and its identification requires complex approaches that are often misunderstood. The consequence is that decisions are based on limited perception rather than the full value and meaning of what risk is, as a result, the way it is being tackled is incorrect. The individuals are often limited in their perceptions and ideas and do not embrace the full multifaceted nature of risk. Regulators and individuals want to follow norms and checklists or overuse models, simulations, and templates, thereby reducing responsibility for decision-making. At the same time, the wider use of technology and rules reduces the critical thinking of individuals. We advance the automation process by building robots that follow protocols and forget about the part of risk assessment that cannot be programed. Therefore, with this study, the objective of this study was to discover how people define risk, the influencing factors of risk perception and how they behave toward this perception. The authors also determine how the perception differed with age, gender, marital status, education level and region. The novelty of the research is related to individual risk perception during COVID-19, as this is a new and unknown phenomenon. Methodology: The research is based on the analysis of the self-administered purposely designed questionnaires we distributed across different social media platforms between February and June 2020 in Europe and in some cases was carried out as a interview over communication platforms such as “Skype,” “Zoom” and “Microsoft Teams.” The questionnaire was divided into four parts: Section 1 was designed to collect demographic information from the participants; Section 2 included risk definition statements obtained from literature and a preliminary discussion with peers; Section 3 included risk behavior statements; and Section 4 included statements on risk perception experiences. A five-point Likert Scale was provided, and participants were required to answer along a scale of “1” for “Strongly Agree” to “5” for “Strongly Disagree.” Participants also had the option to elaborate further and provide additional comments in an open-ended box provided at the end of the section. 466 valid responses were received. Thematic analysis was carried out to analyze the interviews and the open-ended questions, while the questionnaire responses were analyzed using various quantitative methods on IBM SPSS (version 23). Findings: The results of the analysis indicate that individuals evaluate the risk before making a decision and view risk as both a loss and opportunity. The study identifies nine factors influencing risk perception. Nevertheless, it must be emphasized that we can continue to develop models and rules, but as long as the risk is not understood, we will never achieve anything.

This paper examines the exchange risk sensitivity of Japanese firms, and the exchange risk pricing in the Japanese stock market for the period of 1975–2001. We find that an appreciation of the yen is positively associated with industry portfolio returns. This supports the dominance of wealth effects over cash flow effects. This is in contrast to U.S. studies that report a weak, negative relationship between stocks and the domestic currency. The results are more pronounced in the pre-Crash period, and vary somewhat depending on the exchange risk measures used. Similarly, the exchange risk is priced in the pre-Crash period, but not in the post-Crash period. These results suggest that the exchange rate elasticity of the Japanese economy has declined in the post-bubble period of economic stagnation.

Purpose – This research pinpoints the limitations of conventional models for evaluating the performance of hedge funds and attempts to provide a new framework for modeling the dynamics of risk structures of hedge funds.

Methodology/approach – This chapter aims to explore how the systematic risk exposures of hedge funds vary over time and depend on exogenous variables that managers are supposed to use in their dynamic investment strategies. To achieve this, we used a Bayesian time-varying CAPM-based beta model within a state space technology.

Findings – The results showed that the volatility, term spread rate, and shocks in liquidity influence significantly on the time variation of hedge funds. Besides, the dynamics of beta indicates that the transmission channels of systematic risk are mainly the leverage levels of hedge funds and liquidity shocks.

Originality/value of chapter – These results are original because they help to explain how expected and unexpected hedge fund returns are correlated with the systematic risk factors via the beta dynamics.

In the build-up of an investment decision, the existence of both active and passive investment vehicles triggers a puzzle for investors. Indeed the confrontation between active and index replication equity funds in terms of risk-adjusted performance and alpha generation has been a bone of contention since the inception of these investment structures. Accordingly, the objective of this chapter is to distinctly underscore whether an investor should be concerned in choosing between active and diverse passive investment structures.

The survivorship bias-free dataset consists of 776 equity funds which are domiciled either in America or Europe, and are likewise exposed to the equity markets of the same regions. In addition to geographical segmentation, equity funds are also categorised by structure and management type, specifically actively managed mutual funds, index mutual funds and passive exchange traded funds (‘ETFs’). This classification leads to the analysis of monthly net asset values (‘NAV’) of 12 distinct equally weighted portfolios, with a time horizon ranging from January 2004 to December 2014. Accordingly, the risk-adjusted performance of the equally weighted equity funds’ portfolios is examined by the application of mainstream single-factor and multi-factor asset pricing models namely Capital Asset Pricing Model (Fama, 1968; Fama & Macbeth, 1973; Lintner, 1965; Mossin, 1966; Sharpe, 1964; Treynor, 1961), Fama French Three-Factor (1993) and Carhart Four-Factor (1997).

Solely examination of monthly NAVs for a 10-year horizon suggests that active management is equivalent to index replication in terms of risk-adjusted returns. This prompts investors to be neutral gross of fees, yet when considering all transaction costs it is a distinct story. The relatively heftier fees charged by active management, predominantly initial fees, appear to revoke any outperformance in excess of the market portfolio, ensuing in a Fool’s Errand Hypothesis. Moreover, both active and index mutual funds’ performance may indeed be lower if financial advisors or distributors of equity funds charge additional fees over and above the fund houses’ expense ratios, putting the latter investment vehicles at a significant handicap vis-à-vis passive low-cost ETFs. This chapter urges investors to concentrate on expense ratios and other transaction costs rather than solely past returns, by accessing the cheapest available vehicle for each investment objective. Put simply, the general investor should retreat from portfolio management and instead access the market portfolio using low-cost index replication structures via an execution-only approach.

The battle among actively managed and index replication equity funds in terms of risk-adjusted performance and alpha generation has been a grey area since the inception of mutual funds. The interest in the subject constantly lightens up as fresh instruments infiltrate financial markets. Indeed the mutual fund puzzle (Gruber, 1996) together with the enhanced growth of ETFs has again rejuvenated the active versus passive debate, making it worth a detailed analysis especially for the benefit of investors who confront a dilemma in choosing between the two management styles.

Over the past two years, fraudulent financial reporting has become a major concern of both the Securities and Exchange Commission and investors. These concerns have been spurred by evidence that several high-profile companies such as Enron, Tyco, WorldCom, and HealthSouth have published false and/or misleading financial reports. Statement on Auditing Standards (SAS) No. 82 specifies that auditors have a responsibility to assess the likelihood of management fraud and identifies specific risk factors that should be considered when making that assessment. Apostolou et al. (2001b) examined how internal and external auditors rate the relative importance of these factors. This study extends Apostolou et al. (2001b) by examining how forensic experts at four Big 5 professional service firms assess the factors specified in SAS No. 82. These assessments produced two different models of relative importance: (a) a statistical model (produced by the Analytic Hierarchy Process); and (b) a subjective model (based on subjects’ assessment of the relative weights). These models are then used to assess the self-insight of and the degree of agreement among the forensic experts. The results indicate that forensic experts have a moderately high degree of self-insight. A moderate to high degree of consensus among experts’ judgments about the relative importance of fraud risk factors was noted.