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The purpose of this paper is to test whether financial analysts’ rationality in making stocks’ earnings forecasts is homogenous or not across different information regimes in stocks’ past returns.

By treating stocks’ past returns as the information variable in this study, the authors employ a threshold regression model to capture and test threshold effects of stocks’ past returns on financial analysts’ rationality in making earnings forecasts in different information regimes.

The results show that three significant structural breaks and four respective information regimes are identified in stocks’ past returns in the threshold regression model. Across the four different information regimes, financial analysts react to stocks’ past returns quite differently when making one-quarter ahead earnings forecasts. Furthermore, the authors find that financial analysts are only rational in a certain information regime of stocks’ past returns depending on a certain return-window such as one-quarter, two-quarter or four-quarter time period.

This study is different from those in the existing literature by arguing that there could exist heterogeneity in financial analysts’ rationality in making earnings forecasts when using stocks’ past returns information. The finding that financial analysts react to stocks’ past returns differently in the different information regimes of past returns adds value to the research on financial analysts’ rationality.

For Chinese companies that cross-list in Chinese A share and Hong Kong (H share) markets, the H share price has been consistently lower than the A share price by an average of 85% in recent years. This is puzzling because most institutional differences between the two markets have been eliminated since 2007. The purpose of this study is to explain the puzzle of the price difference of A+H companies.

Using all A and H share Chinese firms in the period 2007–2013 and a simultaneous equations approach, this study identifies three new explanations for the recent price difference.

First, utilizing a unique earning quality measure that is directly related to non-persistent components of fair value accounting under International Financial Reporting Standards (IFRS), this study finds that the lower the earnings quality, the lower the H share price relative to the A share price, and hence the greater the price difference. Second, the higher the myopic investor ownership in A share firms, the larger the A share price relative to the H share price. Third, the short-selling mechanism introduced to the A share market since 2010 helps reduce the price difference.

First, this study identifies three new explanations for the puzzle of the AH price difference which remains substantial even after the institutional and accounting standards differences between the two markets were eliminated. Second, we examine the impact of the implementation of fair value accounting under IFRS in an emerging market on the pricing difference of cross-listed shares and reveal that it can induce an unintended negative consequence on the pricing difference of cross-listed shares. Third, this study contributes to the literature on short sales by providing its mitigating role in pricing differences across two different markets. Finally, this study makes improvements in research design, which utilizes a unique measure of earnings quality that is directly related to the implementation of IFRS and a simultaneous equations approach that minimizes endogeneity concern.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

As the influence of institutional investors over managerial decision-making grows, so does the importance of understanding the effect of institutional investor ownership (IO) on firm outcomes. The authors take a comprehensive approach to studying the effect of IO on earnings management (EM).

The authors study the relation between IO and EM using a sample of 59,503 listed U.S. firm-year observations from 1981–2019. The authors proxy EM with earnings surprises and with accrual-based and real activity measures. The authors test for nonlinear relations and analyze changes resulting from the passage of the Sarbanes–Oxley Act.

The findings support a positive IO-EM relation overall, but show that the relation is dynamic and heavily context-dependent with evidence of nonlinearity. The authors also find evidence that IO positively affects accrual-based EM and real activities EM negatively.

To the authors’ knowledge, this is the first study of the IO-EM relation to consider evidence of nonlinearity in the U.S. context, measuring changes to the relation over time, and with the use of several measures of EM.

The authors examine the contemporaneous and causal association between tweet features (bullishness, message volume and investor agreement) and market features (stock returns, trading volume and volatility) using 140 South African companies and a dataset of firm-level Twitter messages extracted from Bloomberg for the period 1 January 2015 to 31 March 2020.

Panel regressions with ticker fixed-effects are used to examine the contemporaneous link between tweet features and market features. To examine the link between the magnitude of tweet features and stock market features, the study uses quantile regression.

No monotonic relationship is found between the magnitude of tweet features and the magnitude of market features. The authors find no evidence that past values of tweet features can predict forthcoming stock returns using daily data while weekly and monthly data shows that past values of tweet features contain useful information that can predict the future values of stock returns.

The study is among the earlier to examine the association between textual sentiment from social media and market features in a South African context. The exploration of the relationship across the distribution of the stock market features gives new insights away from the traditional approaches which investigate the relationship at the mean.

Many entrepreneurs want to reach high to the heavens to achieve unlimited success. These hardworking, often underappreciated, venturers often crave fame and fortune as they strive to create their personal business legacy. One strategic path many have wandered down is that of the Initial Public Offering (IPO), whereby shares of the company are sold to the public. The IPO has many strong attractions. Large amounts of capital can be brought into the company.The company's stock can be used as currency to acquire other companies. Early investors realize a good ROI. Employees can perceive real value in their stock options. Customers, banks, vendors, and other stakeholders pay more respect to the company. Is this truly the entrepreneurʼs nirvana? Or is it a case of “Be careful of what you wish for because it may really come true?” Read on.

The purpose of this paper is to investigate and clarify “irrationality” problem through the maritime industry practices and leading incentives behind common investors.

This paper includes a review of broader business and economics literature; review of shipping business practices and detection of institutional pathways and misleading mechanisms behind the irrational preferences; investigation of data (for some arguments); and introduction of a theoretical approach.

There are several industry practices and norms well established and followed by decision makers, which may cause and initiate illogical and irrational (long-run) preferences. Short-termism is an erroneous habit of common shipping investors, which is embedded and forced through traditional financial math (i.e. discounted cash flow), financial system (e.g. initial public offerings with high-frequency transactions, interest rate governance and asset valuation mechanism) or flawed contracting tradition (i.e. commission bias).

Both shipping business and financial institutions need to redesign their working mechanisms, evaluation systems, risk detection and assessment procedures. As discussed in Section 4.7, commission-based (float) services must be converted to regular flat rate payments with long-term contracts to protect investors from rational choices of intermediaries in the short-run which encourages investor’s irrationality. Having a long-term service contract will also improve sustainability of intermediaries and lower their business risk (win-win).

The impact of this paper is two-fold. First, it raises critical questions about professional decay and drawbacks of some traditional instruments in the shipping business. For the first time, this paper emphasises on various challenges which deteriorate credibility of the industry and causes ill-defined investments. Some arguments have extreme priority for strengthening the foundations of the industry. Second, this paper establishes a new stream of scholarly research highlighting weaknesses of conventional economic approach and demand for outsourcing other schools of economics (e.g. institutional and behavioural) into the shipping business.

The article applies the citizen science phenomenon – i.e. lay people involvement in research endeavours aimed at pushing forward scientific knowledge – to healthcare. Attention is paid to initiatives intended to tackle the COVID-19 pandemic as an illustrative case to exemplify the contribution of citizen science to system-wide innovation in healthcare.

A mixed methodology consisting of three sequential steps was developed. Firstly, a realist literature review was carried out to contextualize citizen science to healthcare. Then, an account of successfully completed large-scale, online citizen science projects dealing with healthcare and medicine has been conducted in order to obtain preliminary information about distinguishing features of citizen science in healthcare. Thirdly, a broad search of citizen science initiatives targeted to tackling the COVID-19 pandemic has been performed. A comparative case study approach has been undertaken to examine the attributes of such projects and to unravel their peculiarities.

Citizen science enacts the development of a lively healthcare ecosystem, which takes its nourishment from the voluntary contribution of lay people. Citizen scientists play different roles in accomplishing citizen science initiatives, ranging from data collectors to data analysts. Alongside enabling big data management, citizen science contributes to lay people's education and empowerment, soliciting their active involvement in service co-production and value co-creation.

Citizen science is still underexplored in healthcare. Even though further evidence is needed to emphasize the value of lay people's involvement in scientific research applied to healthcare, citizen science is expected to revolutionize the way innovation is pursued and achieved in the healthcare ecosystem. Engaging lay people in a co-creating partnership with expert scientist can help us to address unprecedented health-related challenges and to shape the future of healthcare. Tailored health policy and management interventions are required to empower lay people and to stimulate their active engagement in value co-creation.

Citizen science relies on the wisdom of the crowd to address major issues faced by healthcare organizations. The article comes up with a state of the art investigation of citizen science in healthcare, shedding light on its attributes and envisioning avenues for further development.