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Using the next-day and next-week returns of stocks in the Korean market, we examine the association of option volume ratios – i.e. the option-to-stock (O/S) ratio, which is the total volume of put options and call options scaled by total underlying equity volume, and the put-call (P/C) ratio, which is the put volume scaled by total put and call volume – with future returns. We find that O/S ratios are positively related to future returns, but P/C ratios have no significant association with returns. We calculate individual, institutional, and foreign investors’ option ratios to determine which ratios are significantly related to future returns and find that, for all investors, higher O/S ratios predict higher future returns. The predictability of P/C depends on the investors: institutional and individual investors’ P/C ratios are not related to returns, but foreign P/C predicts negative next-day returns. For net-buying O/S ratios, institutional net-buying put-to-stock ratios consistently predict negative future returns. Institutions’ buying and selling put ratios also predict returns. In short, institutional put-to-share ratios predict future returns when we use various option ratios, but individual option ratios do not.

Recent studies suggested the ratio of option to stock volume reflected the private information. Informed traders were drawn to the options market for its leverage effect and relatively low transaction costs. Informed traders use different intervals of option moneyness to execute their strategies. The question is which types of option moneyness were traded by informed traders and what information was reflected in the market. In this study, the authors focused on this question and constructed a method for capturing the activity of informed traders in the options and stock markets.

The authors constructed the daily measure, moneyness option trading volume to stock trading volume ratio (MOS), to capture the activity of informed traders in the market. The authors formed quintile portfolios sorted with respect to the moneyness option to stock trading volume ratio and provided the capital asset pricing model and Fama–French five-factor alphas. To determine whether MOS had predictive ability on future stock returns after controlling for company characteristic effects, the authors formed double-sorted portfolios and performed Fama–Macbeth regressions.

The authors found that the firms in the lowest moneyness option trading volume to stock trading volume ratio for put quintile outperform the highest quintile by 0.698% per week (approximately 36% per year). The firms in the highest moneyness option trading volume to stock trading volume ratio for call quintile outperform the lowest quintile by 0.575% per week (approximately 30% per year).

The authors first propose the measures, moneyness option trading volume to stock trading volume ratio, that combined with the trading volume and option moneyness. The authors provide evidence that the measures have the predictive ability to the future stock returns.

Facing the challenges posed by the pandemic of COVID-19, this paper aims to contribute to the resilience of businesses through the development of a real options approach (ROA) that provides alternatives and opportunities for a decision process under situations when future events and outcomes are unknown and not capable of being known from current information.

This paper involves a stochastic modelling process in generating a set of absolute option values, using available data and scenarios from the COVID-19 pandemic event. The modelling and simulations using ROA suggest how strategic portfolios resolve the growing problem during the endemic to all but in the most isolated societies.

This study finds the emergent correlation between circuit breakers and lockdowns, which have brought about a “distorted gravity” effect (inverse growth of global businesses and trades). However, “time-to-build” real options (i.e. deferral, expand, switch and compound exchange) start to function in the adaptive-transformative capabilities for growth opportunities of both government and corporate sectors. Significantly, some sectors grow faster than others while the compound exchange remains primarily challenging. Clearly, the government and corporate sectors are entangled, inevitably, the decoherence allows for the former to change uncertainty in the latter; therefore, government sector options change option values in the corporate sector.

The ROA by empirically focusing on both government and corporate sectors demonstrates under conditions of uncertainty how options in decision-making generate opportunities that hitherto have not been recognised and exercised upon by research in the immediate context of the COVID-19 pandemic. Importantly, the ROA provides an insightful concatenation (capability–behaviour approach) that drives resilience.

Previous literature shows that market sentiment and the steepness of index option's implied volatility slope have a negative relation. This paper investigates the relation between firm-specific sentiment and individual option's implied volatility slope both theoretically and empirically.

The authors develop a simple model with option traders' sentiment heterogeneity to show that sentiment and the steepness of individual option's implied volatility slope have a positive relation.

When firm-specific sentiment is higher (more bullish), individual option's implied volatility slope becomes steeper. The positive relation is stronger when option traders' beliefs on risk are more dispersed. Empirical results support the theoretical model predictions.

Although both firm-specific sentiment and individual options implied volatility slope predict future stock returns, there is no research exploring the relation between them. In particular, none of previous studies associates implied volatility slope's stock return predictability to investor behavior such as sentiment. The authors’ findings provide a behavior-based explanation on why steep implied volatility slope negatively predicts cross-sectional stock returns.

The purpose of this study is to examine the relationship between stock options compensation and firm strategic risk-taking, employing a quantile regression (QR) model. This study aims to analyze whether the impact of stock options on firm strategic risk-taking changes across various quantiles and investigates the moderating role of firm performance.

This study is based on a sample of 90 French firms for the period extending from 2008 to 2019. To deal with the non-uniform association, the authors use a panel quantile method.

The results reveal that the impact of chief executive officer (CEO) stock options on firm strategic risk-taking varies across risk-taking quantiles. More specifically, the study’s results show a positive association at low quantile levels of strategic risk-taking, measured by research and development (R&D) and a negative linkage at high levels. The authors also find that firm performance moderates the impact of CEO stock options on strategic risk-taking.

The non-uniform relationship between CEO stock options and firm strategic risk-taking shows that the weight of CEO stock options in the total compensation can be a major determinant of the firm's strategic risk-taking attitude.

This study extends existing research on executive compensation and strategic risk-taking. Thus, this study has the potential to help stakeholders, board of directors and regulators, who are attempting to understand how the compensation contract – in particular, stock option pay – is related to the risk behavior of the agents and guide them to structure the executive compensation in an optimal way.

This study aims to develop a scale for new ways of working (NWW) in higher education institutions in the Gulf Cooperation Council (GCC) countries. The study also intends to validate the psychometric properties of the developed scale.

This study targeted the academic staff of universities in the GCC region. Out of the 1,200 questionnaires distributed, only 1,016 questionnaires represented valid responses. Because there was not a unified theory for NWW, the authors developed a six-dimension tool that covered all virtual work aspects and psychometrically validated.

The results of exploratory and confirmatory factor analyses confirmed a structural model of six factors: flexible work location; work–life balance; communication; workplace design at home; culture and motivation; and satisfaction. The model showed a satisfactory fit. The scale consisted of 32 items with a high Cronbach’s alpha value of 0.85, which demonstrated good internal consistency. The results also suggested that the NWW scale had adequate convergent and divergent validity.

The data for the current study is a cross-sectional that represents a single sector; therefore, it would be more interesting to include more sectors. The study findings contribute to the ongoing debate in feasibility and usefulness of NWW pre, during and post-Covid-19 crisis. This research has offered a new scale for measuring NWW that fits dynamic educational environment where continuous learning and innovation are the key critical factors for survival. For this reason, further future studies need to refine, validate and improve the current scale structure. Also, because the current scale is by no means conclusive, future studies may look at other work characteristics and contextual factors that determine the success of NWW.

Practitioners can use the results of the current study as an intervention tool to leverage NWW acceptance to regain benefits and mitigate negative consequences. In addition, policymakers may use the scale as an evaluation tool to examine the readiness of higher education institutions to counter the COVID-19 crisis.

The originality of this work stems from the fact that it is the first study to develop a scale for NWW and test its psychometric properties in higher education institutions in the GCC countries, a domain that has been ignored by the extant literature.

This study aims to examine the value of the call option contract in hedging the risks in the supply chain. The decentralized supply chain without call option contract is first studied as the criterion model for evaluations. This paper addresses several questions: What will be the optimal manufacturer’s production quantity, retailer’s ordering and pricing policies in the presence of random demand and random yield by applying the downconversion approach? How will the call option contract influence the optimal decisions for the members of the supply chain? Can the risk from randomness be divided among the members in the supply chain through the call option contract?

This paper considers a two-level decentralized supply chain under random yield and random demand in which the manufacturer takes advantage of the downconversion approach with two scenarios, with and without option contract. To the best of the authors’ knowledge, no article or study uses the downconversion approach in a supply chain regarding random yield and random demand. Furthermore, the paper considers pricing with option contract in the supply chain, which makes this article stands out significantly from other articles in the literature.

This study shows that the downconversion approach would reduce the risk caused by the random yield, which appears to be the appropriate method for the environmental goal of the supply chains. Moreover, adopting a call option contract can increase flexibility and mitigate risks, resulting in more expected members’ profits.

To simplify the model, the authors assume one manufacturer and one retailer, so extending the model to consider multiple retailers instead of one retailer and inventory sharing between them would be interesting. Considering the option and exercise prices as decision variables would be important future research topics. Put option and bidirectional option contracts could be investigated in the future. Another extension is modeling asymmetry of information in supply chain.

This paper provides managerial insights on dealing with both demand and yield risks in a manufacturer–retailer supply chain. The manufacturer has a random yield production and produces two types of vertical products: low-end and high-end. To reduce waste caused by the random yield, the manufacturer uses a downconversion approach in which low-end products are made by converting the defective high-end products. The manufacturer purchased a shortage of high-end products from the secondary market (i.e. emergency sourcing). High-end products are sold through the retailer, and low-end products are sold directly by the manufacturer. The customer demand for high-end products in the end market is random and depends on the selling price, and the customer demand for the low-end products in the secondary market is independent and random. The retailer contracts the manufacturer with the call option to obtain high-end products to meet a random demand; in fact, by using the call option contract, the authors try to balance the risks between two members. Two scenarios of with and without call option contract are proposed. After the high-end product demand is observed, the retailer would exercise the option order quantity in the call option contract scenario and then place an instant order with the manufacturer if necessary. In each scenario, the manufacturer and the retailer make their decisions simultaneously (static game) to determine the retailer’s optimal ordering and pricing policies and the optimal production quantity of the manufacturer (Nash equilibrium) by maximizing their expected profits. Finally, the impact of the model parameters on the supply chain is expressed through numerical examples. The numerical analysis shows that the call option contract provides greater profit than the wholesale price contract.

This paper provides a structural model to value startup companies and determine the optimal level of research and development (R&D) spending by these companies.

This paper describes a new variant of float-the-money options, which can act as a financial instrument for financing R&D expenses for a specific time horizon or development stage, allowing the investor to share in the startup's value appreciation over that duration. Another innovation of this paper is that it develops a structural model for evaluating optimal level of R&D spending over a given time horizon. The paper deploys the Gompertz-Cox model for the R&D project outcomes, which facilitates investigation of how increased level of R&D input can enhance the company's value growth.

The author first introduces a time-varying drift term into standard Black-Scholes model to account for the varying growth rates of the startup at different stages, and the author interprets venture capital's investment in the startup as a “float-the-money” option. The author then incorporates the probabilities of startup failures at multiple stages into their financial valuation. The author gets a closed-form pricing formula for the contingent option of value appreciation. Finally, the author utilizes Cox proportional hazards model to analyze the optimal level of R&D input that maximizes the return on investment.

The integrated contingent claims model links the change in the financial valuation of startups with the incremental R&D spending. The Gompertz-Cox contingency model for R&D success rate is used to quantify the optimal level of R&D input. This model assumption may be simplistic, but nevertheless illustrative.

Once supplemented with actual transaction data, the model can serve as a reference benchmark valuation of new project deals and previously invested projects seeking exit.

The integrated structural model can potentially have much wider applications beyond valuation of startup companies. For instance, in valuing a company's risk management, the level of R&D spending in the model can be replaced by the company's budget for risk management. As another promising application, in evaluating a country's economic growth rate in the face of rising climate risks, the level of R&D spending in this paper can be replaced by a country's investment in addressing climate risks.

This paper is the first to develop an integrated valuation model for startups by combining the real-world R&D project contingencies with risk-neutral valuation of the potential payoffs.

This study aims to explore the mediating effect of digital options on the relationship between emerging information technology investments (ITIs) and firm performance (FP). In particular, it analyses the performance impacts of investments in five emerging technologies of IT or non-IT firms.

Secondary data are collected from Chinese A-share listed companies from 2010 to 2018. The authors propose an econometric model focusing on the impact of ITIs on a firm’s market value and profit. A propensity score matching model is applied to control endogeneity.

The ITIs’ effect on FP is found to be completely mediated by digital options, and the reach of digital options plays a more positive role in the relationship between ITIs and Tobin’s Q, whereas the richness of digital options is stronger between ITIs and return on net assets (ROE). The group study shows that the impact of process technologies such as cloud computing and the Internet of Things has a more profound impact on Tobin’s Q, and the knowledge technologies represented by artificial intelligence, blockchain and big data strongly affect ROE. In addition, the positive relationship between ITIs and FP is unrelated to IT/non-IT firms.

First, the data are based on 219 publicly announced emerging ITIs in China and thus may not be generalizable to other cultural/national contexts. Second, there is a lack of a large sample data set of emerging ITI information in China, and the duration of this study is constrained to the relatively short rise of emerging technologies.

This study provides firm decision-makers with practical implications. The results imply that the effect of ITIs on FP depends on digital options, so both IT firms (e.g., Big Tech giants) and non-IT firms (e.g., incumbents) should discover how to balance firm value and profit in their management of emerging technology investment projects with digital options thinking.

To the best of the authors’ knowledge, this is the first empirical study to investigate the relationship between ITIs and FP from the perspective of digital options, exploring five emerging technologies and considering firm life, size, and state ownership in a sample of Chinese listed firms.

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.