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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 paper analytically models and numerically investigates two operating levers, namely optimization of product price and optimization of product quality in the context of a manufacturer that sells the products directly in the marketplace. The study attempts to identify how optimizing product quality and product price can fulfill a manufacturer's economic aims such as maximization of the manufacturer's profit and market demand. Anchored to the extant literature, the demand is modeled as a parametric joint multiplicative function of price and quality. Further, price is modeled as a function of product quality.

First, the authors evolve the analytical expression for the manufacturer's profit. Thereafter, following the mathematical principles of unconstrained optimization, the authors arrive at the conditions for optimal product quality and product price. The authors further perform numerical experiments to understand the behavior of economic dimensions such as profit and demand with respect to sensitivities associated with cost, quality and price.

The authors find that under product quality optimization, the optimal product quality is a unique solution in that a highest possible theoretical manufacturer's profit is obtained. However, in the case of product price optimization, the optimal product price is non-unique and is a function of product quality. The authors further find that in the context of functional quality-level expectations, product quality optimization as an operating lever gives a better dividend. However, in the case of higher product quality expectations, product price optimization performs better than product quality optimization. Further, several novel findings are also obtained from numerical experimentations.

The findings of the authors' study have implications for types of industries characterized by relatively low as well as relatively high competitive intensity. Further, as opposed to several extant studies that have often carried out joint optimization of quality and price, the authors' study is one of the first to study the impact of product price and product quality on the manufacturer's economic objective in a disparate and focused manner, thus capturing individual effects.

This paper analyzes the advance selling and pricing strategies of fresh products supply chain where the e-retailer provides wholesale contract or agency contract to the fresh products supplier.

This paper constructed a two-period sequential-move game of fresh products supply chain members.

This analysis showed that the supply chain members had different preferences for contracts under different market conditions. The advance selling of fresh products was not a decision of the seller, but also required the support of other supply chain members. And the advance selling strategy was not always beneficial to all supply chain parties. Under the two contracts, there were market conditions in which the profits of supply chain members were Pareto-improved through the implementation of advance selling.

The model presented in this study focuses solely on the context of monopoly, overlooking the competition from alternative suppliers or retailers. Consequently, exploring the competitive landscape within the fresh products supply chain, particularly in relation to pre-sale pricing, emerges as a crucial avenue for further investigation. By employing empirical research methods, valuable insights are gleaned, thereby significantly augmenting the existing body of relevant theories.

The decision to pre-sell fresh products should be based on market conditions. Supply chain members can control production costs and fresh products circulation losses to maximize profits.

From the perspective of game theory, this study analyzed the optimal advance selling and pricing strategies of fresh products supply chain members under two kinds of contracts. These results can provide practical implications for fresh products suppliers and e-retailers.

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.

This paper investigates the dynamic pricing strategy of a firm for the successive-generation products under the conditions of the limited trade-in duration and strategic customers. Further, it explores the effect of a limited trade-in duration on the choice of the myopic and strategic customers, besides the optimal dynamic pricing and trade-in strategy of the firm.

Based on the choice behavior of the myopic and strategic customers, the authors have developed a two-period game-theoretic analytical model to decide the optimal retail prices of the successive-generation products and the optimal trade-in rebate when the firm adopts a dynamic pricing strategy and then investigate three extensions of the basic model to discuss the change in the results owing to the relaxation of certain conditions.

The authors find from the results that, in terms of profit maximization, it is better to extend the limited trade-in duration, and hence, the firm should implement a dynamic pricing strategy. However, in the situation of using a static pricing strategy, the firm should extend the limited trade-in duration only if the incremental value of the new generation products is below a certain threshold. Moreover, the firm should use a dual rollover strategy instead of a single rollover one. If all customers in the market are myopic, then the firm should also extend the limited trade-in duration.

This study mainly discusses the impact of limited trade-in duration on the firm's dynamic pricing strategy when facing strategic customers, which provides several directions for future research. First, if the government offers subsidies to consumers, how will strategic consumers make purchase decisions? How would the enterprise make its pricing decision? Second, when asymmetric information exists between consumers and firms, how will it affect consumers' choice behavior and firms' pricing decisions? All these issues are worth exploring in the future.

These results offer certain managerial insights for the firm in the decision making on pricing within the trade-in program.

This is the first work to study the dynamic pricing strategy of the firm for the successive-generation products under the conditions of the limited trade-in duration and strategic customers. Further, this work discusses the changes in results owing to the relaxation of certain conditions.

By using higher moments, this paper extends the quadratic local risk-minimizing approach in a general discrete incomplete financial market. The local optimization subproblems are convex or nonconvex, depending on the moment variants used in the modeling. Inspired by Lai et al. (2006), the authors propose a new multiobjective approach for the combination of moments that is transformed into a multigoal programming problem.

The authors evaluate financial derivatives with American features using local risk-minimizing strategies. The financial structure is in line with Schweizer (1988): the market is discrete, self-financing is not guaranteed, but deviations are controlled and reduced by minimizing the second moment. As for the quadratic approach, the algorithm proceeds backwardly.

In the context of evaluating American option, a transposition of this multigoal programming leads not only to nonconvex optimization subproblems but also to the undesirable fact that local zero deviations from self-financing are penalized. The analysis shows that issuers should consider some higher moments when evaluating contingent claims because they help reshape the distribution of global cumulative deviations from self-financing.

A detailed numerical analysis that compares all the moments or some combinations of them is performed.

The quadratic approach is extended by exploring other higher moments, positive combinations of moments and variants to enforce asymmetry. This study also investigates the impact of two types of exercise decisions and multiple assets.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

The purpose of this study is to analyze the affordability and sustainability of residential water consumption in Ecuador, a developing country.

To determine affordability, the authors assess water consumption and its distribution across different income levels of the population in the three main cities: Cuenca, Guayaquil and Quito. The authors then simulate the effects of a pricing policy on sustainability by deriving demand elasticities through a quadratic almost ideal demand system (QUAIDS).

Results show that Ecuador has low water tariffs with respect to other cities in Latin America, which points to its affordability. However, sustainability might not be guaranteed since consumption is overall higher than the level suggested by the World Health Organization. From an economic point of view, this could be motivated by the low tariffs. Indeed, the simulation of a pricing policy shows that higher prices can significantly reduce demand for most of the sample. However, in low-income households this reduction may compromise vital consumption.

This research has important implications in terms of stimulating the discussion of how water affordability and sustainability can be achieved. For the case study, Ecuador, the results indicate that sustainability may not be guaranteed and that policies need to be designed to encourage it. Another implication is that population income levels should be considered to avoid negative effects for the most vulnerable groups. The main limitations of this study are methodological. First, the QUAIDS model is based on a conditional demand model (Zhen et al., 2013), which does not take into account spending on other goods. Second, data availability is limited and prevents a deeper analysis.

To the best of the authors' knowledge, this is the first study to analyze these issues in Ecuador, a country that recognizes the human right to water at the constitutional level.

A growing body of research finds that gig economy platforms use gamification to enhance managerial control. Focusing on technologically mediated forms of gamification, this literature reveals how platforms mobilize gig workers’ work effort by making the labour process resemble a game. This chapter contends that this tech-centric scholarship fails to fully capture the historical continuities between contemporary and much older occurrences of game-playing at work. Informed by interviews and participatory observations at two food delivery platforms in Amsterdam, I document how these platforms’ piece wage system gives rise to a workplace dynamic in which severely underpaid delivery couriers continuously employ game strategies to maximize their gig income. Reminiscent of observations from the early shop floor ethnographies of the manufacturing industry, I show that the game of gig income maximization operates as an indirect modality of control by (re)aligning the interests of couriers with the interests of capital and by individualizing and depoliticizing couriers’ overall low wage level. I argue that the new, algorithmic technologies expand and intensify the much older forms of gamified control by infusing the organizational activities of shift and task allocation with the logic of the piece wage game and by increasing the possibilities for interaction, direct feedback and immersion. My study contributes to the literature on gamification in the gig economy by interweaving it with the classic observations derived from the manufacturing industry and by developing a conceptualization of gamification in which both capital and labour exercise agency.

The authors investigate the effects of Chinese acquirer’s chief executive officer (CEO) risk preference on mergers and acquisitions (M&A) payment method and the moderating roles played by acquirer’s ownership, industry relatedness and whether the M&A is cross-border.

Using 4,624 worldwide M&A deals conducted by Chinese firms from 2009 to 2021, the authors conduct multiple linear regression and ordered probit regression. And comprehensive indexes constructed based on the observed features of acquirer’s CEOs are used to be the proxy for CEO risk preference.

The results show that the higher-level Chinese acquirer’s CEO risk preference is overall positively associated with using more stock in payment. Moreover, the above relationship is strengthened if the ownership of the acquirer is state-owned.

The authors highlight the importance of the non-economic factors and demonstrate a relationship between the Chinese acquirer’s CEO risk preference and the M&A payment method, providing support for and enriching the upper echelons theory (UET). Moreover, the unique risk priorities of Chinese acquirers’ CEOs are revealed.