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In recent years, with the rapid development of Internet technology, the integration of platform economy and e-commerce has become a popular business model. Two-sided platforms have a specific impact on sales, customer experience and transaction efficiency of both sides. In the current severe situation caused by the coronavirus pandemic, both the traditional unilateral market platform and the emerging two-sided market platform are in urgent need of a change in operation mode to reduce the marketing cost. Inspired by the cooperation between Meituan, a two-sided platform, and WeChat, a social media platform, this paper investigates the two-sided platform's scalable decisions on when to cooperate and how to optimize the pricing and investment decisions. We analyze how the two-sided platform makes decisions by considering the changes of network externalities from the cooperation with the social network platform. Compared with the scenario of non-cooperation, we derive the conditions under which platform cooperation can increase demands and increase platforms' profits, and analyze how cooperation affects the optimal pricing strategies. We find that the cooperation leads to a larger demand and a higher total profit, but might lead to higher registration prices for the platform users. Furthermore, we adopt the Nash bargaining framework and introduce platform bargaining power parameters to obtain the optimal cooperation and sharing strategy. Finally, we show how to adjust the investment strategy of the two-sided platform under platform cooperation.

The aim of this paper is to assess whether three well-known commodity-specific variables (basis, hedging pressure, and momentum) may improve the predictive power for commodity futures returns of models otherwise based on macroeconomic factors. We compute recursive, out-of-sample forecasts for the monthly returns of fifteen commodity futures, when estimation is based on a stepwise model selection approach under a probability-weighted regime-switching regression that identifies different volatility regimes. We systematically compare these forecasts with those produced by a simple AR(1) model that we use as a benchmark and we find that the inclusion of commodity-specific factors does not improve the forecasting power. We perform a back-testing exercise of a mean–variance investment strategy that exploits any predictability of the conditional risk premium of commodities, stocks, and bond returns, also consider transaction costs caused by portfolio rebalancing. The risk-adjusted performance of this strategy does not allow us to conclude that any forecasting approach outperforms the others. However, there is evidence that investment strategies based on commodity-specific predictors outperform the remaining strategies in the high-volatility state.

This work investigates financial volatility cascades generated by SARS-CoV-2 related news using concepts developed in the field of seismology. We analyze the impact of socio-economic and political announcements, as well as of financial stimulus disclosures, on the reference stock markets of the United States, United Kingdom, Spain, France, Germany and Italy. We quantify market efficiency in processing SARS-CoV-2 related news by means of the observed Omori power-law exponents and we relate these empirical regularities to investors’ behavior through the lens of a stylized Agent-Based financial market model. The analysis reveals that financial markets may underreact to the announcements by taking a finite time to re-adjust prices, thus moving against the efficient market hypothesis. We observe that this empirical regularity can be related to the speculative behavior of market participants, whose willingness to switch toward better performing investment strategies, as well as their degree of reactivity to price trend or mispricing, can induce long-lasting volatility cascades.

In this paper we consider the problem of choosing the optimal pension fund in the second pillar of Lithuanian pension system by providing some guidelines to individuals with defined contribution pension plans. A multistage risk-averse stochastic optimization model is proposed that can be used to plan a long-term pension accrual under two different cases: minimum and maximum accumulation plans as possible options in the system. The investment strategy of personal savings is based on the optimal solutions over possible scenario realizations generated for a particular participant. The concept of the risk-averse decision-maker is implemented by choosing the conditional value at risk as the risk measure defined by a nested formulation that guarantees the time consistency in the multistage model. The paper focuses on three important decision-making moments corresponding to the duration of periods to be modelled. The first period is a short-term accumulation, while the second period is a long-term accumulation with possibly high deviation of objective function value. The third period is designed to implement the concept of target date fund in the second pillar pension scheme as the subsequent need to protect against potential losses at risky pension funds. The experimental findings of this research provide insights for individuals as decision-makers to select pension funds, as well as for policy-makers by revealing the vulnerability of pension system.

In the current low interest-rate and highly-regulated environment investing capital efficiently is one of the most important challenges insurance companies face. Certain quantitative parts of regulatory requirements (e.g. Solvency II capital requirements) result in constraints on the investment strategies. This paper mathematically describes the implications of Solvency II constraints on the investment strategies of insurance companies in an expected utility framework with a focus on the market risk module. For this constrained expected utility problem, we define a two-step approach that leads to closed-form approximations for the optimal investment strategies. This proposal circumvents the technical difficulties encountered when applying the convex duality approach or the theory of viscosity solutions. The investment strategies found using the two-step approach can be understood as the optimal investment strategies for constraint problems according to Solvency II. The impact of such constraints on the asset allocation and the performance of these strategies is assessed in a numerical case study.

We study a dynamic portfolio optimization problem related to convergence trading, which is an investment strategy that exploits temporary mispricing by simultaneously buying relatively underpriced assets and selling short relatively overpriced ones with the expectation that their prices converge in the future. We build on the model of Liu and Timmermann (2013) and extend it by incorporating unobservable Markov-modulated pricing errors into the price dynamics of two co-integrated assets. We characterize the optimal portfolio strategies in full and partial information settings both under the assumption of unrestricted and beta-neutral strategies. By using the innovations approach, we provide the filtering equation that is essential for solving the optimization problem under partial information. Finally, in order to illustrate the model capabilities, we provide an example with a two-state Markov chain., Comment: 25 pages, 5 figures

Recent years have witnessed a venture capital boom. By offering capitals and mentoring, venture investors would receive high returns if their portfolio companies successfully exit, namely being acquired or going Initial Public Offering. However, the screening and evaluation of startups for investment largely depends on the investors’ personal experiences, social relationships, and qualitative evaluation on firms. The entrepreneurial finance industry thus has a strong call for the methodologically sound, quantitative study of venture capital deals. Plus, more accessible data and more sophisticated analytics techniques signal the opportunity for methodical decision-making in investing the venture financing market. To this end, in this paper, we aim at developing a personalized portfolio strategy for assisting investors to target the right startups and determine the proper amount of capital to fund. Specifically, we first develop a Probabilistic Latent Factor model to estimate the investment preferences of all the investors in a collaborative way. The model is fitted with not only the historical investment records but also the profiles of the venture capitalists and the startups. Then, we assess the startups’ investment outcomes by regressing the potential returns and risks. We improve the regression performances by nonparametric methods. At last, we use modern portfolio theory to optimize the investment strategy over the startups recommended by the investment preference model. As a result, the investment strategy can yield maximized returns with suppressed potential risks, and meanwhile meet the investment preferences of the venture capitalists. The proposed method is evaluated using data from venture financing markets in USA, and the results show that our method outperforms other state-of-the-art methods on various metrics.

We consider Cover’s universal portfolio and the problem of risk management in a distribution-free setting when learning from experts. We aim to find optimal portfolios without modelling the financial market at the outset. Although it exists, the price distribution of the constituent assets is neither known nor given as part of the input. We consider the portfolio selection problem from the perspective of online algorithms that process input piece-by-piece in a serial fashion. Under the minimax regret criterion, we propose two risk-adjusted algorithms that track the expert with the lowest maximum drawdown. We obtain upper bounds on the worst-case performance of our algorithms that equal the bounds obtained by Cover (Math Finance 1(1):1–29, 1991). We also present computational evidence using NYSE data over a 22-year period, which shows superior performance of investment strategies that take risk management into account.

Cooperative investment consists of two problems: finding an optimal cooperative investment strategy and fairly dividing investment outcome among participating agents. In general, the two problems cannot be solved separately. It is known that when agents’ preferences are represented by mean-deviation functionals, sharing of optimal portfolio creates instruments that, on the one hand, satisfy individual risk preferences but, on the other hand, are not replicable on an incomplete market, so that each agent is strictly better off in participating in cooperative investment than investing alone. This synergy effect is shown to hold when agents’ acceptance sets are represented by cash-invariant utility functions in the case of multiperiod investment with an arbitrary feasible investment set. In this case, a set of all Pareto-optimal allocations is characterized, and an equilibrium-based method for selecting a “fair” Pareto-optimal allocation is suggested. It is also shown that if exists, the “fair” allocation belongs to the core of the corresponding cooperative game. The equilibrium-based method is then extended to the case of arbitrary utility functions. The obtained results are demonstrated in a multiperiod cooperative investment problem with investors imposing drawdown constraints on investment strategies.

This paper studies the issue of coordinating equipment maintenance operations with capital investment strategy in the presence of random equipment failures. The traditional approach, developed by Kamien and Schwartz (KS) in their celebrated paper published in 1971, is to formulate the problem as a deterministic optimal control problem with the probability of machine failure as the state variable. With this approach, the optimal policy is deterministic. As a major departure from the KS approach, we explicitly model the underlying stochastic process of machine failures. Our analysis of the stochastic dynamic programming model offers new insights into the problem. Under a long planning horizon with a limited replacement opportunity, each individual machine serves as a revenue generator and contributes a significant amount to the profit of the system. In contrast, when the replacement budget is quite generous over a relatively short planning horizon, adding one extra machine only helps as a backup for unexpected failures of the machines purchased before it. An interesting result derived from this comparison is that a deterministic policy turns out to be optimal for the former, while a state-contingent policy must be applied to the latter. In other words, the deterministic KS approach does not work in general when a chain of machine replacement is considered. We further characterize the effects of the discount rate, productivity deterioration, learning, decision delay, and technology advancement on the optimal policy.

Supplier development is a critical competitive endeavor for organizations and their supply chains. Investigation into this area has increased over the past few years, yet further understanding of suppler development is needed. Specifically, recent supplier development investment practices have shown a shift from standalone actions by organization or supplier to joint actions between organization and suppliers. Faced with this phenomenon, the goal of this paper is to develop a theoretic model based on the Cobb–Douglas production function. The study focuses on determining optimal supplier development investment strategies with respect to joint actions that increase supplier production capability for the benefit of both the focal organization and its suppliers. The supplier development investment strategies mainly refer to joint actions between an organization and multiple suppliers through capital resources (tangible) investments, knowledge (intangible) investments, and sharing cost of capital resources (tangible) investments. Using various game theoretic models, we reveal how supplier development investment strategies and profits of all the members are affected by various buyer-supplier relationships and investment returns to scale reasons. Whether the focal organization (buyer) has any incentives to share cost of capital resources (tangible) investments is also investigated. Our first finding is that supplier development investment activities motivation is derived from increases in supply volume for the “increasing returns to scale” situation, and derives from increasing the organization and suppliers’ marginal profit in the “decreasing returns to scale” situation. Secondly, the cooperative relationship is more economically beneficial to the supply chain, but it also requires more capital resources and knowledge expenditures (investments) than a non-cooperative relationship. Thirdly, through numerical analysis it is found that the cooperative relationship can not obtain Pareto efficiency for all the members of the supply chain when using the Nash bargaining model. Additional gaming insights and implications are also provided from parametric analysis. Opportunities for further research are also presented.

The food quality has always played an important role in the retail process since it has been considered as a direct factor to influence a consumer’s purchase decision. In this paper, we formulate an inventory model for perishable foods, in which the demand depends on the price and quality that decays continuously. The objective is to determine a joint dynamic pricing and preservation technology investment strategy while maximizing the total profit from selling a given initial inventory of foods. We first prove the existence of an optimal solution based on Filippov–Cesari theorem. Then, we obtain all the candidates and provide the conditions that make a certain candidate be an optimal solution according to Pontryagin’s maximum principle. Next, we present an effective algorithm to search for the optimal strategy. Finally, two numerical examples are employed to illustrate the solution procedure and the results, followed by sensitivity analysis and managerial insights.

It is possible to model a wide range of portfolio management problems using stochastic programming. This approach requires the generation of input scenarios and probabilities, which represent the evolution of the return on investment, the stream of liabilities and other random phenomena of the problem and respect the no-arbitrage properties. The quality of the recommended capital allocation depends on the quality of the input scenarios and a validation of results is necessary. Appropriate scenario generation techniques and output analysis methods are described in the context of defined contribution pension fund and applied to the specific model of a Czech pension fund. The numerical results indicate various components that influence the recommended investment decisions and the fund’s achievements. In particular, the initial balance sheet position of the pension fund is important for the optimal investment strategy because of the accounting rules embedded in the model and tracking of both the market and purchasing value of assets.

We extend the classic mean-variance framework to a broad class of investment decisions under risk where investors select optimal portfolios of risky assets that include perfectly divisible as well as perfectly indivisible assets. We develop an algorithm for solving the associated mixed-integer nonlinear program and report on the results of a computational study. We then study the mean-variance structure of the investment frontier facing an individual investor in the presence of investment opportunities in both risky divisible and indivisible assets. Finally, we analyze the economic implications of the presence of investment opportunities in risky indivisible assets on the investor’s investment strategy and on his risk evaluation.

In this paper we describe a 2-phase simulated annealing heuristic approach for a special class of portfolio management problems: the problem of optimizing a stock fund with respect to tracking error and transaction costs over time subject to a set of complex constraints with a linear factor return model “feeding” the objective function with data. Our results on managing two real-world funds of a major German capital investment company have shown that this meta-heuristic provides proposals for the fund manager which are feasible with respect to the investment guidelines and excellent in quality in acceptable time. Thus the approach is ideally suited to be used routinely and interactively within a decision support system to assist the fund manager in his complex task of portfolio control and optimization.

Investors consider mutual funds as an interesting investment opportunity. This is the result of the impressive growth shown by these financial products in recent times. In this paper we propose a mixed integer linear programming model dealing with the portfolio selection problem on mutual funds in a single period investment strategy. We propose some heuristics and compare their performance. According to the results obtained on real instances, heuristics have proved to be effective and efficient.

In this paper we use a stochastic programming approach to develop currency option hedging models which can address problems with multiple random factors in an imperfect market. The portfolios considered in our model are rebalanced at the end of each time period, and reinvestments are allowed during the hedging process. These sequential decisions (reinvestments) are based on the evolution of random parameters such as exchange rates, interest rates, etc. We also allow the inclusion of a variety of instruments in the hedging portfolio, including short term derivative securities, short term options, and futures. These instruments help generate strategies that provide good liquidity and low trade intensity. One of the important features of the model is that it incorporates constraints on sensitivity measures such as Delta and Gamma. By ensuring that these hedge parameters track a desired trajectory (e.g., the parameters of a target option), the new model provides investment strategies that are robust with respect to the perturbations measured by Delta and Gamma. In order to manage the explosion of scenarios due to multiple random factors, we incorporate sampling within a scenario aggregation algorithm. We illustrate that when compared with other myopic hedging methods in imperfect markets, the new stochastic programming model can provide better performance. Our examples also illustrate stochastic programming as a practical computational tool for realistic hedging problems.

In a differential game between two symmetric firms, provided with a clean and a dirtyproduction activity, it is analyzed how investment and emissions are affected by environmentalregulation. If both firms face the same environmental policy, a stricter policy reduceslong‐run investment in the dirty technology, while the effect on the clean one is ambiguous.Long‐run emissions of each firm, and consequently total emissions, decrease. This resultneed not necessarily hold if both firms face different policy instruments: Each firm's investmentlevels, and consequently also its emissions, increase when its competitor faces a stricterenvironmental policy.

This paper presents a two-stage multi-period decision model for allocation of the individual's savings into several investment plans. Although the U.S. economy is used as the background, the modelling methods are general enough to accommodate any tax law. The first stage of the model uses an asset-allocation method based on the single-index model. Because this method is static and does not provide for tax considerations and other constraints, it alone is not enough. The output of this optimal selection is used as exogenous parameters and controls for the second stage of the model which is an integer program. The IP includes fixed charges, statutory and budgetary constraints, a discount rate, and the risk level. We provide an example of this approach to illustrate how an individual can achieve his goals of terminal accumulations while maintaining the risk level, measured by the aggregate beta, he prefers. A linear programming relaxation of the IP model is utilized for sensitivity analysis to examine whether future adjustments in investment strategies are required. The model remains tractable enough for implementation by individuals who may not be experts in mathematical programming and financial planning.