The development of deep learning technique has granted firms with new opportunities to substantially improve their risk management strategies for sustainable growth. This paper introduces a novel deep learning-based financial hedging (DL-HE) strategy to leverage the salient ability of deep learning in extracting nonlinear features from complex high dimensional data, thus boosting the management of inventory risks arising from erratic commodity prices. Using real-world data, we find that the average annualized economic benefit of the proposed strategy is at least 1.21 million CNY for a typical aluminum firm carrying an average level of inventory in China, as compared with those of the traditional hedging strategies. Further analysis reveals that such an economic benefit can largely be explained by the efficacy of the proposed DL-HE strategy in terms of significantly improving return while still effectively controlling risk. Moreover, the superior of this strategy remains robust when extending to copper and zinc.

We investigate the process innovation and contracting decisions of a dynamic supply chain consisting of a supplier and a manufacturer, with the manufacturer possessing private information about her efficiency of process innovation. To overcome the potential adverse selection problem due to the asymmetric infor-mation, the supplier designs a menu of supply contracts that stipulates both the wholesale price and the purchasing quantity. We find that under information asymmetry, the supplier will optimally set a higher wholesale price but a lower purchasing quantity for the manufacturer with high innovation efficiency than that for the manufacturer with low innovation efficiency. As a consequence, the manufacturer with high innovation efficiency will significantly underinvest in innovation due to information asymmetry in addition to the impact of the double marginalization effect. Moreover, although a longer contract period tends to better motivate innovation, it can also magnify the influences of adverse selection on supply chain contracting, leading to a higher wholesale price for the manufacturer with high innovation effi-ciency. (C) 2020 Elsevier B.V. All rights reserved.

We investigate the product innovation, green R&D investments and the emission tax policy in an oligopoly market with network externality. It is shown that an appropriate tax policy should be deployed to effectively control pollution and motivate innovation. At the early stage of the market, the emission tax should gradually reduce to motivate firms to achieve optimal investments. Later at the mature stage, the emission tax policy should carefully consider both the market competition and green technology levels.

Forecasting the copper price volatility is an important yet challenging task. Given the nonlinear and time-varying characteristics of numerous factors affecting the copper price, we propose a novel hybrid method to forecast copper price volatility. Two important techniques are synthesized in this method. One is the classic GARCH model which encodes useful statistical information about the time-varying copper price volatility in a compact form via the GARCH forecasts. The other is the powerful deep neural network which combines the GARCH forecasts with both domestic and international market factors to search for better nonlinear features; it also combines the long short-term memory (LSTM) network with traditional artificial neural network (ANN) to generate better volatility forecasts. Our method synthesizes the merits of these two techniques and is especially suitable for the task of copper price volatility prediction. The empirical results show that the GARCH forecasts can serve as informative features to significantly increase the predictive power of the neural network model, and the integration of the LSTM and ANN networks is an effective approach to construct useful deep neural network structures to boost the prediction performance. Further, we conducted a series of sensitivity analyses of the neural network architecture to optimize the prediction results. The results suggest that the choice between LSTM and BLSTM networks for the hybrid model should consider the forecast horizon, while the ANN configurations should be fine-tuned depending on the choice of the measure of prediction errors.

We study an optimal investment policy of a risky project when there exists the possibility that a firm may permanently exit the business under deeply deteriorated market conditions in the future. To capture the riskiness of the investment return rate, a Geometric Brownian motion is adopted to model the firm's profit stream. Applying the real options framework, this paper aims at characterizing the firm's optimal investment policy of the risky project under permanent exit option. It is shown that the investment threshold is no longer a monotonic function of the market uncertainty. Specifically, the investment threshold can decrease with market uncertainty for moderate uncertainty. And the investment threshold will eventually increase with market uncertainty if the uncertainty becomes sufficiently high. Extensive numerical experiments are conducted to check the robustness of the theoretic results. Some managerial implications are derived for investment decisions under the exit option.

We consider a two-stage supply chain comprising one risk-neutral manufacturer (he) and one risk-averse retailer (she), where the manufacturer procures consumption commodities in spot market as major inputs for production and sells the final products to the retailer. The retailer then sells the final products to the market at a stochastic clearance price. We investigate a flexible price contract that allows the manufacturer to determine the product wholesale price, and the retailer to determine the order quantity, based on the future spot price of consumption commodities. Compared with the simple wholesale price contract, a win-win situation can be achieved under the flexible price contract when the manufacturer's postponed processing cost is lower than a threshold. However, under this flexible price contract the retailer may suffer from the commodity price volatility, even if she does not procure the commodities directly. We further investigate how the risk-averse retailer conducts mean-variance financial hedging by purchasing consumption commodity futures contracts. We formulate the problem using a dynamic programming model and derive a closed-form time-consistent financial hedging policy. Through numerical experiments, we show that the commodity price risk from the manufacturer to the retailer is effectively mitigated with the hedging, and the benefits of the flexible price contract are maintained. (C) 2018 Elsevier B.V. All rights reserved.

This article analyses the price-quality and price-goodwill relationships under the influence of quality on goodwill through the effects of cost, sales, and mark-up. We identify the conditions under which a negative price-quality or price-goodwill relationship will arise, that is, price falls as quality or goodwill increases over time. We show that the price-quality or price-goodwill relationship could be negative even if the demand function is linearly additive, and this relationship will tend to be positive when the customer demand becomes more sensitive to the product quality.

Inspired by the growing use of financial hedging among competitive firms nowadays, we develop a game-theoretical model to investigate the problem of applying financial hedging to improve a firm’s competitive strategy. A distinctive setting of the model is that the firm value is a concave function of the firm profit, which is consistent with the empirical evidences in finance literature. After proving the unique existence of the Nash equilibrium, we examine the effects of financial hedging on the equilibrium and yield some novel results. In particular, our analysis suggests that in a competitive market, financial hedging is not just to protect a firm’s bottom line; perhaps more importantly, effective financial hedging schemes can help increase the firm value by boosting the firm’s production, raising the market share, and improving its profitability.

This paper presents a model to mitigate the risk of budget overruns during procurement of raw materials. A case example in rebar procurement fora typical metallurgical manufacturing company in China is used. The demand data was supplied by the company and the rebar price data was obtained from Shanghai Futures Exchanges. The novelty of this study lies in the incorporation of budgetary control into the objective function of the model. A multistage rebalancing strategy of the futures position is used to reduce the impact of unexpected changes in rebar supply price and finished product demand. The complex objective function is approximated by a quadratic expression, and suboptimal solutions are obtained for cases where raw the material price is independent of the customer demand. When compared with the company's spot buying approach without hedging, the results from the model show the proposed multistage futures balancing model can help reduce the budget overspending by as much as 32%. Further experiments are carried out to study the effects of changing price and demand volatilities, and the results confirm the usefulness of model in mitigating the underlying risk.

This paper studies the capacity management problem for a firm that uses debt financing. This is done by analyzing the effect of the associated agency problem when making capacity decisions. The agency problem arises when there are potential conflicts of interest between the firm owner and the lender. We show that this agency problem can constrain the firm's optimal capacity decision, because the borrowing rate will increase as the risk of default increases with capacity level chosen. The firm will therefore try to optimally choose the level so as to reduce the risk of bankruptcy, which the lender will take into account, and as a consequence the firm will try to control the risk associated with potentially high borrowing costs. However, even when the expected bankruptcy cost is carefully controlled, the optimal capacity decision is still made at the risk of incurring considerable agency costs. In addition, the corporate tax level can also play a significant role in capacity choice. We show that although a higher tax rate leads to bigger tax benefit of debt and lower agency cost, it also gives rise to a higher tax liability. After balancing the tax benefit of debt with the agency cost, the firm can make an optimal decision on the capacity level required. The efficacy of financial hedging for mitigating the agency cost is also analyzed. Finally, we compare and contrast our analysis with existing studies, and it appears that we have been able to obtain a deeper insight into the problem. (C) 2017 Elsevier B.V. All rights reserved.

A risk-averse firm׳s financial hedging activity can impact the decision making in its daily operations. We introduce a CE-based approach that can help the firm to simplify the procedure in making hedging-consistent decisions. A key feature of this new approach is that it allows for the existence of nonfinancial random factors, which give rise to the risk exposure that cannot be hedged in the financial market. By using a CE operator, we show that the optimal operational policy can be obtained by maximizing the CE-based value function. Although the CE operator may bring additional nonlinearity to the value function, we find that the commonly desired base-stock policy can remain optimal under specific conditions. We hope that this new approach can help pave the way for future investigation on joint operations management and financial hedging problems in dynamic settings.

This paper investigates the optimal control problem of a monopolist’s investments in process and product innovation under learning-by-doing in a dynamic setting. We show that: (i) there exists the saddle stable steady state under monopolist optimum and social optimum; (ii) the learning rates of product and process innovation affect not only the monopolist’s process or product innovation investments, but also the complementarity (substitutability) relationship between product and process innovation; (iii) the social incentive towards both product and process innovation is always larger than the private incentive characterizing the profit-seeking monopolist. These results are valuable complement and development to the results drawn from the standard product and process innovation model.

Risk-averse procurement models are proposed under unreliable supply.Models are found to have unique solutions.Inventory strategy is effective to control supply risk, but not for a risk-neutral retailer.Analytical solution is derived for the single-period model.Order quantity of a more risk-averse retailer is more sensitive to various parameters. This study investigates an effective procurement/inventory strategy for a risk-averse retailer facing unreliable supply and stochastic demand. By using an increasing and concave utility function to describe risk aversion, we construct a basic newsvendor (single-period) model and its multi-period extension. Both models are found to have unique solutions, as the optimized expected utility is strictly concave in initial inventory level. As a result, there is a unique optimal order quantity for the effective control of supply risk. For the single-period model, the optimal order quantity is derived in its analytical form. We then show by numerical analysis that the value of the optimized expected utility is a function of the initial inventory level when the retailer is risk-averse, becomes less sensitive to initial inventory level when the degree of risk aversion decreases, and is insensitive for the risk-neutral case. This finding suggests that in our setting the inventory holding matters only when the retailer is risk-averse. For the multi-period model, we propose a solution procedure using backward induction since a direct extension of the single-period solution is impossible. We also conduct a sensitivity analysis of demand and supply with the aim of giving some managerial suggestions for demand risk control and supplier selection.

Procurement and replenishment are always susceptible to uncertain customer demand and also to purchase price volatility. Single factor approaches such as long-term contracts, spot procurements, or supply contracts with options, can mitigate some specific aspect of the overall risk, but such approaches are often of limited value when several types of risk prevail. This study contributes to the problem of procurement by presenting a portfolio approach that simultaneously deals with the two major types of procurement risk, price and inventory. The specific model presented jointly considers both the procurement planning and risk hedging problems. The model is in the form of a multi-stage stochastic program in which replenishment decisions are made at various stages along a time horizon, with replenishment quantities being jointly determined by the stochastic demand and the price dynamics of the spot market. The model attempts to minimise the risk exposure of procurement decisions measured as conditional value-at-risk. Numerical experiments to test the effectiveness of the proposed model. The results indicate that the proposed model can fairly reliably outperform other approaches, especially when either the demand and/or prices exhibit significant variability.

In this paper, we study the opportunities of financial hedging to mitigate inventory risks when the demand process is correlated with the price of a financial asset. Firstly, we develop a continuously reviewed inventory model with uncertain demand and mean-variance criterion to address the financial hedging problem. Then, we propose a simple but effective strategy of financial hedging that allows a manager to exploit various financial securities as the instrument for mitigation of inventory risks. Finally, we provide a numerical experiment using Monte Carlo simulation to assess the effectiveness of the financial hedging approach.

In today's fiercely competing market environment, a growing number of companies have begun to realize the importance of implementing an integrated logistics management. Thus, hire party logistics service providers (3PLs) must improve their logistics networks to support such integrated supply chain management incorporating forward and reverse logistics flows. This paper presents a mixed integer nonlinear programming model to optimize the integrated logistics network for 3PLs. It is a NP hard problem. Therefore, we propose a hybrid immune genetic algorithm (IGA) based heuristic containing a simplex algorithm as a sub-search process. The numerical results obtained from the proposed method are compared with the outcomes gained by a common hybrid genetic algorithm(GA).The result of the comparison indicates that the performance of the proposed IGA is much better than the common GA.