Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative performance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors.

This paper derives an equilibrium asset price when there exist three kinds of traders in financial market: a risk-averse informed trader, noise traders, and risk neutral market makers. This paper is an extended version of Kyle's (1985, Econometrica) continuous time model by introducing insider's risk aversion. We obtain not only the equilibrium asset pricing and market depth parameter but also insider's value function and optimal insider's trading strategy explicitly. The comparative static shows that the market depth (the reciprocal of market pressure) increases with time and volatility of noise traders' trading.

It has been studied that retailer's using a suboptimal (R, T) policy is often more desirable to make the best use of information flows than the locally optimal (s, S) policy in a two-stage serial supply chain. In this paper, by performing an extensive computational study, we tabulate the benefit of the retailer's using (R, T) policy instead of (s, S) policy in a supply chain with information sharing, and compare it to a maximum possible benefit that could be achieved in a centralized supply chain. We can understand the mechanisms of how the cost parameters and demand variance affect the benefit of the retailer's using (R, T) policy instead of (s, S) policy, by comparing decentralized and centralized systems.

Consider the inverse bin-packing number problem. Given a set of items and a prescribed number K of bins, the inverse bin-packing number problem, IBPN for short, is concerned with determining the minimum perturbation to the item-size vector so that all the items can be packed into K bins or less. It is known that this problem is NP-hard (Chung, 2012). In this paper, we investigate some special cases of IBPN that can be solved in polynomial time. We propose an optimal algorithm for solving the IBPN instances with two distinct item sizes and the instances with large items.

This paper considers common due-date assignment and scheduling on parallel machines. The main decisions are: (a) deter-mining the common due-date; (b) allocating jobs to machines; and (c) sequencing the jobs assigned to each machine. The objective is to minimize the sum of the penalties associated with common due-date assignment, earliness and tardiness. As an extension of the existing studies on the problem, we consider sequence-dependent setup times that depend on the type of job just completed and on the job to be processed. The sequence-dependent setups, commonly found in various manufacturing systems, make the problem much more complicated. To represent the problem more clearly, a mixed integer programming model is suggested, and due to the complexity of the problem, two heuristics, one with individual sequence-dependent setup times and the other with aggregated sequence-dependent setup times, are suggested after analyzing the characteristics of the problem. Computational experiments were done on a number of test instances and the results are reported.

This study presents an application of stochastic model for limit order book (LOB) dynamics to Korean Stock Index Futures (KOSPI 200 Futures). Since KOSPI 200 futures market is widely known as one of the most liquid markets in the world, direct application of an existing model is hardly possible. Therefore, we modified an existing model to successfully model and predict the dynamics of extremely liquid KOSPI 200 futures market.

We investigate the effect of bank's market power on financing constraints of non-financial firms in 11 Asian countries between 1995 and 2009. Using firm-level data we analyze financial constraints with the Euler equation derived from the dynamic investment model. We find that with a highly concentrated banking sector firms which have high market power are less financially constrained. These results are consistent with an information-based hypothesis that more market power increases bank's advantage to produce information on potential borrowers.

In this paper we provide a valuation formula for a variance swap with regime switching. A variance swap is a forward contract on variance, the square of realized volatility of the underlying asset. We assume that the volatility of underlying asset is governed by Markov regime-switching process with finite states. We find that the proposed model can provide ease of calculation and be superior to the models currently available.