On the Efficiency of Market Equilibrium in Production Economies

This paper introduces the notion of Market Equilibrium With Active Consumers (MEWAC), in order to characterize the efficiency of market outcomes in production economies. We show that, no matter the behaviour followed by the firms, a market equilibrium is efficient if it is a MEWAC. And also that every efficient allocation can be decentralized as a MEWAC in which firms follow the marginal pricing rule.     

Trading grid services – a multi-attribute combinatorial approach

The Grid is a promising technology for providing access to distributed high-end computational capabilities. Thus, computational tasks can be performed spontaneously by other resources in the Grid that are not under the user’s control. However, one of the key problems in the Grid is deciding which jobs are to be allocated to which resources at what time. In this context, the use of market mechanisms for scheduling and allocating Grid resources is a promising approach toward solving these problems. This paper proposes an auction mechanism for allocating and scheduling computer resources such as processors or storage space which have multiple quality attributes. The mechanism is evaluated according to its economic and computational performance as well as its practical applicability by means of a simulation.     

Market model with heterogeneous buyers

In market modeling, one often treats buyers as a homogeneous group. In this paper we consider buyers with heterogeneous preferences and products available in many variants. Such a framework allows us to successfully model various market phenomena. In particular, we investigate how is the vendor’s behavior influenced by the amount of available information and by the presence of correlations in the system.     

Heterogeneity and increasing returns may drive socio-economic transitions

There are clear benefits associated with a particular consumer choice for many current markets. For example, as we consider here, some products might carry environmental or ‘green’ benefits. Some consumers might value these benefits while others do not. However, as evidenced by myriad failed attempts of environmental products to maintain even a niche market, such benefits do not necessarily outweigh the extra purchasing cost. The question we pose is, how can such an initially economically-disadvantaged green product evolve to hold the greater share of the market? We present a simple mathematical model for the dynamics of product competition in a heterogeneous consumer population. Our model preassigns a hierarchy to the products, which designates the consumer choice when prices are comparable, while prices are dynamically rescaled to reflect increasing returns to scale. Our approach allows us to model many scenarios of technology substitution and provides a method for generalizing market forces. With this model, we begin to forecast irreversible trends associated with consumer dynamics as well as policies that could be made to influence transitions.     

Market selection decisions for inventory models with price-sensitive demand

In the majority of classical inventory theory literature, demand arises from exogenous sources upon which the firm has little or no control. In many practical contexts, however, aggregate demand is comprised of individual demands from a number of distinct customers or markets. This introduces new dimensions to supply chain planning problems involving the selection of markets or customers to include in the demand portfolio. We present a nonlinear, combinatorial optimization model to address planning decisions in both deterministic and stochastic settings, where a firm constructs a demand portfolio from a set of potential markets having price-sensitive demands. We first consider a pricing strategy that dictates a single price throughout all markets and provide an efficient algorithm for maximizing total profit. We also analyze the model under a market-specific pricing policy and describe its optimal solution. An extensive computational study characterizes the effects of key system parameters on the optimal value of expected profit, and provides some interesting insights on how a given market’s characteristics can affect optimal pricing decisions in other markets.     

A model of market power and efficiency in private electronic exchanges

We investigate how private electronic markets (PEMs) can be used as a strategic tool by a large producer to compete against a consortium of smaller producers. We model the competition between a Large Producer and Consortium of producers in a two-tier supply chain as a game and characterize the resulting Subgame Perfect Nash Equilibrium. Our results demonstrate that as the costs of inputs to production increase, there are greater returns to ownership of a private exchange. Further, we demonstrate strong welfare enhancing effects of the PEM as the production efficiency of upstream suppliers declines. Finally, from a policy standpoint we show that when upstream suppliers are highly efficient, the creation of a private electronic exchange by the Large Producer will result in significant welfare loss.     

An optimization method to estimate models with store-level data: A case study

The quality of the estimation of a latent segment model when only store-level aggregate data is available seems to be dependent on the computational methods selected and in particular on the optimization methodology used to obtain it. Following the stream of work that emphasizes the estimation of a segmentation structure with aggregate data, this work proposes an optimization method, among the deterministic optimization methods, that can provide estimates for segment characteristics as well as size, brand/product preferences and sensitivity to price and price promotion variation estimates that can be accommodated in dynamic models. It is shown that, among the gradient based optimization methods that were tested, the Sequential Quadratic Programming method (SQP) is the only that, for all scenarios tested for this type of problem, guarantees of reliability, precision and efficiency being robust, i.e., always able to deliver a solution. Therefore, the latent segment models can be estimated using the SQP method when only aggregate market data is available.     

从投资情绪角度看股票市场流动性——来自B股向境内居民开放的研究

我们借鉴行为方面的模型,即投资情绪的变化会影响股票市场的流动性,从而影响股票的未来收益。在卖空限制条件下市场高流动性预示市场充斥着非理性投资者。本文通过中国股市具备卖空限制和2001年2月B股向境内居民开放前后B股投资主体(投资情绪)发生变化这一自然事件,实证分析了B股市场向境内居民开放后,投资者情绪变化引起市场流动性增加,对股票预期收益的流动性溢价影响也由开放前的不显著变化为显著。     

L1正则化Logistic回归在财务预警中的应用

线性模型和广义线性模型已广泛地用于社会经济、生产实践和科学研究中的数据分析和数据挖掘等领域,如公司财务预警,引入L1范数惩罚技术的模型在估计模型系数的同时能实现变量选择的功能,本文将L1范数正则化Logistic回归模型用于上市公司财务危机预报,结合沪深股市制造业ST公司和正常公司的T-2年财务数据开展实证研究,舛比Logistic回归和L2正则化Logistic回归模型进行对比分析.实验结果表明L1正则化Logistic回归模型的有效性,其在保证模型预测精度的同时提高模型的解释性.     

Heuristic option pricing with neural networks and the neuro-computer synapse 3

Today’s option and warrant pricing is based on models developed by Black, Scholes and Merton in 1973 and Cox, Ross and Rubinstein in 1979. The price movement of the underlying asset is modeled by continuous-time or discrete-time stochastic processes. Unfortunately these models are based on severely unrealistic assumptions. Permanently an unsatisfactory and quite artificial adaption to the true market conditions is necessary (future volatility of the underlying price). Here, an alternative heuristic approach with a highly accurate neural network approximation is presented. Market prices of options and warrants and the values of the influence variables form the usually very large output/ input data set. Thousands of multi-layer perceptrons with various topologies and with different weight initializations are trained with a fast sequential quadratic programming (SQP) method. The best networks are combined to an expert council network to synthesize market prices accurately. All options and warrants can be compared to single out overpriced and underpriced ones for each trading day. For each option and warrant overpriced and underpriced trading days can be used to ascertain a better buy and sell timing. Furthermore the neural model gains deep insight into the market price sen-sitivities (option Greeks), e.g., ?, Г, Θ and Ω. As an illustrative example we inves-tigate BASF stock call warrants. Time series from the beginning of 1996 to mid 1997 of 74 BASF call warrant prices at the Frankfurter Wertpapierborse (Frankfurt Stock Exchange) form the data basis. Finally a possible speed up of the training with the neuro-computer SYNAPSE 3 is briefly discussed