How social comparison influences reference price formation in a service context

What is the influence on reference price when the source of price information is anonymous versus social? This article investigates the formation of reference prices given an observed sequence of past prices in a service context. An experimental study suggests that, considering the same price information, if the source is social (i.e., the prices paid by colleagues), then consumers want to pay less. More specifically, social comparison changes the way individuals weigh information, attributing more importance to the lowest historical prices and to the range in price variations.     

Supply Chain Consequences of Subsidies for Corporate Social Responsibility

The use of government incentives tied to market prices as means of boosting corporate social responsibility (CSR) has expanded notably in recent decades. Enhanced business tax deductions for charitable donations and credits for conservation easements are notable cases. While providing incentives for socially desirable behavior to achieve legislative goals has intuitive appeal, the broader economic consequences are not always fully understood. In this study, we examine such wider consequences for supply chains when subsidies for CSR are offered. One effect we identify is that since incentives are typically tied to market value, firms have not only an added incentive to achieve societal objectives (say by donating inventory) but also an incentive to raise output (retail) market prices. A second consequence is that since firms forgo potential revenues by engaging in socially desired behavior, they become increasingly sensitive to supplier pricing; in an uncoordinated supply chain this leads to input (wholesale) price concessions. Among other things, the results underscore that incentives put in place to meet broader societal objectives also have notable ramifications for suppliers, retailers, and consumers in primary markets.     

Inventory‐Based Dynamic Pricing with Costly Price Adjustment

We study an average‐cost stochastic inventory control problem in which the firm can replenish inventory and adjust the price at anytime. We establish the optimality to change the price from low to high in each replenishment cycle as inventory is depleted. With costly price adjustment, scale economies of inventory replenishment are reflected in the cycle time instead of lot size—An increased fixed ordering cost leads to an extended replenishment cycle but does not necessarily increase the order quantity. A reduced marginal cost of ordering calls for an increased order quantity, as well as speeding up product selling within a cycle. We derive useful properties of the profit function that allows for reducing computational complexity of the problem. For systems requiring short replenishment cycles, the optimal solution can be easily computed by applying these properties. For systems requiring long replenishment cycles, we further consider a relaxed problem that is computational tractable. Under this relaxation, the sum of fixed ordering cost and price adjustment cost is equal to (greater than, less than) the total inventory holding cost within a replenishment cycle when the inventory holding cost is linear (convex, concave) in the stock level. Moreover, under the optimal solution, the time‐average profit is the same across all price segments when the inventory holding cost is accounted properly. Through a numerical study, we demonstrate that inventory‐based dynamic pricing can lead to significant profit improvement compared with static pricing and limited price adjustment can yield a benefit that is close to unlimited price adjustment. To be able to enjoy the benefit of dynamic pricing, however, it is important to appropriately choose inventory levels at which the price is revised.     

一种混合建模方法及其在ETF期权定价中的应用

科学合理的交易型开放式指数基金（ETF）期权定价有利于充分发挥其风险对冲功能,也是一个需要准确掌握市场规律并兼顾经济学意义的复杂建模过程。本文提出了一种新的混合建模方法,将嵌套长短时记忆神经网络模型（NLSTM）与Heston模型结合,实现ETF期权定价偏差的动态修正,并基于华夏上证50ETF、嘉实沪深300ETF和华泰柏瑞沪深300ETF的高频期权数据,实验验证了所提方法的有效性。研究结果表明,不同类型ETF期权价格的波动特征差异显著,无论是基于BS定价模型还是Heston定价模型都难以准确刻画ETF期权价格的复杂变化规律。通过将NLSTM神经网络模型与Heston模型结合,能够有效地捕捉不同类型ETF期权的动态变化规律,从而提升ETF期权定价的准确性。     

单指标非参数期权定价——改进的非参数定价方法

本文建立一种改进的非参数期权定价模型,称为单指标非参数期权定价模型。相比现有非参数回归期权定价模型是期权价格关于各个因素的多元回归函数,本模型通过变量变换把期权价格多个因素指标转换为一个综合变量——单指标,得到期权价格关于单指标的一元非参数回归方程。改进的模型实现了多元非参数期权定价模型的降维和简化了模型计算;还通过多个期限期权的单指标组合解决了非参数估计的样本数量问题;以及通过期限平滑解决了现有非参数定价模型中的日历效应问题。选取上证50ETF期权数据实证分析表明,无论是样本内的估计结果还是样本外的预测结果都比传统的Black-Scholes模型、半参数Black-Scholes模型和多元非参数回归期权定价模型估计效果有提高。     

中国农村民间借贷市场融资约束环境下利率定价模型分析——以广东农村实地调查数据为例

通过对广东农户民间借贷行为实地调查的问卷进行数据分析,了解农村民间借贷中的资金供求关系,发现存在的融资约束问题,进而对融资约束环境下民间借贷资金利率定价过程进行实证分析;着重考察农村民间借贷利率受公共信息和私人信息影响的程度,从借款人和贷款人的角度分别建立定价模型进行经验分析。结果显示,定价模型在F检验1%水平上显著,其他模型具有R2的统计显著性;反映借款用途的变量在10%水平上显著,其他变量均在5%水平上显著。这说明该市场利率能够反映公共信息的影响,借款人和贷款人的利率定价也反映了各自私人信息中相关风险和财务能力因素的影响,得到的经验结论主要是:第一,农村民间借贷市场是自主交易的金融市场;第二,其利率定价过程基本市场化。     

基于聚类分析的IPO定价实证研究

股票发行定价作为股市运行极为关键的一环,它的合理与否将直接关系到整个股市运行的效率。选取沪A近4年来发行新股作为研究样本,然后利用多元线性回归方法作分析,可找出影响IPO发行价格的8个主要因素。将定价研究集中到行业中进行,以多元回归得到的主要因素作为聚类指标,选取某一行业(如电力行业)已发行股票为样本,对其进行聚类分析和判别分析,建立该行业新股发行定价的衡量体系,可为拟发行新股的公司提供定价指导。     

小微企业网贷风险定价模型研究

对小微企业网贷平台的运营模式进行深入分析,发现其运行机制包括需求的同质化解构、需求的模块化生产、需求的风险评估、个性化需求加总,在此基础上构建小微企业网贷风险定价模型。为验证模型的有效性,抽取国内典型互联网金融平台“人人贷”上近期发生的小微企业标的并进行风险定价,将定价结果与实际利率进行比较,发现该模型适用于“卖方市场”的小微企业网贷,能够提供差异化的贷款利率,从而向小微企业提供个性化的网贷产品。     

零售商双渠道供应链定价及协调研究

研究了零售商双渠道供应链的定价策略和协调问题,建立制造商和零售商两方博弈模型,分析了集中决策和分散决策两种情境下的定价决策均衡解,并设计收入共享契约来解决零售商双渠道存在的双重边际化效应。结果表明,零售商需要提供合理的收入分配比例才能实现供应链各成员利润的Pareto改进,并最终协调整个双渠道供应链。在双渠道竞争环境下,线上线下不能采取完全同价策略,企业应针对不同渠道的具体情况制定不同的销售价格。