Abstract

United Airlines Inc. (UAL) is a major American airline headquartered in Chicago with nine domestic and international hubs. It serves 342 destinations, more than any other airline does, and ranks second in the world by the number of passenger-kilometers flown by an airline. United Airlines Flight 3411 made headline news on April 9, 2017 after an involuntarily denied boarding (IDB) selection process resulted in passenger David Dao being forcibly removed from the aircraft by airport police. United management had demanded that Dao give up his seat on the overbooked flight for United employees making a connecting flight, but Dao refused and the situation escalated into violence with airport police. Airlines routinely sell more tickets than available seats to compensate for cancellations and no-shows. Overbooking helps airlines maximize revenues and is perfectly legal. However, the practice also comes with risks, as evidenced by the United Airlines incident that publicly exposed flaws of the airline overbooking profit model. Why do airlines overbook? How do they use the model to maximize revenues? What are the appropriate booking limits? In this case, students will address these questions after learning how to model cancellation and no-show uncertainty using the simulation-based overbooking model and hence improve their decision-making ability in a complex and uncertain environment.

Keyword

booking limits ;  data analytics ;  decision-making ;  Monte Carlo Simulation ;  optimization model formulation ;  profit model ;  revenue management

Abstract

随着技术创新与消费升级，零售业正发生着深刻的变革。2016年10月阿里巴巴云栖大会上，马云首先提出了“新零售”的概念，指出在未来的10～20年，新零售将取代电子商务成为趋势。2017年3月，张近东在全国两会上提出了“智慧零售”概念，标志着苏宁正式入局，并于2017年底提出了“智慧零售大开发”战略。刘强东也于2017年7月发文提出了第四次零售革命和京东“无界零售”战略。经过几年的跑马圈地，各方战略布局已初见端倪，但成败还有待市场的考验。 苏宁以线下起家，逐步发展线上，再到“双线融合”，成为中国零售巨头之一，其发展可以说是中国零售业的缩影。伴随着业务的发展，苏宁也经历了数字化转型，积累了自身的科技实力。在新型零售时代，与天生具有互联网基因的阿里巴巴和京东的竞争中，苏宁不甘示弱，提出了“智慧零售大开发”战略，围绕着“场景互联网”与“智能供应链”，苏宁的布局层层铺开。苏宁下一步应该怎么走？首先，需要仔细复盘苏宁是如何随着时代的变化一步步走到今天的。更重要的是，认清新零售、智慧零售和无界零售的异同和本质。相比其他零售企业，苏宁又应发挥哪些自身的竞争优势？

Keyword

苏宁 ;  电商 ;  实体零售 ;  数字化转型 ;  智慧零售战略

Abstract

本案例聚焦传统零售巨头之一苏宁的信息技术变革、科技在其智慧零售战略中的作用以及苏宁IT部门从业务后台逐渐演变为独立集团的过程。苏宁的IT变革主要包括建设初期、中心化的IT后台、互联网时代的开放系统和智慧零售时代的苏宁云四个阶段。在变革过程中，苏宁IT部门也不断壮大，从最初隶属于财务部门，到2018年升级为独立的产业集团，达到1.2万人的规模，并积累了大量数字资产。随着苏宁智慧零售战略的实施，科技体现在其战略落地的方方面面，包括场景互联网、智能供应链、会员管理、内部效率提升，等等，苏宁的科技实力不断提升，并已超出了自身内部需求。苏宁科技集团的成立标志着苏宁开始用技术对外赋能泛生态伙伴，IT部门的角色不再只是苏宁的内部技术体系，而是转变为：对内全面支撑苏宁智慧零售战略落地以及全产业协同发展，对外打造智慧零售生态圈，输出“零售即服务”的云产品，从“成本中心”逐渐变为“利润中心”。 在零售行业快速发展以及产业数字化发展的趋势下，本案例对于零售企业和其他传统企业的信息化建设、数字化转型都有一定借鉴意义，同时启发学生思考，科技部门对于企业的价值及其在发展过程中面临的机遇和挑战。

Keyword

苏宁 ;  IT系统建设 ;  科技集团 ;  科技驱动业务发展 ;  对外赋能

Abstract

美国联合航空公司（以下简称“美联航”）拥有世界最广的航线：9个国内国际航空港枢纽，覆盖全球342个目的地，其客运量全球排名第二。 然而在2017年4月9日，美联航机组人员为前往目的地执飞另一航班，临时转乘3411航班，导致该航班座位超售。美联航启动非自愿被拒绝登机（IDB）筛选程序，要求陶先生放弃座位改乘后续航班，遭其拒绝，事态最终升级为机场警察暴力逐客。 航空公司超售机票，以弥补退票或误机旅客造成的机位浪费，这是航空界的通行做法，有助航空公司实现收入最大化。但这种操作也有风险，在美联航事件中，航空公司超售利润模型的弊端暴露无遗。航空公司为何要超售机票？他们如何利用这一模型实现收入最大化？售票限额应以多少为宜？通过本案例，学员将学会如何运用基于模拟的超售模型，对退票与误机的不确定性建模，从而为上述问题找到答案,提高在复杂不确定性环境中的决策能力。

Keyword

优化模型建模 ;  蒙特卡罗模拟 ;  决策制定 ;  商业分析 ;  收入管理 ;  售票限额 ;  利润模型

Abstract

Families of distributions are commonly used to model insurance claims data that require flexible distributional forms in a satisfactory manner, but the specification problem to assess the goodness-of-fit of the hypothesized model can sometimes be a challenge due to the complexity of the likelihood function of the family of distributions involved. The previous work shows that these specification problems can be attacked by means of semi-parametric tests based on generalized method of moment (GMM) estimators. While the approach can be directly applied to both discrete and continuous families of distributions, the paper focuses on developing a testing strategy within a framework of discrete families of distributions. Both the local power analysis and the approximate slope method demonstrate the excellent performance of these tests. The finite-sample performance of the tests, based on both asymptotic and bootstrap critical values, are also discussed and are compared with established methods that require the complete specification of likelihood functions.

Keyword

Claims data ;  goodness-of-fit ;  model specification

Abstract

Keyword

Abstract

The availability of high-frequency financial data has led to substantial improvements in our understanding of financial volatility. Most existing literature focuses on estimating the integrated volatility over a fixed period. This article proposes a non-parametric threshold kernel method to estimate the time-dependent spot volatility and jumps when the underlying price process is governed by Brownian semimartingale with finite activity jumps. The threshold kernel estimator combines the threshold estimation for integrated volatility and the kernel filtering approach for spot volatility when the price process is driven only by diffusions without jumps. The estimator proposed is consistent and asymptotically normal and has the same rate of convergence as the estimator studied by Kristensen (2010) in a setting without jumps. The Monte Carlo simulation study shows that the proposed estimator exhibits excellent performance over a wide range of jump sizes and for different sampling frequencies. An empirical example is given to illustrate the potential applications of the proposed method.

Keyword

High‐frequency data ;  jump ;  kernel estimation ;  threshold ;  time dependent ;  volatility

Abstract

The game option, which is also known as Israel option, is an American option with callable features. The option holder can exercise the option at any time up to maturity. This article studies the pricing behaviors of the path-dependent game option where the payoff of the option depends on the maximum or minimum asset price over the life of the option (i.e., the game option with the lookback feature). We obtain the explicit pricing formula for the perpetual case and provide the integral expression of pricing formula under the finite horizon case. In addition, we derive optimal exercise strategies and continuation regions of options in both floating and fixed strike cases.

Keyword

American options ;  Callable feature ;  Lookback game options ;  Path dependent

Abstract

This paper explores whether the regulation of price limits in financial markets will result in the autocorrelation of stock return series. The results of Monte Carlo Experiment under different error term distribution hypothesis suggest that such price limit mechanism will result in a positive first-order autocorrelation of return series. The results indicates that some statistics in empirical finance literature for testing random walk or market efficiency, for example, the variance ratio of Lo and MacKinlay(1988), may be biased when the stock price is subject to the price limit. This paper also suggests that the further research on the price limit is necessary when more exchanges adopt such regulations in the world.

Keyword

error term distribution hypothesis ;  financial markets ;  Monte Carlo experiment ;  price limit regulation ;  random walk testing ;  stock price ;  stock return series autocorrelation

Abstract

Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both effective and efficient. Among different kinds of machine learning models, kernel methods are well accepted since they are more robust and accurate than traditional models, such as neural networks. However, learning from multiple data sources is still one of the main challenges in the financial forecasting area. In this paper, we focus on applying the multiple kernel learning models to the multiple major international stock indexes. Our experiment results indicate that applying multiple kernel learning to the financial forecasting problem suffers from both the short training period problem and non-stationary problem. Therefore we propose a novel multiple kernel learning model to address the challenge by introducing the Gompertz model and considering a non-linear combination of different kernel matrices. The experiment results show that our Gompertz multiple kernel learning model addresses the challenges and achieves better performance than the original multiple kernel learning model and single SVM models.

Keyword

financial forecasting ;  multiple kernel learning ;  non-linear kernel combination