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MODELING THE INFORMATION CONTENT OF THE LIMIT ORDER BOOK BY BAGGING
Title
MODELING THE INFORMATION CONTENT OF THE LIMIT ORDER BOOK BY BAGGING
Creator
Li, Wenyi
Date
2018
Description
I propose a bagging tree framework to study the information content of the limit order book in U.S. equity market. By measuring the...
Show moreI propose a bagging tree framework to study the information content of the limit order book in U.S. equity market. By measuring the predictability and profitability of the order book data up to 5 levels, I find that the limit orders book is informative. In addition to market orders, limit orders behind the best bid and ask prices also contributes to short-term future price movements. Finally, I design simple strategies to show that this information content can be effectively and consistently translated to economic value. My results may provide important implications for both researchers and market practitioners.
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CORPORATE SOCIAL RESPONSIBILITY AND SUSTAINABLE ECONOMIC DEVELOPMENT IN CHINA
Title
CORPORATE SOCIAL RESPONSIBILITY AND SUSTAINABLE ECONOMIC DEVELOPMENT IN CHINA
Creator
Cheng, Weiquan
Date
2021
Description
This paper examines corporate social responsibility (CSR) strategies and their anticipated impacts on both company’s performance and climate...
Show moreThis paper examines corporate social responsibility (CSR) strategies and their anticipated impacts on both company’s performance and climate change mitigation in mainland China. It performs analysis on the effectiveness of the policies/efforts undertaken by Chinese publicly traded companies to carry on CSR projects through CSR disclosure system, and specifically focuses on determining if CSR projects could help to enhance companies’ profitability while promoting sustainable development in China. It utilizes companies’ financial statements and CSR reports from China Stock Market & Accounting Research Database (CSMAR), and regional macroeconomic data from National Bureau of Statistics of China from 2006 to 2016. The modeling results indicate that industry types, and socioeconomic conditions within which they operate control the anticipated outcome of implementing CSR projects specifically if those projects are designed to reduce companies' carbon emissions. This research provides valuable insights for CSR development in the future according to company types and socioeconomic imbalance in China.
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Mathematics of Civil Infrastructure Network Optimization
Title
Mathematics of Civil Infrastructure Network Optimization
Creator
Rumpf, Adam Andrew
Date
2020
Description
We consider a selection of problems from civil infrastructure network design that are of great importance in modern urban planning but have,...
Show moreWe consider a selection of problems from civil infrastructure network design that are of great importance in modern urban planning but have, until relatively recently, gone largely ignored in mathematical literature. Each of these problems is approached from the perspective of network optimization-based modeling, with a major focus placed on the development of efficient solution algorithms.We begin with a study of the phenomenon of interdependent civil infrastructure networks, wherein the functionality of one network (such as a telecommunications system) requires the input of resources from another network (such as the electrical power grid). We first consider a linear relaxation of an established binary interdependence minimum-cost network flows model, including its unique modeling applications and its use as part of a randomized rounding approximation algorithm for the mixed integer model. We also develop a generalized network simplex algorithm for the efficient solution of this generalized minimum-cost network flows problem. We then move on to consider a trilevel network interdiction game for use in planning the fortification of interdependent networks subject to targeted attacks. A variety of solution algorithms are developed for both the binary and the linear interdependence models, and the linear interdependence model is used to develop an approximation algorithm for the more computationally expensive binary model.We then develop a public transit network design model which incorporates a social access objective in addition to traditional operator cost and user cost objectives. The model is meant for use in planning minor modifications to a public transit network capable of improving equity of access to important services while guaranteeing that service levels remain within a specified tolerance of their initial values. A hybrid tabu search/simulated annealing algorithm is developed to solve this model, which is then applied to a test case based on the Chicago public transit network with the objective of improving equity of primary health care access across the city.
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Scale and Scope Economies Drive Asymmetric Competition in Tech Industries
Title
Scale and Scope Economies Drive Asymmetric Competition in Tech Industries
Creator
Ryali, Balajirao
Date
2020
Description
This research is motivated by my industry experience of working with small manufacturers in the high technology industry market space and ...
Show moreThis research is motivated by my industry experience of working with small manufacturers in the high technology industry market space and large manufacturers in the telecom and healthcare industry market spaces. In these industries, small manufacturers thrive on specialization and focus on breakthrough innovation to maintain product differentiation and premium positioning and to sustain competition. In contrast, large manufacturers enjoy the benefits of economies of scale that provide cost efficiencies and use price as major differentiating factor. This research work endeavors to model asymmetric competition that emerges endogenously in industries where scale and scope economies interact to force firms to adopt specialized strategies and address the below research questions:1. How does the cost structure shaped by scope and scale economies in engineering, sales and service drive asymmetric product line choices?2. What channel coordination problems arise in this context?3. How can manufacturers redesign their operating mechanism and sales force to optimize the channel?
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Algorithms for Discrete Data in Statistics and Operations Research
Title
Algorithms for Discrete Data in Statistics and Operations Research
Creator
Schwartz, William K.
Date
2021
Description
This thesis develops mathematical background for the design of algorithms for discrete-data problems, two in statistics and one in operations...
Show moreThis thesis develops mathematical background for the design of algorithms for discrete-data problems, two in statistics and one in operations research. Chapter 1 gives some background on what chapters 2 to 4 have in common. It also defines some basic terminology that the other chapters use.Chapter 2 offers a general approach to modeling longitudinal network data, including exponential random graph models (ERGMs), that vary according to certain discrete-time Markov chains (The abstract of chapter 2 borrows heavily from the abstract of Schwartz et al., 2021). It connects conditional and Markovian exponential families, permutation- uniform Markov chains, various (temporal) ERGMs, and statistical considerations such as dyadic independence and exchangeability. Markovian exponential families are explored in depth to prove that they and only they have exponential family finite sample distributions with the same parameter as that of the transition probabilities. Many new statistical and algebraic properties of permutation-uniform Markov chains are derived. We introduce exponential random ?-multigraph models, motivated by our result on replacing ? observations of a permutation-uniform Markov chain of graphs with a single observation of a corresponding multigraph. Our approach simplifies analysis of some network and autoregressive models from the literature. Removing models’ temporal dependence but not interpretability permitted us to offer closed-form expressions for maximum likelihood estimators that previously did not have closed-form expression available. Chapter 3 designs novel, exact, conditional tests of statistical goodness-of-fit for mixed membership stochastic block models (MMSBMs) of networks, both directed and undirected. The tests employ a ?²-like statistic from which we define p-values for the general null hypothesis that the observed network’s distribution is in the MMSBM as well as for the simple null hypothesis that the distribution is in the MMSBM with specified parameters. For both tests the alternative hypothesis is that the distribution is unconstrained, and they both assume we have observed the block assignments. As exact tests that avoid asymptotic arguments, they are suitable for both small and large networks. Further we provide and analyze a Monte Carlo algorithm to compute the p-value for the simple null hypothesis. In addition to our rigorous results, simulations demonstrate the validity of the test and the convergence of the algorithm. As a conditional test, it requires the algorithm sample the fiber of a sufficient statistic. In contrast to the Markov chain Monte Carlo samplers common in the literature, our algorithm is an exact simulation, so it is faster, more accurate, and easier to implement. Computing the p-value for the general null hypothesis remains an open problem because it depends on an intractable optimization problem. We discuss the two schools of thought evident in the literature on how to deal with such problems, and we recommend a future research program to bridge the gap those two schools. Chapter 4 investigates an auctioneer’s revenue maximization problem in combinatorial auctions. In combinatorial auctions bidders express demand for discrete packages of multiple units of multiple, indivisible goods. The auctioneer’s NP-complete winner determination problem (WDP) is to fit these packages together within the available supply to maximize the bids’ sum. To shorten the path practitioners traverse from from legalese auction rules to computer code, we offer a new wdp formalism to reflect how government auctioneers sell billions of dollars of radio-spectrum licenses in combinatorial auctions today. It models common tie-breaking rules by maximizing a sum of bid vectors lexicographically. After a novel pre-solving technique based on package bids’ marginal values, we develop an algorithm for the WDP. In developing the algorithm’s branch-and-bound part adapted to lexicographic maximization, we discover a partial explanation of why classical WDP has been successful in using the linear programming relaxation: it equals the Lagrangian dual. We adapt the relaxation to lexicographic maximization. The algorithm’s dynamic-programming part retrieves already computed partial solutions from a novel data structure suited specifically to our WDP formalism. Finally we show that the data structure can “warm start” a popular algorithm for solving for opportunity-cost prices.
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