Breaking the Cycle: How the News and Markets Created a Negative Feedback Loop in COVID-19
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
Adapted from “Global Value Chains in Developing Countries: A Relational Perspective from Coffee and Garments,” by Laura Boudreau of Columbia Business School, Julia Cajal Grossi of the Geneva Graduate Institute, and Rocco Macchiavello of the London School of Economics.
Adapted from “Online Advertising as Passive Search,” by Raluca M. Ursu of New York University Stern School of Business, Andrey Simonov of Columbia Business School, and Eunkyung An of New York University Stern School of Business.
This paper from Columbia Business School, “Meaning of Manual Labor Impedes Consumer Adoption of Autonomous Products,” explores marketing solutions to some consumers’ resistance towards autonomous products. The study was co-authored by Emanuel de Bellis of the University of St. Gallen, Gita Johar of Columbia Business School, and Nicola Poletti of Cada.
Co-authored by John B. Donaldson of Columbia Business School, “The Macroeconomics of Stakeholder Equilibria,” proposes a model for a purely private, mutually beneficial financial agreement between worker and firm that keeps decision-making in the hands of stockholders while improving the employment contract for employees.
At Columbia Business School, our faculty members are at the forefront of research in their respective fields, offering innovative ideas that directly impact the practice of business today. A quick glance at our publication on faculty research, CBS Insights, will give you a sense of the breadth and immediacy of the insight our professors provide.
As a student at the School, this will greatly enrich your education. In Columbia classrooms, you are at the cutting-edge of industry, studying the practices that others will later adopt and teach. As any business leader will tell you, in a competitive environment, being first puts you at a distinct advantage over your peers. Learn economic development from Ray Fisman, the Lambert Family Professor of Social Enterprise and a rising star in the field, or real estate from Chris Mayer, the Paul Milstein Professor of Real Estate, a renowned expert and frequent commentator on complex housing issues. This way, when you complete your degree, you'll be set up to succeed.
Columbia Business School in conjunction with the Office of the Dean provides its faculty, PhD students, and other research staff with resources and cutting edge tools and technology to help push the boundaries of business research.
Specifically, our goal is to seamlessly help faculty set up and execute their research programs. This includes, but is not limited to:
All these activities help to facilitate and streamline faculty research, and that of the doctoral students working with them.
This article treats the dynamic lot size model with quantity discount in purchase price. We study the problem with two different cost structures: the all-units-discount cost structure and the incremental-discount cost structure. We solve the problem under both discount cost structures by dynamic programming algorithms of complexity O(T3) and O(T2), respectively, with T the number of periods in the planning horizon.
We consider a single-server queueing system with Poisson arrivals and general service times. While the server is up, it is subject to breakdowns according to a Poisson process. When the server breaks down, we may either repair the server immediately or postpone the repair until some future point in time. The operating costs to the system include customer holding costs, repair costs and running costs. The objective is to find a corrective maintenance policy which minimizes the long-run average operating costs of the system. The problem is formulated as a semi-Markov decision process.
This paper is motivated by two facts: failure of log-linear empirical exchange rate models of the 1970's and the observed variability of risk premiums in the forward market. Rational maximizing models predict that changes in conditional variances of monetary policies, government spendings, and income growths affect risk premiums and induce conditional volatility of exchange rates.
In this article we construct a model in which a consumer's utility depends on the consumption history. We describe a general equilibrium framework similar to Cox, Ingersoll, and Ross (1985a). A simple example is then solved in closed form in this general equilibrium setting to rationalize the observed stickiness of the consumption series relative to the fluctuations in stock market wealth. The sample paths of consumption generated from this model imply lower variability in consumption growth rates compared to those generated by models with separable utility functions.
The financial payback to the City of Boston from the development of Faneuil Hall Marketplace provides a starting point for analyzing the benefits of public-private downtown project development deals.
A bilateral moral-hazard problem provides a rationale for "up-or-out" employment contracts. The employer sets a wage higher than opportunity cost to induce the worker to invest in firm-specific capital. If the individual does not make the grade, it is in the firm's interest ex post to fire him. Had the initial arrangement not included provisions for firing individuals, the firm would underreport the value of the employee, wrecking the incentive scheme. The basic model permits both firm and worker to be risk neutral.
This paper considers an M/G/c queueing system serving a finite number (J) of distinct customer classes. Performance of the system, as measured by the vector of steady-state expected waiting times of the customer classes (the performance vector), may be controlled by adopting an appropriate priority discipline.
We provide a deterministic example in which parties sign a contract which they anticipate will be subsequently renegotiated. The renegotiation is socially desirable. In the example, the cost of writing and enforcing contracts increases their complexity.
This paper presents a strategic theory of contract renegotiation. In this theory, suboptimal contracts are put in place initially to protect one party against undesirable actions by another party and are renegotiated once the danger is past. We develop a model to establish the cases in which simple contracts cannot achieve desirable outcomes, so that only a complicated contract or renegotiation will serve. Unlike most previous accounts of contract renegotiation, this theory does not rely on exogenous uncertainty to motive renegotiation.
This papers introduces a market-based typology of corporate strategy, which builds on previous typologies (Rumelt 1974, 1982). We argue that, because different markets require different skills for success, firms which concentrate in one market area (consumer or industrial), at given levels of diversification, should achieve superior performance. Empirical tests with a sample of manufacturing firms support this proposed relationship between diversification strategy and financial performance.
This papers introduces a market-based typology of corporate strategy, which builds on previous typologies (Rumelt 1974, 1982). We argue that, because different markets require different skills for success, firms which concentrate in one market area (consumer or industrial), at given levels of diversification, should achieve superior performance. Empirical tests with a sample of manufacturing firms support this proposed relationship between diversification strategy and financial performance.
This paper considers general (single facility) queueing systems with exponential service times, dealing with a finite number J of distinct customer classes. Performance of the system, as measured by the vector of steady state expected sojourn times of the customer classes (the performance vector) may be controlled by adopting an appropriate preemptive priority discipline.
We consider the polymatroidal flow network model which incorporates two important extensions of the standard maximal flow problem: general concave objective functions of the vector of supplies to a collection of sinks, as well as polymatroidal capacity restrictions on sets of arcs emanating from or pointing to a common node. A number of important applications are reviewed.
We present an exact solution method for a single-server queueing system which alternates between periods in which service can be provided (on-periods) and periods in which the server is out of operation (off-periods). The arrival process is Poisson, on-periods are assumed to have a phase-type distribution, and service times and off-periods are assumed to be arbitrary.
We present an exact solution method for a single-server queueing system which alternates between periods in which service can be provided (on-periods) and periods in which the server is out of operation (off-periods). The arrival process is Poisson, on-periods are assumed to have a phase-type distribution, and service times and off-periods are assumed to be arbitrary.
Heuristic solution methods for combinatorial optimization problems are often based on local neighborhood searches. These tend to get trapped in a local optimum and the final result is often heavily dependent on the starting solution. Simulated annealing methods attempt to avoid these problems by randomizing the procedure so as to allow for occasional changes that worsen the solution. In this paper we provide probabilistic analyses of different designs of these methods.
We consider general queueing models dealing with multiple classes of customers and address the question under what conditions and in what (stochastic) sense the marginal increase in various performance measures, resulting from the addition of a new class of customers to an existing system, is larger than if the same class were added to a system dealing with only a subset of its current customer base.
A model for the systematic evaluation and management of a company's technological resources is proposed as a first step to developing an integrated corporate marketing-technology strategy. The proposed framework raises 4 issues: 1. technology identification, 2. technology additions, 3. technological commercialization, and 4. treatment of individual technologies as interdependent elements making up an integrated, coherent plan. The technological decision nexus involves decisions related to the firm's development and commercialization of its technology.
An analysis of the environments of leading manufacturing firms operating in the United States and in Australia produced a series of hypothesized differences in the strategies, organization structures, and market environments of firms in the two countries. Parallel hypotheses about differences between domestic Australian firms and subsidiaries of foreign multinationals operating in Australia were also developed. The hypotheses were by and large supported when tested on data obtained from leading corporations in the two countries.
An analysis of the environments of leading manufacturing firms operating in the United States and in Australia produced a series of hypothesized differences in the strategies, organization structures, and market environments of firms in the two countries. Parallel hypotheses about differences between domestic Australian firms and subsidiaries of foreign multinationals operating in Australia were also developed. The hypotheses were by and large supported when tested on data obtained from leading corporations in the two countries.
A nonstationary Markov chain is weakly ergodic if the dependence on the state distribution on the starting state vanishes as time tends to infinity. A chain is strongly ergodic if it is weakly ergodic and converges in distribution. In this paper we show that the two ergodicity concepts are equivalent for finite chains under rather general (and widely verifiable) conditions. We discuss applications to probabalistic analyses of general search methods for combinatorial optimization problems (simulated annealing).
Evidence of excess volatilities of asset prices compared with those of market fundamentals is often attributed to speculative bubbles. This study demonstrates that bubbles could in theory lead to excess volatility, but it shows that certain variance bounds tests preclude bubbles as an explanation. The evidence ought to be attributed to model misspecification or inappropriate statistical tests. One important misspecification occurs if a researcher incorrectly specifies the time series properties of market fundamentals.
Most quantities of interest in discounted and undiscounted (semi-) Markov decision processes can be obtained by solving a system of functional equations. This paper derives bounds and variational characterizations for the solutions of such systems.
This paper considers a single-item, periodic-review inventory model with uncertain demands. In contrast to prior treatments of this problem we assume a finite production capacity per period. Assuming stationary data, a convex one-period cost function and a discrete demand distribution, we show (under a few additional unrestrictive assumptions) that a modified base-stock policy is optimal under the average-cost criterion; in addition, we characterize the optimal base-stock level.
This paper considers a single-item, periodic review inventory model with uncertain demands. We assume a finite production capacity in each period. With stationary data, a convex one-period cost function and a continuous demand distribution, we show (under a few additional unrestrictive assumptions) that a modified basic-stock policy is optimal under the discounted cost criterion, both for finite and infinite planning horizons. In addition we characterize the optimal base-stock levels in several ways.
We consider the problem of scheduling n jobs, each with a specific processing requirement, release time and due date on m uniform parallel machines. It is shown that a feasible schedule can be obtained by determining the maximum flow in a network, thus permitting the use of standard network flow codes. Using a specialized maximum flow procedure, the complexity reduces to O(tn3) operations when t is the number of distinct machine types.
This paper presents an allocation model for a perishable product, distributed from a regional center to a given set of locations with random demands. We consider the combined problem of allocating the available inventory at the center while deciding how these deliveries should be performed. Two types of delivery patterns are analyzed: the first pattern assumes that all demand points receive individual deliveries; the second pattern subsumes the frequently occuring case in which deliveries in multistop routes traveled by a fleet of vehicles. Computational experience is reported.
Some companies consistently enjoy share prices that exceed book value. Such value creators range from giants like Coca-Cola Co., IBM, and Procter & Gamble to less-known small and medium-sized companies like Pall and Shoney's. Other enterprises trade below book value year after year, in both bear and bull markets. Many managers believe that these differences in price to book ratio do not stem from real differences in competitive performance but rather from the capriciousness of the stock market.
In the classical maximal flow problem, the objective is to maximize the supply to a single sink in a capacitated network. In this paper we consider general capacitated networks with multiple sinks: the objective is to optimize a general "concave" preference relation on the set of feasible supply vectors. We show that an optimal solution can be obtained by a marginal allocation procedure. An efficient implementation results in an adaptation of the augmenting path algorithm. We also discuss an application of the procedure for an investment company that deals in oil and gas ventures.
In many resource allocation problems, the objective is to allocate discrete resource units to a set of activities so as to maximize a concave objective function subject to upper bounds on the total amounts allotted to certain groups of activities. If the constraints determine a polymatroid and the objective is linear, it is well known that the greedy procedure results in an optimal solution. In this paper we extend this result to objectives that are "weakly concave," a property generalizing separable concavity.
Special algorithms have been developed to compute an optimal (s,S) policy for an inventory model with discrete demand and under standard assumptions (stationary data, a well-behaved one-period cost function, full backlogging and the average cost criterion). We present here an iterative algorithm for continuous demand distributions which avoids any form of prior discretization. The method can be viewed as a modified form of policy iteration applied to a Markov decision process with continuous state space. For phase-type distributions, the calculations can be done in closed form.
The paper analyzes the use of information in companies planning strategically versus those which are not. This contrast is used to build the case for developing strategic forecasting capability which focuses on a variety of environments, is proactive and interactive, and creates a need for different kinds of data bases and forecasting techniques.
The paper analyzes the use of information in companies planning strategically versus those which are not. This contrast is used to build the case for developing strategic forecasting capability which focuses on a variety of environments, is proactive and interactive, and creates a need for different kinds of data bases and forecasting techniques.
This paper establishes a simple existence proof for a solution to the optimality equations arising in finite undiscounted Markov Renewal Programs, by applying Brouwer's fixed point theorem to the so-called reduced value-iteration operator. Because of its simplicity, our approach lends itself to new existence results for more general models.
This paper presents a successive approximation method for solving systems of nested functional equations which arise, e.g., when considering Markov renewal programs in which policies that are maximal gain or optimal under more selective discount — and average overtaking optimality criteria are to be found. In particular, a successive approximation method is given to find the optimal bias vector and bias-optimal policies. Applications with respect to a number of additional stochastic control models are pointed out.
In many practical applications of multi-item inventory systems significant economies of scale can be exploited when coordinating replenishment orders for groups of items. This paper considers a continuous review multi-item inventory system with compound Poisson demand processes; excess demands are backlogged and each replenishment requires a lead time. There is a major setup cost associated with any replenishment of the family of items, and a minor (item dependent) setup cost when including a particular item in this replenishment. Moreover there are holding and penalty costs.
We address the combined problem of allocating a scarce resource among several locations, and planning deliveries using a fleet of vehicles. Demands are random, and holding and shortage costs must be considered in the decision along with transportation costs. We show how to extend some of the available methods for the deterministic vehicle routing problem to this case. Computational results using one such adaptation show that the algorithm is fast enough for practical work, and that substantial cost savings can be achieved with this approach.
Consider a central depot that supplies several locations experiencing random demands. Periodically, the depot may place an order for exogenous supply. Orders arrive after a fixed leadtime, and are then allocated among the several locations. Each allocation reaches its destination after a further delay. We consider the special case where the penalty-cost/holding-cost ratio is constant over the locations. Several approaches are given to approximate the dynamic program describing the problem.
This paper presents an algorithm to compute an optimal (s,S) policy under standard assumptions (stationary data, well-behaved one-period costs, discrete demand, full backlogging, and the average-cost criterion). The method is iterative, starting with an arbitrary, given (s,S) policy and converging to an optimal policy in a finite number of iterations. Any of the available approximations can thus be used as an initial solution. Each iteration requires only modest computations. Also, a lower bound on the true optimal cost can be computed and used in a termination test.
Consider a central depot (or plant) which supplies several locations experiencing random demands. Orders are placed (or production is initiated) periodically by the depot. The order arrives after a fixed lead time, and is then allocated among the several locations. (The depot itself does not hold inventory.) The allocations are finally received at the demand points after another lag. Unfilled demand at each location is backordered. Linear costs are incurred at each location for holding inventory and for backorders. Also, costs are assessed for orders placed by the depot.
Clark and Scarf [1960] characterize optimal policies in a two-echelon, two-location inventory model. We extend their result to the infinite-horizon case (for both discounted and average costs). The computations required are far easier than for the finite horizon problem. Further simplification is achieved for normal demands. We also consider the more interesting case of multiple locations at the lower echelon. We show that, under certain conditions, this problem can be closely approximated by a model with one such location.
Corporate investment in an economy without a complete set of contingent claims markets has the characteristic of a public good in the sense that the stockholders' consumption plans cannot be separated from, but depend on, the specific investment plans of the firms. The purpose of this article is to develop an internal allocation mechanism capable of attaining production plans that are unanimously preferred by stockholders and that satisfy a natural notion of optimality applicable to the stock market economy.
This paper establishes the existence of a solution to the optimality equations in undiscounted semi-Markov decision models with countable state space, under conditions generalizing the hitherto obtained results. In particular, we merely require the existence of a finite set of states in which every pair of states can reach each other via some stationary policy, instead of the traditional and restrictive assumption that ever stationary policy has a single irreducible set of states.
In continuous review models with a fixed delivery lag T, the state of the system is conveniently described by the net inventory position = (inventory on hand) plus (outstanding orders), in spite of most cost components depending on the actual inventory on hand. To relate these two inventory concepts one observes that the distribution of the inventory on hand at time t + T is determined by the inventory position at time t.
This paper presents methods for solving allocation problems that can be stated as convex knapsack problems with generalized upper bounds. Such bounds may express upper limits on the total amount allocated to each of several subsets of activities. In addition our model arises as a subproblem in more complex mathematical programs. We therefore emphasize efficient procedures to recover optimality when minor changes in the parameters occur from one problem instance to the next. These considerations lead us to propose novel data structures for such problems.