Combining Support Vector Machine and Data Envelopment Analysis to Predict Corporate Failure for Nonmanufacturing Firms

Workshop 2015 -Advances in DEA Theory and Applications (December 1-2, 2015)Research on corporate failure prediction has drawn numerous scholars’ attention because of its usefulness in corporate risk management, as well as in regulating corporate operational status. Most previous research related to this topic focused on manufacturing companies and relied heavily on corporate assets. The asset size of a manufacturing company plays a vital role in traditional research methods; Altman’s Z score model is one such traditional method. However, very limited number of research studied corporate failure prediction for nonmanufacturing companies as the operational status of such companies is not solely correlated to their assets. In this manuscript we use support vector machines (SVMs) and data envelopment analysis (DEA) to provide a new method for predicting corporate failure of nonmanufacturing firms. We first generate efficiency scores using a slack-based measure (SBM) DEA model, using the recent three years historical data of nonmanufacturing firms; then we used SVMs to classify bankrupt firms and healthy ones. We show that using DEA scores as the only inputs into SVMs predict corporate failure more accurately than using the entire raw data available.The workshop is supported by JSPS (Japan Society for the Promotion of Science), Grant-in-Aid for Scientific Research (B), #25282090, titled “Studies in Theory and Applications of DEA for Forecasting Purpose.本研究はJSPS科研費 基盤研究(B) 25282090の助成を受けたものです

National and Store Brand Advertising Strategies

As the propensity of premium store brands (SBs) increases, retailers must consider different ways to drive sales besides promotional strategies. With this in mind, we consider a national brand (NB) and a (premium) SB co-existing in a market. Each brand has to decide the amount to invest in advertising its product and the prices to charge its customers, which can be determined separately or in unison. When either advertising expenditures or pricing decisions are set, each brand must keep in mind that the advertising efforts and revenue may spillover between the two brands, customers who intend to purchase the NB may end up purchasing the SB and vice versa. We derive an analytical model of the situations described and characterize equilibrium advertising decisions. We find that the characteristics of a premium SB may depend on which marketing/promoting instrument (advertising or pricing) is the primary method for driving demand; and in some situations an NB may be better off to not advertise at all and instead let the premium SB carry out all of the advertising

Promoting Resiliency in Emergency Communication Networks: A Network Interdiction Modeling Approach

Emergency communication networks provide the basis for preparing for, and responding to, manmade and natural disasters. With the increasing importance of information security, emergency network operators such as non-governmental organizations (NGOs), local and national governmental agencies, and traditional network operators must deal with the possibility of sabotage and hacking of such networks. A network interdiction modeling approach is proposed that can be utilized for planning purposes in order to identify and protect critical parts of the network infrastructure. These critical nodes or links represent opportunities where investment or hardening of such infrastructure may reduce or prevent reductions in network traffic flows created by nefarious actors prior, during, or after an emergency or disaster

Determining the causality between U.S. presidential prediction markets and global financial markets

This is the peer reviewed version of the following article: Abolghasemi, Y, Dimitrov, S. Determining the causality between U.S. presidential prediction markets and global financial markets. Int J Fin Econ. 2020; 1– 23., which has been published in final form at https://doi.org/10.1002/ijfe.2029. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Prediction markets trade securities with final prices contingent on the outcome of future events, for example, who will win the next political election. We show how the outcome of a United States presidential election, information captured by prediction markets, impacts global financial markets. We investigate the existence of a causal relationship between various prediction markets and global financial markets time series for over 27 different countries and regions using Dow Jones Global Indexes. We construct vector auto‐regressive models and use the Toda–Yamamoto causality test to deal with non‐stationary time series. Preliminary results indicate that prediction markets may be used to predict some global financial markets.The Natural Sciences and Engineering Research Council of Canad

Data Envelopment Analysis may Obfuscate Corporate Financial Data: Using Support Vector Machine and Data Envelopment Analysis to Predict Corporate Failure for Nonmanufacturing Firms

This is an Accepted Manuscript of an article published by Taylor & Francis in INFOR: Information Systems and Operational Research in 2017, available online: https://doi.org/10.1080/03155986.2017.1282290Corporate failure prediction has drawn numerous scholars’ attention because of its usefulness in corporate risk management, as well as in regulating corporate operational status. Most research on this topic focuses on manufacturing companies and relies heavily on corporate assets. The asset size of manufacturing companies play a vital role in traditional research methods; Altman’s score model is one such traditional method. However, a limited number of researchers studied corporate failure prediction for nonmanufacturing companies as the operational status of such companies is not solely correlated to their assets. In this paper we use support vector machines (SVMs) and data envelopment analysis (DEA) to provide a new method for predicting corporate failure of nonmanufacturing firms. We show that using only DEA scores provides better predictions of corporate failure predictions than using the original, raw, data for the provided dataset. To determine the DEA scores, we first generate efficiency scores using a slack-based measure (SBM) DEA model, using the recent three years historical data of nonmanufacturing firms; then we used SVMs to classify bankrupt and non-bankrupt firms. We show that using DEA scores as the only inputs into SVMs predict corporate failure more accurately than using the entire raw data available.Natural Sciences and Engineering Research Council of Canad

On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules

We study market scoring rule (MSR) prediction markets in the presence of risk-averse or risk-seeking agents that have unknown yet bounded risk preferences. It is well known that if agents can be prescreened, then MSRs can be corrected to elicit agents’ beliefs. However, agents cannot always be screened, and instead, an online MSR mechanism is needed. We show that agents’ submitted reports always deviate from their beliefs, unless their beliefs are identical to the current market estimate. This means, in most cases it is impossible for a MSR prediction market to elicit an individual agent’s exact belief. To analyze this issue, we introduce a measure to calculate the deviation between an agent’s reported belief and personal belief. We further derive the necessary and sufficient conditions for a MSR to yield a lower deviation relative to another MSR. We find that the deviation of a MSR prediction market is related to the liquidity provided in the MSR’s corresponding cost-function prediction market. We use the relation between deviation and liquidity to present a systematic approach to help determine the amount of liquidity required for cost-function prediction markets, an activity that up to this point has been described as “art” in the literature.Natural Sciences and Engineering Research Council of Canad

Information Procurement and Delivery: Robustness in Prediction Markets and Network Routing.

In this dissertation we address current problems in information procurement and delivery. Uncertainty commonly reduces the efficacy of information procurement systems, such as prediction markets, and information delivery systems, such as Internet backbone networks. We address the problems of uncertainty by designing robust algorithms and protocols that function well under uncertainty. Telecommunication backbone networks are used for delivering information across the Internet. Current backbone networks mostly employ protocols that include sender-receiver based congestion control. However, as protocols that do not have congestion control available become more prevalent, the network routers themselves must perform congestion control. In order to maximize network throughput, routing policies for backbone networks that take into account router based congestion control must be devised. We propose a mathematical model that can be used to design improved routing policies, while also taking into account existing flow management methods. Our model incorporates current active congestion control methods, and takes into account demand uncertainty when creating routing policies. The resulting routing policies tended to be at least 20% better than those currently used in a real world network in our experiments. Prediction markets are information aggregation tools in which participants trade on the outcome of a future event. One commonly used form of prediction market, the market scoring rule market, accurately aggregate the beliefs of traders assuming the traders are myopic, meaning they do not consider future payoffs, and are risk neutral. In currently deployed prediction markets neither of these assumptions typically holds. Therefore, in order to analyze the effectiveness of such markets, we look at the impact of non-myopic risk neutral traders, as well as risk averse traders on prediction markets. We identify a setting where non-myopic risk neutral traders may bluff, and propose a modified prediction market to disincentivize such behavior. Current prediction markets do not accurately aggregate all risk averse traders' beliefs. Therefore, we propose a new prediction market that does. The resulting market exponentially reduces the reward given to traders as the number of traders increases; we show that this exponential reduction is necessary for any prediction market that aggregates the beliefs of risk averse traders.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75962/1/sdimitro_1.pd

Open Shortest Path First Routing Under Random Early Detection

This is the peer reviewed version of the following article: Liu, J. and Dimitrov, S. (2018), Open shortest path first routing under random early detection. NETWORKS, 71: 120-135., which has been published in final form at https://doi.org/10.1002/net.21792. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.In this article, we consider a variant of Open Shortest Path First (OSPF) routing that accounts for Random Early Detection (RED), an Active Queue Management method for backbone networks. In the version of OSPF we consider in this article we only require a single network path be available between each origin and destination, a simplification of the OSPF protocol. We formulate a mixed integer non‐linear program to determine the data paths, referred to as a routing policy. We prove that determining an optimal OSPF routing policy that accounts for RED is NP‐Hard. Furthermore, in order for the generated routing policies to be real‐world implementable, referred to as realizable, we must determine weights for all arcs in the network such that solving the all‐pairs shortest path problem using these weights reproduces the routing policies. We show that determining if a set of all‐pairs routes is realizable is also NP‐Hard. Fortunately, using traffic data from three real‐world backbone networks, we are able to find realizable routing policies for these networks that account for RED, using an off‐the‐shelf solver, and policies found perform better than those used in each network at the time the data was collected.This work was partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC)

Mobile commerce and device specific perceived risk

This is a post-peer-review, pre-copyedit version of an article published in Electronic Commerce Research. The final authenticated version is available online at: https://doi.org/10.1007/s10660-015-9204-5This study examines the role of perceived risk and access device type on consumers’ on-line purchase decisions. We use a two-step hurdle approach to estimate consumer behavior. In the first step, the decision of whether to engage in eCommerce is estimated and in the second step, how many orders to place is estimated. We use a large multi-year survey sample of households from Canada’s national statistical agency—Statistics Canada. The sample size is such that we are able to conduct sub-sample analysis of PC-only users, mobile-only users, and other-users. We show that online security and price significantly influence mobile eCommerce. We also show that there is a statistically significant difference in the intensity of eCommerce engagement across device type and consumer risk type (high/low). One of our main findings is that perceived risk affects purchase decisions for mobile users more than PC users, however additional comparisons are carried out with our analysis