Quantitative Applications in Economics & Finance Research Paper Starter

Quantitative Applications in Economics & Finance

The business manager's toolkit is filled with common tools to manage the financial performance of an organization, among them financial ratios, variance analyses, and budget projections. Capital-budgeting tools assume that decision makers have access to remarkably complete and reliable information, yet most strategic decisions must be made under conditions of great uncertainty (Courtney, Lovallo $ Clarke, 2013). A core function of financial oversight is having intimate knowledge of strategies to effectively manage changing environments, shrinking operating margins and increasing accountability. Managers are being held to specific performance goals by their organizations and stakeholders. It is readily apparent that quantitative analytic skills, or the ready access to these skills, are necessary to maintain a secure position in the industry. Historically, finance and economics experts' tools tended toward tried and true empirical models, long accepted in the business sector. One must ask, however: How successful would a manager be with access to the tools of measurement and inference that are necessary for market and business processes? Common trajectories can be uncovered from where they are hidden behind the coevolution of a large array of indicators (Du & Kamakura, 2012). This essay offers a snapshot of several economic and financial modeling techniques that organizations are using successfully to decrease variance and error. The models develop a hypothesis, apply analytic tools, and strengthen analysis and projection positions. Some industries are further ahead than others in the statistical analysis playing field and they are finding remarkable benefits to being on the cutting edge. It is not within the scope of this essay to instruct the reader in the science of statistics, rather to deliver the concept and its relevance to business and economics today.

Keywords Analysis; Econometrics; Financial Analysis; Forecasting; Hypothesis; Inference; Metrics; Models — Modeling Techniques; Predictive Behaviors; Probability; Process Control; Quantitative Analysis; Regression Analysis; Statistics; Underwriter; Variance

Economics: Quantitative Applications in Economics


Quantitative analysis, the process of applying mathematics to business, suggests that operational success can be enhanced beyond current systems, which look very much like an “applied intuition” method of management. The literature suggests that the setting of budgets, assessing changes in the marketplace, forecasting and strategy can be improved with the use of econometrics, the business of mathematically studying the underlying causes of varying outcomes. Variance is often poorly understood, and is always inefficient, wasteful and costly.


Case Study Number One: Initial Public Stock Offering

Who: The seller of common or preferred stock often enlists the services of an underwriter to determine pricing and timing of an Initial Public Stock Offering to investors. In the September, 2007 Journal of Financial Analysis, a study by Binay, Gatchev, and Pirinsky hypothesized that there is a predictable impact on IPO's sales related to the IPO underwriter's relationship with known investors (Binay et al., 2007). The group defined the event (the element to which a probability can be applied) in terms of a relational participation of the investor and the underwriter to determine the relationship's impact on future investment tendencies.

What: Sales of an initial public offering (IPO) present a risky investment option because the stock offering is usually brand new to the market and provides no historical performance with which the investor can compare. Uncertainty for the investor is the hallmark of an IPO; these offerings are commonly sold by newer companies in an early growth phase. To the casual observer, it would appear that there is much unpredictability in how well the stock will sell in its first days of going public. Given factors like market volatility, investor confidence, and risk perception, predictions are primarily speculative. Intuitively, the underwriter considers the question: Is there method, a model for predicting the relative success of the early offering; what investors should be approached early? It would appear very difficult to find any predictability in the answer to this question. History suggests that empirical experience drives the underwriters' assumptions. However, the following example just goes to show that where predictability seems unlikely, conditional probability analysis can provide a more robust prediction. Probability is simply the measure of the chance that an event will occur, given that another event has already taken place; the outcome of this type of study can support the analyst's prediction using inferential analysis.

How: Analysis is a process, and the deliverable is the recommendations for change and improvement. What Binay and others were searching for follows: "Despite the importance of underwriter-investor relationships, empirical research provides little evidence on the role of regular investors in the equity issue process in part because data on actual IPO allocations are proprietary and rarely disclosed by investment banks. [In this paper] we empirically examine the role of underwriter-investor relationships in the IPO process by examining institutional positions in IPO's as disclosed in quarterly 13F filings with the SEC. We construct a measure of relationship-based participation by institutional investors in IPO's as the difference of two probabilities-the probability that an institution investor participates in an IPO conditional on that investor's past participation in the same lead underwriters' IPO's, and the unconditional probability that an institutional investor participates in the IPO" (Binay et al., 2007). The writers hypothesized the following: "In this paper we investigate the role of regular IPO investors in the going-public process. IPO allocation practices often leave the impression that underwritings unfairly reward favorite clients and neglect other investors. Economic theory, however predicts that favoritism toward regular investors is an efficient way to extract information relevant for IPO firms” (Binay, 2007). The reader can examine in more detail, from the EBSCO Online Research Database (see bibliography) how the study was derived and administered, as well as the statistical strength of its findings.

Why: Refined and innovative analysis is costly: Time, experience and knowledge are required. The organization that makes the investment of enhancing its managers' skills or bringing in expertise is positioning itself to an advantage.

Where: The study on relational impact revealed that "We further find that regular institutional investors are more likely than casual investors to participate in IPO's with higher under pricing" (Binay, 2007). This is but a snapshot of quantitative analysis at work, validating and verifying predictive behaviors in the market clearly can prove instructive to underwriters and sellers. Absent statistically significant findings, the exercise may still be of value due to the inquiry and opportunity it brings to the investigators.

Case Study Number Two: Entrepreneurial Benefit of Storytelling

Who: Martens, Jennings, and Jennings published in the Academy of Management Journal a qualitative and quantitative study on the effects that storytelling has on potential investors' behavior response; for this research they used the statistical method of regression analysis. The reader will recall that some quantitative analyses involve developing a hypothesis, running the experiment, and gauging the effect of the independent variable on the outcome. For purposes of this essay, the quantitative work (not the qualitative component) of Martens et al. will be discussed. The group developed three distinct hypotheses surrounding resource acquisition, the premise of which is described by Jennings and Jennings in a subsequent paragraph.

Why: Applying logical (statistical) techniques to compare data can be a powerful means to support strategic initiatives in an organization. The adage that time is money supports...

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