Isaac Justin Faber

Simulation-based costing for early phase life cycle cost analysis: Example application to an environmental remediation project

Simulation-based costing (SBC) has been slow to be adopted by the traditional cost estimating community. This can be attributed to many factors, including complexity, how to gather data and develop probabilistic inputs, cost of SBC software, and a lack of understanding of the benefits of developing cost versus risk profiles. This article presents an overview of how SBC can be effectively utilized for early phase life cycle cost (LCC) estimation. A formal process for conducting LCC incorporating SBC is presented not only to provide a structured approach but to also convey to stakeholders how such a study is conducted. This article also presents a case study where total ownership cost versus risk profiles were developed using this proposed process in order to support budgetary and planning considerations for a large environmental remediation project. This research argues that SBC is needed during the concept exploration phase because this is when budgets are often fixed and expectations set.

An Exploration of Using the Empirical Implied Distribution to Predict the Distribution of Future Returns

This paper presents a new approach to applying information from forward-looking options data to better predict the distribution of realized returns. First, pricing data in options chains for five exchange traded funds across various time periods are modeled as distributions. All four moments are then calculated from these distributions and used as parameters in a linear regression that predicts all four moments of the realized distribution. This methodology yields promising results as the model generated a statistically significant correlation coefficient for all moments of every ETF at a 95% confidence level. These improved estimates are valuable to investors as they can be leveraged to better match risk preferences and hedge against predicted uncertainties.

An Improved Composite Estimate for Realized Volatility

The purpose of this study is to determine whether a superior estimation for security volatility can be derived by finding a balance between historical data, the implied volatility and an empirical implied distribution placed on the options chains of four exchange traded funds. Data is collected for 30-day option contracts expiring on the first trading day of every month over a minimum of three years and analyzed to see whether a weighted combination of the three estimates provides an estimate with a higher correlation of realized volatility. A linear optimization solution was formulated to determine the best possible composite volatility estimate. The results of a hypothesis test showed that there is statistically significant evidence in which the composite volatility estimate is preferred at a 95% confidence level. With a better predictor for security volatility, this optimization process could be applied to the creation of portfolios that better meet investor risk preference. The final version of this paper is published in the Journal of Statistical and Econometric Methods.

War Profiteering Comparing Military-Industry Stock Portfolio Returns versus Market Returns During the Iraq and Afghan Wars

The Department of Defense (DoD) relies on a privatized defense industry for tools of war. Following the Cold War, the majority of this Defense Industry consolidated into 5 large corporations: Boeing, United Technologies Corporation, Honeywell, Raytheon, and Lockheed Martin. In this study, an equally weighted portfolio of the 5 aforementioned companies stocks is constructed, and then analyzed during Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF). The equally weighted portfolio is then compared to the market to see whether or not a defense-industry portfolio can outperform the market during wartime. Both the portfolio and the market index are assessed by average monthly return and the Sharpes Slope. These metrics are compared between the portfolio and the market through a Difference in Means Statistical Test.

Leveraging Microfinance: A Tool to Aid Ground Commanders in Distributing Economic Development Funds

Military leaders do not currently have an effective method to guide the allocation of economic development funds. The purpose of this article is to present a tool that will help guide how a ground commander can best allocate available funds in order to raise a communities economic output. The tool is data driven using information from a microfinance organization, Kiva. Data from microfinance loans are utilized due to the similar purpose with that of financial aid provided by ground commanders through economic development funds. The Kiva dataset is consolidated to five sectors: Industry, Services, Agriculture, Health, and Education. These sectors act as the individual stocks that make up an ’investment’ portfolio; the sectors then can be analyzed for optimization in terms of long term economic growth. Given a country of operation, the tool will return a portfolio recommendation with percent allocation by economic sector to be used as guidance in economic development fund distribution. This tool serves to provide much needed guidance in providing a base of economic knowledge of a country of interest and a proposal about the distribution of development funds.

Cashing in on the Holidays Searching for the Holiday Effect Among Some of Americas Top Retail Companies

Investors often analyze historical stock data to find an emerging pattern and establish a trading rule the holiday effect is one such pattern. Traders benefited from a predictable increase in stock prices during the holiday season and bought stocks on pre-holidays. However, recent scholarship has shown a disappearance in the holiday effect, and this study confirms that finding. Analysis of stock prices from five major retail companies from 2000 to 2013 shows drastic unpredictability in stock performance. Even if a trader minimizes risk by trading an equally weighted stock portfolio and makes optimal trades to maximize returns, his performance is usually worse than, and rarely better than, market performance. The study shows among Americas top retail companies, the holiday effect has vanished since 2000. Interestingly, the results indicate that the holiday effect may persist in the overall market, but more analysis is needed to confirm this assertion.

A QUAINT Tool: Quantitative Uniform Analysis of Instructor Talent

In todays educational landscape, subject areas that involve science, technology, engineering and math (STEM) are receiving renewed attention in part due to the rapid proliferation of technology in our daily lives and in the marketplace. One of the most important factors affecting the quality of a STEM education is the quality of the instructor delivering the material. Despite the recognized importance of the instructor in the educational process, there exists no common, quantitative method to determine how effective or valuable the instructor actually is. We present the QUAINT methodology for determining the value of a college level instructor using an analytical and quantitative process. This methodology employs a systems analysis approach in understanding the primary functions and value measures that define an instructors worth. The model incorporates information from a set of stakeholder interviews, focus groups and surveys from all levels of the academic spectrum ranging from Dean of Academics to everyday students. Using value modeling, we are able to create a quantitative assessment based on the input of stakeholders for use in assessing individual instructors or comparing a group of instructors with diverse strengths.

Evaluating the Military Retirement System: An Adaptive Choice-Based Conjoint Analysis Approach

As the U.S. Government moves into an era of fiscal responsibility, addressing national spending goes hand-in-hand with addressing the specific programs in place that accrue the greatest costs in the future. Reducing these expenses is paramount in dealing with the budget strategy and finding the means to make programs more efficient should be the forward focus. From the realm of National Defense, the Military Retirement System is a growing expenditure that threatens both current and future benefits for the Service Member it is designed to service. The issue is sensitive in that its stakeholders encompass the men and women of the U.S. Armed Forces, but simultaneously the growing costs threaten the strategic defense of the United States. This study looks to address the inefficiencies of the current Military Retirement System by evaluating which attributes of the retirement plan that the Service Member finds most attractive. Using Adaptive Choice-Based Conjoint Analysis a resourceful, unadorned surveying technique to evaluate the potential military retiree, this study demonstrates an effective methodology at evaluating consumer (the Service Member) preferences and provides an examination of potential policy changes to combat the growing financial dilemma.

Comparing Historical and Implied Volatility Estimates in Efficient Portfolios

This paper evaluates the performance of efficient portfolios with differing sources of volatility estimation. One of the primary assumptions of modern portfolio theory is that the parameters, asset means, standard deviations and covariance, are known. In practice these values are not known and have to be derived from reliable and accurate sources. Two sources of volatility estimation are compared in this paper; the classical statistical approach and Black-Sholes implied volatility. The time horizon preceding, during and after the financial crisis of 2008-9 is used to evaluate the two sources of information by generating competing monthly tangency portfolios. The results suggest that while historical statistical estimates outperform there is no statistically significant difference between the two over the given time horizon. However, this is partially due to there being a tendency for implied volatility to remain over inflated beyond the crisis into the recovery period. This then leads to significant losses in portfolios that solely use the implied volatility measure.