Efficient online portfolio simulation using dynamic moving average model and benchmark index

Online portfolio selection and simulation are some of the most important problems in several research communities, including finance, engineering, statistics, artificial intelligence, machine learning, etc. The primary aim of online portfolio selection is to determine portfolio weights in every investment period (i.e., daily, weekly, monthly, etc.) to maximize the investor’s final wealth after the end of investment period (e.g., 1 year or longer). In this paper, we present an efficient online portfolio selection strategy that makes use of market indices and benchmark indices to take advantage of the mean reversal phenomena at minimal risks. Based on empirical studies conducted on recent historical datasets for the period 2000 to 2015 on four different stock markets (i.e., NYSE, S&P500, DJIA, and TSX), the proposed strategy has been shown to outperform both Anticor and OLMAR — the two most prominent portfolio selection strategies in contemporary literature.

Entropy Optimization, Maxwell–Boltzmann, and Rayleigh Distributions

In physics, communication theory, engineering, statistics, and other areas, one of the methods of deriving distributions is the optimization of an appropriate measure of entropy under relevant constraints. In this paper, it is shown that by optimizing a measure of entropy introduced by the second author, one can derive densities of univariate, multivariate, and matrix-variate distributions in the real, as well as complex, domain. Several such scalar, multivariate, and matrix-variate distributions are derived. These include multivariate and matrix-variate Maxwell–Boltzmann and Rayleigh densities in the real and complex domains, multivariate Student-t, Cauchy, matrix-variate type-1 beta, type-2 beta, and gamma densities and their generalizations.

A crosswalk to align the GSS and NIH grants databases based on university names

Notable reports over the past dozen years have recommended the federal government and others improve data collection on the research workforce in the United States. The federal government already collects a wealth of data, but important datapoints, like the number of biomedical postdocs working in the U.S., for example, are still not well defined. Furthermore, of the data that are collected, differences in collection method and data naming conventions, like inconsistent naming of universities, hinders our ability to merge information across different datasets. Here I describe the creation of three macros meant to align the National Center for Science and Engineering Statistics’ Survey of Graduate Students and Postdoctorates in Science and Engineering (GSS) with NIH grants databases based on consolidating university names under a single name common to both databases. Aligning these databases will allow for a deeper understanding of how various federal and university policies affect the number trainees, grants, and funding at individual institutions.

“Collect, acquire, analyze, report, and disseminate statistical data related to the science and engineering enterprise…”: The National Center for Science and Engineering Statistics

The National Center for Science and Engineering Statistics (NCSES) is one of the thirteen principal statistical agencies of the United States and is tasked with providing objective data on the status of the science and engineering enterprise in the U.S. and other countries. NCSES sponsors or co-sponsors data collection on 15 surveys and produces two key publications: Science and Engineering Indicators, and Women, Minorities, and Persons with Disabilities in Science and Engineering. Though policy-neutral, the data and reports produced by NCSES are used by policymakers when making policy decisions regarding STEM education and research funding in the U.S. Given NCSES’s importance to the science and engineering community, raising awareness of NCSES and increasing participation by individuals in STEM fields is an important priority.

IAMM: A Maturity Model for Measuring Industrial Analytics Capabilities in Large-scale Manufacturing Facilities

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility.

Adaptive Learning Implementation – A Cognitive Description Experiment for First Year Engineering Students at a Distance Education University

A study was performed on a first year industrial engineering statistics course to improve the statistics pass rate. Statistics is a requisite for other engineering courses. The pass rate for the statistic course was below 50%. The primary purpose is to enable learners to build a capacity to comprehend module content and establish a deeper level of learning that will enable learners to achieve goals and objectives of TL lessons. An intervention program was instructionally designed to develop a personalized and differentiated learning process that breaks down lessons into lower and basic components, for struggling learners, and improves lessons to a complex high level and challenging activities for excelling students. Forty students were considered for the study. Moore’s theory of transactional distance was used as a theoretical framework. The data consisted of exam and assignment scores. A quantitative method was used to analyse the data. Hypothesis testing suggests that the intervention program is significant. The overall pass rates improved by 25%.