This paper investigates inter-relationships among the price behavior of oil, gold and the euro using time series and neural network methodologies. Traditionally gold is a leading indicator of future inflation. Both the demand and supply of oil as a key global commodity are impacted by inflationary expectations and such expectations determine current spot prices. Inflation influences both short and long-term interest rates that in turn influence the value of the dollar measured in terms of the euro. Certain hypotheses are formulated in this paper and time series and neural network methodologies are employed to test these hypotheses. We find that the markets for oil, gold and the euro are efficient but have limited inter-relationships among themselves.

Five inter-related energy products are forecasted one month into the future using both linear and nonlinear techniques. Both spot prices and data derived from those prices are used as input data in the models. The models are tested by running data from the following year through them. Results show that, even though all products are highly correlated, the prediction results are asymmetric. In forecasts for crude oil, heating oil, gasoline and natural gas, the nonlinear forecasts were best, while for propane, the linear model gave the lowest error.

This paper studies the reasons for low response rates in online evaluations. Survey data are collected from the students to understand factors that might affect student participation in the course evaluation process. When course evaluations were opened to the student body, an email announcement was sent to all students, and a reminder email was sent a week later. Our study showed that participation rates increased not only when emails were sent, but also when faculty used in-class time to emphasize the importance of completing the evaluations.

The phenomenal growth of individual retirement accounts in the US, and globally, challenges both individuals and their advisors to rationally manage these investments. The two essential differences between an individual retirement account and an institutional portfolio are the length of the investment horizon and the regularity of monthly contributions. The purpose of this paper is to contrast principles of institutional investing with the management of individual retirement accounts. Using monthly historical data from 1926 to 2005 we evaluate the suitability for managing individual retirement portfolios of seven principles employed in institutional investing. We discover that some of these guidelines can be beneficially applied to the investment management of individual retirement accounts while others need to be reconsidered.

The global financial crisis of 2007–2009 caused major economic disturbances in the oil market. In this paper, we consider five variables that describe the microeconomics of the supply of and demand for oil, and evaluate their importance before, during and after the global financial crisis. We consider five dissimilar regimes during the period of January 1986 to the end of 2020: two regimes prior to the global financial crisis, the regime during the crisis, and two regimes after the crisis. The main hypothesis tested is that oil fundamentals of supply and demand remained important, even though the five regimes were dissimilar. We built five boosted and over-fitted neural networks to capture the exact relationships between spot oil prices and oil data related to these prices. This analysis shows that, while the inputs into an accurate neural network can remain the same, the impact of each variable can change considerably during different regimes.