نبذة مختصرة : This dissertation provides a collection of management and decision analysis approaches that may be used to analyze financial data by creating investment portfolios, ranking mutual funds and mutual fund companies, and that aid in advising investors when to invest in one of the three major U.S. stock exchanges. A topic introduced and examined throughout the paper involves efficient frontiers. These efficient frontiers help identify the best portfolio, mutual fund, or individual security based on financial metrics. Chapters 1 and 2 examine 9 data envelopment analysis (DEA) models that rank mutual funds and mutual fund companies. Chapter 1 examines the relationship between implementing standardized versus non-standardized investment data while Chapter 2 compares between each of the nine different DEA models. In each of the first two chapters, a stochastic model is created by multiple-linear regression to estimate the efficiency scores used to rank the mutual funds and mutual fund companies. Chapter 3 examines patterns within the three major U.S. stock market exchanges by first finding all the occurrences of n time periods with negative returns and then calculating averages of geometric rate of returns for the return periods immediately following the n consecutive negative return periods. Additional analysis is repeated with examining n consecutive positive return periods. The prime objective of chapter 3 is to motivate and inform investors about when is the best time to invest in a stock for a given U.S. stock exchange given passed stock return patterns. Chapter 4 examines Markowitz’s Mean-variance portfolio efficient frontier by creating structural equations that estimates the portfolio weights to produce investment portfolios that perform best; ones that lie on this efficient frontier. Chapter 5 illustrates a literature review that gives a synopsis of the vast amounts of research conducted via Markowitz’s mean-variance portfolio problem.
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