Date of Graduation
Bachelor of Science in Industrial Engineering
The global investment industry offers a wide variety of investment products especially for individual investors. One such product, index funds, which are younger than actively managed mutual funds, have typically outperformed managed funds. Despite this phenomenon, investors have displayed a tendency to continue investing in actively managed funds. Although only a small percentage of actively managed funds outperform index funds, the costs of actively managed funds are significantly higher. Also, managed fund performances are most often determined by their fund category such as growth or real estate. I wanted to answer the following question for individual investors: can we forecast the future performances of actively managed funds taken from multiple categories and build an optimized portfolio to outperform index funds. The goal of my research was to provide quality information to individual investors and to gain investment knowledge myself so that I can make wise investments in the future. Through my analyses, I discovered that creating fund forecasts often results in high error rates and requires macroeconomic factor stabilization, and global events can alter forecast accuracies severely. When optimizing a portfolio using returns, I determined that a constraint must be added to require diversification. Based on my results, individual investors should identify a broad spectrum of possible funds to invest in, select simple factors to make price predictions, be hesitant to respond eagerly to price forecasts, and understand how much return they are willing to give up for diversification. As an individual investor myself, this study gave me the knowledge to think far more strategically about my own investments and challenged me to understand my own risk tolerance.
Global investment, index funds, quality stocks, dividend stocks, mutual funds, investment information
Weiner, L. (2022). Comparing Actively Managed Mutual Fund Categories to Index Funds using Linear Regression Forecasting and Portfolio Optimization. Industrial Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/ineguht/80
Computer and Systems Architecture Commons, Digital Communications and Networking Commons, Finance and Financial Management Commons, Geotechnical Engineering Commons, Industrial Engineering Commons, Operational Research Commons, Risk Analysis Commons, Systems Engineering Commons