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Thesis
KNAPSACK OPTIMAL RISK PORTFOLIO ALLOCATION USING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS
University of West Florida
Master of Science (MS), University of West Florida
2016
Abstract
Previous research provided many controversial investment strategies for the stock market industry. However, the risk of an investment is often overlooked when stock market paths are analyzed through time series analysis. With the combination of a knapsack-based optimization approach and autoregressive integrated moving average models, an optimal risk portfolio allocation strategy, subject to the willingness of risk of an investor, is developed and assessed. Several stock market paths within the technology industry are examined to predict future behavior, as well as the optimal time of sale to maximize the profit of an investment. Results indicate that a high-risk portfolio allocation does in fact produce a higher profit than a low-risk portfolio allocation. However, the data suggests that a medium-risk portfolio allocation for the stock market industry performs superior to any other portfolio allocation model. To explore this outcome in depth, further research is recommended.
Details
- Title
- KNAPSACK OPTIMAL RISK PORTFOLIO ALLOCATION USING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODELS
- Resource Type
- Thesis
- Publisher
- University of West Florida
- Format
- text
- Identifiers
- 99380090737006600
- Academic Unit
- Mathematics and Statistics
- Language
- English
- Awarding Institution
- University of West Florida; Master of Science (MS)
- Theses and Dissertations
- Master of Science (MS), University of West Florida