Chapter 1 of this dissertation delves into the economic challenges faced by oil-exporting countries that rely heavily on a single income source, with a particular focus on Saudi Arabia as a case study. The primary objective is to examine the efforts of Saudi Arabia's sovereign wealth fund in diversifying revenue streams and mitigating risks associated with an excessive dependence on oil. To achieve this, the study proposes an adaptation of the subset-optimization algorithm within the mean-variance model, aiming to enhance portfolio construction in sovereign wealth funds. Chapter 2 of the dissertation conducts a comparative analysis between portfolios constructed using the subset-optimization algorithm and a benchmark portfolio that does not employ the algorithm. The findings show that the subset-optimized portfolios outperform the benchmark across various performance metrics. Notably, these portfolios exhibit higher Sharpe ratios, greater investor utility, and lower volatility compared to the benchmark. Additionally, the use of the algorithm leads to reduced exposure to oil beta across different subset sizes. Notably, as the subset size decreases, the portfolio's volatility also decreases, suggesting the algorithm's effectiveness in diversification. In Chapter 3, the research explores advanced estimation strategies for portfolio construction, considering two distinct cases. The first case incorporates a four-factor model, including the Carhart four-factor model, along with an additional factor specifically related to oil. The second case uses the Bayesian-shrinkage estimator for estimating the variance–covariance matrix and incorporates an informative prior within a Bayesian framework to estimate expected returns. Comparisons among the different models and inputs demonstrate that these advanced estimation techniques lead to improved portfolio performance. Specifically, the Sharpe ratio and investor utility are enhanced, indicating the contribution of these cutting-edge techniques to the creation of more effective and efficient portfolios. Overall, the findings highlight the algorithm's potential to enhance risk-adjusted returns, reduce exposure to specific market factors such as oil, and ultimately contribute to the overall enhancement of portfolio management.
","abstract_html":"<p>Chapter 1 of this dissertation delves into the economic challenges faced by oil-exporting countries that rely heavily on a single income source, with a particular focus on Saudi Arabia as a case study. The primary objective is to examine the efforts of Saudi Arabia's sovereign wealth fund in diversifying revenue streams and mitigating risks associated with an excessive dependence on oil. To achieve this, the study proposes an adaptation of the subset-optimization algorithm within the mean-variance model, aiming to enhance portfolio construction in sovereign wealth funds. Chapter 2 of the dissertation conducts a comparative analysis between portfolios constructed using the subset-optimization algorithm and a benchmark portfolio that does not employ the algorithm. The findings show that the subset-optimized portfolios outperform the benchmark across various performance metrics. Notably, these portfolios exhibit higher Sharpe ratios, greater investor utility, and lower volatility compared to the benchmark. Additionally, the use of the algorithm leads to reduced exposure to oil beta across different subset sizes. Notably, as the subset size decreases, the portfolio's volatility also decreases, suggesting the algorithm's effectiveness in diversification. In Chapter 3, the research explores advanced estimation strategies for portfolio construction, considering two distinct cases. The first case incorporates a four-factor model, including the Carhart four-factor model, along with an additional factor specifically related to oil. The second case uses the Bayesian-shrinkage estimator for estimating the variance–covariance matrix and incorporates an informative prior within a Bayesian framework to estimate expected returns. Comparisons among the different models and inputs demonstrate that these advanced estimation techniques lead to improved portfolio performance. Specifically, the Sharpe ratio and investor utility are enhanced, indicating the contribution of these cutting-edge techniques to the creation of more effective and efficient portfolios. Overall, the findings highlight the algorithm's potential to enhance risk-adjusted returns, reduce exposure to specific market factors such as oil, and ultimately contribute to the overall enhancement of portfolio management.</p>","abstract_has_math":false,"creators":["Alshowaikhat, Mohammed Saeed"],"institution":null,"degree_name":"Economics, PhD","degree_level":"Open Access Dissertation","degree_discipline":"School of Social Science, Politics, and Evaluation","degree_department":null,"school":null,"contributors":["Pierangelo De Pace","Benjamin Gillen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-01T08:00:00Z","date_published":"2023-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:22Z","subjects":["Investment management","Machine learning","Mean-variance","Optimization","Portfolio management","Sovereign wealth funds","Economics","Finance"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/575","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Pierangelo De Pace","Benjamin Gillen"]},{"key":"dc:creator","label":"Author","values":["Alshowaikhat, Mohammed Saeed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-11-08T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["School of Social Science, Politics, and Evaluation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Economics, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Investment management","Machine learning","Mean-variance","Optimization","Portfolio management","Sovereign wealth funds","Economics","Finance"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/575"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Chapter 1 of this dissertation delves into the economic challenges faced by oil-exporting countries that rely heavily on a single income source, with a particular focus on Saudi Arabia as a case study. The primary objective is to examine the efforts of Saudi Arabia's sovereign wealth fund in diversifying revenue streams and mitigating risks associated with an excessive dependence on oil. To achieve this, the study proposes an adaptation of the subset-optimization algorithm within the mean-variance model, aiming to enhance portfolio construction in sovereign wealth funds. Chapter 2 of the dissertation conducts a comparative analysis between portfolios constructed using the subset-optimization algorithm and a benchmark portfolio that does not employ the algorithm. The findings show that the subset-optimized portfolios outperform the benchmark across various performance metrics. Notably, these portfolios exhibit higher Sharpe ratios, greater investor utility, and lower volatility compared to the benchmark. Additionally, the use of the algorithm leads to reduced exposure to oil beta across different subset sizes. Notably, as the subset size decreases, the portfolio's volatility also decreases, suggesting the algorithm's effectiveness in diversification. In Chapter 3, the research explores advanced estimation strategies for portfolio construction, considering two distinct cases. The first case incorporates a four-factor model, including the Carhart four-factor model, along with an additional factor specifically related to oil. The second case uses the Bayesian-shrinkage estimator for estimating the variance–covariance matrix and incorporates an informative prior within a Bayesian framework to estimate expected returns. Comparisons among the different models and inputs demonstrate that these advanced estimation techniques lead to improved portfolio performance. Specifically, the Sharpe ratio and investor utility are enhanced, indicating the contribution of these cutting-edge techniques to the creation of more effective and efficient portfolios. Overall, the findings highlight the algorithm's potential to enhance risk-adjusted returns, reduce exposure to specific market factors such as oil, and ultimately contribute to the overall enhancement of portfolio management.
"]},{"key":"dc:title","label":"Title","values":["Optimal Portfolio Construction for Oil-Based Sovereign Wealth Funds"]}]}],"canonical_facts":{"dc:contributor":["Pierangelo De Pace","Benjamin Gillen"],"dc:creator":["Alshowaikhat, Mohammed Saeed"],"dc:date.available":["2023-11-08T08:00:00Z"],"dc:description.abstract":["Chapter 1 of this dissertation delves into the economic challenges faced by oil-exporting countries that rely heavily on a single income source, with a particular focus on Saudi Arabia as a case study. The primary objective is to examine the efforts of Saudi Arabia's sovereign wealth fund in diversifying revenue streams and mitigating risks associated with an excessive dependence on oil. To achieve this, the study proposes an adaptation of the subset-optimization algorithm within the mean-variance model, aiming to enhance portfolio construction in sovereign wealth funds. Chapter 2 of the dissertation conducts a comparative analysis between portfolios constructed using the subset-optimization algorithm and a benchmark portfolio that does not employ the algorithm. The findings show that the subset-optimized portfolios outperform the benchmark across various performance metrics. Notably, these portfolios exhibit higher Sharpe ratios, greater investor utility, and lower volatility compared to the benchmark. Additionally, the use of the algorithm leads to reduced exposure to oil beta across different subset sizes. Notably, as the subset size decreases, the portfolio's volatility also decreases, suggesting the algorithm's effectiveness in diversification. In Chapter 3, the research explores advanced estimation strategies for portfolio construction, considering two distinct cases. The first case incorporates a four-factor model, including the Carhart four-factor model, along with an additional factor specifically related to oil. The second case uses the Bayesian-shrinkage estimator for estimating the variance–covariance matrix and incorporates an informative prior within a Bayesian framework to estimate expected returns. Comparisons among the different models and inputs demonstrate that these advanced estimation techniques lead to improved portfolio performance. Specifically, the Sharpe ratio and investor utility are enhanced, indicating the contribution of these cutting-edge techniques to the creation of more effective and efficient portfolios. Overall, the findings highlight the algorithm's potential to enhance risk-adjusted returns, reduce exposure to specific market factors such as oil, and ultimately contribute to the overall enhancement of portfolio management.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/575"],"dc:subject":["Investment management","Machine learning","Mean-variance","Optimization","Portfolio management","Sovereign wealth funds","Economics","Finance"],"dc:title":["Optimal Portfolio Construction for Oil-Based Sovereign Wealth Funds"],"thesis:degree_discipline":["School of Social Science, Politics, and Evaluation"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Economics, PhD"]},"updated_at":"2026-07-24T01:40:22Z"}