{'fraction_non_alphanumeric': 0.014014014014014014, 'fraction_numerical': 0.001001001001001001, 'mean_word_length': 5.622516556291391, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Constraint Programming Approach to Portfolio Selection

The key idea in Modern Portfolio Theory, as introduced by Markowitz, is to minimize risk which is deened as mean-variance in returns. One method to solve the resulting quadratic programming problem is by converting it into a linear complementarity problem and then solving the latter using specialized algorithms. An analysis reveals that a linear complementarity problem can be understood as a disjunc-tive linear programming problem which can be modeled and solved using constraint programming systems such as 2LP or ILOG. Representing the problem in this way facilitates the inclusion of additional constraints (integrality requirements, mutually exclusive stocks, personal preferences etc.) which might be required given the speciic needs and wishes of an investor. Interesting issues that arise in solving the resulting constraint programs include the use of cooperating constraint solvers and identifying appropriate search strategies.