Portfolio managers are often constrained by turnover limits, minimum and maximum stock positions, cardinality, a target market capitalization and sometimes the need to hew to a style (  growth or value). In addition, many portfolio managers choose stocks based upon fundamental data, e. g. price-to-earnings and dividend yield in an effort to maximize return. All of these are typical real-world constraints a portfolio manager faces. Another constraint, of sorts, is the need to outperform a stock index benchmark. Performance higher than the benchmark means a better compensation package. Underperforming the benchmark means a lesser compensation package. We use MOEAs to satisfy the above real-world constraints and consistently outperform typical performance benchmarks . Our first MOEA solves all the constraints (except turnover and position limits) and generates feasible portfolios.The second MOEA tests each of the potential feasible portfolios of the first MOEA by trading off mean return, variance, turnover and position limits. The best portfolio is chosen from these feasible portfolios and becomes the portfolio of choice for the next quarter. The MOEAs are applied to the following problems -- generate a series of monthly portfolios that outperform the S P 500 over the past 30 years and generate a set of monthly portfolios that outperform the Russell 1000 Growth index over the last 15 years. Our two MOEAs accomplish both these goals on a risk adjusted and non-risk adjusted return basis
Portfolio managers are typically constrained by turnover limits, minimum and maximum stock positions, cardinality, a target market capitalization and sometimes the need to hew to a style ( such as growth or value). In addition, portfolio managers often use multifactor stock models to choose stocks based upon their respective fundamental data. We use multiobjective evolutionary algorithms (MOEAs) to satisfy the above real-world constraints . The portfolios generated consistently outperform typical performance benchmarks and have statistically significant asset selection.
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many portfolio managers
portfolio managers often use multifactor stock models to
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fundamental data, e. g. price-to-earnings and dividend yield in an effort to maximize return. All of these are typical real-world constraints a portfolio manager faces. Another constraint, of sorts, is the need to outperform a stock index benchmark. Performance higher than the benchmark means a better compensation package. Underperforming the benchmark means a lesser compensation package. We use MOEAs
their respective fundamental data. We use multiobjective evolutionary algorithms (MOEAs)
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. The portfolios generated
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. Our first MOEA solves all the constraints (except turnover and position limits) and generates feasible portfolios.The second MOEA tests each of the potential feasible portfolios of the first MOEA by trading off mean return, variance, turnover and position limits. The best portfolio is chosen from these feasible portfolios and becomes the portfolio of choice for the next quarter. The MOEAs are applied to the following problems -- generate a series of monthly portfolios that outperform the S
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P 500 over the past 30 years and generate a set of monthly portfolios that outperform the Russell 1000 Growth index over the last 15 years. Our two MOEAs accomplish both these goals on a risk adjusted and non-risk adjusted return basis
and have statistically significant asset selection.