Pub Date : 2019-07-03DOI: 10.1080/0013791X.2019.1619887
O. Strub, S. Brandinu, D. Lerch, J. Schaller, N. Trautmann
Abstract Enhanced index tracking is an emerging strategy for investing money in the stock market and is aimed at achieving outperformance over a given benchmark index while achieving a low tracking error. We consider the problem of rebalancing a portfolio for an enhanced index tracking strategy subject to various real-life constraints, including a lower bound and an upper bound on the expected tracking error. To solve this problem, we propose a three-phase approach consisting of preprocessing, optimization, and learning. In a computational experiment, we applied this approach to rebalance a given portfolio on a monthly basis over a time horizon of 10 years; the data for the S&P 500 benchmark index were provided by the investment company Principal Global Investors. Our approach generated portfolios that were provably close to optimality for all monthly rebalancing decisions. Over the entire horizon of 10 years, the portfolios devised by our approach yielded cumulative returns higher than the S&P 500 index after transaction costs with a moderate tracking error.
摘要增强指数跟踪是一种新兴的投资股市策略，旨在实现优于给定基准指数的表现，同时实现低跟踪误差。我们考虑了在各种现实约束条件下，为增强的指数跟踪策略重新平衡投资组合的问题，包括预期跟踪误差的下限和上限。为了解决这个问题，我们提出了一种由预处理、优化和学习组成的三阶段方法。在一个计算实验中，我们应用这种方法在10年的时间范围内每月重新平衡给定的投资组合；标准普尔500指数的数据由投资公司Principal Global Investors提供。我们的方法生成的投资组合在所有月度再平衡决策中都接近最优。在整个10年的时间里，我们的方法设计的投资组合在扣除交易成本后产生的累积回报高于标准普尔500指数，跟踪误差适中。
{"title":"A Three-phase Approach to an Enhanced Index-tracking Problem with Real-life Constraints","authors":"O. Strub, S. Brandinu, D. Lerch, J. Schaller, N. Trautmann","doi":"10.1080/0013791X.2019.1619887","DOIUrl":"https://doi.org/10.1080/0013791X.2019.1619887","url":null,"abstract":"Abstract Enhanced index tracking is an emerging strategy for investing money in the stock market and is aimed at achieving outperformance over a given benchmark index while achieving a low tracking error. We consider the problem of rebalancing a portfolio for an enhanced index tracking strategy subject to various real-life constraints, including a lower bound and an upper bound on the expected tracking error. To solve this problem, we propose a three-phase approach consisting of preprocessing, optimization, and learning. In a computational experiment, we applied this approach to rebalance a given portfolio on a monthly basis over a time horizon of 10 years; the data for the S&P 500 benchmark index were provided by the investment company Principal Global Investors. Our approach generated portfolios that were provably close to optimality for all monthly rebalancing decisions. Over the entire horizon of 10 years, the portfolios devised by our approach yielded cumulative returns higher than the S&P 500 index after transaction costs with a moderate tracking error.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"64 1","pages":"227 - 253"},"PeriodicalIF":1.2,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0013791X.2019.1619887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44022265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-03DOI: 10.1080/0013791X.2019.1636169
T. Jiang, Shuo Wang, Ruochen Zhang, Lang Qin, Jinglian Wu, Delin Wang, S. Ahipaşaoğlu
Abstract We analyze and solve a single-period portfolio optimization problem with non-convex constraints, which address practical concerns of investment such as the active share weights of sectors and the number of stocks held in a portfolio. We reformulate the problem to simplify the computation and propose an inexact l2-norm penalty method to solve the problem.
{"title":"An inexact l2-norm penalty method for cardinality constrained portfolio optimization","authors":"T. Jiang, Shuo Wang, Ruochen Zhang, Lang Qin, Jinglian Wu, Delin Wang, S. Ahipaşaoğlu","doi":"10.1080/0013791X.2019.1636169","DOIUrl":"https://doi.org/10.1080/0013791X.2019.1636169","url":null,"abstract":"Abstract We analyze and solve a single-period portfolio optimization problem with non-convex constraints, which address practical concerns of investment such as the active share weights of sectors and the number of stocks held in a portfolio. We reformulate the problem to simplify the computation and propose an inexact l2-norm penalty method to solve the problem.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"64 1","pages":"289 - 297"},"PeriodicalIF":1.2,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0013791X.2019.1636169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47257559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-03DOI: 10.1080/0013791X.2019.1636440
Ç. N. Ötken, Z. B. Organ, E. Yildirim, Mustafa Çamlıca, Volkan S. Cantürk, E. Duman, Z. M. Teksan, Enis Kayış
Abstract The purpose of this study is to find a portfolio that maximizes the risk-adjusted returns subject to constraints frequently faced during portfolio management by extending the classical Markowitz mean–variance portfolio optimization model. We propose a new two-step heuristic approach, GRASP & SOLVER, that evaluates the desirability of an asset by combining several properties about it into a single parameter. Using a real-life data set, we conduct a simulation study to compare our solution to a benchmark (S&P 500 index). We find that our method generates solutions satisfying nearly all of the constraints within reasonable computational time (under an hour), at the expense of a 13% reduction in the annual return of the portfolio, highlighting the effect of introducing these practice-based constraints.
{"title":"An extension to the classical mean–variance portfolio optimization model","authors":"Ç. N. Ötken, Z. B. Organ, E. Yildirim, Mustafa Çamlıca, Volkan S. Cantürk, E. Duman, Z. M. Teksan, Enis Kayış","doi":"10.1080/0013791X.2019.1636440","DOIUrl":"https://doi.org/10.1080/0013791X.2019.1636440","url":null,"abstract":"Abstract The purpose of this study is to find a portfolio that maximizes the risk-adjusted returns subject to constraints frequently faced during portfolio management by extending the classical Markowitz mean–variance portfolio optimization model. We propose a new two-step heuristic approach, GRASP & SOLVER, that evaluates the desirability of an asset by combining several properties about it into a single parameter. Using a real-life data set, we conduct a simulation study to compare our solution to a benchmark (S&P 500 index). We find that our method generates solutions satisfying nearly all of the constraints within reasonable computational time (under an hour), at the expense of a 13% reduction in the annual return of the portfolio, highlighting the effect of introducing these practice-based constraints.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"64 1","pages":"310 - 321"},"PeriodicalIF":1.2,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0013791X.2019.1636440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49262923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-03DOI: 10.1080/0013791X.2019.1633450
J. Díaz, M. Cortés, Juan C. Hernandez, Óscar Clavijo, Carlos J. Ardila, Sergio Cabrales
Abstract Index funds consist of a subset of stocks, an index tracking portfolio, included in the market index. The index tracking portfolio aims to match the performance of the benchmark index. In this paper, we propose a hybrid model for solving the multiperiod index tracking problem, which includes rebalancing concerns, transaction costs, limits on the number of stocks, and diversification by sector, market capitalization, and stock weight. Our hybrid model combines the genetic algorithm (GA) to select stocks of the index tracking portfolio and mixed-integer nonlinear programming (MINLP) to estimate its weights. Finally, we apply our proposed hybrid model to the S&P500 to find an index tracking portfolio that includes those constraints. The results show that our hybrid model is able to create an index fund whose return rate is similar to the market index with significantly lower risk.
{"title":"Index fund optimization using a hybrid model: genetic algorithm and mixed-integer nonlinear programming","authors":"J. Díaz, M. Cortés, Juan C. Hernandez, Óscar Clavijo, Carlos J. Ardila, Sergio Cabrales","doi":"10.1080/0013791X.2019.1633450","DOIUrl":"https://doi.org/10.1080/0013791X.2019.1633450","url":null,"abstract":"Abstract Index funds consist of a subset of stocks, an index tracking portfolio, included in the market index. The index tracking portfolio aims to match the performance of the benchmark index. In this paper, we propose a hybrid model for solving the multiperiod index tracking problem, which includes rebalancing concerns, transaction costs, limits on the number of stocks, and diversification by sector, market capitalization, and stock weight. Our hybrid model combines the genetic algorithm (GA) to select stocks of the index tracking portfolio and mixed-integer nonlinear programming (MINLP) to estimate its weights. Finally, we apply our proposed hybrid model to the S&P500 to find an index tracking portfolio that includes those constraints. The results show that our hybrid model is able to create an index fund whose return rate is similar to the market index with significantly lower risk.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"64 1","pages":"298 - 309"},"PeriodicalIF":1.2,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0013791X.2019.1633450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48143311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-18DOI: 10.1080/0013791X.2019.1620391
Yerin Kim, Daemook Kang, Mingoo Jeon, Chungmok Lee
Abstract During recent decades, the traditional Markowitz model has been extended for asset cardinality, active share, and tracking-error constraints, which were introduced to overcome the drawbacks of the original Markowitz model. The resulting optimization problems, however, are often very difficult to solve, whereas those of the original Markowitz model are easily solvable. In order to resolve the portfolio optimization problem for the new extensions, we developed a novel heuristic algorithm that combines GAN (Generative Adversarial Networks) with mathematical programming: the GAN-MP hybrid heuristic algorithm. To the best of our knowledge, this is the first attempt to bridge neural networks (NN) and mathematical programming to tackle a real-world portfolio optimization problem. Computational experiments with real-life stock data show that our algorithm significantly outperforms the existing non-linear optimization solvers.
{"title":"GAN-MP hybrid heuristic algorithm for non-convex portfolio optimization problem","authors":"Yerin Kim, Daemook Kang, Mingoo Jeon, Chungmok Lee","doi":"10.1080/0013791X.2019.1620391","DOIUrl":"https://doi.org/10.1080/0013791X.2019.1620391","url":null,"abstract":"Abstract During recent decades, the traditional Markowitz model has been extended for asset cardinality, active share, and tracking-error constraints, which were introduced to overcome the drawbacks of the original Markowitz model. The resulting optimization problems, however, are often very difficult to solve, whereas those of the original Markowitz model are easily solvable. In order to resolve the portfolio optimization problem for the new extensions, we developed a novel heuristic algorithm that combines GAN (Generative Adversarial Networks) with mathematical programming: the GAN-MP hybrid heuristic algorithm. To the best of our knowledge, this is the first attempt to bridge neural networks (NN) and mathematical programming to tackle a real-world portfolio optimization problem. Computational experiments with real-life stock data show that our algorithm significantly outperforms the existing non-linear optimization solvers.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"64 1","pages":"196 - 226"},"PeriodicalIF":1.2,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0013791X.2019.1620391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45936782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-16DOI: 10.1080/0013791X.2019.1619888
Andrijana Bačević, Nemanja Vilimonović, Igor Dabić, Jakov Petrović, Darko Damnjanović, Dušan Džamić
Abstract In this article we consider a portfolio optimization problem under multiple real-world constraints, such as: cardinality constraints, tracking error, active share, and turnover. We propose a heuristic based on variable neighborhood search (VNS) that effectively addresses additional constraints that introduce non-convexities. In the VNS-based heuristic, several neighborhood structures are introduced and fast local search is implemented. We develop a VNS portfolio rebalancing framework (VNS-PRF) with two rebalance strategies. Data sets provided by a financial investment firm are used to evaluate the validity and reliability of the proposed VNS-PRF. Computational experiments and different portfolio performance measures indicate that our approach is able to obtain solutions with competitive quality and can be applied on large-scale data sets.
{"title":"Variable neighborhood search heuristic for nonconvex portfolio optimization","authors":"Andrijana Bačević, Nemanja Vilimonović, Igor Dabić, Jakov Petrović, Darko Damnjanović, Dušan Džamić","doi":"10.1080/0013791X.2019.1619888","DOIUrl":"https://doi.org/10.1080/0013791X.2019.1619888","url":null,"abstract":"Abstract In this article we consider a portfolio optimization problem under multiple real-world constraints, such as: cardinality constraints, tracking error, active share, and turnover. We propose a heuristic based on variable neighborhood search (VNS) that effectively addresses additional constraints that introduce non-convexities. In the VNS-based heuristic, several neighborhood structures are introduced and fast local search is implemented. We develop a VNS portfolio rebalancing framework (VNS-PRF) with two rebalance strategies. Data sets provided by a financial investment firm are used to evaluate the validity and reliability of the proposed VNS-PRF. Computational experiments and different portfolio performance measures indicate that our approach is able to obtain solutions with competitive quality and can be applied on large-scale data sets.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"64 1","pages":"254 - 274"},"PeriodicalIF":1.2,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0013791X.2019.1619888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48322681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-28DOI: 10.1080/0013791X.2019.1677837
V. Bier, Yuqun Zhou, Hongru Du
Abstract Sea-level rise due to climate change is clearly an important problem. This paper uses game theory in conjunction with discounting to explore strategies by which governments might encourage pre-disaster relocation by residents living in areas at high risk of flooding due to sea-level rise. We find that offering a subsidy (e.g., a partial buyout) can be effective if government has a significantly lower discount rate than residents. We also present extensions to our model, exploring the use of a fixed annual benefit after relocation (instead of a one-time subsidy), and hyperbolic instead of standard exponential discounting. Numerical sensitivity analysis elucidates many important factors affecting the timing of anticipatory relocation, since for example relocating too soon may be costly to both residents and government if flooding risk is increasing only gradually. This conceptual model also provides a foundation for future studies that quantify the model with more realistic parameter values (e.g., realistic estimates of flooding probabilities), and alternative behavioral models of resident decision making.
{"title":"Game-theoretic modeling of pre-disaster relocation","authors":"V. Bier, Yuqun Zhou, Hongru Du","doi":"10.1080/0013791X.2019.1677837","DOIUrl":"https://doi.org/10.1080/0013791X.2019.1677837","url":null,"abstract":"Abstract Sea-level rise due to climate change is clearly an important problem. This paper uses game theory in conjunction with discounting to explore strategies by which governments might encourage pre-disaster relocation by residents living in areas at high risk of flooding due to sea-level rise. We find that offering a subsidy (e.g., a partial buyout) can be effective if government has a significantly lower discount rate than residents. We also present extensions to our model, exploring the use of a fixed annual benefit after relocation (instead of a one-time subsidy), and hyperbolic instead of standard exponential discounting. Numerical sensitivity analysis elucidates many important factors affecting the timing of anticipatory relocation, since for example relocating too soon may be costly to both residents and government if flooding risk is increasing only gradually. This conceptual model also provides a foundation for future studies that quantify the model with more realistic parameter values (e.g., realistic estimates of flooding probabilities), and alternative behavioral models of resident decision making.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"65 1","pages":"113 - 89"},"PeriodicalIF":1.2,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0013791X.2019.1677837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45135935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-23DOI: 10.1080/0013791X.2019.1597239
Sha Liu, Shouyi Wang, W. Chaovalitwongse, S. Bowen
Abstract Cost-effectiveness analysis (CEA) in medicine is a form of economic study that compares the relative value of medical technologies and health care services. It helps decision makers to formally evaluate proposed interventions and make informed choices based on the estimated health gains per dollar spent under each intervention. This study employs a CEA framework to assess an emerging imaging technology to determine whether its adoption will be appropriate in routine patient care. A significant challenge in lung cancer radiotherapy (RT) is respiration-induced tumor motion during positron emission tomography/computed tomography (PET/CT). Respiratory gating may improve the image quality and delivery of curative doses to tumor. Respiratory-gated PET/CT is especially useful for locally advanced and inoperable non–small cell lung cancer (NSCLC). Due to the heterogeneity in patients’ respiratory patterns, questions remain regarding who will benefit from respiratory gating. The effectiveness of respiratory gating can be measured by using quantitative improvements in PET/CT images. We previously developed a patient-specific motion management (PSMM) paradigm to identify patients who benefited from respiratory-gated PET/CT based on respiratory pattern analysis. This article presents a new CEA framework to evaluate the cost-effectiveness of PSMM compared to the population-based radiation oncology practice of motion management in more than 1,500 cancer patients.
{"title":"Cost-effectiveness of patient-specific motion management strategy in lung cancer radiation therapy planning","authors":"Sha Liu, Shouyi Wang, W. Chaovalitwongse, S. Bowen","doi":"10.1080/0013791X.2019.1597239","DOIUrl":"https://doi.org/10.1080/0013791X.2019.1597239","url":null,"abstract":"Abstract Cost-effectiveness analysis (CEA) in medicine is a form of economic study that compares the relative value of medical technologies and health care services. It helps decision makers to formally evaluate proposed interventions and make informed choices based on the estimated health gains per dollar spent under each intervention. This study employs a CEA framework to assess an emerging imaging technology to determine whether its adoption will be appropriate in routine patient care. A significant challenge in lung cancer radiotherapy (RT) is respiration-induced tumor motion during positron emission tomography/computed tomography (PET/CT). Respiratory gating may improve the image quality and delivery of curative doses to tumor. Respiratory-gated PET/CT is especially useful for locally advanced and inoperable non–small cell lung cancer (NSCLC). Due to the heterogeneity in patients’ respiratory patterns, questions remain regarding who will benefit from respiratory gating. The effectiveness of respiratory gating can be measured by using quantitative improvements in PET/CT images. We previously developed a patient-specific motion management (PSMM) paradigm to identify patients who benefited from respiratory-gated PET/CT based on respiratory pattern analysis. This article presents a new CEA framework to evaluate the cost-effectiveness of PSMM compared to the population-based radiation oncology practice of motion management in more than 1,500 cancer patients.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"64 1","pages":"368 - 386"},"PeriodicalIF":1.2,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0013791X.2019.1597239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45841922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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