Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

Andriosopoulos, Kostas; Doumpos, Michael; Papapostolou, Nikos C.; Pouliasis, Panos K.

doi:10.1016/j.tre.2012.11.006

Cited by 39 publications

(16 citation statements)

References 51 publications

(25 reference statements)

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“…Given the growing importance of shipping equity markets as a source of funding and the heightened volatility inherent in asset values and cash flows, risk measurement becomes fundamental for investment decisions. Asset allocation and risk-return attributes in the shipping equity market have been examined by Andriosopoulos et al (2013) who employ evolutionary algorithmic approaches to derive investment strategies replicating the performance of equity and physical freight indices in a cost-effective way. In an effort to gain further understanding of shipping equity risk and its implications for asset allocation, Pouliasis et al (2017) develop a comprehensive model to examine the dynamics of stock price volatility for different vessel segments.…”

Section: Cost Of Capital and Sources Of Risk In Shipping Investmentsmentioning

confidence: 99%

A survey of shipping finance research: Setting the future research agenda

Alexandridis

Kavussanos

Kim

et al. 2018

Transportation Research Part E: Logistics and Transportation Re

108

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Financing shipping related investment projects has always been a focal area of debate and research within the international maritime industry since access to funding can determine the competitiveness of a capitalintensive business as well as its success or failure under adverse market conditions. This paper provides, for the first time, a comprehensive and structured survey of all published research in the area of shipping finance and investment. The review spans approximately four decades (1979-2018) of empirical evidence, including 162 studies published in 48 scholarly journals, complemented with select books and book chapters. The study provides a bibliometric analysis and comprehensive synthesis of existing research offering an invaluable source of information for both the academic community and business practice, shaping the future research agenda in shipping finance and investment. JEL Code: G10, G20, G30.

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Section: Cost Of Capital and Sources Of Risk In Shipping Investmentsmentioning

confidence: 99%

A survey of shipping finance research: Setting the future research agenda

Alexandridis

Kavussanos

Kim

et al. 2018

Transportation Research Part E: Logistics and Transportation Re

108

View full text Add to dashboard Cite

show abstract

“…The study by Andriosopoulos et al [3] generated portfolios using a subset of shipping stocks selected from the Dow Jones Composite Average indices. The index tracking problem was addressed using a differential evolution algorithm and a genetic algorithm.…”

Section: Recent Literature On Partial Index Trackingmentioning

confidence: 99%

Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics

García

Guijarro

Oliver

2017

Neural Comput & Applic

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The aim of this study was to compare the performance of the well-known genetic algorithms and tabu search heuristics with the financial problem of the partial tracking of a stock market index. Although the weights of each stock in a tracking portfolio can be efficiently determined by means of quadratic programming, identifying the appropriate stocks to include in the portfolio is an NP-hard problem which can only be addressed by heuristics. Seven real-world indexes were used to compare the above techniques and results were obtained for different tracking portfolio cardinalities. The results show that tabu search performs more efficiently with both real and artificial indexes. In general, the tracking portfolios obtained performed well in both in-sample and out-of-sample periods, so that these heuristics can be considered as appropriate solutions to the problem of tracking an index by means of a small subset of stocks.

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“…Andriosopoulos et al [4] tackle the problem of reproducing the performance of international market capitalization shipping stock indices and physical shipping indices. The authors propose a model that aims at optimizing a weighted function of the tracking error, measured by the standard deviation of the portfolio return from the benchmark, and the excess portfolio return over the benchmark.…”

Section: Enhanced Index Tracking and Multi-objective Portfolio Optimimentioning

confidence: 99%

A heuristic framework for the bi-objective enhanced index tracking problem

Filippi

Guastaroba

Speranza

2016

Omega

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The index tracking problem is the problem of determining a portfolio of assets whose performance replicates, as closely as possible, that of a financial market index chosen as benchmark. In the enhanced index tracking problem the portfolio is expected to outperform the benchmark with minimal additional risk. In this paper, we study the bi-objective enhanced index tracking problem where two competing objectives, i.e., the expected excess return of the portfolio over the benchmark and the tracking error, are taken into consideration. A bi-objective Mixed Integer Linear Programming formulation for the problem is proposed. Computational results on a set of benchmark instances are given, along with a detailed out-of-sample analysis of the performance of the optimal portfolios selected by the proposed model. Then, a heuristic procedure is designed to build an approximation of the set of Pareto optimal solutions. We test the proposed procedure on a reference set of Pareto optimal solutions. Computational results show that the procedure is significantly faster than the exact computation and provides an extremely accurate approximation.

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Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

Cited by 39 publications

References 51 publications

A survey of shipping finance research: Setting the future research agenda

A survey of shipping finance research: Setting the future research agenda

Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics

A heuristic framework for the bi-objective enhanced index tracking problem

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