Growth optimal investment in discrete-time markets with proportional transaction costs

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dc.citation.epage238en_US
dc.citation.spage226en_US
dc.citation.volumeNumber48en_US
dc.contributor.authorVanli, N. D.en_US
dc.contributor.authorTunc, S.en_US
dc.contributor.authorDonmez, M. A.en_US
dc.contributor.authorKozat, S. S.en_US
dc.date.accessioned2018-04-12T10:55:32Z
dc.date.available2018-04-12T10:55:32Z
dc.date.issued2016en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe investigate how and when to diversify capital over assets, i.e., the portfolio selection problem, from a signal processing perspective. To this end, we first construct portfolios that achieve the optimal expected growth in i.i.d. discrete-time two-asset markets under proportional transaction costs. We then extend our analysis to cover markets having more than two stocks. The market is modeled by a sequence of price relative vectors with arbitrary discrete distributions, which can also be used to approximate a wide class of continuous distributions. To achieve the optimal growth, we use threshold portfolios, where we introduce a recursive update to calculate the expected wealth. We then demonstrate that under the threshold rebalancing framework, the achievable set of portfolios elegantly form an irreducible Markov chain under mild technical conditions. We evaluate the corresponding stationary distribution of this Markov chain, which provides a natural and efficient method to calculate the cumulative expected wealth. Subsequently, the corresponding parameters are optimized yielding the growth optimal portfolio under proportional transaction costs in i.i.d. discrete-time two-asset markets. As a widely known financial problem, we also solve the optimal portfolio selection problem in discrete-time markets constructed by sampling continuous-time Brownian markets. For the case that the underlying discrete distributions of the price relative vectors are unknown, we provide a maximum likelihood estimator that is also incorporated in the optimization framework in our simulations.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:55:32Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1016/j.dsp.2015.08.009en_US
dc.identifier.issn1051-2004
dc.identifier.urihttp://hdl.handle.net/11693/36852
dc.language.isoEnglishen_US
dc.publisherElsevier Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.dsp.2015.08.009en_US
dc.source.titleDigital Signal Processing: A Review Journalen_US
dc.subjectDiscrete-time stock marketen_US
dc.subjectGrowth optimal portfolioen_US
dc.subjectProportional transaction costen_US
dc.subjectThreshold rebalancingen_US
dc.titleGrowth optimal investment in discrete-time markets with proportional transaction costsen_US
dc.typeArticleen_US

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