Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.703455
Title: Constructing smart financial portfolios from data driven quantitative investment models
Author: Mehra, Chetan Saran
ISNI:       0000 0004 6061 7766
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2016
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Abstract:
Portfolio managers have access to large amounts of financial time series data, which is rich in structure and information. Such structure, at varying time horizons and frequencies, exhibits different characteristics, such as momentum and mean reversion to mention two. The key challenge in building a smart portfolio is to first, identify and model the relevant data regimes operating at different time frames and then convert them into an investment model targeting each regime separately. Regimes in financial time series can change over a period of time, i.e. they are heterogeneous. This has implications for a model, as it may stop being profitable once the regime it is targeting has stopped or evolved into another one over a period of time. Changing regimes or those evolving into other regimes is one of the key reasons why we should have several independent models targeting relevant regimes at a particular point in time. In this thesis we present a smart portfolio management approach that advances existing methods and one that beats the Sharpe ratio of other methods, including the efficient frontier. Our smart portfolio is a two-tier framework. In the first tier we build four quantitative investment models, with each model targeting a pattern at different time horizon. We build two market neutral models using the pairs methodology and the other two models use the momentum approach in the equity market. In the second tier we build a set of meta models that allocate capital to tier one, using Kelly Criterion, to build a meta portfolio of quantitative investment models. Our approach is smart at several levels. Firstly, we target patterns that occur in financial data at different time horizons and create high probability investment models. Hence we make better use of data. Secondly, we calculate the optimal bet size using Kelly at each time step to maximise returns. Finally we avoid making investments in loss making models and hence make smarter allocation of capital.
Supervisor: Gerding, Enrico Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.703455  DOI: Not available
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