Liquidity and Portfolio Optimisation
2009 (English)Doctoral thesis, monograph (Other academic)
This thesis presents research within empirical financial economics with focus on liquidity and portfolio optimisation in the stock markets. The discussion on liquidity is focussed on measurement issues, including TAQ data processing and measurement of systematic liquidity factors. The portfolio optimisation section evolves around the properties of full-scale optimisation (FSO). Furthermore, a framework for treatment of the two topics in combination is provided.
The liquidity part of the thesis gives a conceptual background to liquidity and discusses several different approaches to liquidity measurement. It contributes to liquidity measurement by providing detailed guidelines on the data processing needed for applying TAQ data to liquidity research. The main focus, however, is the derivation of systematic liquidity factors. The principal component approach to systematic liquidity measurement is refined by the introduction of moving and expanding estimation windows, allowing for time-varying liquidity co-variances between stocks. Under several liquidity specifications this improves the ability to explain stock liquidity and returns, as compared to static window PCA and market average approximations of systematic liquidity. The highest ability to explain stock returns is obtained when using inventory cost as a liquidity measure and a moving window PCA as the systematic liquidity derivation technique. Systematic factors of this setting also have a strong ability in explaining cross-sectional liquidity variation.
Portfolio optimisation in the FSO framework is tested in two empirical studies. These contribute to the assessment of FSO by expanding the applicability to stock indexes and individual stocks, by considering a wide selection of utility function specifications, and by showing explicitly how the full-scale optimum can be identified using either grid search or the heuristic search algorithm of differential evolution. The studies show that relative to mean-variance portfolios, FSO performs well in these settings and that the computational expense can be mitigated dramatically by application of differential evolution.
Place, publisher, year, edition, pages
Birmingham: Aston University , 2009. , 153 p.
stock market, liquidity, systematic liquidity, portfolio optimisation, high-frequency data
IdentifiersURN: urn:nbn:se:su:diva-42232OAI: oai:DiVA.org:su-42232DiVA: diva2:344474
Binner, Jane M., Dr.