Abstract
Data driven rank ordering is a the search for optimal ordering in data based on rules inherent in historical data. This is a challenging problem gaining increasing attention in the machine learning community. We apply our methodology based on pairwise preferences derived from historical data to financial portfolio construction and updating. We use share price data from the FTSE100 between 2003 and 2007. It turned out the portfolio of shares constructed and updated this way produced significant outperformance compared to two benchmarks - firstly a portfolio constructed by a buy and hold strategy and secondly a portfolio created and updated based on neural network predictions.
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Dobrska, M., Wang, H., Blackburn, W. (2009). Data Driven Rank Ordering and Its Application to Financial Portfolio Construction. In: Karagiannis, D., Jin, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2009. Lecture Notes in Computer Science(), vol 5914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10488-6_25
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DOI: https://doi.org/10.1007/978-3-642-10488-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10487-9
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