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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Patterns and Stylized Facts in the Field of Sector Rotation Strategies |
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| Institution | University of Zurich |
| Faculty | Faculty of Economics, Business Administration and Information Technology |
| Date | 2014 |
| Zusammenfassung | Through dynamic investment strategies portfolios are actively allocated with the aim of performing better than investors who passively buy-and-hold a benchmark. This has become increasingly dicult due to technological progress, resulting in fast availability of information and high computational power to arbitrage away anomalies, which has made nancial markets much more ecient over the last few decades. Nevertheless, many practitioners and academics claim that there are still some anomalies in the markets, which can be exploited to generate alpha. It is questionable though, if such evidence has correctly taken into account risk-adjusted returns, transaction costs, liquidity issues and short-selling constraints. One of endless opportunities to actively invest is trying to over- and underweight prospective best- and worst-performing industrial sectors. In the fi nance literature, research on this topic is done under the terms of 'sector rotation' or 'industry momentum'. Derived from mechanics, the word 'momentum' describes the observed anomaly of assets that continue to perform well after having yielded well in the previous period, which would even reject the weakest form of the market effciency hypothesis as described by Fama (1970). Alternatively used, 'momentum' simply means that an asset is currently performing well. After the Introduction in section 1, an overview of the existing sector rotation literature is provided in chapter 2. The purpose is to shed some light on the huge variety of methods used to predict future relative sector strength. It is explained how industry momentum can be measured, e.g. through the moving average convergence divergence. Besides momentum, other indicators are utilized to time the rotation of an investor's sector exposure, including macroeconomic variables (e.g. monetary policy shifts), valuation measures (e.g. industry-wide P/E ratios) and dynamic models (e.g. multivariate Bayesian processes). |
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