Proceedings of the 3rd International Conference on Simulation in Industry and Services, Bussels - Belgium, pp. 79-98, 2005
24 Pages Posted: 21 Mar 2012
Date Written: March 18, 2012
Brokers publish research showing that quantitative stock selections simply based on ranked indicators outperform the market by several percent annually but descriptions often remain anecdotic and partial. On the other hand there is little convincing evidence that managed funds outperform and the debate on market efficiency lingers.
This paper shows portfolio simulations based on simple indicators that evidently outperform. The potential of size and value styles is confirmed and growth is shown to be effective if applied to a value base. Price momentum proves more complex and requires non-linear allocation models. Focus is on the dependency of the results on simulation parameters like backtesting time span, periodicity of reallocation and allocation schemes and indicator models. The variation of results across regions and sectors is discussed as well as turnover and liquidity issues and simulation pitfalls like survival bias and data availability lags. Goal is to provide a sufficiently broad and consistent picture and understanding of stock portfolio simulations to interpret published research confidently and to understand stock markets on a statistical level in order to make ones own assessment of market’s and manager’s efficiency. Given the broad area to cover to deliver a coherent overview this paper favours listing full results over selective interpretation. Time-dependent style allocation falls outside the scope of this paper.
Keywords: style investing, quantitative stock selection, portfolio backtesting
JEL Classification: G11, G14, G15
Suggested Citation: Suggested Citation
Verbiest, Eddy H., Understanding Style Investing Portfolio Simulations (March 18, 2012). Proceedings of the 3rd International Conference on Simulation in Industry and Services, Bussels - Belgium, pp. 79-98, 2005 . Available at SSRN: https://ssrn.com/abstract=2025582
By Andrew Ang