Singer Y
AT&T Labs, Florham Park, NJ 07932, USA.
Int J Neural Syst. 1997 Aug;8(4):445-55. doi: 10.1142/s0129065797000434.
A constant rebalanced portfolio is an asset allocation algorithm which keeps the same distribution of wealth among a set of assets along a period of time. Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight (Cover, 1991; Helmbold et al., 1996; Cover and Ordentlich, 1996). By their nature, these algorithms employ the assumption that high returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the wealth achieved by a constant rebalanced portfolio is much smaller than the wealth achieved by an ad hoc investment strategy that adapts to changes in the market. In this paper we present an efficient portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including the transactions cost models recently investigated in (Blum and Kalai, 1997). We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22-year period. On this data, our algorithm outperforms all the algorithms referenced above, with and without transaction costs.
恒定再平衡投资组合是一种资产配置算法，它在一段时间内保持一组资产之间相同的财富分配。最近，出现了一些在线投资组合选择算法，这些算法与事后确定的最佳恒定再平衡投资组合具有竞争力（Cover，1991；Helmbold等人，1996；Cover和Ordentlich，1996）。从本质上讲，这些算法采用了这样一种假设，即使用固定的资产配置策略可以实现高回报。然而，股票市场远非静止不变，在许多情况下，恒定再平衡投资组合所获得的财富远小于通过适应市场变化的临时投资策略所获得的财富。在本文中，我们提出了一种能够跟踪变化市场的高效投资组合选择算法。我们还描述了该算法在一般交易成本情况下的简单扩展，包括（Blum和Kalai，1997）最近研究的交易成本模型。我们对该算法的竞争力进行了简单分析，并在22年期间积累的纽约证券交易所真实股票数据上检验了其性能。在这些数据上，我们的算法在有交易成本和无交易成本的情况下均优于上述所有算法。
