Sparse, Mean Reverting Portfolio Selection Using Simulated Annealing
Sparse, Mean Reverting Portfolio Selection Using Simulated Annealing
复制标题
使用模拟退火进行稀疏均值回归投资组合选择
DOI：
10.3233/af-13026
复制
发表时间：
2013
影响因子：
0.5
通讯作者：
J. Levendovszky
中科院分区：
文献类型：
--
作者：
N. Fogarasi;J. Levendovszky
我们研究的问题，找到稀疏的，均值回复投资组合的基础上，多元历史时间序列。在将最优投资组合问题转化为广义特征值问题之后，我们提出了一种基于模拟退火算法的最优投资组合优化方法。该方法通过将基数约束嵌入到迭代邻域选择函数中，保证了在每一步优化过程中基数约束都能自动得到满足。我们的经验表明，该方法产生更好的均值回归系数比其他启发式方法，但也表明，这并不一定会导致更高的利润在收敛交易。这意味着，更复杂的目标函数应该开发的问题，这也可以使用所提出的方法在基数约束下进行优化。
We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.