Sequential optimizing investing strategy with neural networks
Sequential optimizing investing strategy with neural networks
复制标题
使用神经网络顺序优化投资策略
DOI：
10.1016/j.eswa.2011.04.098
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发表时间：
2011
影响因子：
8.5
通讯作者：
A.
中科院分区：
文献类型：
--
作者：
Adachi;R.;Takemura;A.
在本文中，我们提出了一个投资策略的基础上，神经网络模型结合的想法，从博弈论的概率Shafer和Vovk。我们提出的策略使用神经网络的参数值，直到上一轮（交易日）的最佳性能，以决定在当前一轮的投资。我们比较了我们提出的策略与各种策略，包括基于监督神经网络模型的策略的性能，并表明我们的过程与其他策略具有竞争力。
In this paper we propose an investing strategy based on neural network models combined with ideas from game-theoretic probability of Shafer and Vovk. Our proposed strategy uses parameter values of a neural network with the best performance until the previous round (trading day) for deciding the investment in the current round. We compare performance of our proposed strategy with various strategies including a strategy based on supervised neural network models and show that our procedure is competitive with other strategies.