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摘要翻译：
本文结合博弈论中Shafer概率和VOVK的思想，提出了一种基于神经网络模型的投资策略。我们所提出的策略使用在前一轮（交易日）前表现最好的神经网络的参数值来决定当前轮的投资。我们将我们提出的策略与包括基于监督神经网络模型的策略在内的各种策略的性能进行了比较，并表明我们的过程与其他策略是有竞争力的。
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英文标题：
《Sequential optimizing investing strategy with neural networks》
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作者：
Ryo Adachi and Akimichi Takemura
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最新提交年份：
2010
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/1002.2265
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