基于随机网络优化的股票市场交易

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摘要翻译：
我们考虑了具有随机价格波动的$N$股票集合的股票动态买卖问题。为限制投资风险，我们对任何时候持有的股份总数设置了上限。假设价格按照一个具有轻微衰减记忆特性的遍历过程演变，并假设在任何时候可以买卖的股票总数受到限制，我们开发了一个交易策略，该策略任意接近于实现对未来事件完全了解的理想策略的利润。接近最优利润伴随着最大所需库存水平和与收敛相关的时间尺度的相应权衡。然后，我们考虑任意（可能是非遍历的）价格过程，并表明相同的算法接近于基于帧的策略的利润，该策略可以将固定数量的时隙展望未来。我们的分析使用了Lyapunov优化技术，我们最初为随机网络优化问题开发了Lyapunov优化技术。
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英文标题：
《Stock Market Trading Via Stochastic Network Optimization》
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作者：
Michael J. Neely
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最新提交年份：
2009
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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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：
  We consider the problem of dynamic buying and selling of shares from a collection of $N$ stocks with random price fluctuations. To limit investment risk, we place an upper bound on the total number of shares kept at any time. Assuming that prices evolve according to an ergodic process with a mild decaying memory property, and assuming constraints on the total number of shares that can be bought and sold at any time, we develop a trading policy that comes arbitrarily close to achieving the profit of an ideal policy that has perfect knowledge of future events. Proximity to the optimal profit comes with a corresponding tradeoff in the maximum required stock level and in the timescales associated with convergence. We then consider arbitrary (possibly non-ergodic) price processes, and show that the same algorithm comes close to the profit of a frame based policy that can look a fixed number of slots into the future. Our analysis uses techniques of Lyapunov Optimization that we originally developed for stochastic network optimization problems.
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PDF链接：
https://arxiv.org/pdf/0909.3891

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关键词：网络优化 股票市场 股票市 Optimization Quantitative 假设 any 股票 接近 过程