英文标题：
《Learning Threshold-Type Investment Strategies with Stochastic Gradient
Method》
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作者：
Zsolt Nika, Mikl\\\'os R\\\'asonyi
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最新提交年份：
2019
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英文摘要：
In online portfolio optimization the investor makes decisions based on new, continuously incoming information on financial assets (typically their prices). In our study we consider a learning algorithm, namely the Kiefer--Wolfowitz version of the Stochastic Gradient method, that converges to the log-optimal solution in the threshold-type, buy-and-sell strategy class. The systematic study of this method is novel in the field of portfolio optimization; we aim to establish the theory and practice of Stochastic Gradient algorithm used on parametrized trading strategies. We demonstrate on a wide variety of stock price dynamics (e.g. with stochastic volatility and long-memory) that there is an optimal threshold type strategy which can be learned. Subsequently, we numerically show the convergence of the algorithm. Furthermore, we deal with the typically problematic question of how to choose the hyperparameters (the parameters of the algorithm and not the dynamics of the prices) without knowing anything about the price other than a small sample.
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中文摘要：
在在线投资组合优化中，投资者根据不断收到的有关金融资产（通常是其价格）的新信息做出决策。在我们的研究中，我们考虑了一种学习算法，即随机梯度法的Kiefer-Wolfowitz版本，它在阈值类型的买卖策略类中收敛到对数最优解。该方法的系统研究在投资组合优化领域是新颖的；我们旨在建立用于参数化交易策略的随机梯度算法的理论和实践。我们在各种各样的股票价格动态（例如随机波动性和长记忆）上证明，存在一种可以学习的最优阈值型策略。随后，我们用数值方法证明了算法的收敛性。此外，我们还处理了一个典型的问题，即如何选择超参数（算法的参数，而不是价格的动态），而不知道除了小样本以外的任何价格。
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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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