Creating investment scheme with state space modeling
Creating investment scheme with state space modeling
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DOI：
10.1016/j.eswa.2017.03.045
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
M. Nakano;Akihiko Takahashi;Soichiro Takahashi
中科院分区：
文献类型：
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作者：
M. Nakano;Akihiko Takahashi;Soichiro Takahashi
本文提出了一种统一的方法来为投资者创建具有各种理想属性的投资策略。特别是，我们为状态空间模型提供了新的解释和由此产生的公式，以实现我们的投资目标，这些目标可能被指定为产生超过基准股票指数的额外回报或实现目标风险调整回报。我们采用粒子过滤算法的状态空间模型用于开发高度复杂的金融市场中投资策略的专家系统。更具体地说，在我们的状态空间框架中，我们应用系统模型来表示具有各种约束的投资组合权重过程，以及标准的基础状态变量，例如波动率过程。此外，我们制定了一个观察模型来代表具有观察变量和潜在变量的非线性函数的目标价值过程。数值实验通过创造超过标准普尔 500 指数的超额回报并生成具有良好风险回报特征的投资组合来证明我们方法的有效性。
This paper proposes a unified approach to creating investment strategies with various desirable properties for investors. Particularly, we provide a new interpretation and the resulting formulations for state space models to attain our investment objectives, which are possibly specified as generating additional returns over benchmark stock indexes or achieving target risk-adjusted returns.Our state space models with particle filtering algorithm are employed to develop expert systems for investment strategies in highly complex financial markets. More concretely, in our state space framework, we apply a system model to representing portfolio weight processes with various constraints, as well as the standard underlying state variables such as volatility processes. Further, we formulate an observation model to stand for target value processes with non-linear functions of observed and latent variables.Numerical experiments demonstrate the effectiveness of our methodology through creating excess returns over S&P 500 and generating investment portfolios with fine risk-return profiles.