Randomized signature methods in optimal portfolio selection
展示了随机签名方法在多变量金融市场中用于非线性、非参数漂移估计的实证结果，帮助投资者从历史数据中学习最优投资组合策略，并考虑了实际市场数据和交易成本。
We present convincing empirical results on the application of Randomized Signature Methods for non-linear, non-parametric drift estimation for a multi-variate financial market. Even though drift estimation is notoriously inaccurate due to small signal to noise ratio, one can still try to learn optimal non-linear maps from past data to conditional expectations of future returns for the purposes of portfolio optimization. Randomized Signatures, in contrast to classical signatures, allow for high dimensional markets and provide features on the same scale. We do not contribute to the theory of Randomized Signatures here, but rather present our empirical findings on portfolio selection in real world settings including real market data and transaction costs.