Kavli Affiliate: Jia Liu
| First 5 Authors: Jiayu Shen, Jia Liu, Zhiping Chen, ,
| Summary:
This paper presents an innovative online portfolio selection model, situated
within a meta-learning framework, that leverages a mixture policies strategy.
The core idea is to simulate a fund that employs multiple fund managers, each
skilled in handling different market environments, and dynamically allocate our
funding to these fund managers for investment. To address the non-stationary
nature of financial markets, we divide the long-term process into multiple
short-term processes to adapt to changing environments. We use a clustering
method to identify a set of historically high-performing policies,
characterized by low similarity, as candidate policies. Additionally, we employ
a meta-learning method to search for initial parameters that can quickly adapt
to upcoming target investment tasks, effectively providing a set of well-suited
initial strategies. Subsequently, we update the initial parameters using the
target tasks and determine the optimal mixture weights for these candidate
policies. Empirical tests show that our algorithm excels in terms of training
time and data requirements, making it particularly suitable for high-frequency
algorithmic trading. To validate the effectiveness of our method, we conduct
numerical tests on cross-training datasets, demonstrating its excellent
transferability and robustness.
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