Online Portfolio Selection with Long-Short Term Forecasting

被引：0
|
作者：
Li R. [1 ]
Liu J. [1 ]
机构：
[1] School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an
来源：
Operations Research Forum | / 3卷 / 4期
基金：
中国国家自然科学基金;
关键词：
Conditional value-at-risk; Long-short term forecasting; Online portfolio selection;
D O I：
10.1007/s43069-022-00169-1
中图分类号：
学科分类号：
摘要：
This work considers an online portfolio selection problem with reward and risk criteria. We use short-term historical data to forecast the reward term, reflecting the current market trend. We use conditional value-at-risk estimated by long-term historical data to measure the investment risk implied in the market. We reformulate the online portfolio selection model with long-short term forecasting as a linear programming problem. Numerical experiments in various data sets examine the superior out-of-sample performance of the proposed model. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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