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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