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Online Portfolio Selection with Long-Short Term Forecasting

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Abstract

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.

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Data Availability

The data that support the findings of this study are openly available at https://github.com/Marigold/universal-portfolios/tree/master/universal/data.

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Funding

This research was supported by the National Natural Science Foundation of China under Grant Numbers 11991023 and 11901449.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, China

    Roujia Li & Jia Liu

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  1. Roujia Li
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  2. Jia Liu
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Correspondence to Jia Liu.

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Li, R., Liu, J. Online Portfolio Selection with Long-Short Term Forecasting. Oper. Res. Forum 3, 56 (2022). https://doi.org/10.1007/s43069-022-00169-1

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