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Online Portfolio Selection with Long-Short Term Forecasting

Author

Listed:
  • Roujia Li

    (Xi’an Jiaotong University)

  • Jia Liu

    (Xi’an Jiaotong University)

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.

Suggested Citation

  • Roujia Li & Jia Liu, 2022. "Online Portfolio Selection with Long-Short Term Forecasting," SN Operations Research Forum, Springer, vol. 3(4), pages 1-15, December.
    • Handle: RePEc:spr:snopef:v:3:y:2022:i:4:d:10.1007_s43069-022-00169-1
    DOI: 10.1007/s43069-022-00169-1
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    References listed on IDEAS

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