Quantile Factor-augmented Prediction Model and Its Applications to Alpha-arbitrage Strategy in China’s Stock Market

DOI

10.2991/acsr.k.191223.017How to use a DOI?

Keywords

factor-augmented, predictors, quantile regression, arbitrage strategy

Abstract

Quantitative equity portfolio management has become a fundamental building block of the investment management. The development of general equilibrium asset pricing models enables statistical arbitrage strategies to capture the effect factors of the market returns. In empirical analysis, a crucial step in the model-building process is the selection of an essential factors which may contribute to the positive excess returns. However, it could be challenging since thousands of candidate factors can be obtained. In this study, we employed a factor-augmented model to identify the effect factors for excess returns, and rank the portfolios according the selected factors. A trading strategy of the combination of buying stock portfolio and stock index futures hedging is then perform for Alpha arbitrage.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Siyu Wang
AU  - Xiaoxia Wang
AU  - Qingqing Zhao
AU  - Xiaorong Yang
PY  - 2019
DA  - 2019/12/24
TI  - Quantile Factor-augmented Prediction Model and Its Applications to Alpha-arbitrage Strategy in China’s Stock Market
BT  - Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019)
PB  - Atlantis Press
SP  - 73
EP  - 78
SN  - 2352-538X
UR  - https://doi.org/10.2991/acsr.k.191223.017
DO  - 10.2991/acsr.k.191223.017
ID  - Wang2019
ER  -