New technical indicators and stock returns predictability

By Zhifeng Dai, Huan Zhu, Jie Kang. 2021

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Abstract We find that combining de-noising stock returns by wavelet transform with new proposed technical indicators can significantly improve the accuracy of stock returns forecasts, in which the new technical indicators can directly reflect the trend of stock returns series. Empirical results indicate the stock returns forecasts generated by new technical indicators are statistically and economically significant both in-sample and out-of-sample prediction performance. And when multivariate information is used to predict stock returns, its predictability is also significant. In addition, it is robust for the prediction performance of new indicators using some extension and robustness analysis.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/308134e3cda94f4630c36d440b1c1bfd8eb9ea64