TITLE:
How External Trends and Internal Components Decomposition Method Improve the Predictability of Financial Time Series?
AUTHORS:
Fatene Dioubi, Adnan Khurshid
KEYWORDS:
Stock Market, Returns, Predictability, Decomposition Method, Internal Components, Approximate Entropy
JOURNAL NAME:
Journal of Financial Risk Management,
Vol.11 No.3,
August
30,
2022
ABSTRACT: Accurately predicting stock market returns can pay off economically not
by yielding significant profit but rather by preventing the loss of a large sum
of money. Therefore, stocks with a high degree of predictability gain more
attention. This study is intended to investigate the impact of decomposing
returns on improving predictability. The decomposition method separates the
return’s internal components from their external trend. The approximate entropy
technique is then applied to quantify their randomness amounts. The results
reveal that the decomposition method improved the predictability of returns
from S & P 500,
Nasdaq 100, SSE and SZSE 500 stocks. Furthermore, the outcomes show that using the return’s absolute value can
further enhance its performance. Moreover, this study shows that S & P 500 intraday data are more
predictable than their daily data. These findings propose incorporating the
decomposition method in the prediction process to improve the predictability
and maximise the investor’s profit and minimise their risk.