Identifying actionable serial correlations in financial markets

Financial markets are complex systems where information processing occurs at multiple levels. One signature of this information processing is the existence of recurrent sequences. In this paper, we developed a procedure for finding these sequences and a process of statistical significance testing to...

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Main Authors: Cheong, Siew Ann, Lee, Yann Wei, Li, Ying Ying, Lim, Jia Qing, Tan, Jadie Jiok Duan, Teo, Joan Xin Ping
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/151067
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1510672023-02-28T20:00:32Z Identifying actionable serial correlations in financial markets Cheong, Siew Ann Lee, Yann Wei Li, Ying Ying Lim, Jia Qing Tan, Jadie Jiok Duan Teo, Joan Xin Ping School of Physical and Mathematical Sciences Complexity Institute Science::Mathematics Financial Markets Serial Correlations Financial markets are complex systems where information processing occurs at multiple levels. One signature of this information processing is the existence of recurrent sequences. In this paper, we developed a procedure for finding these sequences and a process of statistical significance testing to identify the most meaningful ones. To do so, we downloaded daily closing prices of the Dow Jones Industrial Average component stocks, as well as various assets like stock market indices, United States government bonds, precious metals, commodities, oil and gas, and foreign exchange. We mapped each financial instrument to a letter and their upward movements to words, before testing the frequencies of these words against a null model obtained by reshuffling the empirical time series. We then identify market leaders and followers from the statistically significant words in different cross sections of financial instruments, and interpret actionable trends that can be traded upon. Ministry of Education (MOE) Published version YWL, YYL, JL, JT, and XT thank the Singapore Ministry of Education Science Mentorship Program for the opportunity to participate in this study. 2021-06-28T02:02:32Z 2021-06-28T02:02:32Z 2021 Journal Article Cheong, S. A., Lee, Y. W., Li, Y. Y., Lim, J. Q., Tan, J. J. D. & Teo, J. X. P. (2021). Identifying actionable serial correlations in financial markets. Frontiers in Applied Mathematics and Statistics, 7, 641595-. https://dx.doi.org/10.3389/fams.2021.641595 2297-4687 https://hdl.handle.net/10356/151067 10.3389/fams.2021.641595 2-s2.0-85105558031 7 641595 en Frontiers in Applied Mathematics and Statistics © 2021 Cheong, Lee, Li, Lim, Tan and Teo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Mathematics
Financial Markets
Serial Correlations
spellingShingle Science::Mathematics
Financial Markets
Serial Correlations
Cheong, Siew Ann
Lee, Yann Wei
Li, Ying Ying
Lim, Jia Qing
Tan, Jadie Jiok Duan
Teo, Joan Xin Ping
Identifying actionable serial correlations in financial markets
description Financial markets are complex systems where information processing occurs at multiple levels. One signature of this information processing is the existence of recurrent sequences. In this paper, we developed a procedure for finding these sequences and a process of statistical significance testing to identify the most meaningful ones. To do so, we downloaded daily closing prices of the Dow Jones Industrial Average component stocks, as well as various assets like stock market indices, United States government bonds, precious metals, commodities, oil and gas, and foreign exchange. We mapped each financial instrument to a letter and their upward movements to words, before testing the frequencies of these words against a null model obtained by reshuffling the empirical time series. We then identify market leaders and followers from the statistically significant words in different cross sections of financial instruments, and interpret actionable trends that can be traded upon.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Cheong, Siew Ann
Lee, Yann Wei
Li, Ying Ying
Lim, Jia Qing
Tan, Jadie Jiok Duan
Teo, Joan Xin Ping
format Article
author Cheong, Siew Ann
Lee, Yann Wei
Li, Ying Ying
Lim, Jia Qing
Tan, Jadie Jiok Duan
Teo, Joan Xin Ping
author_sort Cheong, Siew Ann
title Identifying actionable serial correlations in financial markets
title_short Identifying actionable serial correlations in financial markets
title_full Identifying actionable serial correlations in financial markets
title_fullStr Identifying actionable serial correlations in financial markets
title_full_unstemmed Identifying actionable serial correlations in financial markets
title_sort identifying actionable serial correlations in financial markets
publishDate 2021
url https://hdl.handle.net/10356/151067
_version_ 1759857857227915264