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...
Saved in:
Main Authors: | , , , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/151067 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-151067 |
---|---|
record_format |
dspace |
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 |