Mapping individual behavior in financial markets: synchronization and anticipation
Abstract In this paper we develop a methodology, based on Mutual Information and Transfer of Entropy, that allows to identify, quantify and map on a network the synchronization and anticipation relationships between financial traders. We apply this methodology to a dataset containing 410,612 $410\te...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-03-01
|
| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1140/epjds/s13688-019-0188-6 |
| _version_ | 1819086771902742528 |
|---|---|
| author | Mario Gutiérrez-Roig Javier Borge-Holthoefer Alex Arenas Josep Perelló |
| author_facet | Mario Gutiérrez-Roig Javier Borge-Holthoefer Alex Arenas Josep Perelló |
| author_sort | Mario Gutiérrez-Roig |
| collection | DOAJ |
| description | Abstract In this paper we develop a methodology, based on Mutual Information and Transfer of Entropy, that allows to identify, quantify and map on a network the synchronization and anticipation relationships between financial traders. We apply this methodology to a dataset containing 410,612 $410\text{,}612$ real buy and sell operations, made by 566 non-professional investors from a private investment firm on 8 different assets from the Spanish IBEX market during a period of time from 2000 to 2008. These networks present a peculiar topology significantly different from the random networks. We seek alternative features based on human behavior that might explain part of those 12,158 $12\text{,}158$ synchronization links and 1031 anticipation links. Thus, we detect that daily synchronization with price (present in 64.90% of investors) and the one-day delay with respect to price (present in 4.38% of investors) play a significant role in the network structure. We find that individuals reaction to daily price changes explains around 20% of the links in the Synchronization Network, and has significant effects on the Anticipation Network. Finally, we show how using these networks we substantially improve the prediction accuracy when Random Forest models are used to nowcast and predict the activity of individual investors. |
| first_indexed | 2024-12-21T21:25:33Z |
| format | Article |
| id | doaj.art-3821d23a72294d7b967b04caa238e66e |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-21T21:25:33Z |
| publishDate | 2019-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-3821d23a72294d7b967b04caa238e66e2022-12-21T18:49:46ZengSpringerOpenEPJ Data Science2193-11272019-03-018111810.1140/epjds/s13688-019-0188-6Mapping individual behavior in financial markets: synchronization and anticipationMario Gutiérrez-Roig0Javier Borge-Holthoefer1Alex Arenas2Josep Perelló3Data Science Lab, Warwick Busniess School, University of WarwickInternet Interdisciplinary Institute (IN3), Universitat Oberta de CatalunyaDepartament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i VirgiliDepartament de Física de la Matèria Condensada, Universitat de BarcelonaAbstract In this paper we develop a methodology, based on Mutual Information and Transfer of Entropy, that allows to identify, quantify and map on a network the synchronization and anticipation relationships between financial traders. We apply this methodology to a dataset containing 410,612 $410\text{,}612$ real buy and sell operations, made by 566 non-professional investors from a private investment firm on 8 different assets from the Spanish IBEX market during a period of time from 2000 to 2008. These networks present a peculiar topology significantly different from the random networks. We seek alternative features based on human behavior that might explain part of those 12,158 $12\text{,}158$ synchronization links and 1031 anticipation links. Thus, we detect that daily synchronization with price (present in 64.90% of investors) and the one-day delay with respect to price (present in 4.38% of investors) play a significant role in the network structure. We find that individuals reaction to daily price changes explains around 20% of the links in the Synchronization Network, and has significant effects on the Anticipation Network. Finally, we show how using these networks we substantially improve the prediction accuracy when Random Forest models are used to nowcast and predict the activity of individual investors.http://link.springer.com/article/10.1140/epjds/s13688-019-0188-6Financial marketsBehavioral economicsTransfer of entropyMutual informationNetworks |
| spellingShingle | Mario Gutiérrez-Roig Javier Borge-Holthoefer Alex Arenas Josep Perelló Mapping individual behavior in financial markets: synchronization and anticipation EPJ Data Science Financial markets Behavioral economics Transfer of entropy Mutual information Networks |
| title | Mapping individual behavior in financial markets: synchronization and anticipation |
| title_full | Mapping individual behavior in financial markets: synchronization and anticipation |
| title_fullStr | Mapping individual behavior in financial markets: synchronization and anticipation |
| title_full_unstemmed | Mapping individual behavior in financial markets: synchronization and anticipation |
| title_short | Mapping individual behavior in financial markets: synchronization and anticipation |
| title_sort | mapping individual behavior in financial markets synchronization and anticipation |
| topic | Financial markets Behavioral economics Transfer of entropy Mutual information Networks |
| url | http://link.springer.com/article/10.1140/epjds/s13688-019-0188-6 |
| work_keys_str_mv | AT mariogutierrezroig mappingindividualbehaviorinfinancialmarketssynchronizationandanticipation AT javierborgeholthoefer mappingindividualbehaviorinfinancialmarketssynchronizationandanticipation AT alexarenas mappingindividualbehaviorinfinancialmarketssynchronizationandanticipation AT josepperello mappingindividualbehaviorinfinancialmarketssynchronizationandanticipation |