Statistical predictions of trading strategies in electronic markets
We build statistical models to describe how market participants choose the direction, price, and volume of orders. Our dataset, which spans sixteen weeks for four shares traded in Euronext Amsterdam, contains all messages sent to the exchange and includes algorithm identification and member identifi...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2024
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| _version_ | 1811141326205353984 |
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| author | Cartea, Á Cohen, SN Graumans, R Labyad, S Sanchez Betancourt, L van Veldhuijzen, L |
| author_facet | Cartea, Á Cohen, SN Graumans, R Labyad, S Sanchez Betancourt, L van Veldhuijzen, L |
| author_sort | Cartea, Á |
| collection | OXFORD |
| description | We build statistical models to describe how market participants choose the direction, price, and volume of orders. Our dataset, which spans sixteen weeks for four shares traded in Euronext Amsterdam,
contains all messages sent to the exchange and includes algorithm identification and member identification. We obtain reliable out-of-sample predictions and report the top features that predict direction,
price, and volume of orders sent to the exchange. The coefficients from the fitted models are used to
cluster trading behaviour and we find that algorithms registered as Liquidity Providers exhibit the widest
range of trading behaviour among dealing capacities. In particular, for the most liquid share in our study,
we identify three types of behaviour that we call (i) directional trading, (ii) opportunistic trading, and
(iii) market making, and we find that around one third of Liquidity Providers behave as market markers. |
| first_indexed | 2024-09-25T04:36:06Z |
| format | Journal article |
| id | oxford-uuid:3c42f141-6410-4040-814a-f895a67fe6b3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:36:06Z |
| publishDate | 2024 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:3c42f141-6410-4040-814a-f895a67fe6b32024-09-20T14:19:16ZStatistical predictions of trading strategies in electronic marketsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:3c42f141-6410-4040-814a-f895a67fe6b3EnglishSymplectic ElementsOxford University Press2024Cartea, ÁCohen, SNGraumans, RLabyad, SSanchez Betancourt, Lvan Veldhuijzen, LWe build statistical models to describe how market participants choose the direction, price, and volume of orders. Our dataset, which spans sixteen weeks for four shares traded in Euronext Amsterdam, contains all messages sent to the exchange and includes algorithm identification and member identification. We obtain reliable out-of-sample predictions and report the top features that predict direction, price, and volume of orders sent to the exchange. The coefficients from the fitted models are used to cluster trading behaviour and we find that algorithms registered as Liquidity Providers exhibit the widest range of trading behaviour among dealing capacities. In particular, for the most liquid share in our study, we identify three types of behaviour that we call (i) directional trading, (ii) opportunistic trading, and (iii) market making, and we find that around one third of Liquidity Providers behave as market markers. |
| spellingShingle | Cartea, Á Cohen, SN Graumans, R Labyad, S Sanchez Betancourt, L van Veldhuijzen, L Statistical predictions of trading strategies in electronic markets |
| title | Statistical predictions of trading strategies in electronic markets |
| title_full | Statistical predictions of trading strategies in electronic markets |
| title_fullStr | Statistical predictions of trading strategies in electronic markets |
| title_full_unstemmed | Statistical predictions of trading strategies in electronic markets |
| title_short | Statistical predictions of trading strategies in electronic markets |
| title_sort | statistical predictions of trading strategies in electronic markets |
| work_keys_str_mv | AT carteaa statisticalpredictionsoftradingstrategiesinelectronicmarkets AT cohensn statisticalpredictionsoftradingstrategiesinelectronicmarkets AT graumansr statisticalpredictionsoftradingstrategiesinelectronicmarkets AT labyads statisticalpredictionsoftradingstrategiesinelectronicmarkets AT sanchezbetancourtl statisticalpredictionsoftradingstrategiesinelectronicmarkets AT vanveldhuijzenl statisticalpredictionsoftradingstrategiesinelectronicmarkets |