Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers
<p>One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Taylor and Francis
2024
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| _version_ | 1826311796159414272 |
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| author | Arroyo, A Cartea, A Moreno-Pino, F Zohren, S |
| author_facet | Arroyo, A Cartea, A Moreno-Pino, F Zohren, S |
| author_sort | Arroyo, A |
| collection | OXFORD |
| description | <p>One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional-Transformer encoder and a monotonic neural network decoder. We use <em>proper scoring rules</em> to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Finally, we carry out a statistical analysis of the fill probability of orders placed in the order book (e.g. within the bid-ask spread) for assets with different queue dynamics and trading activity.</p> |
| first_indexed | 2024-03-07T08:16:31Z |
| format | Journal article |
| id | oxford-uuid:03367c56-81e6-4fea-b51e-18885e3f6680 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:16:31Z |
| publishDate | 2024 |
| publisher | Taylor and Francis |
| record_format | dspace |
| spelling | oxford-uuid:03367c56-81e6-4fea-b51e-18885e3f66802024-01-11T09:26:00ZDeep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformersJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:03367c56-81e6-4fea-b51e-18885e3f6680EnglishSymplectic ElementsTaylor and Francis2024Arroyo, ACartea, AMoreno-Pino, FZohren, S<p>One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional-Transformer encoder and a monotonic neural network decoder. We use <em>proper scoring rules</em> to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Finally, we carry out a statistical analysis of the fill probability of orders placed in the order book (e.g. within the bid-ask spread) for assets with different queue dynamics and trading activity.</p> |
| spellingShingle | Arroyo, A Cartea, A Moreno-Pino, F Zohren, S Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers |
| title | Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers |
| title_full | Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers |
| title_fullStr | Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers |
| title_full_unstemmed | Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers |
| title_short | Deep attentive survival analysis in limit order books: estimating fill probabilities with convolutional-transformers |
| title_sort | deep attentive survival analysis in limit order books estimating fill probabilities with convolutional transformers |
| work_keys_str_mv | AT arroyoa deepattentivesurvivalanalysisinlimitorderbooksestimatingfillprobabilitieswithconvolutionaltransformers AT carteaa deepattentivesurvivalanalysisinlimitorderbooksestimatingfillprobabilitieswithconvolutionaltransformers AT morenopinof deepattentivesurvivalanalysisinlimitorderbooksestimatingfillprobabilitieswithconvolutionaltransformers AT zohrens deepattentivesurvivalanalysisinlimitorderbooksestimatingfillprobabilitieswithconvolutionaltransformers |