FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading
Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices,...
Gespeichert in:
Veröffentlicht in: | Electronics (Basel) 2024-11, Vol.13 (22), p.4482 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 22 |
container_start_page | 4482 |
container_title | Electronics (Basel) |
container_volume | 13 |
creator | Pan, Qingyi Sun, Suyu Yang, Pei Zhang, Jingyi |
description | Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel architecture called FuturesNet, which uses an InceptionTime module to capture the short-term fluctuations between ask and bid orders, as well as a long-short-term-memory (LSTM) module with skip connections to capture long-term temporal dependencies. We evaluated the performance of FuturesNet using datasets numbered 50, 300, and 500 from the domestic financial market. The comprehensive experimental results show that FuturesNet outperforms other competitive baselines in most settings. Additionally, we conducted ablation studies to interpret the behaviors of FuturesNet. Our code and collected futures datasets are released. |
doi_str_mv | 10.3390/electronics13224482 |
format | Article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_3133015458</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A818202667</galeid><sourcerecordid>A818202667</sourcerecordid><originalsourceid>FETCH-LOGICAL-c196t-ed6e975800efe2304553f1d6cab9f7d30b50bfd4205394477bac9821ced3830b3</originalsourceid><addsrcrecordid>eNptUD1PAzEMjRBIVKW_gCUS85Ukvq-wVYUCUgUdynzK5Zwq1fVSktzAvydVOzBgD7af_J6fTMg9Z3MAyR6xRx29G6wOHITI81pckYlglcykkOL6T39LZiHsWQrJoQY2IZvVGEeP4QPjE12qYxrssKMbFSP6IVBn6MZbjXTVjzqOKlqXUDvQZ3fAEK2mFwG69apL1DtyY1QfcHapU_K1etku37L15-v7crHONJdlzLArUVZFzRgaFMDyogDDu1KrVpqqA9YWrDVdLlgBMs-rqlVa1oJr7E7GW5iSh7Pu0bvvMVlp9m70QzrZAAdgvMiLOm3Nz1s71WNjB-OiVzplhwer3YDGJnxR81owUZZVIsCZoL0LwaNpjt4elP9pOGtO727-eTf8AngRddA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3133015458</pqid></control><display><type>article</type><title>FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Pan, Qingyi ; Sun, Suyu ; Yang, Pei ; Zhang, Jingyi</creator><creatorcontrib>Pan, Qingyi ; Sun, Suyu ; Yang, Pei ; Zhang, Jingyi</creatorcontrib><description>Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel architecture called FuturesNet, which uses an InceptionTime module to capture the short-term fluctuations between ask and bid orders, as well as a long-short-term-memory (LSTM) module with skip connections to capture long-term temporal dependencies. We evaluated the performance of FuturesNet using datasets numbered 50, 300, and 500 from the domestic financial market. The comprehensive experimental results show that FuturesNet outperforms other competitive baselines in most settings. Additionally, we conducted ablation studies to interpret the behaviors of FuturesNet. Our code and collected futures datasets are released.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics13224482</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Ablation ; Datasets ; Deep learning ; Domestic markets ; Economic forecasting ; Electronic trading systems ; Financial analysis ; Financial markets ; Forecasts and trends ; Futures market ; Futures trading ; Global economy ; High frequency trading ; Machine learning ; Modules ; Neural networks ; Prices ; Securities markets ; Statistical methods ; Stochastic models ; Strategic planning (Business) ; Trends ; Volatility</subject><ispartof>Electronics (Basel), 2024-11, Vol.13 (22), p.4482</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c196t-ed6e975800efe2304553f1d6cab9f7d30b50bfd4205394477bac9821ced3830b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Pan, Qingyi</creatorcontrib><creatorcontrib>Sun, Suyu</creatorcontrib><creatorcontrib>Yang, Pei</creatorcontrib><creatorcontrib>Zhang, Jingyi</creatorcontrib><title>FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading</title><title>Electronics (Basel)</title><description>Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel architecture called FuturesNet, which uses an InceptionTime module to capture the short-term fluctuations between ask and bid orders, as well as a long-short-term-memory (LSTM) module with skip connections to capture long-term temporal dependencies. We evaluated the performance of FuturesNet using datasets numbered 50, 300, and 500 from the domestic financial market. The comprehensive experimental results show that FuturesNet outperforms other competitive baselines in most settings. Additionally, we conducted ablation studies to interpret the behaviors of FuturesNet. Our code and collected futures datasets are released.</description><subject>Ablation</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Domestic markets</subject><subject>Economic forecasting</subject><subject>Electronic trading systems</subject><subject>Financial analysis</subject><subject>Financial markets</subject><subject>Forecasts and trends</subject><subject>Futures market</subject><subject>Futures trading</subject><subject>Global economy</subject><subject>High frequency trading</subject><subject>Machine learning</subject><subject>Modules</subject><subject>Neural networks</subject><subject>Prices</subject><subject>Securities markets</subject><subject>Statistical methods</subject><subject>Stochastic models</subject><subject>Strategic planning (Business)</subject><subject>Trends</subject><subject>Volatility</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUD1PAzEMjRBIVKW_gCUS85Ukvq-wVYUCUgUdynzK5Zwq1fVSktzAvydVOzBgD7af_J6fTMg9Z3MAyR6xRx29G6wOHITI81pckYlglcykkOL6T39LZiHsWQrJoQY2IZvVGEeP4QPjE12qYxrssKMbFSP6IVBn6MZbjXTVjzqOKlqXUDvQZ3fAEK2mFwG69apL1DtyY1QfcHapU_K1etku37L15-v7crHONJdlzLArUVZFzRgaFMDyogDDu1KrVpqqA9YWrDVdLlgBMs-rqlVa1oJr7E7GW5iSh7Pu0bvvMVlp9m70QzrZAAdgvMiLOm3Nz1s71WNjB-OiVzplhwer3YDGJnxR81owUZZVIsCZoL0LwaNpjt4elP9pOGtO727-eTf8AngRddA</recordid><startdate>20241101</startdate><enddate>20241101</enddate><creator>Pan, Qingyi</creator><creator>Sun, Suyu</creator><creator>Yang, Pei</creator><creator>Zhang, Jingyi</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20241101</creationdate><title>FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading</title><author>Pan, Qingyi ; Sun, Suyu ; Yang, Pei ; Zhang, Jingyi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c196t-ed6e975800efe2304553f1d6cab9f7d30b50bfd4205394477bac9821ced3830b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Ablation</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Domestic markets</topic><topic>Economic forecasting</topic><topic>Electronic trading systems</topic><topic>Financial analysis</topic><topic>Financial markets</topic><topic>Forecasts and trends</topic><topic>Futures market</topic><topic>Futures trading</topic><topic>Global economy</topic><topic>High frequency trading</topic><topic>Machine learning</topic><topic>Modules</topic><topic>Neural networks</topic><topic>Prices</topic><topic>Securities markets</topic><topic>Statistical methods</topic><topic>Stochastic models</topic><topic>Strategic planning (Business)</topic><topic>Trends</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pan, Qingyi</creatorcontrib><creatorcontrib>Sun, Suyu</creatorcontrib><creatorcontrib>Yang, Pei</creatorcontrib><creatorcontrib>Zhang, Jingyi</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pan, Qingyi</au><au>Sun, Suyu</au><au>Yang, Pei</au><au>Zhang, Jingyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading</atitle><jtitle>Electronics (Basel)</jtitle><date>2024-11-01</date><risdate>2024</risdate><volume>13</volume><issue>22</issue><spage>4482</spage><pages>4482-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel architecture called FuturesNet, which uses an InceptionTime module to capture the short-term fluctuations between ask and bid orders, as well as a long-short-term-memory (LSTM) module with skip connections to capture long-term temporal dependencies. We evaluated the performance of FuturesNet using datasets numbered 50, 300, and 500 from the domestic financial market. The comprehensive experimental results show that FuturesNet outperforms other competitive baselines in most settings. Additionally, we conducted ablation studies to interpret the behaviors of FuturesNet. Our code and collected futures datasets are released.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics13224482</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2079-9292 |
ispartof | Electronics (Basel), 2024-11, Vol.13 (22), p.4482 |
issn | 2079-9292 2079-9292 |
language | eng |
recordid | cdi_proquest_journals_3133015458 |
source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
subjects | Ablation Datasets Deep learning Domestic markets Economic forecasting Electronic trading systems Financial analysis Financial markets Forecasts and trends Futures market Futures trading Global economy High frequency trading Machine learning Modules Neural networks Prices Securities markets Statistical methods Stochastic models Strategic planning (Business) Trends Volatility |
title | FuturesNet: Capturing Patterns of Price Fluctuations in Domestic Futures Trading |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T03%3A50%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=FuturesNet:%20Capturing%20Patterns%20of%20Price%20Fluctuations%20in%20Domestic%20Futures%20Trading&rft.jtitle=Electronics%20(Basel)&rft.au=Pan,%20Qingyi&rft.date=2024-11-01&rft.volume=13&rft.issue=22&rft.spage=4482&rft.pages=4482-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics13224482&rft_dat=%3Cgale_proqu%3EA818202667%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3133015458&rft_id=info:pmid/&rft_galeid=A818202667&rfr_iscdi=true |