Cross-Lingual News Event Correlation for Stock Market Trend Prediction
In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset...
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| creator | Arshad, Sahar Azhar, Nikhar Sajid, Sana Latif, Seemab Latif, Rabia |
| description | In the modern economic landscape, integrating financial services with
Financial Technology (FinTech) has become essential, particularly in stock
trend analysis. This study addresses the gap in comprehending financial
dynamics across diverse global economies by creating a structured financial
dataset and proposing a cross-lingual Natural Language-based Financial
Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing
sentiment analysis, Named Entity Recognition (NER), and semantic textual
similarity, we conducted an analytical examination of news articles to extract,
map, and visualize financial event timelines, uncovering the correlation
between news events and stock market trends. Our method demonstrated a
meaningful correlation between stock price movements and cross-linguistic news
sentiments, validated by processing two-year cross-lingual news data on two
prominent sectors of the Pakistan Stock Exchange. This study offers significant
insights into key events, ensuring a substantial decision margin for investors
through effective visualization and providing optimal investment opportunities. |
| doi_str_mv | 10.48550/arxiv.2410.00024 |
| format | Article |
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Financial Technology (FinTech) has become essential, particularly in stock
trend analysis. This study addresses the gap in comprehending financial
dynamics across diverse global economies by creating a structured financial
dataset and proposing a cross-lingual Natural Language-based Financial
Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing
sentiment analysis, Named Entity Recognition (NER), and semantic textual
similarity, we conducted an analytical examination of news articles to extract,
map, and visualize financial event timelines, uncovering the correlation
between news events and stock market trends. Our method demonstrated a
meaningful correlation between stock price movements and cross-linguistic news
sentiments, validated by processing two-year cross-lingual news data on two
prominent sectors of the Pakistan Stock Exchange. This study offers significant
insights into key events, ensuring a substantial decision margin for investors
through effective visualization and providing optimal investment opportunities.</description><identifier>DOI: 10.48550/arxiv.2410.00024</identifier><language>eng</language><subject>Computer Science - Learning ; Quantitative Finance - Statistical Finance</subject><creationdate>2024-09</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2410.00024$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2410.00024$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Arshad, Sahar</creatorcontrib><creatorcontrib>Azhar, Nikhar</creatorcontrib><creatorcontrib>Sajid, Sana</creatorcontrib><creatorcontrib>Latif, Seemab</creatorcontrib><creatorcontrib>Latif, Rabia</creatorcontrib><title>Cross-Lingual News Event Correlation for Stock Market Trend Prediction</title><description>In the modern economic landscape, integrating financial services with
Financial Technology (FinTech) has become essential, particularly in stock
trend analysis. This study addresses the gap in comprehending financial
dynamics across diverse global economies by creating a structured financial
dataset and proposing a cross-lingual Natural Language-based Financial
Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing
sentiment analysis, Named Entity Recognition (NER), and semantic textual
similarity, we conducted an analytical examination of news articles to extract,
map, and visualize financial event timelines, uncovering the correlation
between news events and stock market trends. Our method demonstrated a
meaningful correlation between stock price movements and cross-linguistic news
sentiments, validated by processing two-year cross-lingual news data on two
prominent sectors of the Pakistan Stock Exchange. This study offers significant
insights into key events, ensuring a substantial decision margin for investors
through effective visualization and providing optimal investment opportunities.</description><subject>Computer Science - Learning</subject><subject>Quantitative Finance - Statistical Finance</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMgEKGBgYGJlwMrg5F-UXF-v6ZOallybmKPillhcruJal5pUoOOcXFaXmJJZk5ucppOUXKQSX5CdnK_gmFmWnliiEFKXmpSgEFKWmZCaDVPAwsKYl5hSn8kJpbgZ5N9cQZw9dsI3xBUWZuYlFlfEgm-PBNhsTVgEAQcA4gg</recordid><startdate>20240916</startdate><enddate>20240916</enddate><creator>Arshad, Sahar</creator><creator>Azhar, Nikhar</creator><creator>Sajid, Sana</creator><creator>Latif, Seemab</creator><creator>Latif, Rabia</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240916</creationdate><title>Cross-Lingual News Event Correlation for Stock Market Trend Prediction</title><author>Arshad, Sahar ; Azhar, Nikhar ; Sajid, Sana ; Latif, Seemab ; Latif, Rabia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2410_000243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Learning</topic><topic>Quantitative Finance - Statistical Finance</topic><toplevel>online_resources</toplevel><creatorcontrib>Arshad, Sahar</creatorcontrib><creatorcontrib>Azhar, Nikhar</creatorcontrib><creatorcontrib>Sajid, Sana</creatorcontrib><creatorcontrib>Latif, Seemab</creatorcontrib><creatorcontrib>Latif, Rabia</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Arshad, Sahar</au><au>Azhar, Nikhar</au><au>Sajid, Sana</au><au>Latif, Seemab</au><au>Latif, Rabia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cross-Lingual News Event Correlation for Stock Market Trend Prediction</atitle><date>2024-09-16</date><risdate>2024</risdate><abstract>In the modern economic landscape, integrating financial services with
Financial Technology (FinTech) has become essential, particularly in stock
trend analysis. This study addresses the gap in comprehending financial
dynamics across diverse global economies by creating a structured financial
dataset and proposing a cross-lingual Natural Language-based Financial
Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing
sentiment analysis, Named Entity Recognition (NER), and semantic textual
similarity, we conducted an analytical examination of news articles to extract,
map, and visualize financial event timelines, uncovering the correlation
between news events and stock market trends. Our method demonstrated a
meaningful correlation between stock price movements and cross-linguistic news
sentiments, validated by processing two-year cross-lingual news data on two
prominent sectors of the Pakistan Stock Exchange. This study offers significant
insights into key events, ensuring a substantial decision margin for investors
through effective visualization and providing optimal investment opportunities.</abstract><doi>10.48550/arxiv.2410.00024</doi><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_arxiv_primary_2410_00024 |
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| subjects | Computer Science - Learning Quantitative Finance - Statistical Finance |
| title | Cross-Lingual News Event Correlation for Stock Market Trend Prediction |
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