[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-563285-105":3,"doc-detail-563285-en":74,"detail-sidebar-cat-1-en-105":89},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":67,"head_meta":69,"extra_data":71,"updated_unix":73},105,"en","predicting-oil-prices-with-llms-tapping-into-opec-and-iea-reports","Predicting Oil Prices with LLMs - Tapping into OPEC and IEA Reports","","Central banks need dependable tools to forecast oil price drivers. This work tests whether large language models can extract signal from the qualitative content of OPEC and IEA monthly reports, which traditional approaches may miss. It proposes an LLM forecasting pipeline using GPT-4o report summarization and semantic RAG over historical summaries, then produces Brent forecasts with agnostic and structural decompositions plus qualitative rationales. Results show strongest accuracy at short horizons and informative handling of structural supply and demand drivers, including supply-shock narratives that aid interpretation.",{"@graph":14,"@context":66},[15,34,49],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/posters/","Posters",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/predicting-oil-prices-with-llms-tapping-into-opec-and-iea-reports/563285/",4,{"url":32,"name":10,"@type":35,"author":36,"headline":10,"publisher":39,"fileFormat":42,"inLanguage":8,"description":12,"dateModified":43,"datePublished":43,"encodingFormat":42,"isAccessibleForFree":44,"interactionStatistic":45},"DigitalDocument",{"name":37,"@type":38},"Patrick","Person",{"url":19,"name":40,"@type":41},"DocShare","Organization","application/pdf","2026-10-12",true,{"@type":46,"interactionType":47,"userInteractionCount":4},"InteractionCounter",{"@type":48},"ViewAction",{"@type":50,"mainEntity":51},"FAQPage",[52,58,62],{"name":53,"@type":54,"acceptedAnswer":55},"What is the main question the document addresses?","Question",{"text":56,"@type":57},"Can LLMs use OPEC and IEA reports to improve oil price forecasts, and do these sources differ in predictive content?","Answer",{"name":59,"@type":54,"acceptedAnswer":60},"How does the proposed forecasting pipeline use LLMs and reports?",{"text":61,"@type":57},"It summarizes monthly OPEC/IEA reports with GPT-4o, retrieves similar past summaries via semantic RAG using embeddings, and then uses an LLM to forecast Brent prices and driver decompositions.",{"name":63,"@type":54,"acceptedAnswer":64},"What do the results indicate about forecast accuracy?",{"text":65,"@type":57},"Forecast performance is strongest at short horizons: at 1-month, both OPEC and IEA outperform futures in the pre-cutoff period, while post-cutoff performance favors OPEC at 1-month and IEA at longer horizons.","https://schema.org",{"og:url":32,"og:type":68,"og:title":10,"og:site_name":40,"og:description":12},"article",{"robots":70,"canonical":32},"index,follow",{"doc_id":72,"site_id":7},563285,1791826455,{"code":4,"msg":75,"data":76},"success",{"doc_id":72,"user_id":77,"nickname":37,"user_avatar":78,"doc_module":22,"category_id":79,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":80,"file_id":81,"file_url":82,"file_type":83,"file_size":84,"view_count":4,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":22,"language":85,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":86,"faqs":87,"seo_title":88,"seo_description":12,"update_tm":73,"read_time":4},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",15,"Predicting Oil Prices with LLMs: Tapping into OPEC and IEA Reports  \nG. Lopardo, A-S. Manu, A. Stalla-Bourdillon, I. Van Robays European Central Bank  \n{gianluigi.lopardo, ana-simona.manu, arthur.stalla-bourdillon}@ecb.europa.eu  \n1 . Motivation and Contributions  \nContext:  \n􀀄 Central banks need reliable tools to forecast and analyze oil price drivers  \n􀀄 OPEC and IEA reports offer rich qualitative information on oil supply & demand factors which traditional methods fail to capture  \nQ1: Can LLMs tap into OPEC and IEA reports to improve oil price forecasts? Q2: Are there differences in the predictive content of OPEC vs. IEA reports?  \nContributions:  \n1. Novel LLM + Semantic RAG framework for oil price forecasting at 1-12 month horizons  \n2. First paper to forecast Structural drivers (forward demand, current demand, supply) from SVAR of Gazzani et al. (2024)  \n3. Qualitative rationales: the model provides explanations for each forecast  \n2 . Methodology  \nStep 1—Data Processing & Summarization  \n􀀄 Monthly Oil Reports from IEA and OPEC (May 2014 – Dec 2025)  \n􀀄 Reports summarized via GPT-4o → concise summaries St( s )  \n􀀄 Embeddings via OpenAI’s text-embedding-3-large  \nStep 2—Semantic Retrieval-Augmented Generation (RAG)  \n􀀄 For each forecast at t, retrieve k most similar historical summaries via cosine similarity on embeddings  \n􀀄 Provides relevant context from similar past market episodes  \nStep 3—LLM Forecasting (OpenAI o3)  \nInput: summary St + retrieved context Rt + prices, futures, past SVAR drivers  \n4 Outputs:  \n􀀄 Price forecast for Brent at horizon h  \n􀀄 Agnostic decomposition: Predicted price change into four drivers: target-month demand/supply and forward-looking demand/supply  \n􀀄 Structural decomposition forecast (Gazzani et al. 2024): forward demand, current demand, oil supply  \n􀀄 Qualitative rationale for price and driver forecasts  \nForecasting Pipeline  \n3 . Forecasting Results  \n􀀄 Predicted variable: Brent price and Structural drivers from Gazzani et al. (2024);  \n􀀄 Pre-cutoff: Jan 2015 – May 2024 (∼111 obs.)—before o3’s knowledge cutoff  \n􀀄 Post-cutoff: Jun 2024 – Dec 2025 (6–17 obs.)—out-of-sample  \nPrice Forecast & Structural Driver Accuracy  \n\n| Horizon | Source | Price Forecast (RMSE) |  | Structural Drivers – Pre-Cutoff (RMSE) |  |  |  | N pre | N post |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n|  |  | Pre | Post | Fwd Dem. | Cur. Dem. | Supply | Avg. |  |  |\n| 1m | OPEC | 3.893*** | 3.175** | 2.821*** | 2.326** | 3.295*** | 2.814*** | 111 | 17 |\n|  | IEA | 4.229*** | 3.660 | 3.173** | 2.197*** | 3.351*** | 2.907*** | 111 | 17 |\n|  | Futures | 4.826 | 3.645 | — | — | — | — | — | 17 |\n|  | Zero | — | — | 2.960 | 2.609 | 3.704 | 3.091 | 111 | — |\n| 3m | OPEC | 10.356*** | 5.868 | 5.195** | 4.325*** | 6.658** | 5.393*** | 112 | 15 |\n|  | IEA | 11.549 | 5.295*** | 5.197** | 4.605** | 7.024 | 5.608** | 112 | 15 |\n|  | Futures | 11.613 | 5.849 | — | — | — | — | — | 15 |\n|  | Zero | — | — | 5.453 | 4.783 | 6.894 | 5.710 | 112 | — |\n| 6m | OPEC | 14.658*** | 4.371 | 6.870* | 5.812*** | 10.370 | 7.684** | 112 | 12 |\n|  | IEA | 14.866*** | 3.234*** | 6.789** | 5.989** | 10.141 | 7.640** | 112 | 12 |\n|  | Futures | 15.715 | 3.566 | — | — | — | — | — | 12 |\n|  | Zero | — | — | 7.060 | 6.447 | 9.880 | 7.796 | 112 | — |\n| 12m | OPEC | 18.274*** | 10.210 | 8.929* | 7.265** | 11.890* | 9.361*** | 112 | 6 |\n|  | IEA | 19.181** | 6.714 | 9.050 | 7.189** | 12.631 | 9.624*** | 112 | 6 |\n|  | Futures | 20.959 | 5.295 | — | — | — | — | — | 6 |\n|  | Zero | — | — | 9.460 | 8.414 | 12.627 | 10.167 | 112 | — |\n\nPrice: Clark-West test vs. futures (***p\u003C0.01, **p\u003C0.05, *p\u003C0.1). Drivers: Clark-West test vs. zero benchmark.  \nKey findings:  \n􀀄 Performance is strongest at short horizons: at 1-month, OPEC & IEA significantly outperform futures in the pre-cutoff period  \n􀀄 OPEC dominates pre-cutoff, consistent with richer supply-side information  \n􀀄 Post-cutoff: OPEC remains best at 1-month; IEA performs better at ","cbCaitWZvSgdDgjF","https://ap.wps.com/l/cbCaitWZvSgdDgjF","pdf",499589,"English","# Motivation and Contributions\n# Methodology\n## Step 1—Data Processing & Summarization\n## Step 2—Semantic Retrieval-Augmented Generation (RAG)\n## Step 3—LLM Forecasting\n# Forecasting Results\n## Price Forecast & Structural Driver Accuracy\n# Interpretability & OPEC vs. IEA","[{\"question\":\"What is the main question the document addresses?\",\"answer\":\"Can LLMs use OPEC and IEA reports to improve oil price forecasts, and do these sources differ in predictive content?\"},{\"question\":\"How does the proposed forecasting pipeline use LLMs and reports?\",\"answer\":\"It summarizes monthly OPEC/IEA reports with GPT-4o, retrieves similar past summaries via semantic RAG using embeddings, and then uses an LLM to forecast Brent prices and driver decompositions.\"},{\"question\":\"What do the results indicate about forecast accuracy?\",\"answer\":\"Forecast performance is strongest at short horizons: at 1-month, both OPEC and IEA outperform futures in the pre-cutoff period, while post-cutoff performance favors OPEC at 1-month and IEA at longer horizons.\"}]","Predicting Oil Prices with LLMs - Tapping into OPEC and IEA Reports | PDF",{"code":4,"msg":75,"data":90},[91,96,101,106,109,114,119,124,129],{"id":92,"doc_module":22,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},11,"Presentations",90,"presentations",{"id":97,"doc_module":22,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},12,"Resumes",80,"resumes",{"id":102,"doc_module":22,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},14,"Invoices",70,"invoices",{"id":79,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":107,"slug":108},60,"posters",{"id":110,"doc_module":22,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},16,"Social Media",50,"social-media",{"id":115,"doc_module":22,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},17,"Forms",40,"forms",{"id":120,"doc_module":22,"doc_module_name":25,"category_name":121,"show_sort_weight":122,"slug":123},18,"Letters",30,"letters",{"id":125,"doc_module":22,"doc_module_name":25,"category_name":126,"show_sort_weight":127,"slug":128},21,"Paper Templates",5,"papers-templates",{"id":130,"doc_module":22,"doc_module_name":25,"category_name":131,"show_sort_weight":4,"slug":132},158,"General","general-158"]