Access historical and point-in-time financial statements, ratios, multiples, and press releases, with LSEG's S&P Compustat Database.

The dataset is downloaded from the "Compustat Global - Annual Fundamentals" database. The dataset contains all Dutch listed firms in the period 1998-2020. The variables are mainly financial statement line items related to firm fundamentals and financial reporting / audit quality.

The global financial database market is experiencing robust growth, driven by increasing demand for real-time data analytics and insights across various financial sectors. The market, currently estimated at $15 billion in 2025, is projected to expand at a compound annual growth rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This expansion is fueled by several key factors. The rise of algorithmic trading and quantitative finance necessitates access to high-quality, comprehensive financial data, driving demand for both real-time and historical databases. Moreover, regulatory compliance requirements are pushing financial institutions to invest in robust data management systems, contributing to market growth. The increasing adoption of cloud-based solutions and advanced analytical tools further accelerates market expansion. The market is segmented by application (personal and commercial use) and database type (real-time and historical). The commercial segment currently dominates, propelled by the needs of large financial institutions, investment banks, and asset management firms. However, the personal use segment is expected to witness significant growth driven by the increasing accessibility of financial data and analytical tools to individual investors. Geographical distribution shows a strong presence in North America and Europe, which are expected to remain dominant markets due to the established financial infrastructure and advanced technological capabilities. However, Asia-Pacific is anticipated to demonstrate the fastest growth, driven by increasing economic activity and the expansion of financial markets in emerging economies. Competition is intense, with established players like Bloomberg and Refinitiv (Thomson Reuters) alongside emerging niche players. The competitive landscape is marked by both established giants and agile newcomers. Established players, like Bloomberg, Thomson Reuters, and WRDS, leverage their extensive data networks and brand reputation. However, these are challenged by newer entrants offering innovative solutions and specialized datasets targeting specific niche markets. The ongoing technological advancements, such as the rise of big data analytics and artificial intelligence, presents both opportunities and challenges. While AI-powered analytics unlock deeper insights from financial data, the need to adapt to evolving technologies and data security concerns require substantial investment. Regulatory changes and data privacy concerns also represent potential restraints, requiring continuous adaptation and compliance measures. The future of the market hinges on the ability of players to innovate, adapt to evolving regulations, and meet the increasing demand for speed, accuracy, and comprehensive financial data insights. The market's trajectory strongly suggests a promising future for both established and emerging companies.

We examine how firms hedge in financial distress. Using hand-collected data from oil and gas producers, we find that derivative portfolios in these firms are characterized by short put options. These positions are part of a composite three-way collar strategy that combines buying put options and selling put and call options with differing strike prices. We show that because liquidity demand varies with the degree of financial distress, the three-way collar strategy is the optimal risk management strategy that preserves incentives for future growth.

The sample consists of publicly traded oil and gas producers in the US (SIC code 1311) between Q1:2013 and Q4:2015. Hedging strategies are hand-coded based on quarterly reports (10Q/10-Q reports). We sum each firm's outstanding derivatives positions regardless of maturity for each quarter and create a variable per hedging strategy that takes the value 1 if the sum is positive, zero otherwise. We classify individual firms’ hedge portfolios into five distinct hedging strategies based on the character of the provided protection and the cash flow impact. The dataset contains the classifiers for these five hedging strategies and is identified by quarter and global company key (GVKEY).

The dataset contains quarterly classification of US oil companies' hedging strategies over the period 2013-2015. The strategies are classified based on reporting in each company's quarterly report. Five strategies are identified (described in the data file). Companies are identified by Global Company Key. The Global Company Key or GVKEY is a unique six-digit number key assigned to each company in the Capital IQ Compustat database

We examine how firms hedge in financial distress. Using hand-collected data from oil and gas producers, we find that derivative portfolios in these firms are characterized by short put options. These positions are part of a composite three-way collar strategy that combines buying put options and selling put and call options with differing strike prices. We show that because liquidity demand varies with the degree of financial distress, the three-way collar strategy is the optimal risk management strategy that preserves incentives for future growth. The sample consists of publicly traded oil and gas producers in the US (SIC code 1311) between Q1:2013 and Q4:2015. Hedging strategies are hand-coded based on quarterly reports (10Q/10-Q reports). We sum each firm's outstanding derivatives positions regardless of maturity for each quarter and create a variable per hedging strategy that takes the value 1 if the sum is positive, zero otherwise. We classify individual firms’ hedge portfolios into five distinct hedging strategies based on the character of the provided protection and the cash flow impact. The dataset contains the classifiers for these five hedging strategies and is identified by quarter and global company key (GVKEY). The dataset contains quarterly classification of US oil companies' hedging strategies over the period 2013-2015. The strategies are classified based on reporting in each company's quarterly report. Five strategies are identified (described in the data file). Companies are identified by Global Company Key. The Global Company Key or GVKEY is a unique six-digit number key assigned to each company in the Capital IQ Compustat database

Aħna ninvestigaw kif il-kumpaniji f’diffikultà finanzjarja jużaw id-derivattivi għall-iħħeġġjar. Aħna nistudjaw il-produtturi taż-żejt tal-Istati Uniti matul il-perjodu Q1:2013 sa Q4:2015. L-istrateġiji tal-iħħeġġjar huma kkodifikati bl-idejn bl-użu tar-rapporti trimestrali tal-kumpaniji. Noħolqu varjabbli għal kull strateġija ta’ ħħeġġjar li tassumi valur ta’ 1 jekk il-kumpanija tuża l-istrateġija ta’ ħħeġġjar, inkella żero. Aħna nikklassifikaw il-portafolli tad-derivattivi tal-kumpaniji f'ħames strateġiji distinti ta 'hedging ibbażati fuq il-protezzjoni li joffru u kif jaffettwaw il-flussi tal-flus tal-kumpanija. Is-sett tad-data fih il-varjabbli għall-ħames strateġiji ta’ ħħeġġjar u huwa identifikat skont it-trimestru u l-kjavi globali tal-kumpanija (GVKEY). Is-sett ta’ data fih klassifikazzjoni trimestrali tal-istrateġiji ta’ ħħeġġjar tal-kumpaniji taż-żejt tal-Istati Uniti matul il-perjodu 2013-2015. L-istrateġiji huma kklassifikati abbażi tar-rapportar fir-rapport trimestrali ta’ kull kumpanija. Ġew identifikati ħames strateġiji (deskritti fil-fajl tad-data). Il-kumpaniji huma identifikati bil-Kjavi Globali tal-Kumpanija. Global Company Key jew GVKEY huwa kodiċi uniku ta 'sitt ċifri assenjat lil kull kumpanija fid-database Capital IQ Compustat. Aħna ninvestigaw kif il-kumpaniji f’diffikultà finanzjarja jużaw id-derivattivi għall-iħħeġġjar. Aħna nistudjaw il-produtturi taż-żejt tal-Istati Uniti matul il-perjodu Q1:2013 sa Q4:2015. L-istrateġiji tal-iħħeġġjar huma kkodifikati bl-idejn bl-użu tar-rapporti trimestrali tal-kumpaniji. Noħolqu varjabbli għal kull strateġija ta’ ħħeġġjar li tassumi valur ta’ 1 jekk il-kumpanija tuża l-istrateġija ta’ ħħeġġjar, inkella żero. Aħna nikklassifikaw il-portafolli tad-derivattivi tal-kumpaniji f'ħames strateġiji distinti ta 'hedging ibbażati fuq il-protezzjoni li joffru u kif jaffettwaw il-flussi tal-flus tal-kumpanija. Is-sett tad-data fih il-varjabbli għall-ħames strateġiji ta’ ħħeġġjar u huwa identifikat skont it-trimestru u l-kjavi globali tal-kumpanija (GVKEY). Is-sett ta’ data fih klassifikazzjoni trimestrali tal-istrateġiji ta’ ħħeġġjar tal-kumpaniji taż-żejt tal-Istati Uniti matul il-perjodu 2013-2015. L-istrateġiji huma kklassifikati abbażi tar-rapportar fir-rapport trimestrali ta’ kull kumpanija. Ġew identifikati ħames strateġiji (deskritti fil-fajl tad-data). Il-kumpaniji huma identifikati bil-Kjavi Globali tal-Kumpanija. Global Company Key jew GVKEY huwa kodiċi uniku ta 'sitt ċifri assenjat lil kull kumpanija fid-database Capital IQ Compustat. Aħna ninvestigaw kif il-kumpaniji f’diffikultà finanzjarja jużaw id-derivattivi għall-iħħeġġjar. Aħna nistudjaw il-produtturi taż-żejt tal-Istati Uniti matul il-perjodu Q1:2013 sa Q4:2015. L-istrateġiji tal-iħħeġġjar huma kkodifikati bl-idejn bl-użu tar-rapporti trimestrali tal-kumpaniji.Noħolqu varjabbli għal kull strateġija ta’ ħħeġġjar li tassumi valur ta’ 1 jekk il-kumpanija tuża l-istrateġija ta’ ħħeġġjar, inkella żero. Aħna nikklassifikaw il-portafolli tad-derivattivi tal-kumpaniji f'ħames strateġiji distinti ta 'hedging ibbażati fuq il-protezzjoni li joffru u kif jaffettwaw il-flussi tal-flus tal-kumpanija. Is-sett tad-data fih il-varjabbli għall-ħames strateġiji ta’ ħħeġġjar u huwa identifikat skont it-trimestru u l-kjavi globali tal-kumpanija (GVKEY). Is-sett ta’ data fih klassifikazzjoni trimestrali tal-istrateġiji ta’ ħħeġġjar tal-kumpaniji taż-żejt tal-Istati Uniti matul il-perjodu 2013-2015.L-istrateġiji huma kklassifikati abbażi tar-rapportar fir-rapport trimestrali ta’ kull kumpanija. Ġew identifikati ħames strateġiji (deskritti fil-fajl tad-data). Il-kumpaniji huma identifikati bil-Kjavi Globali tal-Kumpanija. Global Company Key jew GVKEY huwa kodiċi uniku ta 'sitt ċifri assenjat lil kull kumpanija fid-database Capital IQ Compustat. Aħna ninvestigaw kif il-kumpaniji f’diffikultà finanzjarja jużaw id-derivattivi għall-iħħeġġjar. Aħna nistudjaw il-produtturi taż-żejt tal-Istati Uniti matul il-perjodu Q1:2013 sa Q4:2015. L-istrateġiji tal-iħħeġġjar huma kkodifikati bl-idejn bl-użu tar-rapporti trimestrali tal-kumpaniji.Noħolqu varjabbli għal kull strateġija ta’ ħħeġġjar li tassumi valur ta’ 1 jekk il-kumpanija tuża l-istrateġija ta’ ħħeġġjar, inkella żero. Aħna nikklassifikaw il-portafolli tad-derivattivi tal-kumpaniji f'ħames strateġiji distinti ta 'hedging ibbażati fuq il-protezzjoni li joffru u kif jaffettwaw il-flussi tal-flus tal-kumpanija. Is-sett tad-data fih il-varjabbli għall-ħames strateġiji ta’ ħħeġġjar u huwa identifikat skont it-trimestru u l-kjavi globali tal-kumpanija (GVKEY). Is-sett ta’ data fih klassifikazzjoni trimestrali tal-istrateġiji ta’ ħħeġġjar tal-kumpaniji taż-żejt tal-Istati Uniti matul il-perjodu 2013-2015. L-istrateġiji huma kklassifikati abbażi tar-rapportar fir-rapport trimestrali ta’ kull kumpanija. Ġew identifikati ħames strateġiji (deskritti fil-fajl tad-data).Il-kumpaniji huma identifikati bil-Kjavi Globali tal-Kumpanija. Global Company Key jew GVKEY huwa kod

Indaghiamo su come le società in difficoltà finanziaria utilizzano i derivati per la copertura. Studiamo i produttori di petrolio statunitensi durante il periodo dal Q1:2013 al Q4:2015. Le strategie di copertura sono codificate a mano utilizzando i rapporti trimestrali delle società. Creiamo una variabile per strategia di copertura che assume un valore di 1 se l'azienda utilizza la strategia di copertura, altrimenti zero. Classifichiamo i portafogli di derivati delle società in cinque distinte strategie di copertura in base alla protezione che offrono e al modo in cui influenzano i flussi di cassa della società. Il set di dati contiene le variabili per le cinque strategie di copertura ed è identificato per chiave aziendale trimestrale e globale (GVKEY). Il dataset contiene una classificazione trimestrale delle strategie di copertura delle compagnie petrolifere statunitensi nel periodo 2013-2015. Le strategie sono classificate in base alla rendicontazione nella relazione trimestrale di ciascuna società.Vengono identificate cinque strategie (descritte nel file di dati). Le aziende sono identificate con Global Company Key. Global Company Key o GVKEY è un codice univoco a sei cifre assegnato a ciascuna società nel database Capital IQ Compustat. Indaghiamo su come le società in difficoltà finanziaria utilizzano i derivati per la copertura. Studiamo i produttori di petrolio statunitensi durante il periodo dal Q1:2013 al Q4:2015.Le strategie di copertura sono codificate a mano utilizzando i rapporti trimestrali delle società. Creiamo una variabile per strategia di copertura che assume un valore di 1 se l'azienda utilizza la strategia di copertura, altrimenti zero. Classifichiamo i portafogli di derivati delle società in cinque distinte strategie di copertura in base alla protezione che offrono e al modo in cui influenzano i flussi di cassa della società. Il set di dati contiene le variabili per le cinque strategie di copertura ed è identificato per chiave aziendale trimestrale e globale (GVKEY). Il dataset contiene una classificazione trimestrale delle strategie di copertura delle compagnie petrolifere statunitensi nel periodo 2013-2015. Le strategie sono classificate in base alla rendicontazione nella relazione trimestrale di ciascuna società. Vengono identificate cinque strategie (descritte nel file di dati). Le aziende sono identificate con Global Company Key. Global Company Key o GVKEY è un codice univoco a sei cifre assegnato a ciascuna società nel database Capital IQ Compustat.