Value relevance of the components of oil and gas reserve quantity change disclosures of upstream oil and gas companies in the london stock exchange

Tega Anighoro

Home/Archive/arXiv

arXiv · 2005.14659

Value relevance of the components of oil and gas reserve quantity change disclosures of upstream oil and gas companies in the london stock exchange

Tega Anighoro

View original source ↗Read source document ↗Export metadata ↓

Abstract

The high level of risk and uncertainty in harnessing oil and gas reserves poses an accounting dilemma in the reporting of reserves quantity information; information which is critical and relied on by investors for decision making. Different studies have indicated that reserves disclosure information is fundamental to understanding the value of the firm. This study attempts to contribute to the growing value relevance literature on reserves disclosures by examining the value relevance of the components of oil and gas reserve quantity change disclosures of upstream oil and gas companies in the London Stock Exchange. Particularly, it investigates the relationship between average historical share returns and changes in reserves from explorations, acquisitions, production, revisions and sale. It also examines the value relevance of the quality of these disclosures. Using archival data from LSE, databases and annual reports, and applying a multifactor framework, the empirical results suggested that changes in reserves as well as the components of these changes where associated with share returns though insignificantly due to the significant impact of oil price and longitudinal effect posed by applying the measurement approach with utilizes historical returns. However, the quality of reserves disclosures has a positively significant relationship with share returns. The volatility and decline in oil price is also reflected in both low average share returns at -0.4% and low average growth in reserves at 8.94% for the last 8 years in the sector.

Explore related subjects

q-fin.GN

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Tega Anighoro. 2020-05-06. Value relevance of the components of oil and gas reserve quantity change disclosures of upstream oil and gas companies in the london stock exchange. https://arxiv.org/abs/2005.14659

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Prediction Markets Beat the Weather Forecast on Tomorrow's High Temperature

The sooner we receive information, and the more accurate it is, the better planning decisions we can make. Every day, prediction markets let anyone bet on tomorrow's high temperature in cities around the world, creating a market-implied forecast built on dispersed information. We use the past five years of market data from the Kalshi exchange for seven American cities to extract, hour by hour, the market-implied forecast. We use this forecast as a measuring instrument to see how much information about the temperature the market makes public before the public forecasting system does. We race it against the leading American and European weather forecasts. In six of the seven cities we study, the market beats the most accurate single public forecast, the National Blend of Models (NBM). Aggregating every city-day, at the end of the market's first hour of trading it beats the best single public product by about 10 percent in root-mean-square error, and holds its lead through the day, overnight, and into the target day. Looking at how the forecasts move over time, we find the National Blend travels four times further toward the market between its postings than the market travels toward the NBM. The market does not react to new weather forecast updates; instead, the forecast slowly publishes information that the market had already shared publicly.

q-fin.GN↗

arXiv2026

Firm Valuation When AI Shapes the Business Model: A Milestone-Based Real-Options Framework for the AI Valuation Uncertainty Problem

Standard valuation methods, including discounted cash flow, the income approach standard IDW S 1 of the Institute of Public Auditors in Germany, and market multiples, compress milestone probabilities, continuation options, and risk shifts into opaque aggregate parameters; none provides a structured protocol for decomposing AI integration into auditable option-level assumptions. We propose an industry-agnostic taxonomy separating AI Integrators from AI Providers. AI Integrators are further classified by their Integration Depth Level, ranging from no integration to AI at the core of the product or process. A milestone-gated real-options overlay decomposes milestone state value into five components, and an Analytic Hierarchy Process-based Success Readiness Index derives per-option probabilities from structured pairwise comparisons for scenario analysis. Applied to an AI-native energy software-as-a-service firm, the framework yields a coherent valuation band traceable to identifiable option-level assumptions. Risk concentrates in later-stage continuation options, matching the structural prediction for AI Providers. The protocol applies across the firm lifecycle, including mergers and acquisitions due diligence. The case is a single-firm demonstration of protocol coherence, not empirical validation; multi-case testing against realised post-exit valuations is left to future research.

q-fin.GN↗

arXiv2026

Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?

Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted trees, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 2.08 for classification and 1.39 for regression. This outperformance strengthens with class granularity and persists across subsamples and after transaction costs. Spanning tests show that classification retains economically large alphas after we control for regression, whereas regression alphas shrink substantially once we control for classification. These results indicate that classification extracts more return information than matched regression. Our diagnostics trace classification's advantage to more precise separation of return deciles.

q-fin.GN↗