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排出価格ショックの企業への影響に関するイベント研究 (AI 翻訳)
Rok Mazej
🤖 gxceed AI 要約
日本語
本論文は炭素価格ショックが欧州企業の株価に与える影響をイベントスタディで分析。機械学習モデルも用い、炭素集約企業では異常収益率が低下、再生可能エネルギー企業では上昇する傾向を確認。ただし累積異常収益率は統計的に有意でない。また、炭素ベータ推定やWACC分析を通じて炭素リスクと資本コストの関係を検証。
English
This thesis examines the impact of carbon price shocks on European firms' stock returns using event study methodology and machine learning. It finds a slight decline in abnormal returns for carbon-intensive firms and an increase for renewable firms on the event day, though cumulative abnormal returns are largely insignificant. It also estimates carbon betas and analyzes WACC over 2015-2025, showing carbon-intensive firms face higher financing costs.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でも2026年度のGX排出量取引制度本格稼働を控え、炭素価格が企業価値に与える影響評価の重要性が増している。本論文のイベントスタディ手法は、日本の排出量取引市場への企業反応分析の参考となる可能性がある。
In the global GX context
This paper contributes to the growing literature on carbon pricing and financial markets, relevant to global frameworks like TCFD and ISSB. Its event study approach and carbon beta estimation can inform investors and regulators about how carbon price shocks transmit to firm valuation, particularly in the EU ETS context.
👥 読者別の含意
🔬研究者:Provides a framework for event studies on carbon pricing with machine learning, and estimates firm-level carbon betas and WACC implications.
🏢実務担当者:Helps sustainability and finance teams understand how carbon price spikes may affect stock performance and cost of capital.
🏛政策担当者:Offers evidence on market reactions to carbon price increases, useful for designing carbon pricing mechanisms.
📄 Abstract（原文）
Carbon pricing has become an important topic in finance and economics, making it essential to quantify its effects for policy evaluation and corporate decision-making.As financial markets increasingly price in carbon risk, firms must adapt to evolving regulation and transition towards greener operations.For investors and regulators, understanding how markets respond to carbon price shocks provides valuable insight.Carbon pricing can affect firms' stock performance, which in turn can influence both the systematic risk of the firm and its cost of capital.To assess systematic carbon risk at the firm level, I estimated carbon betas by regressing stock returns on emission allowance futures returns for each firm in my sample.Carbon-intensive firms show the highest sensitivity, with a median carbon beta of 0.13, while renewable firms show the lowest (0.075).Energy firms fall in between.The results do not provide strong evidence that carbon risk is fully reflected in asset prices in this small sample.Analysis of the weighted average cost of capital (WACC) over the 2015-2025 period further indicates that carbon-intensive firms have historically faced the highest financing costs, while energy firms have faced the lowest.The core of this thesis applies an event study methodology to examine the market reaction of European firms to a significant increase in carbon prices.The empirical analysis, complemented by machine learning models, evaluates abnormal returns across three subgroups: carbon-intensive firms, energy firms, and renewable firms.Results indicate a slight decline in average abnormal returns for carbon-intensive firms and a slight increase for energy and renewable firms on the event day.However, cumulative average abnormal returns are statistically insignificant for carbonintensive and energy firms, while, for renewable firms, cumulative average abnormal returns are negative and significant over the full event window.When the analysis is split into preand post-event periods, statistical significance appears only in the pre-event period.An important aspect of this thesis is the evaluation of machine-learning techniques within the event study framework.Across all firm groups, linear regression outperforms alternative models on average, producing the lowest root mean square error when two explanatory variables are included in the market model.
🔗 Provenance — このレコードを発見したソース
- openalex https://repozitorij.uni-lj.si/IzpisGradiva.php?id=183446first seen 2026-07-07 04:37:28
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