気候リスクの組み込みは株式市場リターンの予測可能性を高めるか？
Does the incorporation of climate risk enhance the predictability of stock market returns? (原題)
Xiao-Xin Li, Chi Xie, Wei-Ying Wang, Jin-Zuo He, Zhi-Yu Zhang
🤖 gxceed AI 要約
日本語
本論文は、物理的リスクと移行リスクの8つの代理変数をオートエンコーダーで統合した新たな気候リスク指標を構築し、株式市場リターンの予測性を検証する。気候リスクは1〜12ヶ月のイン・アウトサンプルで強い予測力を持ち、平均分散投資家に取引コスト控除後も資産配分上の便益をもたらす。予測力は主にキャッシュフロー経路を通じて生じ、割引率経路ではないことを示す。
English
This paper builds a novel climate risk measure using an autoencoder that integrates eight proxies for physical and transition risks. The measure strongly predicts stock market returns over 1- to 12-month horizons in- and out-of-sample, delivers sizable asset allocation benefits for mean-variance investors even after transaction costs, and outperforms benchmarks. Its forecasting power stems mainly from the cash flow channel rather than the discount rate channel.
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
気候リスクを定量的に資産価格へ織り込む手法は、SSBJ・TCFD開示を投資判断に接続する日本企業・機関投資家にとって、気候リスクの財務影響評価の高度化に資する。
In the global GX context
As ISSB/TCFD disclosure matures, this work demonstrates that aggregated climate risk signals carry predictive information for market returns, supporting the integration of climate risk into asset pricing and portfolio construction globally.
👥 読者別の含意
🔬研究者:気候リスク指標の構築と予測力の実証、キャッシュフロー経路の特定は、気候ファイナンス研究の新たなベンチマークとなる。
🏢実務担当者:気候リスクを投資判断や資産配分に組み込む際の定量的根拠として活用できる。
🏛政策担当者:気候リスク情報の開示が市場価格形成に与える影響を考慮した政策設計の参考になる。
📄 Abstract（原文）
This paper investigates whether climate risk contains valuable information for predicting future stock market returns and examines its implications for asset pricing. We develop a novel measure of climate risk based on the autoencoder (AE) by integrating diverse proxies for both physical and transition risks. The climate risk (1) possesses strong in-sample and out-of-sample predictive ability over the 1- to 12-month horizon; (2) provides the sizable asset allocation benefits for mean-variance investors at all levels of risk aversion, even after accounting for the transaction costs; (3) achieves higher cumulative returns than the competitors, such as historical average, market benchmark and alternative climate risk, indicating its superiority in guiding investment decisions; and (4) derives its forecasting power primarily from the cash flow channel rather than the discount rate channel, shedding light on the mechanism through which climate risk affects future stock market returns. Firstly, we develop a comprehensive measure of climate risk based on the AE, which systematically integrates eight proxies that capture both the physical and transition dimensions of climate risk. Secondly, we present the robust empirical evidence that the climate risk contains the strong predictive content for future stock market returns and delivers sizable economic benefits for mean-variance investors, thereby underscoring its practical implications in asset allocation. Finally, we shed light on the potential economic mechanisms underlying the forecasting power of climate risk by confirming that the climate risk primarily influences the stock market returns through the cash flow channel.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1108/cfri-03-2026-0195first seen 2026-10-28 05:39:19 · last seen 2026-11-05 06:10:39
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