Quant ESG: 12x new research on tariffs, costly pollution, electric vehicles, steel, costly policy uncertainty, green/brown split, quantitative ESG, brown mortgage costs, diversification, bitcoin and financial AI (#shows the SSRN full paper downloads as of April 10th, 2025)
Social and ecological research
Trump trades: Anticipating Trade Turbulence: Assessing the Economic Impacts of President Trump’s Proposed Tariff Scenarios by Jianwei Ai, Wu Huang, Minghao Li, Terry Zhang, and Wendong Zhang as of Jan. 28th, 2025 (#885): “This paper explores the potential economic consequences of President Donald Trump’s proposed tariff scenarios in his second term. … we study six tariff scenarios based on President Trump’s proposed tariffs … . The scenarios include both escalating US tariffs and retaliatory measures by trade partners. … we find that the proposed tariffs could significantly disrupt global trade and lead to mostly negative impacts on the US and its trading partners. In terms of welfare loss, Canada and Mexico will suffer the most from a trade war with the US. China also will experience substantial welfare decreases from an escalation of its existing trade war with the US but stands to benefit from US tariffs on Mexico and Canada. US will experience mixed welfare changes in different scenarios with smaller magnitudes” (abstract).
Costly pollution: The Cost of Air Pollution for Workers and Firms by Marion Leroutier and Hélène Ollivier as of April 8th, 2025 (#9): “… even moderate air pollution levels, such as those in Europe, harm the economy by reducing firm performance. Using monthly firm-level data from France, we estimate the causal impact of fine particulate matter (PM2.5) on sales and worker absenteeism. Leveraging exogenous pollution shocks from local wind direction changes, we find that a 10 percent increase in monthly PM2.5 exposure reduces firm sales by 0.4 percent on average over the next two months, with sector-specific variation. Simultaneously, sick leave rises by 1 percent. However, this labor supply reduction explains only a small part of the sales decline. Our evidence suggests that air pollution also reduces worker productivity and dampens local demand” (abstract).
EV mania: The EV Shakeout by Rob Arnott, Bradford Cornell, Forrest Henslee, and Thomas Verghese as of April 8th, 2025 (#19): “The electric vehicle (EV) sector’s dramatic trajectory from a 900% market capitalization surge in 2020–2021 to a widespread collapse by 2025 exemplifies a „big market delusion“—where investors overestimated the success of early entrants in a transformative industry, ignoring historical consolidation patterns. This paper analyzes how speculative fervor, rather than economic fundamentals, drove valuations to unsustainable heights, only for competition and capital constraints to trigger a brutal shakeout. We document substantial losses, with billions evaporated as firms like Rivian and Nikola faltered or failed. Tesla’s outsized valuation, defying traditional metrics, fueled the bubble, while Chinese manufacturers, leveraging state-backed innovation and pricing, reshaped global competition and pressured Western startups“ (abstract).
Green steel? Estimating firms’ emissions from asset level data helps revealing (mis)alignment to net zero targets by Hamada Saleh, Thibaud Barreau, Stefano Battiston, Irene Monasterolo, and Peter Tankov as of Feb.21st, 2025 (#35): “We develop a new bottom-up methodology to estimate companies’ (mis)alignment to net-zero scenarios. It uses companies’ asset-level data on greenhouse gas emissions at production units. We apply the methodology to the steel sector globally and we find that companies’ projected emissions at 2030 exceed by up to 20% the levels of the corresponding net-zero scenario of the International Energy Agency …. Further, we find that projected emissions at 2030 exceed companies’ aggregate stated targets, even in the optimistic case of electricity supply decarbonization rate following the net-zero scenario. Moreover, the discrepancy is driven by the largest steel companies” (abstract).
ESG investment research (in: Quant ESG)
Green political cost: Climate Policy Uncertainty and Firms‘ and Investors‘ Behavior by Piero Basaglia, Clara Berestycki, Stefano Carattini, Antoine Dechezleprêtre, and Tobias Kruse as of April 7th, 2025 (#14): “… This paper introduces a novel Climate Policy Uncertainty (CPU) index … we find that uncertainty surrounding climate policies negatively impacts firm financial outcomes, innovation, and stock-market outcomes for firms that are in CO2-intensive sectors, i.e. exposed to climate policy. Higher CPU reduces capital expenditures, employment, and research and development, which in turn translates to a decrease in innovation (patent filings), particularly for clean technologies. On the stock market, CPU leads to increased stock volatility and decreased returns for exposed firms” (abstract).
Green/brown split: The Political Economy of Green Investing: Insights from the 2024 U.S. Election by Marco Ceccarelli, Stefano Ramelli, Anna Vasileva, and Alexander F. Wagner as of April 1st, 2025 (#62): “… We provide evidence from incentivized surveys of U.S. investors before and after the 2024 U.S. presidential election. After Trump’s victory, investors reduced green investments on average. However, investors who strongly disapprove of his climate policy increased their green investment taste. These “contrarians” placed greater weight on non-pecuniary considerations and less on financial ones, suggesting they view green investing as a way to compensate for perceived climate inaction. Empirical analyses of real-world ETF flows align with this interpretation …“ (abstract).
Quant ESG (1)? A Systematic Approach to Sustainable and ESG Investing by Andrew Ang, Gerald T. Garvey, and Katharina Schwaiger from Blackrock as of April 3rd, 2025 (#58): “… Divesting from industries like tobacco, defense, coal, oil, and utilities can only decrease the investment opportunity set. Over 1969 to 2023, excluding these industries has resulted in lower returns: the average return comparing the market constructed excluding these industries is 1.1% lower than the full market. According to the framework of the dividend discount model, sustainable or ESG signals can predict stock prices only if they predict cashflows, or they are related to discount rates, or both. An important insight from this analysis is that, all else equal, higher demand for stocks with more sustainable characteristics or higher ESG ratings would result in lower expected returns. … The second avenue that sustainable signals and ESG ratings can affect returns is if they are related to style factors. Since 2014, MSCI ESG scores have exhibited positive correlations to quality and low volatility factors which, all else equal, would contribute to higher expected returns. By construction, the E, S, and G signals measure or can help effect a real-world ESG outcome: firms with higher carbon-adjusted profits have, by definition, abnormally high earnings relative to the carbon they emit, geographical areas with higher CFPB complaints experience more consumers’ financial vulnerability, and greater board diversity may be an important signal of equality in and of itself …“ (p. 37/38). My comment: This research also shows that the divestment effect depends on the period chosen and taht stock price prediction is problematic. In my opinion, similar performance of sustainable and traditional investments clearly speaks for sustainable investments.
Quant ESG (2): ESG Return and Portfolio Attribution via Shapley Values by Andrew Ang, Debarshi Basu, and Marco Corsi as of April 3rd, 2025 (#48): “… We show that over 2017 to 2024, Social and Governance scores contribute positively to the ESG portfolio outperforming the MSCI World Index, whereas Environmental scores detracted. Most of the realized active risk of the ESG portfolio is due to Environmental and Governance scores. There is very little contribution to performance, risk, and turnover from reducing carbon emission intensities” (p. 24/25).
Brown mortgage costs: From flood to fire: is physical climate risk taken into account in banks’ residential mortgage rates? by Adele Fontana, Barbara Jarmulska, Benedikt Scheid, Christopher Scheins, and Claudia Schwarz from the European Central Bank as of March 13th, 2025 (#65): “Physical climate risks can have a large regional impact, which can influence mortgage loans’ credit risk and should be priced by the lenders. … We show that on average banks seem to demand a physical climate risk premium from mortgage borrowers and the premium has increased over recent years. … Banks that were identified as “adequately” considering climate risk in the credit risk management by the ECB Banking Supervision charge higher risk premia which have been increasing particularly after the publication of supervisory expectations. In contrast, the lack of risk premia of certain banks shows that ECB diagnostics in the Thematic Review on Climate were accurate in identifying the banks that need stronger supervisory focus” (abstract).
Other investment research
Implicit diversification: Reclassifying Investment Indexes Based on Sales and Earnings Instead of Legal Domicile by Erblin Ribari as of April 2nd, 2025 (#21): “Based on two decades of financial and macroeconomic data, it challenges the common view of the S&P 500 as a purely domestic U.S. index, highlighting that around 45 percent of its constituent companies’ revenues come from international markets … the paper shows that the S&P 500 responds strongly to global economic factors“ (abstract).
Bitcoin speculation: The Optimal Long-term Portfolio Share of Bitcoin is Negative (or Zero) by Alistair Milne as of April 1st, 2025 (#39): ” “Applying the standard Markovitz mean-variance framework to a two asset portfolio consisting of US stocks (S&P500) and Bitcoin (BTC) … With risk (variance and covariance) estimated using data from 02/14 to 02/25 and long-term returns based on standard efficient markets assumptions, the optimal portfolio share for Bitcoin-1.6% (full sample) and-7.3% (recent sample), regardless of investor preferences towards risk. Other studies of BTC in portfolio management report that a positive BTC portfolio share improves risk-return trade-offs. This difference is explained by their focus on short-term dynamic asset allocation strategies and the more recent data used here, exhibiting an increased +ve correlation between BTC and stock returns” (abstract).
Finance AI: Large Language Models in Equity Markets: Applications, Techniques, and Insights by Aakanksha Jadhav and Vishal Mirza as of April 9th, 2025 (#18): “… This paper presents a comprehensive review of 84 research studies conducted between 2022 and early 2025, synthesizing the state of LLM applications in stock investing. … Our analysis identifies key research trends, commonalities, and divergences across studies, evaluating both their empirical contributions and methodological innovations. We highlight the strengths of existing research, such as improved sentiment extraction and the use of reinforcement learning to factor market feedback, alongside critical gaps in scalability, interpretability, and real-world validation. Finally, we propose directions for future research …“ (abstract).
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Der Fonds konzentriert sich auf die UN-Ziele für nachhaltige Entwicklung mit durchschnittlich einzigartig hohen 99% SDG-vereinbaren Umsätzen der Portfoliounternehmen und sehr hohen E-, S- und G-Best-in-Universe-Scores sowie einem besonders umfangreichen Aktionärsengagement (siehe auch My fund).
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Insgesamt hat der von mir beratene Fonds seit der Auflage im August 2021 eine ähnliche Performance wie traditionelle globale Small- und Mid-Cap-Fonds (vgl. z.B. Fonds-Portfolio: Mein Fonds | CAPinside und Globale Small-Caps: Faire Benchmark für meinen Artikel 9 Fonds?).
Ein Fondsinvestment war also bisher ein „Free Lunch“ in Bezug auf Nachhaltigkeit: Ein besonders konsequent nachhaltiges Portfolio mit marktüblichen Renditen und (eher niedrigeren) Risiken. Vergangene Performance ist allerdings kein guter Indikator für künftige Performance.