The Green Innovation Premium
Markus Leippold1,2, Tingyu Yu1
1University of Zurich, Switzerland; 2Swiss Finance Institute (SFI)
This paper introduces a novel firm-level green innovation measure utilizing ClimateBERT and GPT-3 language models, capturing a broader range of innovative activities than green patents and categorizing firms into inventors and adopters. Green innovating firms, including many from carbon-intensive sectors, experienced lower expected returns than their less innovative industry peers in both groups. These firms exhibit reduced carbon emissions and fewer climate incidents. They demonstrate a notable value increase in response to more stringent environmental regulations and recent heightened attention to green innovation. Climate policies effectively incentivize green innovation but predominantly among financially unconstrained companies within the green inventors.
Animating Stock Markets
Kuntara Pukthuanthong1, Tomasz Kaczmarek2
1University of Missouri, United States of America; 2Poznan University
Our study presents a revolutionary method called Variational Recurrent Neural
Networks (VRNNs) that utilizes a series of graphs to predict future stock price trends.
It works like an animated movie about price trends. We analyze data from the S&P500
index constituents, which are known to be less predictable than other traded stocks,
between 1993 and 2021. Our model generates a Sharpe ratio of 2.94 for equally weighted portfolios and 2.47 for value-weighted portfolios. Even after taking into account a 10 basis points transaction cost, our Sharpe ratio remains approximately twice as high as that of Jiang, Kelly, and Xiu, 2020. By adopting our graph-based approach, we achieve a substantial alpha of 55 basis points per week after controlling for established risk factors (FF3, FF5, FF6, Q5, and DHS). Furthermore, our prediction of price changes, after adjusting for various price trend strategies and firm traits, strongly forecasts the weekly returns of firms.
The Impact of Social Media Influencers on the Financial Market Performance of Firms
Zhengfa Zhang, Kevin Keasey, Costas Lambrinoudakis, Danilo V. Mascia
University of Leeds, United Kingdom
A key development in social media has been the remarkable growth of influencers and their increasing use by firms to manage their online presence and image, and to promote their products. Despite the huge growth of influencers and their use by firms, there is a lack of analysis of social media influencers and their impact on the financial market performance of firms. Anecdotal evidence suggests mega influencers are able to affect the stock prices of firms via social media. We ask whether the effect on stock prices identified in anecdotal evidence is generalizable to all mega influencers and other financial market characteristics of firms. After developing hypotheses from the Noise Trader model and using a hand collected dataset of more than 11,000,000 mega influencer posts on Instagram (2012–2019), we find that mega influencers affect investors’ attention, volatility, trading volume and, through extreme sentiment posts, stock returns. The effect on returns is, however, very short lived. Companies need to be aware of these stock market consequences if they intend to use influencers for external image purposes and/or product promotion.
Weather Variance Risk Premia
Joon Woo Bae1, Yoontae Jeon2, Stephen Szaura3, Virgilio Zurita4
1Case Western Reserve University; 2McMaster University; 3BI Norwegian School of Business, Norway; 4Baylor University
We analyze the information content of a variance risk premia extracted from the
weather derivatives contracts written on the local temperature of individual U.S. cities.
We term this the Weather Variance Risk Premia (WVRP). By constructing the WVRP
measure from the CME’s weather futures and options contracts, we examine the role of
weather variance risk on bond credit spreads of local corporations and municipalities.
Our results indicate informativeness of weather derivatives market as a local risk factor
priced in the bond returns of local corporations and municipalities. Our results are
robust to controlling state level economic uncertainty measures.
Contextualized Sentiment Analysis using Large Language Models
Christian Breitung, Garvin Kruthof, Sebastian Müller
Technical University of Munich, Germany
The impact of economic news varies widely among firms, industries, and countries. Conventional sentiment analysis methods often fail to capture this heterogeneity, potentially leading to misjudged economic consequences for various stakeholders. This study explores the capabilities of large language models (LLMs) in employing economic reasoning to predict industry-specific news impacts.
Our experiments use commodity price news headlines to reveal the capability of LLMs to derive industry-specific sentiments. This capability positively correlates with context granularity, with more detailed information on the industry resulting in more accurate estimates. Furthermore, our experiments show significant variations of this capability along topic areas, language models, and prompting strategies.
Information cherry-picking: When confirmation bias met anchoring bias
Shiyang Huang1, Tse-Chun Lin1, Yan Luo2, Ningyu Zhou2
1HKU Business School, The University of Hong Kong; 2School of Management, Fudan University
We hypothesize that a surge in availability of information coupled with investors’ confirmation bias could aggravate retail investors’ behavioral biases due to their cherry-picking of information that only confirms their priors. We use the staggered EDGAR implementation to provide causal inference for our hypothesis. We find that retail investors’ anchoring tendency is amplified in the post-EDGAR period. The effect is most evident shortly after the release of corporate 10K/Q reports, for Internet users and sophisticated investors, and among stocks with higher information uncertainty. The exacerbated anchoring bias in the post-EDGAR period also makes retail investors lose more money in trading.
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