Do Managers Walk the Talk on Environmental and Social Issues?

Chava, Sudheer; Du, Winncy Y.; Malakar, Baridhi

doi:10.2139/ssrn.3900814

Cited by 9 publications

(4 citation statements)

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“…It is also directional, which means that it takes into consideration both preceding and subsequent context to generate the embeddings of a word, in contrast to unidirectional models (like ELMo and ULMFit). For this reason, BERT can interpret texts with more precision and has been increasingly employed in other studies; for example, see Chava, Du, and Malakar (2021) and Gorodnichenko et al (2023).…”

Section: Bert-based Estimates Of Risk Attentivenessmentioning

confidence: 99%

The FOMC versus the Staff: Do Policymakers Add Value in Their Tales?

Filippou,

Mitchell,

Nguyen

2023

Working Paper (Federal Reserve Bank of Cleveland)

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Using close to 40 years of textual data from FOMC transcripts and the Federal Reserve staff's Greenbook/Tealbook, we extend Romer and Romer (2008) to test if the FOMC adds information relative to its staff forecasts not via its own quantitative forecasts but via its words. We use methods from natural language processing to extract from both types of document text-based forecasts that capture attentiveness to and sentiment about the macroeconomy. We test whether these text-based forecasts provide value-added in explaining the distribution of outcomes for GDP growth, the unemployment rate, and inflation. We find that FOMC tales about macroeconomic risks do add value in the tails, especially for GDP growth and the unemployment rate. For inflation, we find value-added in both FOMC point forecasts and narrative, once we extract from the text a broader set of measures of macroeconomic sentiment and risk attentiveness.

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Section: Bert-based Estimates Of Risk Attentivenessmentioning

confidence: 99%

The FOMC versus the Staff: Do Policymakers Add Value in Their Tales?

Filippou,

Mitchell,

Nguyen

2023

Working Paper (Federal Reserve Bank of Cleveland)

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“…[3] used embeddings created using RoBERTa model for identification of emerging technologies. [4] create a dictionary of Environmental and Social (E&S) phrases, while [42] leveraged word-embeddings to measure the corporate culture. Moreover, multimodal machine learning was used by [2] and [43] for credit rating prediction and measurement of persuasiveness respectively.…”

Section: Related Workmentioning

confidence: 99%

“…The surge in machine learning and its applications has opened up a new arena of possibilities in diverse fields ranging from image recognition, natural language processing to finance [1], [2], [3], [4], [5]. However, a major challenge for building or training predictive models is the scarcity of labelled data [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Further the sentences with a claim have a higher density of forward-looking information. Related, extraction of numerical ESG claims from earnings call transcripts, can help better understand whether companies do walk the talk on their environment and social responsibility claims [4]. The importance of mentioned examples necessitates the numerical claim detection task in the Finance domain.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Claim Detection in Finance: A Weak-Supervision Approach

Shah¹,

Banerjee²,

Shah³

et al. 2022

Preprint

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<p>In the past few years, Transformer based models have shown excellent performance across a variety of tasks and domains. However, the black-box nature of these models, along with their high computing and manual annotation costs have limited adoption of these models. In this paper, we employ a weak-supervision-based approach to alleviate these concerns. We build and compare models for financial claim detection task using sentences with numerical information in analyst reports for more than 1500 public companies in the United States from 2017 to 2020. The proposed aggregation function for the weak-supervision model outperforms existing aggregation functions. In addition to standard performance metrics, we provide cost-value analysis of human-annotation and weak-supervision labeling along with estimates of the carbon footprint of our models. We also analyze the performance of our claim detection models across various industry sectors given the considerable variation in numerical financial claims across industries. Our work highlights the potential of weak supervision models for research at the intersection of Finance and Data Engineering. </p>

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