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- 01GX3SPY4VR556GGAK87NXPYA3 classification B2.
- 01GX3SPY4VR556GGAK87NXPYA3 date "2021".
- 01GX3SPY4VR556GGAK87NXPYA3 language "eng".
- 01GX3SPY4VR556GGAK87NXPYA3 type bookChapter.
- 01GX3SPY4VR556GGAK87NXPYA3 hasPart 01GYF7C8T38NYN5V16R4RYDA25.pdf.
- 01GX3SPY4VR556GGAK87NXPYA3 subject "Business and Economics".
- 01GX3SPY4VR556GGAK87NXPYA3 doi "10.1007/978-3-030-66891-4_10".
- 01GX3SPY4VR556GGAK87NXPYA3 isbn "9783030668907".
- 01GX3SPY4VR556GGAK87NXPYA3 isbn "9783030668914".
- 01GX3SPY4VR556GGAK87NXPYA3 abstract "We present a general monitoring methodology to summarize news about predefined entities and topics into tractable time-varying indices. The approach embeds text mining techniques to transform news data into numerical data, which entails the querying and selection of relevant news articles and the construction of frequency- and sentiment-based indicators. Word embeddings are used to achieve maximally informative news selection and scoring. We apply the methodology from the viewpoint of a sustainable asset manager wanting to actively follow news covering environmental, social, and governance (ESG) aspects. In an empirical analysis, using a Dutch-written news corpus, we create news-based ESG signals for a large list of companies and compare these to scores from an external data provider. We find preliminary evidence of abnormal news dynamics leading up to downward score adjustments and of efficient portfolio screening.".
- 01GX3SPY4VR556GGAK87NXPYA3 author C13D4B2E-2676-11E9-B3C7-45AB5607D3EF.
- 01GX3SPY4VR556GGAK87NXPYA3 author urn:uuid:7e080331-1573-4f31-a07a-68742903447d.
- 01GX3SPY4VR556GGAK87NXPYA3 author urn:uuid:988b7564-d05d-48e3-8f78-36812e3de72f.
- 01GX3SPY4VR556GGAK87NXPYA3 author urn:uuid:a95e6264-758f-45a3-bb4e-e61f6b036c8f.
- 01GX3SPY4VR556GGAK87NXPYA3 dateCreated "2023-04-03T14:53:04Z".
- 01GX3SPY4VR556GGAK87NXPYA3 dateModified "2024-07-09T15:48:08Z".
- 01GX3SPY4VR556GGAK87NXPYA3 editor urn:uuid:37aad99a-a885-45b7-848f-4d5b69885c0b.
- 01GX3SPY4VR556GGAK87NXPYA3 editor urn:uuid:85e8d7b2-3aad-41d8-b773-458c6ae74840.
- 01GX3SPY4VR556GGAK87NXPYA3 editor urn:uuid:dc15b849-ae2d-423b-9c67-613af0797250.
- 01GX3SPY4VR556GGAK87NXPYA3 name "Semi-supervised text mining for monitoring the news about the ESG performance of companies".
- 01GX3SPY4VR556GGAK87NXPYA3 pagination urn:uuid:f5f1e974-b21c-4a70-9d55-6729e76c4469.
- 01GX3SPY4VR556GGAK87NXPYA3 publisher urn:uuid:95b179a7-fde9-4b6a-b53c-9b636aa18f1c.
- 01GX3SPY4VR556GGAK87NXPYA3 sameAs LU-01GX3SPY4VR556GGAK87NXPYA3.
- 01GX3SPY4VR556GGAK87NXPYA3 sourceOrganization urn:uuid:c4886fac-7e78-4aea-aabc-1e961b10a1eb.
- 01GX3SPY4VR556GGAK87NXPYA3 type B2.