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Sentiment as a shipping market predictor: Testing market-specific language models.
- Source :
-
Transportation Research Part E: Logistics & Transportation Review . Sep2024, Vol. 189, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- • We use language models to construct sentiment indices for the shipping market. • Similar to the stock market, shipping market sentiment also has a reversal phenomenon. • Shipping market sentiment is an important factor for pricing other than fundamentals. • Shipping sentiment indices eliminate the reverse causal relationship with market prices. • Language models significantly outperform the lexicon-based approaches for sentiment analysis. This paper applies language models to the shipping market for the first time and studies the impact of changes in shipping market sentiment on freight rates. First, based on language models and Clarksons' commentary reports, this paper proposes the sentiment indices for the entire shipping market and the sub-markets for bulk ships, tankers, and container ships. Second, empirical results indicate that, apart from the container shipping market sentiment index, all other shipping sentiment indices including the total shipping market sentiment index, the dry bulk shipping market sentiment index and the tanker shipping market sentiment index serve as positive predictive indicators for shipping freight rate indices. Third, this paper investigates the interaction between the shipping sentiment index and market prices through a vector autoregressive model and the Granger causality test. We find that the total shipping market sentiment index is the Granger cause of the Baltic Dry Index and the Baltic Dirty Tanker Index. The dry bulk shipping market sentiment index and the container shipping market sentiment index are the Granger causes of the Baltic Dry Index and the China Containerized Freight Index, respectively. Last, this paper compares the shipping sentiment index constructed by market-specific language models and lexicon-based sentiment analysis. It is evident that language models significantly outperform the lexicon-based approaches for sentiment analysis and are expected to be useful for analyzing textual sentiment in the field of asset pricing research. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13665545
- Volume :
- 189
- Database :
- Academic Search Index
- Journal :
- Transportation Research Part E: Logistics & Transportation Review
- Publication Type :
- Academic Journal
- Accession number :
- 178942890
- Full Text :
- https://doi.org/10.1016/j.tre.2024.103651