Salmon Stock Returns Around Market News
Clemens Knoppe et al.
What the paper says
We examine the relationship between media news and trading behavior in the salmon market. For this, we create a share price index based on five salmon aquaculture companies trading on the Oslo Stock Exchange. We use the latent Dirichlet allocation algorithm to obtain news topics and a lexicon-based sentiment analysis. We find that topics relating to COVID-19 and sustainability have a significant negative impact on the salmon market, while topics on land-based aquaculture have a significant positive impact. The sentiment series based on the Loughran-McDonald lexicon is found to have a negative and insignificant effect on stock returns. Hence, we expand the lexicon with industry-specific words. A negative shock to sentiment within the news related to competitors foreshadows a significant increase in returns owing to the market’s competitive nature. Through our out-of-sample forecasting experiment, we find that the incorporation of news data can improve the predictive performance.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.