Name
Abstract | ||
|---|---|---|
Big Data analytics has recently fostered significant research on the influence of news sentiment in finance. This paper thus examines the effect of news sentiment on crude oil prices for different investor types according to the noise trader approach. The noise trader approach assumes the presence of informed and uninformed investors. Informed investors possess a perfect information horizon, whereas uninformed investors trade upon noise signals, such as sentiment. Methodologically, we decompose the crude oil price with a Kalman filter into a Kalman-smoothed, fundamental price component and a noise residual. We then regress news sentiment on both decomposed oil price components. Our findings suggest that news sentiment not only has a significant positive effect on the noise residual (as suggested by the noise trader approach), but also on the fundamental price. Thus, we find empirical evidence contradicting the noise trader model, which assumes that only uninformed investors trade on sentiment. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.2139/ssrn.2520445 | ECIS |
Keywords | Field | DocType |
information processing,text mining,kalman filter,sentiment analysis | Economics,Financial economics,Information processing,Empirical evidence,Noise trader,Sentiment analysis,Crude oil,Kalman filter,Perfect information,Big data,Marketing | Conference |
Citations | PageRank | References |
3 | 0.41 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Jonas Alfano | 1 | 3 | 0.41 |
| Stefan Feuerriegel | 2 | 219 | 31.91 |
| Dirk Neumann | 3 | 294 | 37.29 |