Name
Abstract | ||
|---|---|---|
There has been an increasing interest recently in examining the possible relationships between emotions expressed online and stock markets. Most of the previous studies claiming that emotions have predictive influence on the stock market do so by developing various machine learning predictive models, but do not validate their claims rigorously by analysing the statistical significance of their findings. In turn, the few works that attempt to statistically validate such claims suffer from important limitations of their statistical approaches. In particular, stock market data exhibit erratic volatility, and this time-varying volatility makes any possible relationship between these variables non-linear, which tends to statistically invalidate linear based approaches. Our work tackles this kind of limitations, and extends linear frameworks by proposing a new, non-linear statistical approach that accounts for non-linearity and heteroscedasticity. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-17091-6_15 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Econometrics,Heteroscedasticity,Economics,Actuarial science,Social web,Granger causality,Anxiety,Stock market,Volatility (finance) | Conference | 9047 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.37 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rapheal Olaniyan | 1 | 2 | 1.19 |
| Daniel Stamate | 2 | 66 | 36.68 |
| Doina Logofatu | 3 | 17 | 16.74 |