Having a set of news articles (or press releases) about a company (including timestamps of publication) and a time-series of the related company's stock price (at a rather high-resolution, like 1-minute OHLC bars or individual ticks): Is it possible to estimate which news article might have influenced the stock price? For ex. evaluate some kind of score for each news article?
My first approach was to subtract the actual price after publication from the expected price: Something like, $\epsilon = y_{t+n} - E[y_{t+n}]$, with...
- $\epsilon$ the score of the article,
- $t$ the time of release of the article,
- $y_{t+n}$ the actual price after $n$-ticks and
- $E[y_{t+n}]$ the expected price after $n$-ticks (estimated through the momentum before the publication).
I've implemented a simple Python script to evaluate my assumption and tried different values for $n$ (like 5, 10, 20 minutes...). But, the results didn't look as convincing as I'd had expected (seemingly significant news ranked at low score and completely irrelevant news with high scores).
I also tried to get rid of choosing a value for $n$, by summing and discounting different values of $n$: $\epsilon = \sum_{t} \gamma * (y_{t+n} - E[y_{t+n}])$, with $\gamma = 0.975$ as discounting factor. However, that also didn't help.
I am aware that with my approach would also rate irrelevant articles high, when they are released at the same time as relevant ones. Then there is also a chance that some news might have leaked before the publication of the news, etc. But, I would have at least expected to get somewhat reasonable estimates.
Is there any generally accepted solution to what I am trying to solve? It seems like such a basic issue to me, and I would be very thankful if somebody could point me into the right direction.