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README.md
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@@ -18,7 +18,8 @@ It is capable of extracting price movements from the news, making it a useful to
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I also compared it with FinBERT, and the results are quite similar.
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Interestingly, it can detect the sentiment of financial news, even though it was trained using a different methodology than the one used with FinBERT.
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Also, as we know, not all stocks react the same way to a specific event. That’s why I created additional models tailored for individual stocks.
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You can find them on my profile, along with the datasets used.
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This particular model is designed for general news — try it with phrases like “The stock market crashed” or “Tesla’s price dropped.”
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When evaluating news related to investment opportunities, this general model might provide a neutral score, whereas the stock-specific models can estimate potential price variations that could occur after such events.
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The predictions are tailored to each individual stock.
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| 18 |
I also compared it with FinBERT, and the results are quite similar.
|
| 19 |
Interestingly, it can detect the sentiment of financial news, even though it was trained using a different methodology than the one used with FinBERT.
|
| 20 |
Also, as we know, not all stocks react the same way to a specific event. That’s why I created additional models tailored for individual stocks.
|
| 21 |
+
You can find them on my profile, along with the datasets used.
|
| 22 |
+
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| 23 |
This particular model is designed for general news — try it with phrases like “The stock market crashed” or “Tesla’s price dropped.”
|
| 24 |
When evaluating news related to investment opportunities, this general model might provide a neutral score, whereas the stock-specific models can estimate potential price variations that could occur after such events.
|
| 25 |
The predictions are tailored to each individual stock.
|