How Financial News Can Be Used to Train Good Financial Models π° Numbers tell you what happened, but news tells you why. Iβve written an article explaining how news can be used to train AI models for sentiment analysis and better forecasting. Hope you find it interesting!
Given a news title, it calculates a sentiment score : if the score crosses a certain threshold, the strategy decides to buy or sell. Each trade lasts one day, and the strategy then computes the daily return. For Tesla the best model seems to be the regression π Just a quick note: the model uses the closing price as the buy price, meaning it already reflects the impact of the news.
Given a news title, it calculates a sentiment score : if the score crosses a certain threshold, the strategy decides to buy or sell. Each trade lasts one day, and the strategy then computes the daily return.
Just a quick note: the model uses the closing price as the buy price, meaning it already reflects the impact of the news. If I had chosen the opening price, the results would have been less biased but less realistic given the data available.
I found it excellent and very well done. One of the best explanations of embedding I've ever read. Well done, @hesamation! Had to share this: hesamation/primer-llm-embedding
Finally, I uploaded the model I developed for my masterβs thesis! Given a financial event, it provides explained predictions based on a dataset of past news and central bank speeches. Try it out here: SelmaNajih001/StockPredictionExplanation (Just restart the space and wait a minute)