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Welcome to Natural Language Processing
Financial news provides information to the general public. Consumers rely on information they read or hear before buying a product. The internet makes content easily accessible and more relevant than ever.
Remember the game where you guess how many jellybeans are in the jar? Your guess, and all your friend’s guesses probably spanned across a wide range. Wisdom of crowds states that the average of a mass populations’ guesses will be closer to reality than an individual expert guess. This phenomenon holds true for predicting jelly beans, or sentiment around a stock.
Consumer sentiment plays an important role in financial markets. We apply natural language processing to analyze text and understand what consumers are reading or talking about. We prove that using financial articles to predict stock movements outperforms a random guess investment strategy.
We test three methods in our analysis. First, we use deep learning to test article and sentence level prediction. Next we create features from characters, words, and sentences in each data source. Finally, we create sentiment scores from each article to then predict stock returns.
Our research is broken up as follow:
1.) Data explained.
2.) Method 1: Computer vision applied to sentiment analysis
3.) Method 2: Decomposing each article into features for stock return prediction.
2.1: Feature construction
2.2: Hyperparameter Optimization
2.3: Model training
2.4: Experiment Results
4.) Method 3: Using our features to predict sentiment of each article.
5.) Future work.
We collect articles from ten unique financial data sources, as listed in Table 1. In this research we focus exclusively on Seeking Alpha articles.