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© unsplash/@markuswinkler
Banks & Insurances

Stock trading based on sentiment analysis

Context

Share prices of publicly listed companies are influenced by bank analysts evaluating a company's financials and investor relation communications. Analysts and journalists are constantly publishing articles and reports to evaluate the companies performance in the market space and comparing it to competitors. After the quarterly earnings report, analysts and journalists evaluate if the companies stock price is still reasonable or if it is under- or overvalued and if the company met market expectations.

Challenges

When companies publish ad-hoc statements or financial statements, (potential) investors are investigating articles and analysts' reports to conclude out if there is an arbitrage opportunity, i.e. if the stock is under- or overvalued. However, apparent arbitrage opportunities vanish quickly as professional stock traders react within a short period of time to generate profits. Evaluating the effect of news on stock prices in a matter of seconds or even less is therefore, of the essence, to capitalize on arbitrage opportunities.

Potential solution approaches

Sentiment analysis is a machine learning technique to classify text into different classes of sentiments the author carried while writing the text. The most simple form is a classification into "positive", "negative" or "neutral". More sophisticated algorithms classify the emotional state (e.g. "angry") or the magnitude of sentiment (e.g. on a scale from 1 to 5). Algorithms used for sentiment analysis include Naive Bayes regression, support vector machines or deep neural networks for more sophisticated tasks.

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