Portfolio optimization with sentiment analysis

Quantitative finance practices focus on developing solutions to financial markets using mathematics, statistics, and computational techniques. The objective is to maximize the return while minimizing the risk of trading. To achieve that, an optimized portfolio that is balanced between return and risk must be constructed. After the optimal portfolio is created, price predictions have to be made. Sentiment analysis is a machine learning technique to understand the sentiment in a text. It is one of the most commonly used methods to predict price direction of financial instruments. After the prediction process, portfolio is optimized using the Markowitz model developed by Harry Markowitz in 1952 by selecting a group of financial instruments. In this research, these methodologies are used for predicting the financial price and optimizing the portfolio respectively. To develop a sentiment analysis framework, words in texts must be represented as numeric values. Bag of words is a method that converts text into numeric values by using word frequency and counts. Another useful technique is word embeddings, that is learning the distributed representations of words. Several machine learning algorithms such as Naive Bayes, probabilistic machine learning model based on bayessian theorem, support vector machines, finding optimum hyperplane to maximize distances between data points which belongs to different classes, and recurrent neural network model, a neural network architecture that learns sequential patterns in data with the help of long short term memory are used. The text data is gathered from X (formerly Twitter) and the daily price data of the components of bist100 index of Borsa Istanbul A.S ̧. are employed to solve portfolio optimization problem. Our experiments show that developing a sentiment model on X data to have an optimized portfolio is useful to increase the return while minimizing the risk.