Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management

Financial markets analysis has been and remains a topic of intense research interest since the seminal work of Markowitz [1] detailing his theory on portfolio choice, for which he was awarded the Nobel Prize in 1990. The rapid advancements of Machine Learning (ML) and, more specifically those made in the field of Deep Learning (DL) and Deep Reinforcement Learning (DRL), further fueled interest in the field. Financial markets analysts began using ML-based techniques and combining them with their own knowledge of the field [2]. As early as 1992, Neural Networks (NNs) were already being used for equity index futures trading [3]. More recently, DL research in financial market analysis has focused on high frequency trading, i.e., an algorithmic financial trading method where high speeds and large volumes are the main characteristics. The kind of data used in works that focus on this type of trading include Limit Order Book (LOB) data [4] as well as candle data for assets such as FOREX or Cryptocurrencies [5]. Candle data contain the Open, High, Low and Close prices for assets in a requested frequency, e.g., at the minute or hour level. Price forecasting is a first step towards solving the very complex task of portfolio management, and has proved to be a sufficiently difficult problem to tackle itself. One way to sufficiently solve it is by transforming the problem into one of classification, i.e., predicting the price movement instead of its actual value in the next step [4].