The explosive development of electronic media has brought to the market participants thousands of pieces of financial news which are released on different platforms every day. Many news wires published online are editorially controlled and can be relied as factual summary as opposed to fake news or disinformation. These news items provide a rich source of textual information which in a summative way represents the sentiment of the market. The sentiments influence or impact the asset price as well as the volatility of individual assets. In this study we have tested sentiment enhanced daily trading strategies. Alexandria Technology has provided us news sentiment metadata, which is used in this study. We have also resorted to ‘asset filters’ which we use to restrict the universe of assets chosen for daily trades. We have considered quantified news sentiment and its impact on the movement of asset prices as a second time series data, which is used together with the asset price/return time series data. Our asset allocation strategy uses Second Order Stochastic Dominance (SSD); see Roman et al. (2006, 2013). Following this modelling paradigm we compute daily trade schedules using a time series of historical equity price data. In contrast to classical mean-variance method this approach improves the tail risk as well as the upside of the return. In our recent research we have introduced news sentiment indicators such as News RSI (NRSI) and Derived RSI (DRSI) filters. These filters restrict the choice of asset universe for trading. Consistent performance improvement achieved in back-testing vindicates our approach.
Key Words: Trading Strategy, Sentiment Analysis, News Meta Data, Asset Filter