Asset Allocation Strategies: Enhanced by Micro-Blog

Abstract

The rapid rise of social media communication has touched upon all aspects of our social and commercial life. In particular, the rise of social media as the most preferred way of connecting people online has led to new models of information communication amongst the peers. Of these media, Twitter has emerged as a particularly strong platform and in the financial domain tweets by market participants are of great interest and value. News in general, and commercial and financial news wires, in particular provide the market sentiment and in turn influence the asset price behaviour in the financial markets. In a comparable way micro-blogs of tweets generate sentiment and has an impact on market behaviour, that is, the price as well as the volatility of stock prices. In our recent research [?], we have introduced news sentiment based filters such as News RSI (NRSI) and Derived RSI (DRSI), which restrict the choice of asset universe for trading. In this present study, we have extended the same approach to StockTwit's data. We use the filter approach of asset selection and restrict the available asset universe. We then apply our daily trading strategy using the Second-Order Stochastic Dominance (SSD) as an asset allocation model. Our trading model is instantiated by two time series data, namely, (i) historical market price data and (ii) StockTwits sentiment (scores) data. Instead of NRSI we compute the Micro-blog RSI (MRSI) and using this a DRSI is computed. The resulting combined filter (DRSI) leads to an enhancement of the SSD-based trading and asset allocation strategy. Empirical experimental results of constructing portfolios are reported for S&P 500 Index constituents.