Social sentiment and exchange-specific liquidity at a Eurasian stock exchange outside of US market hours

Abstract

We perform a neural network analysis of the impact of Russian retail investors´ sentiment on the stock price behavior of well-known American companies. We study American stocks in a situation of a time-segmentation of the stock market. A special feature of our analysis is the separate time trading mode, when trading is active at the SPB (formerly St. Petersburg) exchange and inactive at the US stock exchanges. Building on the unique local exchange data and original technique for constructing a neural network to identify the sentiment of messages from several Internet forums, we uncover the existence of behavioral anomalies in a non-English-speaking emerging market and analyze sentiment and attention metrics in social networks. We construct several sentiment metrics based on AI text analysis and use panel regression to identify their statistical significance under the selected hypotheses. The impact of sentiment is examined across the entire sample of US companies available to investors on the SPB exchange and a separate zooming is made at the top 10, 25, 50, and 100 stocks that are under special interest manifested by volume of discussions and trading volume. We also analyze the impact of sentiment on price reaction for individual popular stocks and by industry. We find that retail investors’ sentiment exercises a statistically significant influence on price spikes. The stocks, most sensitive to sentiment, are healthcare and high tech.