Сентимент частных инвесторов в объяснении различий в биржевых характеристиках акций российского рынка

Журнал Новой экономической ассоциации. 2022. Т. 1. № 53. С. 53–84.

Teplova T., Sokolova T., Tomtosov A., Бучко Д. В., Никулин Д. Д.

Abstract. In our paper, for the first time, we examine the influence of the sentiment of private investors in social networks on the trade characteristics of stocks in the Russian market. Monthly return rates and trading volumes are analyzed under the control of financial indicators and indicators of the quality of corporate governance of stock issuers, as well as the changing external environment in the period from 2013 to 2020. The sample for various sentiment metrics is based on unique data: messages in the Telegram and mfd.ru platforms. The tonality of messages is diagnosed according to the authors’ method using artificial intelligence (neural network). The main conclusion is: the sentiment can be seen as an explanatory factor in pricing and trading activity. The influence of sentiment is non-linear. The author’s HYPE indicator of sentiment is proposed and compared in terms of explanatory ability of the trade characteristics with a wide range of proxy variables. The explanatory ability to identify differences is realized through regression constructions on panel data. It is shown that trade characteristics are more sensitive to the growth of negative messages, which is consistent with the postulates of behavioral finance. An increase in messages’ number of both positive and negative sentiment contributes to the growth of trading activity. An important practical conclusion is: following the crowd when the company is most intensely discussed will not result in high returns to an investor.

Keywords: поведенческие финансы, mood, neural networks, доходность акций, stock returns, торговая активность, text processing, обработка текста, сентимент инвесторов, investor sentiment, tone of messages, trading activity, настроения инвесторов, тональность сообщений, нейросети