Abstract: Abstract This study uses fintech approaches, including web crawler technology with distributed architecture to select internet news messages largely and efficiently and a dictionary-based linguistic text mining to create sentiment variables, to explore the respective impacts of investors' optimism and pessimism on stock returns. The construction of sentiment variables in network- and dictionary-based messages is more precise and variable than that in traditional-based messages. Our results show that firms with investors' optimistic sentiments h...
(read more)
Topics: 
Financial economics
Monetary economics