  Financial market analysis has focused primarily on extracting signals from
accounting, stock price, and other numerical hard data reported in P&L
statements or earnings per share reports. Yet, it is well-known that the
decision-makers routinely use soft text-based documents that interpret the hard
data they narrate. Recent advances in computational methods for analyzing
unstructured and soft text-based data at scale offer possibilities for
understanding financial market behavior that could improve investments and
market equity. A critical and ubiquitous form of soft data are earnings calls.
Earnings calls are periodic (often quarterly) statements usually by CEOs who
attempt to influence investors' expectations of a company's past and future
performance. Here, we study the statistical relationship between earnings
calls, company sales, stock performance, and analysts' recommendations. Our
study covers a decade of observations with approximately 100,000 transcripts of
earnings calls from 6,300 public companies from January 2010 to December 2019.
In this study, we report three novel findings. First, the buy, sell and hold
recommendations from professional analysts made prior to the earnings have low
correlation with stock price movements after the earnings call. Second, using
our graph neural network based method that processes the semantic features of
earnings calls, we reliably and accurately predict stock price movements in
five major areas of the economy. Third, the semantic features of transcripts
are more predictive of stock price movements than sales and earnings per share,
i.e., traditional hard data in most of the cases.
