Machine learning in finance has been on the rise in the past decade. The applications of
machine learning have become a promising methodological advancement. The paper's …

Investor sentiment based on social media Word-of-Mouth (WoM) has been shown to be
predictive of stock returns under normal market conditions. However, the COVID-19 …

We derive a measure of firm-level regulatory exposure from the text of corporate earnings
calls. We use this measure to study the effect of regulation on companies' growth, leverage …

Relationships between companies serve as key channels through which the effects of past
stock price movements and news events propagate and influence future price movements …

The feasibility of making profitable trades on a single asset on stock exchanges based on
patterns identification has long attracted researchers. Reinforcement Learning (RL) and …

We study the performance of many traditional and novel, text-based variables for in-sample
and out-of-sample forecasting of oil spot, futures, and energy company stock returns, and …

A time-series of numerical data and a sequence of time-ordered documents are often
correlated. This paper aims at modeling the impact that the underlying themes discussed in …

We introduce FDIF, a measure of Fed communication surprise based on the text of FOMC
statements. FDIF measures the difference between text-implied and actual values of key …

This paper examines the impact of economic narratives disseminated by influential Twitter
users on the returns of the S&P500 index. The users are prominent economists working as …

This dissertation is a study of fundamental questions in macro-finance using modern tools
from textual analysis. These questions include how financial constraints affect firm …