Word Power: A New Approach for Content Analysis

By Narasimhan Jegadeesh, Di Wu. 2013

472 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Finance

Citations in the corpus

Sentiment in Central Banks’ Financial Stability Reports* (2017, unknown): “Our method to test the robustness of the dictionary is similar to the method in Jegadeesh and Wu (2013). The main difference is that their method removes words from the dictionary controlling for frequency of use.”
Readability of 10‐K Reports and Stock Price Crash Risk (2019, unknown): “…of the reports, such as the Management Discussion and Analysis, provide investors with new and important supplementary information (Brown and Tucker 2011; Feldman et al. 2010; Griffin 2003; Jegadeesh and Wu 2013; Kothari et al. 2009a; Li 2010a; Loughran and McDonald 2011; You and Zhang 2009).”
Media Content and Stock Returns: The Predictive Power of Press (2015, unknown): “3 Recently a few papers (executed simultaneously), such as Jegadeesh and Wu (2013) and Garcia (2013), examine the effects of positive and negative tone in newspaper columns on asset prices.”
On the Importance of Text Analysis for Stock Price Prediction (2014, methodology): “We further experimented with sentiment lexicons designed specifically for finance (Jegadeesh and Wu, 2013).”
Hyperbole or Reality? Investor Response to Extreme Language in Earnings Conference Calls (2019, methodology): “(2008), Li (2010a), Rogers, Van Buskirk, and Zechman (2011), Price et al.”
Hear Me Write: Does CEO Narcissism Affect Disclosure? (2019, methodology): “Likewise, numerous studies have also documented that positive tone of these disclosures significantly increases stock returns in the short term (e.g., Price et al. 2012; Jegadeesh and Wu 2013), but leads to negative stock returns in the longer-term (Huang et al. 2014).”
Text as Data (2017, methodology): “The use of text regression for asset pricing is exemplified by Jegadeesh and Wu (2013).”
Textual analysis in finance (2019, methodology): “Some authors use dictionaries with different weights for each word (Loughran and McDonald, 2011; Jegadeesh and Wu, 2013).”
News-based trading strategies (2016, unknown): “For example, dictionary-based approaches are very frequently used in recent financial text mining research [17, 9, 18, 19, 8].”
Intraday online investor sentiment and return patterns in the U.S. stock market (2017, background): “Second, except for rare exceptions (Jegadeesh and Wu (2013)), the vast majority of dictionary-based approaches uses an equal-weighting scheme, where each word in the dictionary is supposed to have the same explanatory power.”
Executive Extraversion: Career and Firm Outcomes (2018, unknown): “analyzes the text of company disclosures to measure tone (Tetlock, Saar-Tsechansky, and Macskassy, 2008; Loughran and McDonald, 2011; Jegadeesh and Wu, 2013), uncertainty (Loughran and McDonald, 2013), readability (Li, 2008; Loughran and McDonald, 2014), and uncover deception (Larcker and Zakolyukin”
Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (2023, unknown): “Our paper makes a unique contribution to this literature as being the ﬁrst to evaluate the text processing capabilities of recently developed LLMs such as ChatGPT in forecasting stock market movements.”