Don’t Believe the Hype: Local Media Slant, Local Advertising, and Firm Value

THE JOURNAL OF FINANCE • VOL. LXVII, NO. 2 • APRIL 2012

Don’t Believe the Hype: Local Media Slant, Local Advertising, and Firm Value

UMIT G. GURUN and ALEXANDER W. BUTLER∗

ABSTRACT

When local media report news about local companies, they use fewer negative words

compared to the same media reporting about nonlocal companies. We document that

one reason for this positive slant is the firms’ local media advertising expenditures.

Abnormal positive local media slant strongly relates to firm equity values. The effect

is stronger for small firms; firms held predominantly by individual investors; and

firms with illiquid or highly volatile stock, low analyst following, or high dispersion of

analyst forecasts. These findings show that news content varies systematically with

the characteristics and conflicts of interest of the source.

When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks

THE JOURNAL OF FINANCE • VOL. LXVI, NO. 1 • FEBRUARY 2011

When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks

TIM LOUGHRAN and BILL MCDONALD∗

ABSTRACT

Previous research uses negative word counts to measure the tone of a text. We show

that word lists developed for other disciplines misclassify common words in financial

text. In a large sample of 10-Ks during 1994 to 2008, almost three-fourths of the words

identified as negative by the widely used Harvard Dictionary are words typically not

considered negative in financial contexts. We develop an alternative negative word

list, along with five other word lists, that better reflect tone in financial text. We link

the word lists to 10-K filing returns, trading volume, return volatility, fraud, material

weakness, and unexpected earnings.

Measuring Readability in Financial Disclosures

Measuring Readability in Financial Disclosures

Tim Loughran 

University of Notre Dame

Bill McDonald 

University of Notre Dame – Mendoza College of Business – Department of Finance
July 16, 2013
Journal of Finance, Forthcoming

Abstract: 
Defining and measuring readability in the context of financial disclosures becomes important with the increasing use of textual analysis and the SEC’s plain English initiative. We propose defining readability as the effective communication of valuation relevant information. The Fog Index — the most commonly applied readability measure — is shown to be poorly specified in financial applications. Of Fog’s two components, one is misspecified and the other is difficult to measure. We report that 10-K document file size provides a simple readability proxy that outperforms the Fog Index, does not require document parsing, facilitates replication, and is correlated with alternative readability constructs.

Using 10-K Text to Gauge Financial Constraints

Using 10-K Text to Gauge Financial Constraints

Andriy Bodnaruk 

University of Notre Dame – Mendoza College of Business

Tim Loughran 

University of Notre Dame

Bill McDonald 

University of Notre Dame – Mendoza College of Business – Department of Finance
November 18, 2013

Abstract: 
Financial constraints — the wedge between the costs of external and internal funds — dictate the relevance of financial structure. We propose a new measure of financial constraints based on qualitative information contained in corporate disclosures. We parse 10-K disclosures filed with the Securities and Exchange Commission (SEC) to measure a document’s tone as indicated by the percentage of negative words. We find that the frequency of negative words exhibits very low correlation with traditional measures of financial constraints such as size and predicts subsequent liquidity events — like dividend cuts or omissions, debt downgrades, and asset growth — better than widely-used financial constraint indexes.

Accounting Variables, Deception and a Bag of Words, Assessing the Tools of Fraud Detection

ACCOUNTING VARIABLES, DECEPTION AND A BAG OF WORDS: ASSESSING THE TOOLS OF FRAUD DETECTION

Lynnette Purda

Queen’s School of Business

Queen’s University

Kingston, Ontario, Canada

(lpurda@business.queensu.ca)

David Skillicorn

School of Computing

Queen’s University

Kingston, Ontario, Canada

(skill@cs.queensu.ca)

Abstract

We develop a data-generated list of words most predictive of fraudulent financial reporting and compare

its success in correctly classifying truthful and fraudulent reports with predictions made by models based

on quantitative financial statement variables and four alternative fixed word lists previously shown to be

associated with fraud. We find that the data-generated list can be a useful complement to alternative

methods, greatly reducing the number of false positives produced by models based on accounting

variables and correctly identifying a higher proportion of frauds than alternative “bag of word” methods.

We study the time series of annual and interim reports of firms eventually accused of fraud and assign a

probability of truthfulness to each using the various word lists. On average, the data-generated word list

shows declines in truthfulness approximately three quarters prior to the actual instance of fraud while

other word lists measuring negative tone and the presence of litigation generate probabilities that decline

by a lesser extent much closer to the event. We find that a word list designed to capture conscious

deception has no predictive power in this setting, consistent with financial reports being written by

multiple individuals, many of whom are likely unaware that misrepresentation is occurring. Our results

contribute not only the development of a new detection tool but also to improving our understanding of

how textual analysis may ultimately contribute to the financial investigators’ toolkit.