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Ronen Feldman

The Hebrew University of Jerusalem, School of Busienss Administration, Faculty Member

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Professor Feldman is a world-leading expert in the field of text-mining: in fact, he coined the term “text mining” over two decades ago, authored the Text Mining Handbook published by Cambridge University Press and has authored 80+ academic treatises on text mining and information extraction. His first text mining systems were developed for use by top intelligence units, and twenty years of improvements have given rise to Amenity Analytics’ advanced 5th-generation technologies. Leveraging his technologies, Prof. Feldman co-founded two companies prior to joining Amenity Analytics (ClearForest, acquired by Reuters in 2007 and Digital Trowel), and served as a consultant for Nice Systems Ltd., the Bank of Israel, and Standard & Poors. A graduate of the IDF’s elite Talpiot program, Prof. Feldman holds a Ph.D in Computer Science from Cornell University and is a full professor in Hebrew University’s School of Business Administration.

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Papers by Ronen Feldman

Earnings Guidance After Regulation FD

The Journal of Investing, Nov 30, 2003

This study investigates market reactions to voluntary earnings guidance provided by managers afte... more This study investigates market reactions to voluntary earnings guidance provided by managers after the enactment of Regulation FD, which requires companies to disseminate material news to all investors simultaneously. More managers now issue their guidance to the public instead of disclosure to a selective group of analysts, in conformity with Regulation FD. We examine a very large set of earnings guidance disclosures based on identification of these announcements using text mining techniques. Our results indicate that guidance provided with the disclosure of earnings is not associated with significant market reactions, but guidance provided between earnings releases is associated with significant negative reactions. We further show that market reactions are consistent with the trend implied by management even when it is in the form of qualitative disclosure. Finally, we show that market reactions are stronger (more negative, typically) for NASDAQ firms than NYSE or AMEX firms, larger firms, and when the disclosure involves revenues and not earnings.

Accounting and Control Group

The authors gratefully acknowledge comments made by seminar participants at Rutgers University an... more

Orders Backlog in Earnings Conference Calls

SSRN Electronic Journal, 2020

Firm disclosure of order backlog (OB) is considered important to assess future sales and profits.... more Firm disclosure of order backlog (OB) is considered important to assess future sales and profits. The extant literature on OB has generally documented positive associations between increases in OB and market returns. These associations were based on annual disclosures of backlog in 10-K filings, and could have been caused by other simultaneous disclosures that were also correlated with order backlog. To focus on the direct effects of backlog on market participants, we use references to OB in earnings conference call transcripts. We find incremental market reactions to OB after controlling for earnings surprise and other information communicated during the conference call. Our findings also reveal that OB disclosures are more relevant when they are supported by numbers and when firms derive a material amount of their demand from OB.

Optimal Portfolio Construction Using Qualitative and Quantitative Signals

Advances in Linguistics and Communication Studies

Finance and accounting research has recently focused on extracting the tone or sentiment of a doc... more Finance and accounting research has recently focused on extracting the tone or sentiment of a document (such as an earnings press release, cover story about a company, or management’s presentations to analysts) by using positive or negative words/phrases in the document. This chapter shows that signals based on tone or sentiment (extracted from qualitative data) can achieve abnormal returns, and in some studies, incremental abnormal returns beyond quantitative signals. In this chapter, the authors exploit the information content of qualitative data in addition to quantitative signals in selecting optimal portfolios. Using optimization techniques developed by Brandt, Santa-Clara, and Valkonov (2009), and later extended by Hand and Green (2011), the authors show that significantly higher returns can be obtained by combining quantitative and qualitative data obtained from firms’ Management Discussion and Analysis (MD&A) sections of their Form 10-Q (10-K) SEC filings than using quantita...

Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions

Cambridge University Press eBooks, Dec 24, 2009

Cambridge University Press eBooks, Dec 24, 2009

Cambridge University Press eBooks, Dec 24, 2009

Visualization Approaches

Cambridge University Press eBooks, Dec 24, 2009

The Text Mining Handbook: Bibliography

Information Extraction

Cambridge University Press eBooks, Dec 24, 2009

Information extraction: Theory and practice

... Different types of style · Scientific papers · Newspapers · memos · Emails · Speech transcrip... more

An integrated framework for knowledge representation and theory revision

Analyzing Product Comparisons on Discussion Boards

Lecture Notes in Computer Science, 2014

Product discussion boards are a rich source of information about consumer sentiment about product... more Product discussion boards are a rich source of information about consumer sentiment about products, which is being increasingly exploited. Most sentiment analysis has looked at single products in isolation, but users often compare different products, stating which they like better and why. We present a set of techniques for analyzing how consumers view product markets. Specifically, we extracted relative sentiment analysis and comparisons between products, to understand what attributes users compare products on, and which products they prefer on each dimension. We illustrate these methods in an extended case study analyzing the sedan car markets.

Extracting Product Comparisons from Discussion Boards

Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007

In recent years, product discussion forums have become a rich environment in which consumers and ... more In recent years, product discussion forums have become a rich environment in which consumers and potential adopters exchange views and information. Researchers and practitioners are starting to extract user sentiment about products from user product reviews. Users often compare different products, stating which they like better and why. Extracting information about product comparisons offers a number of challenges; recognizing and normalizing entities (products) in the informal language of blogs and discussion groups require different techniques than those used for entity extraction in the more formal text of newspapers and scientific articles. We present a case study in extracting information about comparisons between running shoes and between cars, describe an effective methodology, and show how it produces insight into how consumers view the running shoe and car markets.

Mine Your Own Business: Market-Structure Surveillance Through Text Mining

Marketing Science, 2012

W eb 2.0 provides gathering places for Internet users in blogs, forums, and chat rooms. These gat... more W eb 2.0 provides gathering places for Internet users in blogs, forums, and chat rooms. These gathering places leave footprints in the form of colossal amounts of data regarding consumers' thoughts, beliefs, experiences, and even interactions. In this paper, we propose an approach for firms to explore online user-generated content and "listen" to what customers write about their and their competitors' products. Our objective is to convert the user-generated content to market structures and competitive landscape insights. The difficulty in obtaining such market-structure insights from online user-generated content is that consumers' postings are often not easy to syndicate. To address these issues, we employ a text-mining approach and combine it with semantic network analysis tools. We demonstrate this approach using two cases-sedan cars and diabetes drugs-generating market-structure perceptual maps and meaningful insights without interviewing a single consumer. We compare a market structure based on user-generated content data with a market structure derived from more traditional sales and survey-based data to establish validity and highlight meaningful differences.

Using text mining to analyze user forums

2008 International Conference on Service Systems and Service Management, 2008

Abstract Product discussion boards are a rich source of information about consumer sentiment abou... more Abstract Product discussion boards are a rich source of information about consumer sentiment about products, which is being increasingly exploited. Most sentiment analysis has looked at single products in isolation, but users often compare different products, stating which they like better and why. We present a set of techniques for analyzing how consumers view product markets. Specifically, we extracted relative sentiment analysis and comparisons between products, to understand what attributes users compare products on, ...

FRST - An Interactive Revision System for Forward Chaining Rule Bases

One of the main problems in building rule based systems is that the knowledge elicited from exper... more One of the main problems in building rule based systems is that the knowledge elicited from experts is not always correct. Therefore there is a need for means of revising the rule base whenever an inaccuracy is discovered. The rule base revision is the problem of how best to go about revising a deficient rule base using information contained in cases that expose inaccuracies. The revision process is very sensitive to implicit and explicit biases that are encoded in the specific revision algorithm employed. In a sense, each revision algorithm must provide two forms of biases. The first bias governs the preferred location in the rule base for the correction, while the second bias governs the type of correction performed. In this paper we present a system for incremental revision of rule bases called FRST (Forward chaining Revision SysTem). This system enables the user to analyze the impact of different revisions and to select the most appropriate revision operator. The user provides t...

Identifying and Following Expert Investors in Stock Microblogs

Information published in online stock investment message boards, and more recently in stock micro... more Information published in online stock investment message boards, and more recently in stock microblogs, is considered highly valuable by many investors. Previous work focused on aggregation of sentiment from all users. However, in this work we show that it is beneficial to distinguish expert users from non-experts. We propose a general framework for identifying expert investors, and use it as a basis for several models that predict stock rise from stock microblogging messages (stock tweets). In particular, we present two methods that combine expert identification and per-user unsupervised learning. These methods were shown to achieve relatively high precision in predicting stock rise, and significantly outperform our baseline. In addition, our work provides an in-depth analysis of the content and potential usefulness of stock tweets.

The Incremental Information Content of Tone and Sentiment in Management Discussion and Analysis

This study explores whether the Management Discussion and Analysis (MD&A) section of Form 10-Q an... more This study explores whether the Management Discussion and Analysis (MD&A) section of Form 10-Q and 10-K has incremental information content beyond financial measures such as earnings surprises, accruals and operating cash flows (OCF). It uses a well-established classification scheme of words into positive and negative categories to measure the tone and sentiment in a specific MD&A section as compared to those of the prior four filings. Our results indicate that short window market reactions around the SEC filing are significantly associated with the tone of the MD&A section, even after controlling for accruals, OCF and earnings surprises. We also show that the tone of the MD&A section adds significantly to portfolio drift returns in the window of two days after the SEC filing date through one day after the subsequent quarter’s preliminary earnings announcement, beyond financial information conveyed by accruals, OCF and earnings surprises. The Incremental Information Content of Tone ...

Earnings Guidance After Regulation FD

The Journal of Investing, Nov 30, 2003

This study investigates market reactions to voluntary earnings guidance provided by managers afte... more This study investigates market reactions to voluntary earnings guidance provided by managers after the enactment of Regulation FD, which requires companies to disseminate material news to all investors simultaneously. More managers now issue their guidance to the public instead of disclosure to a selective group of analysts, in conformity with Regulation FD. We examine a very large set of earnings guidance disclosures based on identification of these announcements using text mining techniques. Our results indicate that guidance provided with the disclosure of earnings is not associated with significant market reactions, but guidance provided between earnings releases is associated with significant negative reactions. We further show that market reactions are consistent with the trend implied by management even when it is in the form of qualitative disclosure. Finally, we show that market reactions are stronger (more negative, typically) for NASDAQ firms than NYSE or AMEX firms, larger firms, and when the disclosure involves revenues and not earnings.

Accounting and Control Group

The authors gratefully acknowledge comments made by seminar participants at Rutgers University an... more

Orders Backlog in Earnings Conference Calls

SSRN Electronic Journal, 2020

Firm disclosure of order backlog (OB) is considered important to assess future sales and profits.... more Firm disclosure of order backlog (OB) is considered important to assess future sales and profits. The extant literature on OB has generally documented positive associations between increases in OB and market returns. These associations were based on annual disclosures of backlog in 10-K filings, and could have been caused by other simultaneous disclosures that were also correlated with order backlog. To focus on the direct effects of backlog on market participants, we use references to OB in earnings conference call transcripts. We find incremental market reactions to OB after controlling for earnings surprise and other information communicated during the conference call. Our findings also reveal that OB disclosures are more relevant when they are supported by numbers and when firms derive a material amount of their demand from OB.

Optimal Portfolio Construction Using Qualitative and Quantitative Signals

Advances in Linguistics and Communication Studies

Finance and accounting research has recently focused on extracting the tone or sentiment of a doc... more Finance and accounting research has recently focused on extracting the tone or sentiment of a document (such as an earnings press release, cover story about a company, or management’s presentations to analysts) by using positive or negative words/phrases in the document. This chapter shows that signals based on tone or sentiment (extracted from qualitative data) can achieve abnormal returns, and in some studies, incremental abnormal returns beyond quantitative signals. In this chapter, the authors exploit the information content of qualitative data in addition to quantitative signals in selecting optimal portfolios. Using optimization techniques developed by Brandt, Santa-Clara, and Valkonov (2009), and later extended by Hand and Green (2011), the authors show that significantly higher returns can be obtained by combining quantitative and qualitative data obtained from firms’ Management Discussion and Analysis (MD&A) sections of their Form 10-Q (10-K) SEC filings than using quantita...

Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions

Cambridge University Press eBooks, Dec 24, 2009

Cambridge University Press eBooks, Dec 24, 2009

Cambridge University Press eBooks, Dec 24, 2009

Visualization Approaches

Cambridge University Press eBooks, Dec 24, 2009

The Text Mining Handbook: Bibliography

Information Extraction

Cambridge University Press eBooks, Dec 24, 2009

Information extraction: Theory and practice

... Different types of style · Scientific papers · Newspapers · memos · Emails · Speech transcrip... more

An integrated framework for knowledge representation and theory revision

Analyzing Product Comparisons on Discussion Boards

Lecture Notes in Computer Science, 2014

Product discussion boards are a rich source of information about consumer sentiment about product... more Product discussion boards are a rich source of information about consumer sentiment about products, which is being increasingly exploited. Most sentiment analysis has looked at single products in isolation, but users often compare different products, stating which they like better and why. We present a set of techniques for analyzing how consumers view product markets. Specifically, we extracted relative sentiment analysis and comparisons between products, to understand what attributes users compare products on, and which products they prefer on each dimension. We illustrate these methods in an extended case study analyzing the sedan car markets.

Extracting Product Comparisons from Discussion Boards

Seventh IEEE International Conference on Data Mining (ICDM 2007), 2007

In recent years, product discussion forums have become a rich environment in which consumers and ... more In recent years, product discussion forums have become a rich environment in which consumers and potential adopters exchange views and information. Researchers and practitioners are starting to extract user sentiment about products from user product reviews. Users often compare different products, stating which they like better and why. Extracting information about product comparisons offers a number of challenges; recognizing and normalizing entities (products) in the informal language of blogs and discussion groups require different techniques than those used for entity extraction in the more formal text of newspapers and scientific articles. We present a case study in extracting information about comparisons between running shoes and between cars, describe an effective methodology, and show how it produces insight into how consumers view the running shoe and car markets.

Mine Your Own Business: Market-Structure Surveillance Through Text Mining

Marketing Science, 2012

W eb 2.0 provides gathering places for Internet users in blogs, forums, and chat rooms. These gat... more W eb 2.0 provides gathering places for Internet users in blogs, forums, and chat rooms. These gathering places leave footprints in the form of colossal amounts of data regarding consumers' thoughts, beliefs, experiences, and even interactions. In this paper, we propose an approach for firms to explore online user-generated content and "listen" to what customers write about their and their competitors' products. Our objective is to convert the user-generated content to market structures and competitive landscape insights. The difficulty in obtaining such market-structure insights from online user-generated content is that consumers' postings are often not easy to syndicate. To address these issues, we employ a text-mining approach and combine it with semantic network analysis tools. We demonstrate this approach using two cases-sedan cars and diabetes drugs-generating market-structure perceptual maps and meaningful insights without interviewing a single consumer. We compare a market structure based on user-generated content data with a market structure derived from more traditional sales and survey-based data to establish validity and highlight meaningful differences.

Using text mining to analyze user forums

2008 International Conference on Service Systems and Service Management, 2008

Abstract Product discussion boards are a rich source of information about consumer sentiment abou... more Abstract Product discussion boards are a rich source of information about consumer sentiment about products, which is being increasingly exploited. Most sentiment analysis has looked at single products in isolation, but users often compare different products, stating which they like better and why. We present a set of techniques for analyzing how consumers view product markets. Specifically, we extracted relative sentiment analysis and comparisons between products, to understand what attributes users compare products on, ...

FRST - An Interactive Revision System for Forward Chaining Rule Bases

One of the main problems in building rule based systems is that the knowledge elicited from exper... more One of the main problems in building rule based systems is that the knowledge elicited from experts is not always correct. Therefore there is a need for means of revising the rule base whenever an inaccuracy is discovered. The rule base revision is the problem of how best to go about revising a deficient rule base using information contained in cases that expose inaccuracies. The revision process is very sensitive to implicit and explicit biases that are encoded in the specific revision algorithm employed. In a sense, each revision algorithm must provide two forms of biases. The first bias governs the preferred location in the rule base for the correction, while the second bias governs the type of correction performed. In this paper we present a system for incremental revision of rule bases called FRST (Forward chaining Revision SysTem). This system enables the user to analyze the impact of different revisions and to select the most appropriate revision operator. The user provides t...

Identifying and Following Expert Investors in Stock Microblogs

Information published in online stock investment message boards, and more recently in stock micro... more Information published in online stock investment message boards, and more recently in stock microblogs, is considered highly valuable by many investors. Previous work focused on aggregation of sentiment from all users. However, in this work we show that it is beneficial to distinguish expert users from non-experts. We propose a general framework for identifying expert investors, and use it as a basis for several models that predict stock rise from stock microblogging messages (stock tweets). In particular, we present two methods that combine expert identification and per-user unsupervised learning. These methods were shown to achieve relatively high precision in predicting stock rise, and significantly outperform our baseline. In addition, our work provides an in-depth analysis of the content and potential usefulness of stock tweets.

The Incremental Information Content of Tone and Sentiment in Management Discussion and Analysis

This study explores whether the Management Discussion and Analysis (MD&A) section of Form 10-Q an... more This study explores whether the Management Discussion and Analysis (MD&A) section of Form 10-Q and 10-K has incremental information content beyond financial measures such as earnings surprises, accruals and operating cash flows (OCF). It uses a well-established classification scheme of words into positive and negative categories to measure the tone and sentiment in a specific MD&A section as compared to those of the prior four filings. Our results indicate that short window market reactions around the SEC filing are significantly associated with the tone of the MD&A section, even after controlling for accruals, OCF and earnings surprises. We also show that the tone of the MD&A section adds significantly to portfolio drift returns in the window of two days after the SEC filing date through one day after the subsequent quarter’s preliminary earnings announcement, beyond financial information conveyed by accruals, OCF and earnings surprises. The Incremental Information Content of Tone ...