Is tone really that important? And if so, can it be measured and scaled?

Why might tone be important?

Analysts and investors are seeking an edge as to where a company is headed next. So, they focus not only on the numbers, but on the words the executives use in their introductory remarks and in the answers to analysts’ questions. Executives can unintentionally or even intentionally “tip their hand” and provide a clue as to the company’s direction.

But aside from making good CNBC commentary, is there a connection between tone and future company performance? Is it possible to detect tone without listening to the call?

Financial textual analysis has matured

Before the advent of natural language programming (NLP), text analytics and enhanced computing power, written transcripts and audio recordings were all analysts had to go on. There is only so much that the human mind can discern. Subtle shifts in tone and word usage can go unnoticed when listening and tone seems lost when reading transcripts of calls you didn’t have time to cover.

But now, new tools have emerged that can help. Going beyond simple sentiment, nuances in tone emerge when analyzed with advanced computational linguistics. A new academic field of inquiry developed, financial textual analysis, out of this ability to decompose earnings transcripts and financial documents into their most granular bits. Academic researchers were (and are) keen to determine if tone can presage future company performance.

Although academic researchers have always been interested in studying financial disclosures, it wasn’t until the early 2000’s that interest in financial textual analysis really took off. The bibliography of a survey of financial textual analysis studies shows the exponential growth in the number of studies starting in the early 2000’s. The authors attribute this growth to the application of “highly evolved” computational linguistics technologies to financial documents and enhanced computing power.

So, does tone matter? Simple answer is YES.

What do these academic studies conclude about tone? Can executives’ tone on earnings calls “tell” analysts something about future company performance?

It is beyond the scope of this post to survey the entire field of financial textual analysis. But we can say the answer is “yes,” tone matters. Here is a taste of what the academic literature has found:

We note that these studies tend to be sophisticated statistical studies that attempt to estimate the relationship between tone and company or market performance after controlling for other factors that may affect such performance (i.e. they tend to be more than simple correlation studies).

A study by Price, Doran, Peterson and Bliss finds that “conference call linguistic tone is a significant predictor of abnormal returns and trading volume.” The researchers further find that the Q&A portion of calls has an incrementally greater effect than the introductory portion, which is typically a duplication of the earnings press release. The study concludes that “conference call tone dominates earnings surprises over the sixty trading days following the call.”

Another study by Price with Blau and DeLisle finds a positive relation between companies’ stock returns and earning call tone. But the researchers go on to argue that tone can be subtle and that more sophisticated investors (e.g. short sellers) process the information differently than the average investor. Empirically they find that “short sellers target firms with simultaneous high earnings surprise and abnormally high management tone.”

Druz, Petzev, Wagner and Zeckhauser looked at changes in earnings call tone from quarter to quarter finding that higher negativity of executives’ tone “strongly predicts lower future earnings and greater uncertainty.” Interestingly, the researchers find that decreased negativity only “weakly predicts the opposite.”

Finally, Chen, Demers, and Lev’s research showed that the overall negative tone of earnings call Q&A increased as the morning progressed, dipped slightly after lunch and then rose again until the market close. They also found that the more negative the tone, the more negative the stock returns over the five trading hours after the call. Those negative stock returns associated with negative calls continued downward for up to 15 trading days.

So, it’s clear that “tone matters” and computational linguistics can detect it. Analysts, investors and the market react to the choice of words used by executives in their earnings calls. Investors need to factor this information into their trading strategies, but with care to consider external factors as well.

Boulder Earnings Call Tracker

KDD Analytics is partnering with Boulder Equity Analytics in the development of the Boulder Earnings Call Tracker. This Tableau-based set of dashboards examines earnings call tone among other attributes of earnings calls. It provides our clients with highly relevant signals for sophisticated trading strategies, especially short sellers. On the flip side it provides CEOs and investor relations with unbiased metrics to improve their conference call performance.

The post Negative Earnings Call Tone? Go Short (but not always) appeared first on KDD Analytics.

Our partner at BEA takes textual analysis a step further and teaches artificial intelligence (AI) software to find concepts and themes, not just words.  It’s one thing to find all occurrences of the word “decline” in an earnings call transcript.  It is another thing altogether to understand the concept of “decline” within the context of a paragraph.  Is it a decline in sales?  or a decline in bad accounts?

In another guest article, Tom Marsh, CTO at Boulder Equity Analytics (BEA), talks about keyword vs. theme detection and why “concepts are key, not words”.

A critical skill for the analyst during earnings season is detecting changes in the key indicators or themes for a company and its peers. Keyword detection is often passed off as theme detection but it’s not and the difference is critical.  Here at BEA, teaching software (AI) to find themes buried in SEC filings, earnings calls and press coverage from investor relations is a critical technology advantage.

First, understand the terms. Analysts tell us that with all the buzzwords and claims by vendors, it’s hard to understand the difference between real and apparent performance.  For us, the goal is to replicate an expert analyst’s ability to read and understand a document, whether its a filing, earnings call or interview.

While there’s more, in this post I want to make sure we understand each other when we use the term “theme”, “topic” or “concept”.

What is a “concept”?

Since we claim to teach software agents to find “concepts”, let’s check the definition of the term “concept” to make sure we are using it correctly. While I didn’t expect this to lead me all the way back to Philosophy class with references to Kant, Locke, Mill etc., our approach and use of this term are fundamentally consistent with the excerpts below from Wikipedia.

Concept– definition from Wikipedia

A concept is a general idea, or something conceived in the mind.

Notable definitions:

John Locke‘s description of a general idea corresponds to a description of a concept. According to Locke, a general idea is created by abstracting, drawing away, or removing the uncommon characteristic or characteristics from several particular ideas. The remaining common characteristic is that which is similar to all of the different individuals.

John Stuart Mill argued that general conceptions are formed through abstraction. A general conception is the common element among the many images of members of a class. “…When we form a set of phenomena into a class, that is, when we compare them with one another to ascertain in what they agree, some general conception is implied in this mental operation” (A System of Logic, Book IV, Ch. II).

Philosopher Arthur Schopenhauer argued that concepts are “mere abstractions from what is known through intuitive perception, and they have arisen from our arbitrarily thinking away or dropping of some qualities and our retention of others.” (Parerga and Paralipomena, Vol. I, “Sketch of a History of the Ideal and the Real”).

By contrast to the above philosophers, Immanuel Kant held that the account of the concept as an abstraction of experience is only partly correct. He called those concepts that result from abstraction “a posteriori concepts”.

A concept is a common feature or characteristic. Kant investigated the way that empirical a posteriori concepts are created.

“The logical acts of the understanding by which concepts are generated as to their form are:

1. comparison, i.e., the likening of mental images to one another in relation to the unity of consciousness;

2. reflection, i.e., the going back over different mental images, how they can be comprehended in one consciousness; and finally

3. abstraction or the segregation of everything else by which the mental images differ …

In order to make our mental images into concepts, one must thus be able to compare, reflect, and abstract, for these three logical operations of the understanding are essential and general conditions of generating any concept whatever. For example, I see a fir, a willow, and a linden. In firstly comparing these objects, I notice that they are different from one another in respect of trunk, branches, leaves, and the like; further, however, I reflect only on what they have in common, the trunk, the branches, the leaves themselves, and abstract from their size, shape, and so forth; thus I gain a concept of a tree.”

— Logic, §6

Optimization of AI software

We worked with our ai-one partner to optimize their AI for this task. 

At the core, our application processes each line of text much the way our brains do it, learning the patterns of language, the “key” words, their importance and the words most closely associated with them. The AI provides commands to extract as an array those key words and associations, their direction and values (strengths).

While the ability to score the similarity of concepts is important, my observation from years of applying it to problems from NASA (Topic Mapping pg.198) to SwissRe is that it’s even more proficient at filtering out the noise, giving lowest values to the unimportant words and associations.

Filtering is fundamental to our brain’s ability to find the topic that’s important.

Locke describes a concept as an idea “created by abstracting, drawing away, or removing the uncommon characteristic or characteristics”. This is very close to the way our solution builds a model of a concept after learning from the examples provided to teach it. The fingerprint we extract from that text is an array that represents the concept in the same way. The similarity score for that comparison is a powerful attribute we use in a number of ways to deliver a great user experience.

Building powerful qualitative analytics for financial analysts and investors starts with the right core technology. Finding concepts buried inside documents is the first part and foundation of extracting actionable insight.

Now that I think about it, maybe Philosophy 101 wasn’t a liberal arts waste of money after all.

Tom

@tom_semantic

KDD Analytics and Boulder Equity Analytics are partnering to deliver collaborative artificial intelligence to the financial and competitive analysis industries.

The post Concepts are Key, Not Words appeared first on KDD Analytics.