The quarterly earnings call is a critical event for publicly traded companies. Each call serves multiple purposes. It is both an important source of information for investors and an opportunity for a company to present a narrative of operational performance, financial health, and strategic vision in their own terms. It’s also an ideal opportunity for executives seeking to manage and optimize outcomes.
The advent of AI makes it plausible for the Chief Financial Officer (CFO) or Chief Executive Officer (CEO) to predict the outcome of a conference call by means of business intelligence (BI) analysis. Working with unstructured textual data, an AI engine can discover, investigate, and draw relationships between context to provide a meta-view of a company’s operations, performance, strategy, and alignment with wider industry trends. This enables a company to maximize the outcome of a quarterly earnings call presentation.
The Earnings Call Challenge of Publicly Traded Companies
Most enterprise implementations of AI are predominantly based on machine learning (ML) – solving problems using past experience, known variables and outcomes. This type of AI tends to make humans more passive when a complete solution to a problem is formulated and rendered by machine. We see examples in applications such as Google Lens, Alexa/Echo, Apple/Siri. These applications use automation to perform jobs humans can do. The only difference is in cost, time, and precision.
When applied to a quarterly earnings presentation or company outlook, a ML approach would be hindered by the lack of relevant historical data. Moreover, while knowledge base models can play an important role in many AI applications, these models generally fail when faced with unprecedented scenarios and “one-off” variables. For example, IBM has executed a hardware-centric strategy throughout its corporate history. The serial downturn of mainframe demand year-on-year forced IBM to confront a new business paradigm that past experience could not account for. IBM is facing 20 consecutive quarters of declining revenue. Retail companies like Walmart and Macy’s have never previously encountered the threat of online commerce. Faced with a slow-growth market for switch & routers, Cisco is forced to seek alternatives. Under these circumstances, it’s no longer a matter of improving an enterprise within the sector, but to transition into different business models altogether.
Forecasting business outlook against historical trends can overlook material risks and opportunities. There is another branch of AI that enables business teams to manage the spectrum of information found in quarterly earnings call BI. This approach to AI augments human intelligence and actively works with humans to solve problems. It is based on man-machine interactions – bionic fusion – via visual feedback and expert inferences derived from instinct and automated meta-object discovery. For this reason, we believe bionic fusion can help executive teams create and grow a winning competitive edge.
Vision and Meta-Vision
Vision is one of the most efficient ways for living things to evaluate their surroundings for opportunity and risk. Computer vision mimics human vision to detect objects through image registration. Vision, however, does not have to be based on the physical view of an environment. Bats use sound and sonar to “see”. Scientists use radio signals and light spectrum, among other signals, to reconstruct the views of remote galaxies that may be several million light-years away. Similarly, Context Discriminant, an alternative approach to machine learning outlined in my previous post, applied on a body of textual information, maps a topological view of meta-objects that elevates domain expert insight discovery and execution. The resulting “Meta-Vision” helps business teams gain comprehensive insight across a wide spectrum of information from navigating market forces and assessing the competition to monitoring public relations media engineering. Applied to current events, Meta-Vision enables one to observe and triangulate the shift in public opinion over time. The ability to navigate risk and identify opportunity in qualitative information sources such as earnings call transcripts, paid media, earned media, voice of customer and investor sentiment enables business leaders to realize tactical and strategic execution.
The Meta-Vision Approach in predicting reaction to earning calls
Our AI development represents a necessary divergence from conventional statistical-based machine learning and rendering engines. As noted, the dynamic nature of business intelligence surrounding quarterly earnings calls severely limits the effectiveness of machine learning. Data may be new, volume may be low, terminology may not exist in the data dictionary or ontology. Context Discriminant technology enables business teams to discover and penetrate a wide spectrum of business intelligence. Meta-Vision enables a business team to create a topology where perceived strengths and weaknesses are manifested as “meta-objects”. Meta-object topology provides both visual cues into sentiment polarity and supporting facts for context and validation. Concerns can be addressed; head-winds, whether from direct competitors or market conditions, can be mitigated. Meta-Vision facilitates bionic fusion that help domain experts to navigate risk and opportunity in real-time.
Meta-Vision inputs can vary from current and relevant BI to earned media or “quarterly conference call” transcripts presented by most publicly traded companies. Predictive analytics on unstructured data (textual media) enable executive teams to better prepare their quarterly earnings call presentation in the following areas:
Consequently, executive teams are able to proactively take actions to mitigate weaknesses and embolden areas of strength in preparation ahead of each conference call:
Context Discriminant and Meta-Vision have been deployed on earnings conference calls of DOW 30 and FTSE-100 companies with substantial results. Analysis was performed on quarterly earnings calls, earned media, and validated against analyst Question-and-Answers. The predictive insights drawn are consistent with market reaction to respective earnings calls.
 IBM Revenue Declines for 20th Consecutive Quarter. Bloomberg. April 18, 2017. https://www.bloomberg.com/news/videos/2017-04-18/ibm-revenue-declin...
 Introducing Context Discriminant - Artificial Intelligence for Tactical Strategic Execution with Bionic Fusion. http://www.datasciencecentral.com/profiles/blogs/introducing-contex...