Analyzing a company's earnings call gives an investor a first hand heads-up on the company's latest status with regards to operational and financial health. Investors can read the transcript, look at the numbers, and draw their own conclusions. <\/p>\n
In addition to the traditional approach of evaluating an earnings call, we used our Artificial Intelligence engine to objectively analyze a call transcript. The purpose of this exercise is to acquire additional insights directly from the company's perspective. This write-up focuses on the Executive Statement from the IBM (NYSE:IBM) 2017 Q1 Earnings Call. <\/p>\n
The following is a summary of findings: <\/p>\n
Analytics with Artificial Intelligence <\/p>\n
Our AI Analytics is based on symbolic logic and propositional calculus. In other words, our algorithm discovers symbols that represent some level of importance based on propositional logic to drive a causational model. The causational model seeks out supporting context surrounding these situations. Thus, for each of the points, we expect AI to tell us the rationale. <\/p>\n
In a nutshell, the AI part of the analysis is to read the transcript like a human researcher and bring out positive points, negative points, and points with both positive and negative aspects. It does so in an objective way using Meta-Vision. <\/p>\n
Our AI analysis of the earnings call Executive Statement resulted in the following Meta-Vision: <\/p>\n<\/p>\n
Meta-Vision Legend: <\/p>\n
Our AI engine discovers important points we call 'Meta-Objects'. There are two type of Meta Objects, namely, Machine Generated Hashtag (or MGH) nodes and Supporting Fact (or SF) nodes. MGH nodes are important points discovered by CIF from the given dataset. SF nodes are the text that is being analyzed. 'Meta-Vision' is the topological mapping of Meta-Objects across a quadrant chart by semantics, context, and polarity. The quadrant chart connects Meta-Objects (MGH and SF nodes) by edges to depict their respective relationships. Clicking on a node opens a new window showing corresponding context for that node. The North-East \"NE\" quadrant is called the \"common-positive quadrant.\" The North-West \"NW\" quadrant is called the \"common-negative quadrant.\" The South-West \"SW\" quadrant is the \"negative quadrant.\" The South-East \"SE\" quadrant is the \"positive quadrant.\" The name of each quadrant denotes the connotation (common, negative, positive). Placement of nodes are determined by the AI. Machine generated hashtag nodes are labeled. The relative location from the X-axis denotes the strength of a MGH node. The closer the FN nodes are to the center, the higher the number of MGH nodes that it supports. <\/p>\n
For each of the important points (MGH node), the co-ordinate indicates the connotation. Clicking a MGH will bring out all the corresponding quotes in verbatim from the transcript (supporting facts and context). MGH nodes are also connected to fact nodes. Each Fact node represents the excerpts from the original document. Clicking a fact node will bring out the semantic and sentiment analytics on that excerpt. <\/p>\n
In summary, without any human interaction or influence, our AI algorithm has determined that the following points, represented by machine generated hashtags, are negatively stated in the earnings call: #Income, #GBS, #Earning, #Workforce <\/p>\n
Our AI algorithm determined that the following points, represented by machine generated hashtags, are positively stated in the earning call: #Cloud, #Solutions, #Digital, #Profit, #Investment, #IBM <\/p>\n
Our AI algorithm determined two points carried a negative connotation, but also has positive aspects. They are: #Software, #Track <\/p>\n
Our AI algorithm determined that the following points contained both positive and negative supporting facts, while the positive supporting facts are dominant: #Margin, #Client <\/p>\n
Our AI algorithm determined that the following points contained both negative and positive supporting facts, while the negative supporting facts are dominant: #Performance, #Revenue <\/p>\n
Evaluating the Executive Statement with Meta-Vision <\/p>\n
Based on our examination, we identified strategic points and corresponding supporting facts. We did so with the following agendas in mind: <\/p>\n
The following are points (MGH nodes) that we picked out are based on the above criteria: <\/p>\n
#income #workforce <\/p>\n
#gbs <\/p>\n
#cloud <\/p>\n
#ibm <\/p>\n
#margin, #solutions, #profit <\/p>\n
#clients <\/p>\n
Deriving Insights through Bionic Fusion <\/p>\n
While the details of the technology behind the analysis is beyond of scope of this article, the general concept is not difficult to understand. The idea is to equip a software system with the ability to master a language, such as English, to the equivalent of a graduate student or researcher who can learn a core subject from a lecture or research medium. In this scenario, the medium uses English to introduce new subjects. In the process of knowledge transfer, the medium draws relationships between subjects and expresses the properties of the underlying context. The researcher, using English as a medium, can learn any subject and acquire new knowledge by listening to lectures. In a similar manner, the software system uses visual charts to depict the discovered subjects, relationships, underlying context, properties, and references to source documents. When a user navigates through these properties, together with human thinking, it forms a bond of bionic fusion which enables the user to gain insights by drawing inference from these visuals. <\/p>\n
The AI algorithm did the work of identifying important points, connotation, and supporting facts. We examined each point and supporting fact to draw inference into perceived strengths and weaknesses. To corroborate our findings, we also referred to our enterprise data lake for business intelligence around competitive marketspace and external market forces. <\/p>\n
RE: GBS, Strategic Imperatives <\/p>\n
If management saw growth in its Strategic Imperatives, IBM would need the following: <\/p>\n
This needs upfront investment, a substantial increase in human capital, and a faster time to market with industry-specific vertical applications. This proposition is contradicted by the decline in Global Business Services (or GBS). If management was dedicated to building a backlog and pipeline in its GBS unit, the subsequent rebalance of workforce should result in an increase in expense. Judging from the continuing rebalancing of workforce in the negative column, and the need to build industry specific solutions, GBS will have problems with scale. Customers cannot put their business on hold and will seek for alternative competitive solutions in the marketplace such as open source or off-the-shelf solutions. Consequently, we do not believe that management is confident in GBS pipeline growth. <\/p>\n
RE: Cloud <\/p>\n
IBM is transforming their business into a 'data and cloud first' company. The super set of cloud business consists of private cloud (enterprise cloud), public cloud, and hybrid cloud. IBM's cloud is not a public cloud like Amazon (NASDAQ:AMZN)'s AWS offering. IBM only focuses on enterprise. The public cloud space has a market cap that is projected to exceed $500 billion by 2020. IBM's Executive Statement did not reflect any initiative that would position IBM for a share of this huge market. The enterprise cloud space has major competitors such as HP (NYSE:HPE), Microsoft (NASDAQ:MSFT), and Google (NASDAQ:GOOG). Moreover, IBM's enterprise cloud is a service that will compete with IBM's legacy mainframe business for the same customer IT budget. IBM recognizes that this shift will require a level of investment a longer return profile which is already being reflected in their margins and will require continued investment. <\/p>\n
RE: Cognitive <\/p>\n
Cognitive is industry-specific. It will cost substantial time and additional investment in building each of the vertical problem domains. Artificial Intelligence is becoming a crowded market. IBM will have to compete with new startups. Time, cost and efficiency will weigh against IBM just like its legacy Personal Computing and server business. Technology is changing at a fast pace; custom-built solutions that takes years to materialize will face obsolescence before it is put to use. <\/p>\n
Conclusion: <\/p>\n
Products and services that make up the Strategic Imperatives are part of the \"red-ocean\" in a crowded market. If Strategic Imperatives as identified by IBM is its main turnaround strategy, it is going to face a lot of competition. Based on the Meta-Vision analysis of IBM's 2017 Q1 earnings call, we do not see any counter initiatives that will improve IBM's outlook in near-term. <\/p>\n
Additional Notes - Process of Analysis: <\/p>\n
Disclosure: I\/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. <\/p>\n
I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. <\/p>\n
Additional disclosure: I am neither a certified investment advisor nor a certified tax professional. The data presented here is for informational purposes only and is not meant to serve as a buy or sell recommendation. The analytic tools used in this analysis are products of SiteFocus. <\/p>\n
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An Artificial Intelligence Retrospective Analysis Of IBM 2017 Q1 Earnings Call - Seeking Alpha<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Analyzing a company's earnings call gives an investor a first hand heads-up on the company's latest status with regards to operational and financial health. Investors can read the transcript, look at the numbers, and draw their own conclusions Continue reading