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IBM

International Business Machines NYSE:IBM Stock Report

Last Price

US$134.01

Market Cap

US$121.0b

7D

1.2%

1Y

-6.4%

Updated

12 Aug, 2022

Data

Company Financials +
IBM fundamental analysis
Snowflake Score
Valuation5/6
Future Growth2/6
Past Performance4/6
Financial Health1/6
Dividends5/6

IBM Stock Overview

International Business Machines Corporation provides integrated solutions and services worldwide.

International Business Machines Corporation Competitors

Price History & Performance

Summary of all time highs, changes and price drops for International Business Machines
Historical stock prices
Current Share PriceUS$134.01
52 Week HighUS$146.00
52 Week LowUS$114.56
Beta0.85
1 Month Change-2.31%
3 Month Change0.31%
1 Year Change-6.40%
3 Year Change0.19%
5 Year Change-4.07%
Change since IPO446.98%

Recent News & Updates

Aug 11

IBM Research Tech Makes Edge AI Applications Scalable

Edge computing promises to bring distributed intelligence across a multitude of interconnected computing resources. But in the real world, trying to distribute computing tasks across multiple locations and then coordinate those various efforts is a lot harder than it first appears. IBM's Research Group, partnering with IBM Sustainability Software and IBM Consulting, has been working to help overcome some of these challenges for several years now. Recently, the group has begun to see success in industrial environments like automobile manufacturing. In particular, the company has been rethinking how data is being analyzed at various edge locations and how AI models are being shared with other sites. One of the more intriguing topics driving evolution in the technology world is edge computing. After all, how can you not get excited about a concept that promises to bring distributed intelligence across a multitude of interconnected computing resources all working together to achieve a singular goal? The real-world problem is that early iterations of edge computing turned out to be a lot more exciting in theory than in practice. Trying to distribute computing tasks across multiple locations and then coordinate those various efforts into a cohesive, meaningful whole is a lot harder than it first appears. This is particularly true when attempting to scale small proof-of-concept (POC) projects into full-scale production. Issues like the need to move enormous amounts of data from location to location - which, ironically, was supposed to be unnecessary with edge computing - as well as overwhelming demands to label that data are just two of several factors that have conspired to make successful edge computing deployments the exception as opposed to the rule. IBM's (IBM) Research Group, partnering with IBM Sustainability Software and IBM Consulting, has been working to help overcome some of these challenges for several years now. Recently, the group has begun to see success in industrial environments like automobile manufacturing by taking a different approach to the problem. In particular, the company has been rethinking how data is being analyzed at various edge locations and how AI models are being shared with other sites. At car manufacturing plants, for example, most companies have started to use AI-powered visual inspection models that help spot manufacturing flaws that may be difficult or too costly for humans to recognize. Proper use of tools like IBM’s Maximo Applications Suite’s Visual Inspection Solution with Zero D (Defects or Downtime), for example, can both help save car manufacturers significant amounts of money in avoiding defects, and keep the manufacturing lines running as quickly as possible. Given the supply chain-driven constraints that many auto companies have faced recently, that point has become particularly critical lately. The real trick, however, is getting to the Zero D aspect of the solution, because inconsistent results based on wrongly interpreted data can actually have the opposite effect, especially if that wrong data ends up being promulgated across multiple manufacturing sites throughout inaccurate AI models. To avoid costly and unnecessary production line shutdowns, it’s critical to make sure that only the appropriate data is being used to generate the AI models and that the models themselves are checked for accuracy on a regular basis in order to avoid any flaws that wrongly labelled data might create. This “recalibration” of the AI models is the essence of the secret sauce that IBM Research is bringing to manufacturers and, in particular, a major US automotive OEM. IBM is working on something they call Out of Distribution (OOD) Detection algorithms that can help determine if the data being used to refine the visual models is outside an acceptable range and might therefore cause the model to perform an inaccurate inference on incoming data. Most importantly, it’s doing this work on an automated basis to avoid potential slowdowns that would occur from time-consuming human labelling efforts, as well as enable the work to scale across multiple manufacturing sites. A byproduct of OOD Detection, called Data Summarization, is the ability to select data for manual inspection, labeling and updating the model. In fact, IBM is working on a 10x-100x reduction in the amount of data traffic that currently occurs with many early edge computing deployments. In addition, this approach results in 10x better utilization of person hours spent on manual inspection and labeling by eliminating redundant data (near-identical images). In combination with state-of-the-art techniques like OFA (Once For All) model architecture exploration, the company is hoping to reduce the size of the models by as much as 100x as well. This enables more efficient edge computing deployments. Plus, in conjunction with automation technologies designed to more easily and accurately distribute these models and data sets, this enables companies to create AI-powered edge solutions that can successfully scale from smaller POCs to full production deployments. Efforts like the one being explored at a major US automotive OEM are an important step in the viability of these solutions for markets like manufacturing. However, IBM also sees the opportunity to apply these concepts of refining AI models to many other industries as well, including telcos, retail, industrial automation and even autonomous driving. The trick is to create solutions that work across the inevitable heterogeneity that occurs with edge computing and leverage the unique value that each edge computing site can produce on its own.

Jul 25

IBM: Recent Earnings Are Not A Sign Of Fundamental Change

The recent rally in IBM share price seems to be largely influenced by market-wide forces. We also take a closer look at IBM's most recently reported quarterly numbers. From business fundamentals and strategy point of view, little seems to have changed at IBM. In the past few months International Business Machines (IBM) has turned into one of the best performing tech names. Since I first covered the company in January of 2021 IBM returned 17%, compared to merely 8% for the broader equity market. Data by YCharts During this timeframe the spin-off of Kyndryl (KD) was completed and now that the underperforming assets have been unloaded, expectations around the 'New IBM' are running high. Unfortunately, however, the strong share price performance since November of last year has little to do with IBM's fundamentals. As we see in the graph below, the iShares Edge MSCI USA Momentum Factor ETF (MTUM) peaked also in November of last year and since then the gap with the iShares Edge MSCI USA Value Factor ETF (VLUE) has been expanding. Data by YCharts As expectations of monetary tightening begun to surface and inflationary pressures intensified, high duration and momentum stocks begun to underperform the lower duration value companies. I talked about this dynamic in my recent analysis called 'The Cloud Space In Numbers: What Matters The Most', where I showed why the high-growth names were at risk. More specifically, I distinguished between the companies in the bottom left-hand corner and those in the upper right-hand corner in the graph below. prepared by the author, using data from Seeking Alpha As we see in the graph below, the high flyers, such as Workday (WDAY), Salesforce (CRM) and Adobe (ADBE), have become the worst performers, while companies like IBM and Oracle (ORCL) that were usually associated with low expected growth and low valuation multiples became the new stars. Data by YCharts Although this was good news for value investors as a whole and is a trend that could easily continue, we should distinguish between strong business performance and market-wide forces. Having said that, IBM shareholders should not simply assume that the strong share price performance is a sign of strong execution. Needless to say, the Kyndryl disastrous performance of losing 75% of its value in a matter of months also lies on the shoulders of current management of IBM. Data by YCharts A Closer Look At IBM's Recent Earnings IBM's recently reported quarterly numbers once again disappointed and the management seems to have largely attributed the U.S. dollar movement to the slightly lower guidance. IBM Earnings Release Alongside the guidance gross margins also fell across the board, with the exception of the Financing division, which is relatively small to the other business units. IBM Q2 2022 Earnings Release Rising labour and component costs were also to blame during the quarter and the management is addressing these through pricing actions which should take some time. Although this is likely true, IBM is also reducing spend on research and development and selling, general and administrative functions. Such actions are usually taken as a precaution during downturns, however, consistent lower spend in those areas could often have grave consequences. prepared by the author, using data from annual and quarterly reports Last but not least, the reported EPS numbers from continuing operations should also be adjusted as I have outlined before. IBM Q2 2022 Earnings Release I usually exclude the royalty income and all income/expenses grouped in the 'other' category. These expenses/income usually have little to do with IBM's ongoing business and as such I deem them to be irrelevant for long-term shareholders. IBM Annual Report 2021 IBM Annual Report 2021 On an adjusted basis, EPS increased from $1.08 in Q2 2021 to $1.33 in Q2 2022, which although is a notable increase remains low. Just as a back of the envelope calculation, if we annualize the last quarterly result, we end up with a total EPS number of $5.3 or a forward P/E ratio of almost 25x. Given all the difficulties facing IBM and its growth profile, this still appears as too high. Has Anything Changed Following The Recent Earnings? As expected, IBM continued on its strategy to fuel its growth through a frenzy of acquisitions and divestitures. Following the Kyndryl spin-off, the company completed four deals in a matter of just few months. Seeking Alpha Seeking Alpha Seeking Alpha Seeking Alpha As I have said before, all that does not bode well for the prospects of IBM's legacy businesses. Moreover, the management does not seem to be focused on organic growth numbers in their quarterly reviews which is even more worrisome. Now that the underperforming assets have been off-loaded, IBM's dividend payments are still too high relative to its adjusted income. prepared by the author, using data from annual and quarterly reports * adjusted for Intellectual property and custom development income, Other (income) and expense and Income/(loss) from discontinued operations, net of tax

Jul 20
Should You Investigate International Business Machines Corporation (NYSE:IBM) At US$131?

Should You Investigate International Business Machines Corporation (NYSE:IBM) At US$131?

International Business Machines Corporation ( NYSE:IBM ) saw significant share price movement during recent months on...

Shareholder Returns

IBMUS ITUS Market
7D1.2%1.2%3.2%
1Y-6.4%-26.5%-10.1%

Return vs Industry: IBM exceeded the US IT industry which returned -27.3% over the past year.

Return vs Market: IBM exceeded the US Market which returned -11.7% over the past year.

Price Volatility

Is IBM's price volatile compared to industry and market?
IBM volatility
IBM Average Weekly Movement3.4%
IT Industry Average Movement9.2%
Market Average Movement7.7%
10% most volatile stocks in US Market16.9%
10% least volatile stocks in US Market3.2%

Stable Share Price: IBM is less volatile than 75% of US stocks over the past 3 months, typically moving +/- 3% a week.

Volatility Over Time: IBM's weekly volatility (3%) has been stable over the past year.

About the Company

FoundedEmployeesCEOWebsite
1911282,100Arvind Krishnahttps://www.ibm.com

International Business Machines Corporation provides integrated solutions and services worldwide. The company operates through four business segments: Software, Consulting, Infrastructure, and Financing. The Software segment offers hybrid cloud platform and software solutions, such as Red Hat, an enterprise open-source solutions; software for business automation, AIOps and management, integration, and application servers; data and artificial intelligence solutions; and security software and services for threat, data, and identity.

International Business Machines Corporation Fundamentals Summary

How do International Business Machines's earnings and revenue compare to its market cap?
IBM fundamental statistics
Market CapUS$121.04b
Earnings (TTM)US$5.63b
Revenue (TTM)US$59.68b

21.5x

P/E Ratio

2.0x

P/S Ratio

Earnings & Revenue

Key profitability statistics from the latest earnings report
IBM income statement (TTM)
RevenueUS$59.68b
Cost of RevenueUS$27.00b
Gross ProfitUS$32.23b
Other ExpensesUS$26.61b
EarningsUS$5.63b

Last Reported Earnings

Jun 30, 2022

Next Earnings Date

Oct 18, 2022

Earnings per share (EPS)6.23
Gross Margin54.01%
Net Profit Margin9.43%
Debt/Equity Ratio257.8%

How did IBM perform over the long term?

See historical performance and comparison

Dividends

4.9%

Current Dividend Yield

105%

Payout Ratio