According to the Flossbach von Storch Research Institute, 328 of the S&P 500 companies in 2024 had a negative Other Comprehensive Income (OCI) of $4.5 billion…
According to the July 12, 2026, Consumer Price Index Release from the U.S. Bureau of Labor Statistics, the 12-month inflation rate rose by 3.4 percent and the month-over-month measure rose by 0.1 percent in July 2026…
Over the past few years, artificial intelligence (AI) has evolved from a futuristic concept into a core engine of modern enterprise strategy. Organizations across every major industry are now using AI to automate complex workflows…
What makes AI unique is that risks associated with it emerge from the way systems learn, generate outputs, and make decisions to influence customers, employees, and business outcomes.
Modern businesses face several distinct risk vectors:
Biased or discriminatory decisions Automated recruitment, lending, or credit-scoring models trained on flawed data can produce systematically unfair outcomes. This can result in regulatory penalties, civil rights litigation, and damaged brand reputation.
Hallucinations and inaccurate outputs AI models can confidently generate inaccurate or misleading information. A customer-facing AI assistant that provides incorrect financial, legal or medical guidance could create significant liability exposure.
Intellectual property and copyright disputes Models trained on vast, unvetted datasets reproduce copyrighted material, exposing organizations to costly intellectual property infringement claims.
Data privacy violations Unintentional exposure of proprietary trade secrets or personally identifiable information (PII) during model training can trigger regulatory investigations under frameworks such as the EU AI Act, the General Data Protection Regulation (GDPR), or state-level privacy laws.
Cybersecurity vulnerabilities AI introduces new attack vectors, including prompt injection, data poisoning, and model extraction. Malicious actors can exploit these to compromise business integrity.
Financial losses Autonomous trading agents or algorithmic pricing models operating at high speeds can execute erroneous transactions, leading to immediate financial losses.
Why Traditional Insurance May Not Be Enough
Existing coverage was not designed for current AI issues. Cyber policies were designed around data breaches and network intrusion. This does not cover an AI model making a biased hiring decision or fabricating a financial projection.
Professional indemnity and E&O policies assume a human professional exercised judgment. So, when an algorithm makes a mistake, an insurer may dispute whether the policy was intended to respond. For general liability policies, the focus is on bodily injury and property damage. If an AI program causes bodily injury, insurers can debate whether the policy applies.
Several incidents have caused some insurance companies to exclude AI from their corporate policies. For instance, Google was sued by a Minnesota-based company after its AI Overviews feature named it as a defendant in a lawsuit. This is just one case that highlights the growing concern around “silent insurance” when policies do not explicitly address AI-related risks. However, businesses may assume they are covered when they are not.
The challenge is compounded by the rapidly evolving legal landscape, with governments worldwide introducing new regulations.
The Rise of AI Liability Coverage
In response, a new category is beginning to take shape. This is AI liability insurance. These policies are designed to explicitly address the development, deployment, and use of AI systems. While offerings may vary across providers, AI liability covers incidents such as AI-driven discrimination claims, IP infringement from generative outputs, financial losses from automated decision-making, and regulatory penalties tied to AI non-compliance.
Insurers are approaching underwriting as they did with early cyber policies. They are starting cautiously, requiring detailed disclosure of how AI is used, existing governance controls, and how models are tested and monitored.
Beyond Insurance: Building Comprehensive AI Resilience
Insurance alone cannot eliminate AI risk and should not be a substitute for operational resilience. Organizations building genuine AI resilience are investing in:
Formal AI governance frameworks
Meaningful oversight of consequential decisions
Ongoing model monitoring and auditing
Employee training on responsible AI use
Clearly articulated responsible AI principles
Tested incident response plans specifically for AI-related failures.
A well-governed AI program will also make a business significantly more insurable, as underwriters increasingly price risk based on demonstrated controls.
Conclusion
AI has become one of the greatest sources of competitive advantage as well as a new source of liability. As regulatory scrutiny increases and AI-driven decisions become more consequential, executives must broaden their understanding of enterprise risk. Insurance should not be viewed as a substitute for governance, oversight or responsible AI practices.
For businesses increasingly relying on AI, the question is no longer whether AI creates liability risk, but whether existing insurance is equipped to respond to it.
Powell CPA PLLC
Insurance for AI Risk: Is It Time to Consider AI Liability Coverage?
September 1, 2026 · Blog, What's New in Technology
⏱ 4 min read
Over the past few years, artificial intelligence (AI) has evolved from a futuristic concept into a core engine of modern enterprise strategy. Organizations across every major industry are now using AI to automate complex workflows, augment customer service operations, drive predictive decision-making, and unlock greater operational productivity.
Understanding AI Risk
AI is not an easily defined category, as it spans several dimensions that traditional risk frames are not built to accommodate. The Gallagher report, Smart Systems, Blind Spots: Rethinking Insurance for the AI Era, found that the pace of AI adoption surpassed the insurance industry’s capacity to develop responsive products.
What makes AI unique is that risks associated with it emerge from the way systems learn, generate outputs, and make decisions to influence customers, employees, and business outcomes.
Modern businesses face several distinct risk vectors:
Biased or discriminatory decisions Automated recruitment, lending, or credit-scoring models trained on flawed data can produce systematically unfair outcomes. This can result in regulatory penalties, civil rights litigation, and damaged brand reputation.
Hallucinations and inaccurate outputs AI models can confidently generate inaccurate or misleading information. A customer-facing AI assistant that provides incorrect financial, legal or medical guidance could create significant liability exposure.
Intellectual property and copyright disputes Models trained on vast, unvetted datasets reproduce copyrighted material, exposing organizations to costly intellectual property infringement claims.
Data privacy violations Unintentional exposure of proprietary trade secrets or personally identifiable information (PII) during model training can trigger regulatory investigations under frameworks such as the EU AI Act, the General Data Protection Regulation (GDPR), or state-level privacy laws.
Cybersecurity vulnerabilities AI introduces new attack vectors, including prompt injection, data poisoning, and model extraction. Malicious actors can exploit these to compromise business integrity.
Financial losses Autonomous trading agents or algorithmic pricing models operating at high speeds can execute erroneous transactions, leading to immediate financial losses.
Why Traditional Insurance May Not Be Enough
Existing coverage was not designed for current AI issues. Cyber policies were designed around data breaches and network intrusion. This does not cover an AI model making a biased hiring decision or fabricating a financial projection.
Professional indemnity and E&O policies assume a human professional exercised judgment. So, when an algorithm makes a mistake, an insurer may dispute whether the policy was intended to respond. For general liability policies, the focus is on bodily injury and property damage. If an AI program causes bodily injury, insurers can debate whether the policy applies.
Several incidents have caused some insurance companies to exclude AI from their corporate policies. For instance, Google was sued by a Minnesota-based company after its AI Overviews feature named it as a defendant in a lawsuit. This is just one case that highlights the growing concern around “silent insurance” when policies do not explicitly address AI-related risks. However, businesses may assume they are covered when they are not.
The challenge is compounded by the rapidly evolving legal landscape, with governments worldwide introducing new regulations.
The Rise of AI Liability Coverage
In response, a new category is beginning to take shape. This is AI liability insurance. These policies are designed to explicitly address the development, deployment, and use of AI systems. While offerings may vary across providers, AI liability covers incidents such as AI-driven discrimination claims, IP infringement from generative outputs, financial losses from automated decision-making, and regulatory penalties tied to AI non-compliance.
Insurers are approaching underwriting as they did with early cyber policies. They are starting cautiously, requiring detailed disclosure of how AI is used, existing governance controls, and how models are tested and monitored.
Beyond Insurance: Building Comprehensive AI Resilience
Insurance alone cannot eliminate AI risk and should not be a substitute for operational resilience. Organizations building genuine AI resilience are investing in:
Formal AI governance frameworks
Meaningful oversight of consequential decisions
Ongoing model monitoring and auditing
Employee training on responsible AI use
Clearly articulated responsible AI principles
Tested incident response plans specifically for AI-related failures.
A well-governed AI program will also make a business significantly more insurable, as underwriters increasingly price risk based on demonstrated controls.
Conclusion
AI has become one of the greatest sources of competitive advantage as well as a new source of liability. As regulatory scrutiny increases and AI-driven decisions become more consequential, executives must broaden their understanding of enterprise risk. Insurance should not be viewed as a substitute for governance, oversight or responsible AI practices.
For businesses increasingly relying on AI, the question is no longer whether AI creates liability risk, but whether existing insurance is equipped to respond to it.
Disclaimer
These articles provide general information on tax, accounting, and financial topics for small businesses and individuals. They are educational in nature and are not specific legal, accounting, financial, tax, or other professional advice, and should not be relied upon as such. This content was prepared by Service2Client and may have been reviewed or edited by the website owner for accuracy and compliance. Look for a trust mark below for verification details. No representation is made that any approach described will achieve a particular result, and no regulatory or professional body has reviewed or endorsed this content. Because each situation is different, readers should consult a qualified professional about their specific circumstances before acting. Images accompanying these articles are protected by copyright and may not be copied or reused.
strong>21st Century ROAD to Housing Act (HR 6644) – This bipartisan, White House-endorsed bill addresses housing affordability by placing ownership restrictions on…
Surprising at it may seem, Q4 is at your doorstep, knocking and asking for attention. What’s more, it’s that time of year when everything starts getting busy: kids go back to school, football starts…
Create SMART goals. You might have heard of this acronym, but it stands for:
Specific: Needs to be concrete, not vague.
Measurable: Must be specific dollar amounts.
Attainable: Within your budget but still challenging.
Relevant: Aligned with your five-year goals, your future dreams.
Time-bound: Hard deadlines.
Separate your goals into buckets. Those would be long-, medium-, and short-term.
Long-term: This is 5-plus years. Early retirement by XX years old with a specific amount of money in the bank. Paying off your house by a certain date. Having a certain amount of cash saved for your kiddos after you’re gone.
Medium-term: This is 1-5 years. The usual suspects include paying off your car, student loans, consumer debt, or even building up a (dollar amount goes here) reserve for a down payment on a house or second property.
Short-term goals and quarterly goals: This is less than 1 year – hot items you cannot ignore. Starting, or adding to, your emergency fund that will equal, let’s say, $5,000. Or, for instance, saving $8,000 for a family vacation. You can also look at these small goals as subsets of larger goals: paying off X% of your house or car note by a certain date.
In sum, all of the above are simple ways to wrap your head around how to navigate Q4 financial goals – and beyond – by carving them up into smaller, digestible steps. If you can get organized, take on the last half of the year with intention, and make some real progress, there’s nothing in the (fiscal) world you can’t accomplish if you set your mind to it.
Surprising as it may seem, Q4 is at your doorstep, knocking and asking for attention. What’s more, it’s that time of year when everything starts getting busy: kids go back to school, football starts, and then the holidays are just up ahead. During this time, you might also be hearing “cha-ching, cha-ching” as what lies ahead can be financially challenging. Consider a few ways to frame this and strategies to set up goals as you bring the year to a close.
Map out the big picture. While all the things in your immediate future might be at the forefront of your mind, take a step back. What’s your five-year vision? Where are you with your big life goals? Do they include saving for a down payment for a house, a dream vacay, or setting up a college fund for your kids? Decide on completion dates and work backward. What needs to happen for these things to become realities?
Focus on the next 90 days. Now you can get a bit more granular. What’s looming in the future, before the year ends? What are your holiday plans? Usually this involves expenses (travel and food). Brainstorm about how to economize. Can you cost-share with family and friends? (Thinking here about your five-year vision.) What about your home and cars? Do they need work, and what might you spend? Do you have an emergency fund to help with all this? If you don’t, start one! Keep all of these things in mind as you make your way toward next year and beyond.
Set up a tracker. It can be an Excel spreadsheet, a notebook, or a whiteboard – whatever works for you. Color code different milestones and then brainstorm (yes, again) to see how you can reach these goals. Do you need to cut expenses in some areas? Pick up a side hustle? Purge your closet (house, too), and sell some things? To get started, here are a few tracker templates to kick things off.
Create SMART goals. You might have heard of this acronym, but it stands for:
Specific: Needs to be concrete, not vague.
Measurable: Must be specific dollar amounts.
Attainable: Within your budget but still challenging.
Relevant: Aligned with your five-year goals, your future dreams.
Time-bound: Hard deadlines.
Separate your goals into buckets. Those would be long-, medium-, and short-term.
Long-term: This is 5-plus years. Early retirement by XX years old with a specific amount of money in the bank. Paying off your house by a certain date. Having a certain amount of cash saved for your kiddos after you’re gone.
Medium-term: This is 1-5 years. The usual suspects include paying off your car, student loans, consumer debt, or even building up a (dollar amount goes here) reserve for a down payment on a house or second property.
Short-term goals and quarterly goals: This is less than 1 year – hot items you cannot ignore. Starting, or adding to, your emergency fund that will equal, let’s say, $5,000. Or, for instance, saving $8,000 for a family vacation. You can also look at these small goals as subsets of larger goals: paying off X% of your house or car note by a certain date.
In sum, all of the above are simple ways to wrap your head around how to navigate Q4 financial goals – and beyond – by carving them up into smaller, digestible steps. If you can get organized, take on the last half of the year with intention, and make some real progress, there’s nothing in the (fiscal) world you can’t accomplish if you set your mind to it.
These articles provide general information on tax, accounting, and financial topics for small businesses and individuals. They are educational in nature and are not specific legal, accounting, financial, tax, or other professional advice, and should not be relied upon as such. This content was prepared by Service2Client and may have been reviewed or edited by the website owner for accuracy and compliance. Look for a trust mark below for verification details. No representation is made that any approach described will achieve a particular result, and no regulatory or professional body has reviewed or endorsed this content. Because each situation is different, readers should consult a qualified professional about their specific circumstances before acting. Images accompanying these articles are protected by copyright and may not be copied or reused.
With over $4 trillion in merger and acquisition transactions happening in 2025, understanding the necessary accounting considerations is essential to see how tax professionals can navigate…
No matter how well you know someone, you usually learn a lot more once you’ve traveled with them. We are all different in this activity, from people who prefer aisle seats over window seats, to Airbnb renters or hotel…
For two decades, enterprise software has been built around a simple assumption: people log into multiple applications to retrieve information, make decisions and complete work. A CRM, a project tracker, a business intelligence…
A Harvard Business Review study revealed that digital workers toggle between different applications and websites about 1,200 times a day. This tool-switching alone costs employees an average of 44 hours per year due to tool fatigue. Meanwhile, most of the enterprise SaaS stack goes completely unused, and this is a weighty business cost.
What is Actually Changing
The shift in business computing is not about adding another dashboard to the stack, but rather usurping its purpose. The enterprise interface is beginning to shift toward intent-native workspaces, reducing the need to navigate traditional dashboards for routine work.
In comes agentic AI, which collapses the decision chain. Instead of opening a chart to figure out what it means, the user states an intent and an agent queries the underlying systems directly, synthesizes across them, and gives the user an answer or takes the action itself. For example, instead of a user logging into five different systems, a finance agent pulls real-time vendor invoices from an ERP, a legal agent scans contract terms, and a risk agent cross-references historical delivery delays. All coordinated by an orchestration layer.
Generative user interface (GenUI) technology pairs with this orchestration. Instead of presenting the same dashboard to everyone, a GenUI system generates a temporary interface tailored to the user’s immediate request. Once the task is complete, that interface disappears. If a user inputs their intention, such as checking which supplier poses a risk, the system dynamically renders a clean, interactive panel showing only the relevant vendor risk scores.
A survey by CrewAI on 2026 State of Agentic AI Survey found that adoption of agentic AI is moving fast. Of the 500 senior enterprise executives surveyed, 65 percent are already using AI agents, 81 percent have fully adopted and are actively scaling, and 100 percent plan to expand agentic AI use in 2026.
What Still Matters
Dashboards aren’t disappearing; their role is changing. The shift is not toward a better dashboard; it is to create systems that decide and act directly, with humans overseeing outcomes and not every step. Modern AI-driven operations demand speed that previous tools can’t cope with. Having insights without action is now a bottleneck. Static views, manual interpretation, and the lack of proactive alerts and personalized framing are limitations that drive the shift toward agents.
However, while agentic AI determines what happens next, the dashboards will keep documenting the process. They will also exist mainly as audit trails and compliance records, but not as the primary way work gets done.
What This Means for Your Business
For businesses evaluating software, appearance is becoming less important than accessibility. A polished dashboard matters little if AI agents can’t access its data or trigger actions. As enterprises increasingly rely on AI agents to automate work across multiple systems, software without strong AI integration risks becoming difficult to use, costly to upgrade, and easier to replace.
Logistically, this means businesses should start auditing their software stack for API maturity and AI agent readiness. Before renewing or purchasing new software contracts, a business should evaluate whether the platform has robust APIs, allows AI agents to securely access its data and perform actions, and is built to support an AI-driven workflow.
Conclusion
The biggest disruption is not the end of SaaS dashboards – it’s the end of software that waits for human input. The next generation of enterprise software won’t compete on who has the prettiest dashboard. It will compete on which platform gives AI agents the fastest, safest access to data and actions. Businesses that continue buying interfaces instead of intelligent access may soon find themselves paying for software no one opens.
Powell CPA PLLC
The Death of the App: Why Your Business Will Sideline SaaS Dashboards
August 1, 2026 · Blog, What's New in Technology
⏱ 4 min read
For two decades, enterprise software has been built around a simple assumption: people log into multiple applications to retrieve information, make decisions, and complete work. A CRM, a project tracker, a business intelligence dashboard, a support ticketing system, and more. All this is because these applications operate in isolation.
There is a shift whose intention is not eliminating SaaS applications. It’s about eliminating the need to constantly switch between them.
Why Dashboards Existed
Dashboards were built because software couldn’t interpret business intent. Humans had to retrieve, interpret charts, and decide what to do next. While dashboards were designed for human navigation, these static SaaS front ends are being replaced by dynamic, real-time interface synthesis.
The dashboard model worked when companies relied on a handful of applications. Today, enterprises manage hundreds of SaaS tools. An average large enterprise runs multiple SaaS applications – about 291 with large organizations scaling over 400. This makes constant switching a productivity problem rather than convenience.
A Harvard Business Review study revealed that digital workers toggle between different applications and websites about 1,200 times a day. This tool-switching alone costs employees an average of 44 hours per year due to tool fatigue. Meanwhile, most of the enterprise SaaS stack goes completely unused, and this is a weighty business cost.
What is Actually Changing
The shift in business computing is not about adding another dashboard to the stack, but rather usurping its purpose. The enterprise interface is beginning to shift toward intent-native workspaces, reducing the need to navigate traditional dashboards for routine work.
In comes agentic AI, which collapses the decision chain. Instead of opening a chart to figure out what it means, the user states an intent and an agent queries the underlying systems directly, synthesizes across them, and gives the user an answer or takes the action itself. For example, instead of a user logging into five different systems, a finance agent pulls real-time vendor invoices from an ERP, a legal agent scans contract terms, and a risk agent cross-references historical delivery delays. All coordinated by an orchestration layer.
Generative user interface (GenUI) technology pairs with this orchestration. Instead of presenting the same dashboard to everyone, a GenUI system generates a temporary interface tailored to the user’s immediate request. Once the task is complete, that interface disappears. If a user inputs their intention, such as checking which supplier poses a risk, the system dynamically renders a clean, interactive panel showing only the relevant vendor risk scores.
A survey by CrewAI on 2026 State of Agentic AI Survey found that adoption of agentic AI is moving fast. Of the 500 senior enterprise executives surveyed, 65 percent are already using AI agents, 81 percent have fully adopted and are actively scaling, and 100 percent plan to expand agentic AI use in 2026.
What Still Matters
Dashboards aren’t disappearing; their role is changing. The shift is not toward a better dashboard; it is to create systems that decide and act directly, with humans overseeing outcomes and not every step. Modern AI-driven operations demand speed that previous tools can’t cope with. Having insights without action is now a bottleneck. Static views, manual interpretation, and the lack of proactive alerts and personalized framing are limitations that drive the shift toward agents.
However, while agentic AI determines what happens next, the dashboards will keep documenting the process. They will also exist mainly as audit trails and compliance records, but not as the primary way work gets done.
What This Means for Your Business
For businesses evaluating software, appearance is becoming less important than accessibility. A polished dashboard matters little if AI agents can’t access its data or trigger actions. As enterprises increasingly rely on AI agents to automate work across multiple systems, software without strong AI integration risks becoming difficult to use, costly to upgrade, and easier to replace.
Logistically, this means businesses should start auditing their software stack for API maturity and AI agent readiness. Before renewing or purchasing new software contracts, a business should evaluate whether the platform has robust APIs, allows AI agents to securely access its data and perform actions, and is built to support an AI-driven workflow.
Conclusion
The biggest disruption is not the end of SaaS dashboards – it’s the end of software that waits for human input. The next generation of enterprise software won’t compete on who has the prettiest dashboard. It will compete on which platform gives AI agents the fastest, safest access to data and actions. Businesses that continue buying interfaces instead of intelligent access may soon find themselves paying for software no one opens.
Disclaimer
These articles provide general information on tax, accounting, and financial topics for small businesses and individuals. They are educational in nature and are not specific legal, accounting, financial, tax, or other professional advice, and should not be relied upon as such. This content was prepared by Service2Client and may have been reviewed or edited by the website owner for accuracy and compliance. Look for a trust mark below for verification details. No representation is made that any approach described will achieve a particular result, and no regulatory or professional body has reviewed or endorsed this content. Because each situation is different, readers should consult a qualified professional about their specific circumstances before acting. Images accompanying these articles are protected by copyright and may not be copied or reused.
For the first time since 2022, the IRS is changing standard mileage rates in the middle of the tax year. If you track business, medical or moving miles, this matters. Starting July 1, 2026, the numbers go up, and your recordkeeping needs…
The ratio assesses how many shares the company that’s purchasing the takeover company must issue per share of the takeover business. Transactions that use shares for part or whole of the payment are able to leverage this integral benchmark. It’s important to keep in mind that the exchange ratio may provide parties helpful insight on transactions involving all or part equity, but it won’t be beneficial for all cash deals.
The formula to calculate the ratio is as follows:
Exchange Ratio = Offer Price for Target’s Shares / Acquirer’s Share Price
Looking at the acquiring firm’s and the target or acquired firm’s share prices illustrates the exchange ratio. If the target firm has 30,000 shares outstanding and trades at $34.60, and the acquiring firm offers to pay a 20 percent takeover, it results in a share price of $41.52 per share. The acquiring firm’s share price currently trades at $23.50.
Putting the formula into practice, it’s as follows:
= $41.52 / $23.50
= 1.77
Based on the resulting Exchange Ratio of 1.77, the acquiring firm must issue 1.77 shares of its equity for each share of the target firm it wants to acquire.
For transactions with different proportions of cash and stock, the percentage of stock is what’s factored into the exchange ratio. Deals conducted with 100 percent stock provide the most value to an exchange ratio.
Real World Example
If an acquiring business offers the acquisition target two of its shares for a single share of the acquired company, the deal can take the following circumstances. Before the deal announcement, the purchasing company’s shares might be trading at $20, with the target company’s shares trading at $30. With a 2-to-1 exchange ratio, the purchaser is bidding $40 for the seller’s share at $30.
After the deal announcement, there’s usually a valuation difference between buyer and seller due to the time value of money and risks. Risks include potentially being blocked by regulators, shareholder rejection or changing economic conditions. One important consideration is that merger arbitration may occur by investors when they try to get ahead of a deal ultimately completing before the uncertainty is removed.
If the deal ultimately closes, and investors get two buyer shares in exchange for one seller share and the acquiring company’s share increases to $37 from $30, investors who bet against the buyer’s stock via short-selling will be rewarded a difference of $3 per share (2 shares from the acquiring company 2 X $20 = $40 minus the $37 single share price of the target company). Investors who close out their short position will see the difference from the seller’s price for a profit. This tactic is frequently executed by opportunistic investors who have no direct interest in owning the equity, but only for a trade.
While each deal is different, understanding the process is essential to break down the internal details for all interested merger and acquisition parties.
Powell CPA PLLC
Understanding the Exchange Ratio
August 1, 2026 · Blog, General Business News
⏱ 3 min read
With more than $57 trillion in mergers and acquisitions, according to the Institute for Mergers, Acquisitions & Alliances, understanding how the Exchange Ratio works is essential for businesses and investors to maximize these processes.
The ratio assesses how many shares the company that’s purchasing the takeover company must issue per share of the takeover business. Transactions that use shares for part or whole of the payment are able to leverage this integral benchmark. It’s important to keep in mind that the exchange ratio may provide parties helpful insight on transactions involving all or part equity, but it won’t be beneficial for all cash deals.
The formula to calculate the ratio is as follows:
Exchange Ratio = Offer Price for Target’s Shares / Acquirer’s Share Price
Looking at the acquiring firm’s and the target or acquired firm’s share prices illustrates the exchange ratio. If the target firm has 30,000 shares outstanding and trades at $34.60, and the acquiring firm offers to pay a 20 percent takeover, it results in a share price of $41.52 per share. The acquiring firm’s share price currently trades at $23.50.
Putting the formula into practice, it’s as follows:
= $41.52 / $23.50
= 1.77
Based on the resulting Exchange Ratio of 1.77, the acquiring firm must issue 1.77 shares of its equity for each share of the target firm it wants to acquire.
For transactions with different proportions of cash and stock, the percentage of stock is what’s factored into the exchange ratio. Deals conducted with 100 percent stock provide the most value to an exchange ratio.
Real World Example
If an acquiring business offers the acquisition target two of its shares for a single share of the acquired company, the deal can take the following circumstances. Before the deal announcement, the purchasing company’s shares might be trading at $20, with the target company’s shares trading at $30. With a 2-to-1 exchange ratio, the purchaser is bidding $40 for the seller’s share at $30.
After the deal announcement, there’s usually a valuation difference between buyer and seller due to the time value of money and risks. Risks include potentially being blocked by regulators, shareholder rejection or changing economic conditions. One important consideration is that merger arbitration may occur by investors when they try to get ahead of a deal ultimately completing before the uncertainty is removed.
If the deal ultimately closes, and investors get two buyer shares in exchange for one seller share and the acquiring company’s share increases to $37 from $30, investors who bet against the buyer’s stock via short-selling will be rewarded a difference of $3 per share (2 shares from the acquiring company 2 X $20 = $40 minus the $37 single share price of the target company). Investors who close out their short position will see the difference from the seller’s price for a profit. This tactic is frequently executed by opportunistic investors who have no direct interest in owning the equity, but only for a trade.
While each deal is different, understanding the process is essential to break down the internal details for all interested merger and acquisition parties.
Disclaimer
These articles provide general information on tax, accounting, and financial topics for small businesses and individuals. They are educational in nature and are not specific legal, accounting, financial, tax, or other professional advice, and should not be relied upon as such. This content was prepared by Service2Client and may have been reviewed or edited by the website owner for accuracy and compliance. Look for a trust mark below for verification details. No representation is made that any approach described will achieve a particular result, and no regulatory or professional body has reviewed or endorsed this content. Because each situation is different, readers should consult a qualified professional about their specific circumstances before acting. Images accompanying these articles are protected by copyright and may not be copied or reused.
According to the Flossbach von Storch Research Institute, 328 of the S&P 500 companies in 2024 had a negative Other Comprehensive Income (OCI) of $4.5 billion. This is attributed to rising interest rates since 2022 had OCI figures of negative $325 billion. Understanding OCI and Accumulated Other Comprehensive Income (AOCI) is essential to see what this means and how it’s calculated.
