What is Beta
What is beta, levered beta and unlevered beta? Beta is a measure of the systematic risk associated with
The post Net Cash Flow: Definitions, Formula and Examples appeared first on Capital City Training Ltd.
]]>You must have heard the adage that circulates the world of finance…… Cash is King! Of course, the followon tag is “…. but Tax is Emperor!”, but let’s not go there. What analysts love about cash, is that cash is cash is cash. You can’t change what cash is, so cash flow is one of the least manipulated indicators of a business’s health. However, there are many ‘levels’ of cash flow and different categories that indicate corporate performance, efficiency and even financial stress.
Understanding the flow of cash within a business is a fundamental block in understanding its financial performance. This article delves into the intricacies of net cash flow, exploring its definitions, formulas, and applications, empowering readers to grasp its significance in the financial world.
Topic  Key Takeaways 
Definition of Net Cash Flow  The net amount of cash a business generates or consumes through all three standard cash flow categories: operating, investing, and financing activities. 
Formula  Net Cash Flow = Cash Flow from Operating Activities + Cash Flow from Investing Activities + Cash Flow from Financing Activities 
Importance  Indicates a company’s liquidity and ability to meet financial obligations; less manipulated than other financial indicators; critical for business survival and growth. 
Components 

Net Income vs Net Cash Flow  Net income reflects profitability (P&L), while net cash flow focuses on actual cash movements. Differences arise due to accrual accounting, credit sales, and noncash expenses like depreciation. 
Interpretation  Positive NCF is generally favorable, indicating sufficient liquidity. Negative NCF is a potential cause for concern if persistent. Consistency and allocation across activities are important to analyze. 
Limitations  Does not account for noncash transactions; potential for shortterm accounting manipulation; provides a snapshot, may not reflect longterm health. 
Complementary Metrics  Free Cash Flow, Cash Conversion Cycle, Cash Flow Statements 
Importance for Business  Reflects liquidity, ability to service debt, fund growth, and ensure longterm sustainability. 
Although it seems simple, ‘what is Net Cash Flow?’ may actually pose more questions than it answers. Net of what? Which cash flow – just operating? Or net of investing cash flows? What about financing? You should never be shy of asking ‘which category of cash flow?’
But to start, if asked to measure Net cash flow, often referred to as NCF, it would normally be ALL cash inflows net of ALL cash outflows within a specified period, typically a financial year. So it is the net amount of cash a business generates or consumes through all three of the standard cash flow categories – operating, investing and financing activities.
The concept of net cash flow is rooted in the fundamental principle that cash is the lifeblood of any business. While profitability is essential, a company’s ability to generate positive cash flow is equally, if not more, crucial for its survival and growth. Profitable companies can have cash strain – especially if small and investing heavily in growing. A positive net cash flow indicates that a business has sufficient liquidity to meet its financial obligations, invest in growth opportunities, and reward its stakeholders. The bigger question is whether the cash flow is sustainable.
The formula for calculating net cash flow is as follows:
Net Cash Flow = Cash Flow from Operating Activities + Cash Flow from Investing Activities + Cash Flow from Financing Activities
Cash Flow from Operating Activities: This component represents the cash generated or consumed by a company’s core business operations, such as sales, purchases, and operational expenses. This is the real lifeblood of a company, and if this is not positive then you have to start questioning the business model and whether things will turn around.
Cash Flow from Investing Activities: This component encompasses cash inflows and outflows related to investments, such as the purchase or sale of fixed assets, acquisitions, or the sale of investment securities. Healthy companies will be investing regularly in fixed assets, and growing companies may have significant commitments to achieve their goals. Without appropriate financing in place, this is where the strain can come. Investing activities will often be negative for healthy companies – and the question is whether they generate positive operating cash flows to fund this, or whether they have borrowed (see Financing cash flows).
Cash Flow from Financing Activities: This component includes cash movements resulting from financing activities, such as issuing or repurchasing shares, obtaining or repaying loans, and paying dividends.
By summing these three components, businesses can determine their overall net cash flow, providing a comprehensive view of their cash position.
Here is a good little overview we’ve put together to show the different levels of cash flow presented in a typical Cash Flow Statement: it clearly shows EBITDA, Operating Cash Flow, ‘Free Cash Flow’ and Net Cash Flow so you can see the differences.
It is essential to differentiate between net income and net cash flow, as they represent distinct aspects of a company’s financial performance. Net income, calculated on the income statement (P&L), reflects a company’s profitability by subtracting expenses from revenues. However, it does not necessarily reflect the actual cash movements within the business. The primary reason for this is the use of the ‘matching principle’ on which the P&L is built. The P&L will show income ‘earned’ rather than cash received. So, for example, if a company makes a sale on credit and is slow to collect cash from its credit sales, cash flow can be negative (plenty of P&L revenue earned, but not so much cash received!)
Net cash flow, in other words, focuses solely on the inflows and outflows of cash, providing a more accurate representation of a company’s liquidity and ability to meet its financial obligations. Another reason for profit and cash to differ significantly is depreciation of fixed assets. When companies invest in fixed assets, the cost is allocated to the P&L over its useful life to the business. This could be, say, 10 years. The full cash will be spent up front, but the P&L will only show 1/10^{th} of that cost each year for 10 years. Profit will be much higher than cash for that reason. This is why many analysts prefer to look at Earnings (profit) before depreciation – it is going to be a better proxy for operating cash flows.
Analysing net cash flow is a critical exercise in finance, as it provides valuable insights into a company’s financial health and operational efficiency. Here are some key considerations when interpreting net cash flow:
Positive Net Cash Flow: A positive net cash flow indicates that a company is generating more cash than it is spending, which is generally a favourable sign. It suggests that the business has sufficient liquidity to meet its obligations, invest in growth opportunities, and potentially distribute dividends to shareholders.
Negative Net Cash Flow: A negative net cash flow implies that a company is consuming more cash than it is generating, which can be a cause for concern if the trend persists. This situation may require the business to seek external financing or adjust its operations to improve cash flow.
Cash Flow Consistency: Evaluating the consistency of a company’s net cash flow over multiple periods is crucial. A consistent positive net cash flow indicates a stable and sustainable business model, while erratic or declining cash flows may signal underlying operational or financial challenges.
Cash Flow Allocation: Analysing the allocation of cash flows across operating, investing, and financing activities can provide valuable insights into a company’s strategic priorities and growth trajectory. For instance, a significant cash outflow from investing activities may indicate acquisitions or capital expenditures for expansion.
We have also written a knowledge article on Free Cash Flows – again many definitions! But do look at that article to get a more indepth feel for which cash flows matter, depending on what you want to analyse.
To illustrate the practical application of net cash flow analysis, let’s consider two hypothetical companies, Company A and Company B, operating in the same industry.
Company A:
Company B:
Based on the net income figures alone, Company A appears to be more profitable. However, when considering net cash flow, Company B emerges as the stronger, generating a higher cash surplus of £4 million compared to Company A’s £2 million.
This raises further questions – For example, for Company A: why is net cash flow so low compared to profit? It seems to be investing heavily in working / fixed assets. Had it already raised capital in the previous year to do this, or is it now under financial strain? A more holistic look at the financials is needed – e.g. prior year accounts to look at movements in the balance sheet and previous year cash flows. Also, is company B actually ‘stronger’?
It may be for now, but the higher net cash flow may indicate it is underinvesting. Equally, it may be more conservative with dividend payments, saving the cash to reinvest next year. Looking a net cash flow is only the tip of the iceberg.
This example highlights the importance of analysing net cash flow, but only in conjunction with other financial metrics. While net income provides valuable information about profitability, net cash flow offers a more comprehensive understanding of a company’s liquidity and operational efficiency.
Another scenario could involve a company with negative net income but positive net cash flow. This situation may arise due to noncash expenses, such as depreciation or amortization, which are deducted from net income but do not directly impact cash flow. In such cases, analysing net cash flow can reveal the underlying strength of the business’s operations and its ability to generate cash.
While net cash flow analysis is a valuable tool for assessing a company’s financial health, it is essential to consider its limitations:
To gain a more comprehensive view of a company’s financial health, net cash flow analysis should be used in conjunction with other financial metrics, such as:
As the lifeblood of any enterprise, positive and consistent net cash flow is a critical indicator of a company’s longterm sustainability and growth potential. However, it is essential to consider the limitations of net cash flow analysis and use it in conjunction with other financial metrics for a more comprehensive assessment of a company’s financial performance.
By conducting thorough net cash flow analyses and considering complementary financial metrics, stakeholders can gain a holistic understanding of a business’s financial health, enabling them to navigate the complexities of the financial landscape with confidence.
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]]>The post Basic Excel Formulas and Functions for Finance appeared first on Capital City Training Ltd.
]]>In the fastpaced world of finance, data is king. From tracking stock prices and analysing market trends to managing budgets and forecasting cash flows, finance professionals rely heavily on accurate and uptodate data. Microsoft Excel has long been the goto tool for crunching numbers and performing advanced financial analysis. However, to truly harness the power of this versatile software, a solid understanding of basic Excel formulas is essential.
Excel is an indispensable tool for finance professionals, as it allows them to efficiently manage and analyze large amounts of data. Mastering basic Excel formulas can greatly enhance productivity, reduce errors, and facilitate better decisionmaking. By leveraging the power of these formulas, finance professionals can streamline their workflows, uncover valuable insights, and ultimately drive better financial outcomes for their organizations.
Aspect  Key Takeaways 
Essential Formulas 

Advanced Formulas 

Dynamic Formulas 

Best Practices 

Applications 

Advanced Skills 

At its core, Excel is a calculation powerhouse. Even the most complex financial models are built upon a foundation of basic functions, formulas and builtin data tools. Mastering these fundamentals is an essential skill for finance professionals who need to navigate through vast amounts of data and perform intricate calculations with reliability.
While Excel offers a wide range of formulas and functions, some of the most commonly used ones in finance include:
These are just a few examples of the many formulas and functions available in Excel. By mastering these basic tools, finance professionals can streamline their workflows, reduce manual data entry, and minimize the risk of errors.
While Excel offers a vast array of formulas and functions, some of the most fundamental ones revolve around basic numbercrunching operations. These formulas are the building blocks for more advanced financial analysis and modelling. Let’s explore some of the key formulas in this category:
The SUM function is arguably one of the most widely used formulas in Excel. It adds up a range of values, making it indispensable for calculating totals, summing up expenses, or aggregating financial data. For example, you can use the SUM function to calculate the total revenue for a given period or to sum up the values in a portfolio.
The AVERAGE function calculates the arithmetic mean of a set of numbers. This function is widely used in financial analysis, budgeting, and forecasting. For instance, you can use it to calculate the average monthly expenses or the average return on investment for a particular asset class.
These functions return the smallest and largest values in a given range, respectively. In finance, these functions can be useful for identifying the highest and lowest stock prices, tracking extreme market movements, or determining the minimum and maximum values in a dataset.
Try it for the series of numbers above!
The COUNT function counts the number of cells containing numerical data. This can be useful for tracking the number of transactions, investments, or data points in a financial dataset. Additionally, you can use variations of the COUNT function, such as COUNTA and COUNTBLANK, to count nonnumeric values or blank cells, respectively.
The IF function is a logical function that allows you to perform conditional operations based on a specified criterion. The function logic follows our human logic very closely, so it is probably the most commonly used function – perhaps overused!
The logic is essential: IF a test condition is satisfied, give one outcome / solution, else give the alternative outcome. In this example below – you can see the formula translated as “if the number in cell B8 is greater than 500, the result is ‘1’, else it is ‘0’”. So we are just flagging up which clients have a sale of more than 500 units. The formula has been copied down column D to flag up 1 or 0 for all the clients.
In finance, this function is invaluable for creating decision trees, implementing business rules, and automating repetitive tasks. For example, you can use the IF function to categorize investments based on their risk profile or to apply different tax rates based on income thresholds.
This is categorized as an ‘advanced if’ function by Excel! Why? The logic is not complex, but the syntax for the function can be a bit fiddle. The logic is that the function will count the number of cells in a specified range that satisfy a given condition. Below, we are counting the number clients with sales over 500 units: it’s 118.
These lookup functions allow you to search for and retrieve data from a specified range or table based on a given criterion. VLOOKUP searches for a value in the leftmost column of a table and returns a corresponding value from a specified column. XLOOKUP, on the other hand, is a more versatile function that can search for values in any column and return corresponding results from alternative columns. Let’s see XLOOKUP in action – if you can use XLOOKUP then there is no need to learn VLOOKUP!
The Xlookup asks for several criteria in its formulation:
In this case, it is looking for “Scenario 2” text from B10, finding it in the second row of the “lookup array” (B5 to B7) and giving a result from the corresponding row in the “return array” being C5 to C7.
Using the $ and then copying the formula across row 10 gives a series corresponding to Scenario 2. If the text in B10 is changed to “Scenario 1” then excel will flip the data appropriately. It’s a great way to introduce scenarios into financial models. You need to look at our Excel Modelling eLearning to really get to grips with this – and we introduce switches as well, to save users have to type in scenario names.
These type of lookup functions are particularly useful in finance for tasks such as matching transaction IDs, looking up stock prices, or retrieving account information from a master list, or retrieving data from different scenarios – as we saw.
Financial modelling often involves complex decisionmaking processes and the evaluation of multiple criteria simultaneously. Excel’s logical functions, such as AND, OR, and NOT, provide a powerful toolset for implementing these logical operations within your financial models.
The AND function returns TRUE if all the specified conditions are met, and FALSE otherwise. In finance, you can use the AND function to set conditions for investment decisions, budget allocations, or loan approvals based on multiple criteria. For example, you could use the AND function to identify potential investments that meet specific risk and return thresholds.
The OR function returns TRUE if at least one of the specified conditions is met, and FALSE if none of the conditions are met. This function can be useful in financial modelling when you need to consider alternative scenarios or account for multiple possibilities. For instance, you could use the OR function to identify potential customers who meet certain income or credit score criteria.
The AND and OR functions are most commonly used in conjunction with IF – so you can arrive at appropriate results IF several conditions are met (AND) or if one of several conditions is met (OR). For example:
So the two conditions that both need satisfying are: 1. The Theory mark is >45 AND the Practical mark is >55. You can see both these tests in the formula above. The $s ensure that when the formula is copied down the page, the cell references are fixed.
The NPV function returns the present value of a stream regularfrequency cash flows using a given discount rate. For example, if discounting cash flows to find the value of a business or a project.
Note: the function will discount back to 1 period before the first identified cash flow. One way to address the problem of irregular cashflows is to use the XNPV function which adjusts correctly for precise dates of cash flows, as stated in your model:
Here, you can see the series of cash flows – an investment of £900,000 followed by regular, annual cash inflows for 5 years.
Using NPV function: note that we have excluded the £900,000 investment outflow in the NPV function. Why? Assuming today’s date is 11 July 2024, the £900,000 should not be discounted – it is already a present value. The formula will discount the number if you include it! So we have allowed for it as an add on to the NPV (you can see the “+E11” at the end of the formula.
Using XNPV, you can see we select the rate, the numbers, but also the dates. We get a very slightly lower number of £100,012.2. That’s because XNPV is accurate enough to allow for the extra day in the 2028 leap year!
Similarly, rather IRR and XIRR can be used to determine the compound average rate of return implied by these cash flows. Commonly used in private equity modelling. So the IRR is 15.46/7% depending on which formula you use.
The NOT function returns the opposite logical value of its argument. It can be used to negate a condition or reverse a logical operation. In finance, the NOT function can be useful for identifying exceptions, filtering out unwanted data, or implementing exclusion criteria in your models.
By combining these logical functions with other Excel formulas and functions, you can create sophisticated financial models that accurately reflect the complexities of realworld scenarios. These models can help you make informed decisions, mitigate risks, and optimize your financial strategies.
In the everchanging world of finance, keeping your data and analyses up to date is crucial. Excel’s dynamic formulas, such as TODAY and NOW, can help you automate this process, ensuring that your financial models and reports always reflect the latest information.
The TODAY function returns the current date, based on the system date of your computer. This function is particularly useful when you need to incorporate the latest date into your financial models or reports. For example, you could use the TODAY function to automatically update the “as of” date in your investment portfolio summary or to track the date of the latest market data update.
Similar to the TODAY function, the NOW function returns the current date and time, based on your computer’s system clock. This function can be valuable when you need to timestamp transactions, log trading activities, or record the exact time when a specific market event occurred.
By incorporating dynamic formulas like TODAY and NOW into your Excel models and reports, you can ensure that your analyses always reflect the most uptodate information. This can be particularly important in the fastpaced world of finance, where market conditions can change rapidly, and timely decisionmaking is crucial.
While Excel formulas are powerful tools for financial analysis, it’s essential to be aware of common mistakes that can lead to inaccurate results or flawed decisionmaking. Some of these mistakes include:
By being mindful of these common mistakes and implementing best practices for formula usage, finance professionals can ensure the accuracy and reliability of their Excelbased analyses.
Excel formulas can be invaluable tools for budgeting and forecasting in finance. By leveraging the power of these formulas, finance professionals can create dynamic, datadriven models that help them plan for the future and make informed decisions. Some key applications of Excel formulas in budgeting and forecasting include:
By harnessing the power of Excel formulas in budgeting and forecasting, finance professionals can create more accurate, flexible, and responsive financial plans that help their organizations navigate the complexities of the business landscape.
While mastering basic Excel formulas is essential for finance professionals, there are also many advanced formulas that can further enhance your analytical capabilities and streamline your workflows. Some advanced Excel formulas that finance professionals should consider learning include:
By mastering these advanced Excel formulas, finance professionals can take their analytical skills to the next level, unlocking new insights and driving better decisionmaking within their organizations.
Excel formulas are an indispensable tool for finance professionals, enabling them to efficiently manage, analyse, and interpret vast amounts of financial data. By mastering basic formulas for numbercrunching, data cleaning, logical operations, and dynamic analysis, finance professionals can streamline their workflows, reduce errors, and generate valuable insights that drive better decisionmaking.
As you continue your journey with Excel in finance, remember that the key to success lies in continuous learning, practice, and application. By dedicating yourself to mastering the power of Excel formulas, you’ll be wellequipped to tackle the challenges and opportunities that lie ahead in your financial career.
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]]>The post Market Momentum Tracker Analysis – 240624 appeared first on Capital City Training Ltd.
]]>Following our initial blog, we’ve started setting up monthly portfolios. The approach we took was to make it diversified – taking top performers from four different segments to reduce volatility. This presents real problems at the moment as momentum is so concentrated in technology. This feeds through to sectors like “Global”, which encompasses a range of strategies with many with names like “Global innovation”. These, like the MSCI World index, which we’ve taken as our benchmark, is also US focused and with a big allocation to technology.
Our first portfolio only had only three funds as the HSBC Turkey fund wasn’t available to retail investors in the UK through either Hargreaves Lansdown or Interactive Investor.
The performance was mixed – the announcement of the UK elections cooled this segment very quickly. Otherwise technology was the big focus – with Blue Whale having nearly a 40% allocation to technology. The “fund of funds” has significantly outperformed.
It is a shame that the Turkey fund wasn’t available. Turkey was extremely problematic for a number of years as it followed unconventional economic policies – keeping interest rates down in the face of rampant inflation. Finance ministers came and went. Inflation was rampant and the currency crashed.
The stock market looks to have performed spectacularly but the impact on the currency is terrible.
From the end of 2016 to the end of 2021, the index has risen over tenfold in Turkish Lira terms. In real terms, after taking account of inflation it has more than doubled. If we look in US$ terms, the story is very different: US$100 invested in the BIST100 index at the end of 2016 would have been worth US$62 five years later, even though the index had doubled in Turkish lira terms. The Turkish lira has lost 74% of its purchasing power against the dollar.
Investors face the tantalising prospect of a stabilising in the lira. International inflows have been dramatic.
It will be interesting to look at Technology performance over the next month – to 19^{th} July to see what impact Nvidia will have. The company’s shares corrected starting on the 18^{th} of July, falling over 10%.
Technology is still performing, but interestingly so is Biotechnology. The sector had a terrible run after the end of the COVID crisis, with the Russell biotech index falling 80% from its peak. This will be interesting to watch.
India had a small correction after the surprise loss by Narendra Modi of his majority, but this soon retraced.
Marlborough Far East Growth fits into the same narrative, its biggest sector allocation is to technology – albeit not the “magnificent 7” and its biggest country allocation is to India. So we are not really diversified.
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]]>The post Forecasting Methods in Budgeting and Modelling appeared first on Capital City Training Ltd.
]]>Budgeting and modelling are integral components of financial planning and decisionmaking processes. Forecasting methods are employed to estimate future revenues, expenses, cash flows, and other financial metrics, enabling organisations to develop realistic budgets and create robust financial models. By accurately projecting future outcomes, businesses can optimise resource allocation, identify potential risks, and capitalise on emerging opportunities.
Topic  Key Takeaways 
Financial Forecasting 

Types of Forecasting Models 

Quantitative Methods 

Qualitative Methods 

Selecting the Right Method 

Handling Conflicting Results 

Financial forecasting is the process of estimating future financial metrics based on historical data, current trends, and underlying assumptions. It involves analysing past patterns, taking into account external factors, and employing mathematical models or expert judgments to predict future outcomes. Effective financial forecasting aids in strategic decisionmaking, risk management, and performance evaluation, ultimately contributing to the overall success of an organisation.
Forecasting models can be broadly categorised into two main categories: quantitative and qualitative. Quantitative forecasting methods rely on mathematical and statistical techniques, while qualitative methods incorporate expert opinions and market insights.
Quantitative forecasting methods are datadriven and employ statistical techniques to analyse historical data and identify patterns. These methods are particularly useful when dealing with large datasets and when historical data is readily available.
The straightline forecasting method, also known as the linear trend method, assumes that the historical data follows a linear pattern. This method uses regression analysis to fit a straight line to the data points, and future values are projected based on the linear equation. Straightline forecasting is suitable for shortterm forecasting and when the data exhibits a consistent linear trend.
The great news is that you don’t need to be a stats genius to create this – Excel can do all the hard work. It uses Linear Regression analysis to find a ‘line of best fit’ to the data provided. Note that a linear fit may not be the best assumption – that is for you to decide!
Take for example this set of Revenue data, and say we want to forecast the next 2 quarters using a linear trend:
To analyse this data in Excel, we would:
Here is the forecast equation being used to compute data for 2024Q3 through to 2025Q2:
The moving average method smooths out fluctuations in data by calculating the average of a specific number of consecutive data points. This approach is useful when dealing with seasonal or cyclical data, as it helps identify underlying trends by removing random variations. Moving averages can be simple (equal weights) or weighted (assigning different weights to data points).
This is a great tool in Excel that will use a welltested statistical technique called Exponential Smoothing. There is a function in Excel to do this FORECAST.ETS, but we will show you how to automate this with a chart.
Firstly, we have amended the time period labels so as not to confuse Excel – as per this:
Note that “20231, 20232, 20232 etc……” for example, is now just “1,2,3….”
Based on a 95% confidence interval, Excel produces not only the ‘expected’ value forecast, but also the upper and lower 95% confidence forecasts – as shown in the 3 lines above.
On the sheet it creates, you also get all the numbers supporting the chart. You’ll look like a forecast genius in any presentation!
Although we won’t show all the details here, Excel can also assist with Multiple Regression – where the output is a function of more than one factor. We have onle created a timeseries forecast with the linear regression, but this does not explore the factors that drive revenue. With Multiple Regression you can forecast revenue based on factors like
The regression analysis will use algorithms to determine the best relationship between Revenue and all given independent variables.
Qualitative forecasting methods rely on expert judgments, market surveys, and subjective assessments rather than mathematical models. These methods are often employed when historical data is limited or when dealing with new products, services, or markets.
The Delphi method is a structured communication technique that involves gathering opinions from a panel of experts. In this method, experts are asked to provide their forecasts anonymously, and their responses are summarised and shared with the group. The process is repeated until a consensus or convergence of opinions is reached.
Market surveys involve collecting data directly from potential customers, industry experts, or other stakeholders through questionnaires, interviews, or focus groups. These surveys aim to gather insights into market trends, consumer preferences, and future demand, which can inform forecasting models.
The sales force composite method utilizes the collective knowledge and experience of an organisation’s sales team. Sales representatives, who have direct contact with customers and market conditions, provide their estimates and insights, which are then aggregated to create a comprehensive forecast.
Executive opinion is a qualitative forecasting method that relies on the expertise and judgment of senior management or industry leaders. These individuals possess extensive knowledge and experience in their respective fields and can provide valuable insights into market dynamics, competitive landscapes, and future trends.
Choosing the appropriate forecasting method is crucial for obtaining accurate and reliable results. The selection process should consider several factors, including the nature of the data, the time horizon, the complexity of the problem, and the availability of resources.
Before selecting a forecasting method, it is essential to thoroughly analyse the available data. Assess the quality, completeness, and patterns within the data, as well as any potential outliers or anomalies.
The time horizon for the forecast plays a significant role in determining the appropriate method. Shortterm forecasts may rely on simpler techniques, such as moving averages or linear regression, while longterm forecasts often require more complex models or qualitative methods.
Some forecasting problems are inherently more complex than others, involving multiple variables, nonlinear relationships, or external factors. In such cases, more advanced methods like multiple regression or time series analysis may be required to capture the underlying complexities.
The selection of a forecasting method should also consider the available resources, including time, computational power, and expertise. Certain methods may require specialised software, extensive data processing, or advanced statistical knowledge.
In some instances, combining multiple forecasting methods can yield more accurate and robust results. This approach, known as ensemble forecasting, leverages the strengths of different techniques and can provide a more comprehensive understanding of future trends.
Financial forecasting is an ongoing process that requires regular updates and refinements to maintain accuracy and relevance. As new data becomes available and market conditions evolve, it is essential to revisit and adjust forecasting models accordingly. This iterative approach ensures that forecasts remain aligned with the current business environment and can adapt to changing circumstances.
In some cases, different forecasting methods may yield conflicting results, leading to uncertainty in decisionmaking. When faced with such situations, consider the following approaches:
Examine the assumptions and inputs used in each forecasting method. Identify any differences or inconsistencies that may contribute to the conflicting results. Assess the validity and relevance of each assumption in the current context.
Based on the reliability, historical accuracy, and relevance of each forecasting method, assign appropriate weights to their respective results. This approach allows for a more balanced consideration of different methods and can help reconcile conflicting outcomes.
Consult with subject matter experts, industry professionals, or experienced forecasters to gain additional insights and perspectives on the conflicting results. Their expertise can help identify potential reasons for the discrepancies and provide guidance on how to interpret and reconcile the findings.
Perform sensitivity analysisv to assess how changes in key assumptions or inputs affect the forecasting results. By varying these factors within reasonable ranges, you can identify the most critical variables and understand the robustness of each forecasting method.
Continuously monitor the actual performance against the forecasted values. As new data becomes available, compare it with the predictions from different methods. Adjust the forecasting models based on the observed discrepancies and refine them over time to improve their accuracy and reliability.
Financial forecasting is a critical component of effective decisionmaking and strategic planning in the finance domain. By employing various forecasting techniques, organisations can gain valuable insights into future trends, optimise resource allocation, and mitigate risks. Whether relying on quantitative methods, qualitative approaches, or a combination of both, it is essential to select the appropriate forecasting method based on the nature of the data, the time horizon, the complexity of the problem, and available resources. Continuous monitoring and refinement of forecasting models are also crucial to ensure their accuracy and relevance in an everchanging business landscape.
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]]>The post What is Valuation? appeared first on Capital City Training Ltd.
]]>Capitalism is a system in which private owners control private property and let market forces supply and demand set prices. To make money in such a system, rational allocation of capital is the only legitimate route to success. Being able to value assets, market opportunities and businesses is therefore a cornerstone of modern civilisation!
As an investor, company valuation or as a business manager, project appraisal (i.e. valuation) underpins all decisionmaking processes, ranging from share portfolio investment decisions to mergers and acquisitions, to capital investment. It is a systematic approach to determining the intrinsic worth or value of an asset, business, or company. Are markets efficient? Are assets always at about their right value? History and experience show us that disparities in valuations can persist in and between markets for long times. These disparities create opportunities. We will look at the main valuation techniques used by investors exploring their definitions, types, models, and the circumstances under which it is performed, providing a comprehensive understanding of this critical financial concept.
Valuation is the process of estimating the worth or value of an asset, business, or company based on various factors, such as its current and projected financial performance, cashflow, market conditions, industry trends, and competitive landscape. It is a multifaceted exercise that involves analysing both qualitative and quantitative data to arrive at a defensible and wellreasoned estimate of value.
Topic  Key Takeaways 
Definition of Valuation 

When Valuations are Performed 

Main Valuation Methods 

Limitations and Challenges 

Common Mistakes 

Valuation Standards 

Valuations are conducted in a variety of scenarios within the finance industry. Some of the most common instances include:
Rather than talking generically, let’s talk about the standard approaches taken in different sectors:
For most nonfinancial companies, such as retailers, manufacturers, mobile phone companies, analysts will use three broad methods
The relative weight that analysts put on these methods vary from sector to sector: in supermarkets many analysts rely heavily on DCF. In oil exploration companies, analysts value the oil in the ground by forecasting production and valuing the oil produced at $50 per barrel and discounting the cashflow from sales.
What do you do if you value a company two ways and get very different answers? DCF can help an investor keep their feet on the ground. DCF is Warren buffet’s touchstone: quoting in part his friend Ben Graham, Warren said:
“The market may ignore business success for a while, but eventually will confirm it.”
As Ben said:
“In the short run, the market is a voting machine but in the long run it is a weighing machine.”
The speed at which a business’s success is recognized, furthermore, is not that important as long as the company’s intrinsic value is increasing at a satisfactory rate. In fact, delayed recognition can be an advantage – it may give us the chance to buy more of a good thing at a bargain price.
In commercial property investment, both listed and unlisted, official valuers and investors value properties using discounted cashflow: the rental income from a rented property is treated as a perpetuity and discounted by dividing the annual rent by the required rental yield. This gives the capital value of the property. Like with equities there is more than one way to value a property. Perhaps if you terminate the leases, knock down, refurbish, and add a couple of extra floors, maybe the property will be even more valuable, when relet with more floor space and a higher rent…?
In principle, the precedent transactions method for valuing a company is straightforward. WE look at the prices paid for companies in recent takeover deals and infer valuation multiples – EV/EBITDA typically for our business.
This process reveals a different paradigm for valuation of private and listed companies: in listed company transaction a “control premium” gets paid by the acquirer. In private companies, valuation multiples tend to be lower. Typically, bidders will offer at least a 25% premium to gain the interest of shareholders. Depending on market conditions and individual deals, the premium can be much higher: During the Global Financial crisis, acquirers in the UK typically paid premia of very 60%! In the 2020s we are seeing these high premia being offered and paid for UK listed companies: G4S in 2021 was acquired for a 69% premium, in early 2024, Blackstone offered over a 50% premium to acquire Hipgnosis song fund. In early 2024EQT has offered a 73% premium to acquire Keynote Studios!
In all of these cases you could argue that the companies were all undervalued by UK investors. Interestingly if we look at Hipgnosis (LSE: SONG), it’s assets – song rights – are valued officially by discounting expected future royalty payments. Before the bid, the shares were trading at around a 30% discount to their Net asset value per share. The Blackstone bid is at only a 7.5% premium. So perhaps Blackstone’s bid is fair?
In listed company deals precedents create an investor expectation of the premium they should receive.
In private company deals, precedents re difficult to use reliably: no valuation data is typically published, so multiples must be inferred from potentially very out of date publicly published accounts. If a deal is a “spin out,” i.e. the separation and sale of a division, there may never have been any public data from which to infer a multiple. In the midcap space, the actual levels of valuation in these private deals tends to be valuable proprietary knowledge held by the leading corporate finance advisers.
There is a broader issue with precedents which is timeliness and relevance: how many similar companies have been sold recently? If precedents are too old, they may represent valuation levels from a time when market conditions were totally different. High UK premia reflect the generally low valuation for the UK stock market, which trades on an average P/E of 12X, compared with a multiple of over 20X for the US market (early 2023)!
Suppose an investor is considering investing in ABC Corporation, a publicly traded company in the technology sector. To determine the appropriate value of ABC Corporation, the investor may use a combination of valuation methods, such as the company comparables method and the DCF analysis method.
Using the company comparables method, the investor would identify a group of similar publicly traded companies in the technology sector and analyse their financial ratios and multiples, such as P/E ratios and EV/EBITDA multiples. The investor would then apply these multiples to ABC Corporation’s financial metrics to estimate its value relative to its peers.
Additionally, the investor could perform a DCF analysis by forecasting ABC Corporation’s future cash flows, determining an appropriate discount rate based on the company’s risk profile and market conditions, and calculating the present value of those discounted cash flows. This would provide an estimate of the company’s intrinsic value based on its future earnings potential.
By combining the results of these valuation methods and considering other qualitative factors, such as the company’s competitive position, management team, and growth prospects, the investor can arrive at a wellrounded assessment of ABC Corporation’s value and make an informed investment decision.
Consider a scenario where XYZ Corporation, a large publicly traded company, is interested in acquiring a private company, Alpha Inc., which operates in the same industry. XYZ Corporation would need to perform a valuation of Alpha Inc. to determine an appropriate acquisition price.
In this case, the precedent transactions method may be particularly useful, as there may be limited publicly available financial information for Alpha Inc. as a private company. The valuation team at XYZ Corporation would research recent mergers and acquisitions involving similar companies in the same industry and analyse the valuation multiples and metrics used in those transactions.
Additionally, the valuation team may employ the DCF analysis method by forecasting Alpha Inc.’s future cash flows based on its historical performance and growth projections. They would then determine an appropriate discount rate based on the risk profile of Alpha Inc. and its industry and calculate the present value of those discounted cash flows to estimate the company’s intrinsic value.
By considering the results of these valuation methods, along with other factors such as potential synergies between the two companies and Alpha Inc.’s strategic fit within XYZ Corporation’s overall business plan, XYZ Corporation can make an informed decision regarding the appropriate acquisition price for Alpha Inc.
These examples illustrate the practical application of valuation methods in realworld scenarios and highlight the importance of using multiple valuation techniques and considering various factors to arrive at a comprehensive and wellreasoned valuation.
While valuation methods provide a structured approach to estimating the worth of an asset or company, they are not without limitations and challenges. Some of the key issues include:
To ensure the integrity and reliability of valuations, it is crucial to avoid common mistakes and pitfalls. Some of these include:
Valuation standards and regulations vary across different countries and jurisdictions. However, there are some widely recognized standards and guidelines that aim to promote consistency, transparency, and best practices in the valuation profession. Some of these include:
It is essential for valuation professionals to stay informed about the relevant standards and regulations applicable to their jurisdiction and the specific valuation engagement. Adhering to these standards helps ensure the quality, reliability, and acceptability of valuation results.
[1] Damodaran, A. (2012). Investment valuation: Tools and techniques for determining the value of any asset (Vol. 666). John Wiley & Sons.
[2] Geltner, D., Miller, N., Clayton, J., & Eichholtz, P. (2014). Commercial real estate analysis and investments. OnCourse Learning.
[3] Koller, T., Goedhart, M., & Wessels, D. (2010). Valuation: measuring and managing the value of companies (Vol. 499). John Wiley & Sons.
[4] Damodaran, A. (2009). Valuing young, startup and growth companies: estimation issues and valuation challenges. SSRN Electronic Journal.
[5] Fernández, P. (2007). Company valuation methods. The most common errors in valuations. IESE Business School, 127.
[6] Koller, T., Goedhart, M., & Wessels, D. (2010). Valuation: measuring and managing the value of companies (Vol. 499). John Wiley & Sons.
[7] Hitchner, J. R. (2017). Financial valuation: Applications and models. John Wiley & Sons.
[8] Damodaran, A. (2012). Investment valuation: Tools and techniques for determining the value of any asset (Vol. 666). John Wiley & Sons.
[9] Fernández, P. (2007). Company valuation methods. The most common errors in valuations. IESE Business School, 127.
[10] Koller, T., Goedhart, M., & Wessels, D. (2010). Valuation: measuring and managing the value of companies (Vol. 499). John Wiley & Sons.
[11] Hitchner, J. R. (2017). Financial valuation: Applications and models. John Wiley & Sons.
[12] International Valuation Standards Council. (2020). International Valuation Standards.
[13] American Society of Appraisers. (2009). ASA business valuation standards.
[14] Catty, J. P. (2010). Guide to fair value under IFRS. John Wiley & Sons.
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]]>The post Capital Asset Pricing Model (CAPM): Definition, Formula, and Examples appeared first on Capital City Training Ltd.
]]>The Capital Asset Pricing Model (CAPM) i has significantly influenced the way investors evaluate and price assets. Developed in the 1960s by William Sharpe, John Lintner, and Jan Mossin, the CAPM provides a framework for understanding the relationship between risk and expected returns on investments.
Takeaway  Description 
Definition  CAPM is a financial theory that describes the relationship between risk and expected return of an asset. It suggests that investors should be compensated with higher expected returns for taking on additional risk. 
Formula  E(Ri) = Rf + βi × (E(Rm) – Rf), where E(Ri) is the expected return, Rf is the riskfree rate, βi is the asset’s beta (systematic risk), and E(Rm) – Rf is the market risk premium. 
Beta (β)  Beta measures the volatility of an asset’s returns relative to the overall market. It quantifies the asset’s systematic risk that cannot be diversified away. 
Security Market Line (SML)  The SML is a graphical representation of the CAPM equation, plotting the expected return on the yaxis and the asset’s beta on the xaxis. Assets above the SML are undervalued, and those below are overvalued. 
Applications  CAPM is used for asset valuation, portfolio optimization, determining the cost of equity capital, and evaluating investment performance. 
Assumptions  CAPM assumes efficient markets, rational investors, a singleperiod model, and the ability to hold a welldiversified portfolio. 
Limitations  CAPM has been criticized for not accounting for size, value, and momentum effects, leading to alternative models like the FamaFrench ThreeFactor Model. 
The Capital Asset Pricing Model (CAPM) is a widely accepted financial theory that describes the relationship between the risk of an asset and its expected return. It is based on the principle that investors should be compensated for taking on additional risk, and it provides a framework for determining the appropriate expected rate of return for an asset, given its level of risk.
At the heart of CAPM is modern portfolio theory – all about risk, return and diversification. In short, this concludes that investors should only expect to be ‘compensated’ for risk they take that cannot be diversified away – systematic risk.
The CAPM formula is expressed as:
E(Ri) = Rf + βi × (E(Rm) – Rf)
Where:
In practice, CAPM is applied to the narrower world of public equities and ‘the market’ is an allmarket stock index such as the FTSE All Share.
The riskfree rate represents the return that an investor can expect from an investment with no risk, such as government bonds or Treasury bills. It serves as the baseline return for any investment, as investors should expect a higher return for taking on additional risk
.
Beta is a measure of an asset’s systematic risk, which is the risk that cannot be eliminated through diversification. It quantifies the volatility of an asset’s returns relative to the overall market. It is measured statistically using observed market returns. The actual computation is:
β = Covariance (i,m) / Variance (m)
Covariance (i,m) being the covariance between the investment returns and the market returns. You can see here that it is a measure of relative risk. It is NOT the same as correlation, but correlation does come into it. If a security has zero correlation with the market, it will have a beta of zero.
It can also be visualized conceptually as the slope of the line of best fit, when plotting excess market returns vs excess security returns:
Note that the axes are the surplus return of the market (Rm) and the Security (Rs) over and above the riskfree rate. The reason being that we are only interested in the excess returns achieved by taking risk.
Also note, that by taking the line of best fit we ignore the ‘scattering’ of the plot points around the line which is the returns uncorrelated to the market. That is, the extent to which a scatter point is not ‘on the line’ is due to stock specific risk that can be diversified away.
A beta greater than 1 indicates that the asset is more volatile than the market, while a beta less than 1 suggests that the asset is less volatile.
The market risk premium is the additional return that investors expect to receive for investing in the market portfolio, which includes all risky assets, over the riskfree rate. It represents the compensation for taking on market risk (systematic risk that cannot be diversified away).
The Capital Asset Pricing Model can be graphically represented using the Security Market Line (SML). The SML is a graphical representation of the CAPM equation, plotting the expected return on the yaxis and the asset’s beta on the xaxis.
The slope of the SML is determined by the market risk premium (E(Rm) – Rf), and the yintercept represents the riskfree rate (Rf). Assets that fall above the SML are considered undervalued, as their expected returns are higher than what the CAPM predicts, while assets below the SML are considered overvalued, with expected returns lower than the CAPM prediction.
The Capital Asset Pricing Model provides valuable insights for investors and portfolio managers in finance:
While the Capital Asset Pricing Model provides a useful framework for understanding risk and return, it is important to acknowledge its underlying assumptions and limitations:
Despite these limitations, the Capital Asset Pricing Model remains a widely used and influential theory in finance, providing a useful framework for understanding risk and expected returns.
While the Capital Asset Pricing Model has been widely accepted and applied in finance, it has also faced criticism and been subject to empirical testing. Some of the key empirical findings and criticisms include:
Despite these criticisms, the CAPM remains a foundational model in finance, and its insights continue to influence asset pricing and portfolio management practices.
While the Capital Asset Pricing Model has been widely accepted and applied in finance, it has also faced criticism and been subject to empirical testing. Some of the key empirical findings and criticisms include:
Despite these criticisms, the CAPM remains a foundational model in finance, and its insights continue to influence asset pricing and portfolio management practices.
To apply the Capital Asset Pricing Model, investors need to estimate the required inputs: riskfree rate, beta, and market risk premium. Here are some common approaches:
It is important to note that these inputs are estimates and subject to uncertainty and potential errors. Investors should regularly review and update their estimates to ensure the accuracy of their CAPM calculations.
To illustrate the practical application of the Capital Asset Pricing Model, let’s consider the following examples:
Suppose an investor is considering investing in Company XYZ’s stock. The riskfree rate is currently 3%, and the expected return on the market portfolio is 10%. Company XYZ’s beta is 1.2.
Using the CAPM formula:
Based on the CAPM calculation, the expected return on Company XYZ’s stock should be 11.4%. If, based on current prices, the total return is only, say, 8% then it is not compensating investors fairly for the risk take. It is overvalued. risk associated with the investment.
A company is evaluating a new project and needs to determine its cost of equity capital to assess the project’s viability. The riskfree rate is 4%, the market risk premium is estimated to be 6%, and the company’s beta has been measured at 1.5.
Using the CAPM formula:
The company’s cost of equity capital, calculated using the CAPM, is 13%. This information can be used to evaluate the potential profitability of the new project and make informed capital budgeting decisions.
By applying the CAPM formula and considering its components, investors and financial professionals can make more informed decisions about investment opportunities, capital budgeting, and risk management. However, it is important to recognize the model’s assumptions and potential limitations, and to use it in conjunction with other financial analysis tools and market insights.
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]]>The post Guide to Leveraged Finance (Lev Fin) appeared first on Capital City Training Ltd.
]]>Leveraged finance, as we know it today, originated in the 1980s as the private equity industry developed and grew. Since then, the range of products and investors has grown and changed, with the most radical development – the rise of “debt funds” or direct lenders occurring as a result of the Global financial crisis. leveraged finance is a critical tool for companies seeking to finance major transactions, such as acquisitions, leveraged buyouts (LBOs), and leveraged recapitalisations. This article will explain some of the intricacies of leveraged finance, exploring its definition, instruments, risks, rewards, and realworld applications.
Topic  Key Takeaways 
Leveraged Finance Definition 

Key Instruments 

Market Evolution 

Risks 

Exit Strategies 

Key Players 

Regulatory Oversight 

Leveraged finance, often referred to as “Lev Fin,” involves using high debt levels. The average debt/EBITDA of nonfinancial companies in the FTSE100 is only 1.5X. In a leveraged transaction, like a Leveraged Buyout, we might see total Debt/EBITDA of 6.5X, immediately putting a new deal on the “naughty list” at the US Comptroller’s office!
If we talk in credit rating terms, Lev Fin lenders operate in the BBB and below investment grade areas. Banks typically bring leveraged and acquisition finance under one umbrella, so not everything acquisition they do is highly leveraged. Common definitions use debt/Equity >50% as a benchmark, or Debt/EBITDA>3 or 4X. Tax authorities use tests such as “is interest more than 50% of EBITDA” to decide if a deal is “leveraged” and to then restrict taxdeductibility of interest on the “excess” debt.
Prior to the 2008 financial crisis or credit crunch, leveraged finance and P/E led leveraged buyout financing was straightforward: For BBB and crossover credits (i.e. companies with one investment grade and one below investment grade rating) companies making acquisitions, banks provided senior debt, usually on an unsecured basis.
In leveraged buyouts where maximising leverage was usually a target, banks would provide senior secured debt; Mezzanine funds or high yield bond investors provided subordinated debt, or senior unsecured debt (so still effectively ranking after the banks). Private equity funds provided the equity.
Three distinct developments happened between 2000 and 2006:
This senior secured debt was traditionally provided by banks in two tranches: an “A” tranche that amortised over 56 years and a B tranche a “Term loan B or TLB” which had a maturity of 1 year longer and a bullet repayment. Interest rates for leveraged loans can vary depending on market conditions and the borrower’s creditworthiness, but they typically range from LIBOR + 300 to 800 basis points.
Banks, because of their shortterm funding base preferred the A loan and generally the B would be syndicated pro rata with the A loan. So, banks were forced to participate.
A CLO – a collateralised loan obligation is a fund which invests in floatingrate leveraged loans. It then finances itself by issuing a series of floating rate notes, each tranche of which is subordinated to the ones above it. Here is a typical structure.
The growth of the number of CLOs was a revolution in the European markets. Unlike banks the funds preferred the longer duration TLBs. The bond investors wanted long term assets of clearly defined duration. The opposite of the banks! This suited borrowers: “A loans” put progressively more pressure on cashflow as the amortisations increased over the life of the deal. B loans could just be refinanced in full on exit from the deal. Borrowers started to get all B loan deals and banks largely left the senior financing, being left with only revolving credit for working capital finance.
The second impact of the CLOs was on the Mezzanine funds. Mezzanine debt was expensive typically with a coupon of SOFR +5.5% cash and 5.5% PIK or payinkind. PIK means that the interest is added to the principal amount, so your debt balance goes up each year (and you interest bill!) In a small or mediumsized deal (i.e. too small to do a high yield bond), then Mezzanine was the only game in town if you wanted to extend leverage.
Leveraged finance CLOs are typically covenanted to have more than 85 or 90% of their investments in senior secured debt. Some innovative corporate financiers had the clever idea of marketing second lien debt as an alternative to Mezzanine. A “second lien” is a generic term for a second ranking claim over an asset. A mortgage is an example of a lien. In practice in the debt markets second lien lenders will share exactly the same collateral as the first lien lenders (here the A & B loans) but on a second ranking basis: they only get the proceeds of the asset sales after the A & B lenders have been paid in full.
This “innovation” suddenly meant that CLO funds became a cheap, alternative to Mezzanine in the form of Second lien debt. The second lien debt would go into the 8090% pot of the CLOs with an enhanced yield.
Another popular instrument in the leveraged finance realm is highyield bonds. These bonds carry credit ratings below investment grade, reflecting the higher risk of default. However, they offer higher yields to investors willing to take on additional risk. Highyield bonds are frequently used to finance leveraged buyouts, mergers, and acquisitions, as well as to refinance existing debt. Yields on highyield bonds can range from 5% to 12% or higher, depending on the issuer’s credit quality and market conditions.
High yield bonds can have different structures: they can be issued as senior debt, this would typically be the case for a “fallen Angel,” i.e. a company that has fallen on hard times and has fallen below investment grade, or for companies like Fresenius that just like being leveraged.
In large leveraged transactions, High yield bonds may be issued on a senior unsecured basis, but and it is a big BUT, with large amounts of senior secured debt ranking ahead of the high yield bonds the bonds are effectively very subordinated.
The growth of the high yield bond market in Europe and the growth of CLOs created great competition to Mezzanine funds. Using high yield and second lien could lower the cost of subordinated debt by several percentage points. These developments largely killed off Mezzanine as a product in medium and large LBOs.
Mezzanine finance is subordinated debt which ranks below senior debt but above common equity in terms of repayment priority. Mezzanine financing is often used as a complement to leveraged loans or alternative to highyield bonds, providing additional capital for leveraged transactions. Mezzanine debt typically carries interest rates in the range of 12% to 20%, reflecting its higher risk profile.
During the credit crunch banks reduced lending to the leveraged market and the number of banks in a leveraged loan syndication fell dramatically. Fund managers and in particular the private equity fund managers saw an opportunity to replace the banks and create a new business line for themselves. The funds began to market debt funds to investors. They were well received: putting money into these funds gave investors like pension funds and insurance companies access to a new asset class – corporate loans, which had previously been the monopoly of the banks.
As well as providing the Bloan financing, like the CLOs these funds created a new instrument – Unitranche debt. In short, the funds would provide all of the leveraged financing replacing A, B, second lien with a single bilateral or club facility, offered by a handful of funds. This created a new and even bigger pressure of competition to the banks. Many of the former mezzanine fund providers have changed the format of their funds in line with the P/E partnership debt funds.
Despite careful planning and structuring, leveraged finance transactions can sometimes result in debt restructuring or distressed situations if the borrower faces financial difficulties or default. Historical default rates for leveraged loans and highyield bonds have varied over time, with peak default rates reaching 1012% during economic downturns. In such cases, various stakeholders become involved, each with their own interests and priorities:
Potential outcomes in debt restructuring and distressed situations include:
Leveraged finance transactions often involve significant debt burdens, and companies may seek exit strategies or refinancing options to manage their debt levels over time. Some common approaches include:
While leveraged finance can provide companies with access to substantial capital for growth and strategic initiatives, it also carries significant risks. High levels of debt can strain a company’s cash flow and increase its vulnerability to economic downturns or industryspecific challenges. Additionally, leveraged finance transactions often involve complex legal and regulatory considerations, requiring careful due diligence and compliance.
However, the potential rewards of leveraged finance can be substantial. Successful leveraged transactions can generate significant returns for investors and enable companies to pursue transformative growth strategies. Moreover, the use of leverage can amplify returns on equity, creating value for shareholders.
Credit rating agencies play a crucial role in assessing the risk of leveraged finance transactions, particularly those involving highyield bonds. The three major credit rating agencies – Moody’s, Standard & Poor’s, and Fitch – assign credit ratings to bond issuances based on their evaluation of the issuer’s creditworthiness and ability to meet its debt obligations.
These ratings range from investment grade (AAA to BBB) to noninvestment grade or highyield (BB+ and below). Credit rating agencies consider various factors when assigning ratings, including the issuer’s financial strength, industry dynamics, competitive position, and management quality. Investors rely on these ratings to gauge the risk associated with investing in leveraged finance instruments and to determine appropriate pricing and yields.
While leveraged finance is a global phenomenon, there are some key differences between the US and European markets:
Despite these differences, the US and European leveraged finance markets are increasingly interconnected, with many global investors and banks participating in both markets.
To better understand the practical applications of leveraged finance, let’s explore a few realworld examples and case studies:
In 2023 Worldpay, the merchant payments business of Fidelity National Information Services, Inc. (FIS), was spun off. A majority 55% stake in the company was sold to the private equity fund, GTCR. GTCR paid $10.175Bn in total and raised nearly 75% through debt: Worldpay raised USD7.375Bn through a USD5.25Bn first lien TLB, a €500m TLB a US$2.175bn secured bond, a £600m secured bond. The debt was rated BA3/BB. Issuing secured loans and bonds allowed GTCR to tap into different pools of investors, increasing demand and competition for the financing. There was lot of demand for the “moderately leveraged loans from CLOs.
The US$ loan priced at SOFR +300bp, while the € tranche priced at Euribor +350bp. The $ bonds priced at 7.5%, the £ at 8.5%.
Interestingly CTGR did not raise any subordinated debt in this transaction.
In 2013, Dell Inc., one of the world’s largest computer manufacturers, underwent a leveraged buyout led by its founder, Michael Dell, and private equity firm Silver Lake Partners. The $24.9 billion deal involved a combination of leveraged loans and highyield bonds totalling $19.6 billion, making it one of the largest leveraged buyouts in history. This transaction allowed Dell to go private and pursue longterm strategies without the pressures of public markets.
Valeant Pharmaceuticals, a Canadian pharmaceutical company, embarked on an aggressive acquisition strategy in the 2010s, fuelled by leveraged finance. The company utilized a combination of leveraged loans and highyield bonds to finance the acquisitions of various pharmaceutical firms, such as Bausch & Lomb and Salix Pharmaceuticals. However, Valeant’s heavy debt load and controversial pricing practices ultimately led to scrutiny and a significant drop in its stock price.
Mezzanine finance has played a crucial role in the renewable energy sector, where projects often require significant upfront capital investment. For instance, in 2021, Invenergy, a leading developer of sustainable energy solutions, secured $180 million in mezzanine financing from Blackstone Credit to support the construction of a portfolio of wind and solar projects across the United States.
In 2020, Dunkin’ Brands, the parent company of Dunkin’ Donuts and BaskinRobbins, undertook a leveraged recapitalization transaction. The company issued $3.8 billion in leveraged loans and highyield bonds to fund a special dividend to shareholders and refinance existing debt. This transaction allowed Dunkin’ Brands to optimize its capital structure while returning cash to investors.
These examples illustrate the diverse applications of leveraged finance across various industries and transaction types, highlighting both the potential rewards and risks associated with this financing strategy.
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]]>The post Market Momentum Tracker Analysis – 280524 appeared first on Capital City Training Ltd.
]]>The table shows which category of funds in this case UK Equity Income had the largest proportion of funds in the top one hundred performers over the last four weeks. This is “rank 1”. So, UK equity income have moved up by two places from third place by number of winners since the week before. Europe inc./exc. UK has improved its position dramatically.
In our portfolio experiment we looked at 6 monthly returns. In this analysis we are looking at 4weekly returns. The focus here is to potentially spot trends as they begin to develop, rather than when they are quite mature
.
The UK’s strong performance continues with the Small and All company funds staying in the top three places. UK equity income has fallen, so momentum is consolidating in smaller and UK mid cap companies, the larger more mature segments dominating the UK equity income – so banks, oil and gas and mining have eased a little but are still performing well. This theme is echoed in Europe, where large caps have good momentum, but small caps have suddenly started moving quickly! This suggests that we are perhaps starting to see a cyclical rotation into small caps now that the spectre of recession is receding Europe (and the UK), and the prospect of lower interest rates will boost demand and small company earnings. Small caps tend to be more volatile. Less liquidity means less money moves the stock a lot. They are also disproportionately affected by interest rates.
Global (+21) and Technology (+19) have both improved momentum. There is quite a lot of overlap, many “Global funds” in the group we cover have broad mandates and lots of technology stocks/subfunds. The NASDAQ has performed well following NVIDIA’s strong results – quarterly revenue tripled year on year, with net profit increasing sevenfold.
China has lost momentum over the last two weeks. It will be interesting to see if this is just a brief interlude; China has been in the news as central and regional governments provide new support to the housing market and broader stimulus through issuing longdated 20 and 50 year bonds for the first time. Credit growth has slowed slightly. US tariffs on cars is not good news for corporate China.
More broadly a lot of other segments are infected by the moderating of US stock performance over the last month. US stocks make up more than 50% of the MSIC ACWI high dividend yield index for example.
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]]>The post Weighted Average Cost of Capital (WACC): Formula, Analysis, Examples appeared first on Capital City Training Ltd.
]]>The Weighted Average Cost of Capital (WACC) is a key component in discounted Cash flow valuation (or “DCF” for short). In a nutshell it is the marketvalue weighted average AFTERTAX cost of debt and equity of a company.
DCF is one the two most fundamental company valuation tools, the other being comparable company analysis.
As well as being the rate at which we discount a company’s cashflows, WACC is also used in performance metrics like ROIC/WACC, as a fundamental measure of the ability of a company to generate value.
Understanding WACC is crucial for financial professionals, investors, and business owners alike. It drives equity markets and impacts merger and acquisition activity: As interest rates and inflation rose during 2022 and 2023, the WACC of all companies rose. This depressed equity markets and contributed to falls in new issue of shares (global volumes fell over 50%) and also led to reduced levels of merger and acquisition activity: Private Equity funds found they could not realise their investments at the high valuations they had hoped for.
Key Takeaway  Description 
Definition  WACC is the marketvalue weighted average aftertax cost of debt and equity of a company. It represents the overall cost of capital. 
Importance  WACC is crucial for financial professionals, investors, and business owners. It drives equity markets and impacts mergers & acquisitions. 
Formula  WACC = (E/V) x Re + (D/V) x Rd x (1T), where E is market value of equity, D is market value of debt, V is total market value of capital, Re is cost of equity, Rd is cost of debt, and T is corporate tax rate. 
Components  WACC calculation involves three primary components: cost of equity, cost of debt, and their respective weights. 
Cost of Equity  Calculated using the Capital Asset Pricing Model (CAPM) or Gordon’s Dividend Growth Model (DGM). 
Cost of Debt  Determined by synthesizing longterm government bond yield, credit spread, and tax effects. 
Market Values  Market values of equity and debt should be used for accurate WACC calculation. 
Applications  WACC is used in investment evaluation, capital budgeting, determining optimal capital structure, valuation, and performance evaluation. 
Sensitivity Analysis  Analysts perform sensitivity analysis on WACC inputs to assess the impact on valuation and investment decisions. 
Interpretation  A lower WACC is generally better as it implies a lower overall cost of capital for the company. 
The concept of Weighted Average Cost of Capital (WACC) derives from the Capital Asset Pricing Model (CAPM). This is a valuation framework developed by Jack Treynor, William Sharpe, John Lintner and Jan Mossin (“3JW”) built on earlier work by Harry Markowitz (his book portfolio Selection is brilliant) and Merton Miller. CAPM is still very influential today, Sharpe, Markowitz and Miller shared a Nobel prize for economics. Even though there are lots of valid criticisms of the CAPM and the idea of WACC, it is such a simple, elegant and useful model, it has not been supplanted.
What the authors critically wanted to do was to unravel where value came from in a company. How much was to do with:
To help answer this question, 3JW chose to define the cashflow to be discounted as the “prefinancing posttax operating cashflow”. The notional tax used in this definition is the cash tax paid on Earnings before interest and tax (EBIT). This conveniently omits the tax benefit of financing with debt pay interest, you pay less tax (this is often called the “tax shield” in textbooks). The logical consistent definition of the discount rate then is to use the weighted average AFTER TAX cost of debt and equity. At a stroke they had separated capital structure from operating cashflow.
The calculation of WACC involves three primary components:
The cost of equity represents the expected rate of return that shareholders demand for investing in a company’s equity. It reflects the risk associated with the company’s operations and the opportunity cost of (not) investing in alternative investments with similar risk profiles.
The cost of equity is calculated using the Capital Asset Pricing Model (CAPM). One can also use Gordon’s Dividend Growth Model (DGM) in principle, but a steady dividend history is needed. It is not widely used.
CAPM is a “single factor” model. The logic of the derivation of the cost of equity goes like this. If I hold a share in a portfolio of shares, the idiosyncratic risks of the individual stocks (bad weather ice cream makers go down, umbrella makers go up) get eliminated and I am left with the general riskiness of the market. i.e. my portfolio will go up and down with the market in general. In maths speak it will have the same variability (“σ”) as the market. If we drill down into my portfolio, the variability of the portfolio “σ_{p}” is just the weighted average of the variabilities of the individual stocks.
The leap the CAPM takes from there is to define the “Securities market line”: If I take no risk by buying Government bonds, then I should receive the riskfree rate (“r_{f}”) at the moment, this is around 4.5% in dollars. If I “own the market”, by owing a market portfolio of shares, then I should get the market return (r_{m}). In CAPM we define the “risk” of owning an asset as the variability of its return. My risk in owning the market portfolio is the variability of the value and returns on the equity markets. To put this in perspective, the longterm average return on the S&P500 is 9.24% (over 150 years) but with a standard deviation of 15%. i.e. in any year you have a roughly 60% chance of getting a return in the range 9.24% +/15%! That’s variable.
This chart shows the securities market line, the variability of the market is given the value 1 in this chart. The idea of the line is that it allows us to “price” the risk of a share. If the impact of adding a share to my portfolio is that it contributes variability, then a fair return for a share (r_{s}) is given by the formula:
r_{s} = r_{f }+β*( r_{m }– r_{f})
β is the relative volatility of the stock versus the market. We can measure Beta by scattering weekly excess returns from the stock vs the market and then fitting to a straight line and measuring the slope of the line.
In short if a stock is twice as volatile as the market, I should earn rf +2*( rm – rf) on the stock.
The pretax cost of debt represents the current cost of raising new longterm debt for a company. This cost of debt typically needs to be “synthesised”.
If we unpick this, the first important point is “longterm”. Instead of taking the current cost of floating rate short term debt as a basis, we will typically take a longterm Government bond yield, i.e. a bond of 10 years or longer. The point of this is that short term rates are driven by Governments managing inflation and growth in the short run. Longterm rates reflect total returns required by lenders factoring in longterm inflation expectation.
To calculate the allin cost, we will then need to add a “Creditspread”, i.e. the margin that lenders will require to take the credit risk of the company.
Having established the pretax cost of debt, the last step is to discount this at the tax rate, i.e. multiplying by (1Tax rate). This discount reflects the fact that interest is tax deductible, giving us the aftertax cost of debt.
To calculate WACC, it is necessary to determine the market values of equity and debt. The market value of equity is typically represented by the company’s market capitalization, which is the total value of outstanding shares multiplied by the current share price.
The market value of debt is more complex to determine, as it depends on the specific debt instruments and their respective market values. In practice, the book value of debt is often used as an approximation, especially for privately held companies or those with limited publicly traded debt.
Market weighting value weighting is very important: The typical Price/book ratio for the S&P500 is over 4.5X. Investors are not trying to earn a return on the book value of their shares, but on the market value. Using book value would seriously underestimate the actual cost of equity.
The formula for calculating WACC is as follows:
WACC = (E / V) × Re + (D / V) × Rd × (1 – T)
Where:
The steps to calculate WACC are as follows:
The WACC has several important implications and applications in finance:
While the standard WACC formula is widely used, there are some alternative approaches and variations that can be considered in certain circumstances:
Instead of using the current market values of equity and debt, some analysts prefer to use the target or optimal capital structure for the company. This approach assumes that the company aims to maintain a specific debttoequity ratio over the long term, which may be different from the current proportions. Using the target capital structure can provide a more forwardlooking perspective on the company’s cost of capital. Large, listed companies in sectors like Supermarkets where analysts valuations are often based largely on their DCF valuations will often guide analysts as to their “target capital structure”.
Some analysts like to “iterate “to get a “selfconsistent” WACC.
There is a potential circularity to calculating WACC:
Obviously, we can keep doing this many times, but typically, after one “iteration”, we have captured over 95% of the potential change from further iterations. The valuation converges very quickly to its “selfconsistent” value.
Sensitivity analysis is a useful tool for understanding the impact of changes in the input parameters on the WACC calculation. By varying the individual components of WACC, analysts can gain insights into the sensitivity of the overall cost of capital to these changes.
Analysts can examine how changes in the cost of equity (Re), such as variations in the riskfree rate, market risk premium, or the company’s beta, can affect the WACC. This analysis can help identify the key drivers of the cost of equity and their influence on the overall cost of capital. This is critical in DCF as there is tremendous subjectivity around key variables like Beta.
Similarly, the sensitivity of WACC to changes in the cost of debt (Rd) can be analysed. Factors like interest rate fluctuations, changes in the company’s credit rating, or modifications to the debt structure can impact the cost of debt and, consequently, the WACC.
Exploring the sensitivity of WACC to changes in the capital structure, represented by the weights of equity (E/V) and debt (D/V), can provide valuable insights. This analysis can help determine the optimal capital structure that minimizes the overall cost of capital.
By combining sensitivity analyses on multiple input parameters, analysts can create different scenarios to assess the impact on WACC on valuation and establish a range of “fair value”. This can include stresstesting the WACC under various market conditions, leverage ratios, or growth assumptions, providing a more comprehensive understanding of the company’s cost of capital and its implications for investment decisions and valuation.
To illustrate the practical application of WACC, let’s consider a few examples and case studies:
Market value of equity: $100 million
Market value of debt: $50 million
Cost of equity (Re): 12%
Cost of debt (Rd): 6%
Corporate tax rate (T): 25%
Using the WACC formula:
Company A’s WACC is 9.5%, which means that any investment or project with an expected return higher than 9.5% would be considered profitable and valuecreating.
In 2022, Apple Inc. reported a market capitalization of $2.37 trillion and a total debt of $120.1 billion. Analysts estimated Apple’s cost of equity to be around 9% and its aftertax cost of debt to be approximately 2.5%.
Using these figures, Apple’s WACC can be calculated as follows:
With a WACC of 8.69%, Apple can evaluate potential investments and projects based on this hurdle rate, ensuring that only those with expected returns exceeding 8.69% are considered valuecreating.
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]]>At Capital City Training, we’ve been looking into the concept of momentum investing, its history, practice and whether it actually works or not!
With momentum investing, the idea is simple: buy what has performed well, then hold it until it stops performing. One the most wellknown research papers in the practice, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency by Jegadeesh and Titman, looks at performance history and examines how long a period of growth should we look for – its momentum – before following it to make money. The next question of obvious import, is how long should we stay invested for before momentum runs out or reverses? The key results from J&T suggest that nine months of performance will typically be followed by a further six months of good performance.
Momentum investing has had a lot of research coverage, which broadly says that momentum investing works. From our experience, we have run momentum portfolios in the past quite successfully, but never with a “control investment” to benchmark against – we just looked at absolute returns.
So, we thought – why not run our own experiment in momentum investing?
There are retail services that provide you with momentum data – like the Salty Dog investor platform that we’ve written about before.
The idea of momentum is that it persists. Markets overshoot – in both directions. As such, we decided to assemble a series of momentum portfolios, with data over 12 months and compare its performance with the MSCI World index.
Our monitor looks at over eight hundred funds available to retail investors in the UK and shows which sectors are performing best and critically which sectors are seeing improving performance – i.e. developing momentum.
Before we start making up portfolios using this idea, you might ask exactly that question: why bother? “Momentumfactor” indices exist and are investable. Why not just do that?
We benchmarked our portfolio performance against the MSCI world index. If we look at the momentum factor version of this, you will see that it is a variation on a theme, changing the weights of individual stocks, rather than picking completely different stocks in a focused way. The key word also is factor, so it’s a shift in weightings to the same stocks, not a different set of stocks. The design of the index is, in part, to ensure the mix of stocks doesn’t change much overtime. A key point here is that the index is designed to be investible at scale.
Why is this relevant to us? If we are retail investors and not moving the markets when we sell and buy stocks in $100m blocks, then we don’t need to suffer these major constraints. These constraints are so serious that it takes the best part of 10 years to see a material difference in the performance of the parent and momentum weighted indices. We would argue that momentum is only a small factor in these kinds of indices.
The conditions for our test were as follows:
We’ll look at the impact of only picking one or two or more funds as well.
We picked portfolios starting in May 2023. The following year was a very strong period for equity returns, with the S&P500 rising over 25% over the period we looked at.
The average total return on our portfolios was 15.62% over their 6 month lives, compared with 12.81% for the MSCI world over the same period. So, a 2.8% outperformance over 6 months!
If we look at each of the individual portfolios and the MSCI over the same periods, i.e. May to November, then June to December and so on, we see:
(this table shows returns from sequential 6month periods each beginning onemonth after the former).
Interestingly, the best performance came from taking the top three funds. What we haven’t done is statistical testing to see if this is significant or just by chance, we’d need to do a much bigger study!
This table shows the performance of the “different funds” versus the MSCI.
n.b. – “1 fund” = top performer only, 2 = top 2 from different sectors etc.
The way to read this is, in the first column we show how a portfolio of just the top fund, then the top two funds …and so on, performed vs. the MSCI over 6 months. Unsurprisingly, we never got a negative half year in this verystrong market, but in the first two portfolios starting in July and August, Tech gave a net negative return, falling over 10% then 5%. Then it recovered going up 9%, 26% and 20%. So, choosing more than one fund achieved what we wanted.
It is interesting to look at why our portfolio did so well:
The MSCI world is dominated by the US (over 70% weight) and the technology stocks like Microsoft have big weightings, Microsoft alone is 4.4% of the MSCI World. So, technology, which was often a big component in our portfolio, also helped drive the MSCI world. The US was also heavily represented in our portfolios. There was a lot of overlap. Out of the twentyfour selections we made, technology funds appeared six times and North America four times.
A big difference in our portfolio is that in four out of six periods India funds were the top performer. Here are the portfolios in the first and last months:
Portfolio in month one  Portfolio in month six 
Technology & Innovations  India 
North America all caps  Technology & Innovations 
Technology &Innovations  Global 
Global  Global 
The obvious omission is emerging market stocks, but had we used the MSCI emerging markets index, which is 17.25% India, we would have performed even better on a relative basis! Over the same period the MSCI emerging markets index rose only 9.9%. By following momentum, we cherrypicked the brightest star from the EM universe at that time, India. We could have picked a 60/40 equity bond fund, mimicking a pension fund, but this wouldn’t have been a fair comparison; our momentum fund is going to be dominated by equities and have their volatility of returns and risk profile. An allequity index (as we chose) is a fairer comparison.
We also need to take a broader perspective here: we, by chance chose one of the best performing indices to benchmark against because of its US and tech component. By following momentum, we allocated strongly to those sectors and then when India and Global stocks performed better, we got those!
This is the big issue but given the outperformance and the fact we are using funds, there is a very good probability that we would have outperformed even after spreads and dealing costs.
Given the intriguing results of this retroactive analysis, and potential gaps in the analysis, we decided to gather more data. As such, we’ll continue running a momentum investing portfolio as time progresses to see if it yields positive results, rather than a hindsight analysis.
We are going to start publishing a biweekly momentum survey, the Capital City Training Market Momentum Tracker, or the CCT MMT for short! This tracker will show which categories of funds are increasing in momentum and which are losing momentum. We’ll be sharing this analysis as updates on our blog and social media going forward.
We must emphasise at this point that this is not investment advice, and we aren’t doing this to try and promote day trading! Instead, our aim is to correct one of the biggest mistakes that small investors make – having “homemarket bias.” It is exactly what it says; if you are a French investor, the chance of the French market being the best performer in a year is low. Diversifying internationally will expose you to potentially higher performing markets. Following momentum indicators may make you consider markets that you were just not aware of, or never considered. Had you thought of investing in £ strategic bond funds?
Reading the financial press isn’t really very helpful in finding trends. In my experience, journalists only report on market performance at new peaks; when there is a sudden dramatic “against trend” move (as happened in gold in April 2024); at turning points when a market has fallen 10% or so from a recent peak; or at a new low for the year, half year or quarter. In short you will only read commentary when something has happened. It then is usually a contraindicator – do the opposite.
We hope you’ve found this stimulating and that is has made you curious about momentum. If you would like a list of the actual funds we used and the performance data, feel free to get in touch with us at info@capitalcitytraining.com or reach out Mark Woolhouse on LinkedIn. Similarly, if you want to get our biweekly momentum reports and our monthly commentaries, keep an eye on our LinkedIn posts, or signup for our Newsletter in our blog to be notified when a new update goes live.
Remember stocks can go down as well as up!
The table shows which category of funds – in this case UK Equity Income – had the largest proportion of funds in the top one hundred performers over the last four weeks. This is “rank 1”. So, UK equity income have moved up by two places from third place by number of winners since the week before. Europe inc./exc. UK has improved its position dramatically.
In our portfolio experiment we looked at 6 monthly returns. In this analysis we are looking at 4weekly returns. The focus here is to potentially spot trends as they begin to develop, rather than when they are quite mature.
The story here, which we’ll elaborate on in another post, is how UK equities are performing very strongly at the moment. This momentum has been broadening, so spreading from large caps to small caps. UK all companies are performing. The whole UK market is going up. You can read our recent blogs to get the background to this story. This could be the proof that Goldman’s forecast for the UK performing strongly in 2024 could be correct. Maybe 2024 will be the UK market’s year in the sun!
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