capital modelling

capital structure modelling is a crucial component of financial decision-making that enables businesses to achieve their objectives. It is a process that involves the determination of the optimum mix of equity, debt, and other financing instruments that a firm should use to finance its operations. The decision-making process is not straightforward and requires an in-depth analysis of the company's financial profile, risk tolerance, and other key factors. The process is further complicated by the fact that there is no one-size-fits-all solution for capital structure modelling and what works for one company may not necessarily work for another.

To fully understand capital structure modelling, it is important to break it down into its individual components. Here are some key concepts to keep in mind:

1. debt financing: Debt financing is one of the most common ways for businesses to raise capital. It involves borrowing money from lenders and paying back the principal and interest over a predetermined period. Debt financing can take many forms, including bank loans, bonds, and lines of credit. One of the advantages of debt financing is that the interest paid on the debt is tax-deductible, which can lower a company's tax bill.

2. equity financing: Equity financing involves raising funds by selling ownership shares in the company. equity financing can take the form of initial public offerings (IPOs), private placements, or venture capital. One of the key advantages of equity financing is that there is no obligation to repay the funds raised, which can provide greater flexibility for a company.

3. Optimal capital structure: The optimal capital structure is the mix of debt and equity financing that maximizes a company's value. Finding the optimal capital structure requires a balance between the benefits and costs of debt and equity financing. A company that is too heavily reliant on debt financing may be at risk of default, while a company that is too reliant on equity financing may miss out on the tax benefits of debt financing.

4. weighted Average Cost of capital (WACC): WACC is the average cost of the company's capital, taking into account the cost of both debt and equity financing. WACC is a critical component of capital structure modelling, as it helps companies determine the minimum return they need to earn on their investments to satisfy their investors.

Capital structure modelling is a complex process that requires careful consideration of a company's financial profile, risk tolerance, and other key factors. By understanding the key concepts involved, companies can make informed decisions about their financing options and achieve their objectives.

Introduction to Capital Structure Modelling - Capital Structure Modelling: Tools for Effective Financial Decision Making

2. Understanding the Components of Capital Structure

Components of Capital

Capital structure is a crucial aspect of financial decision making for any business organization. It refers to the combination of debt and equity that a company uses to finance its operations and growth. The capital structure of a company can have a significant impact on its ability to generate profits, manage risks, and meet its financial obligations. Understanding the components of capital structure is essential for making informed decisions about financing options and managing financial risks. There are different perspectives on the optimal capital structure, and the choice of capital structure will depend on factors such as the company's industry, growth potential, and risk profile.

Here are some of the key components of capital structure:

1. Debt: Debt refers to the amount of money that a company borrows from external sources, such as banks, bondholders, or other lenders. Debt is generally cheaper than equity financing, but it comes with the obligation to pay interest and principal payments on a regular basis. Too much debt can increase financial risk, but the right amount of debt can help a company to achieve its financial goals.

Example: A company that is expanding rapidly may need to borrow significant amounts of debt to finance its growth. However, if the company takes on too much debt, it may struggle to make interest payments and may face the risk of default.

2. Equity: Equity refers to the ownership interest in a company that is held by its shareholders. Equity financing can come from sources such as issuing stocks, retained earnings, or venture capital. Equity financing is generally more expensive than debt financing, but it does not require regular interest and principal payments. Equity financing also provides investors with an opportunity to share in the company's profits and growth.

Example: A startup company may seek equity financing from venture capitalists to fund its operations. In exchange for the investment, the venture capitalists receive an ownership stake in the company and the potential for significant returns if the company is successful.

3. hybrid securities: Hybrid securities, such as convertible bonds or preferred stock, have characteristics of both debt and equity. For example, convertible bonds can be converted into equity shares at a predetermined price, while preferred stock pays a fixed dividend like debt but can be converted into equity shares.

Example: A company that wants to raise capital without diluting the ownership stakes of its existing shareholders may issue convertible bonds. If the company's stock price rises, the bondholders can convert their bonds into equity shares at a lower price, giving them an opportunity to benefit from the company's growth.

Understanding the components of capital structure is important for making informed financial decisions. By balancing debt and equity financing, companies can manage their risks and achieve their financial goals. However, the optimal capital structure will depend on a variety of factors, and companies should carefully consider their financing options before making a decision.

Understanding the Components of Capital Structure - Capital Structure Modelling: Tools for Effective Financial Decision Making

3. Key Drivers of Capital Structure Modelling

Capital structure modelling is an important aspect of financial decision making for businesses. It involves analyzing the different ways that a company can fund its operations, such as through equity or debt financing. The choice between these two options can have significant implications for a company's financial health, risk profile, and overall performance. In order to make informed decisions about capital structure, it is important to consider a variety of key drivers that can influence the modelling process.

1. Business Risk: This refers to the level of risk associated with a company's operations, including factors such as industry trends, competitive pressures, and regulatory changes. A company with high business risk may be more reliant on debt financing to fund its operations, as equity investors may be less willing to take on the associated risks.

2. Financial Risk: This relates to a company's ability to meet its debt obligations, including factors such as interest rates and credit ratings. A company with high financial risk may need to rely more heavily on equity financing to avoid defaulting on its debt obligations.

3. Tax Considerations: The tax implications of different capital structure decisions can be significant, as interest payments on debt financing are tax-deductible while dividends on equity financing are not. Companies may therefore choose to use debt financing to take advantage of tax benefits, although this can also increase financial risk.

4. Market Conditions: The availability and cost of different forms of financing can vary depending on market conditions, such as interest rates and investor sentiment. For example, in a low interest rate environment, debt financing may be more attractive due to lower borrowing costs.

5. Company Size: The size of a company can also play a role in capital structure decisions. Smaller companies may have fewer financing options available to them, while larger companies may be able to access a wider range of sources of capital.

It is important to note that there is no one-size-fits-all approach to capital structure modelling, as each company's circumstances and goals are unique. However, by considering these key drivers and their implications, companies can make more informed decisions about how to structure their financing in a way that supports their long-term success. For example, a company with high business risk may choose to use a mix of equity and debt financing to balance risk and reward, while a company with high financial risk may prioritize equity financing to reduce the risk of default.

Key Drivers of Capital Structure Modelling - Capital Structure Modelling: Tools for Effective Financial Decision Making

4. Types of Capital Structure Models

When it comes to capital structure modelling, there are various types of models that a company can choose from. Each model has its own advantages and disadvantages, and the appropriate model for a company will depend on its unique financial situation, goals, and risk tolerance. In this section, we will explore the different types of capital structure models that are available to companies, including their characteristics and how they can be used to inform effective financial decision-making.

1. Modigliani and Miller (M&M) Model: This model suggests that the value of a company is independent of its capital structure. The model assumes that there are no taxes, transaction costs, or bankruptcy costs, and that investors have access to the same information. The M&M model provides a useful starting point for understanding capital structure, but it does not reflect the real-world complexities that companies face.

2. Trade-Off Theory: This theory suggests that there is an optimal level of debt that a company should have. The optimal level of debt balances the tax benefits of debt with the costs of financial distress. The trade-off theory acknowledges that there are costs associated with debt, such as bankruptcy costs and agency costs, but suggests that these costs can be offset by the tax benefits of debt.

3. pecking Order theory: This theory suggests that companies prefer to use internal financing (such as retained earnings) before resorting to external financing (such as debt or equity). The pecking order theory is based on the idea that external financing is more expensive than internal financing, and that companies want to avoid sending negative signals to investors by issuing new equity.

4. Agency Cost Theory: This theory suggests that the cost of debt is related to the potential conflicts of interest between shareholders and management. The agency cost theory suggests that debt can be used to align the interests of management with those of shareholders, as debt holders have a priority claim on the company's assets.

For example, a company that is in a stable industry with reliable cash flows may choose to use more debt to take advantage of the tax benefits of debt. On the other hand, a company that is in a volatile industry with uncertain cash flows may choose to use less debt to avoid the costs of financial distress. By understanding the characteristics of different capital structure models, companies can make informed decisions about their financing choices and optimize their financial performance.As the performance of the dominant modelling platforms has inched forwards, demands and expectations on modelling teams continue to increase unabated. The conflict between operating a capital model focused on regulatory imperatives versus a model that adds real business value, and the ensuing trade-offs, is a frequent topic of conversation between risk and capital professionals.

In a perverse way, modelling teams have perhaps become victims of their own success – the deep and sustained investment in management education and embedding model use has translated into increased demand.

Issues such as an overly narrow focus on one part of the curve driving model parameterisation and validation have been documented extensively. As model use and understanding has developed over time, approaches to addressing these shortcomings have become increasingly sophisticated.

However, challenges remain around model convergence, flexibility, validation and the speed at which management questions can be answered. Often, these issues are glossed over by a tacit acceptance that we need to work within the constraints of the computing performance at our disposal.

But recent advancements in stochastic modelling platforms and other analytical tools have begun to erode the credibility of this excuse.

In order to appreciate the size of the gap between the current and potential state, a good starting point is last year's market study conducted by Grant Thornton UK1 on model use, resources, performance, challenges and future priorities.

The thorny issue of simulations and model convergence

Around three quarters of respondents to Grant Thornton's survey run their models at no more than 100,000 simulations. Anecdotally, we expect many of those selecting "50,000 to 100,000" are at or near the bottom of that range.

Whilst perhaps not a great surprise, consider the fact this is the maximum used for production runs and over half of respondents acknowledged that different volumes were applied for intermediate runs (such as sensitivity testing to support validation or decision support).

Considering that for even a relatively mid-tier worldwide portfolio the natural catastrophe model component could be drawing from an event catalogue of greater than half a million unique events, it is clear that even at 100,000 simulations we are missing out on a wealth of information about potential volatility.

When we look at convergence, testing on our own model purely on gross / net underwriting risk demonstrates that at 50,000 simulations we are suffering from simulation error of ~3%. This reduces to ~1.5% at 100k, ~0.5% at 500k and ~0.1% at 1m simulations, at a typical 99.5% value-at-risk measure as used in Solvency II.

Dependencies is an area which is mired by issues such as coherence, positive-semidefinite adjustment, etc. The limitations are exacerbated by the inability to run sufficient number of simulations. As an example, the sensitivity tests to assess a change of +/- 5% change in the initial levels of dependencies are easily lost within the noise when running 50,000 simulations; even at 100,000 simulations, the differentiation between signal and noise is weak at best.

We must ask ourselves how much credibility should we really attach to the model when we test the sensitivity of an expert judgement at a lower simulation run.

Even a simple model relies on hundreds of expert judgements and parameterisation decisions, all of which should be sensitivity tested in order to help us focus on validation of the key drivers. How do we balance the opposing forces of acceptable simulation count, the range of judgements to be tested and the omnipresent limiting factor of time?

Which one do we give way on? What are the implications in terms of the validity of our results? When management asks a question, would we be able to answer it before they forget the question?

For actuaries, adopting new technologies to improve capital modelling may well become a matter of professional responsibility. For them and the rest of the business, there is a stark choice: embrace the possibilities and build a case for change or struggle on with legacy models until the step-change in capabilities offered by new technologies becomes the default expectation of regulators and rating agencies.