From The Developing Economist VOL. 1 NO. 1
The Relationship Between Monetary Policy and Asset Prices: A New Approach Analyzing U.S. M&A Activity
III. A New Approach: Mergers and Acquisitions
To begin this section, I emphasize that M&A activity has not been studied in relation to the effects of monetary policy on asset prices.11 The only two types of assets considered in the literature have been stock prices and housing prices, even though M&A transactions are ideal for several reasons. First,M&Aactivity involves the equity prices of companies, just as stock prices reflect the equity value of public companies.12 It follows that, if stock prices are relevant to study the effects of monetary policy on asset prices, then M&A activity must be relevant as well because they both measure the same type of asset. However, M&A transactions involve a multi-month process. Contrary to only observing one-day movements in stock prices, M&Aprocesses have the time to absorb shocks in monetary policy and respond accordingly. This allows empirical research to more consistently observe the effects of shocks. Unlike investing in a house that covers multiple decades or the perpetuity nature of stock valuations, M&A investments often cover a three to seven year window. This is more likely to reflect the effect of monetary policy, which controls the shortterm nominal rate. In the very least, M&A activity is a relevant asset class due to its enormous market size. In 2012 alone, over 14,000 M&A transactions were completed with an average value above $200 million. This empirical analysis of the effects of monetary policy on M&A activity provides an original approach to this literature and helps further understand the relationship between asset prices and monetary policy.
M&A transactions are either the merger of or purchase of companies, generally involving the sale of a majority stake or the entirety of a company. Broadly speaking, M&A involves two classes of acquirers: 1) a company acquiring or merging with another company; or 2) an investment institution, primarily a private equity firm, that acquires companies to include in an investment portfolio. The latter sort of acquisitions often involve a large portion of debt with only a minority of the acquisition being funded with equity. To understand this process, consider a typical private equity firm. The firm will raise capital in an investment fund and then acquire a group of companies, financing the acquisitions with debt. Each portfolio company has two primary goals: 1) use the investment from the acquisition to grow the company; and 2) generate profits that are used to pay down the debt. As the companies grow and the debt paid down, the private equity firm re-sells each company hopefully at a higher price due to growth. What is more, the firm receives a quantity worth the entire value of the company, which is sizably more than the original investment that was financed only partially with equity and mostly with debt. Even if only some of the portfolio companies grow and not all the debt paid down, the portfolio can post remarkable returns. Harris et al. (2013) has found that the average U.S. private equity fund outperformed the S&P 500 by over 3% annually. The next sections discuss more fully the elements of this market.
Asymmetric information is especially of concern in the M&A market. As noted earlier, over 14,000 M&A transactions occurred in 2012. Although this is a large market due to the size of each transaction, the frequency of transactions pales in comparison, say, to the thousands of stocks traded daily. The market value of a stock is readily available because of the high frequency of transactions that signal the price to market participants. In contrast, there may only be a few transactions each year that are similar in terms of size, sector, maturity, geography, etc. This asymmetry is further compounded when considering reporting requirements. Public companies are required to publish quarterly and annual financial reports. What is more, these reports include sections of management discussion, providing deeper insight into the prospects and growth of the companies. However, because many M&A transactions deal with private companies, this information is often not available. For this very reason, investment banks are hired to advise M&A transactions, gather and present company information to potential buyers, and provide a credible reputation to stand behind the company information, thus removing the asymmetry problem. Posing even more of a challenge for empirical research such as this, not all M&A deals are required to disclose the transaction price or valuation multiples. Therefore, particularly when assessing the aggregate market, one must be prudent in selecting relevant variables that are still reliable and consistent despite this lack of information.
When analyzing aggregate data on M&A, four variables reflect the overall market activity: 1) the aggregate value of all disclosed deals, 2) the average size of each deal, 3) the number of deals in each period, and 4) the average valuation multiple of each deal. I argue that the first two are inconsistent metrics due to reporting requirements. Because information is only available on disclosed transactions, the aggregate value of all deals does not represent the entire market and can fluctuate from period to period simply if more or fewer firms disclose deal information. Similarly, the average size of each deal can also fluctuate from period to period as this average size comes from only the sample of deals that are disclosed. For these variables, there is the potential for inconsistency from one period to the next based only on fluctuations in reporting.
The next two variables, however, account for these issues. The total number of M&A transactions represents both disclosed and nondisclosed deals, thus removing the disclosure problem altogether. Looking at the final variable, valuation multiples are disclosed for only a portion of transactions. However, unlike the average deal size, multiples are independent of the size of a company and reflect the real price of the company. If a company with $100 million in revenue is sold for $200 million, the enterprise value (EV) to its revenue, or the revenue multiple, would be 2.0x.13 If a company with $1 billion in revenue is sold for $2 billion, the revenue multiple would still be 2.0x. Regardless of company size, the average multiple is not distorted. Several common multiples are a ratio of EV to revenue, EBIT (earnings before interest and taxes), and EBITDA (earnings before interest, taxes, depreciation, and amortization). Without digging deeply into the accounting for each multiple, this study looks at the EBITDA multiple which is the most commonly used multiple in the investment banking industry. EBITDA indicates a company's operational profitability. In other words, it reflects how much a company can earn with its present assets and operations on the products it manufactures or sells. This multiple is comparable across companies regardless of size, gross margin levels, debt-capital structures, or any one time costs that affect net income. EBITDA is generally considered the truest accounting metric for the health and profitability of a company. Thus, the EBITDA multiple is an excellent pricing metric to determine the value of a company relative both across time and to other companies of varying sizes. When assessing aggregate data, the average EBITDA multiple is a proxy for the average real price of transactions. With these metrics in mind, I now discuss common valuation methods.
Valuation methods can be broken into two main categories: 1) Relative methods that value the company in comparison to similar companies; and 2) Intrinsic methods that value the company based on its own performance. Two types of relative methods are precedent transactions and comparable public companies ("comps"). Precedent transactions look at the financial metrics of similar M&A deals and then apply those multiples and ratios to the target company. Similarly, comps analysis examines the trading multiples of a group of similar public companies and applies them to the financials of the company. In each method, the sample is based on criteria such as industry, financial metrics, geography, and maturity. An analysis will take the multiples of the group of companies, say the EBITDA multiple, and then apply it to the company at hand. As an example, if the average EBITDA multiple of the precedent transactions or comps is 10.0x and the EBITDA of the company is $20 million, the relative methods imply a value of $200 million.
In addition to relative methods, intrinsic methods value a company based solely on its individual financials. Discounted cash flow ("DCF") analysis is the present value of a company's future cash flow, as the real worth of a company is determined by how much cash (income) it can generate in the future. This mirrors the basic asset price valuations discussed previously. A DCF is usually split into two parts. The first component of a DCF is the forecast of a company's free cash flow over a five to ten year period that is then divided by a discount rate to yield a present value. The most commonly used discount rate is the WACC which is broken into components based on a firm's capital structure. Debt and preferred stock are easy to calculate as they are based on the interest rate of debt or the effective yield of preferred stock. The cost of equity is determined using the Capital Asset Pricing Model ("CAPM") by taking the risk-free rate, i safe, and adding the product of the market risk premium, φ, and a company-specific risk-factor, β.14 Within the CAPM, the risk-free rate is often a 10-year Treasury bond whereas the market risk premium is generally the percentage that stocks are expected to outperform the riskless rate. The CAPM is given by Equation 7:
The three components must be added back together to determine one discount rate, usually calculated by theWeighted Average Cost of Capital ("WACC"). Depicted in Equation 8 below, WACC multiplies each cost by that component's percentage of the total capital structure.
The last part of the DCF is a terminal value to reflect the earnings of the company that are generated beyond the projection period. The Gordon Growth Method, a common terminal value, takes the final year of projected free cash flow, multiplied by a projected annual growth rate of the company and then divided by the difference between the discount rate and the growth rate.15 Adding the discounted free cash flows and the terminal value, the total DCF with a five-year projection period is the following: