Present financial position and performance of the firm



Handout #4

Agricultural Economics 432

Part I - Financial Analysis

Topic #4

Spring Semester 2007

John B. Penson, Jr.

Agricultural Economics 432

Part I - Financial Analysis

Outline

Topic 1: Review of Financial Analysis Concepts

A. Introduction to Terminology

B. Key Financial Indicators to Track

C. Other Variables to Track

D. Financial Strength and Performance of the Firm

Topic 2: Growth of the Firm

A. Economic Climate for Growth

B. Economic Growth Model

Topic 3: Valuing Investment Opportunities

A. Time Value of Money

B. Capital Budgeting Methods

C. Overview of Capital Budgeting Information Needs

D. Specific Applications of Net Present Value Method

Topic 4: Valuation of Externalities

A. Historical Assessments (p. 42)

B. Projecting Future Values (p. 45)

Topic 3. Valuation of Externalities

A. Historical Assessments

Another important consideration in the financial decision making processes of firms is the nature of the markets in which the firm sells its products and acquires its inputs. Many farmers often find themselves as price takers in both product and factor markets. This makes understanding the trends and structure of these markets imperative to making sound investment and financing decisions.

Successful firms will find use of all the information available at its disposal, including historical on past commodity and input prices and yields (e.g., bushels per acre, gain per pound of feed). Crop revenue, for example, is given by

(74) Revenue ( Commodity price ( yield ( acres planted

Historical commodity and input prices can help explain deviations from historical trends in the financial indicators discussed earlier in equations (1) through (17). Future trends and deviations from trend are influenced by external global market events that may be influenced by new policies in Washington, financial crises in client nations and other factors that present new risks and returns.

Yields in crop and livestock production are more local in nature. Understanding long run trends in these yields and deviations about these trends can be instrumental to making projections of future revenue flows. Assume the annual yields in wheat production over the last ten years on the farm were as depicted in the scatter diagram below:

where this scatter reflects the following observations:

1996 = 35.6 1997 = 34.1 1998 = 40.2 1999 = 36.5

2000 = 31.8 2001 = 37.7 2002 = 39.1 2003 = 36.4

2004 = 41.2 2005 = 36.8

The average or mean of this time series is 36.94, which was found by using the AVE function in the Excel spreadsheet. The standard deviation for this same time series is equal to 2.80, which was found by using the STD function in the Excel spreadsheet.

These elements of this historical probability distribution can be depicted as shown below:

This information suggests that we can be approximately 70 percent confident that the yield on the firm’s tract of land planted to wheat will range between 34.14 bushels and 39.74 bushels.

The “least squares” line passing through the scatter diagram above can be found by using regression analysis in Excel. Using the SLOPE and INTERCEPT functions in the Excel spreadsheet, we see that the linear time trend over the 1991 – 2000 time period is given by:

(75) Yield = - 630.645 + 0.334545(Year)

The percent deviations about this historical time trend can be super-imposed on the forecasted long run yield trend starting in 2001 to assess how sensitive an investment decision is to past weather variability. More will be said about this topic in the next section.

Armed with this information, we can assess the long run yield trend for the firm’s local yields by substituting successive years into equation (75). This equation suggests that the yield for wheat in 2005, for example, would be:

(76) Yield = - 630.645 + 0.334545(2006)

= 38.78

or roughly 39 bushels per acre. This process can be extended over the life of the investment project to forecast future trends in yields, giving us a projection like that illustrated in the figure below:

[pic]

We can then superimpose observed yield deviations in the past on this long term trend to examine the sensitivity of the investment project’s feasibility to known weather patterns.

B. Projecting Future Values

One of the more important dimensions to financial decision making is the formation of expected future values of income and cost streams over time. Accountants and economists alike have a term they use for this; it is called pro forma analysis. There are a varied of approaches one can take when conducting pro forma analysis. Some are simple, such as using last year’s price or buying projections from private consultants, while others are more sophisticated, as we will demonstrate. We will start with the simple approaches.

Market outlook information approach

Perhaps the easiest approach to acquiring forward information on commodity and input prices is from government and university sources. Many universities, in conjunction with government agencies, provide 12-24 month outlook materials for many crop and livestock commodities. The information is free, but often dated. The Agricultural Outlook publication available at usda. is a prime example of monthly market assessments.

More detailed and frequently-updated price information is available at a cost from private sources. This can range from market newsletters to contractual consulting arrangements with consulting companies specializing in agricultural commodity sources.

Historical-based approaches

Another option is to make your own projections of what future trends in commodity and unit input prices will be over the economic life of an investment project. For example, one can use the naïve model approach, which assumes that

(77) Pi = Pi-1

where Pi is the projected price in the ith year and Pi-1 is the price in the previous year. While this approach is used because of its simplicity, recent commodity price trends for wheat easily refute the validity of this approach.

Another historical-based approach is to employ the Olympic average approach. This involves using past prices for 5 or more years, dropping the “high” and the “low” price, and computing the arithmetic average for the remaining observations. This approach will most likely out-perform the naïve model approach, but still ignores probabilistic future events that can lead to sharp departures from historical-based expectations.

Structural econometric simulation

As an alternative to the market outlook information and historical-based approaches, one can estimate market demand-supply relationships for the commodities produced by the firm or employ elasticity estimates from previous studies for these commodities. Let the demand for the ith crop be given by:

where the demand curve will shift with changes in the price of substitutes, consumer income, exchange rate relationships with client nations and other domestic and export demand developments. The supply curve will shift with changes in productivity, marginal input costs and other domestic and import supply developments.

Time series data on these variables, combined with the use of regression analysis to estimate the demand and supply relationships in double log form, provides elasticity estimates that are useful in pro forma analysis in absence of simulating estimated demand and supply equations. For example, the reciprocal of the econometrically-estimated own price elasticity of supply for a commodity of say 0.25 gives us the price flexibility for the commodity, or

(78) %(P = 4.0(%(Q)

This suggests that a one percent increase in the supply of a commodity coming onto the market will cause a 4 percent drop in the price of the commodity. This begins to help the firm assess the magnitude of future price fluctuations.

More expansive econometric analyses that capture the structure of multi-market relationships can provide an appropriate basis for making long run projections of commodity and unit costs of projections. Simulation of these relationships is referred to as structural pro forma analysis. This involves the assessment of alternative scenarios that result in a distribution of market prices for commodities and inputs to the firm’s operations as it considers investment projects. These scenarios include potential developments in farm programs, weather and disease, macroeconomic policies, foreign

trade policies and global market developments. Much like we did for historical observations for the firm’s yield history, we can compute the standard deviations and coefficients of variation for annual distributions of net cash flows reflecting the effects of each scenario.

Triangular probability distributions

We shall assume for the moment that we dealing with three scenarios, a “best case” scenario, a “worst case” scenario and a “most likely” scenario. This results in a triangular probability distribution where the two tails of this distribution reflect the subjective probabilities associated with the “best case” and “worst case” scenarios. The probabilities assigned to these two tails will likely increase over time, reflecting the increasing uncertainty as we move away from the current period and out over the remainder of the investment project’s economic life.

See Slide Show #8

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Plus and minus one standard deviation

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[pic]

Demand

Supply

PE

QE

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