Analyzing Forecasts

Ratio analysis and EPS are used to compare the strengths and weaknesses of various companies with industry or company benchmarks.

TERMS

• Diluted EPS

  Diluted Earnings Per Share (diluted EPS) is a company's earnings per share (EPS) calculated using fully diluted shares outstanding (i.e. including the impact of stock option grants and convertible bonds).

• Preferred dividends

  Preferred stock usually carries no voting rights, but may carry a dividend and may have priority over common stock in the payment of dividends and upon liquidation.

• Financial Accounting Standards Board (FASB)

  The Financial Accounting Standards Board (FASB) is a private, not-for-profit organization whose primary purpose is to develop generally accepted accounting principles (GAAP) within the United States in the public's interest.

Ratio Analysis

A financial ratio (or accounting ratio) is a relative magnitude of two selected numerical values taken from an enterprise's financial statements. Often used in accounting, there are many standard ratios used to try to evaluate the overall financial condition of a corporation or other organization. Financial ratios may be used by managers within a firm, by current and potential shareholders (owners) of a firm, and by a firm's creditors. Financial analysts use financial ratios to compare the strengths and weaknesses in various companies. If shares in a company are traded in a financial market, the market price of the shares is used in certain financial ratios.

Values used in calculating financial ratios are taken from the balance sheet, income statement, statement of cash flows or (sometimes) the statement of retained earnings. These comprise the firm's "accounting statements" or financial statements. The statements' data is based on the accounting method and accounting standards used by the organization.

Financial ratios quantify many aspects of a business and are an integral part of the financial statement analysis. Financial ratios are categorized according to the financial aspect of the business which the ratio measures .

Liquidity Ratios:

Liquidity ratios measure the availability of cash to pay debt .

Current ratio (Working Capital Ratio): Current assets / Current liabilities

Acid-test ratio (Quick ratio): (Current assets - Inventory - Prepayments) / Current liabilities

Activity Ratios:

Activity ratios measure how quickly a firm converts non-cash assets to cash assets.

Average collection period: Accounts receivable / (Annual credit sales / 365 days)

Average payment period: Accounts payable / (Annual credit purchases / 365 days)

Inventory conversion ratio: 365 days / Inventory turnover

Cash Conversion Cycle: Inventory conversion period + Receivables conversion period - Payables conversion period

Debt Ratios

Debt ratios measure the firm's ability to repay long-term debt.

Debt ratio: Total liabilities / Total assets

Times interest earned ratio (Interest Coverage Ratio): EBIT / Annual interest expense

Profitability Ratios

Profitability ratios measure the firm's use of its assets and control of its expenses to generate an acceptable rate of return .

Gross margin, Gross profit margin or Gross Profit Rate: Gross profit / Net sales

Profit margin, net margin or net profit margin: Net profit / Net sales

Return on equity (ROE): Net income / Average shareholders equity

Return on assets (ROA ratio or Du Pont Ratio): Net income / Average total assets

Market Ratios

Market ratios measure investor response to owning a company's stock and also the cost of issuing stock. These are concerned with the return on investment for shareholders, and with the relationship between return and the value of an investment in company's shares.

Earnings per share (EPS): Net earnings / Number of shares

Payout ratio: Dividends / Earnings

P/E ratio: Market price per share / Diluted EPS

Ratios generally are not useful unless they are benchmarked against something else, like past performance or another company. Thus, the ratios of firms in different industries, which face different risks, capital requirements, and competition are usually hard to compare.

Earnings Per Share (EPS)

Earnings per share (EPS) is the amount of earnings per each outstanding share of a company's stock. In the United States, the Financial Accounting Standards Board (FASB) requires companies' income statements to report EPS for each of the major categories of the income statement: continuing operations, discontinued operations, extraordinary items, and net income.

The EPS formula does not include preferred dividends for categories outside of continued operations and net income. Earnings per share for continuing operations and net income are more complicated in that any preferred dividends are removed from net income before calculating EPS. This is because preferred stock rights have precedence over common stock.

Earnings Per Share (Basic Formula):

Earnings per Share = Profit / Weighted average common shares

Ratios generally are not useful unless they are benchmarked against something else, like past performance or another company. Thus, the ratios of firms in different industries, which face different risks, capital requirements, and competition are usually hard to compare.

Since actual business activities are planned in relation to forecasting, it is very important that realistic expectations and estimations be undertaken.

TERMS

• supply chain management

  Supply chain management (SCM) is the management of a network of interconnected businesses involved in the provision of product and service packages required by the end customers in a supply chain.

• Egain Forecasting

  a method of controlling building heating by calculating demand for heating energy that should be supplied to the building in each time unit.

• inflation

  In economics, inflation is a rise in the general level of prices of goods and services in an economy over a period of time.

Business planning and forecasting refers to the set of activities where business operations are planned against the business strategy, and what forecast activities or results may occur from operational execution during a particular time period. In preparing financial forecasts, firms should always assume they will be reviewed by a bank manager, regulatory agency, or investor. With this goal in mind, the firm should be guided to keep forecasts tidy and easy to understand by grouping cash inflows and outflows in simple ways that are easy to understand quickly.

Forecasting financial statements comprises the estimation of several values - including sales, costs, and expected interest rates. Since actual business activities are planned in relation to these estimations, it is very important that realistic expectations and estimations be undertaken. With this in mind, there are specific points of interest to lenders and investors that need to be addressed. The profitability of a business reflects a sound relationship between market-driven sales projections and accurate cost estimates.

• Have you planned to have sufficient cash to meet your regular bills and also non-regular costs (like annual insurance premiums)?
• Does the financial position of the business remain sound when growth is forecast (this is what the balance sheet is for)?
• Is there a sensible balance between borrowings and the amount contributed by the owner (when the business is raising capital in its own right)?
• Are short and long-term obligations matched with relevant finance options?
• Do key business ratios remain within sensible bounds?

It is always easier to forecast future performance of a business when the business is already up and running because there are past trading results to look at. When a completely new venture is being planned a certain amount of imagination and estimation is required. However, this is in no way a license to be overly optimistic.

Forecasting has applications in many situations and impacts multiple aspects of a business. One such aspect is Supply Chain Management. Forecasting can be used in Supply Chain Management to make sure that the right product is at the right place at the right time. Accurate forecasting will help retailers reduce excess inventory and therefore increase the profit margin. Accurate forecasting will also help them meet consumer demand. On a broader level, economic forecasting is the process of making predictions about the economy as a whole. Forecasts can be carried out at a high level of aggregation -- for GDP, inflation, unemployment or the fiscal deficit -- or at a more disaggregated level -- for specific sectors of the economy or even specific firms .

Other important areas of forecasting include:

• Egain Forecasting
• Land use forecasting
• Player and team performance in sports
• Political Forecasting
• Product forecasting
• Sales Forecasting
• Technology forecasting
• Telecommunications forecasting
• Transport planning and Transportation forecasting
• Weather forecasting

Regression Analysis is a causal / econometric forecasting method that is widely used for prediction and forecasting improvement.

TERMS

• Ordinary least squares regression

  In statistics, ordinary least squares (OLS) or linear least squares is a method for estimating the unknown parameters in a linear regression model. This method minimizes the sum of squared vertical distances between the observed responses in the data set and the responses predicted by the linear approximation.

• linear regression

  In statistics, linear regression is an approach to modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X.

• independent

  not contingent or depending on something else

Regression Analysis

Regression Analysis is a causal / econometric forecasting method. Some forecasting methods are based on the assumption that it is possible to identify underlying factors that might influence a variable that is being forecast. For example, including information about weather conditions might improve the ability of a model to predict umbrella sales. This is a model of seasonality that shows a regular pattern of up and down fluctuations. In addition to weather, seasonality can also result from holidays and customs such as predicting that sales in college football apparel will be higher during football season as opposed to the off season.

Regression analysis includes a large group of methods that can be used to predict future values of a variable using information about other variables. These methods include both parametric (linear or non-linear) and non-parametric techniques.

Classical assumptions for regression analysis include:

• The sample is representative of the population for the inference prediction.
• The error is a random variable with a mean of zero conditional on the explanatory variables.
• The independent variables are measured with no error. (Note: If this is not so, modeling may be performed instead, using errors-in-variables model techniques).
• The predictors are linearly independent, i.e. it is not possible to express any predictor as a linear combination of the others.
• The errors are uncorrelated, that is, the variance– co-variance matrix of the errors is diagonal, and each non-zero element is the variance of the error.
• The variance of the error is constant across observations (homoscedasticity). (Note: If not, weighted least squares or other methods might instead be used).

In statistics, regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.

Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution.

Forecast Improvement

Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables is related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. However, this can lead to illusions or false relationships, so caution is advisable.

A large body of techniques for carrying out regression analysis has been developed. Familiar methods, such as linear regression and ordinary least squares regression, are parametric, in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data. Nonparametric regression refers to techniques that allow the regression function to lie in a specified set of functions, which may be infinite-dimensional.

The performance of regression analysis methods in practice depends on the form of the data generating process and how it relates to the regression approach being used. Since the true form of the data-generating process is generally not known, regression analysis often depends to some extent on making assumptions about this process. These assumptions are sometimes testable if a large amount of data is available. Regression models for prediction are often useful even when the assumptions are moderately violated, although they may not perform optimally. However, in many applications, especially with small effects or questions of causality based on observational data, regression methods give misleading results.

Modifying inputs such as accounts receivable, inventory, and accounts payable will significantly influence forecasting and business operations.

TERMS

• solvency

  The state of having enough funds or liquid assets to pay all of one's debts; the state of being solvent.

• liquidity

  Availability of cash over short term: ability to service short-term debt.

• forecast

  An estimation of a future condition.

Inputs

The inputs of accounts receivable, inventory, accounts payable, and other line items on financial statements provide important data for financial forecasting. Modifying any one of these inputs can lead to major changes in forecasts. Similarly, drastic differences in expected values and actual values in regard to these inputs can cause problems for a company, possibly even leading to insolvency.

Accounts Receivable

Accounts receivable is money owed to a business by its customers and shown on its balance sheet as an asset. It is one of a series of accounting transactions dealing with the billing of a customer for goods and services that the customer has ordered. A business must not only anticipate the level of sales that will be made on credit, but it must also anticipate when payment on these accounts will occur and account for the fact that some of these credit accounts will default. Accounts receivable has a great effect on a firm's expected cash inflows, and thus modifying this input on a forecast will affect how much cash a company decides to have on hand.

Inventory

Inventory management is primarily about specifying the scope and percentage of stocked goods. It is required at different locations within a facility or within many locations of a supply network to precede the regular and planned course of production and stock of materials. The scope of inventory management concerns the fine lines between replenishment lead time, carrying costs of inventory, asset management, inventory forecasting, inventory valuation, inventory visibility, future inventory price forecasting, physical inventory, available physical space for inventory, quality management, replenishment, returns and defective goods, and demand forecasting. Balancing these competing requirements leads to optimal inventory levels, which is an on-going process as the business needs shift and react to the wider environment.

Companies that rely on the sale of physical goods -- i.e., those that must carry inventory -- must manage inventory in such as way as to decrease expenses as much as possible. Since inventory is such a prevalent expense, accurate forecasting is of the utmost importance. Moreover, the modification of this particular input will have expansive effects on all of the financial statements a firm must forecast.

Accounts Payable

Accounts payable is money owed by a business to its suppliers and is shown on its balance sheet as a liability. Commonly, a supplier will ship a product, issue an invoice, and collect payment later, which describes a cash conversion cycle. This is the period of time during which the supplier has already paid for raw materials but hasn't been paid in return by the final customer.

Accounts payable will influence the current liabilities of a business, which will accordingly influence the liquidity of the business. A major requirement for a business to continue its operations is for that business to maintain solvency. Modifying accounts payable will drastically change the amount of cash-on-hand required for a business.