Author: indiafreenotes

In order to evaluate the alternatives, certain quantitative techniques have been developed which facilitate in making objective decisions.

Important decision-making techniques are four and they have been discussed as under:

(1) Marginal Analysis:

This technique is also known as ‘marginal costing’. In this technique the additional revenues from additional costs are compared. The profits are considered maximum at the point where marginal revenues and marginal costs are equal.” This technique can also be used in comparing factors other than costs and revenues.

For Example – If we try to find out the optimum output of a machine, we have to vary inputs against output until the additional inputs equal the additional output. This will be the point of maximum efficiency of the machine. Modern analysis is the ‘Break-Even Point’ (BEP) which tells the management the point of production where there is no profit and no loss.

(2) Co-Effectiveness Analysis:

This analysis may be used for choosing among alternatives to identify a preferred choice when objectives are far less specific than those expressed by such clear quantities as sales, costs or profits. Koontz, O’Donnell and Weihrich have written that “Cost models may be developed do show cost estimates for each alternative and its effectiveness. Social objective may be to reduce pollution of air and water which lacks precision. Further, he has emphasised for synthesizing model i.e., combining these results, may be made to show the relationships of costs and effectiveness for each alternative.”

(3) Operations Research:

This is a scientific method of analysis of decision problems to provide the executive the needed quantitative information in making these decisions. The important purpose of this is to provide the managers with scientific basis for solving organisational problems involving the interaction of components of the organisation. This seeks to replace the process by an analytic, objective and quantitative basis based on information supplied by the system in operation and possibly without disturbing the operation.

This is widely used in modern business organisations. For Example – (a) Inventory models are used to control the level of inventory, (b) Linear Programming for allocation of work among individuals in the organisation.

Further, some theories have also been propounded by eminent writers of management to analyse the problems and to take decisions. Sequencing theory helps the management to determine the sequence of particular operations. Queuing theory, Games theory, Reliability theory and Marketing theory are also important tools of operations research which can be used by the management to analyse the problems and take decisions.

(4) Linear Programming:

It is a technique applicable in areas like production planning, transportation, warehouse location and utilisation of production and warehousing facilities at an overall minimum cost. It is based on the assumption that there exists a linear relationship between variables and that the limits of variations can be ascertained.

It is a method used for determining the optimum combination of limited resources to achieve a given objective. It involves maximisation or maximisation of a linear function of various primary variables known as objective function subject to a set of some real or assumed restrictions known as constraints.

Models represent the behaviour and perception of decision-makers in the decision-making environment. There are two models that guide the decision-making behaviour of managers.

These are:

1. Rational/Normative Model-Economic Man
2. Non-Rational/Administrative Model
3. Rational/Normative Model:

These models believe that decision-maker is an economic man as defined in the classical theory of management. He is guided by economic motives and self-interest. He aims to maximise organisational profits. Behavioural or social aspects are ignored in making business decisions.

These models presume that decision-makers are perfect information assimilators and handlers. They can collect complete and reliable information about the problem area, generate all possible alternatives, know the outcome of each alternative, rank them in the best order of priority and choose the best solution. They follow a rational decision-­making process and, therefore, make optimum decisions.

This model is based on the following assumptions:

1. Managers have clearly defined goals. They know what they want to achieve.
2. They can collect complete and reliable information from the environment to achieve the objectives.
3. They are creative, systematic and reasoned in their thinking. They can identify all alternatives and outcome of each alternative related to the problem area.
4. They can analyse all the alternatives and rank them in the order of priority.
5. They are not constrained by time, cost and information in making decisions.
6. They can choose the best alternative to make maximum returns at minimum cost.

Group decision-making suffers from the following limitations:

(a) It is costly and more time consuming than individual decision-making.

(b) Some members accept group decisions even when they do not agree with them to avoid conflicts.

(c) Sometimes, groups do not arrive at any decision. Disagreement and disharmony amongst group members leads to interpersonal conflicts.

(d) Some group members dominate others to agree to their viewpoint. Social pressures lead to acceptance of alternatives which all group members do not unanimously agree to.

(e) If there is conflict between group goals and organisational goals, group decisions generally promote group goals even if they are against the interest of the organisation.

Though cost of group decision-making is more than individual decision-making, its benefits far outweigh the costs and enable the managers to make better decisions.

Mathematical expectation, also known as the expected value, which is the summation of all possible values from a random variable.

It is also known as the product of the probability of an event occurring, denoted by P(x), and the value corresponding with the actually observed occurrence of the event.

For a random variable expected value is a useful property. E(X) is the expected value and can be computed by the summation of the overall distinct values that is the random variable. The mathematical expectation is denoted by the formula:

E(X)= Σ (x₁p₁, x₂p₂, …, x_np_n),

where, x is a random variable with the probability function, f(x),

p is the probability of the occurrence,

and n is the number of all possible values.

The mathematical expectation of an indicator variable can be 0 if there is no occurrence of an event A, and the mathematical expectation of an indicator variable can be 1 if there is an occurrence of an event A.

For example, a dice is thrown, the set of possible outcomes is { 1,2,3,4,5,6} and each of this outcome has the same probability 1/6. Thus, the expected value of the experiment will be 1/6*(1+2+3+4+5+6) = 21/6 = 3.5. It is important to know that “expected value” is not the same as “most probable value” and, it is not necessary that it will be one of the probable values.

Properties of Expectation

1. If X and Y are the two variables, then the mathematical expectation of the sum of the two variables is equal to the sum of the mathematical expectation of X and the mathematical expectation of Y.

Or

E(X+Y)=E(X)+E(Y)

2. The mathematical expectation of the product of the two random variables will be the product of the mathematical expectation of those two variables, but the condition is that the two variables are independent in nature. In other words, the mathematical expectation of the product of the nnumber of independent random variables is equal to the product of the mathematical expectation of the n independent random variables

Or

E(XY)=E(X)E(Y)

3. The mathematical expectation of the sum of a constant and the function of a random variable is equal to the sum of the constant and the mathematical expectation of the function of that random variable.

Or,

E(a+f(X))=a+E(f(X)),

where, a is a constant and f(X) is the function.

4. The mathematical expectation of the sum of product between a constant and function of a random variable and the other constant is equal to the sum of the product of the constant and the mathematical expectation of the function of that random variable and the other constant.

Or,

E(aX+b)=aE(X)+b,

where, a and b are constants.

5. The mathematical expectation of a linear combination of the random variables and constant is equal to the sum of the product of  ‘n’ constant and the mathematical expectation of the ‘n’ number of variables.

Or

E(∑a_iX_i)=∑ a_i E(X_i)

Where, a_i, (i=1…n) are constants.

The scope of statistics was primarily limited in the sense that the ruling kings used to collect data so as to frame suitable military and fiscal policies only. Hence they heavily depended upon statistics. As time went on, statistics came to be regarded as a method of handling and analyzing the numerical facts and figures.

In recent years, the activities of the state have increased tremendously. Statistical facts and figures are of immense help in promoting human welfare. Today, the scope of statistics is so vast and ever expanding. It influences everybody’s life. Even an entry into the world and exit are systematically recorded.

There is no branch of human activity that can escape the attention of statistics. It is a tool of all sciences. It is indispensable for research and intelligent judgment. It has become a recognized discipline in its own right. A few specific areas of application are mentioned below.

• Finance and Accounting: Cash flow analysis, Capital budgeting, Dividend and Portfolio management, Financial planning.
• Marketing Management: Selection of product mix, Sales resources allocation and Assignments.
• Production Management: Facilities planning, Manufacturing, Aggregate planning, Inventory control, Quality control, Work scheduling, Job sequencing, Maintenance and Project planning and scheduling.
• Personnel Management: Manpower planning, Resource allocation, Staffing, Scheduling of training programs.
• General Management: Decision Support System and Management of Information Systems, MIS, Organizational design and control, Software Process Management and Knowledge Management.

Applications

Applications of Quantitative Analysis in the Business Sector

Business owners are often forced to make decisions under conditions of uncertainty. Luckily, quantitative techniques enable them to make the best estimates and thus minimize the risks associated with a particular decision. Ideally, quantitative models provide company owners with a better understanding of information, to enable them to make the best possible decisions.

Project Management

One area where quantitative analysis is considered an indispensable tool is in project management. As mentioned earlier, quantitative methods are used to find the best ways of allocating resources, especially if these resources are scarce. Projects are then scheduled based on the availability of certain resources.

Production Planning

Quantitative analysis also helps individuals to make informed product-planning decisions. Let’s say a company is finding it challenging to estimate the size and location of a new production facility. Quantitative analysis can be employed to assess different proposals for costs, timing, and location. With effective product planning and scheduling, companies will be more able to meet their customers’ needs while also maximizing their profits.

Marketing

Every business needs a proper marketing strategy. However, setting a budget for the marketing department can be tricky, especially if its objectives are not set. With the right quantitative method, marketers can find an easy way of setting the required budget and allocating media purchases. The decisions can be based on data obtained from marketing campaigns.

Finance

The accounting department of a business also relies heavily on quantitative analysis. Accounting personnel use different quantitative data and methods such as the discounted cash flow model to estimate the value of an investment. Products can also be evaluated, based on the costs of producing them and the profits they generate.

Purchase and Inventory

One of the greatest challenges that businesses face is being able to predict the demand for a product or service. However, with quantitative techniques, companies can be guided on just how many materials they need to purchase, the level of inventory to maintain, and the costs they’re likely to incur when shipping and storing finished goods.

The Bottom Line

Quantitative analysis is the use of mathematical and statistical techniques to assess the performance of a business. Before the advent of quantitative analysis, many company directors based their decisions on experience and gut. Business owners can now use quantitative methods to predict trends, determine the allocation of resources, and manage projects.

Data Analysis can be defined as the process of reviewing and evaluating the data that is gathered from different sources.  Data cleaning is very important as this will help in eliminating the redundant information and reaching to the accurate conclusions. Data analysis is the systematic process of cleaning, inspecting and transforming data with the help of various tools and techniques. The objective of data analysis is to identify the useful information which will support the decision-making process. There are various methods for data analysis which includes data mining, data visualization and Business Intelligence. Analysis of data will help in summarizing the results through examination and interpretation of the useful information. Data analysis helps in determining the quality of data and developing the answers to the questions which are of use to the researcher.

In order to discover the solution of the problem and to reach to the specific and quality results, various statistical techniques can be applied. These techniques will help the researcher to get accurate results by drawing relationships between different variables. The statistical techniques can mainly be divided into two

A) Parametric Test

B) Non-Parametric Test

Parametric Test

Parametric statistics considers that the sample data relies on certain fixed parameters. It takes into consideration the properties of the population. It assumes that the sample data is collected from the population and population is normally distributed. There are equal chances of occurrence of all the data present in the population. The parametric test is based on various assumptions which are needed to be holding good. Various parametric tests are Analysis of Variance (ANOVA), Z test, T test, Chi Square test, Pearson’s coefficient of correlation, Regression analysis.

T- Test

T- test can be defined as the test which helps in identifying the significant level of difference in a sample mean or between the means of two samples. It is also called as a T- Distribution. The t-test is conducted when the sample size of the population is small, and variance of the population is not known. The t-test is used when the population (n) is not larger than 30. There are two types of T-Test:

1. Dependent mean T Test- It is used when same variables or groups are experimented.
2. Independent mean T Test-It is used when two different groups experimented. The two different groups have faced different conditions.

The formula for T-Test is:

Z Test

This test is used when the population is normally distributed. The sample size of the population is large or small, but the variance of the population is known. It is used for comparing the means of the population or for identifying the significance level of difference between the means of two independent samples. Z test is based on the single critical value which makes the test more convenient.

The formula for z test is:

X- Main value

µ – Sample Mean

σ – Standard Deviation

Analysis of Variance (ANOVA)

When there are two or more categorical data, then Analysis of Variance is used. Analysis of variance can be mainly of two types a) one-way ANOVA, b) Two-way ANOVA. One way ANOVA is used when the mean of three or more than three groups are compared. The variables in each group are same. Two-way ANOVA is used to discover if there is any relationship between two independent variables and dependent variables. Analysis of Variance is based on many assumptions. ANOVA assumes that there is a dependent variable which can be measured at continuous intervals. There are independent variables which are categorical, and there should be at least two categories. It also assumes that the population is normally distributed and there is no unusual element is present.

Chi Square Test

This test is also known as Pearson’s chi-square test. This test is used to find a relationship between two or more independent categorical variables. The two variables should be measured at the categorical level and should consist of two or more independent groups.

Coefficient of Correlation

Pearson’s coefficient of correlation is used to draw an association between two variables. It is denoted by ‘r’. The value of r ranges between +1 to -1. The coefficient of correlation is used to identify whether there is a positive association, negative association or no association between two variables. When the value is 0, it indicates that there is no association between two variables. When it is less than 0, it indicates a negative association, and when the value is more than 0, then it indicates a positive association.

Regression Analysis

This is used to measure the value of one variable which is based on the value of another variable. The variable whose value is predicted is the dependent variable, and the variable which is used to predict the value of another variable is called independent variable. The assumptions of regression analysis are that the variables should be measured at the continuous level and there should be a linear relationship between two variables.

Non-Parametric Tests

Non-Parametric Statistics does not take into account any assumption relating to the parameters of the population. It explains that data is ordinal and is not necessary to be normally distributed. The non-parametric test is also known as a distribution-free test. These tests are comparatively simpler than the parametric test. Various non-parametric tests include Fisher- Irwin Test, Wilcoxon Matched –Pairs Test (Signed rank test), Wilcoxon rank-sum test, Kruskal- Wallis Test, Spearman’s Rank Correlation test.

Probability theory is the foundation for statistical inference.  A probability distribution is a device for indicating the values that a random variable may have.  There are two categories of random variables.  These are discrete random variables and continuous random variables.

Discrete random variable

The probability distribution of a discrete random variable specifies all possible values of a discrete random variable along with their respective probabilities.

Examples can be

• Frequency distribution
• Probability distribution (relative frequency distribution)
• Cumulative frequency

Examples of discrete probability distributions are the binomial distribution and the Poisson distribution.

Binomial Distribution

A binomial experiment is a probability experiment with the following properties.

1. Each trial can have only two outcomes which can be considered success or failure.
2. There must be a fixed number of trials.
3. The outcomes of each trial must be independent of each other.
4. The probability of success must remain the same in each trial.

The outcomes of a binomial experiment are called a binomial distribution.

Poisson Distribution

The Poisson distribution is based on the Poisson process.  

1. The occurrences of the events are independent in an interval.
2. An infinite number of occurrences of the event are possible in the interval.
3. The probability of a single event in the interval is proportional to the length of the interval.
4. In an infinitely small portion of the interval, the probability of more than one occurrence of the event is negligible.

Continuous probability distributions

A continuous variable can assume any value within a specified interval of values assumed by the variable.  In a general case, with a large number of class intervals, the frequency polygon begins to resemble a smooth curve.

A continuous probability distribution is a probability density function.  The area under the smooth curve is equal to 1 and the frequency of occurrence of values between any two points equals the total area under the curve between the two points and the x-axis.

The Normal Distribution

The normal distribution is the most important distribution in biostatistics.  It is frequently called the Gaussian distribution.  The two parameters of the normal distribution are the mean (m) and the standard deviation (s).  The graph has a familiar bell-shaped curve.

Graph of a Normal Distribution

Characteristics of the normal distribution

1. It is symmetrical about m.
2. The mean, median and mode are all equal.
3. The total area under the curve above the x-axis is 1 square unit.  Therefore 50% is to the right of m and 50% is to the left of m.
4. Perpendiculars of:
    ± s contain about 68%; 
       ±2 s contain about 95%;
       ±3 s contain about 99.7%
   of the area under the curve.

The standard normal distribution

A normal distribution is determined by m and s.  This creates a family of distributions depending on whatever the values of m and s are.  The standard normal distribution has m =0 and s =1.

 Finding normal curve areas

1. The table gives areas between – and the value of .  
2. Find the z value in tenths in the column at left margin and locate its row.  Find the hundredths place in the appropriate column.
3. Read the value of the area (P) from the body of the table where the row and column intersect.  Note that P is the probability that a given value of z is as large as it is in its location.  Values of P are in the form of a decimal point and four places.  This constitutes a decimal percent.

Finding probabilities

We find probabilities using the table and a four-step procedure as illustrated below.

a) What is the probability that z < -1.96?

    (1) Sketch a normal curve
    (2) Draw a line for z = -1.96
    (3) Find the area in the table
    (4) The answer is the area to the left of the line P(z < -1.96) = .0250

b)  What is the probability that -1.96 < z < 1.96?

    (1) Sketch a normal curve
    (2) Draw lines for lower z = -1.96, and upper z = 1.96
    (3) Find the area in the table corresponding to each value
    (4) The answer is the area between the values–subtract lower from upper P(-1.96 < z < 1.96) = .9750 – .0250 = .9500

c)  What is the probability that z > 1.96?

    (1) Sketch a normal curve
    (2) Draw a line for z = 1.96
    (3) Find the area in the table
    (4) The answer is the area to the right of the line; found by subtracting table value from 1.0000; P(z > 1.96) =1.0000 – .9750 = .0250

Applications of the Normal distribution

The normal distribution is used as a model to study many different variables.  We can use the normal distribution to answer probability questions about random variables.  Some examples of variables that are normally distributed are human height and intelligence.

Solving normal distribution application problems

In this explanation we add an additional step.  Following the model of the normal distribution, a given value of x must be converted to a z score before it can be looked up in the z table.

(1) Write the given information
(2) Sketch a normal curve
(3) Convert x to a z score
(4) Find the appropriate value(s) in the table
(5) Complete the answer

Illustrative Example:  Total fingerprint ridge count in humans is approximately normally distributed with mean of 140 and standard deviation of 50.  Find the probability that an individual picked at random will have a ridge count less than 100.  We follow the steps to find the solution.

(1) Write the given information

m = 140
s = 50
x = 100

(3) Convert x to a z score

(4) Find the appropriate value(s) in the table

    A value of z = -0.8 gives an area of .2119 which corresponds to the probability P (z < -0.8)

(5) Complete the answer

    The probability that x is less than 100 is .2119.

In these other discipline, we can include production and its department, marketing and its department and personnel and its department. Relationship shows balanced behavior of officers of finance department and other department’s officers. They should concentrate on one target of company and many other things, they should know for creating good relation.

Relationship of finance with other discipline can be explained in following way:

(i) Finance and Accounting

The two are embedded with different disciplines. The finance is the discipline which is mainly based on the cash basis of operations but the accounting is totally governed by the accrual system.

Accounting is mainly vested with the collection and presentation of data, but the finance is closely connected with the decision making of the organization.

Till this moment, the differences are discussed only to know the role of finance over the accounting of any organization. The following is the major relationship which lies in between the finance and accounting as follows “Finance begins where accounting ends”

(ii) Relationship of Finance with Production

Production department’s main duty is to produce the goods. For producing goods, it needs raw material, labor and other expenses. For paying all expenses, production department needs money and fund which will be fulfilled by finance department. Finance department checks the budget of production department and allow funds for production department. With this view, we can understand that production department is dependent on finance department’s decision. Now, if production department performs his duty honestly and products are produced and sold on time, it will be helpful for increase sale and profitability and it will again recycle the fund with high profit in finance department. So, we can say both are dependent on each other. Both are players of business team. Both should be adopt co-operative view for each other. After this, business team can succeed in business.

(iii) Relationship of Finance with Marketing

Marketing department’s main duty is to sell maximum goods and satisfy the consumers. Its product’s input cost will decrease if all products are sold by marketers of company. For developing the product, promotion activities and distribution activities of marketing department need some money for paying salesmen, advertising budget and other promotional expenses. For this marketing department makes his marketing budget and it is cleared by finance department, but sometime finance department will not all specific marketing expenses but marketing department need that type of expenses for promotion of sales. This will create confliction. Good relations will be helpful for both departments. If both department does meeting and show behavior like good relative, the problem can easily solve. Both departments should think that both are the part of company’s organization and co-ordination between them is must. Sometime, marketing department obtains big order for supplying the goods, at that time finance department should help marketing department for arrangement of money for buying raw material and supplying fastly without any delay.

(iv) Relationship of Finance with Personnel

Personnel are that science which manages the employees of company and finance is that science which manages the money. If personnel department and finance department work together with co-operation, both departments can satisfy the objectives of company. It is the objective of company to satisfy employee by fulfilling their financial needs. It is also objective of company to reduce the misuse of fund by paying excess salary that required cost of doing work by employee. So, both department should understand each other’s objective and should help other department for fulfilling the objectives. One more thing, financial decisions are also very necessary in human resource area. Corporate are moving to the development of employees. They are human resource capital of company. Now, investment in training of employees, incentive schemes and retirement schemes etc. should be calculated like other investment and both departments should take maximum advantages from this asset.

(v) Relationship of Finance with Economics

There are two areas of economics with which the financial manager must be familiar: micro-economics and macroeconomics. Microeconomics deals with the economic decisions of individuals, households, and firms, whereas macroeconomics looks at the economy as a whole.

The typical firm is heavily influenced by the overall performance of the economy and is dependent upon the money and capital markets for investment funds. Thus, financial managers should recognize and understand how monetary policies affect the cost of funds and the availability of credit. Financial managers should also be versed in fiscal policy and how it affects the economy. What the economy can be expected to do in the future is a crucial factor in generating sales forecasts as well as other types of forecasts.

The financial manager uses microeconomics when developing decision models that are likely to lead to the most efficient and successful modes of operation within the firm. Specifically, financial managers use the microeconomic concept of setting marginal cost equal to marginal revenue when making long-term investment decisions (capital budgeting) and when managing cash, inventories, and accounts receivable (working capital management).

Business financing is just it what it sounds like: the activity of funding the many aspects of a business, whether the funding be for starting a business, running it, or expanding it. Regardless of the size or type of business, there are fundamental questions involving financing that must be addressed.

For example, most businesses purchase a variety of items, such as buildings, machinery, or office furniture and equipment that are intended to be useful for a long time. Such items are called long-term investments. Any business making long-term investments must carefully consider what those investments will be, how much they will cost, and how much they will hold their value over time. Just as important is the question of where to get the money needed to pay for them.

When a business is just starting, it typically borrows money from banks or other financial institutions, or it brings in additional individuals or institutions (that is, investors) to share ownership in the business in order to procure the initial capital it needs to cover the costs of building a new business. Capital is the term given to the money or other things of worth that are needed to produce goods or services. Capital can take the form of human beings, physical goods, or some means of financial exchange. Examples of capital are skilled labor, factories, office space, tools, machinery, and money.

When a businesses is up and running and managing the everyday financial operations, it may likewise turn to banks and investors for financing, but it typically relies on its customers for generating the money needed to finance the business. If the business is profitable and the company saves some of the money it makes from commercial activity, it may use that money to make new investments that will further expand its business. There are many different methods businesses use to acquire the financing they need to fund large projects and to improve their profitability.

Recorded instances of business financing date back to ancient times when wealthy Greeks arranged loans to shipping concerns that needed financing to transport freight. Greek lenders also funded miners and erectors of public buildings. In the Middle Ages Jewish merchants living in Italy loaned money to Christian Italian farmers. This practice established merchants in Europe as the main source of loans for farms and businesses and originated the concept of the “merchant bank.”

In 1781 the first commercial bank was established in the United States. Named the Bank of North America, it extended short-term loans to merchants who then passed them on to wholesalers of imported goods. The wholesalers, in turn, extended loans to retailers, often country stores and independent peddlers.

Another step in the development of business financing in the United States was taken in 1904, when the American banker A.P. Giannini (1870–1949; later to be nicknamed “America’s banker,”) opened the Bank of Italy in San Francisco in 1904. Immigrants who sought to borrow money to start businesses but had been turned down because they had no established wealth were supported by the Bank of Italy, which became the Bank of America in 1930. California industry and agriculture and Hollywood filmmaking were among the many interests supported by Giannini’s financing enterprise.

The Small Business Investment Act of 1958 established ways to make venture capital (funds from investors seeking to share ownership in new businesses) and long-term loans available to small, independent businesses in the United States. This program was the first to give small American businesses the financing they needed to start, maintain, and expand their operations.

Aspects of Business Financing

As businesses grow, their financing needs evolve and typically become more complex. In the case of a small business, the owner generally makes the financial decisions for the firm. In the case of a large company, the owner or owners (who in some cases are the stockholders) do not get involved in financial decisions. Instead, they hire managers who take on the financial responsibilities. In large companies, this person is known as the chief financial officer (CFO) or vice-president of finance.

The process of planning and managing the long-term investments of a business is known as capital budgeting. Usually this process involves seeking those business opportunities that will earn the company more than they will cost the company. For each type of business, these opportunities are distinct. For example, for a commercial airline the decision about whether to begin regular service to a new city would be an important capital budgeting decision. For a large discount retailer the decision about whether to introduce a new line of gardening products would be one. Other types of capital budgeting opportunities (for example, investments in computer systems or human labor) are common to almost all businesses.

When a company’s financial manager reviews a proposed capital budgeting decision, he or she must respond to several issues concerning the flow of cash associated with an investment. Primary considerations are the amount of cash the company is likely to make from an investment, when the company can collect the cash, and how high is the risk of the investment (that is, the chance of losing money in it). Basically, all capital investments must be evaluated for their size, timing, and risk. In the example of the airline’s beginning a new route, mentioned above, the financial manager must estimate how much money the company will make once the route is established, when it will earn the money, and how reliable the new market will be.

When a company makes a long-term investment, as it does when it decides to develop a new product or open a new division, it must know from where the needed money will come: from outside the company, from within the company, or some combination of the two. Long-term investments require what is called long-term financing. The financial structure (sometimes called the capital structure) of a company is the particular mix of long-term borrowing and the equity that it can use to pay for its operations and new investments. The company, for example, may have a certain amount of long-term debt incurred from borrowing money for its startup or to make new investments. The equity is the market value of the business’s property held by the owners and shareholders. Financial managers constantly weigh the levels of debt and equity. Equity allows a company to keep growing and gives it its value. Debts must be kept under control because they represent the level of financial risk a company takes upon itself; the greater its debts, the greater it risks financial instability.

The term “working capital” refers to the short-term investments a business can draw upon. A business’s short-term investments may be the inventory of goods it has produced. A business also may have short-term debts in the form of money it owes to the suppliers who provide materials to the business. The owners or the financial manager of the business must manage these short-term investments and debts of the firm on a daily basis so that the firm does not lose track of its costs, run out of ready cash, or interrupt its operations.

Recent Trends in Business Financing

An area of business finance that has grown steadily in the late twentieth and early twenty-first centuries is the practice of extending small loans to poor entrepreneurs who live in developing countries. The practice is known as microlending, and the loan is often called microcredit. The purpose of microlending is to assist individuals in creating income for themselves (for instance, by farming, weaving, or making crafts) and therefore to improve their living standards. Usually the individuals borrowing money have no existing property to use as collateral and no credit history and so would not qualify for a traditional bank loan.

Microcredit is generally issued for a short time period (one year or less), and the terms mandate that it be paid back on a weekly basis. Interest rates on the loans generally are high (in some places 40-50 percent) because costs of running the programs are high. The loan programs also generally seek to improve the education and health care of those enrolled. Microcredit is extended to women more often than men, and usually it is arranged as a community program, which cultivates responsibility to repay the loans because the whole community takes on the risk involved in improving the financial situation of its constituents.