Trade Off Supporting Analysis Risk

Published Date: 02 Nov 2017

There are many definitions of risk and many that focus purely on the negative potential outcomes, however, the most appropriate definition I have found which relates to both financial and project management is defined as ‘an uncertain event or condition that, if it occurs, has a positive or negative effect’ (Project Management Institute, 2000) to a project, event or investment.

Project Management Risk

Projects are different from ‘business as usual’ activities in that they are unique activities with a defined start and finish point undertaken to achieve a specific target or objective within defined success criteria. As projects are unique, there will always be some element of uncertainty or risks associated, therefore PRAM is essential throughout (Turner, 1999). To determine if a project and investment is worth undertaking and which option provides the optimum solution, in financial terms, companies will use a ‘capital budgeting’ decision making process. Capital budgeting decisions on investment are paramount where companies are looking to undertake large capital investment as, unless the project is completed for social or public benefit, investments are always based on the expected or likely financial return over time (Gardiner, 2005). Initial analysis of the most viable project is determined through the planning stages of a project, for example within the feasibility study which inform the Business Case. Within this analysis process, the feasibility of the project is determined through a number of specific criteria such as an evaluation of market potential, feasibility, cost-effectiveness, assessment of risks and preliminary project cost and duration estimates utilising methods such as NPV, internal rate of return utilising discounted cash flow, sensitivity analysis, SWOT analysis and pay-back periods.

Financial Risk

In today’s markets it could be stated that virtually all company financial investments, from shares to projects, are uncertain or have some degree of risk involved. It should be made clear that risk and uncertainty are 2 separate terms. An investment termed as ‘risky’ means that, based on past experience, observation and knowledge, a probable likely result will occur i.e. the probability of the an expected return on investment can be calculated as 85% based on previous experience. If the probability within an investment cannot be determined, due to limited historical or previously gained knowledge, then the probability outcome also cannot be determined, and in this case the investment is deemed as ‘uncertain’ (Lumby & Jones, 2011). For uncertain investments, every possible outcome needs to be identified and assessed in order to identify each occurrences likely potential outcome. Uncertain investments are therefore inherently more risky.

Influence of Risk and Return Trade-Off

Generally speaking, investors can be classed as risk adverse, i.e. they do not like the idea of taking a risk on losing money on their investment. A risk free investment can be achieved by investing in Government Bonds or placing your investment into a bank or building society to earn interest, however, the level of return will be low and further reduced when the effect of inflation is taken into account. If a greater return on investment is sought then a higher level of risk is required as a reward for undertaking the risky investment. The basic principle outlining the relationship between capital market risk and the expected return trade-off can be summarised as:

‘The reward for investing in higher risk ventures or investments is a higher reward or ‘risk premium’. The higher the risk premium, the greater the investments risk (Hillier, et al., 2011).’

Risk and Return Relationship

The relationship between risk and return, how to measure the level of risk involved and how to determine what the expected return will be on a specific investment risk are essential elements for company financial planning and decision makers.

In order to evaluate how a risk premiums and return on investments is analysed we must provide some basic principles.

Expected Return. The probability of an expected return will be different each year depending on a number of economic factors such as the state of the economy, i.e. whether the market is in recession or boom. When analysing the economy over a long term investments it can be seen that the probabilities stay the same throughout time, therefore an expected return can be calculated based on these probabilities (Hillier, et al., 2011).

For example if the probable return for an investment (A) in recession is 10% and during boom is 50%. Historically in this instance we know that the possibility of a being in recession will occur 70% of the time. To determine the expected return:

Expected Return (E(RA) = % Recession x Prob Return + % Boom x Prob Return

This approach can be completed for each potential investment, to determine the expected return on each investment / equity, however, does not take into account the risk premium applied to each investment.

Risk Premium. The risk premium is the difference between the certain return on a risk free (Rf) investment and the projected or expected return (using the previous investment ‘A’ (E(RA)) on a risky investment and is calculated as:

Risk Premium = Expected Return – Risk Free Rate

Assuming that you can obtain a risk free investment return of 8%, the risk free rate is 8%. If the expected return is 12.5% we can determine the projected risk premium as:

Calculating the Variance. When analysing the risk of an investment, we must take into account the probability of the expected return on the investment will vary dependant on the state of the economy. By calculating the variance, we can determine what the expected standard deviation from the expected return will be. This standard deviation is an indicator of the assets risk. To determine the variance we must first determine the squared deviation from the expected return and then multiply this by its probability of occurring. By adding the results together we determine the variance. The standard deviation is the square root of the variance (Hillier, et al., 2011).

Using the previous values for investment ‘A’, to determine the expected return (E(RA) = 12.5%), we noted that in a year the actual return will be either 5% or 30%. Therefore the possible deviations are 5% - 12.5% = -7.5% or 30% - 12.5% = 17.5% from the expected return. In this case, the variance and standard deviation can be seen as:

By analysing the standard deviation, it can be seen that there is an 11% chance that the expected return will be less than or more than the expected return value of 12.5%. Conversely there is an 89% chance that we will receive the expected return value of 12.5%.

Types of Risk. There are many types of risks that may have an impact on investments, however, some have underlying factors which will affect all investments and some are factors which are specific to an individual investment (McLaney, 2011), therefore can be subdivided into 2 main categories:

Systematic Risks. Are political or macroeconomic risks which affect all companies market-wide and are not controllable, such as war, labour costs, inflation, interest-rate and GDP.

Unsystematic. Are unique risks which are specific to the company or investment, such as the company’s financial, liquidity and business risks, but also include internal company factors such as its management and business strategies.

Minimising Impact of Risk. Sensible diversification of portfolio (or spreading your investment across a number of assets) effectively eliminates the unsystematic risk which may be present in a single investment.

The portfolio diversification table, Figure A-1, illustrates that not all risk can be eliminated by diversification. Studies have shown that, on average, 65% of the total risk can be diversified away, the remaining 35% of the non-diversifiable or systematic risk remains. In addition, by analysing Figure A-1, it can be seen that obtaining between 15-20 different assets within your portfolio eliminates about 90% of the maximum diversifiable risk possible. It should be remembered that the effect of diversification reduces extremes of returns, this can be positive returns as well as negative returns.

Figure A-1. Effects of Portfolio Diversification

The formula for calculating the total return can now be seen as:

Return (R) = Expected Return (E(R)) + Unknown Element (or Risk Element)

Or Return (R) = E(R) + Systematic Element + Unsystematic Element

R = E(R) + m + ε

Where:

m = Systematic element (market risk)

ε = Unsystematic element

In order to measure the level of assets systematic risk, in relation to its expected return, each investment is assessed and given a specific measure, termed a ‘beta (β) coefficient’, to enable a comparison against an average risk asset. The baseline measure for an average risk asset has a Beta coefficient of 1, an asset with a score less than 1 has less systematic risk than average and an asset with a score above 1 has greater systematic risk. Again, assets with higher than 1 beta values have the potential to have greater positive or negative returns.

Relationship between Asset Risk and Required Return. By using the beta coefficient and risk premium calculation we could determine the reward-to-risk ratio for Asset ‘A’, which can be plotted as a straight line slope as shown in Figure A-2 and by the example equation below. In the example I am using an expected return E(RA) of 25%, a risk free rate (Rf) of 6% and an asset beta (βA) of 1.5:

This tells us that Asset ‘A’ offers a reward-to-risk ratio of 12.667%, or Asset ‘A’ has a risk premium of 12.667% per unit of systematic risk.

Figure A-2. Example of Reward to Risk Ratio

The same analysis can be completed for different assets and, when plotted on the same graph, would show the different gradients and reward-to-risk ratios. Clearly in this situation investors would only be attracted to the investments which offer the greatest reward-to-risk ratio, therefore the effect would be that the asset price of the higher reward-to-risk ratio asset would increase and the share price of the other asset would fall. The impact of the asset value changes would then alter the risk-to-reward ratio and the opposite situation would occur, where the price of the higher asset would fall and the lower value asset would rise. This situation would continue until equilibrium is reached where both assets risk-to-reward ratios are plotted on exactly the same gradient line, where Asset ‘A’ and Asset ‘B’ ratio are equal:

Therefore, the fundamental relationship between risk and return with regards to assets within a well-established and competitive market, such as the UKs, is that ‘the reward to risk ratio must be the same for all assets in the market’. The line used to plot the expected returns and the beta coefficients of investments is crucial as it provides the tool to analyse the relationship between systematic risk and expected return and is called the Security Market Line (SML).

All assets in the market must plot on the SML, the beta for the entire market (βM) must also be 1 (the value of average systematic risk through complete market diversification), therefore analysing the SML slope to determine the entire Market Risk Premium we can see that:

Figure A-3 below shows that the market risk premium on a portfolio (slope of the SML) is the difference between the expected market return and the risk free rate and effectively informs what the standard risk rate is for bearing risk investing in the market.

Figure A-3. The Security Market Line.

http://i.investopedia.com/inv/articles/site/portmgmt17c.gif

Source: This SML diagram is the property of http://www.investopedia.com, accessed on 01 Jan 13.

Reference For Business (2013) ‘Assumptions of the CAPM Model’. Available at http://www.referenceforbusiness.com (accessed on 01 Jan 13).

The Capital Asset Pricing Model (CAPM). The CAPM analyses an investments expected return (E(Ri)) and the beta (βi) and plots them on the SML, as the reward-to-risk ratio is the same as the markets. This can be shown by the following equation and diagrammatically in Figure A-4:

Figure A-4. The Capital Asset Pricing Model.

Capital Asset Pricing Model (CAPM)

Reference For Business (2013) ‘Assumptions of the CAPM Model’. Available at http://www.referenceforbusiness.com (accessed on 01 Jan 13).

Or to determine the expected return on an investment (E(Ri)):

In summary the CAPM shows that the expected return on an asset depends upon:

The amount of systematic risk, measured by the assets βi relative to an average asset.

The risk premium reward for the systematic risk, measured by the market risk premium [E(RM)-Rf].

The time value of money, measured by the risk free (Rf) rate of return for the possibility of investing without taking any risk.

General

Shareholders have lost a significant amount of money on inadequately managed investments in the last 2 decades. In response to this, the UK government Turnbull report was produced on the request of the London Stock Exchange to offer guidance on an internal risk-based control and review system to enable a review of its effectiveness. UK company boards are now mandated to complete strategic risk management (Environmental Risk Management (ERM)) in order to ensure they attract investors (Emblemsvag & Endre Kjolstad, 2002).

Variance

Variance as a measure of risk is only accurate for the particular time it was assessed and does not allow for the analysis of a company’s long term risk taking strategy (Lehner, 2000).

Risk Evaluation

Risk is perceived differently to everyone depending on their gender, age and culture. Women are generally more risk adverse then men and more experienced managers are also more risk adverse than those with less experience (MacCrimmon & Wehrung, 1986). When analysing cross-cultural risk perception on financial investments, Weber and Hsee found that across the four country cultures examined (America, German, Polish and Chinese), the distribution of results were effectively the same. All cultures were perceived to be risk adverse and although the Americans had the greatest degree of risk aversion and the Chinese the least degree of risk aversion, the resultant trend was not significant (Weber & Hsee, 1998).

Risk Analysis

Most forms of risk analysis requires some form of assumption, calculated decision or best guess in order to evaluate a likely outcome, therefore are susceptible to error.

Analysis on which investment to undertake will be taken at senior company management level and will require a comparison of options. Decisions at this level eventually require intuition and subjective judgement (Isenberg, 1984). Decision makers’ attitudes towards risks are not the only influence on strategic decision making and risk taking, specific company and environmental factors are equally influential and essential. Strategic decision makers are also responsible to board members, shareholders and company employees and each groups potential outlook and perspective on risk and appropriate investment often differ (Lehner, 2000).

Risk analysis whether it is in project, finance or company strategy and planning does not provide solutions to problems, just identifies areas that require further action, monitoring and controlling. As an investor in an asset, you have no influence on how the asset is managed; therefore, have no control over any outcome other than when to withdraw your investment.

CAPM Analysis

Bali and Peng (2006) analysed the aggregate stock market to assess its volatility and determine if it accurately reacts to company and market risks. The analysis captured high frequency data to establish a more precise measure of market risk, but found that any difference using this method to that of the CAPM was insignificant, but potentially useful if this method was used in conjunction to support a decision making process (Bali & Peng, 2006).

Investigating if all companies use the CAPM as the industry median reference point for decision making, Lehner’s (2000) analysis determined that in general they do. The study found some evidence that certain companies create their own individual company reference points based on 2 specific reasons. Firstly companies who consistently produce high annual performance returns may attempt to manage the ‘over positive’ expectations of stakeholders by creating an independent variation reference point. Secondly, a company that is struggling to remain afloat may set independently low reference points, as company survival may be seen as more important than its perceived performance. In general it was suggested that the CAPM reference points across the market are a little conservative, making actual company performance appear more positive (Lehner, 2000).

Diversification

When analysing diversification within company’s strategic decision making approach, it was seen that the main reason for most conglomerate mergers is risk reduction. Menon and Subramanian (2008) suggest for a corporate manager that, whilst diversification across a variety of industries reduces the industry specific risks a company may have, the negative effect is that the company does not assimilate skills and knowledge of an industry specific area as quickly. In this situation, the opportunity to develop, learn and improve processes and therefore reduce risks through greater integration of industry specific knowledge is lost or slowed (Menon & Subramanian, 2008).

Economic Influence on Asset Risk

Purda (2007) examined if a country’s financial system influenced how the country’s asset risks are judged by investors. Purda found that country’s with a strong banking and financial systems are seen to pose a lower credit risk and, therefore, the assets from the country had higher credit ratings. This positively influences how investors from inside and outside the countries perceive these assets and led to the development of a specific measure of a country’s risk or creditworthiness. Each country’s credit worthiness is impacted by factors such as its economic conditions, political risk and financial policies (Purda, 2008).

Conclusions on Risk and Return Trade-Off

The referenced financial studies agree that the industry mean reference point for investment risk, CAPM, offers an accurate appraisal of an investment assets level of risk and that diversification of portfolio can eliminate systematic risk, for the majority of risk adverse investors. It is natural for most investors to consider risk as a purely negative term, however, risk must also be seen as an opportunity for potential higher returns on investment. In addition, concentrating an organisations portfolio of investments in the same market area allows greater knowledge sharing, efficiencies and development opportunities to be achieved at greater speed, which is not possible through a diversified organisation portfolio. Throughout the different analysis processes reviewed, all required some form of assumption, calculated decision or best guess in order to evaluate a likely outcome, therefore are susceptible to error. Standard deviation is employed within some analysis, to calculate the variation from the expected return value, and offers a method of determining the level of expected accuracy.

Annex B

Monte Carlo Simulation Supporting Analysis

The results from financial appraisals influence company decision makers on whether or not to invest large sums of company finance in a certain investment, development or project, yet most financial appraisals provide positive net present values based on estimator judgements without any indication on prediction certainty. Quite often estimators get elements of these appraisals wrong and the output fails to deliver the expected benefits (Lock, 2007). There are 2 common methods to analyse certainty within these estimators’ judgements, sensitivity analysis and Monte Carlo simulation, however this analysis concentrates on the latter.

Monte Carlo is a computer generated probability simulator used to help more accurately forecast the impact of risk and uncertainty. Although this simulator was originally developed for the project management industry to determine the probability of finishing a project on time, it can be used in a number of different fields including financial and cost forecasting. Monte Carlo uses both standard deviation (a concept used within measuring risk and coefficient of variation) and error (a component of the CAPM model used for measuring risk) within the simulation.

Monte Carlo in Project Management

Monte Carlo simulations were developed for the Project Management industry to help determine if a project will finish on time, however, the same principles are used to predict the probability of the project cost (i.e. being under or over budget). Both of these factors are analysed, amongst others, within the risk quantification process of PRAM. When the Monte Carlo method is used, the project manager is empowered with the necessary evidence to ensure appropriate finance and contingencies are attributed to delivering the project (Kwak & Ingall, 2007) and adequate project delivery duration is allocated.

Monte Carlo in Finance

Monte Carlo simulation can be used when making capital budgeting and investment decisions on potential future programme and portfolio management strategies. Simulations assist decision makers in choosing potential investments and projects by exchanging approximations of net cash flow, for each year, with probability distributions for each element affecting net cash flow. From this, decision makers can develop a range or distribution of possible future NPVs of an investment instead of a single value. This range of results helps the decision maker understand the risk or uncertainty levels in the potential investment, but also how likely the specific outcome will be. This is useful when comparing investments with the same mean NPV but differing levels of variance in NPV distribution (Kwak & Ingall, 2007).

Monte Carlo Model

The Monte Carlo simulation model is based on the following relationship:

%

Where:

= Probability (%)

= Uniform Monte Carlo estimated expected value after simulation i.e. expected result after simulation

= Number of iterations

= continuous uniform distribution variable – generates a complex random sample distribution

= Expected value or average value of a function

= Total error

= Standard Deviation

Monte Carlo Process

Where possible, it is advisable to include key decision makers and stakeholders in the process of producing the variables, maximum and minimum parameters and distribution pattern to be used and actively simulated. In doing so, higher stakeholder confidence levels are obtained for the results produced, it also breads some ownership of the outcome and helps these critical members understand some of the key factors such as risk that are involved within the potential investment or project. The following stages are required in order to produce a Monte Carlo simulation of each variable or parameter required to be analysed.

Stage 1. Identify the particular variable or series of inter-related variables you require to analyse, this may be critical project risks or assumptions of expected return on an investment.

Stage 2. Define the expected range limits (maximum and minimum value) for the variable being analysed, using historical data, past experience or expertise in the particular variable area.

Stage 3. Allocate the probability of occurrence, based on the expected probability distribution i.e. normal, uniform or triangular depending on the variable. All these have a defined range with a varying concentration towards the centre. A normal probability distribution (bell curve) is the most common and represents a uniform distribution and is depicted in Figure B-1.

Figure B-1. Example Normal Probability Distribution Curve for Two Quantities, Highlighting its Mean (µ) and its Standard Deviation (σ).

http://www.math.armstrong.edu/statsonline/5/curve.gif

Source: This distribution curve example is the property of www.math.Armstrong.EDU, accessed on 01 Jan 13.

Reference For Business (2013) ‘Assumptions of the CAPM Model’. Available at http://www.referenceforbusiness.com (accessed on 01 Jan 13).

Stage 4. Calculate the standard deviation (σ) between the maximum, minimum and average value of the variable. Standard deviation determines the amount of variation or how spread out the distribution is from the mean or expected value. The smaller the standard deviation from the mean, the more closely the simulated results are to the average, giving higher confidence of the actual end value.

Standard deviation () is calculated on Microsoft Excel by:

Or mathematically by:

Where:

= Standard deviation

Σ = Sum of range, using the mathematical term sigma

= Value of samples

= Mean of samples, sometimes referred to as mew (µ)

n = Number of samples

Broken into stages:

Firstly we find the mean average of the sample numbers, as an example we will use the following range of numbers:

5 (or x1), 2 (or x2), 3 (or x3), 4 (or x4), 6 (or x5)

Secondly, we find the squared variation of the sample number from the mean value, by taking the sample number, subtracting the mean and then squaring the value. Note that the direction of the number disappears when squared (i.e. the negative factor disappears when squared):

Thirdly, find the mean value of the results of the variations from above:

As we squared the sample variation numbers, to find the standard deviation we need to square root the result.

This whole process can be simplified into the single equation:

Or

Where:

n = Final number value in the series of samples

i = Starting number value of samples

Σ = Sum of range, using the mathematical term sigma

= Value of samples

µ = Mean of samples, sometimes referred to as mew (µ)

N = Number of samples

Stage 5. Define an acceptable total error for the simulation; an initial error figure of 2% (average variable range value ÷ 50) is an acceptable starting point and is calculated using the standard deviation of the variable and the number of simulated sample iterations.

To determine an absolute error () of 2%, divide the average variable value by 50 (i.e. 2% = ):

Stage 6. Calculate the minimum number (N) of randomly generate iterations required to generate the most accurate resultant value with an error factor of 2%:

Where:

= Number of iterations

= Standard Deviation

= Absolute error

Stage 7. Randomly generate the required number of samples to provide the expected value, each randomly generated number represents one single probability of the simulated event occurring. Figure B-2 illustrates an example of the typical range and distribution of expected exam results.

Figure B-2. Example of Expected Frequency Histogram Showing the Range and Distribution Results.

Frequency histogram

Source: This histogram is the property of http://www.microbiologybytes.com, accessed on 01 Jan 13.

Reference For Business (2013) ‘Assumptions of the CAPM Model’. Available at http://www.referenceforbusiness.com (accessed on 01 Jan 13).

Stage 8. Define the mean average total simulated variable value (this is your true expected value) based on all results.

Stage 9. Calculate the median value of the randomly generated simulated numbers. Note this is a process check as the median figure should be close to the mean average figure generated above otherwise there is an error in the random number generation process.

Stage 10. Calculate the new standard deviation across all generated results and then re-calculate the revised true error factor based on the new standard deviation and the exact number of samples taken.

Stage 11. We can now revise the true error figure based on the revised standard deviation figure. The true error () is calculated by:

Where:

= Revised standard deviation

= Number of iterations generated

Stage 12. Represent the true error figure as a percentage:

Monte Carlo Critical Analysis

The following critical analysis has been based on the ‘standard’ Monte Carlo simulation model, although it is recognised that variations exist which attempt to place greater control within large sample group sizes by weighting (Glasserman & Yu, 2005) or by controlling variants in Quasi-Monte Carlo simulation (Hickernell, et al., 2005).

PERT was the previous method of choice for evaluating project schedule networks, however this method does not statistically account for the impact of one schedule task on the critical path and therefore generally underestimates project duration. Monte Carlo runs all potential scenarios therefore accounts for these path convergence implications (Kwak & Ingall, 2007).

When using Monte Carlo to analyse a large number of risks in a project, the shear quantity of results produced, to determine the most likely solution, make it difficult to determine which of the risks are the main contributors to variability of output and therefore more difficult to determine where to put additional management controls to reduce the risk (Turner, 1999).

There is a danger of blindly accepting Monte Carlo simulations with extended project delivery durations without scrutiny. However, project managers should be aware that the simulation will complete numerous simulations unintelligently, without any adjustment provided for the real life situation, where the project manager would potentially intervene, complete remedial action and potentially bring the project delivery back on schedule (Kwak & Ingall, 2007). Similarly, in finance if the expected results are not being realised, decision makers could abandon the project and cut its losses, therefore the simulated worst case scenario may never come to fruition. Options to modify projects are known as ‘real options’ (Brealey, et al., 2011).

Monte Carlo simulations are only as accurate as the information and parameters inputted into the simulation process. If the project information and delivery model is lacking then the simulations will not accurately reflect actual delivery, therefore are susceptible to error (Kwak & Ingall, 2007).

Monte Carlo does not account for the fact that organisations are seldom able to appoint a dedicated project team to the delivery of a project and resources are often distributed and diluted across numerous projects and business as usual activities (matrix organisation), therefore variances such as these need to be accounted for during the simulation model design (Button, 2003).

Although the benefits of Monte Carlo simulations are clear, the process has not been popular with project managers due to the perceived amount of statistical expertise required to generate and interpret results (Kwak & Ingall, 2007). Recent off-the-shelf software packages are now available which integrate with other project management software making the process more user friendly (Lock, 2007).

The technique employed within Monte Carlo is similar to that of the well-used weighted average (or 3 point estimating) technique used in estimating, scheduling and budgeting. This technique takes a value for the most optimistic outcome, the most pessimistic outcome and the most likely outcome to produce a weighted average result (weighted average involves adding together the most optimistic, four times the most likely and the most pessimistic results and divides by 6) (Gardiner, 2005). Although useful for simple variables where the range of expected outcomes is small or well defined, it is less useful in more complex situations and has a tendency to produce overly optimistic or biased results.

Conclusions on Monte Carlo

Monte Carlo simulation is an extremely useful technique for analysing risks, costs and schedule related uncertainties, with results created by numerous simulations rather than weighted average or a basic expected return technique. The range of results produced helps decision maker understand the risk or uncertainty levels in the potential investment, but also how likely the specific outcome will be, whilst removing any kind selection bias. The benefits of the Monte Carlo simulation are numerous; however, the reliability of the outputs, as with all computer generated simulations, is dependent on the accuracy of the information and parameters inputted. Monte Carlo simulations are an unintelligent process and do not allow for the human intervention or ‘real option’ modifications that would occur during investments and projects.

Annex C

Short Term Decision in Financial Planning Supporting Analysis

Short Term Financial Planning Introduction

Short-term planning is often called ‘cash budgeting’, where the most important current assets are cash, marketable securities, accounts receivable and inventories. The most important liabilities are short-term loans and accounts payable. Managers involved in short-term financial decisions often only concentrate on the next 12 month forecast, ensuring the company has enough cash to pay its bills and making sensible short-term borrowing decisions and lending. Short-term focusing financial managers very rarely look at the intricacies involved with long-term financial strategies, options, planning and goal setting (Brealey, et al., 2011).

The basic principle behind short term financial management is to keep the level of investment in ‘holding cash’ and short term assets as low as possible, yet enable the company to continue to operate efficiently, conduct transactions and ensure banking services are funded. To achieve this, organisations must invest any ‘idle cash’ in short-term asset investments, bought and sold in the financial markets. As previously detailed in the risk analysis Appendix, a diverse portfolio of investments or short-term marketable securities offer little or no systematic risk (diverse portfolio) or are risk free (marketable securities) (Hillier, et al., 2011).

Short-Term Financial Planning Relationships

In order to evaluate short-term financial planning relationships I must first provide some basic principles.

Cash

Net Float. The difference between a company’s available balance and its ‘book balance’ is the ‘net float’. Net float accounts for uncollected finances submitted to the bank that have not yet cleared, therefore the company financial manager must always plan work on the collected cash balance and not the book balance to ensure sufficient cash available in the bank and therefore avoid financial penalties.

Improving Cash Balance. A historical method of improving the cash balance figure was to speed up the payment collection process and slow down the payment process, however, electronic bank transfers now limit this application to management the collection and disbursement of cash.

Target Cash Balance. Companies require to hold a temporary surplus of cash as a contingency reserve to finance planned expenditures throughout the year and balance seasonal and cyclic events. In order to maximise the return on this ‘idle cash’, companies tend to invest in short-term marketable securities, which it buys and sells to ensure the best balance of trading cost against opportunity costs. This balance is called the ‘target cash balance’.

Cash Management Systems. The Baumol-Allais-Tobin (BAT) and Miller-Orr model are examples of 2 cash management systems designed to manage cash inflows and outflows that may be predictable or randomly fluctuating from day to day. Figure C-1 and C-2 below highlights the difference analysis techniques used within each model.

Figure C-1. Example of BAT Model.

Starting Cash (C)

Cash Expenditure

C/2

Ending Cash

0

0

1

2

Weeks

3

Average Cash

4

Figure C-2. Example of Miller-Orr Model.

http://www.themanagementor.com/enlightenmentorareas/finance/imgs/millerorr.gif

Source: This balance table is the property of http://www.themanagementor.com, accessed on 01 Jan 13.

Reference For Business (2013) ‘Assumptions of the CAPM Model’. Available at http://www.referenceforbusiness.com (accessed on 01 Jan 13).

The 2 cash management models differ in their complexity but are based on the same following principles:

The higher the interest rate, the lower the target cash balance should be.

The higher the order costs, the higher the target cash balance needs to be.

The Miller-Orr model measures and analysis the effect of uncertainty and variations in net cash inflow using standard deviation [1] , the greater the uncertainty, the greater the difference between the target and minimum balance level.

Credit and Working Capital

Credit and working capital require an understanding of the following concepts:

a. Terms of Sale. The terms of sale are the conditions in which a company sells its goods for cash or credit.

b. Credit Analysis. Credit analysis is the process to determine the probability of whether a customer will pay for the goods sold under credit.

c. Collection Policy. Collection policy is the method or policy the company establishes in order to collect the cash after authorising credit.

d. Credit Period. Credit period is the length of time for which credit has been granted.

e. Invoice. An invoice is the bill given to the purchaser for the goods provided by the seller.

f. Cash Discount. Cash discount, or sales discount, is a reduction in price used as an incentive for prompt payment.

Credit Policy. The decision to grant credit or not can be calculated using a Net Present Value (NPV) format and depends on 5 factors:

Revenue Effects. By allowing credit there will be a delay in the revenue collected by the sale of the goods. This delay in revenue may be balanced by a greater quantity of goods sold and a potentially higher product selling price.

b. Cost Effect. The cost effect is the immediate cost of the goods sold which is required to be met by the seller in order to produce, procure or provide the product.

c. Cost of Debt. When a company approves credit, it must finance the produce or product supplied (cost effect) and therefore may incur short-term borrowing costs itself to finance the credit agreement.

d. Probability of Non-Payment. Unlike when a product is sold for cash, when authorising credit, there is a percentage probability that the credit buyers will default or not pay.

e. Cash Discount. Cash discounts may be used as part of the credit terms as an incentive for early repayment.

In order to evaluate if a proposed credit policy is financially viable from an existing cash purchasing customer the NPV can be calculated by:

Where:

P = Price per unit

v = Variable cost

Q = Current quantity sold per month

Q’ = Quantity sold under new policy

R = Monthly return required

In this instance the break-even point is equal to an NPV of 0, therefore transposing the equation we can determine the break-even point as:

The following NPV can be calculated to determine when credit should be granted, by analysing all costs involved with credit. Note, the equations differ for a one time sale to repeat business:

The NPV of granting credit on a one time sale is:

Where:

V = Variable cost per unit.

= Probability or percentage of customers who will not pay

P = Cost per unit

R = Required return (payment per month)

The NPV of granting credit on repeat business can be rearranged based on the assumption that the customer paid for the last business deal therefore will continue to pay, i.e. not default, and continue to be a customer forever:

The key factors to granting credit are the product selling price and the opportunity of achieving repeat business.

Optimum Credit. The optimum amount of credit a company can offer depends on the company’s internal policies and market competition, however, must try to minimise the credit carrying costs and the opportunity costs should the sale be lost due to refusing credit.

Ageing Schedule. The collection policy requires the monitoring of credit payments made on goods sold, against the particular credit policy. An ‘ageing schedule’ can be used to monitor the percentage of payments made on goods to highlight payment delays or failures.

Inventory Management

Inventory Costs. Inventory costs can represent a large proportion, up to 15%, of a company’s investment. Both credit and inventory prices are used to maximise sales and it is imperative that the processes are aligned and coordinated. For accounting purposes, there are 3 types of inventory categories:

Raw materials – are the component parts to produce the product to be sold.

Work in Progress – are the unfinished products during the manufacturing process.

Finished Goods – are the products ready to be sold.

There are 2 basic costs associated with inventories:

Carrying Costs – are all the direct and opportunity costs associated with keeping the inventory items in stock, such as storage, insurance, losses due to depreciation and theft and the opportunity cost of the capital invested in inventory stock and not the market.

Shortage or Restocking Costs – are the costs associated with having insufficient stock inventory to complete orders. Effective inventory management requires the minimum balance between carrying costs and restocking costs.

Managing Inventory Levels. The Economic Order Quantity (EOQ) is the minimum amount of stock required to be reordered that minimises the total inventory costs and can be determined using a method such as the ABC approach. The ABC approach to inventory management breaks the inventory into 3 or more categories. The basic principle is that a small portion of the inventory stock may have a high financial value and therefore a percentage breakdown of stock, per category, needs careful consideration. Category ‘A’ inventory is high value items that are required infrequently and stock that requires close monitoring, therefore the percentage of this stock should be minimised e.g. 10%. Category ‘C’ inventory are low cost items that must be kept on hand and are critical to production (such as nuts and bolts), therefore constitute the majority of the inventory e.g. 60%. Category B inventory items are items somewhere between the Category A and C items.

The EOQ can be determined using the following equation:

Where:

Q* = Number of units.

T = Total number of unit sales per year.

F = Fixed cost per order.

CC = Carrying costs.

Inventory stock management is important to ensure a minimum stock level is kept to prevent loss of customer sales due to product being out of stock, by means of a ‘safety stock’ level. Stock management also needs to take into consideration the inventory stock ordering lead and delivery timelines required to replenish the inventory, therefore these reorder points also need to be factored into the stock management process.

A number of computer based stock ordering systems have been produced to ensure inventory material stock is ordered at the optimum time. These systems can be placed under the following categories:

Materials Requirement Planning (MRP). MRP is a system based on identifying all inventory elements that make up a finished goods product and keeping sufficient amounts of this stock to finish an entire product item. Ascertaining the quantity of finished products required helps establish the amount of raw materials required to be kept in stock. This process is highly beneficial when the finished product is complex and made up of a large number of components.

Just in Time (JIT) Inventory. The JIT inventory management approach aims to minimise inventories and maximise turnover. The result of the JIT system is that inventory stock is reordered frequently, therefore requiring a high level of coordination and cooperation with suppliers to make the system work effectively.

Short Term Financial Planning Critical Analysis

A company’s reserve of cash holding allows it time to react to short term crisis more flexibly, which may be important if the company find it difficult to raise cash at short notice. Large cash holdings, however, can lead to complacency and managers may not strive for optimum performance and profit, based on the security blanket of large cash holdings (Brealey, et al., 2011).

Financial managers must ensure they explore the consequences of different assumptions, or financial risks, which determine the optimum amount of cash holdings, interest rates on finance loans and returns from marketable securities etc. Computer modelling of these assumptions of risk can be used to help determine this uncertainty (Brealey, et al., 2011). As we have already determined, computer based modelling programs require some form of assumption, calculated decision or best guess in order to evaluate a likely outcome, therefore are susceptible to error (Brealey, et al., 2011).

Kallberg et al (1982) detail the extent of difficulty in predicting the short term cash management in order to adjust the organisations asset and liability mix in order to minimise the net cost of cash surpluses and deficits over a predetermined planning period. They note that there are a number of complex planning models in industry, but all contain high degrees of uncertainty (Kallberg, et al., 1982).

Managers may perceive their position within a company as short-term, before they move on to another company or employment opportunity, and their reputations may be based on their current short-term actions. In this situation, there is a risk they will focus on short-term decisions rather than long-term company benefits. This is known as short-termism (McLaney, 2011).

Company legislation and stakeholder interests are the priority and driving forces behind the extent of working capital, cash flow management and capital budgeting, in which cost of capital calculations are a fundamental part encompassed within short-term planning. The coordination and control of these elements is best achieved through planning down control and is seen as preferable to financial decision makers as it reduces the organisational tendency to fragment towards bottom up financial planning (Morden, 1984).

Although delivering programmes or portfolios of projects is aligned to long-term financial planning, decisions to proceed are required at key intervals which are normally based on specific short-term life cycle results or returns based on long-term assumed financial returns. The decision to continue with the programme or portfolio of projects may depend on strategic short-term interpretations of results.

Morgan (1993) links strategy planning to long-term decisions and operational planning to short-term decision making. He warns that implementing any new control systems, at the operational level, is a complex task which impacts across the organisation and therefore requires all management team members to be involved (Morgan, 1993).

Short-term financing needs to be linked to long-term planning decisions, as all business require capital invested within the company for inventories, machinery replacement and maintenance and all other assets to run a business effectively. These assets can be financed by either short or long term sources of capital, depending on the company’s strategy (Brealey, et al., 2011).

The JIT approach was pioneered by Toyota, in Japan, where the organisations Toyota deal with to supply spares are tightly integrated and work closely to achieve the required high degree of coordination (Hillier, et al., 2011). Toyota are able to operate successfully with such low inventories only because it has contract strategies to ensure that strikes, delivery failings and other resource delivery risks do not halt production (Murphy, 1999), contracts such as these are difficult to implement or have highly inflated delivery prices within the UK.

A method to assist in controlling raw material inventory costs is through long-term fixed price contracts as this helps to produce stability in planning and product pricing and helps to project product costs several quarters into the future. In order to do this long-term planning needs to be coordinated with the short-term planning.

Conclusions on Short Term Financial Planning

Successful short-term planning requires a detailed analysis of the most likely annual forecast and financial managers must ensure they keep the level of investment in ‘holding cash’ and short term assets as low as possible, yet enable the company to continue to operate efficiently with some degree of flexibility. It is imperative that Short-term financing decisions are linked to long-term planning decisions, and vice versa, to ensure the business runs and develops effectively. To aid in planning, financial managers often use computer based modelling programs, however all require some form of initial assumption, calculated decision or best guess in order to evaluate a likely outcome, therefore are susceptible to error. Simulations such as the Miller-Orr model measure and analyse the effect of uncertainty and variations in net cash inflow using standard deviation, and offers a method of determining the level of expected accuracy between the target and minimum balance level.