Making Cash Flow Forecasting More Effective

Businesses can thrive when they generate free cash flow or ‘owner earnings’. A well-conceived cash flow forecasting process will help measure progress towards business strategy goals, drive confident implementation of investment and debt strategy, and signal need for alternative paths in turbulent market environments. To make cash flow forecasting more effective, treasury leaders can look at shared resources to augment the business case for a forecasting process, promoting use of internally-generated or independent local market reports balanced with concentration techniques, increasing transparency of liquidity policy governance and operations, and leveraging complex forecasting models and simulations.

Architecture and Data Capture for Cash Forecasting

Calculating where paying the bills ends and the amount of cash an owner can pocket is a key treasury function. Static cash flow forecasting begins with development of an information architecture. At a minimum, financial data items are defined for reporting purposes, collected as transactions occur and maintained through an enterprise resource planning (ERP) or other automated system. Standard formats for cash budget and cash worksheet are defined and used as a static benchmark for projecting cash flows. These instruments, in the hands of an able local staff, can be used quite reliably for daily, weekly and monthly cash management.

Companies that only have adequate enterprise project discipline can easily design and build a daily collection system, empower field staff with a local market condition reporting mandate, deploy standard statistical tools to help assure forecast quality, establish a periodic market action meeting and create a policy review cycle with appropriate research documents that builds confidence and synchronicity across the treasury function, if not the enterprise.

To reach the next level required for advanced statistical analysis and forecasting, an information architecture must include definition of financial and accounting data series (e.g. Statistical Data and Metadata Exchange (SDMX)1). This will likely include protocols for extracting transactional data and combining it with other variable economic datasets for manipulation in a data mart or other platform to facilitate use of statistical methods and tools.

Organisations may be resistant to added labour and software costs for a statistical forecasting process geared solely to maximise treasury efficiency and inform business strategy development. As a result, a persuasive business case normally includes technology and labour cost mitigation, beginning with the use of shared resources. For example, the same platform and professional talent can support forecasting needs across the enterprise.

Although, depending on size, staff may be assigned to work primarily in diverse business units (sales, marketing, manufacturing, treasury, etc.) and physical locations, each function has important forecasting requirements and can provide complementary, if not direct data flows into the respective models. Coordination of a robust enterprise analytics function can be achieved economically through a matrix director with minimal duplication of resource roles.

As working capital management objectives, investment policy, and debt policy are executed, gaps in information quality and/or availability may appear and must be addressed. Treasury policy seeks to align investment and debt management finance objectives with business strategy. Information quality and availability gaps must be resolved through a best practices assurance process – at least as rigorous as any used for public reporting!

Organisations growing beyond headquarters either organically or through acquisition must charter far-flung teaming to perform the cash management function to standard. This objective may be compromised where a subsidiary has certain legal requirements or there are insurmountable cross-border regulations. Often when entering new locales, practices implemented at HQ need to be refined to accommodate needs of the local business unit. Local laws, business customs, and business strategy may conflict with ‘steady state’ at HQ.

In particular, business strategy for the new unit, and consequently some procedures, may ‘push’ to be done differently due to the nature of entering a new market, current size of the business unit, and competitive forces. On-boarding the local enterprise requires serious commitment, dedication and effort to training, learning, and adaptation. However, the payoff, in business unit growth, should be enormous for incorporating a new far-flung team member as another node of reliable data and local market insight not to mention, cash stream.
Finally, the availability of central sources of market information to use in benchmarking rates before executing investing or loan transactions is increasing.

Recent changes in money market rules in the US has led to the creation of a Resource Center by the Association of Financial Professionals (AFP).2 Tight credit markets have been difficult to penetrate with harsher terms and ongoing opacity. Those seeking credit have their work cut out for them to find and track loan market data. AFP also offers a central credit price benchmarking resource.3

Mechanisms for Governing Liquidity Policy

Liquidity policy is implemented through control of two primary ‘levers’:

  1. Cash reserves – too much undesirably lowers capital return ratios.
  2. Debt – too much is potentially fatal, ranging from raising the cost of capital to solvency issues. Both levers are constantly in motion, typically in non-correlated directions. Cash concentration is a technique that promises to make liquidity policy execution most efficient.

A well-designed and thoughtful operation of a robust cash flow forecasting system can be the steadying hand in a newly uncertain world. Cash forecasting can help identify needs and surpluses of cash, giving the liquidity policy team ample time to prepare the best market tactics. Indeed, many treasurers are faced with competing objectives and balancing between undesirable tradeoffs for both market decisions and internal relationships.

With cash concentration or reporting that bypasses standard distribution, a couple of caveats are in order:

  1. Concentration or non-standard reporting should not be used to exclude necessary involvement of stakeholders in policy development, execution, and compliance audit processes.
  2. As information gaps develop in governance processes, risks increase, particularly with respect to reputation.

Many look to central banks as a source of stability in the markets. Similarly, some routine bank governance practices can be instructive for the way they strike a balance in internal politics while achieving the benefits of cash concentration strategy in policy.

Organisations may benefit from constituting the policy team as a far-flung team with regular face-to-face meetings as opposed to emphasising an exclusive HQ focus. In the US Federal Reserve Open Market Committee4 processes leverage the intellectual diversity of its members. In part, this is done through the ‘Beige Book’, developed through a wide network of business contacts in each region with a summary of economic conditions in each district is produced in a collaborative process. Other summary reports are developed to offer current market environment analysis, forecasts, and policy options.

In day-to-day operations, another bank practice is to hold internal conference calls with diverse teams responsible for market operations, each offering its own analysis and forecasts for implementing strategic targets to achieve policy objectives.5 Any of these governance or operations practices can be tailored to improve what may seem to be uniquely intractable policy or operational execution issues of a business treasury.

It should be noted that the ability of policy executives to use forecasts to gain strategic flexibility in setting policy and executing liquidity objectives can be a key benefit when negotiating cash flow based loans and/or loan covenants with cash flow conditions. The difference between a successful treasury liquidity policy and a trip to the woodshed or worse, a solvency contingency, can be the degree of confidence a treasurer has in the cash forecast.

Understanding Statistical Forecasts

Recent market volatility surrounding Greece’s debt crisis has highlighted the difficulty in producing reliable cash forecasts. Many prognosticators can’t explain why the Greece debt crisis had not manifested itself in the markets sooner. Credit, currency, derivative, short-term income/debt and equity markets have simultaneously converged and declined for periods in response to debt crises. This leaves the treasurer whipsawed, with little margin for error in allocating investment and debt capital. The severest of market conditions seems to have rendered some forecasting systems obsolete or of little value in a low interest rate environment.

Reliable forecasting of multi-year cash flows affected by complex sets of economic, market, business strategy, and execution variables is difficult to achieve. Some compare the frequently unreliable forecasts into the future with the solid, reliable, periodic managerial balance sheet, cash budget projection and cash worksheet tools and ask “why bother?”.

Perhaps a better perspective is to identify some of the key factors that lead to error in forecasts or ambiguity in interpretation of forecast model results. Through a process of model development and examination, valuable insights affecting daily cash flow-working capital management and business strategy alignment can be developed that would otherwise remain hidden. Through analysis of changes in the prediction model, managers can better understand current market forces and adjust their efforts to meet working capital management objectives.

Most popular forecasting methods use multiple variables implemented through a combination of statistical regression and Monte Carlo simulation. Possible variables are analysed in a scatter chart format, tested for line fit, selected based on their descriptive or inferential (when using a sample) correlation/non-correlation, deviation, and ability to ‘explain’ variations in cash flow. Monte Carlo routines execute randomised trials using multiple variables with pre-set ranges to derive likely high, low, middle values for cash over a period of time. Results of Monte Carlo simulation can and should be subjected to statistical regression analysis to understand relevant characteristics of the model and its estimated values.

So, five quick items to improve your understanding of the role of statistical forecasting might be:

  1. Understand that economies and markets are complex systems that can’t be predicted within time frames relevant to cash generation accounting.
  2. The exercise of forecasting cash flows is dialectic: it informs business strategy and business execution informs it.
  3. Regressions done with a data population are imprecise in explaining completely the variation in the target – cash. Regressions done with a sample allow even less precise inference of the estimated variation in the target – cash.
  4. Choose to run multiple simulations using different randomised distributions for your model(s). In particular, normal distributions underestimate the likelihood of unlikely events and few models seek to account for or inform us about the network effects of unexpected market volatility.
  5. Simulations and models add and gain value through dissemination and discussion.

1 Implementations of Data Series – http://sdmx.org/?page_id=14

 

2 AFP Money Market Resource Center, http://www.afponline.org/moneyfunds

 

3 AFP Loan Market Data, http://www.afponline.org/loandata

 

4 Federal Open Market Committee, http://www.newyorkfed.org/aboutthefed/fedpoint/fed48.html

 

5 Open Market Operations, http://www.newyorkfed.org/aboutthefed/fedpoint/fed32.html

 

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