Overview
A common method of forecasting the liquidity posittion of a firm is to forecast the average or expected amount of cash that flows into or out of the firm.
The firm has multiple sources and use of cash over its operarting cycle, and identified as part of its working capital management process. The analyst builds an average forecast of the each cash flow over the next period (or periods).
Due to the linearity of the expectation, one can forecast each line item separately and then just add.
{% \mathbb{E}[c(t)] = Current \, Cash + \mathbb{E}[Cash \; from \;Sales_t] + \mathbb{E}[Cash \, from \, Receivables] + ... %}
here {% c(t) %} is the random variable representing total cash at time {% t %}.
The expected cash model might have models for the following sources:
- Sales
- sales are typically forecast as a part of
marketing
(see
demand forecasting
)
Nevertheless, not all sales are transacted in cash, some will occur using credit. This means that in addition to forecasting sales, the working capital analyst will need to forecast the amount of sales that will produce cash. - Accounts Receivable - accounts receivable are assets, such as sales conducted using credit, for which the company expects to receive cash shortly.
- Current Liabalities - are liabilities that the company owes. This account does not need to be modeled as a random variable as such, because the firm has a choice when to pay these liabilities. At best, the firm will have a date that it must pay each liability by, and can use this date as the pay date in any analysis of liqudity.
Forecasting Variance
Creating an expected value forecast of a firm's liquidty position can indicate whether a company can expect to be liquid in the next period on average, but doesnt give an indication as to the probability that the firm will be illiquid.
A simple way to calculate the probability of shortfall is to assume that the random variables in the liquidity calculation above are independent. Then, the analyst can apply the Variance of a sum formula to calculate the variance of the firms cash.
{% Var(\sum X_i) = \sum Var(X_i) %}
This can then be converted into a standard deviation, which can be used to show how many standard deviations away from the expected value
forecast the illiquidity event is.