Following the end of merger and acquisition (M&A)-sourced expansion growth, Russia’s corporates focused on the optimisation of efficiency, including the planning and forecasting function. In a resource-orientated economy, where most major energy and natural resource corporations emerged from former Soviet industry ministries, the budgeting approach to forecasting was the only approach to operational and financial planning for many years. However, the budget approach on its own was not working anymore -corporate treasuries needed something new to forecast financial results; something with more analytical ability. A lack of a market-forecasting capability within finance functions, coupled with an aggressive M&A policy, brought some corporations to the verge of bankruptcy.
Commodity exports comprise a major percentage of total revenue for most resource corporations in Russia. As a primary tool of market risk management, these corporations relied on high correlations between US dollar (USD)-denominated commodity prices and the rouble (RUB)-USD exchange rate. These natural hedges were stable in the long run, but during certain short-term periods significant deviations from long-term mean levels significantly increased liquidity risks. That is why corporate treasuries, which are typically responsible for liquidity management, have recently focused on advanced methods of forecasting and associated tools for market and liquidity risk management.
In large corporations, complex corporate structures and excessive bureaucracy slows down the information flows within the company. Corporate treasuries have to internally develop the cash flow and liquidity forecasting function, parallel to similar processes in financial planning departments. Cash flow and liquidity forecasts made by corporate treasury typically have more analytics, such as breakdowns of cash flows and liquidity by currency (two, three or four major operating currencies), a breakdown of monthly cash flows by working days and various market scenarios (positive, neutral and negative market outlooks on commodity prices, foreign exchange (FX) rates, sales volumes, etc).
- A breakdown of cash flows and liquidities by currencies helps corporate treasuries re-evaluate liquidity forecasts internally after market FX rate changes, and prepare for possible FX rate fluctuations during times of high market volatility.
- Using daily cash flow forecasts – instead of monthly ones – helps corporate treasuries be prepared for intra-month cash shortages, which are not evaluated in financial plans with monthly figures.
- Using different market scenarios helps corporate treasuries be ready for executing two priority tasks: ensuring that the level of cash on the current accounts is sufficient for a stable payment process, and earning interest revenue on excessive liquidity in line with current market deposit rates. The second task is currently becoming more important, as shareholders begin to view corporate treasury as a profit centre rather than a cost centre.
In terms of measuring the effectiveness of forecasting methods and function, calculating forecasting accuracy is the ‘must have’ method of assessment. It might sound strange, but many of the largest Russian corporations do not periodically measure the accuracy of made forecasts: neither accuracy of results of forecasting (such as cash flow and liquidities), nor accuracy of inputs (such as production and sales volumes) provided by other departments and used in forecasting of cash flows and liquidities. Although the heads of corporate treasuries are mindful just how important performing periodic measurements of forecasting function key performance indicators (KPI) is, some barriers nevertheless prevent it. They include an insufficient level of automation of forecasting algorithms and data collection, as well as a lack of a formalised and working system of KPIs in these areas.
- A structured database will make it easier to keep both prior forecasts of individual operational or cash flows made by the corporation’s other departments, as well as earlier liquidity forecasts made by corporate treasury. This analytical toolset will help the forecasting specialists within corporate treasury calculate the accuracy of provided inputs and make forecasts in a convenient manner, analyse the seasonality of cash flows and detect other patterns which exists in cash flows.
- A formalised KPI system, approved by top management, for forecasting cash flows and liquidity can form the basis for motivating other corporate departments to prepare their best forecasts of inputs and to timely provide these to corporate treasury.
Lack of accuracy has a price. The other side of the coin, improving forecasting efficiency, could easily be utilised by corporate treasury:
- Quantifying cash deficits and their probability could help negotiate credit lines in advance and under improved terms (as opposed to opening such credit lines in times of financial distress). In addition, any early identification of a deficit may lead to revision of the budget towards cost optimisation.
- Forecasts of liquidity, historically prepared with high accuracy, could provide a basis for planned decreasing of the cash reserve level and putting more excessive liquidity in mid and long-term deposits. The current difference between the market overnight deposits interest rate (IR) and the six-month deposit IR is more than 1%. But what is more important is the fact that banks typically apply 40-60% discounts for short-term market rates. These two points could jointly give an additional 4-5% in interest revenue earned on excessive liquidity placements.
Among other important problems characteristic of the forecasting process is a lack of relevant and timely input data for planned financial and investing deals. Details of anticipated- but-not-yet-contracted M&A and financing deals are typically kept secret and regular forecasts are not provided to corporate treasury. As a result, treasury may be forced to prematurely return favourable long-term deposits or other invested cash.
A similar situation exists with derivatives. The essence of hedging is the stabilising of cash flows or values; hence it is crucial for corporate treasury to have information on conducted hedged deals. While FX and IR derivatives are typically well-known within corporate treasury, derivatives embedded in commodity trade contracts (and other aspects of contract formulas) are usually hidden and are not reported to treasury, which creates uncertainty when it comes to forecasting operational revenue.
The level of forecasting automation is also an issue. MS Excel still represents the main instrument for preparing cash flow and liquidity forecasts, even though it has limited analytical functionality and is not convenient for a multi-user approach to preparing forecasts. MS Excel models are not formalised and well-tested on historic data.
Leading forecasting practices employ many advanced forecasting approaches, which include:
- The implementation of specialised treasury software when preparing and analysing forecasts of cash flows and liquidities. Modern treasury management systems (TMSs) typically have functionality for both direct and analytical forecasting methods and are integrated with market-data feeding systems such as Bloomberg and Thompson Reuters.
- Moving from calendar year forecasts to rolling forecasts with forecast horizons of up to 12 months. This helps overcome the ‘end of year’ problem, when the effective forecasting horizon is significantly shorter than the nominal, and managerial decisions on cash management are made on intuition, rather than liquidity projections.
- Adopting advanced forecasting methods, such as the Monte Carlo simulation in preparing joint probabilistic scenarios for market-risk factors and regression models for market and corporate data. Corporate treasuries use a designated team of quants to prepare market forecasts for commodity prices, FX and interest rates.
- Increased integration of cash flow and liquidity forecasting with the market risk management and hedging functions. Corporate treasurers and risk managers have begun to use common IT systems for market forecasting and financial instrument valuation, and to organise regular knowledge-sharing meetings.
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