Forecasting in Russian Corporate Treasuries: Current Situation, New Trends
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).
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.
Lack of accuracy has a price. The other side of the coin, improving forecasting efficiency, could easily be utilised by corporate treasury:
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: