Month: December 2018

An Empirical Study of Insurance Performance Measure

An Empirical Study of Insurance Performance Measure

In the ninth article from the (JACF Spring/Summer issue) this author applies Insurance Performance Measure (IPM) to a set of Indian insurance companies over the period 2005-2016. This is the first article published that applies the IPM model on real industry data and studies its implications.

The IPM was introduced in a Winter 2002 JACF article by Joseph Calandro, Jr., then at General Star management, a subsidiary of Berkshire Hathaway and by Scott Lane, then an accounting professor at the University of New Haven. Those authors explained why financial reporting for insurance companies was so challenging and presented the IPM metric as a better way to assess industry and company performance. Evaluating P&C companies is difficult because the unique format of insurance company financials does not lend itself to traditional financial accounting analysis and because the industry’s preeminent performance measure, the Underwriting Ratio, captures underwriting and claims activity but says nothing about investment and risk distribution (reinsurance).

By contrast, the IPM represents the interrelation of underwriting, investment and reinsurance along with a hurdle rate and is quite consistent with Warren Buffett’s expressed desire for a balanced overview of industry performance. IPM uses financial data without modification thereby simplifying and fastening computation. Operationally, it could help in negotiations for reinsurance renewals and identify “Maximum Profitable capacity”—the threshold limit for overall profitability.

Authored by Sai Ranjani Bharathkumar, XLRI Jamshedpur, India

Processes and Accuracy of Cash Flow Forecasting: A Case Study of a Multinational Corporation

Processes and Accuracy of Cash Flow Forecasting: A Case Study of a Multinational Corporation

The eighth article from the (JACF Spring/Summer issue) discusses how, despite its pivotal importance in enterprise management, cash flow forecasting gets little attention from academics perhaps because few of them have access to internal processes and data. In this article, however, the authors explain how cash flow forecasting is organized at Bayer, a large multinational company headquartered in Germany, and which factors influence the accuracy of its forecasts. The research focuses on cash flow forecasts based on the direct method, prepared three times a yearat Bayer, involving about 62,000 individual forecasting items each time. These forecasts form the basis of the company’s liquidity and financial risk management, in particular, its foreign exchange risk hedging.

The authors explain how local managers in Bayer’s entities across the world derive the forecasts, i.e., what information they use as input, how they validate it, and how they deal with potential bias caused by managerial incentive systems. They also analyze whether forecasting processes are affected by characteristics such as business area, size, region, or specific local conditions, and ultimately whether forecasting practices and entity characteristics affect forecast accuracy.

The findings show that cash flow forecasting procedures vary substantially across Bayer. While the central finance department gives general guidance on the required cash flow forecasting output and provides direction on the input to be used, there are no detailed instructions on how forecasts are to be prepared. Instead, local managers are free to determine their own forecasting practices. They use different forecasting inputs and validate forecasting inputs and output with different intensities, and they also differ in how they treat possible biases in input data. These findings document the limits of standardization and central control in large multinational corporations resulting from local managers’ need for flexibility to cope with the heterogeneity and dynamism of their environments. At the same time, however, local differentiation increases complexity and may increase errors.

Quantitative analysis of forecasting errors shows that forecasts of receipts from customers (cash inflows) are more accurate than forecasts of payments to suppliers (cash outflows). Moreover, forecasting practices affect forecast accuracy. Outflow forecasts are more accurate if managers intensively validate forecasting input; inflow forecasts, if they eliminate input biases that may result from internal target setting or from other managerial incentives, and if they carefully validate their forecasting output.

The study provides several insights.

1. Cash flow forecasting in a large multinational corporation is a complex system of interlaced processes, taking place in many organizational sub- units and located in different environments with different backgrounds.

2. Forecasting processes depend on entity characteristics. Managers at smaller, less complex entities tend to communicate closely with local counterparts and to verify forecasting output, while managers at entities whose liquidity is managed by the central finance department often use only rough assumptions about payment terms of cash inflows and outflows.

3. Quantitative analysis supports the contention that invoices issued to customers can be forecasted more accurately than invoices received from suppliers.

4. Forecasting practices affect forecast accuracy. Forecasts of invoices from suppliers are more accurate if managers intensively validate the forecasting input and prepare their own calculations rather than simply accept at face value input data provided by other departments.

Authored by Martin Glaum, WHU – Otto Beisheim School of Management, Peter Schmidt, Justus-Liebig Universität Giessen, and Kati Schnürer, Bayer AG & Justus-Liebig-Universität Giessen