Forecasting Methods and Uses for Demand Deposits of U.S. Commercial Banks

Article excerpt

INTRODUCTION

During the past decade, the banking industry has witnessed a multitude of dramatic changes, such as the deregulation of the financial sector, competition from other financial institutions, and new information technology such as the Internet. All of these changes have produced a combined effect, leading to the unprecedented present day competitive market environment. In order to survive in this highly volatile industry, competent forecasting and planning have become vital activities for banks. The managers of these institutions require timely and accurate forecasts of variables such as deposits, loans, exchange rates, and interest rates, in order that they might fulfill their planning and control responsibilities in an effective manner. In essence, all of the major budgeting practices of these institutions are dependent upon the forecasting function.

Given the critical role of the forecasting function, insights into current demand deposit forecasting practices and the possible success of these practices should be of major value to bank management. However, most past studies on forecasting have focused on a cross-sectional analysis (Dalrymple, 1987, 1975; Mentzer & Cox, 1984; Sanders, 1992, 1994). Sanders (1997) has noted that since the management of service organizations is in many ways different from that of manufacturing companies, combining information on forecasting practices in manufacturing and service firms can only lead to diffused generalizations and is not helpful in understanding practices in a specific industry segment.

Unfortunately, detailed studies of the forecasting methods employed in the banking industry have not been undertaken, although other aspects of the forecasting function, such as developing forecasting models and comparing their accuracies, have been assessed (Ellis, 1995).

In this study the focus is on U.S. commercial banks. It assesses current bank demand deposit forecasting practices and probes into problems that are specific to this environment. The specific objectives are: (1) to explore the uses of demand deposit forecasts; (2) to evaluate forecasting methods and forecasting time parameters; and (3) to examine the criteria used for evaluating forecasting effectiveness and the measures used for forecasting accuracy.

RESEARCH METHOD

In this study a mail survey was utilized to obtain information about demand deposit forecasting practices in commercial banks. An initial mail questionnaire was developed, based upon questionnaires utilized in previous studies (Dalrymple, 1987, 1975; Giroux, 1980; Mentzer & Cox, 1984; Peterson & Jun, 1999). This preliminary measuring instrument was reviewed by two practitioners from the banking industry and several alterations were produced, based upon their inputs. The final questionnaire was designed and formulized to collect data which could be of value to bank managers.

The survey was forwarded to the presidents of a sample of U.S. banks, requesting them to forward the survey questionnaire to the manager who is responsible for preparing demand deposit forecasts. A total of 400 banks were randomly selected from the Thomson Bank Directory (Thomson Financial Publishing, 1999). Of the responses received, 83 questionnaires were usable. This results in a response rate of 20.8%, which is comparable to similar surveys and can be regarded as an acceptable rate considering the length (seven-page) of the questionnaire.

Table 1 summarizes the characteristics of respondents regarding the approximate size of annual demand deposits, the number of years respondents have been employed with the banks, and the approximate ages of the banks classified by company size: large and small size. In this inquiry a large bank was defined as one with more than $500 million of demand deposits and a small firm as one with less than or equal to $500 million.

The bulk of the respondents were executives whose job titles included chief executive officer, president, vice-president of branch management, forecasting manager, controller, and director of management information systems. …