Data quality has been shown to be a major determinant of the value of systems that utilize
input data feeds and transform them into valuable information under a variety of business
contexts. For this study, we have chosen a financial risk management context to investigate
the relationship between data quality and value of risk management forecasting systems.
Three attributes of data quality, frequency, response time, and accuracy, along with the cost
of data are considered. Joint impacts of attributes are also considered. It is shown that an
increase in report frequency results in an increase in the utility of a risk management
forecasting system, but this increase is limited by the responsiveness of the hedging scheme.
Frequency is shown to improve the utility of the forecasting systems in two ways: First, an
increase in frequency pushes the predicted states closer to the actual states and second, an
increase in frequency causes the reliability of the forecasting model to increase. A delay in
response time of reports is predicted to have a greater impact on utility for high frequency
reports than for low frequency reports. Finally, data inaccuracies are recommended to be the
first concern of a portfolio manager before an attempt is made to increase the reporting
frequency.Information Systems Working Papers Serie
