Original Research

The analysis of data errors in financial information databases: New evidence from the Korean financial markets

Hyung-Chan Jung, Hyun-Jung Nam
South African Journal of Business Management | Vol 49, No 1 | a185 | DOI: https://doi.org/10.4102/sajbm.v49i1.185 | © 2018 Hyung-Chan Jung, Hyun-Jung Nam | This work is licensed under CC Attribution 4.0
Submitted: 13 April 2018 | Published: 27 June 2018

About the author(s)

Hyung-Chan Jung, Division of Business Administration, Pukyong National University, Korea, Democratic People's Republic of
Hyun-Jung Nam, Graduate School of International Studies, Dong-A University, Korea, Democratic People's Republic of

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Background: As financial professionals including policy-makers tend to base decisions on research performed using large machine-readable financial databases, the accuracy of the financial data provided by database companies has a direct impact on the quality of their decisions.


Objectives: The objective of this study was to examine data errors in the DataGuide and KisValue databases which are both primary sources of stock prices and return data for Korea Exchange securities in Korea. This article also discussed the methodological implications of erroneous data on monthly stock returns in empirical studies on Korean financial markets.


Methods: A cross-checking technique was used in this study.


Results: The results suggest that there are material discrepancies between the DataGuide and KisValue databases in monthly stock returns, most of which are attributable to the mishandling of split events and of missing values. The results also indicate that DataGuide provides a more reliable service than KisValue in terms of monthly stock returns.


Conclusion: The results show that extreme monthly returns resulting from serious data errors in the DataGuide and KisValue databases may be enough to sharply change the properties of monthly stock return distributions and to over- or underestimate long-term abnormal stock returns.


financial databases; errors in databases; cross-checking technique; methodological implications; monthly stock returns


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