In the financial industry, data plays a critical role in enabling managers to make informed decisions and manage risk effectively. Financial data can come from a wide range of sources, including economic indicators, company financial statements, market data, customer transaction histories, and social media sentiment. By analyzing this data, financial professionals can identify trends, patterns, and opportunities that would be difficult or impossible to detect otherwise. This information can be used to inform investment decisions, optimize portfolio management, and develop more effective risk management strategies.
Despite the critical importance of financial data, it is often missing or incomplete. Financial data can be difficult to obtain due to a lack of standardization and regulatory requirements. Incomplete or inaccurate data can lead to flawed analysis, incorrect decision-making, and increased risk. As a result, financial professionals must carefully consider the quality and completeness of the data they use and take steps to address any gaps or inconsistencies. Techniques such as data cleaning, imputation, and statistical modeling can be used to address missing data, but it is crucial to ensure that any assumptions or limitations are well understood and accounted for in the analysis.
Reference [1] studied the missing data in firms’ fundamentals and proposed methods for imputing the missing data. The authors pointed out,
This paper focuses on a very widespread yet rarely recognized issue of missing data in firm-specific characteristics. First, we document the systematic feature of missing data: it is pervasive and widespread among the overwhelming majority of firms. In our representative data set of the 45 most often used characteristics, more than 70% of firms are missing at least one of them at any given point of time. We show that firm fundamentals are not missing-completely-at-random, but display complex systematic patterns. We leverage the complicated cross-sectional and time-series dependence in firm characteristics to propose a new imputation method, which is easy to use, and substantially outperforms existing alternatives…
The problem of missing data is not limited to just firm characteristics, and is encountered universally in various applications in finance: I/B/E/S forecast data, ESG ratings of firms, and many others. It is also likely to be more severe in the international context. Given the growth in Big Data applications and new sources of information being available at an increasing speed, we suspect that the issue of missing data will become even more paramount going forward. We hope that our paper lays out foundations and general guidelines for imputing missing data that could be applied in many different settings in the follow-up work.
We think that the proposed data imputation methods can be applied not only to fundamental data but also to financial derivatives data such as options.
Let us know what you think in the comments below or in the discussion forum.
References
[1] Bryzgalova, Svetlana and Lerner, Sven and Lettau, Martin and Pelger, Markus, Missing Financial Data (2022). https://ssrn.com/abstract=4106794
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