Investor Based Industry Classification
Adaptive and Agile Measure of Company Relevancy.
DYNAMIC CLASSIFICATION
Clustering companies by their similarity is a very important preparation step of advanced analysis such as industry momentum and reversal strategies. The traditional approach relies on the self-disclared industry classification code (e.g. SIC, NAISC etc.). The drawback of the approach is that the industry code would deprecated over time as companies' business envolve over time. Data Story' relies on the EDGAR downloading activities to characterize the firm similarity in much more timely manner.
ACTION-BASED CLASSIFICATION
The traditional industry classification of companies are based on the characteristics of product and services. However, investors might not view the firms in their product offering. For example, Tesla would be clustered together with General Motor and Ford Motor by traditional industry code. However, it turns out, based on SEC EDGAR analytics, Tesla is much more correlated to firms such as Amazon and Netflix, implying that investors view Tesla as a technology firm more than a automobile firm. Data Story's analytics more accurately capatured how investors perceived companies.
HIGH MARKET RELEVANCY
Data Story's proprietary industry classification analytics shows high market relevancy. The stock returns correlation of companies in the same Data Story classification group is much higher than the traditional SIC or NAICS classification. The usage of Data Story classification has the potential to outperform in various trading strategies. Given that the Data Story classification is always computed with the most recent activities on EDGAR, the measure can still maintain its high relevancy over time.
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