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Ben Wellington: ML for Finance and Storytelling through Data
The Gradient: Perspectives on AI
Importance of Accurate Timestamp Data in Finance and Data Analysis
Accurate timestamp data is crucial for meaningful financial analysis and backtesting. The concept of 'point in time' data ensures that one knows both the exact moment data was created and its original form. This is vital because data that is retrospectively altered or backfilled can lead to flawed conclusions. For instance, if a topic code in a news story is applied retroactively, as in the case of a news agency introducing a 'subprime' code after the fact, it can mislead a machine learning algorithm into believing that potential trading strategies were viable in the past. Similarly, when analyzing historical Twitter sentiment data against the S&P 500, using today's list of companies skews the reality of past market conditions, as the actual constituents of the index would have differed. Vendors may not be intentionally deceptive; rather, they often overlook the significance of timestamps that correctly reflect market circumstances at specific historical moments. Failing to capture accurate timestamp data can derail trading strategies, as they rely on historical context that reflects real market opportunities rather than projections of what is known today.
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