Tag
#point-in-time
11 articles
-
Building a Factor Scorecard: Turning Definitions Into a Repeatable Score
A factor scorecard converts factor definitions into one comparable number per stock. Here is how to build one that is precise, point-in-time, and honest about what it cannot see.
Read article -
How to Backtest a Stock Strategy in India: An End-to-End Walkthrough
Backtesting a stock strategy in India means testing explicit rules on historical data that was actually knowable at each date, with realistic costs, a fair benchmark, and honest reporting.
Read article -
How to Identify Momentum Stocks: The Measurement Method
Momentum is identified by ranking a defined universe on risk-adjusted past return over a fixed lookback window. Here is the measurement method, step by step, with no lists.
Read article -
Common Modelling Mistakes Analysts Make
The recurring errors that quietly ruin financial models: hardcoding, false precision, restated history, straight-lined growth, circular references, and single-case thinking, with plain fixes for each.
Read article -
Why Point-in-Time Databases Are Hard to Build
A point-in-time database stores every financial number the way it was actually known on each past date. That sounds simple, but restatements, reclassifications, and corporate actions make it one of the hardest things in financial data.
Read article -
Why Restatements Break Models and Backtests
Restatements and reclassifications quietly rewrite a company's past, so a model or backtest built on today's numbers acts on figures nobody could have seen at the time.
Read article -
How to Compare Companies Across 10 Years of Filings
To compare a company across a decade, normalise for restatements, segment redefinitions, and accounting changes first, so every year is measured on the same basis before you read the trend.
Read article -
Structuring Decades of Filings So an AI Can Actually Use Them
A language model cannot reason over a messy pile of filings. Labels drift, statements get restated, formats change, and history is not what it looks like today.
Read article -
Why Point-in-Time Data Matters in Research and Backtests
Point-in-time data means using the numbers that were actually knowable on a given date, not today's restated version. Skip it and your research quietly looks smarter than it was.
Read article -
The Hardest Part of AI in Finance Is Not the Model. It Is the Data.
In financial AI, the model is fast becoming a commodity. The durable edge lives in disciplined data work: units, restatements, point-in-time correctness.
Read article -
Lookahead Bias, Explained: The Silent Killer of Stock Backtests
Lookahead bias is when a backtest uses information it could not have known at the time. It quietly inflates results, and point in time data is the only real fix.
Read article