Tag
#methodology
25 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 -
Common Backtesting Mistakes That Make Results Look Better Than Reality
The most common backtesting mistakes are future information leaking into past decisions, survivorship in the universe, ignored costs, and testing so many variations that something looks good by luck.
Read article -
Factor Crowding Explained: When Too Much Money Chases the Same Signal
Factor crowding is what happens when many investors hold the same factor exposure at once. It raises valuations, correlates positions, and makes unwinds sharper.
Read article -
Factor Cyclicality and Drawdowns: Sizing for the Droughts
Factors go through long periods of underperformance. Factor cyclicality analysis measures how deep and how long those droughts run, so position sizing and governance can survive them.
Read article -
Factor Exposure Analysis: What Your Portfolio Is Actually Exposed To
Factor exposure analysis measures which characteristics, such as value, quality, momentum or size, actually drive a portfolio, using holdings-based scores or returns-based regression.
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 -
In-Sample vs Out-of-Sample Testing: The Minimum Honest Backtest Check
In-sample data is where a strategy is built and tuned. Out-of-sample data is held back and used once to judge it. Splitting the two is the least you can do to avoid fooling yourself.
Read article -
Liquidity Constraints in Backtesting: Why a Paper Strategy Cannot Always Be Filled
Liquidity constraints decide whether a backtested trade could actually have happened. A guide to volume caps, capacity, thin stocks and the filters that keep a test honest.
Read article -
Multi-Factor Investing Explained: Blending vs Integrating
Multi-factor investing combines several return drivers such as value, quality, momentum and low volatility into one portfolio, either by blending sleeves or by integrating scores.
Read article -
Rebalancing Frequency and Backtest Results: How Often You Trade Changes What You Measure
Rebalancing frequency changes turnover, cost and signal decay all at once. A guide to why backtest results move with frequency and how to test frequency without fooling yourself.
Read article -
Sector Rotation Strategy in India: How Rotation Is Measured and Analysed
Sector rotation is the observation that leadership moves between sectors over time. This is how rotation is measured in Indian markets, and where the analysis breaks down.
Read article -
Slippage and Impact Cost: The Gap Between the Modelled Price and the Real One
Slippage is the difference between the price a backtest assumes and the price a trade actually gets. A guide to spread, delay and market impact, and how to model each.
Read article -
SPIVA India Explained: What the Scorecard Measures and How to Read It
SPIVA compares active fund returns against a designated benchmark index, corrected for survivorship and measured net of fees. Here is the methodology and its honest limits.
Read article -
Survivorship Bias in Backtests: Why Today's Index Lies About the Past
Survivorship bias is testing a strategy on companies that survived to today. Delisted, merged, and dropped names disappear from the sample, so historical results improve for reasons unrelated to the strategy.
Read article -
Transaction Costs in Backtests: Brokerage, STT, Stamp Duty and GST
Transaction costs turn a paper strategy into a real one. A guide to the categories of Indian trading cost, how to model them in a backtest, and how turnover multiplies the drag.
Read article -
Walk-Forward Analysis Explained: Rolling Re-Estimation as a Stricter Test
Walk-forward analysis fits a strategy on a past window, applies it to the next unseen window, then rolls forward and repeats. It is a harder test than a single out-of-sample split.
Read article -
What Is Overfitting in Backtesting? Curve-Fitting, Parameters and How to Detect It
Overfitting in backtesting is tuning a strategy until it describes the noise in one sample of history rather than any durable pattern. It looks like a great result and behaves like a coin flip.
Read article -
A Practical Guide to Forensic Accounting for Indian Stocks
Forensic accounting is a set of practical checks you run on reported numbers, cash versus profit, receivables and inventory, related parties, revenue timing, and auditor signals, before you trust the headline.
Read article -
Building KPI Trees for Indian Companies
A KPI tree connects a company's operating drivers to its financial statements as a hierarchy. Here is how to structure one, top-down, with an Indian-company shaped example.
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 Unit Economics Matter More Than Earnings
Unit economics show what one unit of a business earns after the cost to serve it, which reveals whether a company is healthy long before the reported profit line does.
Read article -
Why Deterministic Forecasting Beats LLM Guesses
A forecast used for capital must be reproducible and auditable. A language model's free-form guess is neither, which is why serious forecasts come from an explicit method, not a prompt.
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 -
Data Quality Beats Model Quality: A Year Reading Indian Filings
After a year building AI to read Indian company filings, the biggest gains came from boring data discipline, not from a better model. Here is what actually moved the needle.
Read article