GoQuant — Multi-method Investment Analysis

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What the harness measured

In the past data we checked, we could not tell the stocks this screen picked apart from the ones it left out. We looked back over 64 months. Each month we checked whether the stocks this screen picked did better, over the following month (21 trading days), than the stocks it didn't pick. We score that from -1 (the picks always did worse) to +1 (always did better); 0 means no pattern. This screen scored -0.0007. As a measurement, the score is essentially zero. If the screen had no real edge, a score at least this far from zero would show up about 90 in 100 by luck alone. That does not mean the screen is wrong; it still works for narrowing a list of stocks by your own rules.

This is a record of the past, not a prediction.

Information coefficient-0.0007-1 to +1; 0 means no pattern

IC p-value0.900how often luck alone would do this

Net Sharpe (out-of-sample)0.18per year, after estimated trading costs

Deflated Sharpe probability0.166Deflated against 2 trialsthe chance it truly beats holding the index, after marking down for flukes

Annual turnover11.7how much of the portfolio is replaced each year

Out-of-sample months64months we checked, using only what was known at the time

Universe:: Today's Nifty 500 constituents (survivorship-biased: delisted names are absent)
Data window:: 2019-04-01 → 2026-09-25
IC basis:: Full-sample (not out-of-sample)
Portfolio:: rebalanced monthly, 21 trading days, net of estimated costs, for a fixed position size per stock
Versions:: qp21f-nifty500-2.0.0 / 0.1.0 / 2.0.0
Record:: 97625d1b-564

Live monitoring

Not yet testable — 4 of 6 points needed to chart a rolling information coefficient.

5-day IC 0.010 · signal date 2026-10-06 · n 4475
22-day IC -0.027 · signal date 2026-09-09 · n 3886

This is a historical, statistical measurement produced by FINOME TECHNOLOGIES PVT LTD's research harness on past data. It is not a prediction, and past results do not indicate future results. The universe is today's Nifty 500 constituents, so the sample is affected by survivorship bias. It describes how a screen's selections behaved in the past and does not constitute investment advice or a recommendation.