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Evidence-Driven Investing, defined
The vocabulary of testing an investment idea before it deserves a place in a portfolio. Each entry gives a definition you can quote, why it matters, how it works, and what Opulence Alpha actually does — with a link to the live evidence.
Sector behaviour in every market regime →The Opulence Alpha vocabulary
Evidence-Driven InvestingEvidence-Driven Investing is an operating philosophy in which an investment idea is a hypothesis, not a conclusion: it must be tested on data that was available at the time, validated out of sample, and measured after it goes live — and it earns a place in a portfolio only while the evidence keeps supporting it.Investment intelligenceInvestment intelligence is the category of software that continuously researches, predicts, tests, validates and measures portfolio decisions — decision infrastructure for investors, as opposed to a signal service, a stock picker or a robo-advisor.Investment BookAn investment Book is a living, parameterised portfolio: a declared policy — universe, selection, sizing, constraints, holding rules, rebalancing, execution and risk — applied continuously to fresh forecasts, with every position, outcome and change recorded against that policy.Promotion GateA Promotion Gate is the validation step that stands between attractive research and a live portfolio: a strategy is published for use only if it clears every statistical check — overfitting, data snooping, robustness — and a check that could not be computed counts as a failure, not a pass.Refutation LedgerA Refutation Ledger is a public record of what happened after each investment decision — the prediction, the position, the outcome, the benchmark, and any fault or change — kept with the failures in, so that anyone can check the process against all of its results rather than the flattering ones.
Methods
Walk-forward validationWalk-forward validation tests an investment strategy in time order: it is fitted only on data before a cut-off, tested on the unseen period that follows, and the cut-off then moves forward and the process repeats — so every test result comes from a period the model had not seen.Purged cross-validationPurged cross-validation is a way of testing financial models in which training observations whose outcome window overlaps the test period are removed (purged), with an embargo gap; its combinatorial form (CPCV) recombines the test blocks into many complete backtest paths, giving a distribution of results rather than one.Backtest overfittingBacktest overfitting happens when a strategy's rules or parameters are tuned — deliberately or through repeated trial — to the noise of a particular history, so the backtest looks strong but the edge does not exist out of sample and live performance decays.Deflated Sharpe ratioThe deflated Sharpe ratio (DSR) is the probability that a strategy's true Sharpe ratio is positive after accounting for how many strategies were tried to find it, and for the skewness and fat tails of its returns; it deflates an impressive-looking Sharpe ratio by the size of the search behind it.Multiple testingMultiple testing, or data snooping, is the problem that arises when many strategies, factors or parameters are tested on the same data: some will look statistically significant by chance alone, so each result must be judged against the whole search, not on its own.Point-in-time dataPoint-in-time data records each value as it was known on each date — when a filing was published, before later restatements, including companies that later disappeared — so that a test of a past decision uses only the information that decision could actually have used.Probabilistic forecastingProbabilistic forecasting predicts a distribution of possible outcomes — for example a median return with a calibrated interval around it — rather than a single number, so the forecast states how uncertain it is and that uncertainty can be measured and used to size a position.Market regime detectionMarket regime detection classifies the state the market is in — for example calm or stressed volatility, benign or distressed credit, easy or tight policy — from observable data, so that forecasts, portfolio construction and risk limits can depend on the regime rather than treat all periods alike.Regime-aware portfolio constructionRegime-aware portfolio construction makes the market regime an input to how a portfolio is built — which exposures are preferred, how positions are sized and how much risk is allowed — instead of applying one set of rules in every market condition.