Investing & AI Glossary

Plain-English definitions of the trading, investing, and AI terms we use in our daily model analyses. Every term has a formal definition, a simple explanation, a concrete example, and related concepts.

  • 52-Week HighThe highest price at which a stock has traded in the past year.
  • AI AgentAn AI system that plans and takes actions toward a goal over multiple steps.
  • AI BenchmarkA standardized test that compares AI models on the same task under identical conditions.
  • AI Financial ReasoningA model's ability to analyze markets, weigh evidence, manage risk, and justify its decisions.
  • AI Model EvaluationJudging an AI model on the quality of its decisions, not just its final output.
  • AI TradingUsing artificial intelligence systems to make or assist trading decisions.
  • AlphaThe excess return of an investment above its risk-adjusted benchmark.
  • Bear MarketA prolonged market decline of 20% or more from recent highs.
  • BetaA measure of a stock's volatility relative to the broader market.
  • BreakoutWhen a stock's price pushes decisively through a support or resistance level.
  • Bull MarketA sustained period of rising stock prices and optimism.
  • Calmar RatioAnnualized return divided by maximum drawdown, a return-to-pain measure.
  • CapitulationA panic wave of selling that often marks a market bottom.
  • Context WindowThe maximum amount of text, measured in tokens, a model can consider at once.
  • CorrelationA measure of how closely two assets' prices move together, from -1 to +1.
  • DiversificationSpreading investments across many assets to reduce overall risk.
  • Dividend AristocratAn S&P 500 company that has raised its dividend for 25+ consecutive years.
  • Dividend YieldAnnual dividends per share divided by share price, expressed as a percentage.
  • DrawdownThe peak-to-trough decline of a portfolio or stock before it reaches a new high.
  • EmbeddingA list of numbers that represents the meaning of text so software can compare it.
  • Enterprise ValueA company's total value including debt and net of cash.
  • EPSEarnings Per Share: a company's net profit divided by outstanding shares.
  • Fine-TuningFurther training a general model on specialized data to adapt it to a task.
  • Free Cash FlowThe cash a company generates after funding operations and capital spending.
  • Fundamental AnalysisEvaluating a stock based on a company's financial statements and business prospects.
  • GroundingTying a model's claims to real evidence so its output can be checked against the facts.
  • HallucinationWhen a model states something confidently that is false or unsupported by its data.
  • InferenceRunning a trained model to produce an output from a given input.
  • Intrinsic ValueThe estimated true value of a business based on fundamentals, independent of market price.
  • LiquidityHow easily an asset can be bought or sold without moving its price.
  • MACDA trend-and-momentum indicator built from the difference of two moving averages.
  • Margin of SafetyThe discount between a stock's market price and its estimated intrinsic value.
  • Market CapitalizationThe total market value of a company's outstanding shares.
  • Market CorrectionA decline of 10% to 20% from a recent market peak.
  • Maximum DrawdownThe largest peak-to-trough loss a portfolio suffered over a chosen time window.
  • Model CalibrationHow well a model's stated confidence matches how often it is actually right.
  • MomentumThe tendency of a stock's recent price trend to continue.
  • Moving AverageThe average price of a stock over a rolling window of time.
  • P/E RatioPrice-to-Earnings ratio: a stock's price divided by its earnings per share.
  • Payout RatioThe share of earnings a company pays out as dividends.
  • PEG RatioThe P/E ratio divided by the earnings growth rate.
  • Position SizingDeciding how much capital to allocate to a single trade or holding.
  • Price-to-Book (P/B) RatioA stock's price divided by its book value per share.
  • Prompt EngineeringThe iterative practice of crafting model instructions to get the desired output.
  • Relative Strength Index (RSI)A momentum oscillator measuring the speed and size of recent price moves, from 0 to 100.
  • Retrieval-Augmented GenerationCombining a search step with a language model so answers are grounded in fetched data.
  • Return on Equity (ROE)Net income as a percentage of shareholders' equity.
  • Risk ManagementThe practice of identifying, measuring, and controlling investment losses.
  • Risk-Adjusted ReturnInvestment return measured relative to the amount of risk taken to earn it.
  • S&P 500An index of 500 of the largest US publicly traded companies by market capitalization.
  • Sector RotationShifting investments between sectors based on the economic cycle.
  • Semantic SearchSearching by meaning rather than exact keywords, using embeddings to rank relevance.
  • Sentiment AnalysisMeasuring market or news mood to forecast stock moves.
  • Sharpe RatioA measure of return earned per unit of total risk taken.
  • Short SellingBetting a stock will fall by selling borrowed shares and buying them back later.
  • Sortino RatioA risk-adjusted return measure that penalizes only downside volatility.
  • Standard DeviationA statistical measure of how much returns spread around their average.
  • Stop-Loss OrderA standing order to sell a stock once it falls to a preset price.
  • Structured OutputForcing a model to return data in a fixed, machine-readable format such as JSON.
  • Support and ResistancePrice levels where a stock tends to stop falling (support) or rising (resistance).
  • System PromptThe fixed instruction that sets a model's role, rules, and goals before a conversation.
  • Technical AnalysisForecasting price using historical price and volume data.
  • TokenThe small chunk of text, often a word piece, that models read and generate.
  • Tool CallingWhen a model invokes an external function or API instead of only producing text.
  • Value at Risk (VaR)The maximum expected loss over a period at a given confidence level.
  • Value InvestingBuying stocks trading below their estimated intrinsic value.
  • Vector DatabaseA database that stores and searches embeddings to find items by meaning.
  • VIX (Volatility Index)A real-time index of expected S&P 500 volatility, known as the fear gauge.
  • VolatilityThe magnitude of price fluctuations over time.