Investing & Quant ML Glossary
Clear definitions of the investing, quantitative, and machine-learning concepts behind hedgewing.ai.
- Walk-Forward Backtesting — An out-of-sample model validation method that trains on a rolling historical window, predicts the next unseen period, then slides forward — preventing look-ahead bias.
- Look-Ahead Bias — A backtesting flaw where a model is accidentally fed information that would not have been known at the time of the prediction, inflating apparent performance.
- Overfitting — When a model learns noise specific to its training data instead of the underlying signal, producing strong in-sample results that collapse on new data.
- Ensemble Model — A model that combines predictions from several independent models, typically producing more robust forecasts than any single model alone.
- LSTM (Long Short-Term Memory) — A recurrent neural network architecture designed to learn long-range dependencies in sequential data such as price history.
- Transformer Model — A neural architecture based on self-attention that processes an entire sequence in parallel, capturing relationships between any two time steps directly.
- Sharpe Ratio — A measure of risk-adjusted return: excess return over the risk-free rate divided by the standard deviation of returns.
- Sortino Ratio — A variant of the Sharpe ratio that measures return only against downside volatility, ignoring upside swings.
- Value at Risk (VaR) — An estimate of the maximum expected loss over a given horizon at a chosen confidence level, e.g. 95% or 99%.
- Beta — A measure of how much a stock moves relative to the broad market — beta of 1 moves with the market, above 1 is more volatile, below 1 less.
- Factor Analysis — Decomposing a stock's returns into exposures to systematic factors such as market, size, value, profitability, and investment.
- Alpha — The portion of return not explained by exposure to market or other systematic factors — a measure of genuine skill or edge.
- Calibrated Confidence — A confidence score is calibrated when its stated probability matches the observed frequency — 70% confident predictions should be right about 70% of the time.
- RSI (Relative Strength Index) — A momentum oscillator from 0 to 100 that flags overbought (above 70) and oversold (below 30) conditions.
- MACD (Moving Average Convergence Divergence) — A trend-following momentum indicator built from the difference between two exponential moving averages and a signal line.
- Bollinger Bands — Volatility bands plotted a number of standard deviations above and below a moving average, widening and narrowing as volatility changes.
- Neural Network — A machine-learning model made of interconnected layers of simple computing units (neurons) that learn to map inputs to outputs by adjusting weighted connections during training.
- Deep Learning — A branch of machine learning that uses neural networks with many stacked layers to automatically learn hierarchical patterns directly from raw or lightly processed data.
- Recurrent Neural Network (RNN) — A neural network designed for sequences that processes data one step at a time while carrying forward a hidden state, letting earlier inputs influence later outputs.
- Gated Recurrent Unit (GRU) — A streamlined recurrent neural network cell that uses update and reset gates to retain long-range information, achieving much of the LSTM's power with fewer parameters.
- Temporal Convolutional Network (TCN) — A sequence model that applies stacked, dilated, causal convolutions to time-series data, capturing long histories in parallel without the step-by-step recurrence of an RNN.
- Attention Mechanism — A neural-network technique that lets a model dynamically weight which parts of its input matter most for each output, enabling it to focus on the most relevant information in a sequence.
- Feature Engineering — Feature engineering is the process of transforming raw data into informative input variables that a machine-learning model can use to make better predictions.
- Supervised Learning — Supervised learning is a machine-learning approach where a model learns from labeled examples, mapping known inputs to known outputs so it can predict outcomes for new data.
- Time-Series Forecasting — Time-series forecasting is the task of predicting future values of a sequence from its past, accounting for trends, seasonality, and the order-dependence of observations.
- Regularization — Regularization is a set of techniques that constrain a model's complexity during training to prevent it from memorizing noise and to improve how well it generalizes to new data.
- Cross-Validation — Cross-validation is a model-evaluation method that repeatedly trains on part of the data and tests on the rest to estimate how well a model will generalize to unseen data.
- Hyperparameter Tuning — Hyperparameter tuning is the process of searching for the configuration settings of a model—such as learning rate or depth—that produce the best generalization performance.
- Gradient Descent — Gradient descent is the optimization algorithm that trains most machine-learning models by repeatedly nudging parameters in the direction that most reduces prediction error.
- Stacking (Meta-Learner Ensemble) — Stacking is an ensemble method that combines several base models by training a meta-learner to weigh their predictions, typically outperforming any single model.
- Information Ratio — The information ratio measures a portfolio's excess return over a benchmark per unit of tracking error, gauging how consistently a manager beats that benchmark.
- Maximum Drawdown — Maximum drawdown is the largest peak-to-trough percentage decline in a portfolio's value before a new peak is reached, capturing the worst loss an investor would have endured.
- Calmar Ratio — The Calmar ratio divides an investment's annualized return by its maximum drawdown, measuring return earned per unit of worst-case loss.
- Risk-Adjusted Return — Risk-adjusted return measures investment performance relative to the amount of risk taken, so strategies with different volatility can be compared fairly.
- Standard Deviation — Standard deviation measures how widely a set of returns spreads around its average, serving as the most common statistical proxy for volatility and risk.
- Correlation — Correlation measures how closely two assets' returns move together, ranging from +1 (perfectly in sync) through 0 (unrelated) to -1 (perfectly opposite).
- Volatility — Volatility measures how much an asset's price fluctuates over time, usually expressed as the annualized standard deviation of returns; higher volatility means larger and less predictable price swings.
- Implied Volatility — Implied volatility is the market's forward-looking estimate of an asset's future price variability, reverse-engineered from current option prices rather than measured from past returns.
- Capital Asset Pricing Model (CAPM) — CAPM is a foundational model that estimates an asset's expected return as the risk-free rate plus its beta times the market's risk premium, linking expected reward to non-diversifiable risk.
- Efficient Market Hypothesis (EMH) — The Efficient Market Hypothesis holds that asset prices already reflect available information, making it difficult to consistently earn excess returns without taking on additional risk.
- Monte Carlo Simulation — Monte Carlo simulation estimates the range of possible outcomes by running thousands of randomized scenarios, producing a distribution of results rather than a single point forecast.
- Hierarchical Risk Parity (HRP) — Hierarchical Risk Parity is a portfolio construction method that groups assets by similarity using machine learning, then allocates risk across the resulting hierarchy without inverting an unstable covariance matrix.
- Mean Reversion — Mean reversion is the tendency of an asset's price or other financial variable to drift back toward its long-run average after deviating from it, forming the basis of contrarian trading strategies.
- Momentum (Investing) — Momentum is the empirical tendency of assets that have performed well (or poorly) over the recent past to continue performing in the same direction over the near term.
- Algorithmic Trading — Algorithmic trading is the use of computer programs to automatically generate, route, and execute orders according to predefined rules or models, with little or no manual intervention.
- Quantitative Analysis — Quantitative analysis is the use of mathematical, statistical, and computational methods to model financial markets, value securities, and make systematic, data-driven investment and risk decisions.
- Diversification — Diversification is the practice of spreading capital across assets whose returns are imperfectly correlated, reducing portfolio risk for a given level of expected return.
- Moving Average — A moving average smooths price data by continuously averaging the most recent N periods, producing a single trend line that filters out short-term noise.
- Exponential Moving Average (EMA) — An exponential moving average is a moving average that weights recent prices more heavily, so it reacts faster to new information than a simple moving average of the same length.
- Average True Range (ATR) — Average True Range measures market volatility by averaging the daily true price range over a period, capturing how much an asset typically moves regardless of direction.
- Stochastic Oscillator — The stochastic oscillator is a momentum indicator that measures where the current close sits within its recent high-low range, signaling potential overbought or oversold conditions.
- Support and Resistance — Support and resistance are price levels where buying or selling pressure has repeatedly halted a move, acting as floors and ceilings that traders watch for reversals or breakouts.
- Volume-Weighted Average Price (VWAP) — VWAP is the average price an asset traded at over a period, weighted by volume, serving as a benchmark for execution quality and intraday fair value.
- Price-to-Earnings (P/E) Ratio — The price-to-earnings ratio divides a stock's share price by its earnings per share, showing how much investors pay for each dollar of company profit.
- Earnings Per Share (EPS) — Earnings per share is a company's net profit allocated to each outstanding share of common stock, a core measure of per-share profitability.
- Market Capitalization — Market capitalization is the total market value of a company's outstanding shares, calculated by multiplying share price by the number of shares outstanding.
- Technical Analysis — Technical analysis studies historical price and volume data, using charts and indicators, to forecast future price movements based on patterns and market behavior.
- Fundamental Analysis — Fundamental analysis estimates a security's intrinsic value by examining financial statements, business quality, and economic conditions to judge whether it is mispriced.
- Sentiment Analysis (Markets) — Market sentiment analysis measures the collective mood of investors toward an asset or market, often using news, social media, and survey data to gauge bullish or bearish bias.