Backtesting Glossary – The most common terms and definitions when you backtest

Welcome to this Backtesting Glossary, an essential reference for understanding key terms and concepts in the world of financial backtesting. Whether you’re a seasoned trader or just starting, this Backtesting Glossary guide will hopefully help you better understand backtesting, an important tool for systematic traders. 


Alpha: Alpha is a measure of an investment or portfolio’s excess return compared to a benchmark or a risk-free rate. Positive alpha indicates that the investment has outperformed its benchmark, while negative alpha suggests underperformance. Alpha is often used to assess the skill of a portfolio manager or the effectiveness of a trading strategy in generating returns.


Backtesting: (complete definition) Backtesting is a crucial process in the evaluation of trading strategies. It involves using historical market data to simulate how a particular trading strategy would have performed in the past. By applying the strategy’s rules and criteria to historical price and volume data, traders and investors can assess its potential profitability, risk, and reliability. Backtesting helps identify the strengths and weaknesses of a strategy, aids in fine-tuning its parameters, and provides insights into its historical performance, thereby assisting in decisions when implementing the strategy in real trading environments.

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Benchmark: A benchmark is a reference point, typically an index or asset, used to evaluate the relative performance of a trading strategy or portfolio. It provides a standard against which the strategy’s returns and risk can be compared.

Benchmark Return: Benchmark return is the return achieved by an index or asset used as a benchmark for comparison against a trading strategy. It serves as a basis for evaluating the strategy’s ability to outperform the broader market or a specific reference point.

Beta: Beta measures the sensitivity of an asset’s returns to changes in the overall market or a specific benchmark. A beta of 1 implies the asset moves in line with the market, while a beta greater than 1 indicates higher volatility, and a beta less than 1 implies lower volatility relative to the market.

Bias, Look-Ahead: Look-ahead bias occurs when information that was not available at the time of the test is inadvertently included in the backtesting process, leading to artificially inflated results. Detecting and eliminating look-ahead bias is crucial for accurate performance assessment.

Bias, Survivor: Survivor bias arises when only successful assets or strategies are included in historical data, while unsuccessful ones are omitted. This bias can lead to overestimations of performance because it ignores the impact of failed strategies or assets.

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Candlestick: (complete definition) Candlestick charts are a popular tool in technical analysis used to visualize price movements of an asset over a specified time period. Each candlestick represents that time period and contains information about the opening, closing, high, and low prices during that period. Candlestick patterns provide insights into market sentiment and potential trend reversals. Traders often use these patterns to make decisions about entering or exiting positions.

Correlation: Correlation is a statistical measure that quantifies the degree to which two or more variables move in relation to each other. In the context of trading strategies, it is often used to assess the relationship between the returns of different assets. A correlation coefficient can range from -1 (perfect inverse correlation) to 1 (perfect positive correlation), with 0 indicating no linear relationship. Traders use correlation to evaluate diversification benefits within a portfolio; assets with low or negative correlations can help reduce overall risk.

Cointegration: Cointegration is a statistical concept used in time series analysis to describe the long-term equilibrium relationship between two or more variables. In financial markets, it often refers to the phenomenon where the prices of two assets move together over time, but not necessarily in a one-to-one relationship. Cointegrated assets tend to revert to their common mean after short-term deviations, making them useful in pairs trading strategies where traders exploit temporary price divergences.

Cross-Validation: Cross-validation is a technique used to assess the performance and robustness of a trading strategy by dividing historical data into subsets. Typically, one subset (the “test set”) is used for evaluation, while the other subsets (the “training set”) are used to develop the strategy. By cycling through different subsets, traders can avoid overfitting, where a strategy performs well on historical data but poorly on new, unseen data. Cross-validation helps ensure that a strategy’s performance is more likely to generalize to real-world trading.


Data-driven in the context of backtesting trading strategies refers to the approach of developing and evaluating trading strategies based on historical and real-time market data. It involves using quantitative analysis and statistical methods to make informed decisions about trading, risk management, and strategy optimization. Data-driven backtesting helps traders assess the historical performance of their strategies and refine them to make more informed and data-backed trading decisions in the future.

Delta: In options trading, delta measures the sensitivity of an option’s price to changes in the underlying asset’s price. It quantifies how much the option’s value is expected to change for a one-point move in the underlying asset.

Drawdown: Drawdown is a critical metric in backtesting and refers to the maximum decline in the value of an investment from its peak to its lowest point. It represents the extent to which an investment or trading strategy experiences losses before recovering to a new high. Evaluating drawdown helps assess the risk associated with a strategy, as large drawdowns can be indicative of higher risk and potential emotional stress for investors.


Ergodicity: Ergodicity is a mathematical property in stochastic processes, indicating that the average behavior of a system over time is representative of its long-term behavior. In finance, it is crucial for analyzing asset returns, as an ergodic process ensures that statistical properties estimated from historical data can be applied to predict future behavior with confidence.

Empirical analysis: A systematic examination of historical market data to empirically validate the profitability and feasibility of a trading strategy, using past data as evidence.

Excess Return: Excess return is the return generated by an investment or strategy above a risk-free rate or benchmark return. It is a key metric for evaluating the strategy’s ability to generate profits beyond what could be obtained through passive investment.


Fat Finger Error: A “Fat Finger Error” is a colloquial term in finance, referring to an unintentional or accidental input of an incorrect numerical value when executing a trade or financial transaction. Such errors can lead to significant market disruptions or erroneous trades due to the large order sizes or extreme prices entered by mistake.

Forward Testing: Forward testing is the practice of applying a trading strategy to real-time or out-of-sample data to validate its performance and reliability in a live market environment. Unlike backtesting, which uses historical data, forward testing helps traders assess how their strategy behaves with current market conditions and execution issues, such as slippage and latency. It provides a more realistic assessment of a strategy’s potential for success in real-world trading.

Fundamental Analysis: Fundamental analysis is an approach to evaluating investments by analyzing the underlying financial, economic, and qualitative factors that affect their value. This analysis includes studying financial statements, economic indicators, industry trends, and company-specific information to determine the intrinsic value of an asset. Fundamental analysts seek to identify assets that are overvalued or undervalued based on their analysis and make investment decisions accordingly.

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GARCH (Generalized Autoregressive Conditional Heteroskedasticity): GARCH is a statistical model used to analyze and forecast financial time series data, particularly volatility. It accounts for the conditional heteroskedasticity, or changing volatility, often observed in financial markets. GARCH models are employed to estimate and predict the variance or volatility of asset returns, aiding risk management and options pricing.

Gaussian Distribution: Also known as the normal distribution, the Gaussian distribution is a probability distribution characterized by a symmetric bell-shaped curve. It is frequently used in finance to model asset returns, assuming that returns are normally distributed. Many statistical methods and risk models in finance rely on the assumption of normal distribution, though in reality, asset returns often exhibit fat tails and deviations from perfect normality.

Genetic Algorithm: A genetic algorithm is a heuristic optimization technique inspired by the process of natural selection. It is used in trading to find optimal parameter settings for trading strategies. Genetic algorithms create a population of potential solutions (sets of parameters), evolve and combine them through selection, mutation, and recombination, and iteratively improve the solutions until an optimal or near-optimal set of parameters is found. Traders use genetic algorithms to optimize trading strategies for maximum returns.

Grid Search: Grid search is a systematic method for finding the optimal combination of parameters for a trading strategy. It involves specifying a range of possible values for each parameter and testing all possible combinations within those ranges. Grid search is useful for exploring the entire parameter space of a strategy and finding the parameter values that yield the best performance based on a chosen performance metric.


Hedging: (complete definition) Hedging is a risk management strategy used by traders and investors to protect their portfolios from adverse price movements. It involves taking positions or using financial instruments that offset potential losses in other investments. For example, if an investor holds a portfolio of stocks and fears a market downturn, they may hedge by purchasing put options or short-selling index futures to profit from falling prices. Hedging strategies aim to reduce overall risk while allowing investors to maintain exposure to their desired investments.

Heteroskedasticity: Heteroskedasticity is a statistical term that describes the phenomenon where the variability or dispersion of data points in a dataset is not constant across different levels of an independent variable. In finance, heteroskedasticity in asset returns can have implications for risk assessment and model accuracy, as it violates the assumption of constant volatility.

Historical testing in the context of backtesting trading strategies refers to the process of evaluating the performance and viability of a trading strategy using historical market data. It involves simulating the execution of trades based on the strategy’s rules and parameters to assess how it would have performed in the past. This assessment helps traders and investors gauge the strategy’s effectiveness, risk, and potential profitability. Historical testing allows for the identification of strengths and weaknesses in the strategy and provides valuable insights into its historical performance, aiding in the decision-making process for future trading activities and investment decisions.

Hurst Exponent: The Hurst Exponent is a statistical measure used to assess the long-term memory or persistence in a time series data set. In finance, it helps analyze the trend-following or mean-reverting characteristics of financial prices. A Hurst Exponent value between 0.5 and 1 suggests a trending behavior, while values between 0 and 0.5 indicate mean-reverting behavior.

Holding Period Return (HPR): HPR calculates the total return earned from holding an investment over a specific period. It considers all income received from the investment, including capital gains, dividends, and interest.


Information Coefficient (IC): Information Coefficient measures the correlation between a portfolio manager’s forecasts and the actual returns of the assets they manage. A high IC indicates that the manager’s forecasts are accurate and valuable for generating alpha.

In-Sample Testing: In-sample testing is the initial phase of backtesting where a trading strategy is tested on the historical data it was developed with. While this stage provides a starting point for evaluating strategy viability, it can also lead to overfitting if not followed by out-of-sample testing. Overfitting occurs when a strategy is excessively tailored to historical data and may not perform well in live trading.


Jarque-Bera Test for Normality: The Jarque-Bera Test is a statistical test used to assess whether a given dataset follows a normal distribution. In the context of financial analysis, it helps determine whether asset returns or financial variables exhibit a Gaussian (bell-shaped) distribution. Deviations from normality can have implications for risk modeling and asset pricing, making this test valuable for financial analysts and researchers.

Jensen’s Alpha: Jensen’s Alpha, also known as the Jensen index or Jensen’s measure, is a risk-adjusted performance measure used in finance. It evaluates the excess return generated by an investment compared to its expected return based on the asset’s beta with respect to a market index (often the market portfolio). A positive Jensen’s Alpha indicates that the investment has outperformed its expected return, considering its systematic risk. This measure helps investors assess whether a fund manager’s skill has added value beyond market returns.

Jump Diffusion Model: A Jump Diffusion Model is a mathematical framework used in finance to describe the price movements of assets or securities, particularly when there are sudden, discontinuous jumps or spikes in their values. It combines elements of continuous diffusion processes (Brownian motion) and discrete jump processes to capture the irregular and unpredictable nature of market events. This model is valuable for assessing the risk associated with assets that experience occasional extreme fluctuations, making it useful for pricing options and managing portfolio risk in situations where standard continuous models like the Black-Scholes model fall short.


Kelly Criterion: The Kelly Criterion is a mathematical formula used to determine the optimal bet size or position size for a series of trades or investments. It takes into account the trader’s edge (the expected return on each trade) and the risk of ruin (the likelihood of losing the entire capital). The goal is to maximize long-term capital growth while minimizing the risk of significant losses. The Kelly Criterion provides a precise way to allocate capital to different trades or strategies to achieve the best risk-adjusted returns over time.

Kolmogorov-Smirnov Test: The Kolmogorov-Smirnov Test is a statistical method used to assess whether a sample of data follows a specific probability distribution or whether two samples are drawn from the same distribution. It calculates a test statistic based on the maximum difference between the empirical cumulative distribution function of the data and the theoretical cumulative distribution function. By comparing this statistic to critical values from a reference distribution, such as the Kolmogorov-Smirnov distribution, one can determine if there is a significant difference between the observed data and the expected distribution. This test is valuable for various applications in statistics and hypothesis testing.


Leverage: Leverage refers to the use of borrowed funds or debt to amplify the potential returns or losses of an investment or trading position. In the context of trading, leverage allows traders to control a larger position size with a smaller amount of capital. While leverage can magnify profits, it also increases the risk of substantial losses. It is important for traders to use leverage cautiously and understand the associated risks and margin requirements imposed by brokers.

Limit Order: A limit order is a type of order placed by a trader to buy or sell a financial instrument at a specific price or better. Unlike market orders that execute immediately at the current market price, limit orders only execute when the market reaches the specified price. Buy limit orders are placed below the current market price, while sell limit orders are placed above it. Limit orders allow traders to have more control over their entry and exit prices but may not guarantee execution if the market does not reach the specified price.


Mandelbrot Set: The Mandelbrot Set is a famous fractal in mathematics, named after its discoverer BenoĆ®t B. Mandelbrot. It’s a complex mathematical object generated through iterative calculations in the complex number plane. The set consists of points that, when iterated under a specific mathematical formula, either remain within a certain boundary or escape to infinity. The intricate and self-replicating patterns within the Mandelbrot Set make it a visually stunning example of fractal geometry. It has applications in computer graphics, chaos theory, and the study of complex systems, serving as a symbol of the beauty and complexity of mathematical structures.

Markov Chain Monte Carlo (MCMC): Markov Chain Monte Carlo (MCMC) is a powerful statistical technique used for approximate numerical computation and sampling from complex probability distributions. It employs a Markov chain to generate a sequence of samples that converge to the desired distribution. MCMC methods are invaluable in Bayesian statistics, where they facilitate the estimation of posterior distributions for models with many parameters. By iteratively sampling from the posterior distribution, MCMC enables probabilistic inference, parameter estimation, and uncertainty quantification in a wide range of fields, including machine learning, physics, and social sciences.

Markov Regime-Switching Model: A Markov Regime-Switching Model is a time series model used in economics and finance to describe data with changing regimes or states over time. Each regime represents a distinct set of statistical properties, such as mean and volatility. Transitions between these regimes follow a Markov process, meaning they depend only on the current state and not the historical path. This model allows for capturing structural breaks, regime shifts, and volatility clustering in financial markets and economic data, making it useful for risk management, forecasting, and understanding complex time series behavior.

Mean Absolute Deviation (MAD): Mean Absolute Deviation (MAD) is a statistical measure of the average absolute difference between individual data points and the mean (average) of a dataset. It quantifies the dispersion or variability in the data. MAD is calculated by summing the absolute differences between each data point and the mean, then dividing by the number of data points. It is less sensitive to outliers than the standard deviation, making it a useful measure when robustness to extreme values is desired. MAD is employed in various fields, including finance and data analysis, to assess the spread of data points and evaluate the accuracy of forecasts or models.

Monte Carlo Simulation: Monte Carlo Simulation is a computational technique used in backtesting to model complex systems, such as financial markets. It involves generating random samples to simulate various market scenarios, allowing for the assessment of a strategy’s performance under different conditions and the quantification of risk.


Normal Distribution: The normal distribution, also known as the Gaussian distribution, is a mathematical probability distribution characterized by a symmetric bell-shaped curve. In finance, it is commonly used to model the distribution of asset returns and various financial phenomena. The normal distribution is defined by two parameters: the mean (average) and the standard deviation (a measure of variability). While many financial models assume that returns are normally distributed, in reality, asset returns often exhibit deviations from perfect normality, including fat tails and skewness.


Optimization: Optimization involves the process of fine-tuning a trading strategy by adjusting its parameters to achieve specific objectives, such as maximizing returns or minimizing risk. While optimization can improve a strategy’s performance, it must be performed cautiously to avoid overfitting. Over-optimizing can lead to strategies that perform exceptionally well in historical data but poorly in real-world scenarios.

Outliers: Outliers are data points that significantly deviate from the typical or expected values in a dataset. These anomalies can distort backtesting results, and it is important to handle them appropriately to avoid misinterpretation.

Out-of-Sample Testing: Out-of-sample testing is a critical step in the backtesting process where a trading strategy is evaluated using data that was not used in the strategy’s development. This approach helps determine how well the strategy is likely to perform in real-world, unseen market conditions. It helps identify if the strategy has been overfitted to historical data and whether it can adapt to new market dynamics.

Overfitting: Overfitting is a common pitfall in backtesting where a trading strategy is overly optimized to historical data, capturing noise and false patterns instead of true market dynamics. Overfitted strategies often perform well on historical data but fail to generalize to new market conditions, resulting in poor real-world performance. It’s essential to strike a balance between optimizing a strategy and ensuring its robustness.


Past performance assessment: The retrospective evaluation of a trading strategy’s historical performance, allowing traders to gauge its effectiveness based on historical data.

Performance evaluation: The process of assessing the results and outcomes of a trading strategy, typically measured by returns, risk-adjusted metrics, and other performance indicators.

Performance Measure Item: A performance measure item refers to a specific metric or indicator used to assess the performance of a trading strategy. It could include metrics like return, risk-adjusted return, volatility, and drawdown, among others.

Performance Metrics: Performance metrics are specific measures used to evaluate the success of a trading strategy. They encompass a wide range of indicators, including total return, risk-adjusted return, drawdown, and more, to provide a comprehensive assessment of a strategy’s performance.

Poisson Process: A Poisson Process is a mathematical model used to describe the occurrence of random, rare events over time or space. It is characterized by the properties that events occur independently of one another and at a constant average rate or intensity. The Poisson Process is widely applied in fields such as queuing theory, epidemiology, and finance to model events like customer arrivals, disease outbreaks, or financial market price jumps. Its simplicity and mathematical tractability make it a valuable tool for understanding and predicting rare, unpredictable events in various domains.

Price Action: (Price Action Trading Glossary) Price Action refers to the analysis of historical price movements and patterns in financial markets, primarily focusing on raw price data like open, close, high, and low prices, as well as volume. It is a fundamental component of technical analysis, helping traders by identifying trends, support/resistance levels, and potential future price movements based on past price behavior. Price Action is often used in backtesting to assess the effectiveness of trading strategies by evaluating their performance against historical price data. 


Quantamental Analysis: Quantamental Analysis is an investment approach that combines quantitative (data-driven) and fundamental (qualitative) analysis to make informed investment decisions. It involves leveraging both quantitative models and traditional financial analysis, such as evaluating company financials, management quality, and industry trends. By integrating quantitative factors like statistical models, machine learning algorithms, and alternative data sources, investors aim to gain a more comprehensive understanding of asset performance and market opportunities. Quantamental analysis seeks to enhance investment strategies by harnessing the power of data-driven insights while considering traditional financial fundamentals.

Quantitative analysis: The use of mathematical and statistical techniques to evaluate trading strategies, including measures of risk, return, and other quantitative performance metrics.

Quantile-Quantile Plot (Q-Q Plot): A Quantile-Quantile Plot (Q-Q Plot) is a graphical tool used in statistics to assess the similarity between two probability distributions. It compares the quantiles (ordered data values) of a sample or dataset to the quantiles of a theoretical distribution, typically the normal distribution. If the points on the Q-Q plot approximately follow a straight line, it suggests that the sample distribution is similar to the theoretical distribution. Deviations from the straight line indicate differences in distribution shape or location. Q-Q plots are valuable for checking assumptions and diagnosing distributional characteristics in statistical analyses.

Quantitative Analysis: Quantitative analysis involves using mathematical and statistical methods to evaluate trading strategies and make data-driven investment decisions. It relies on historical data, statistical models, and mathematical formulas to analyze asset prices, risk factors, and market dynamics.


Research: The systematic investigation and study of trading strategies, using historical data and empirical analysis to gain insights and develop more profitable and reliable trading approaches.

Retroactive analysis: The examination of historical trading data to assess the effectiveness and profitability of a trading strategy applied to past market conditions, helping traders refine their approach.

Risk-Adjusted Return: (Definition) Risk-adjusted return is a measure that assesses the performance of an investment or trading strategy in relation to the level of risk taken. Commonly used metrics for risk-adjusted return include the Sharpe ratio and Sortino ratio. These ratios consider not only the total return generated by the strategy but also the volatility or downside risk associated with it. A higher risk-adjusted return indicates a better trade-off between returns and risk.


Sensitivity Analysis: Sensitivity analysis involves systematically varying input parameters of a trading strategy to understand how changes affect its performance. It helps identify robust strategies and potential vulnerabilities to different market conditions or parameter settings.

Simulation: In the context of backtesting trading strategies, simulation refers to the process of mimicking real-market conditions and historical price movements to assess the performance of a trading strategy. Traders use historical data to simulate how their strategy would have performed in the past, allowing them to evaluate its effectiveness and potential risks. This helps in making informed decisions about implementing or refining trading strategies in real-time financial markets.

Slippage: Slippage refers to the difference between the expected execution price of a trade and the actual price at which it is executed. It is a common occurrence in real trading due to market volatility and liquidity constraints. Slippage can impact a strategy’s performance by causing deviations from expected results and must be considered when backtesting to reflect real-world trading conditions accurately.

Spline Interpolation: Spline Interpolation is a mathematical technique used for approximating or interpolating data points with a smooth curve or series of polynomial functions called splines. Splines are piecewise-defined functions that connect data points smoothly while ensuring continuity in both value and derivatives. Spline interpolation is commonly used in computer graphics, engineering, and data analysis to create smooth curves from discrete data, such as interpolating between data points on a graph or fitting curves to experimental data. It provides a flexible and versatile way to capture the underlying trends in datasets.

Strategy verification: The process of rigorously testing a trading strategy using historical data to confirm its viability and effectiveness in real-market conditions.


Test Variable: A test variable is a parameter or factor that is deliberately adjusted during the backtesting process to evaluate its impact on the performance of a trading strategy. This helps in optimizing strategy parameters and assessing sensitivity to changes.

Trading System: A trading system is a set of predefined rules and parameters that guide the decision-making process for buying and selling assets. It forms the core of a trading strategy and serves as a systematic approach to executing trades based on specific criteria and signals.

Transaction Costs: Transaction costs encompass all expenses associated with buying and selling assets, including brokerage fees, commissions, and bid-ask spreads. These costs directly impact the profitability of a trading strategy and should be factored into backtesting to provide a more realistic assessment of strategy performance.

T-statistic: The T-statistic is a statistical measure used to assess the significance of the difference between a sample statistic (e.g., sample mean) and a population parameter when the population standard deviation is unknown. It is calculated by dividing the difference between the sample statistic and the population parameter by an estimate of the standard error of the sample statistic. The resulting T-statistic is then compared to critical values from the Student’s t-distribution to determine if the difference is statistically significant. T-statistics are widely used in hypothesis testing, confidence interval estimation, and comparing means in various fields, including science, medicine, and social sciences.

Turnover: Turnover measures the frequency with which assets are bought or sold within a portfolio over a specific period. High turnover can result in increased transaction costs and tax implications, which need to be considered when evaluating a strategy’s performance.


Underlying Asset: The underlying asset is the financial instrument, security, or commodity on which a derivative contract is based. In options and futures trading, the underlying asset determines the value and characteristics of the derivative contract. For example, in stock options, the underlying asset is the stock itself. Understanding the underlying asset is crucial for traders and investors because changes in its price directly impact the value of the derivative contract.


VaR Backtesting: VaR Backtesting is a risk management technique used in the financial industry to evaluate the accuracy and reliability of Value at Risk (VaR) models. VaR is a statistical measure that estimates the maximum potential loss a portfolio or investment may incur over a specified time horizon at a given confidence level. Backtesting involves comparing the actual losses observed in historical data with the VaR predictions made by the model. If the observed losses consistently exceed the VaR estimates, it may indicate a deficiency in the model’s performance. VaR backtesting is crucial for ensuring that risk assessments are robust and align with actual market conditions, aiding in better risk management and regulatory compliance in financial institutions.

Volatility: Volatility refers to the degree of variation in an asset’s or market’s price over time. It is a key metric for risk assessment in backtesting, as higher volatility implies greater price fluctuations and potential for both gains and losses.


Walk-Forward Testing: Walk-forward testing is a dynamic approach to backtesting where a trading strategy is periodically re-optimized and validated using rolling windows of historical data. This iterative process helps assess the strategy’s adaptability to changing market conditions and enhances its real-world performance.


X-axis: The X-axis, also known as the horizontal axis, is a fundamental component of a graph or chart used in trading and finance, among other fields. It represents the independent variable or the data categories, such as time, asset prices, or other relevant factors. In the context of financial charts, time is often plotted along the X-axis, allowing you to track the performance of a financial instrument (e.g., stock price) over a specific period.


Yield Curve: The yield curve is a graphical representation of interest rates on bonds of varying maturities, typically plotted on a chart. It shows the relationship between the interest rate (or yield) and the time to maturity for a set of bonds. The yield curve is a crucial indicator for assessing the state of the economy and predicting future interest rate changes. Different shapes of the yield curve (e.g., upward-sloping, flat, inverted) can signal various economic conditions and expectations, influencing investment and trading decisions.

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Zero-Coupon Bond: A zero-coupon bond is a type of bond that does not make periodic interest or coupon payments to the bondholder. Instead, it is sold at a discount to its face value and redeemed at face value upon maturity. The difference between the purchase price