Gian
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7 min
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January 21, 2026
Quantitative analysis uses mathematics, statistics, and data to evaluate investments and markets. It focuses on numbers rather than narratives, providing objective insights for decisions.
Core Concepts
Everything starts with good data. Quantitative analysis relies on historical prices, trading volumes, financial ratios (P/E, ROE, debt-to-equity), economic figures (GDP, inflation, interest rates) and sometimes alternative sources like news sentiment or satellite imagery. Data must be accurate and complete, adjust for stock splits, dividends and currency changes. Free sources include Yahoo Finance and Alpha Vantage, paid ones like Bloomberg offer more depth. Clean data is non-negotiable: missing values, errors or outliers can ruin results.
Descriptive Statistics
These numbers summarize what the data shows. The mean return gives the average performance over a period, but the median is often more reliable when extreme gains or losses distort the picture. Standard deviation measures how much returns vary, higher values mean more volatility and risk. Skewness reveals asymmetry: negative skew indicates more frequent large losses, which is common in stocks. Kurtosis shows whether the distribution has fat tails, high kurtosis means extreme events happen more often than a normal bell curve predicts. These metrics help investors quickly understand the character of returns before moving to more complex models.
Probability Distributions
Financial theory often assumes returns follow a normal distribution, but real markets behave differently. Prices follow a lognormal distribution because they cannot go below zero. Returns show fat tails (leptokurtosis), meaning crashes and booms are more common than normal models expect. Understanding these patterns is essential for pricing options, estimating risk and avoiding underestimating tail events like market crashes.
Hypothesis Testing
This method checks whether results are meaningful or just random noise. A t-test compares average returns to see if a strategy beats a benchmark. The p-value shows the probability of observing the data if the null hypothesis (no real effect) is true, p < 0.05 is a common threshold for significance. In backtesting, running many tests increases the chance of false positives. Adjustments like Bonferroni correction help keep conclusions reliable.
Regression Analysis
Regression models the relationship between variables. Simple linear regression predicts one variable from another: stock return = alpha + beta × market return + error. Beta shows how sensitive the asset is to market moves, alpha measures excess return. Multiple regression includes additional factors (size, value, momentum) as in the Fama-French model, helping explain why some assets outperform others.
Time Series Analysis
Financial data is ordered over time, so past values influence future ones. Autocorrelation detects patterns that repeat from previous periods. ARIMA models forecast returns by combining autoregression, differencing and moving averages. GARCH models capture volatility clustering, periods of high volatility tend to follow high volatility. These tools are particularly useful for predicting trends and modeling risk in price series.
Risk Metrics
Value at Risk (VaR) estimates the worst expected loss over a period at a given confidence level (e.g., 95% VaR of 5% means a 5% chance of losing more than 5%). Expected Shortfall (CVaR) averages losses beyond VaR to give a fuller picture of tail risk. The Sharpe ratio = (portfolio return – risk-free rate) / standard deviation measures return per unit of risk. Higher values indicate better risk-adjusted performance.
Portfolio Basics
Modern Portfolio Theory (Markowitz) uses mean-variance optimization to find the efficient frontier, the set of portfolios with the highest expected return for a given level of risk. Correlation and covariance matrices are key: assets with low or negative correlations reduce overall portfolio volatility. This approach helps allocate capital to balance expected return and risk.
Tools
Excel is sufficient for beginners, use functions for averages, standard deviation, correlation and the Data Analysis ToolPak for regressions. Python (pandas for data handling, numpy for calculations, statsmodels for modeling) and R offer more power for advanced work. Free platforms like QuantConnect allow strategy testing without deep coding knowledge.
Common Pitfalls
Overfitting happens when a model fits historical noise instead of real patterns and fails in live markets. Data snooping bias occurs from testing too many ideas until one appears to work by chance. Survivorship bias ignores delisted stocks, inflating historical returns. Look-ahead bias uses future information in backtests. Always test models on out-of-sample data and apply economic reasoning to avoid these errors.
Quantitative analysis brings structure and objectivity to investing. It is powerful when used carefully, combined with sound judgment and proper risk controls.
Disclaimer: The content provided in this blog post is for informational and educational purposes only and does not constitute financial, investment, or other professional advice. All data, figures, and examples are illustrative and should not be interpreted as guarantees of future performance or recommendations for specific investment actions. While we strive to ensure the accuracy of the information presented, we make no representations or warranties as to its completeness, reliability, or suitability for your individual financial situation. Always consult with a qualified financial advisor or professional before making any investment decisions. The author disclaims any liability for actions taken based on the information provided herein.