Financial Toolbox
Analyze financial data and develop financial models
Financial Toolbox™ provides functions for the mathematical modeling and statistical analysis of financial data. You can analyze, backtest, and optimize investment portfolios taking into account turnover, transaction costs, semi-continuous constraints, and minimum or maximum number of assets. The toolbox enables you to estimate risk, model credit scorecards, analyze yield curves, price fixed-income instruments and European options, and measure investment performance.
Stochastic differential equation (SDE) tools let you model and simulate a variety of stochastic processes. Time series analysis functions let you perform transformations or regressions with missing data and convert between different trading calendars and day-count conventions.
Get Started:
Data Preprocessing
Convert date and time formats, taking into account business day conventions, day count conventions, custom trading calendars, and coupon payment dates. Use timetable capabilities in MATLAB® to remove entries with missing data and outliers and to resample, aggregate, and synchronize time-associated data.
Technical Indicators and Financial Charts
Compute technical indicators (including moving averages, momentums, oscillators, volume indicators, and rate of change) and create financial charts (including candlestick, open-high-low-close, and Bollinger band charts).
Financial charts and technical indicators.
Investment Performance Metrics
Evaluate investment performance using built-in functions for calculating metrics such as Sharpe ratio, information ratio, tracking error, risk-adjusted return, sample lower partial moments, expected lower partial moments, maximum drawdown, and expected maximum drawdown.
Equity curve from backtesting with performance metrics.
Portfolio Optimization Approaches
Perform mean-variance, mean absolute deviation (MAD), and conditional value at risk (CVaR) portfolio optimizations.
Portfolio optimization application built using MATLAB and Financial Toolbox.
Efficient Portfolios and Efficient Frontiers
Estimate the efficient portfolio and its weights that maximize Sharpe ratio, visualize efficient frontiers, and calculate portfolio risks, including portfolio standard deviation, MAD, VaR, and CVaR.
Efficient frontier and optimal portfolio.
Portfolio Constraints and Transaction Costs
Apply portfolio optimization constraints, including tracking error, linear inequality, linear equality, bound, budget, group, group ratio, average turnover, one-way turnover, minimum number of assets, and maximum number of assets. Incorporate proportional or fixed transaction costs on either gross or net portfolio return optimization.
Efficient frontiers plot of portfolios at various turnover thresholds.
Strategy Backtesting Framework
Define investment strategies and use the backtesting framework to run backtests, analyze results, and generate performance metrics for your strategies from historical or simulated market data. Incorporate technical indicators, sentiment, and other trading signals into your strategies. The framework also supports custom transaction costs, expanding or rolling lookback windows, margin trading, and long/short portfolios.
Equity curves comparing backtests of multiple investment strategies.
Cash Flow Analysis
Use Financial Toolbox to calculate present and future values; determine nominal, effective, and modified internal rates of return; calculate amortization and depreciation; and determine the periodic interest rate paid on loans or annuities.
Cash flow diagram.
Fixed-Income Analysis and Option Pricing
Calculate price, yield-to-maturity, duration, and convexity of fixed-income securities. Compute analytics such as complete cash flow date, cash flow amounts, and time-to-cash-flow mapping for bonds. Calculate option prices and greeks using Black and Black-Scholes formulas. You can design, price, and hedge complex financial instruments with Financial Instruments Toolbox™.
Gamma (z-axis height) and delta (color) for a portfolio of call options.
Monte Carlo Simulation
Generate random variables for Monte Carlo simulations based on a variety of stochastic differential equation (SDE) models, including Brownian motion, geometric Brownian motion, constant elasticity of variance, Cox-Ingersoll-Ross, Hull-White/Vasicek, and Heston.
Single path of a multidimensional market model.
Backtesting Workflow
Define investment strategies, run backtests, and summarize results
Stochastic Differential Equation Models
Transition density simulation for Heston and Bates models
Portfolio Analysis
Calculate period-over-period (PoP) rolling returns with business calendars
See release notes for details on any of these features and corresponding functions.
Computational Finance Suite
The MATLAB Computational Finance Suite is a set of 12 essential products that enables you to develop quantitative applications for risk management, investment management, econometrics, pricing and valuation, insurance, and algorithmic trading.
