Fixed income analysis doesn't have to feel like deciphering ancient hieroglyphics. Whether you're evaluating corporate bonds for a pension fund or analyzing municipal securities for a portfolio, the right tools can turn mountains of data into crystal-clear investment insights.
Imagine having an AI assistant that can calculate yield to maturity
, assess duration risk
, and build comprehensive credit analysis models—all within a familiar spreadsheet interface. That's the power of modern fixed income analysis.
Transform your bond analysis workflow with these game-changing capabilities
Calculate yield to maturity, current yield, and yield to call instantly. No more manual formulas or Excel crashes with large datasets.
Measure interest rate sensitivity with precise duration and convexity calculations. Understand how rate changes impact your portfolio.
Analyze credit spreads, default probabilities, and rating migration patterns. Make informed decisions about credit exposure.
Optimize asset allocation across different fixed income sectors. Balance risk and return with sophisticated modeling tools.
Connect to live bond prices and yield curves. Keep your analysis current with streaming market data integration.
Model portfolio performance under various interest rate scenarios. Prepare for market volatility with comprehensive stress tests.
Picture this: You're reviewing a portfolio of 200 corporate bonds, each with different maturities, credit ratings, and call provisions. Your stakeholders want to know how a 100 basis point rate increase would affect the portfolio's value. Oh, and they need it by tomorrow morning.
Traditional spreadsheet analysis would have you pulling all-nighters, manually updating formulas and praying your calculations are correct. But with AI-powered analysis, you can model complex scenarios in minutes, not hours.
Today's bond markets are more complex than ever. You're dealing with:
Each of these elements adds layers of complexity to your analysis. The old approach of static spreadsheets simply can't keep pace.
See how professionals use advanced analysis tools to solve complex investment challenges
A portfolio manager needs to rebalance a $500M corporate bond portfolio across different sectors and credit ratings. Using duration-matching algorithms and credit spread analysis, they identify opportunities to improve yield while maintaining risk targets. The analysis reveals that shifting 10% from financials to industrials could increase portfolio yield by 15 basis points without increasing duration risk.
An analyst evaluates municipal bonds from various issuers facing budget pressures. By analyzing revenue trends, debt service coverage ratios, and demographic data, they build a comprehensive credit scoring model. The analysis identifies bonds with strong fundamentals trading at attractive spreads, generating alpha for the fund.
A pension fund needs to match asset duration with liability duration to minimize interest rate risk. Using advanced duration and convexity calculations, they construct a ladder of Treasury bonds and corporate securities that closely matches their 12-year liability profile while maximizing yield pickup opportunities.
An investment team analyzes callable corporate bonds to determine fair value and call risk. Using option-adjusted spread (OAS) analysis and Monte Carlo simulations, they model various interest rate paths to assess the probability of early redemption. This helps them identify bonds trading below fair value with attractive risk-adjusted returns.
A credit analyst evaluates distressed bonds using fundamental analysis combined with technical indicators. By analyzing cash flow coverage, liquidity ratios, and market-based credit metrics, they identify restructuring candidates with potential for significant recovery. The comprehensive analysis framework helps distinguish between value opportunities and value traps.
An emerging markets specialist analyzes sovereign and corporate bonds across different countries and currencies. Using macroeconomic indicators, political risk assessments, and currency hedging strategies, they build a diversified portfolio that balances yield enhancement with capital preservation in volatile markets.
Follow this systematic approach to create professional-grade bond analysis models
Start by gathering bond characteristics: coupon rates, maturity dates, call provisions, and credit ratings. Import market data including current prices, yield curves, and credit spreads. Clean and validate all data to ensure accuracy in your analysis models.
Calculate key yield metrics including current yield, yield to maturity, and yield to worst. For callable bonds, compute yield to call for each call date. Build total return scenarios incorporating price appreciation, coupon income, and reinvestment assumptions.
Measure duration and convexity to assess interest rate sensitivity. Calculate credit risk metrics including probability of default and loss given default. Analyze liquidity risk by examining bid-ask spreads and trading volumes in secondary markets.
Model portfolio performance under different interest rate environments. Test credit stress scenarios including rating downgrades and default events. Analyze the impact of yield curve changes including parallel shifts, twists, and butterfly movements.
Use mean-variance optimization to construct efficient portfolios. Apply constraints for sector limits, credit quality minimums, and duration targets. Incorporate transaction costs and liquidity considerations into the optimization framework.
Create comprehensive reports showing portfolio characteristics, risk metrics, and performance attribution. Set up automated alerts for credit rating changes, call notices, and significant price movements. Establish regular rebalancing triggers based on risk and return criteria.
AI-powered models typically achieve 95%+ accuracy in bond valuation by incorporating more variables and market dynamics than traditional models. They can process real-time market data, credit events, and macroeconomic indicators simultaneously, leading to more precise fair value estimates and better identification of mispriced securities.
Yes, advanced analysis tools can handle complex structured products including MBS, ABS, and CDOs. The platform can model prepayment speeds, credit enhancement structures, and waterfall payment mechanisms. You can analyze cash flow patterns, extension and contraction risk, and option-adjusted spreads for these sophisticated instruments.
Modern analysis platforms integrate ESG scoring data directly into credit risk models. You can weight ESG factors alongside traditional financial metrics, analyze green bond premiums, and assess how sustainability factors affect credit quality over time. This helps identify ESG leaders and laggards within fixed income portfolios.
Effective stress testing involves multiple scenarios: parallel yield curve shifts, curve steepening/flattening, credit spread widening, and correlation breakdowns. Run Monte Carlo simulations with thousands of interest rate paths, model tail risk events, and analyze portfolio performance across different economic cycles to ensure robust risk management.
Set up automated workflows that pull daily market data, update portfolio valuations, and generate regulatory reports. Create alerts for duration drift, credit rating changes, and concentration limits. Automated reporting can handle NAIC filings, ERISA compliance, and custom investor reporting while ensuring data accuracy and timeliness.
Modified duration assumes no change in cash flows and works well for option-free bonds. Effective duration accounts for changing cash flows in bonds with embedded options (callable, putable, or convertible bonds). For these securities, effective duration provides a more accurate measure of price sensitivity to interest rate changes, especially when rates are near option strike levels.
To analyze spreadsheet data, just upload a file and start asking questions. Sourcetable's AI can answer questions and do work for you. You can also take manual control, leveraging all the formulas and features you expect from Excel, Google Sheets or Python.
We currently support a variety of data file formats including spreadsheets (.xls, .xlsx, .csv), tabular data (.tsv), JSON, and database data (MySQL, PostgreSQL, MongoDB). We also support application data, and most plain text data.
Sourcetable's AI analyzes and cleans data without you having to write code. Use Python, SQL, NumPy, Pandas, SciPy, Scikit-learn, StatsModels, Matplotlib, Plotly, and Seaborn.
Yes! Sourcetable's AI makes intelligent decisions on what spreadsheet data is being referred to in the chat. This is helpful for tasks like cross-tab VLOOKUPs. If you prefer more control, you can also refer to specific tabs by name.
Yes! It's very easy to generate clean-looking data visualizations using Sourcetable. Simply prompt the AI to create a chart or graph. All visualizations are downloadable and can be exported as interactive embeds.
Sourcetable supports files up to 10GB in size. Larger file limits are available upon request. For best AI performance on large datasets, make use of pivots and summaries.
Yes! Sourcetable's spreadsheet is free to use, just like Google Sheets. AI features have a daily usage limit. Users can upgrade to the pro plan for more credits.
Currently, Sourcetable is free for students and faculty, courtesy of free credits from OpenAI and Anthropic. Once those are exhausted, we will skip to a 50% discount plan.
Yes. Regular spreadsheet users have full A1 formula-style referencing at their disposal. Advanced users can make use of Sourcetable's SQL editor and GUI, or ask our AI to write code for you.