Creating Multi-Axis Charts

Creating Multi-Axis Charts with ChatGPT

Multi-axis charts display multiple related datasets with different scales on a single visualization. This guide demonstrates how to use ChatGPT to create effective multi-axis charts for CMBS and ABS analysis, enabling complex comparisons and revealing relationships between different metrics.

When to Use Multi-Axis Charts

Multi-axis charts are most effective when you need to:

Compare Different Metrics - Display volume and performance metrics simultaneously
Show Correlations - Reveal relationships between different data types
Optimize Space - Present multiple perspectives without cluttering dashboards
Track Multiple Trends - Monitor various KPIs that move at different scales

Types of Multi-Axis Configurations

Dual Y-Axis Charts

The most common configuration:

Left Axis - Primary metric (often volume or count data)
Right Axis - Secondary metric (often percentage or rate data)
Shared X-Axis - Common time period or category dimension

Effective Axis Pairings

Volume vs. Performance - Deal count vs. average pricing
Absolute vs. Relative - Dollar amounts vs. percentage changes
Leading vs. Lagging - Economic indicators vs. market performance
Actual vs. Benchmark - Real performance vs. target metrics

AI-Powered Multi-Axis Creation

Dual-Axis Chart Generator

Create a dual-axis chart combining these two datasets: AXIS CONFIGURATION: - Use the left Y-axis for [primary metric] with appropriate scale - Use the right Y-axis for [secondary metric] with its own scale - Ensure both axes start at logical zero points or appropriate baselines - Label each axis clearly with units (dollars, percentages, counts, etc.) VISUAL DESIGN: - Use different chart types for each dataset (e.g., bars for volume, line for rates) - Apply distinct colors for each dataset that work well together - Add a clear legend identifying which data series uses which axis - Ensure both datasets are easily distinguishable ANALYTICAL ENHANCEMENT: - Highlight any correlations or inverse relationships between the datasets - Add annotations for significant events that affected both metrics - Include trend lines if they help clarify relationships - Mark any periods where the metrics diverge significantly Focus on revealing the relationship between the two metrics clearly.

Financial Performance Multi-Axis

Create a multi-axis chart for financial performance analysis: PERFORMANCE METRICS SETUP: - Left axis: Volume metrics (loan counts, deal volumes, pool sizes) - Right axis: Performance metrics (yields, spreads, delinquency rates) - Time axis: Monthly or quarterly periods for trend analysis CORRELATION ANALYSIS: - Highlight periods where volume and performance move together - Mark periods where they move in opposite directions - Add correlation coefficient if relevant to the analysis - Include market event annotations that explain major movements RISK PERSPECTIVE: - Use color coding to indicate risk periods vs. stable periods - Add threshold lines for important performance benchmarks - Highlight volume spikes that coincide with performance deterioration - Mark regulatory or market events that impacted both metrics Create a chart that reveals insights about the relationship between market activity and performance.

Design Best Practices

Scale Management

Independent Scaling - Each axis should use its optimal range
Zero Baseline - Consider whether axes should start at zero
Proportional Relationships - Ensure visual proportions reflect data relationships
Range Optimization - Use ranges that highlight important variations

Visual Differentiation

Chart Type Mixing - Combine bars, lines, and areas effectively
Color Coordination - Use complementary colors that work together
Pattern Usage - Add patterns for accessibility and print compatibility
Line Styling - Vary line weights, styles, and markers

Applications in Financial Analysis

CMBS Market Analysis

Issuance vs. Spreads - Volume of new deals vs. pricing trends
Delinquencies vs. Property Values - Problem loans vs. underlying collateral values
Geographic Concentration vs. Performance - Regional exposure vs. market performance
Loan Count vs. Average Size - Deal composition analysis over time

ABS Performance Tracking

Pool Size vs. Delinquency Rates - Volume vs. credit performance
Vintage Performance vs. Economic Indicators - Deal performance vs. macro factors
Origination Volume vs. Credit Quality - Market activity vs. underwriting standards
Prepayment Rates vs. Interest Rates - Borrower behavior vs. market conditions

Common Pitfalls and Solutions

Scale Manipulation Issues

Problem: Manipulating scales to force apparent correlations
Solution: Use consistent, logical scaling principles
Best Practice: Document scaling decisions and methodology

Overcomplexity

Problem: Trying to show too many metrics on one chart
Solution: Limit to 2-3 key metrics maximum
Best Practice: Use multiple simpler charts rather than one complex chart

Correlation vs. Causation

Problem: Visual proximity suggesting causal relationships
Solution: Add clear disclaimers about correlation vs. causation
Best Practice: Include statistical analysis when appropriate

Quality Assurance

Technical Verification

Scale Accuracy - Verify all scales accurately represent the data
Data Alignment - Ensure time periods and categories match correctly
Legend Accuracy - Confirm legends match actual chart elements
Calculation Verification - Double-check any derived metrics or ratios

User Experience Testing

Comprehension Speed - Test how quickly users understand the chart
Interpretation Accuracy - Verify users draw correct conclusions
Action Orientation - Confirm charts lead to appropriate decisions
Accessibility Compliance - Ensure charts work for all user types

Conclusion

Multi-axis charts are powerful tools for revealing complex relationships in financial data, but they require careful design to be effective. By thoughtfully combining related metrics and applying sound design principles, you can create visualizations that provide insights impossible to achieve with single-metric charts.

Whether analyzing CMBS market dynamics, tracking ABS performance correlations, or monitoring competitive positioning, multi-axis charts help stakeholders understand the multifaceted nature of financial markets. Use AI tools like ChatGPT to design charts that balance complexity with clarity.

The key to successful multi-axis charts is restraint—include only the metrics that truly need to be compared directly, and ensure that the resulting visualization enhances rather than complicates understanding.