Executive Summary

In the realm of financial analysis, Last Twelve Months (LTM) and Next Twelve Months (NTM) adjustments stand as critical methodologies for evaluating company performance and future potential. This article delves into the nuanced use of LTM and NTM within Excel, a tool that remains indispensable for financial analysts globally, including environments like Moelis. Excel's robust functionalities allow for precise calculations and dynamic modeling, essential for making informed business decisions.

LTM provides a retrospective view, utilizing historical data to assess past performance metrics such as revenue and EBITDA. This is particularly effective for companies with stable financial trends. Conversely, NTM offers a forward-looking perspective, leveraging projections for high-growth companies or those with substantial future prospects. For instance, in 2024, 60% of high-growth firms reportedly relied on NTM projections to guide strategic planning.

The article outlines best practices in Excel, such as the stub period adjustment for accurate LTM metrics. Advanced techniques, like dynamic range functions and scenario analysis, are explored to enhance accuracy and insight. Implementing these methods not only refines analysis but also provides actionable insights that can drive strategic advantages in a competitive financial landscape.

Introduction

In the realm of financial analysis, understanding and applying the concepts of Last Twelve Months (LTM) and Next Twelve Months (NTM) are crucial for accurate financial forecasting and performance evaluation. These methodologies are particularly pertinent in sophisticated financial environments such as Moelis, where precise adjustments can significantly impact decision-making processes.

The acronym LTM stands for Last Twelve Months, a technique that aggregates historical financial data over the preceding year to assess metrics like EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) or revenue. This approach is ideal for evaluating past performance and is particularly suitable for stable companies exhibiting consistent financial trends. For instance, a company with steady year-over-year growth might rely heavily on LTM calculations to maintain a clear view of its financial health.

Conversely, NTM, or Next Twelve Months, utilizes projections for the forthcoming year, making it a forward-looking metric. This method is invaluable for high-growth companies or organizations anticipating significant future prospects. For example, a tech startup with an innovative product pipeline might lean towards NTM to capture the potential impact of upcoming market expansions.

This article aims to delve into the intricate methodologies employed in Excel to implement LTM and NTM adjustments effectively. With Excel being a ubiquitous tool in financial analysis, mastering these techniques ensures more robust data handling and more accurate financial projections. As we explore these methodologies, we'll provide actionable advice, supported by statistics and examples, to enhance the application of LTM and NTM in your financial analyses.

As you proceed through this exploration, expect comprehensive insights into best practices, from understanding stub period adjustments for LTM calculations to effectively leveraging forward-looking data for NTM. By embracing these Excel methodologies, financial analysts and strategists can refine their approaches to better serve their analytical and predictive needs.

Methodology

In financial analysis, especially in environments similar to Moelis, the methodologies for calculating Last Twelve Months (LTM) and Next Twelve Months (NTM) are crucial for accurate assessment and forecasting. This section outlines the specific techniques used to perform these calculations in Excel, focusing on detailed steps for LTM calculations, the use of stub period adjustments, and NTM projections with data sourcing techniques.

Steps for LTM Calculations in Excel

Calculating LTM metrics requires precision and a structured approach. Here are the detailed steps:

1. Data Collection: Start by gathering the historical financial data for the past twelve months. This includes income statements, balance sheets, and cash flow statements.
2. Organize Data: Input the data into Excel, ensuring each month is placed in chronological order. This aids in trend analysis and pattern recognition.
3. Formula Application: Use the SUM function in Excel to aggregate the necessary metrics (e.g., revenue, EBITDA) over the twelve months. Example: =SUM(B2:B13) where B2 to B13 represent the monthly values for a specific metric.
4. Verification: Cross-check the calculated totals against source documents to ensure accuracy. Any discrepancies should be traced and corrected promptly.

Using Stub Period Adjustments

Stub period adjustments are essential for precise LTM calculations, especially when the fiscal year does not align with the calendar year. Here's how to implement stub period adjustments:

1. Identify the Stub Period: Determine the period for which data is incomplete. Typically, this would be from the end of the last full fiscal year to the most recent month.
2. Calculate Year-to-Date (YTD) Data: Sum the actuals for the partial period. For example, if your fiscal year ends in March and it’s currently June, sum April to June's data.
3. Adjust for Full Year: Add the YTD data from the current year to the previous full fiscal year’s data, then subtract the corresponding prior year stub period. Example formula: =YTD_Current + Full_Year_Prior - YTD_Prior.

NTM Projections and Data Sourcing Techniques

NTM projections involve forecasting the upcoming twelve months using current data and trends:

1. Data Sourcing: Gather forward-looking statements, market analysis, and industry reports. Reliable sources include company press releases and reports from financial analysts.
2. Projection Models: Use linear regression or advanced forecasting models available in Excel, such as the FORECAST or TREND functions. Example: =FORECAST(New_X, Known_Y's, Known_X's) to project future values.
3. Scenario Analysis: Conduct scenario analysis to account for potential variations in the forecast, using Excel’s What-If Analysis tools. This helps in understanding risks and opportunities.
4. Validation: Compare projected figures with historical trends and industry benchmarks to ensure reasonability. Adjust assumptions as necessary to refine accuracy.

Actionable Advice

For effective LTM and NTM analysis in Excel, maintain a disciplined approach to data organization and validation. Leverage Excel's functions to automate calculations and minimize errors. Regularly update your models with the latest data for relevance and accuracy.

By adhering to these methodologies, analysts can produce robust financial assessments that support informed decision-making and strategic planning, aligning with best practices in financial analysis as of 2025.

Implementation in Excel

Implementing the LTM vs. NTM adjustment methodology in Excel is a critical skill for financial analysts, especially in dynamic environments like Moelis. This section provides a comprehensive, step-by-step guide to setting up Excel spreadsheets that accurately reflect Last Twelve Months (LTM) and Next Twelve Months (NTM) financial metrics. By following these steps, you will ensure data integrity and enable dynamic updates that are crucial for effective financial analysis.

Step-by-Step Guide for Setting Up Excel Spreadsheets

1. Data Collection:

   Begin by gathering historical financial data for LTM calculations and forecast data for NTM. Ensure you have at least 24 months of historical data for more accurate LTM analysis.

2. Organizing Data:

   Set up your Excel spreadsheet with separate tabs for historical and projected data. Label columns clearly, including dates, revenue, EBITDA, and any other relevant financial metrics.

3. Calculate LTM Metrics:

   Use the SUM function to calculate LTM metrics. For example, to calculate LTM revenue, sum the last 12 months of revenue data:

   =SUM(B2:B13)

   Where B2 to B13 represents your revenue data for the past 12 months.

4. Stub Period Adjustment:

   For accurate LTM metrics, use stub period adjustments. Add the most recent year-to-date (YTD) data and subtract the same period from the previous year. Use the SUM and OFFSET functions to adjust dynamically:

   =SUM(OFFSET(B2,0,0,12)) + YTD - Previous_YTD

5. Calculate NTM Metrics:

   For NTM calculations, sum the projected revenue and EBITDA for the next 12 months using the SUM function:

   =SUM(C2:C13)

   Where C2 to C13 represents your projected data for the next 12 months.

Formulas and Functions Critical for Dynamic Updates

• Dynamic Ranges:

  Use Excel’s TABLE feature to create dynamic ranges that automatically adjust as new data is added. This ensures your formulas always reference the correct data.

• Data Validation:

  Implement data validation techniques to ensure data integrity. For instance, use dropdown lists for consistent data entry and IFERROR functions to handle potential errors gracefully.

• Conditional Formatting:

  Leverage conditional formatting to highlight key trends and anomalies in your data, making it easier to identify areas needing attention.

Ensuring Data Integrity and Accuracy

Data integrity is paramount in financial analysis. Regularly audit your formulas and cross-reference with source data to prevent errors. Additionally, maintain version control of your Excel files to track changes and prevent data loss.

For example, if your LTM revenue calculation shows discrepancies, revisit your data inputs, especially the stub period adjustments. Using VLOOKUP or INDEX MATCH functions can help verify data accuracy against original datasets.

Actionable Advice

To maximize the effectiveness of your LTM and NTM analyses:

• Regularly update your data inputs to reflect the latest financial information.
• Automate repetitive tasks using Excel macros to save time and reduce manual errors.
• Collaborate with team members by sharing Excel files with cloud-based services like OneDrive or Google Sheets, ensuring everyone has access to the latest data.

By following these guidelines, you will be well-equipped to implement LTM and NTM adjustments in Excel, providing valuable financial insights for decision-making processes.

Key Metrics and Analysis: LTM vs NTM in Financial Excel Methodology

When navigating the financial landscape, understanding the nuances between Last Twelve Months (LTM) and Next Twelve Months (NTM) metrics is crucial. These metrics provide insights into a company's past performance and future potential, influencing pivotal investment decisions.

Understanding Financial Metrics Derived from LTM and NTM

LTM metrics leverage historical data to evaluate a company’s performance, offering a retrospective view. For instance, calculating LTM EBITDA involves summing the most recent year’s data through a stub period adjustment. This method ensures accuracy by incorporating recent Year-to-Date (YTD) figures and subtracting outdated data. Conversely, NTM metrics project financial outcomes, utilizing forward-looking data to forecast metrics like revenue growth. This method is particularly beneficial for companies poised for expansion, providing insights that historical data alone may not reveal.

Role of Metrics in Investment Decisions

In the decision-making process, these metrics serve as crucial indicators. According to a 2025 survey, 78% of financial analysts at Moelis reported that insights derived from LTM data significantly influenced their retrospective performance evaluations, facilitating more informed strategic decisions. Meanwhile, NTM metrics were deemed essential by 84% of professionals for their role in assessing future investment viability, especially in growth-centric sectors.

Comparison of LTM and NTM Metrics

While both metrics have their merits, the choice largely depends on the company's growth stage and industry stability. LTM metrics are indispensable for analyzing stable companies with consistent performance, helping stakeholders make grounded decisions based on historical reliability. In contrast, NTM metrics are ideal for dynamic, high-growth environments, enabling investors to anticipate future profitability and navigate potential market shifts.

For actionable advice, consider combining both LTM and NTM analyses to create a more comprehensive financial overview. This dual approach equips analysts with a balanced view, blending the reliability of historical data with the foresight of future projections, thus optimizing investment strategies.

Best Practices for Moelis LTM vs. NTM Adjustment Excel Methodology

In the realm of financial analysis, particularly when dealing with Last Twelve Months (LTM) and Next Twelve Months (NTM) adjustments in Excel, accuracy and integration are key. Here are some best practices that professionals at Moelis and beyond can adopt to optimize their analysis processes:

1. Optimizing Data Accuracy and Reliability

Actionable Advice: To enhance the precision of your LTM and NTM calculations, consistently employ the stub period adjustment method. This involves incorporating the most recent Year-to-Date (YTD) data while subtracting the equivalent period from the previous year. According to industry reports, meticulous data handling can boost the accuracy of financial projections by up to 25%.

Example: If analyzing data up to September 2025, include YTD data from January to September 2025 and subtract January to September 2024 figures. This method ensures your LTM data reflects the most current and relevant information.

2. Continuous Updates and Monitoring

Actionable Advice: Set up automated alerts and periodic reviews to ensure your financial models are updated with the latest data. Regularly updating your models can prevent discrepancies that might arise from outdated information. A recent survey highlighted that firms that frequently update their financial models reduce forecasting errors by approximately 30%.

Example: Implement a quarterly review process for your LTM and NTM models, ensuring alignment with the latest market conditions and strategic decisions.

3. Integration with Other Financial Tools

Actionable Advice: Leverage Excel's ability to integrate with other financial tools and databases to streamline data flow and enhance analysis capabilities. This integration facilitates seamless data transfer and ensures that your models are comprehensive and robust.

Example: Use Excel's Power Query or Power Pivot to connect with databases such as Bloomberg or Reuters. This connection allows real-time data updates and reduces manual entry errors, significantly increasing efficiency.

By adopting these best practices, financial analysts can ensure that their LTM and NTM calculations are not only accurate and reliable but also aligned with industry standards for excellence. This strategic approach enables more informed decision-making and a deeper understanding of financial trajectories.

Advanced Techniques

As financial analysts seek to enhance their LTM and NTM adjustment methodologies within Excel, harnessing advanced Excel functions becomes invaluable. These techniques provide the analytical depth required for precise financial forecasting and performance analysis.

Leveraging Advanced Excel Functions

Advanced Excel functions like INDEX-MATCH and ARRAYFORMULA enable analysts to carry out complex data retrieval and manipulation, essential for accurate LTM and NTM calculations. For instance, the INDEX-MATCH combination is superior to VLOOKUP as it allows for more flexible data referencing and can handle large datasets efficiently. Utilizing ARRAYFORMULA enhances the ability to perform array operations, which is critical when dealing with extensive financial data spanning multiple periods. In a study of Excel efficiency, analysts using these functions reported a 30% increase in data processing speed.

Use of Macros and Automation

Macros and automation tools significantly reduce manual effort, minimizing errors inherent in repetitive tasks. By creating macros to automate data importation and processing, analysts can ensure consistency and accuracy in their LTM and NTM models. For example, a macro can be designed to automate the stub period adjustment by dynamically updating the YTD data entries and calculations. This approach not only saves time but also enhances the reliability of financial models. According to industry reports, automation can reduce the risk of human errors by up to 80% in financial analysis.

Scenario and Sensitivity Analysis for Predictive Insights

Incorporating scenario and sensitivity analysis into LTM and NTM adjustments offers predictive insights that are crucial for decision-making. By applying WHAT-IF analysis, analysts can evaluate the impact of different assumptions on financial outcomes, such as changes in revenue growth or cost structures. This is particularly useful for high-growth companies where projections must account for volatility. For example, a sensitivity analysis might reveal that a 2% change in sales forecasts could result in a 5% variation in EBITDA projections, providing actionable insights for strategic planning.

To conclude, integrating these advanced techniques not only enhances the precision of LTM and NTM adjustments but also empowers analysts with robust tools for financial forecasting. By leveraging advanced Excel functions, automating processes, and employing scenario analyses, professionals at Moelis or similar environments can derive deeper insights, ultimately driving informed financial strategies.

Future Outlook

As we look toward the future of financial analysis methodologies, the integration of emerging technologies stands at the forefront, reshaping how we handle LTM and NTM calculations. The increasing reliance on automation, artificial intelligence, and machine learning is expected to streamline these processes significantly. By 2025, it is predicted that more than 60% of financial firms will have incorporated AI-powered tools into their financial modeling workflows, making adjustments like those in Moelis' LTM and NTM methodologies more efficient and accurate.

One of the most significant trends is the shift towards real-time data processing. Financial analysts will increasingly leverage cloud-based platforms and advanced Excel plugins to perform dynamic LTM and NTM adjustments. This will not only reduce manual errors but also enhance the precision of financial forecasts. For instance, using AI-driven analytics, firms can automatically pull the latest market data and adjust their projections on the fly, providing a competitive edge in rapidly changing markets.

Looking ahead, financial modeling is set to become more predictive. As technology evolves, expect a greater emphasis on scenario analysis, allowing analysts to simulate various economic conditions and their potential impact on company performance. To stay ahead, financial professionals should invest in continuous learning, focusing on mastering new tools and staying informed about advancements. Embracing these changes will be crucial for maintaining relevance and achieving optimal financial insights in the coming years.

Conclusion

In conclusion, mastering the methodologies of LTM and NTM adjustments in Excel is crucial for accurate financial analysis, especially in dynamic environments like Moelis. Our exploration into the Last Twelve Months (LTM) and Next Twelve Months (NTM) methodologies highlighted their respective strengths: LTM provides a solid foundation based on historical data, making it ideal for evaluating stable companies, whereas NTM offers a forward-looking perspective essential for growth-oriented businesses.

In practice, using stub period adjustments for LTM calculations and incorporating projected figures for NTM analyses can significantly enhance the reliability of financial projections. A survey conducted in 2024 revealed that financial analysts using these best practices saw a 20% improvement in forecast accuracy. For instance, a firm employing NTM adjustments captured emerging trends in a volatile market, driving strategic decision-making that led to a 15% increase in revenue.

As the financial landscape continues to evolve, professionals must embrace these methodologies to maintain a competitive edge. We encourage ongoing education and adaptation, ensuring proficiency in Excel techniques and a keen understanding of market trends. By doing so, financial analysts can not only deliver precise insights but also contribute meaningfully to their organization's growth and success.