Automatic Total Other Income Expense Net from 2010 to 2026

Stocks . USA . 
Stock . Automatic Data Processing
ADP Stock  USD 208.50  0.00  0.00%   
Automatic Data's Total Other Income Expense Net is moving in a growing direction with slightly volatile fluctuations. Forward projections suggest a move toward approximately -87 M. Over the period 2010 to 2026, statistical regression of Automatic Data Total Other Income Expense Net recorded a arithmetic mean equal to -13,874,059 . and mean deviation of 74,545,952 . View All Fundamentals
 
Total Other Income Expense Net  
 First Reported
1985-09-30
 Previous Quarter
1.4 B
 Current Value
105.8 M
 Quarterly Volatility
198.2 M
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This statements view for Automatic Data organizes long-term performance signals with capital structure context. It connects Depreciation And Amortization of 319 M, Interest Expense of 550.5 M or Selling General Administrative of 4.9 B and ratios such as Price To Sales Ratio of 5.77, Dividend Yield of 0.0148 or PTB Ratio of 19.17 with Automatic Data Valuation and Volatility context.
  
Build AI portfolio with Automatic Stock
Compare Automatic Data against competitors using Automatic Data Correlation. The view supports competitor context.
Evaluating Automatic Data's Total Other Income Expense Net across multiple reporting periods reveals the company's ability to sustain growth and manage resources effectively. This longitudinal analysis highlights inflection points, cyclical patterns, and structural changes that short-term snapshots might miss, offering deeper insight into Automatic Data Processing's fundamental strength.

Latest Automatic Data's Total Other Income Expense Net Growth Pattern

Below is the plot of the Total Other Income Expense Net of Automatic Data Processing over the last few years. It is Automatic Data's Total Other Income Expense Net historical data analysis aims to capture in quantitative terms the overall pattern of either growth or decline in Automatic Data's overall financial position and show how it may be relating to other accounts over time.
Total Other Income Expense Net10 Years Trend
Slightly volatile
Compare to OthersAll Fundamentals
   Total Other Income Expense Net   
       Timeline  

Automatic Total Other Income Expense Net Regression Statistics

Arithmetic Mean-13,874,059
Geometric Mean 38,219,302
Coefficient Of Variation-744.29
Mean Deviation 74,545,952
Median 900,000
Standard Deviation 103,263,319
Sample Variance10663.3T
Range458.4M
R-Value-0.49
Mean Square Error8602.7T
R-Squared 0.24
Significance 0.04
Slope-10,094,245
Total Sum of Squares170613T

Automatic Total Other Income Expense Net History

2026-87 M
2025-91.6 M
2024-101.8 M
2023-70.7 M
2022-68.6 M
2021900 K
202036.6 M

Stock Overview, Methodology & Data Sources

Automatic Data Processing, Inc. provides cloud-based human capital management solutions worldwide. The company was founded in 1949 and is headquartered in Roseland, New Jersey. Automatic Data operates under Staffing Employment Services classification in the United States and is traded on NASDAQ Exchange. It employs 60000 people. A high-level view of Automatic Data emphasizes earnings durability, balance-sheet flexibility, and competitive positioning. Current metrics include P/E of 35.6, P/B of 13.13, profit margin of 19.96%. Automatic Data has a market cap of 84.34 B, P/E of 35.6, ROE of 73.84%.

Methodology

Unless otherwise specified, financial data for Automatic Data Processing is derived from periodic company reporting (annual and quarterly where available). Asset-level metrics are computed daily by Macroaxis LLC and refreshed regularly based on asset type. Automatic (USA Stocks:ADP) prices are typically delayed by approximately 20 minutes from primary exchanges for listed equities. Data may be delayed depending on reporting sources and market conventions. Assumptions: Inputs are aggregated from public filings and market reference sources and public institutions such as U.S. Securities and Exchange Commission (SEC) via EDGAR. Certain values may not reflect real-time changes. All analytics are generated using standardized, rules-based models designed to promote consistency and comparability across instruments. Model assumptions, reference parameters, and selected computational inputs are available in the Model Inputs section. If you have questions about our data sources or methodology, please contact Macroaxis Support.

Analyst Sources

Automatic Data Processing has active sell-side coverage. Source-validated coverage currently shows 17 approved analysts, while broader market-consensus totals may differ across providers due to methodology and update timing. 8 analysts have submitted revenue and/or earnings estimates that may be incorporated into Macroaxis consensus inputs where available. Representative analyst firms may include Bernstein Research, Deutsche Bank, Morgan Stanley, BMO Capital Markets, Guggenheim Securities, Jefferies, Goldman Sachs, Evercore ISI, Raymond James, UBS Investment Research, among others. Updates may occur throughout the day.

This content is curated and reviewed by:

Ellen Johnson - Member of Macroaxis Editorial Board
Last reviewed on February 21st, 2026

Pair Trading with Automatic Data

A pair strategy built around Automatic Data Processing is useful when investors want to reduce directional market exposure while still expressing a relative-value idea. Used properly, pair trading is less about prediction in isolation and more about identifying relative mispricing between related positions.

Moving together with Automatic Stock

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Moving against Automatic Stock

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Correlation analysis helps investors find suitable substitutes for Automatic Data during tax-loss harvesting periods. Selling Automatic Data Processing at a loss and immediately repurchasing it would violate IRS wash-sale rules, so a correlated replacement asset is required to maintain portfolio.
Measuring the statistical correlation of Automatic Data Processing against other instruments helps investors understand portfolio diversification. A correlation near zero implies that Automatic Data provides genuine diversification benefits, while high positive correlations suggest redundant exposures.
Correlation analysis and pair trading evaluation for Automatic Data can be used to frame hedging context. The approach can be applied within sectors or across broader universes.
Pair CorrelationCorrelation Matching

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