READY SIGNAL CONTROL DATA

Corporate Profits After Tax (w/out IVA & CCAdj)

Why Use This Data Source In Your Models?

Corporate Profits After Tax (w/out IVA & CCAdj) serves as a pivotal metric for analysts and economists delving into the financial health of businesses and the overall economic landscape. This metric, devoid of inventory valuation adjustment (IVA) and capital consumption adjustment (CCAdj), provides a clear, unadulterated view of corporations' net earnings. For data scientists, it offers a nuanced perspective on corporate performance, enabling them to gauge profitability accurately. By studying this figure, analysts can discern trends in corporate earnings over time, offering crucial insights into economic stability and business sentiment. Policymakers rely on these insights to shape economic policies, ensuring they align with the realities of corporate profitability. Investors utilize this metric to assess the financial robustness of companies, aiding them in making informed investment decisions. Economists leverage this data to model economic forecasts, guiding governments and businesses in strategic planning. In essence, Corporate Profits After Tax (w/out IVA & CCAdj) serves as a reliable compass, guiding a myriad of stakeholders in understanding the financial landscape and making prudent, data-driven choices.

Instantly Download this Data Using Our Automated Feature Engineering Tool.

Automated Data Profiling

Ready Signal automatically profiles each data set and offers up suggested industry standard data science treatments to utilize with these data in your models.

Suggested Treatment:

The data shows auto correlation and a non-normal distribution. The data should be differenced. While the Order Norm transformation, provides the best normality, the Yeo Johnson variable will also perform well.

Grain Transformation:

Data is able to be distributed by time and geography. The roll up method used is Sum.

Auto Correlation Analysis:

Data shows auto correlation indicating a need for differencing

The ACF indicates 1 order differencing is appropriate.

Following first order differencing, no further differencing is required based on the differenced ACF at lag one of 0.00

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.20 p-value = 0.01 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.73 with a p-value =0.00 indicating the data does not follow a normal distribution.

A skewness score of 1.83 indicates the data are substantially skewed.

Hartigan's dip test score of 0.07 with a p-value of 0.17 inidcates the data is unimodal

Statistics (Pearson P/ df, lower => more normal)

No transform

8.14

Box-cox

2.26

Log_b(x-a)

2.54

sqrt(x+a)

2.82

exp(x)

arcsinh(x)

2.54

Yeo-Johnson

1.94

OrderNorm

1.70

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function

Seasonal Impact

Seasonal and Trend Decompostion

Designed For Data Scientists and Analysts

400+ Data Sources

Use our platform to aggregate, normalize, and profile open source and premium control data. Spend less time finding and wrangling data, and more time building efficient and feature-rich machine learning data pipelines.

Data Science Treatments

Instantly apply industry-standard data science treatments and transformations, including (but not limited to) Differencing, Lead/Lag, Box Cox. Easily manipulate data across different time and geographic grains.

Auto Discovery

Our Patent Pending iterative testing engine allows you to upload your target variable, and the platform will test for possible statistical relationships across all available data sources. Saving you time and removing analyst bias.

Data Ingestion

Easily integrate your Ready Signal data to the data science platform of your choice. Connect directly to Ready Signal through our API or using one of our pre-built data connectors or download directly in Excel or CSV format.