READY SIGNAL CONTROL DATA

Stock Market Capitalization to GDP, US

Why Use This Data Source In Your Models?

Stock Market Capitalization to GDP describes the ratio of the value of the US stock market to the value of the total output of goods and services in the US. This is used to determine whether the overall market is undervalued or overvalued.

Stock Market Capitalization to GDP, US

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 seasonality. The data should be differenced and seasonally adjusted.

Grain Transformation:

Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.

Source:
World Bank

Release:
Global Financial Development

Units:
Percent, Not Seasonally Adjusted

Frequency:
Annual

Available Through:
12/31/2019

Suggested Treatment:

The data shows auto correlation and seasonality. The data should be differenced and seasonally adjusted.

Grain Transformation:

Data is unable to be distributed by time or geography. The roll up method used is Weighted Average.

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 -0.04

Trend Analysis:

The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, KPSS Trend = 0.15 p-value = 0.04 indicates that the data is not stationary.

Distribution Analysis:

The Shapiro-Wilk test returned W = 0.89 with a p-value =0.23 indicating the data follows a normal distribution.

A skewness score of -1.10 indicates the data are substantially skewed.

Hartigan's dip test score of 0.08 with a p-value of 0.93 inidcates the data is unimodal

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

No transform
1.00
Box-cox
1.00
Log_b(x-a)
1.00
sqrt(x+a)
1.00
exp(x)
11.63
arcsinh(x)
1.00
Yeo-Johnson
1.63
OrderNorm
0.38

Auto Correlation Function

Auto Correlation Function After Differencing

Partial Auto Correlation Function


Data Notes:

2020 data is not currently available.

Citation:

World Bank, Stock Market Capitalization to GDP for United States [DDDM01USA156NWDB], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/DDDM01USA156NWDB, December 15, 2019.

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.

Scroll to Top