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NVIDIA (UKEX:NVDA) Cyclically Adjusted Price-to-FCF : 275.17 (As of May. 27, 2024)


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What is NVIDIA Cyclically Adjusted Price-to-FCF?

As of today (2024-05-27), NVIDIA's current share price is ₴33612.00. NVIDIA's Cyclically Adjusted FCF per Share for the quarter that ended in Apr. 2024 was ₴122.15. NVIDIA's Cyclically Adjusted Price-to-FCF for today is 275.17.

The historical rank and industry rank for NVIDIA's Cyclically Adjusted Price-to-FCF or its related term are showing as below:

UKEX:NVDA' s Cyclically Adjusted Price-to-FCF Range Over the Past 10 Years
Min: 17.84   Med: 101.04   Max: 375.65
Current: 339.05

During the past years, NVIDIA's highest Cyclically Adjusted Price-to-FCF was 375.65. The lowest was 17.84. And the median was 101.04.

UKEX:NVDA's Cyclically Adjusted Price-to-FCF is ranked worse than
97.03% of 337 companies
in the Semiconductors industry
Industry Median: 39.98 vs UKEX:NVDA: 339.05

The Shiller PE Ratio was first used by professor Robert Shiller. He uses E10 for his Shiller PE Ratio calculation. E10 is the average of the inflation adjusted earnings per share of a company over the past 10 years. The similar calculation is applied by GuruFocus to calculate the Cyclically Adjusted Price-to-FCF. The Cyclically Adjusted FCF per Share is the average of the inflation adjusted free cash flow per share of a company over the past 10 years.

NVIDIA's adjusted free cash flow per share data for the three months ended in Apr. 2024 was ₴242.590. Add all the adjusted free cash flow per share for the past 10 years together and divide 10 will get our Cyclically Adjusted FCF per Share, which is ₴122.15 for the trailing ten years ended in Apr. 2024.

Shiller PE for Stocks: The True Measure of Stock Valuation


NVIDIA Cyclically Adjusted Price-to-FCF Historical Data

The historical data trend for NVIDIA's Cyclically Adjusted Price-to-FCF can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

* Premium members only.

NVIDIA Cyclically Adjusted Price-to-FCF Chart

NVIDIA Annual Data
Trend Jan15 Jan16 Jan17 Jan18 Jan19 Jan20 Jan21 Jan22 Jan23 Jan24
Cyclically Adjusted Price-to-FCF
Get a 7-Day Free Trial Premium Member Only Premium Member Only 85.20 150.59 201.36 138.09 244.76

NVIDIA Quarterly Data
Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22 Apr22 Jul22 Oct22 Jan23 Apr23 Jul23 Oct23 Jan24 Apr24
Cyclically Adjusted Price-to-FCF Get a 7-Day Free Trial Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only 180.69 261.15 196.36 244.76 275.15

Competitive Comparison of NVIDIA's Cyclically Adjusted Price-to-FCF

For the Semiconductors subindustry, NVIDIA's Cyclically Adjusted Price-to-FCF, along with its competitors' market caps and Cyclically Adjusted Price-to-FCF data, can be viewed below:

* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.


NVIDIA's Cyclically Adjusted Price-to-FCF Distribution in the Semiconductors Industry

For the Semiconductors industry and Technology sector, NVIDIA's Cyclically Adjusted Price-to-FCF distribution charts can be found below:

* The bar in red indicates where NVIDIA's Cyclically Adjusted Price-to-FCF falls into.



NVIDIA Cyclically Adjusted Price-to-FCF Calculation

Like the Shiller PE Ratio, the Cyclically Adjusted Price-to-FCF takes the Free Cash Flow per Share from the past 10 years, adjusts it for inflation, and then calculates the average. This average is then used for the P/FCF calculation. Because it considers this 10-year average, it's often referred to as the CAPFCF Ratio.

The Shiller PE Ratio was first used by professor Robert Shiller to measure the valuation of the overall market. The similar calculation is applied by GuruFocus to calculate the Cyclically Adjusted Price-to-FCF.

NVIDIA's Cyclically Adjusted Price-to-FCF for today is calculated as

Cyclically Adjusted Price-to-FCF=Share Price/ Cyclically Adjusted FCF per Share
=33612.00/122.15
=275.17

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

NVIDIA's Cyclically Adjusted FCF per Share for the quarter that ended in Apr. 2024 is calculated as:

For example, NVIDIA's adjusted Free Cash Flow per Share data for the three months ended in Apr. 2024 was:

Adj_FreeCashFlowPerShare=Free Cash Flow per Share/CPI of Apr. 2024 (Change)*Current CPI (Apr. 2024)
=242.59/131.7762*131.7762
=242.590

Current CPI (Apr. 2024) = 131.7762.

NVIDIA Quarterly Data

Free Cash Flow per Share CPI Adj_FreeCashFlowPerShare
201407 1.289 100.520 1.690
201410 3.197 100.176 4.206
201501 7.496 98.604 10.018
201504 3.833 99.824 5.060
201507 2.520 100.691 3.298
201510 4.246 100.346 5.576
201601 8.517 99.957 11.228
201604 4.442 100.947 5.799
201607 2.671 101.524 3.467
201610 6.082 101.988 7.858
201701 9.816 102.456 12.625
201704 3.585 103.167 4.579
201707 10.366 103.278 13.226
201710 17.463 104.070 22.112
201801 15.095 104.578 19.021
201804 21.333 105.708 26.594
201807 12.624 106.324 15.646
201810 5.435 106.695 6.713
201901 11.263 106.200 13.975
201904 9.687 107.818 11.840
201907 13.467 108.250 16.394
201910 25.068 108.577 30.424
202001 21.425 108.841 25.940
202004 12.219 108.173 14.885
202007 21.737 109.318 26.203
202010 12.895 109.861 15.467
202101 28.452 110.364 33.972
202104 25.135 112.673 29.397
202107 39.793 115.183 45.526
202110 20.604 116.696 23.267
202201 43.742 118.619 48.594
202204 21.772 121.978 23.521
202207 13.413 125.002 14.140
202210 -2.226 125.734 -2.333
202301 28.306 126.223 29.551
202304 43.120 127.992 44.395
202307 97.755 128.974 99.879
202310 114.036 129.810 115.764
202401 181.788 130.124 184.096
202404 242.590 131.776 242.590

Add all the adjusted free cash flow per share together and divide 10 will get our Cyclically Adjusted FCF per Share.

Please note that we use the CPI data of the country/region where the company is headquartered. If the CPI data for that country/region is not available, then we will use the CPI data of the United States as default.


NVIDIA  (UKEX:NVDA) Cyclically Adjusted Price-to-FCF Explanation

Compared with the regular Price-to-Free-Cash-Flow, which works poorly for cyclical businesses, the Cyclically Adjusted Price-to-FCF smoothed out the fluctuations of free cash flow during business cycles. Therefore it is more accurate in reflecting the valuation of the company.

If a company has consistent business performance, the Cyclically Adjusted Price-to-FCF should give similar results to regular Price-to-Free-Cash-Flow.


NVIDIA Cyclically Adjusted Price-to-FCF Related Terms

Thank you for viewing the detailed overview of NVIDIA's Cyclically Adjusted Price-to-FCF provided by GuruFocus.com. Please click on the following links to see related term pages.


NVIDIA (UKEX:NVDA) Business Description

Industry
Address
2788 San Tomas Expressway, Santa Clara, CA, USA, 95051
Nvidia is a leading developer of graphics processing units. Traditionally, GPUs were used to enhance the experience on computing platforms, most notably in gaming applications on PCs. GPU use cases have since emerged as important semiconductors used in artificial intelligence. Nvidia not only offers AI GPUs, but also a software platform, Cuda, used for AI model development and training. Nvidia is also expanding its data center networking solutions, helping to tie GPUs together to handle complex workloads.