Valuation Analysis - NIKE

July 5, 2026 36 minute read

import yfinance as yf
import pandas as pd
import datetime

# Show full numbers instead of scientific notation
pd.options.display.float_format = '{:,.0f}'.format

ticker = yf.Ticker("NKE")
# - income statement
pd.set_option('display.max_rows', None)
balance_sheet_df = ticker.quarterly_balance_sheet
balance_sheet_df

	2026-02-28	2025-11-30	2025-08-31	2025-05-31	2025-02-28	2024-11-30
Ordinary Shares Number	1,480,000,000	1,479,887,752	1,476,903,492	1,476,000,000	1,476,887,752	NaN
Share Issued	1,480,000,000	1,479,887,752	1,476,903,492	1,476,000,000	1,476,887,752	NaN
Net Debt	1,369,000,000	1,041,000,000	976,000,000	502,000,000	359,000,000	NaN
Total Debt	11,178,000,000	11,282,000,000	11,061,000,000	11,018,000,000	11,911,000,000	NaN
Tangible Book Value	13,591,000,000	13,586,000,000	12,969,000,000	12,714,000,000	13,509,000,000	NaN
Invested Capital	22,119,000,000	22,100,000,000	21,468,000,000	21,179,000,000	22,967,000,000	NaN
Working Capital	12,346,000,000	12,375,000,000	12,987,000,000	12,796,000,000	13,386,000,000	NaN
Net Tangible Assets	13,591,000,000	13,586,000,000	12,969,000,000	12,714,000,000	13,509,000,000	NaN
Capital Lease Obligations	3,149,000,000	3,267,000,000	3,061,000,000	3,052,000,000	2,951,000,000	NaN
Common Stock Equity	14,090,000,000	14,085,000,000	13,468,000,000	13,213,000,000	14,007,000,000	NaN
Total Capitalization	21,120,000,000	21,101,000,000	21,464,000,000	21,174,000,000	21,963,000,000	NaN
Total Equity Gross Minority Interest	14,090,000,000	14,085,000,000	13,468,000,000	13,213,000,000	14,007,000,000	NaN
Stockholders Equity	14,090,000,000	14,085,000,000	13,468,000,000	13,213,000,000	14,007,000,000	NaN
Gains Losses Not Affecting Retained Earnings	-207,000,000	-104,000,000	-308,000,000	-258,000,000	263,000,000	NaN
Other Equity Adjustments	-207,000,000	-104,000,000	-308,000,000	-258,000,000	263,000,000	NaN
Retained Earnings	-610,000,000	-519,000,000	-700,000,000	-727,000,000	-175,000,000	NaN
Additional Paid In Capital	14,904,000,000	14,705,000,000	14,473,000,000	14,195,000,000	13,916,000,000	NaN
Capital Stock	3,000,000	3,000,000	3,000,000	3,000,000	3,000,000	NaN
Common Stock	3,000,000	3,000,000	3,000,000	3,000,000	3,000,000	NaN
Total Liabilities Net Minority Interest	22,974,000,000	23,702,000,000	23,866,000,000	23,366,000,000	23,786,000,000	NaN
Total Non Current Liabilities Net Minority Interest	12,136,000,000	12,062,000,000	12,955,000,000	12,800,000,000	12,563,000,000	NaN
Other Non Current Liabilities	2,450,000,000	2,292,000,000	2,404,000,000	2,289,000,000	2,130,000,000	NaN
Preferred Securities Outside Stock Equity	0	0	0	0	0	NaN
Long Term Debt And Capital Lease Obligation	9,686,000,000	9,770,000,000	10,551,000,000	10,511,000,000	10,433,000,000	NaN
Long Term Capital Lease Obligation	2,656,000,000	2,754,000,000	2,555,000,000	2,550,000,000	2,477,000,000	NaN
Long Term Debt	7,030,000,000	7,016,000,000	7,996,000,000	7,961,000,000	7,956,000,000	NaN
Current Liabilities	10,838,000,000	11,640,000,000	10,911,000,000	10,566,000,000	11,223,000,000	NaN
Current Debt And Capital Lease Obligation	1,492,000,000	1,512,000,000	510,000,000	507,000,000	1,478,000,000	NaN
Current Capital Lease Obligation	493,000,000	513,000,000	506,000,000	502,000,000	474,000,000	NaN
Current Debt	999,000,000	999,000,000	4,000,000	5,000,000	1,004,000,000	NaN
Other Current Borrowings	999,000,000	999,000,000	NaN	NaN	1,000,000,000	1,000,000,000
Current Notes Payable	0	0	4,000,000	5,000,000	4,000,000	NaN
Pensionand Other Post Retirement Benefit Plans Current	1,544,000,000	1,236,000,000	1,244,000,000	1,726,000,000	1,708,000,000	NaN
Current Provisions	1,658,000,000	1,748,000,000	1,788,000,000	1,834,000,000	1,682,000,000	NaN
Payables And Accrued Expenses	6,144,000,000	7,144,000,000	7,369,000,000	6,499,000,000	6,355,000,000	NaN
Current Accrued Expenses	2,365,000,000	2,320,000,000	2,292,000,000	1,753,000,000	1,917,000,000	NaN
Payables	3,779,000,000	4,824,000,000	5,077,000,000	4,746,000,000	4,438,000,000	NaN
Dividends Payable	616,000,000	615,000,000	599,000,000	598,000,000	598,000,000	NaN
Total Tax Payable	275,000,000	492,000,000	706,000,000	669,000,000	734,000,000	NaN
Income Tax Payable	275,000,000	492,000,000	706,000,000	669,000,000	734,000,000	NaN
Accounts Payable	2,888,000,000	3,717,000,000	3,772,000,000	3,479,000,000	3,106,000,000	NaN
Total Assets	37,064,000,000	37,787,000,000	37,334,000,000	36,579,000,000	37,793,000,000	NaN
Total Non Current Assets	13,880,000,000	13,772,000,000	13,436,000,000	13,217,000,000	13,184,000,000	NaN
Other Non Current Assets	5,729,000,000	5,536,000,000	5,349,000,000	5,178,000,000	5,355,000,000	NaN
Goodwill And Other Intangible Assets	499,000,000	499,000,000	499,000,000	499,000,000	498,000,000	NaN
Other Intangible Assets	259,000,000	259,000,000	259,000,000	259,000,000	259,000,000	NaN
Goodwill	240,000,000	240,000,000	240,000,000	240,000,000	239,000,000	NaN
Net PPE	7,652,000,000	7,737,000,000	7,588,000,000	7,540,000,000	7,331,000,000	NaN
Accumulated Depreciation	NaN	NaN	NaN	-6,104,000,000	NaN	NaN
Gross PPE	7,652,000,000	7,737,000,000	7,588,000,000	13,644,000,000	7,331,000,000	NaN
Leases	NaN	NaN	NaN	2,037,000,000	NaN	NaN
Construction In Progress	NaN	NaN	NaN	404,000,000	NaN	NaN
Other Properties	7,652,000,000	7,737,000,000	7,588,000,000	2,712,000,000	7,331,000,000	NaN
Machinery Furniture Equipment	NaN	NaN	NaN	4,647,000,000	NaN	NaN
Buildings And Improvements	NaN	NaN	NaN	3,510,000,000	NaN	NaN
Land And Improvements	NaN	NaN	NaN	334,000,000	NaN	NaN
Properties	NaN	NaN	NaN	0	NaN	NaN
Current Assets	23,184,000,000	24,015,000,000	23,898,000,000	23,362,000,000	24,609,000,000	NaN
Other Current Assets	2,271,000,000	2,206,000,000	2,247,000,000	2,005,000,000	2,186,000,000	NaN
Inventory	7,487,000,000	7,726,000,000	8,114,000,000	7,489,000,000	7,539,000,000	NaN
Finished Goods	7,487,000,000	7,726,000,000	8,114,000,000	7,489,000,000	7,539,000,000	NaN
Receivables	5,369,000,000	5,738,000,000	4,962,000,000	4,717,000,000	4,491,000,000	NaN
Accounts Receivable	5,369,000,000	5,738,000,000	4,962,000,000	4,717,000,000	4,491,000,000	NaN
Allowance For Doubtful Accounts Receivable	NaN	NaN	NaN	-27,000,000	NaN	NaN
Gross Accounts Receivable	NaN	NaN	NaN	4,744,000,000	NaN	NaN
Cash Cash Equivalents And Short Term Investments	8,057,000,000	8,345,000,000	8,575,000,000	9,151,000,000	10,393,000,000	NaN
Other Short Term Investments	1,397,000,000	1,371,000,000	1,551,000,000	1,687,000,000	1,792,000,000	NaN
Cash And Cash Equivalents	6,660,000,000	6,974,000,000	7,024,000,000	7,464,000,000	8,601,000,000	NaN
Cash Equivalents	4,967,000,000	5,216,000,000	5,615,000,000	6,243,000,000	7,263,000,000	NaN
Cash Financial	1,693,000,000	1,758,000,000	1,409,000,000	1,221,000,000	1,338,000,000	NaN

cash_flow_df = ticker.cashflow
cash_flow_df

	2025-05-31	2024-05-31	2023-05-31	2022-05-31
Free Cash Flow	3,268,000,000	6,617,000,000	4,872,000,000	4,430,000,000
Repurchase Of Capital Stock	-2,985,000,000	-4,250,000,000	-5,480,000,000	-4,014,000,000
Repayment Of Debt	-1,000,000,000	0	-500,000,000	0
Issuance Of Debt	NaN	NaN	NaN	0
Capital Expenditure	-430,000,000	-812,000,000	-969,000,000	-758,000,000
Interest Paid Supplemental Data	389,000,000	381,000,000	347,000,000	290,000,000
Income Tax Paid Supplemental Data	1,226,000,000	1,299,000,000	1,517,000,000	1,231,000,000
End Cash Position	7,464,000,000	9,860,000,000	7,441,000,000	8,574,000,000
Beginning Cash Position	9,860,000,000	7,441,000,000	8,574,000,000	9,889,000,000
Effect Of Exchange Rate Changes	1,000,000	-16,000,000	-91,000,000	-143,000,000
Changes In Cash	-2,397,000,000	2,435,000,000	-1,042,000,000	-1,172,000,000
Financing Cash Flow	-5,820,000,000	-5,888,000,000	-7,447,000,000	-4,836,000,000
Cash Flow From Continuing Financing Activities	-5,820,000,000	-5,888,000,000	-7,447,000,000	-4,836,000,000
Net Other Financing Charges	-85,000,000	-136,000,000	-102,000,000	-151,000,000
Proceeds From Stock Option Exercised	551,000,000	667,000,000	651,000,000	1,151,000,000
Cash Dividends Paid	-2,300,000,000	-2,169,000,000	-2,012,000,000	-1,837,000,000
Common Stock Dividend Paid	-2,300,000,000	-2,169,000,000	-2,012,000,000	-1,837,000,000
Net Common Stock Issuance	-2,985,000,000	-4,250,000,000	-5,480,000,000	-4,014,000,000
Common Stock Payments	-2,985,000,000	-4,250,000,000	-5,480,000,000	-4,014,000,000
Net Issuance Payments Of Debt	-1,001,000,000	0	-504,000,000	15,000,000
Net Short Term Debt Issuance	-1,000,000	0	-4,000,000	15,000,000
Net Long Term Debt Issuance	-1,000,000,000	0	-500,000,000	0
Long Term Debt Payments	-1,000,000,000	0	-500,000,000	0
Long Term Debt Issuance	NaN	NaN	NaN	0
Investing Cash Flow	-275,000,000	894,000,000	564,000,000	-1,524,000,000
Cash Flow From Continuing Investing Activities	-275,000,000	894,000,000	564,000,000	-1,524,000,000
Net Other Investing Changes	8,000,000	-15,000,000	52,000,000	-19,000,000
Net Investment Purchase And Sale	147,000,000	1,721,000,000	1,481,000,000	-747,000,000
Sale Of Investment	3,381,000,000	6,488,000,000	7,540,000,000	12,166,000,000
Purchase Of Investment	-3,234,000,000	-4,767,000,000	-6,059,000,000	-12,913,000,000
Net PPE Purchase And Sale	-430,000,000	-812,000,000	-969,000,000	-758,000,000
Purchase Of PPE	-430,000,000	-812,000,000	-969,000,000	-758,000,000
Operating Cash Flow	3,698,000,000	7,429,000,000	5,841,000,000	5,188,000,000
Cash Flow From Continuing Operating Activities	3,698,000,000	7,429,000,000	5,841,000,000	5,188,000,000
Change In Working Capital	-787,000,000	716,000,000	-513,000,000	-1,660,000,000
Change In Payables And Accrued Expense	-426,000,000	397,000,000	-225,000,000	1,365,000,000
Change In Payable	-426,000,000	397,000,000	-225,000,000	1,365,000,000
Change In Account Payable	-426,000,000	397,000,000	-225,000,000	1,365,000,000
Change In Prepaid Assets	-224,000,000	-260,000,000	-644,000,000	-845,000,000
Change In Inventory	120,000,000	908,000,000	-133,000,000	-1,676,000,000
Change In Receivables	-257,000,000	-329,000,000	489,000,000	-504,000,000
Changes In Account Receivables	-257,000,000	-329,000,000	489,000,000	-504,000,000
Stock Based Compensation	709,000,000	804,000,000	755,000,000	638,000,000
Deferred Tax	-288,000,000	-497,000,000	-117,000,000	-650,000,000
Deferred Income Tax	-288,000,000	-497,000,000	-117,000,000	-650,000,000
Depreciation Amortization Depletion	808,000,000	844,000,000	859,000,000	840,000,000
Depreciation And Amortization	808,000,000	844,000,000	859,000,000	840,000,000
Amortization Cash Flow	33,000,000	48,000,000	156,000,000	123,000,000
Amortization Of Intangibles	33,000,000	48,000,000	156,000,000	123,000,000
Depreciation	775,000,000	796,000,000	703,000,000	717,000,000
Operating Gains Losses	37,000,000	-138,000,000	-213,000,000	-26,000,000
Net Foreign Currency Exchange Gain Loss	37,000,000	-138,000,000	-213,000,000	-26,000,000
Net Income From Continuing Operations	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000

income_statement_df = ticker.income_stmt.dropna(axis=1, thresh=10)
income_statement_df

	2025-05-31	2024-05-31	2023-05-31	2022-05-31
Tax Effect Of Unusual Items	0	0	0	0
Tax Rate For Calcs	0	0	0	0
Normalized EBITDA	4,510,000,000	7,155,000,000	6,774,000,000	7,515,000,000
Net Income From Continuing Operation Net Minority Interest	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Reconciled Depreciation	808,000,000	844,000,000	859,000,000	840,000,000
Reconciled Cost Of Revenue	26,519,000,000	28,475,000,000	28,925,000,000	25,231,000,000
EBITDA	4,510,000,000	7,155,000,000	6,774,000,000	7,515,000,000
EBIT	3,702,000,000	6,311,000,000	5,915,000,000	6,675,000,000
Net Interest Income	107,000,000	161,000,000	6,000,000	-205,000,000
Normalized Income	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income From Continuing And Discontinued Operation	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Total Expenses	42,607,000,000	45,051,000,000	45,302,000,000	40,035,000,000
Diluted Average Shares	1,487,600,000	1,529,700,000	1,569,800,000	NaN
Basic Average Shares	1,484,900,000	1,517,600,000	1,551,600,000	NaN
Diluted EPS	2	4	3	NaN
Basic EPS	2	4	3	NaN
Diluted NI Availto Com Stockholders	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income Common Stockholders	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income Including Noncontrolling Interests	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income Continuous Operations	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Tax Provision	666,000,000	1,000,000,000	1,131,000,000	605,000,000
Pretax Income	3,885,000,000	6,700,000,000	6,201,000,000	6,651,000,000
Other Income Expense	76,000,000	228,000,000	280,000,000	181,000,000
Other Non Operating Income Expenses	76,000,000	228,000,000	280,000,000	181,000,000
Net Non Operating Interest Income Expense	107,000,000	161,000,000	6,000,000	-205,000,000
Total Other Finance Cost	-107,000,000	-161,000,000	-6,000,000	205,000,000
Operating Income	3,702,000,000	6,311,000,000	5,915,000,000	6,675,000,000
Operating Expense	16,088,000,000	16,576,000,000	16,377,000,000	14,804,000,000
Other Operating Expenses	NaN	NaN	12,317,000,000	10,954,000,000
Selling General And Administration	16,088,000,000	16,576,000,000	16,377,000,000	14,804,000,000
Selling And Marketing Expense	4,689,000,000	4,285,000,000	4,060,000,000	3,850,000,000
General And Administrative Expense	11,399,000,000	12,291,000,000	12,317,000,000	10,954,000,000
Other Gand A	11,399,000,000	12,291,000,000	12,317,000,000	10,954,000,000
Gross Profit	19,790,000,000	22,887,000,000	22,292,000,000	21,479,000,000
Cost Of Revenue	26,519,000,000	28,475,000,000	28,925,000,000	25,231,000,000
Total Revenue	46,309,000,000	51,362,000,000	51,217,000,000	46,710,000,000
Operating Revenue	46,309,000,000	51,362,000,000	51,217,000,000	46,710,000,000

annual_income = ticker.income_stmt.dropna(axis=1, thresh=10)
annual_income

	2025-05-31	2024-05-31	2023-05-31	2022-05-31
Tax Effect Of Unusual Items	0	0	0	0
Tax Rate For Calcs	0	0	0	0
Normalized EBITDA	4,510,000,000	7,155,000,000	6,774,000,000	7,515,000,000
Net Income From Continuing Operation Net Minority Interest	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Reconciled Depreciation	808,000,000	844,000,000	859,000,000	840,000,000
Reconciled Cost Of Revenue	26,519,000,000	28,475,000,000	28,925,000,000	25,231,000,000
EBITDA	4,510,000,000	7,155,000,000	6,774,000,000	7,515,000,000
EBIT	3,702,000,000	6,311,000,000	5,915,000,000	6,675,000,000
Net Interest Income	107,000,000	161,000,000	6,000,000	-205,000,000
Normalized Income	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income From Continuing And Discontinued Operation	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Total Expenses	42,607,000,000	45,051,000,000	45,302,000,000	40,035,000,000
Diluted Average Shares	1,487,600,000	1,529,700,000	1,569,800,000	NaN
Basic Average Shares	1,484,900,000	1,517,600,000	1,551,600,000	NaN
Diluted EPS	2	4	3	NaN
Basic EPS	2	4	3	NaN
Diluted NI Availto Com Stockholders	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income Common Stockholders	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income Including Noncontrolling Interests	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Net Income Continuous Operations	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000
Tax Provision	666,000,000	1,000,000,000	1,131,000,000	605,000,000
Pretax Income	3,885,000,000	6,700,000,000	6,201,000,000	6,651,000,000
Other Income Expense	76,000,000	228,000,000	280,000,000	181,000,000
Other Non Operating Income Expenses	76,000,000	228,000,000	280,000,000	181,000,000
Net Non Operating Interest Income Expense	107,000,000	161,000,000	6,000,000	-205,000,000
Total Other Finance Cost	-107,000,000	-161,000,000	-6,000,000	205,000,000
Operating Income	3,702,000,000	6,311,000,000	5,915,000,000	6,675,000,000
Operating Expense	16,088,000,000	16,576,000,000	16,377,000,000	14,804,000,000
Other Operating Expenses	NaN	NaN	12,317,000,000	10,954,000,000
Selling General And Administration	16,088,000,000	16,576,000,000	16,377,000,000	14,804,000,000
Selling And Marketing Expense	4,689,000,000	4,285,000,000	4,060,000,000	3,850,000,000
General And Administrative Expense	11,399,000,000	12,291,000,000	12,317,000,000	10,954,000,000
Other Gand A	11,399,000,000	12,291,000,000	12,317,000,000	10,954,000,000
Gross Profit	19,790,000,000	22,887,000,000	22,292,000,000	21,479,000,000
Cost Of Revenue	26,519,000,000	28,475,000,000	28,925,000,000	25,231,000,000
Total Revenue	46,309,000,000	51,362,000,000	51,217,000,000	46,710,000,000
Operating Revenue	46,309,000,000	51,362,000,000	51,217,000,000	46,710,000,000

annual_cash_flow = ticker.cashflow
annual_cash_flow

	2025-05-31	2024-05-31	2023-05-31	2022-05-31
Free Cash Flow	3,268,000,000	6,617,000,000	4,872,000,000	4,430,000,000
Repurchase Of Capital Stock	-2,985,000,000	-4,250,000,000	-5,480,000,000	-4,014,000,000
Repayment Of Debt	-1,000,000,000	0	-500,000,000	0
Issuance Of Debt	NaN	NaN	NaN	0
Capital Expenditure	-430,000,000	-812,000,000	-969,000,000	-758,000,000
Interest Paid Supplemental Data	389,000,000	381,000,000	347,000,000	290,000,000
Income Tax Paid Supplemental Data	1,226,000,000	1,299,000,000	1,517,000,000	1,231,000,000
End Cash Position	7,464,000,000	9,860,000,000	7,441,000,000	8,574,000,000
Beginning Cash Position	9,860,000,000	7,441,000,000	8,574,000,000	9,889,000,000
Effect Of Exchange Rate Changes	1,000,000	-16,000,000	-91,000,000	-143,000,000
Changes In Cash	-2,397,000,000	2,435,000,000	-1,042,000,000	-1,172,000,000
Financing Cash Flow	-5,820,000,000	-5,888,000,000	-7,447,000,000	-4,836,000,000
Cash Flow From Continuing Financing Activities	-5,820,000,000	-5,888,000,000	-7,447,000,000	-4,836,000,000
Net Other Financing Charges	-85,000,000	-136,000,000	-102,000,000	-151,000,000
Proceeds From Stock Option Exercised	551,000,000	667,000,000	651,000,000	1,151,000,000
Cash Dividends Paid	-2,300,000,000	-2,169,000,000	-2,012,000,000	-1,837,000,000
Common Stock Dividend Paid	-2,300,000,000	-2,169,000,000	-2,012,000,000	-1,837,000,000
Net Common Stock Issuance	-2,985,000,000	-4,250,000,000	-5,480,000,000	-4,014,000,000
Common Stock Payments	-2,985,000,000	-4,250,000,000	-5,480,000,000	-4,014,000,000
Net Issuance Payments Of Debt	-1,001,000,000	0	-504,000,000	15,000,000
Net Short Term Debt Issuance	-1,000,000	0	-4,000,000	15,000,000
Net Long Term Debt Issuance	-1,000,000,000	0	-500,000,000	0
Long Term Debt Payments	-1,000,000,000	0	-500,000,000	0
Long Term Debt Issuance	NaN	NaN	NaN	0
Investing Cash Flow	-275,000,000	894,000,000	564,000,000	-1,524,000,000
Cash Flow From Continuing Investing Activities	-275,000,000	894,000,000	564,000,000	-1,524,000,000
Net Other Investing Changes	8,000,000	-15,000,000	52,000,000	-19,000,000
Net Investment Purchase And Sale	147,000,000	1,721,000,000	1,481,000,000	-747,000,000
Sale Of Investment	3,381,000,000	6,488,000,000	7,540,000,000	12,166,000,000
Purchase Of Investment	-3,234,000,000	-4,767,000,000	-6,059,000,000	-12,913,000,000
Net PPE Purchase And Sale	-430,000,000	-812,000,000	-969,000,000	-758,000,000
Purchase Of PPE	-430,000,000	-812,000,000	-969,000,000	-758,000,000
Operating Cash Flow	3,698,000,000	7,429,000,000	5,841,000,000	5,188,000,000
Cash Flow From Continuing Operating Activities	3,698,000,000	7,429,000,000	5,841,000,000	5,188,000,000
Change In Working Capital	-787,000,000	716,000,000	-513,000,000	-1,660,000,000
Change In Payables And Accrued Expense	-426,000,000	397,000,000	-225,000,000	1,365,000,000
Change In Payable	-426,000,000	397,000,000	-225,000,000	1,365,000,000
Change In Account Payable	-426,000,000	397,000,000	-225,000,000	1,365,000,000
Change In Prepaid Assets	-224,000,000	-260,000,000	-644,000,000	-845,000,000
Change In Inventory	120,000,000	908,000,000	-133,000,000	-1,676,000,000
Change In Receivables	-257,000,000	-329,000,000	489,000,000	-504,000,000
Changes In Account Receivables	-257,000,000	-329,000,000	489,000,000	-504,000,000
Stock Based Compensation	709,000,000	804,000,000	755,000,000	638,000,000
Deferred Tax	-288,000,000	-497,000,000	-117,000,000	-650,000,000
Deferred Income Tax	-288,000,000	-497,000,000	-117,000,000	-650,000,000
Depreciation Amortization Depletion	808,000,000	844,000,000	859,000,000	840,000,000
Depreciation And Amortization	808,000,000	844,000,000	859,000,000	840,000,000
Amortization Cash Flow	33,000,000	48,000,000	156,000,000	123,000,000
Amortization Of Intangibles	33,000,000	48,000,000	156,000,000	123,000,000
Depreciation	775,000,000	796,000,000	703,000,000	717,000,000
Operating Gains Losses	37,000,000	-138,000,000	-213,000,000	-26,000,000
Net Foreign Currency Exchange Gain Loss	37,000,000	-138,000,000	-213,000,000	-26,000,000
Net Income From Continuing Operations	3,219,000,000	5,700,000,000	5,070,000,000	6,046,000,000

annual_balance_sheet = ticker.balance_sheet
annual_balance_sheet = annual_balance_sheet.fillna(0)
annual_balance_sheet

	2025-05-31	2024-05-31	2023-05-31	2022-05-31
Ordinary Shares Number	1,476,000,000	1,503,000,000	1,532,000,000	1,571,000,000
Share Issued	1,476,000,000	1,503,000,000	1,532,000,000	1,571,000,000
Net Debt	502,000,000	0	1,492,000,000	856,000,000
Total Debt	11,018,000,000	11,952,000,000	12,144,000,000	12,627,000,000
Tangible Book Value	12,714,000,000	13,931,000,000	13,449,000,000	14,711,000,000
...	...	...	...	...
Cash Cash Equivalents And Short Term Investments	9,151,000,000	11,582,000,000	10,675,000,000	12,997,000,000
Other Short Term Investments	1,687,000,000	1,722,000,000	3,234,000,000	4,423,000,000
Cash And Cash Equivalents	7,464,000,000	9,860,000,000	7,441,000,000	8,574,000,000
Cash Equivalents	6,243,000,000	8,638,000,000	5,674,000,000	7,735,000,000
Cash Financial	1,221,000,000	1,222,000,000	1,767,000,000	839,000,000

75 rows × 4 columns

# FCFF Calculation using Cash Flow Statement and Income Statement Inputs
free_cash_flow_firm = (cash_flow_df.loc['Free Cash Flow'].astype('int64')) \
                    + (income_statement_df.loc['Net Non Operating Interest Income Expense'].astype('int64') \
                        * (1 - income_statement_df.loc['Tax Provision'].astype('int64') \
                           / income_statement_df.loc['Pretax Income'].astype('int64'))).astype('int64')

# Change Series to a Pandas Dataframe
free_cash_flow_firm_df = free_cash_flow_firm.to_frame().transpose()
print(free_cash_flow_firm_df)
print(cash_flow_df.loc['Free Cash Flow'][0])
print(income_statement_df.loc['Net Non Operating Interest Income Expense'][0])
print(income_statement_df.loc['Tax Provision'][0])
print(income_statement_df.loc['Pretax Income'][0])
print((1 - income_statement_df.loc['Tax Provision'][0] \
                           / income_statement_df.loc['Pretax Income'][0]))
print(income_statement_df.loc['Net Non Operating Interest Income Expense'][0] *(1 - income_statement_df.loc['Tax Provision'][0] \
                           / income_statement_df.loc['Pretax Income'][0]))

   2025-05-31  2024-05-31  2023-05-31  2022-05-31
0  3356657142  6753970149  4876905660  4243647572
3268000000.0
107000000.0
666000000.0
3885000000.0
0.8285714285714285
88657142.85714285

# CAGR of FCFF
latest_free_cash_flow_firm = float(free_cash_flow_firm_df.iloc[0,0])
earliest_free_cash_flow_firm = float(free_cash_flow_firm_df.iloc[0,len(free_cash_flow_firm_df.columns)-1])
free_cash_flow_firm_CAGR = ((latest_free_cash_flow_firm/earliest_free_cash_flow_firm)\
                            **(float(1/(len(free_cash_flow_firm_df.columns)))))-1


print(latest_free_cash_flow_firm)
print(earliest_free_cash_flow_firm)
print(free_cash_flow_firm_CAGR)

3356657142.0
4243647572.0
-0.0569343672541337

# Perpetual (terminal) growth rate.
# The historical FCFF CAGR can be negative, which implies the firm shrinks
# forever - not a valid going-concern assumption. Use a conservative long-run
# rate (~ long-run GDP / inflation) instead.
long_term_growth = 0.025
long_term_growth

0.025

# Forecasted FCFF
forecast_free_cash_flow_firm_df = pd.DataFrame(columns=['Year ' + str(i) for i in range(1,6)])
free_cash_flow_firm_forecast_lst = []
for i in range(1,6):
    if i != 5:
        free_cash_flow_firm_forecast = latest_free_cash_flow_firm*(1+free_cash_flow_firm_CAGR)**i
    else:
        free_cash_flow_firm_forecast = latest_free_cash_flow_firm*(1+free_cash_flow_firm_CAGR)\
                                        **(i-1)*(1+long_term_growth)
    free_cash_flow_firm_forecast_lst.append(int(free_cash_flow_firm_forecast))
forecast_free_cash_flow_firm_df.loc[0] = free_cash_flow_firm_forecast_lst
forecast_free_cash_flow_firm_df

	Year 1	Year 2	Year 3	Year 4	Year 5
0	3165547991	2985319519	2815352241	2655061943	2721438491

# Risk-free Rate
timespan = 100
current_date = datetime.date.today()
formatted_date = current_date.strftime('%Y-%m-%d')
past_date = current_date-datetime.timedelta(days=timespan)
formatted_past_date = past_date.strftime('%Y-%m-%d')
tk = yf.Ticker('^TNX')
risk_free_rate_df = tk.history(period='3mo')
risk_free_rate = (risk_free_rate_df.iloc[len(risk_free_rate_df)-1,3])/100
risk_free_rate

c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()

0.04484999656677246

import requests
from bs4 import BeautifulSoup

def finviz_fundament(symbol):
    # Finviz blocks non-browser requests and splits fundamentals across
    # multiple snapshot-table2 tables, so use a browser UA and aggregate them.
    url = f"https://finviz.com/quote.ashx?t={symbol}"
    headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                             "AppleWebKit/537.36 (KHTML, like Gecko) "
                             "Chrome/126.0 Safari/537.36"}
    resp = requests.get(url, headers=headers, timeout=15)
    resp.raise_for_status()
    soup = BeautifulSoup(resp.text, "lxml")
    cells = []
    for table in soup.find_all("table", class_="snapshot-table2"):
        cells += [td.text for td in table.find_all("td")]
    return {cells[i]: cells[i + 1] for i in range(0, len(cells) - 1, 2)}

tk = finviz_fundament('NKE')
tk

{'Index': 'DJIA, S&P 500',
 'Market Cap': '65.29B',
 'Enterprise Value': '67.30B',
 'Income': '3.11B',
 'Sales': '46.40B',
 'Book/sh': '9.52',
 'Cash/sh': '6.10',
 'Dividend Est.': '1.66 (3.76%)',
 'Dividend TTM': '1.63 (3.70%)',
 'Dividend Ex-Date': 'Jun 01, 2026',
 'Dividend Gr. 3/5Y': '7.28% 8.78%',
 'Payout': '77.67%',
 'Employees': '77800',
 'IPO': 'Dec 02, 1980',
 'P/E': '21.01',
 'Forward P/E': '19.97',
 'PEG': '4.16',
 'P/S': '1.41',
 'P/B': '4.63',
 'P/C': '7.23',
 'P/FCF': '62.30',
 'EV/EBITDA': '18.45',
 'EV/Sales': '1.45',
 'Quick Ratio': '1.36',
 'Current Ratio': '1.96',
 'Debt/Eq': '0.74',
 'LT Debt/Eq': '0.58',
 'Option/Short': 'Yes / Yes',
 'EPS (ttm)': '2.10',
 'EPS next Y': '25.94%',
 'EPS next Q': '0.44',
 'EPS this Y': '-16.54%',
 'EPS next 5Y': '4.80%',
 'EPS past 3/5Y': '-13.39% -10.02%',
 'Sales past 3/5Y': '-3.22% 0.84%',
 'EPS Y/Y TTM': '-2.96%',
 'Sales Y/Y TTM': '-0.01%',
 'EPS Q/Q': '404.83%',
 'Sales Q/Q': '-1.33%',
 'Earnings': 'Jun 30 AMC',
 'EPS/Sales Surpr.': '479.24% 1.13%',
 'Insider Own': '21.06%',
 'Insider Trans': '0.01%',
 'Inst Own': '66.28%',
 'Inst Trans': '-1.43%',
 'ROA': '8.29%',
 'ROE': '22.14%',
 'ROIC': '13.27%',
 'Gross Margin': '44.06%',
 'Oper. Margin': '8.56%',
 'Profit Margin': '6.70%',
 'SMA20': '1.63%',
 'SMA50': '0.57%',
 'SMA200': '-23.08%',
 'Trades': '\n\n',
 'Shs Outstand': '1.48B',
 'Shs Float': '1.17B',
 'Short Float': '4.88%',
 'Short Ratio': '2.28',
 'Short Interest': '57.04M',
 '52W High': '80.17 -45.00%',
 '52W Low': '40.00 10.23%',
 'Volatility': '3.79% 3.25%',
 'ATR (14)': '1.49',
 'RSI (14)': '52.93',
 'Beta': '1.11',
 'Rel Volume': '1.25',
 'Avg Volume': '25.00M',
 'Volume': '31,232,182',
 'Perf Week': '7.80%',
 'Perf Month': '0.82%',
 'Perf Quarter': '-1.21%',
 'Perf Half Y': '-27.95%',
 'Perf YTD': '-30.80%',
 'Perf Year': '-42.28%',
 'Perf 3Y': '-60.05%',
 'Perf 5Y': '-72.40%',
 'Perf 10Y': '-20.72%',
 'Recom': '2.49',
 'Target Price': '50.32',
 'Prev Close': '43.06',
 'Price': '44.09',
 'Change': '2.39%'}

beta = float(tk['Beta']) # float is for decimal and int is for integer
beta

1.11

market_risk_premium = (0.10-risk_free_rate)
market_risk_premium

0.05515000343322755

# Required Cost of Equity

coe = risk_free_rate + (beta*market_risk_premium)

coe

0.10606650037765504

interest_expense = income_statement_df.loc['Net Non Operating Interest Income Expense']
interest_expense_df = interest_expense.to_frame().transpose()
interest_expense_str = interest_expense_df.values[0][0]
interest_expense_int = int(interest_expense_str)


# Total Debt
total_debt = balance_sheet_df.loc['Total Debt']
total_debt_df = total_debt.to_frame().transpose()
total_debt_str = total_debt_df.values[0][0]
total_debt_int = int(total_debt_str)

# Required Cost of Debt
cod = interest_expense_int / total_debt_int


print(interest_expense_str)
print(total_debt_df)

print(interest_expense_int)
print(total_debt_int)
print(cod)

107000000.0
               2026-02-28     2025-11-30     2025-08-31     2025-05-31  \
Total Debt 11,178,000,000 11,282,000,000 11,061,000,000 11,018,000,000   

               2025-02-28  2024-11-30  
Total Debt 11,911,000,000         NaN  
107000000
11178000000
0.009572374306673823

# Effective Tax Rate
effective_tax_rate = income_statement_df.loc['Tax Provision'].astype('int64') \
                           / income_statement_df.loc['Pretax Income'].astype('int64')

print(income_statement_df.loc['Tax Provision'].astype('int64'))
print(income_statement_df.loc['Pretax Income'].astype('int64'))
print(len(effective_tax_rate))
print(sum(effective_tax_rate))
avg_effective_tax_rate = sum(effective_tax_rate) / len(effective_tax_rate)
avg_effective_tax_rate

2025-05-31     666000000
2024-05-31    1000000000
2023-05-31    1131000000
2022-05-31     605000000
Name: Tax Provision, dtype: int64
2025-05-31    3885000000
2024-05-31    6700000000
2023-05-31    6201000000
2022-05-31    6651000000
Name: Pretax Income, dtype: int64
4
0.5940360047261646





0.14850900118154114

market_cap_str = tk['Market Cap']

market_cap_lst = market_cap_str.split('.')
if market_cap_str[len(market_cap_str)-1] == 'T':
    market_cap_length = len(market_cap_lst[1])-1
    market_cap_lst[1] = market_cap_lst[1].replace('T',(12-market_cap_length)*'0')
    market_cap_int = int(''.join(market_cap_lst))
if market_cap_str[len(market_cap_str)-1] == 'B':
    market_cap_length = len(market_cap_lst[1])-1
    market_cap_lst[1] = market_cap_lst[1].replace('B',(9-market_cap_length)*'0')
    market_cap_int = int(''.join(market_cap_lst))

market_cap_int

65290000000

last_cf = cash_flow_df.loc['End Cash Position']
last_cf_df = last_cf.to_frame().transpose()
last_cf_str = last_cf_df.values[0][0]
last_cf_int = int(last_cf_str)

last_equity = balance_sheet_df.loc['Total Equity Gross Minority Interest']
last_equity_df = last_equity.to_frame().transpose()
last_equity_str = last_equity_df.values[0][0]
last_equity_int = int(last_equity_str)


enterprise_value = market_cap_int + total_debt_int - last_cf_int

print(last_cf_int)
print(last_equity_int)
print(enterprise_value)

7464000000
14090000000
69004000000

WACC = ((last_equity_int/(last_equity_int + total_debt_int)) * coe) \
        + ((total_debt_int/(last_equity_int + total_debt_int)) * cod * (1-avg_effective_tax_rate))

WACC

0.0627507728033376

# Equity Value Calculation
discounted_FCFF_lst = []
for year in range(0,5):
    discounted_FCFF = forecast_free_cash_flow_firm_df.iloc[0,year]/(1+WACC)**(year+1)
    discounted_FCFF_lst.append(int(discounted_FCFF))
terminal_value = (forecast_free_cash_flow_firm_df.iloc[0,4]*(1+long_term_growth))/(WACC-long_term_growth)
PV_terminal_value = int(terminal_value/(1+WACC)**5)
firm_value = sum(discounted_FCFF_lst)+PV_terminal_value
equity_value = (firm_value - total_debt_int + last_cf_int)

print(discounted_FCFF_lst)
print(terminal_value)
print(forecast_free_cash_flow_firm_df.iloc[0,4])
print(firm_value)
print(equity_value)

[2978636263, 2643187438, 2345516274, 2081368319, 2007434463]
73891850315.40277
2721438491
66561525614
62847525614

# Total Shares Outstanding
shares_outstanding_str = tk['Shs Outstand']

shares_outstanding_lst = shares_outstanding_str.split('.')
if shares_outstanding_str[len(shares_outstanding_str)-1] == 'T':
    shares_outstanding_length = len(shares_outstanding_lst[1])-1
    shares_outstanding_lst[1] = shares_outstanding_lst[1].replace('T',(12-shares_outstanding_length)*'0')
    shares_outstanding_int = int(''.join(shares_outstanding_lst))
if shares_outstanding_str[len(shares_outstanding_str)-1] == 'B':
    shares_outstanding_length = len(shares_outstanding_lst[1])-1
    shares_outstanding_lst[1] = shares_outstanding_lst[1].replace('B',(9-shares_outstanding_length)*'0')
    shares_outstanding_int = int(''.join(shares_outstanding_lst))
if shares_outstanding_str[len(shares_outstanding_str)-1] == 'M':
    shares_outstanding_length = len(shares_outstanding_lst[1])-1
    shares_outstanding_lst[1] = shares_outstanding_lst[1].replace('M',(6-shares_outstanding_length)*'0')
    shares_outstanding_int = int(''.join(shares_outstanding_lst))

shares_outstanding_int

1480000000

# Two-stage FCFF Valuation Model Stock Price Estimate
stock_price = equity_value / shares_outstanding_int
stock_price = '${:,.2f}'.format(stock_price)
print("Model Stock Price = %s"%(stock_price))

# Actual Stock Price
actual_stock_price = market_cap_int / shares_outstanding_int
actual_stock_price = '${:,.2f}'.format(actual_stock_price)
print("Actual Stock Price = %s"%(actual_stock_price))

Model Stock Price = $42.46
Actual Stock Price = $44.11

   Reportable Operating Segments - Schedule of Operating Segment Information (Details) - USD ($)  $ in Thousands 12 Months Ended                            
   Reportable Operating Segments - Schedule of Operating Segment Information (Details) - USD ($)  $ in Thousands   Mar. 31, 2026 Mar. 31, 2025 Mar. 31, 2024
                                                                   Segment Reporting Information [Line Items]             NaN           NaN           NaN
                                                                                                    Net sales     $ 5,472,296   $ 4,985,612   $ 4,287,763
                                                                                          Less: Cost of sales         2314570       2099949       1902275
                                                                                                 Gross profit     $ 3,157,726   $ 2,885,663   $ 2,385,488
                                                                                         Segment gross margin          57.70%        57.90%        55.60%
                                                                                                        Less:             NaN           NaN           NaN
                                                                                    Payroll and related costs       $ 307,073     $ 265,328     $ 226,926
                                                               Advertising, marketing, and promotion expenses          495838        432198        348852
                                                                                           Rent and occupancy          130571        102615         92661
                                                                         Depreciation and other related costs           20184         19445         22718
                                                                                         Other segment items          231384        180121        146736
                                                                                       Segment SG&A expenses         1185050        999707        837893
                                                                              Segment income from operations     $ 1,972,676   $ 1,885,956   $ 1,547,595
                                                                                    Segment operating margin          36.00%        37.80%        36.10%
                                                                             Impairment of intangible assets             $ 0           $ 0       $ 8,164
                                                                                   UGG | Reportable segments             NaN           NaN           NaN
                                                                  Segment Reporting Information [Line Items]             NaN           NaN           NaN
             

Peer Comparison — NKE vs DECK (Hoka) vs ONON (On) vs ADDYY (Adidas)

Below we reuse the same two-stage FCFF DCF and layer in price-performance metrics and a qualitative read. Note: DECK reports in USD; ONON reports in CHF and ADDYY in EUR, while market data (price, market cap, shares) comes from Finviz in USD — so their model prices mix reporting currency with USD market data and should be read as rough, not precise.

import numpy as np

peers = ['NKE', 'DECK', 'ONON', 'ADDYY']

def _num(s):
    # Parse Finviz shorthand like '65.29B', '1.48B', '29.92M', '44.09'
    if s is None:
        return np.nan
    s = str(s).strip().replace(',', '')
    mult = {'T': 1e12, 'B': 1e9, 'M': 1e6, 'K': 1e3}
    if s and s[-1] in mult:
        try:
            return float(s[:-1]) * mult[s[-1]]
        except ValueError:
            return np.nan
    try:
        return float(s)
    except ValueError:
        return np.nan

def fcff_valuation(symbol, long_term_growth=0.025, market_return=0.10):
    tkr = yf.Ticker(symbol)
    cf  = tkr.cashflow
    inc = tkr.income_stmt.dropna(axis=1, thresh=10)
    bs  = tkr.quarterly_balance_sheet

    # Market data: Finviz first, fall back to yfinance (e.g. OTC ADRs like ADDYY)
    try:
        fv = finviz_fundament(symbol)
        beta = _num(fv.get('Beta')); shares = _num(fv.get('Shs Outstand'))
        mcap = _num(fv.get('Market Cap')); price = _num(fv.get('Price'))
    except Exception:
        info = tkr.info
        beta = info.get('beta'); shares = info.get('sharesOutstanding')
        mcap = info.get('marketCap')
        price = info.get('currentPrice', info.get('regularMarketPrice'))
    beta   = np.nan if beta   is None else float(beta)
    shares = np.nan if shares is None else float(shares)
    mcap   = np.nan if mcap   is None else float(mcap)
    price  = np.nan if price  is None else float(price)

    # Effective tax rate (clip to a sane range, fall back to 21%)
    if 'Tax Provision' in inc.index and 'Pretax Income' in inc.index:
        eff = inc.loc['Tax Provision'].dropna() / inc.loc['Pretax Income'].dropna()
        eff = eff[(eff > 0) & (eff < 0.35)]
        eff_tax = float(eff.mean()) if len(eff) else 0.21
    else:
        eff_tax = 0.21

    # FCFF = Free Cash Flow + after-tax non-operating interest
    fcf = cf.loc['Free Cash Flow'].dropna().astype(float)
    if 'Net Non Operating Interest Income Expense' in inc.index:
        nnoi = inc.loc['Net Non Operating Interest Income Expense'].dropna().astype(float)
        fcff = (fcf + nnoi * (1 - eff_tax)).dropna()
    else:
        fcff = fcf
    fcff = fcff.sort_index(ascending=False)          # newest first
    latest, earliest, n = float(fcff.iloc[0]), float(fcff.iloc[-1]), len(fcff)
    cagr = (latest / earliest) ** (1 / n) - 1 if (latest > 0 and earliest > 0) else 0.0

    # 5-year forecast (fade final year to terminal growth)
    fc = [latest * (1 + cagr) ** i if i != 5 else latest * (1 + cagr) ** (i - 1) * (1 + long_term_growth)
          for i in range(1, 6)]

    # Cost of equity (CAPM), floored at the risk-free rate
    beta = 1.0 if np.isnan(beta) else beta
    coe = max(risk_free_rate + beta * (market_return - risk_free_rate), risk_free_rate)

    # Cost of debt + WACC
    total_debt = int(bs.loc['Total Debt'].dropna().iloc[0]) if 'Total Debt' in bs.index else 0
    equity_bv  = int(bs.loc['Total Equity Gross Minority Interest'].dropna().iloc[0])
    int_exp    = int(inc.loc['Net Non Operating Interest Income Expense'].dropna().iloc[0]) if 'Net Non Operating Interest Income Expense' in inc.index else 0
    cod  = (int_exp / total_debt) if total_debt else 0.0
    wacc = ((equity_bv / (equity_bv + total_debt)) * coe
            + (total_debt / (equity_bv + total_debt)) * cod * (1 - eff_tax)) if (equity_bv + total_debt) else coe

    # Terminal value (keep WACC > g)
    g = long_term_growth if wacc > long_term_growth else wacc - 0.01
    disc = [fc[y] / (1 + wacc) ** (y + 1) for y in range(5)]
    tv    = (fc[4] * (1 + g)) / (wacc - g)
    firm_value = sum(disc) + tv / (1 + wacc) ** 5

    cash = int(cf.loc['End Cash Position'].dropna().iloc[0]) if 'End Cash Position' in cf.index else 0
    equity_value = firm_value - total_debt + cash

    model_price  = equity_value / shares if not np.isnan(shares) and shares else np.nan
    actual_price = price
    if np.isnan(actual_price) and not np.isnan(shares) and shares:
        actual_price = mcap / shares
    upside = (model_price / actual_price - 1) * 100 if (model_price and actual_price) else np.nan

    return {'Ticker': symbol, 'Beta': round(beta, 2), 'WACC': round(wacc, 4),
            'FCFF CAGR': round(cagr, 4), 'Firm Value ($B)': round(firm_value / 1e9, 2),
            'Equity Value ($B)': round(equity_value / 1e9, 2),
            'Model Price': round(model_price, 2), 'Actual Price': round(actual_price, 2),
            'Upside %': round(upside, 1)}

def performance_metrics(symbol, start='2022-01-01', end=None):
    px = yf.Ticker(symbol).history(start=start, end=end, interval='1d')['Close'].dropna()
    if len(px) < 2:
        return {'Ticker': symbol}
    daily = px.pct_change().dropna()
    years = (px.index[-1] - px.index[0]).days / 365.25
    return {'Ticker': symbol,
            'CAGR %': round(((px.iloc[-1] / px.iloc[0]) ** (1 / years) - 1) * 100, 1),
            'Cumulative %': round((px.iloc[-1] / px.iloc[0] - 1) * 100, 1),
            'Volatility %': round(daily.std() * np.sqrt(252) * 100, 1),
            'Expected Yearly %': round(daily.mean() * 252 * 100, 1),
            'Expected Monthly %': round(daily.mean() * 21 * 100, 1)}

dcf_df  = pd.DataFrame([fcff_valuation(t) for t in peers]).set_index('Ticker')
perf_df = pd.DataFrame([performance_metrics(t) for t in peers]).set_index('Ticker')

_dcf_fmt = {'Beta': '{:.2f}'.format, 'WACC': '{:.2%}'.format, 'FCFF CAGR': '{:.2%}'.format,
            'Firm Value ($B)': '{:.1f}'.format, 'Equity Value ($B)': '{:.1f}'.format,
            'Model Price': '${:.2f}'.format, 'Actual Price': '${:.2f}'.format,
            'Upside %': '{:+.1f}%'.format}
print("=== Two-stage FCFF DCF (model vs actual) ===")
print(dcf_df.to_string(formatters=_dcf_fmt))
print("\n=== Price performance (since 2022-01-01) ===")
print(perf_df.to_string(formatters={c: '{:+.1f}%'.format for c in perf_df.columns}))

c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()
c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()
c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()
c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()


=== Two-stage FCFF DCF (model vs actual) ===
       Beta   WACC FCFF CAGR Firm Value ($B) Equity Value ($B) Model Price Actual Price Upside %
Ticker                                                                                          
NKE    1.11  6.28%    -5.74%            66.5              62.8      $42.41       $44.09    -3.8%
DECK   1.16 11.11%    25.21%            28.2              29.7     $212.42      $104.69  +102.9%
ONON   2.12 12.44%     0.00%             2.7               3.1      $10.52       $36.83   -71.4%
ADDYY  1.21  4.84%     0.00%             6.4               2.1       $5.87      $105.15   -94.4%

=== Price performance (since 2022-01-01) ===
       CAGR % Cumulative % Volatility % Expected Yearly % Expected Monthly %
Ticker                                                                      
NKE    -24.3%       -71.3%       +36.6%            -21.1%              -1.8%
DECK   +12.7%       +70.7%       +44.9%            +22.1%              +1.8%
ONON    -1.1%        -4.8%       +54.6%            +13.7%              +1.1%
ADDYY   -6.1%       -24.5%       +38.7%             +1.1%              +0.1%

import matplotlib.pyplot as plt
from math import pi

fig, axes = plt.subplots(1, 3, figsize=(20, 6))

# 1) Normalized price (rebased to 100 at start)
prices = yf.download(peers, start='2022-01-01', progress=False)['Close'].dropna(how='all')
norm = prices / prices.bfill().iloc[0] * 100
norm.plot(ax=axes[0])
axes[0].set_title('Rebased Price (start = 100)'); axes[0].set_ylabel('Index'); axes[0].grid(alpha=.3)

# 2) Model vs Actual price
x = np.arange(len(dcf_df)); w = 0.35
axes[1].bar(x - w/2, dcf_df['Actual Price'], w, label='Actual', color='#888')
axes[1].bar(x + w/2, dcf_df['Model Price'],  w, label='Model (DCF)', color='#112e51')
axes[1].set_xticks(x); axes[1].set_xticklabels(dcf_df.index)
axes[1].set_title('DCF Model vs Actual Price'); axes[1].legend(); axes[1].grid(alpha=.3, axis='y')

# 3) Radar of normalized performance (higher = better; volatility inverted)
radar = perf_df.copy()
radar['Volatility %'] = -radar['Volatility %']              # lower vol scores higher
radar_n = (radar - radar.min()) / (radar.max() - radar.min())
labels = list(radar_n.columns)
angles = np.linspace(0, 2*pi, len(labels), endpoint=False).tolist(); angles += angles[:1]
axr = plt.subplot(1, 3, 3, polar=True)
for t in radar_n.index:
    vals = radar_n.loc[t].tolist(); vals += vals[:1]
    axr.plot(angles, vals, 'o-', linewidth=1.5, label=t); axr.fill(angles, vals, alpha=0.08)
axr.set_thetagrids(np.degrees(angles[:-1]), labels, fontsize=8)
axr.set_title('Normalized Performance (outer = better)'); axr.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))

plt.tight_layout(); plt.show()

c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()
c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()
c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()
c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()
C:\Users\Admin\AppData\Local\Temp\ipykernel_16632\2091169150.py:25: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.
  axr = plt.subplot(1, 3, 3, polar=True)

png

Qualitative Comparison

	NKE (Nike)	DECK (Hoka/UGG)	ONON (On)	ADDYY (Adidas)
Stage	Mature global leader	High-growth mid-cap	Fast-growth small-cap	Turnaround #2 player
Moat	Wide — brand, scale, DTC	Narrowing — brand momentum	Narrow — premium niche	Wide but dented (post-Yeezy)
FCFF trend	Declining (~ -6%/yr)	Strong, compounding	Low/erratic base	Recovering off a weak base
Growth driver	DTC + margin recovery	Hoka running franchise	Premium running + apparel	Terrace/lifestyle (Samba, Gazelle)
Key risk	Wholesale reset, China	Single-brand concentration	Valuation, scale, competition	Execution, FX, brand heat fading
Reporting ccy	USD	USD	CHF	EUR

How the numbers tie together

NKE — model $42 vs actual ~$44 (≈fair). Weak recent price (-71% since 2022) reflects the declining-FCFF story the DCF also captures. Lowest-risk profile (lowest volatility).
DECK — model $212 vs actual ~$105 (+103% “upside”). Driven by a very high FCFF CAGR (~25%) extrapolated forward; treat as optimistic — a 25% growth rate compounding 5 years is aggressive, and the model is highly sensitive to it. Best actual performer (+71%).
ONON — model $11 vs actual ~$37 (-71%). Misleading: On reports in CHF while price/shares are USD ADS, and its FCFF base is small/volatile (CAGR floored to 0). The DCF understates it — the market is pricing high future growth the model doesn’t.
ADDYY — model $6 vs actual ~$105 (-94%). Not usable as-is: EUR financials mixed with a USD ADR price, plus an ADR-to-ordinary share ratio the model ignores. Read the qualitative turnaround story here, not the DCF number.

Takeaway: the DCF is only apples-to-apples for the USD reporters (NKE, DECK). NKE screens fairly valued and defensive; DECK screens cheap but on aggressive growth assumptions. ONON and ADDYY need a currency-consistent model (convert statements to USD and apply the correct ADR share ratio) before their model prices mean anything.

# Revenue comparison (converted to USD $B for apples-to-apples)
report_ccy = {'NKE': 'USD', 'DECK': 'USD', 'ONON': 'CHF', 'ADDYY': 'EUR'}
fx = {'USD': 1.0,
      'EUR': float(yf.Ticker('EURUSD=X').history(period='5d')['Close'].iloc[-1]),
      'CHF': float(yf.Ticker('CHFUSD=X').history(period='5d')['Close'].iloc[-1])}

rev = {}
for t in peers:
    s = yf.Ticker(t).income_stmt.loc['Total Revenue'].dropna().astype(float)
    s.index = s.index.year                      # index by fiscal-year-end year
    rev[t] = s * fx[report_ccy[t]] / 1e9        # -> USD billions
rev_df = pd.DataFrame(rev).sort_index()

# Fiscal-year-ends differ, so compute growth/CAGR on each ticker's own series
latest_yoy = {}
summary = {}
for t in peers:
    s = rev_df[t].dropna()
    yoy = (s.iloc[-1] / s.iloc[-2] - 1) * 100
    cagr = ((s.iloc[-1] / s.iloc[0]) ** (1 / (len(s) - 1)) - 1) * 100
    latest_yoy[t] = yoy
    summary[t] = {'Latest Rev ($B)': f'{s.iloc[-1]:,.1f}',
                  'Latest YoY %': f'{yoy:+.1f}%',
                  'CAGR %': f'{cagr:+.1f}%',
                  'Years': f'{s.index[0]}-{s.index[-1]}'}
summary_df = pd.DataFrame(summary).T

print("=== Total Revenue (USD $B, by fiscal-year-end year) ===")
print(rev_df.to_string(float_format=lambda v: f'{v:,.1f}'))
print("\n=== Revenue growth summary (each on its own fiscal calendar) ===")
print(summary_df.to_string())

import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 3, figsize=(20, 5))

rev_df.plot(marker='o', ax=axes[0])
axes[0].set_title('Total Revenue (USD $B)'); axes[0].set_ylabel('$B'); axes[0].grid(alpha=.3)

rebased = rev_df.apply(lambda c: c / c.dropna().iloc[0] * 100)
rebased.plot(marker='o', ax=axes[1])
axes[1].set_title('Revenue Rebased to 100 (first year each)'); axes[1].grid(alpha=.3)

lg = pd.Series(latest_yoy)
lg.plot(kind='bar', ax=axes[2], color=['#c0392b' if v < 0 else '#27ae60' for v in lg])
axes[2].axhline(0, color='k', lw=.8)
axes[2].set_title('Latest Fiscal-Year Revenue Growth'); axes[2].set_ylabel('% YoY'); axes[2].grid(alpha=.3, axis='y')

plt.tight_layout(); plt.show()

c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()
c:\Users\Admin\anaconda3\lib\site-packages\yfinance\scrapers\history.py:396: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  self._capital_gains = pd.Series()


=== Total Revenue (USD $B, by fiscal-year-end year) ===
      NKE  DECK  ONON  ADDYY
2022 46.7   NaN   1.5   25.8
2023 51.2   3.6   2.2   24.5
2024 51.4   4.3   2.9   27.1
2025 46.3   5.0   3.8   28.4
2026  NaN   5.5   NaN    NaN

=== Revenue growth summary (each on its own fiscal calendar) ===
      Latest Rev ($B) Latest YoY %  CAGR %      Years
NKE              46.3        -9.8%   -0.3%  2022-2025
DECK              5.5        +9.8%  +14.7%  2023-2026
ONON              3.8       +30.0%  +35.1%  2022-2025
ADDYY            28.4        +4.8%   +3.3%  2022-2025

png

Revenue basis — how they differ, and why NKE is losing

Scale & trajectory (USD, latest fiscal year)

Co.	Revenue	Latest YoY	CAGR (window)	Read
NKE	~$46B	−9.8%	−0.3%	Giant, but shrinking — only one going backwards
ADDYY	~$28B	+4.8%	+3.3%	#2 incumbent, recovering (Samba/Gazelle terrace wave)
DECK	~$5.5B	+9.8%	+14.7%	Mid-cap compounder, Hoka is the growth engine
ONON	~$3.8B	+30.0%	+35.1%	Small-cap hyper-growth, premium running

The scale chart shows NKE ~8–12x bigger than the challengers, but the rebased chart tells the real story: NKE is the only line rolling over while ONON nearly triples and DECK keeps climbing. NKE isn’t losing on size — it’s losing on direction and momentum.

Why NKE is losing

DTC over-pivot. Nike cut wholesale accounts (Foot Locker, DSW, Amazon, boutiques) to push direct/app sales. It gave up shelf space and doorway visibility — which On, Hoka, New Balance and Adidas rushed to fill. The margin/relationship damage outlasted the strategy, and Nike is now rebuilding those wholesale ties.
Innovation gap. Growth leaned on retro franchises (Air Force 1, Dunk, Jordan retros) instead of new performance platforms. Flooding the market with Dunks/AF1s diluted scarcity and “brand heat,” while On (CloudTec) and Hoka (max-cushion) owned the fresh running narrative.
Lost the running category. ONON +30% and Hoka/DECK +10–15% are taking premium run share directly. Running is the tip of the spear for credibility, and Nike ceded ground right when the category boomed.
Lifestyle share to Adidas. Adidas’s Samba/Gazelle/terrace revival (+4.8%, recovering off a post-Yeezy trough) recaptured the fashion-sneaker zeitgeist Nike used to own.
China + macro softness. Weak China demand and heavy promotional activity (clearing excess Dunk/AF1 inventory) pressured both revenue and gross margin simultaneously.
Reset underway. Elliott Hill (veteran insider) returned as CEO to un-wind the DTC excess, rebuild wholesale, refresh product, and clean inventory — a multi-quarter turnaround, which is exactly why FY25 revenue (−9.8%) is the trough the market is watching.

Bottom line: the challengers are winning innovation and channel, not just price. Nike’s problem is self-inflicted (strategy + product cadence) more than competitive pricing — which is why a credible product/wholesale reset could stabilize it, but the revenue trend confirms it is currently the loser of the group.

Caveat: revenue converted to USD at current spot FX (EURUSD/CHFUSD); fiscal-year-ends differ (NKE May, DECK Mar, ONON/ADDYY Dec), so year alignment is approximate.

# === SEC filing evidence: put NUMBERS behind the qualitative story ===
# Pulls segment/disaggregation tables straight from 10-K financial-report R-files on EDGAR.
import requests, io, re
from bs4 import BeautifulSoup

SEC_HEADERS = {"User-Agent": "finance-research research@example.com"}

def _cik(tk):
    j = requests.get("https://www.sec.gov/files/company_tickers.json", headers=SEC_HEADERS, timeout=20).json()
    return next(str(r['cik_str']).zfill(10) for r in j.values() if r['ticker'].upper() == tk.upper())

def _latest(cik, forms=('10-K', '20-F')):
    rec = requests.get(f"https://data.sec.gov/submissions/CIK{cik}.json", headers=SEC_HEADERS, timeout=20).json()['filings']['recent']
    i = next(k for k, f in enumerate(rec['form']) if f in forms)
    return rec['accessionNumber'][i].replace('-', ''), rec['reportDate'][i]

def _clean(v):
    if pd.isna(v):
        return None
    s = str(v).replace('$', '').replace(',', '').replace('%', '').replace('(', '-').replace(')', '').strip()
    try:
        return float(s)
    except ValueError:
        return None

def _extract(df, metric='Revenues'):
    out, last = {}, None
    for _, r in df.iterrows():
        lab = str(r.iloc[0]).strip()
        if lab.lower().startswith(metric.lower()):
            vals = [_clean(r.iloc[c]) for c in range(1, min(4, df.shape[1]))]
            if any(v is not None for v in vals) and last:
                out[last] = vals
        elif lab and lab.lower() != 'nan' and '[line items]' not in lab.lower() \
                and 'disaggregation' not in lab.lower() and 'reporting information' not in lab.lower():
            last = re.sub(r'^Operating Segments \| ', '', lab)
    return out

def sec_table(tk, shortname_kw, metric='Revenues', forms=('10-K', '20-F')):
    cik = _cik(tk); acc, rdate = _latest(cik, forms)
    base = f"https://www.sec.gov/Archives/edgar/data/{int(cik)}/{acc}"
    fs = BeautifulSoup(requests.get(f"{base}/FilingSummary.xml", headers=SEC_HEADERS, timeout=20).text, 'lxml-xml')
    fn = next(r.find('HtmlFileName').text for r in fs.find_all('Report')
              if r.find('HtmlFileName') and shortname_kw in r.find('ShortName').text.lower())
    df = pd.read_html(io.StringIO(requests.get(f"{base}/{fn}", headers=SEC_HEADERS, timeout=20).text))[0]
    return _extract(df, metric), rdate

# ---- NIKE: geography, channel, product (from FY2025 10-K, $M, FY25/FY24/FY23) ----
nke_geo, nke_date = sec_table('NKE', 'information by operating segments', 'Revenues', forms=('10-K',))
nke_dis, _        = sec_table('NKE', 'disaggregation of revenue', 'Revenues', forms=('10-K',))
yrs = ['FY25', 'FY24', 'FY23']

geo_keys = {'North America': 'NIKE Brand | NORTH AMERICA', 'EMEA': 'NIKE Brand | EUROPE, MIDDLE EAST & AFRICA',
            'Greater China': 'NIKE Brand | GREATER CHINA', 'APLA': 'NIKE Brand | ASIA PACIFIC & LATIN AMERICA'}
geo_df = pd.DataFrame({k: nke_geo[v] for k, v in geo_keys.items()}, index=yrs).T
chan_df = pd.DataFrame({'Wholesale': nke_dis['Sales to Wholesale Customers'],
                        'DTC (Nike Direct)': nke_dis['Sales through Direct to Consumer']}, index=yrs).T
prod_df = pd.DataFrame({'Footwear': nke_dis['Footwear'], 'Apparel': nke_dis['Apparel'],
                        'Equipment': nke_dis['Equipment']}, index=yrs).T
for d in (geo_df, chan_df, prod_df):
    d['YoY %'] = (d['FY25'] / d['FY24'] - 1) * 100

# ---- Competitor brand growth: HOKA (from DECK 10-K) ----
deck_seg, deck_date = sec_table('DECK', 'operating segment information', 'Net sales', forms=('10-K',))
hoka = next(v for k, v in deck_seg.items() if 'HOKA' in k.upper())          # FY26/FY25/FY24 in $K
hoka_yoy = (hoka[0] / hoka[1] - 1) * 100

_f = lambda v: f'{v:,.0f}'
_p = lambda v: f'{v:+.1f}%'
print(f"NIKE 10-K reportDate {nke_date}  |  DECK 10-K reportDate {deck_date}\n")
print("=== NIKE revenue by geography ($M) — China + macro softness ===")
print(geo_df.to_string(formatters={'FY25': _f, 'FY24': _f, 'FY23': _f, 'YoY %': _p}))
print("\n=== NIKE revenue by channel ($M) — the DTC over-pivot backfiring ===")
print(chan_df.to_string(formatters={'FY25': _f, 'FY24': _f, 'FY23': _f, 'YoY %': _p}))
print("\n=== NIKE revenue by product ($M) — footwear (innovation engine) leads the drop ===")
print(prod_df.to_string(formatters={'FY25': _f, 'FY24': _f, 'FY23': _f, 'YoY %': _p}))
print(f"\n=== Lost running/share: growth of the challengers vs NIKE footwear ===")
print(f"  NIKE Footwear    {(prod_df.loc['Footwear','YoY %']):+.1f}%")
print(f"  HOKA (DECK)      {hoka_yoy:+.1f}%   (net sales {_f(hoka[1]/1000)}M -> {_f(hoka[0]/1000)}M)")
print(f"  On (ONON total)  {latest_yoy['ONON']:+.1f}%")
print(f"  Adidas (total)   {latest_yoy['ADDYY']:+.1f}%")

# ---- Charts ----
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 3, figsize=(20, 5))

geo_df['YoY %'].plot(kind='bar', ax=ax[0], color=['#c0392b' if v < 0 else '#27ae60' for v in geo_df['YoY %']])
ax[0].axhline(0, color='k', lw=.8); ax[0].set_title('NIKE Revenue YoY by Geography (FY25 vs FY24)')
ax[0].set_ylabel('% YoY'); ax[0].grid(alpha=.3, axis='y')

chan_df['YoY %'].plot(kind='bar', ax=ax[1], color=['#c0392b' if v < 0 else '#27ae60' for v in chan_df['YoY %']])
ax[1].axhline(0, color='k', lw=.8); ax[1].set_title('NIKE Revenue YoY by Channel — DTC fell hardest')
ax[1].set_ylabel('% YoY'); ax[1].grid(alpha=.3, axis='y')

comp = pd.Series({'NIKE\nFootwear': prod_df.loc['Footwear', 'YoY %'], 'HOKA': hoka_yoy,
                  'On': latest_yoy['ONON'], 'Adidas': latest_yoy['ADDYY']})
comp.plot(kind='bar', ax=ax[2], color=['#c0392b' if v < 0 else '#27ae60' for v in comp])
ax[2].axhline(0, color='k', lw=.8); ax[2].set_title('Nike is losing the growth: footwear vs challengers')
ax[2].set_ylabel('% YoY (latest FY)'); ax[2].grid(alpha=.3, axis='y')

plt.tight_layout(); plt.show()

NIKE 10-K reportDate 2025-05-31  |  DECK 10-K reportDate 2026-03-31

=== NIKE revenue by geography ($M) — China + macro softness ===
                FY25   FY24   FY23  YoY %
North America 19,572 21,396 21,608  -8.5%
EMEA          12,257 13,607 13,418  -9.9%
Greater China  6,586  7,545  7,248 -12.7%
APLA           6,251  6,729  6,431  -7.1%

=== NIKE revenue by channel ($M) — the DTC over-pivot backfiring ===
                    FY25   FY24   FY23  YoY %
Wholesale         26,758 28,856 28,696  -7.3%
DTC (Nike Direct) 19,477 22,351 22,282 -12.9%

=== NIKE revenue by product ($M) — footwear (innovation engine) leads the drop ===
            FY25   FY24   FY23  YoY %
Footwear  30,967 35,227 35,290 -12.1%
Apparel   13,045 13,868 13,933  -5.9%
Equipment  2,223  2,112  1,755  +5.3%

=== Lost running/share: growth of the challengers vs NIKE footwear ===
  NIKE Footwear    -12.1%
  HOKA (DECK)      +15.9%   (net sales 2,233M -> 2,587M)
  On (ONON total)  +30.0%
  Adidas (total)   +4.8%

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SEC-filing evidence — the numbers behind each theme

All figures below are pulled directly from the companies’ latest 10-K financial-report tables on SEC EDGAR (NIKE FY2025 10-K, fiscal year end May 31, 2025; DECK FY2026 10-K).

Qualitative theme	Hard number (from 10-K)	Verdict
China + macro softness	Greater China revenue $7,545M → $6,586M (−12.7%) — the worst-declining region (NA −8.5%, EMEA −9.9%, APLA −7.1%)	Confirmed — China is the epicenter
DTC over-pivot backfiring	Nike Direct −12.9% vs Wholesale −7.3% — the channel Nike bet on fell nearly 2x harder than the one it walked away from	Confirmed — the DTC pivot is the bigger drag
Innovation gap / franchise fatigue	Footwear −12.1% (vs Apparel −5.9%, Equipment +5.3%) — the core sneaker engine is the single largest source of decline	Confirmed — the problem is footwear, i.e. product
Lost the running category / share	NIKE Footwear −12.1% while HOKA +15.9% ($2,233M → $2,587M), On +30.0%, Adidas +4.8%	Confirmed — challengers are growing double digits as Nike shrinks

Read the three charts together:

Geography — every region is red, but Greater China is the deepest hole (−12.7%), validating the “China + macro” narrative.
Channel — DTC (Nike Direct) fell harder than Wholesale, the numeric fingerprint of the failed direct-to-consumer over-pivot; rebuilding wholesale is exactly the reset underway.
Footwear vs challengers — Nike’s footwear is down ~12% in the same year Hoka (+16%), On (+30%) and Adidas (+5%) grew. That gap is the lost running/lifestyle share — quantified.

One-line takeaway: Nike’s decline is concentrated in footwear, DTC, and China — a product-and-strategy problem, not a pricing one — and the exact categories where On, Hoka and Adidas are simultaneously posting double-digit growth.

Note: fiscal-year-ends differ (NIKE May, DECK Mar, On/Adidas Dec) and Nike segment figures are NIKE Brand only in reported USD; competitor totals are whole-company.

Conclusion — what Nike must do to stop losing

The data points to a clear diagnosis: the decline is concentrated in footwear (−12.1%), Nike Direct (−12.9%) and Greater China (−12.7%) — a product, channel and China problem, not a pricing one — happening in the exact categories where On (+30%), Hoka (+16%) and Adidas (+5%) are growing. The fix has to attack those same three fronts.

1. Win back product & innovation (fixes footwear −12.1%)

Re-establish a performance-running franchise. On and Hoka took share with a clear technology story (CloudTec, max-cushion). Nike needs a flagship cushioning/running platform marketed as hard as Vaporfly was — not another retro drop.
Cut the retro oversupply. Deliberately ration Air Force 1 / Dunk to rebuild scarcity and pricing power; stop letting franchise volume mask the innovation gap.
Rebalance the pipeline toward newness (higher % of revenue from products <2 years old) with faster design-to-shelf cycles.

2. Rebuild wholesale without abandoning DTC (fixes Nike Direct −12.9%)

Re-enter lost doors (Foot Locker, Amazon, DSW, specialty run) to reclaim shelf space and reach — the vacuum challengers filled.
Reposition DTC as premium/full-price, not the whole engine — use it for launches, membership and data, not volume clearance.
Clean inventory to stop margin-destroying promotions that trained shoppers to wait for discounts.

3. Reset Greater China (fixes China −12.7%)

Localize product and marketing (China-specific design, local athletes/creators) rather than exporting the US line.
Rebuild credibility with local competitors (Anta, Li-Ning) taking share; lean on running and basketball where Nike still has authority.

4. Protect the moat & the P&L

Defend lifestyle against Adidas’s terrace wave (Samba/Gazelle) with Nike’s own low-profile silhouettes and culture partnerships.
Reinvest the demand-creation budget into product storytelling (it already rose to ~$4.7B) and measure by full-price sell-through, not shipments.
Restore gross margin through disciplined supply, fewer markdowns, and mix shift back to footwear innovation.

The scorecard to watch (from the same 10-K tables)

Signal that the turnaround is working	Target direction
Footwear revenue YoY	−12% → flat → positive
Nike Direct vs Wholesale	Both stabilize; wholesale re-growing
Greater China revenue	Stops declining faster than the group
Gross margin	Recovers as promotions fade
% revenue from new (<2yr) product	Rising

Bottom line: Nike doesn’t need to out-price On, Hoka or Adidas — it needs to out-innovate in footwear, rebuild the wholesale shelf it walked away from, and re-localize China. The FY25 trough (−9.8% total revenue) is the reset point; success is measured by footwear returning to growth and Nike Direct stabilizing alongside a rebuilt wholesale base — exactly the plan Elliott Hill’s leadership has signaled.