Mean P/E & Regressive P/E Family
Subject : Mean P/E & Regressive P/E Family
.
(A)
Concepts:
1. P/E is a function of Gnet_income, NetROIC, ROA, CICC and CTAC.
.
2. Regressive could be achieved by pairing the High and Low.
.
3. Gnet_income pairs with √(CTAC & CICC)
— √[ Gnet_income × √(CTAC & CICC) ]
.
4. ROA pairs with CTAC
— √[ ROA × CTAC ]
.
5. NetROIC pairs with CICC
— √[ NetROIC × CICC ]
.
6. Regressive P/E is conservatively low, a Potential 52wL candidate during panicking sell down by traders.
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7. Mean P/E would be met before settling down to Regressive P/E in the long run.
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(B) Formulation :
(B1)
If Gnet_income is bumpy and sometimes negative, perhaps pure profitability (ROA & NetROIC) valuation should be considered.
Moreover, ROA & NetROIC are reflections of Gnet_income in mirror.
.
Integrating ROA Pairs and NetROIC Pairs :
.
Regressive P/E (ROA & NetROIC Pairs Based)
=
(ROA × NetROIC × CTAC × CICC)^(1/4)
.
(B2)
Regressive P/E (Gnet_income Pairs & ROA Pairs Based)
=
(Gnet_income^2 × ROA^2 × CTAC^3 × CICC)^(1/8)
.
(B3)
Regressive P/E (Gnet_income Pairs & NetROIC Pairs Based)
=
(Gnet_income^2 × NetROIC^2 × CTAC × CICC^3)^(1/8)
.
(B4)
Full-spectrum Regressive P/E (Gnet_income Pairs, ROA Pairs and NetROIC Pairs Based)
=
(Gnet_income^2 × CTAC^2 × CICC^2 × ROA × NetROIC)^(1/8)
.
(B5)
Mean P/E
= √(ROA × NetROIC)
.
(B6)
Mean P/E
= √(Gnet_income × ROA)
.
(B7)
Mean P/E
= √(Gnet_income × ROIC)
.
(B8)
Full Spectrum Mean P/E
= (Gnet_income^2 × ROA × ROIC)^(1/4)
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(C)
Case Study:
PDD
.
(C1)
Regressive P/E FY2025
(ROA & NetROIC Pairs Based)
= (ROA × NetROIC × CTAC × CICC)^(1/4) 🏀
=(17.03077631×25.5305840264×8.5538486×5.11587169)^(1÷4)
= 11.7447587211 (Non-Gaap) 🏀
=
(15.771028282×23.6421144492×8.5538486×5.11587169)^(1÷4)
= 11.3020410964 (Gaap) 🏀
.
(C2)
Assume Gnet_income_2026 = 16%
.
Forecast Regressive P/E FY2026
(Gnet_income Pairs & ROA Pairs Based)
=
(Gnet_income^2 × ROA^2 × CTAC^3 × CICC)^(1/8)
=
(16^2×17.03077631^2×8.5538486^3×5.11587169)^(1÷8)
= 11.1433083188 (Non-Gaap)
=(16^2×15.771028282^2×8.5538486^3×5.11587169)^(1÷8)
= 10.9312679109 (Gaap)
.
(C3)
Assume Gnet_income_2026 = 16%
.
Forecast Regressive P/E FY2026
(Gnet_income Pairs & NetROIC Pairs Based)
=
(Gnet_income^2 × NetROIC^2 × CTAC × CICC^3)^(1/8)
=
(16^2×25.5305840264^2×8.5538486×5.11587169^3)^(1÷8)
= 10.8432693093 (Non-Gaap)
=
(16^2×23.6421144492^2×8.5538486×5.11587169^3)^(1÷8)
= 10.6369381925 (Gaap)
.
(C4)
Assume Gnet_income_2026 = 16%
Forecast Full-spectrum Regressive P/E FY2026
(Gnet_income Pairs, ROA Pairs and NetROIC Pairs Based)
=
(Gnet_income^2 × CTAC^2 × CICC^2 × ROA × NetROIC)^(1/8)
=(16^2×8.5538486^2×5.11587169^2×17.03077631×25.5305840264)^(1÷8)
= 10.9922651486 (Non-Gaap)
=
(16^2×8.5538486^2×5.11587169^2×15.771028282×23.6421144492)^(1÷8)
= 10.7830988651 (Gaap)
.
(C5)
Mean P/E
= √(ROA × NetROIC) 🏀
=
√(17.03077631×25.5305840264)
= 20.8519942839 (Non-Gaap)
=
√(15.771028282×23.6421144492)
= 19.3095949109 (Gaap)
.
(C6)
Assume Gnet_income_2026 = 16%
.
Forecast Mean P/E FY2026
= √(Gnet_income × ROA) 🏀
=
√(16×17.03077631)
= 16.5073444551 (Non-Gaap) 🏀
=
√(16×15.771028282)
= 15.8851015896 (Gaap) 🏀
.
(C7)
Assume Gnet_income_2026 = 16%
.
Forecast Mean P/E FY2026
= √(Gnet_income × NetROIC)
=
√(16×25.5305840264)
= 20.2111193263 (Non-Gaap)
=
√(16×23.6421144492)
= 19.4492629986 (Gaap)
.
(C8)
Assume Gnet_income_2026 = 16%
Forecast Full Spectrum Mean P/E FY2026
= (Gnet_income^2 × ROA × ROIC)^(1/4)
=
(16^2×17.03077631×25.5305840264)^(1÷4)
= 18.2655935722 (Non-Gaap)
=
(16^2×15.771028282×23.6421144492)^(1÷4)
= 17.5770736636 (Gaap)
.
Reference :
