Value Factor
A-share value factors: PE, PB, PS and dividend yield
Value factors compare market price with earnings, book value, sales or cash distributions. A low multiple can reflect risk, a cyclical peak or a pricing error, so it is a research input rather than a buy signal.
Typical direction
Lower PE/PB/PS is commonly ranked higher; higher dividend yield is commonly ranked higher.
Data
Prices, financial statements and dividend data
Refresh
Monthly or quarterly refresh
Research hypothesis
Write the hypothesis before reading the backtest
After controlling for sector, size, quality and tradability, lower prices relative to fundamentals may contain information about future risk compensation or valuation repair. Test that relationship in a defined universe and period.
A factor is a testable research hypothesis, not an investment recommendation or return promise.
Factor health card
Pre-backtest checks for this factor
Research purpose
Test whether valuation discounts remain informative after quality and sector controls.
Refresh and rebalance
Usually monthly or quarterly after the relevant financial data are disclosed.
Data timing
Record announcement, implementation and adjusted-price conventions.
Neutralisation
Rank within sector; review financials and cyclicals separately.
Overlapping exposures
Often paired with quality or dividend factors.
Check before use
Handle negative PE, negative equity and near-zero denominators before ranking.
Definitions
Core measures
Price-to-earnings (PE)
Market capitalisation ÷ attributable net incomeUse only where earnings are positive and meaningful.
Price-to-book (PB)
Market capitalisation ÷ attributable equityOften more comparable in financial and asset-heavy sectors.
Price-to-sales (PS)
Market capitalisation ÷ revenueDoes not distinguish high- and low-margin businesses.
Dividend yield
Trailing cash dividends ÷ market capitalisationPast distributions do not guarantee future distributions.
Research protocol
Keep the same research conventions across factors
Data availability
Financial, dividend and share data become available on actual disclosure or implementation dates, not report-period end dates.
Universe and exclusions
Document index membership, listing age, ST, suspensions, delistings and missing-data rules.
Processing and neutralisation
Version winsorisation, standardisation, sector/size neutralisation and missing-value rules.
Tradability
Include price limits, suspensions, participation, fees, slippage and market impact.
Out-of-sample review
Report IC, grouped returns, exposures, turnover and rolling out-of-sample evidence together.
Build and validate
What to test
- 1Winsorise and standardise within sector.
- 2Compare raw and sector-neutral portfolio exposures.
- 3Report IC, grouped returns, turnover and out-of-sample results.
Common pitfalls
- ×Treating negative PE as cheaper than low positive PE.
- ×Using report-end dates before actual disclosure.
- ×Confusing a low-valuation sector bet with stock selection.
A-share implementation
A-share checks that belong in the backtest
- Use the actual disclosure or implementation date; do not make a field available at the report-period end date.
- State the universe, listing-age, ST, suspension, delisting and missing-data rules before running the backtest.
- Model price limits, suspensions, fees, slippage and participation limits instead of assuming every close can be traded.
- Use implemented dividends and ex-dividend conventions; a proposal is not a cash payment.
Research prompt
A reviewable starting prompt
“In CSI 300 non-financial stocks, rank PB within sector and combine it with cash-flow quality. Rebalance monthly after disclosure dates and report IC, sector exposure, turnover, costs and rolling out-of-sample results.”
FAQ
Is lower PE always better?
No. It can reflect cyclical earnings peaks, deteriorating fundamentals or risk. Review quality, leverage and out-of-sample evidence.
Why neutralise by sector?
Sector valuation anchors differ materially. Neutralisation helps separate company-level signals from a broad low-multiple sector exposure.