1. Discussion by Alan Huang University of Waterloo
A discussion of “Deviation from Target Dividend Payout and the Cross-Section of Stock Returns” by Jacoby, Li, and Lu
Discussion by Alan Huang
University of Waterloo

2. Summary of findings
A dividend deviation measure predicts stock returns;
A dividend-deviation (DD) mimicking factor acts as a robust pricing factor;
The DD mimicking factor potentially captures a mixture of firm risks, such as profitability (or the lack thereof), volatility, financial constraints, and overinvestments.

3. Assessment
I buy the story—but surprised to find that the effect of DD is so strong; the authors have done a good and convincing job
The paper considered many potential channels and is relatively thorough in exposition.
Empirically inspired asset pricing factors
Fama-French five factors; q factor
Although many firm characteristics are shown to predict returns (e.g., Hou, Xue and Zhang 2017 lists over 300 such characteristics), few move on to construct a “factor”.
Authors are extremely brave in constructing such a factor (publishing a new “factor” has an extremely high bar).

4. The differences between return prediction and factor
Return of t+1 regressed on characteristic at t.
Fama-Macbeth cross sectional regression
Factor:
Return of testing portfolios at time t, regressed on factor-mimicking return at time t.
The benchmark testing portfolios are usually Fama-French 25 size/book to market portfolio.
Is the risk premium significant? Are GRS intercepts zero?
Two stage testing, GMM.
The paper blends the two—feels like that you do two things in one paper. Although the authors did a good job, at times the exposition is confusing.

5. Potential look-ahead bias
Different fiscal-year-ends imply that a portfolio based on ranked values of dividend deviation spans two years in the construction phase, and all future returns must be benchmarked against the very last calendar month of the fiscal year of ALL firms in the portfolio, to avoid look-ahead bias.
Example for calendar year 2010:
Firm 1: fyr of Jan., fiscal year 2009: :01; …
Firm 1,500: fyr of Dec., fiscal year 2010: :12;
Run a cross sectional regression at year 2010 for firms 1 to 1,500 to derive DD.
DD uses information from 2009: :12
Future returns conditioned on DD, across firms 1 to 1,500, will start only in 2011:01.
Note that starting future return for firm 1 from 2010:02 (or anytime before 2011:01, will suffer from look-ahead bias.
A tricky question: what is firm 1’s DD in 2010 now?

6. Perhaps a better alternative to 2-year portfolio construction phase
Use quarterly Compustat, and calculate ttm dividend.
Align firms by calendar time.

7. Non-dividend paying firm-years and other forms of distribution
Many firm-years do not pay dividends, and I believe that the cash dividend value is set to missing in Compustat. This accounts maybe half of the observations.
Starting from the 1980s there is a large surge of share buyback.
 A 1982 SEC regulation states that open-market stock buybacks would not be considered stock manipulation.
These in my view undermine the usefulness and acceptability of dividend-based measures.

8. Per Robert Shiller data

9. Other comments
Denominator is the within industry DPS standard deviation
Dividend yield is another popular measure. Perhaps following dividend yield, and change the dominator to price, or assets per share?
On P9, equating management confidence to the firm being less risky I think is a weak argument.

10. Other comments
DD is also motivated with profitability, which makes sense. If so,
Relationship of DD with profitability, such as with the RMW factor or with Novy-Marx’s profitability measures should be more closely examined.

11. Summary
Integrates corporate finance and empirical asset pricing very well.
Conveys a lot of information in one paper.
Return results are impressive.