Do firms that acquire patents in similar technologies persistently perform similarly? In the October 2017 draft of their paper entitled “Technology and Return Predictability”, Jiaping Qiu, Jin Wang and Yi Zhou examine monthly performance persistence of stocks grouped by similarity in recent firm patent activity. Specifically, they:
- Record the patent activity of each firm by patent class over the most recent three calendar years.
- Quantify similarity of this patent activity for each pair of firms.
- Segregate firms into innovation groups based on patent activity similarity (top fifth of quantified similarities).
- For each month during the next calendar year:
- Rank stocks into fifths (quintiles) based on average prior-month, similarity-weighted return of their respective groups.
- Form a hedge portfolio that is long (short) the equal-weighted or value-weighted stocks in the highest (lowest) return quintile.
They focus on gross average monthly return and stock return factor model alphas of the hedge portfolio as evidence of firm innovation group performance persistence. Using firm patent information by technology class during 1968 through 2010, and monthly stock data, quarterly institutional holdings and analyst coverage for a broad sample of U.S. stocks priced greater than $1 during 1968 through 2011, they find that:
- The number and average strength of innovation group similarities increases over time, but these groups remain mostly distinct from conventional industry groups.
- For the equal-weighted firm innovation group hedge portfolio:
- Average gross monthly return is 0.92% (11.1% annualized).
- Gross monthly 3-factor (market, size, book-to-market) alpha is 1.05%.
- Gross monthly 4-factor (adding momentum) alpha is 0.92%.
- Gross monthly 5-factor (further adding liquidity) alpha is 0.92%.
- The long side drives performance.
- Using capitalization weighting reduces gross average returns/alphas by more than half. For example, average gross monthly return drops to 0.40%.
- Findings are generally robust (focusing on equal-weighted):
- Across decade subsamples.
- After controlling for industry, industry momentum, customer-supplier relationships and strategic alliances.
- After controlling for individual stock past return.
- Based on average gross monthly returns from double sorts, the effect is stronger (focusing on equal-weighted):
- Among the smallest stocks (1.18%) than among the largest (0.53%).
- Among stocks with the lowest institutional ownership (1.52%) than those with the highest (0.52%).
- Among stocks with the lowest analyst coverage (1.44%) than those with the highest (0.72%).
In summary, evidence indicates that investors may be able to construct an outperforming portfolio by focusing on groups of firms with similar patent activity and strong prior-month average return.
Cautions regarding findings include:
- Performance data are gross, not net. Accounting for monthly rebalancing frictions and shorting costs would reduce returns. Shorting may not be feasible as specified, especially for small stocks, due to lack of shares to borrow.
- The strategy design is intricate, with multiple parameters available for snooping, such that data snooping bias is a concern.
- Data collection and analyses required for the strategy are beyond the reach of most investors, who would bear fees for delegating to a fund manager.
