Technological Links And Predictable Returns

Technological Links and Predictable Returns

• Charles Lee, Stephen Sun, Rongfei Wang and Ran Zhang
• Journal of Financial Economics, forthcoming
• A version of this paper can be found here
• Want to read our summaries of academic finance papers? Check out our Academic Research Insight category

Q2 hedge fund letters, conference, scoops etc

What are the Research Questions?

We are living in a knowledge-based economy and technological skill is becoming an increasingly important determinant of firms’ short-term profitability as well as long-term survival. Companies that don’t really have product overlap may have similar technological expertise but these affinities transcend traditional industry boundaries and are typically not readily discernible from firms’ financial reports⁽¹⁾. The authors investigate the implications of technological affinity⁽²⁾ for market price discovery and firms’ stock returns.

They ask the following questions:

1. Do technology-linked firm predict future returns of focal firms?
2. Are these results robust?

What are the Academic Insights?

By studying 1.9 million CRSP matched patents granted by the USPTO, the authors arrive at a final sample of 561,989 firm month observations (spanning from 1963 to 2012) and sort all firms into deciles at the beginning of each month, based on the return earned by their technology-linked peers in the previous month. They find that:

1. YES-Technology-linked returns predict focal firm returns. Specifically, we find that the equal-weighted hedged strategy (Long the top decile and Short the bottom decile), yields average monthly returns of 117 basis points (t=5.47), or roughly 14.0% per year. The corresponding value-weighted returns from the L/S portfolio are 69 basis points per month (t=3.19), or about 8.3% per year.
2. YES- After controlling for common risk factors (firm size, book-to-market, gross profitability, asset growth, R&D intensity, and short-term reversal and medium-term price momentum), the strategy still delivers alpha. The authors also document that it is distinct from,
   and cannot be explained by, previously documented lead-lag effects such as industry momentum, customer-supplier momentum, or standalone-conglomerate momentum.

Why Does it Matter?

This study points to researchers needing to better understand the mechanism through which such technological attributes impact information processing costs, and thus market prices.

In the words of the authors: ” it is difficult to argue that this publicly available mapping should not be taken into account when forming expectations about technology-intensive firms’ future cash flows. Certainly, from an investor’s perspective, greater attention to technology-linkages could lead to better investment decisions. From a firm’s perspective, educating investors on its technological capabilities, perhaps through greater media coverage, may likewise yield improvements in pricing efficiency.”

The Most Important Chart from the Paper

Table 2 reports abnormal returns and factor loadings for a technology momentum strategy. Panel A reports calendar-time portfolio abnormal returns. To construct this table, firms are ranked and assigned into decile portfolios at the beginning of every calendar month, based on the prior-month return to a portfolio of their tech-peers (TECHRET). All stocks are equally (value) weighted within a given portfolio, and the portfolios are rebalanced every calendar month to maintain equal (value) weights. All non-financial stocks with a stock price greater than $1 at portfolio formation are included. Excess return is the raw return of the portfolio over the risk-free rate. Alpha is the intercept from a regression of the monthly excess return on factor returns.

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index.

Abstract

Employing a classic measure of technological closeness between firms, we show that the returns of technology-linked firms have strong predictive power for focal firm returns. A long-short strategy based on this effect yields monthly alpha of 117 basis points. This effect is distinct from industry momentum and is not easily attributable to risk-based explanations. It is more pronounced for focal firms that: (a) have a more intense and specific technology focus, (b) receive lower investor attention, and (c) are more difficult to arbitrage. Our results are broadly consistent with sluggish price adjustment to more nuanced technological news.

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References

1. an example are Illumina Inc. and Regeneron Pharmaceutical Inc. They are not in the same industry and are not product market competitors. Yet, their technology proximity score is very high: technological affinity can often cut across industrial boundaries
2. technological affinity is defined as the uncentered correlation of the patent distributions between all pairs of firms i and j

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