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The R&D-abnormal return anomaly: a transaction cost explanation

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Abstract

Previous research finds a positive and significant relation between current increases in R&D expenditures and future abnormal stock returns. While the existence of this anomalous pattern is well-established, its underlying causes are the subject of much debate. Recent research also shows that transaction costs can lead to apparent market anomalies such as the post-earnings-announcement drift. We combine these two lines of research and posit that the positive relation between R&D increases and future abnormal stock returns is due to transaction costs. Consistent with this hypothesis, we find that abnormal returns on R&D-based, zero-net-investment portfolios disappear after incorporating standard measures of transaction costs. Overall, our results show that the R&D-abnormal return anomaly is more likely due to transaction costs than to the alternative hypotheses of market inefficiency or omitted risk factors.

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Notes

  1. Other studies that document abnormal returns to under-reaction to R&D information include Lev and Sougiannis (1996), Chan et al. (2001), Eberhart et al. (2004), and Chen et al. (2012).

  2. In related work, Ciftci and Zhou (2015) show that disclosure of patent counts and citations increases the value relevance of accounting numbers in explaining stock prices.

  3. However, Ng et al. (2008) find that economically and statistically significant annual excess returns to a PEAD strategy based on analysts’ forecasts persist even after considering transaction costs.

  4. Ali et al. (2012) show that subsequent market reactions to large R&D increases are positive, albeit incomplete. Their evidence suggests investors trade in the correct direction but do not fully react to large R&D increases.

  5. The use of these four R&D-based criteria is useful in identifying economically large increases in R&D, as noted by Ali et al. (2012). This is crucial for our study as investors should only be expected to act on economically large increases. Ali et al. (2012) also note that some studies that claim there is no market under-reaction to R&D increases (e.g., Donelson and Resutek 2012) do not focus on economically large increases.

  6. See the SEC execution cost analysis document at http://www.sec.gov/spotlight/regnms/companalysis121504.pdf for a full discussion of the appropriate statistics to measure trade execution cost for investors.

  7. We use the Lee and Ready (1991) algorithm in matching trades with quotes. In particular, we match each trade with the latest available quote made at least 5 s earlier. Following Huang and Stoll (1997), we combine all trades that take place at the same price and quotes (bid and ask prices) into a single trade.

  8. Lesmond et al. (1999) show that transaction cost estimates from their model are highly correlated with more direct measures (e.g. spreads) of transaction costs.

  9. Later we report results of sensitivity tests using alternative portfolio formation approaches.

  10. Since firms without large R&D increases are sold short, the CRSP return computed for this group (0.54) is multiplied by −1 in tabulating the return to the short strategy and in computing the hedge strategy return.

  11. Penman and Zhang’s (2002) Q-score is a measure of conservative accounting on earnings. In addition to the R&D measure we use above, it includes measures for the LIFO reserve and an advertising asset.

  12. Stocks in the three middle quintiles are excluded from the analysis.

  13. For presentation purposes, we tabulate results using the portfolio formation strategy employed in Table 4; in untabulated analyses we find similar results when using alternative portfolio formation methods.

  14. For stocks under $1 per share the commission is $38 plus 4 % of trade size. The minimum commission is $38 per trade.

  15. However, Goldstein et al. (2009) find that commissions paid to full-service brokers remain higher than the marginal cost of trade execution. Such brokers arguably set commissions as a convenient way to charge fees for long-term access to the broker’s services. Goldstein et al.’s evidence suggests that institutional traders negotiate commissions infrequently and that commissions vary little with trade characteristics. Thus, even traders arguably most able to exploit market mispricing (institutional investors) routinely pay commissions that would greatly reduce the after-transaction cost returns to trading strategies.

  16. We examine the following four ETFs/ETNs: (1) ELEMENTS Benjamin Graham Small Cap Value ETN (ticker: BSC); (2) iShares Russell 1000 Value Index Fund (ticker: IWD); (3) iShares Russell 2000 Value Index Fund (ticker: IWN); and (4) iShares Russell Midcap Value Index Fund (ticker: IWS).

  17. These variables are defined more completely in Table 6.

  18. We also examine whether the trading platform affects our results. Specifically, we re-run our Table 7 results for NASDAQ firms only, and then again for NYSE/AMEX firms only. The (untabulated) results for each trading platform are consistent with the overall results in Table 7; that is, we find predominately insignificant coefficients for large-R&D-increasing firms.

  19. In addition, we also examine two subperiods (i.e., 1992–2001 and 2002–2013) to see whether transaction costs can account for the R&D anomaly in each subperiod. Our untabulated results show that the coefficients on LARGE_ΔRD are significant only in the earlier subperiod. In addition, we perform a separate analysis using data over the height of the financial crisis period, 2008–2009. The coefficient on LARGE_ΔRD during this period is positive but insignificant.

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Authors and Affiliations

  1. College of Business and Economics, Lehigh University, 621 Taylor Street, Bethlehem, PA, 18015, USA

    Paul Brockman

  2. Beedie School of Business, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada

    Dennis Y. Chung

  3. School of Accountancy, Trulaske College of Business, University of Missouri, Columbia, MO, 65211, USA

    Kenneth W. Shaw

Authors
  1. Paul Brockman
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  2. Dennis Y. Chung
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  3. Kenneth W. Shaw
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Correspondence to Paul Brockman.

Additional information

Comments from the editor, two anonymous reviewers, and workshop participants at the University at Buffalo, Florida State University, and the American Accounting Association national meeting are appreciated.

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Brockman, P., Chung, D.Y. & Shaw, K.W. The R&D-abnormal return anomaly: a transaction cost explanation. Rev Quant Finan Acc 48, 385–406 (2017). https://doi.org/10.1007/s11156-016-0555-3

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