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Decomposing the Momentum in the Japanese Stock Market

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Abstract

In this study, we decompose momentum indicators for the Japanese stock market into two components, high-to-price and price-to-high. High-to-price has a lower downside risk and higher Sharpe ratio than price-to-high. We find that a conventional momentum strategy combines the characteristics of high-to-price in a bull market and those of price-to-high in a bear market. In particular, the large drawdowns of momentum strategies reported in previous studies seem to be largely owed to those of price-to-high in bear markets. It is possible that the mechanism generating factor returns differs among the three strategies.

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Notes

  1. In addition to the Tokyo Stock Exchange, Japan has the Osaka Securities Exchange, the Nagoya Stock Exchange, the Fukuoka Stock Exchange, and the Sapporo Securities Exchange. About 97% of the approximately 3,800 listed stocks are listed on the Tokyo Stock Exchange under the following sections: first Section, Second Section, Mothers, and JASDAQ.

  2. We used time series data of stock prices adjusted for stock splits to remove the effect of stock splits.

  3. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html. The market factor returns are converted into US dollars, and since re-conversion into Japanese yen would introduce unnecessary errors, instead, we use the Tokyo Stock Price Index (TOPIX) return inclusive of dividends. The HML factor and the SMB factor are the returns on the long-short portfolios, and although there is a possibility of exchange rate effects due to compounding effects when the returns gap is large, the effects are negligible, so they are used as they are.

  4. In previous studies overseas (e.g., Ang et al., 2006), idiosyncratic volatility is generally calculated using observed daily returns for the past 30 days, while in previous studies in Japan, idiosyncratic volatility was calculated using observed monthly returns for the past 60 months.

  5. Stocks with no more than 15 days of trading volume in a month are excluded from calculations for that month.

  6. However, since CGO was used as a dummy variable, summary statistics were omitted.

  7. For information on quantile portfolios besides Q1 and Q10, see Tables 10, 11, and 12 in the Appendix.

  8. Asness (2011) reports that the effect of MOM is statistically significant when adjusted for the Fama–French three-factor model. In the analysis in this paper, the reason MOM is not statistically significant even when adjusted for the Fama–French three-factor model may be due to differences in the analysis period, compared with Asness’ (2011) findings.

  9. The difference in turnover occurs in the short leg (Q1); HTP's short leg has a smaller regression coefficient on the SMB factor, compared with MOM's, suggesting that it is composed of larger (more liquid) stocks. Thus, the difference in transaction costs may not be as large as the difference in turnover.

  10. We use the market factor returns of the Fama–French three-factor model as market returns.

  11. Asem and Tian (2010) also tested a similar hypothesis.

  12. Büsing et al. (2021) conducted their analysis based on Cooper et al. (2004) and Stivers and Sun’s (2010) research.

  13. For information on quantile portfolios besides than Q1 and Q10, see Tables 13, 14, and 15 in the Appendix.

  14. The additional sensitivity in a reversal decline from a bull market is − βL,U.

  15. Cross-sectional regressions taking individual stock returns as explained variables generally show heteroskedasticity, with larger error variances for smaller stocks. Since this issue is empirically known to be alleviated by weighting with the square root of market capitalization, we weighted the square root of market capitalization in the cross-section regression analysis.

  16. CGO is used as-is in Grinblatt and Han’s (2005) research. However, in this study we felt more appropriate to use it as a dummy variable split into positive and negative (unrealized loss, unrealized gain). Note that there is no difference in the interpreted results in both cases.

  17. Frazzini (2006) and Ye (2014) reported that institutional investors are prone to disposition effects.

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Funding

This work was supported by Japan Society for the Promotion of Science, 21K13330, Yasuhiro Iwanaga.

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Authors and Affiliations

  1. Hakuoh University, 2-2-2, Ekihigashidori, Oyama-City, Tochigi, 323-8586, Japan

    Yasuhiro Iwanaga

  2. Sumitomo Mitsui DS Asset Management Company, Limited, Toranomon Hills Business Tower 26F, 1-17-1 Toranomon, Minato-Ku, Tokyo, 105-6426, Japan

    Takehide Hirose

  3. Nissay Asset Management Corporation, 1-6-6 Marunouchi, Chiyoda-Ku, Tokyo, 100-8219, Japan

    Tomohiro Yoshida

Authors
  1. Yasuhiro Iwanaga
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  2. Takehide Hirose
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  3. Tomohiro Yoshida
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Correspondence to Yasuhiro Iwanaga.

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Appendix: 1

Appendix: 1

See Tables 8

Table 8 Quantile portfolio analysis (Equal-weighting portfolio)
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, 9

Table 9 Bivariate sorts by market capitalization
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, 10

Table 10 Quantile portfolio analysis (MOM)
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, 11

Table 11 Quantile portfolio analysis (HTP)
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, 12

Table 12 Quantile portfolio analysis (PTH)
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, 13

Table 13 Market timing regression analysis (MOM)
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, 14

Table 14 Detailed market timing regression analysis (HTP)
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and 15

Table 15 Market timing regression analysis details (PTH)
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Iwanaga, Y., Hirose, T. & Yoshida, T. Decomposing the Momentum in the Japanese Stock Market. Asia-Pac Financ Markets (2023). https://doi.org/10.1007/s10690-023-09413-y

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