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Financial Markets and Portfolio Management

26-04-2017

Trading strategies based on past returns: evidence from Germany

Published in: Financial Markets and Portfolio Management | Issue 2/2017

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Abstract

Among the various strategies studied in this paper, only momentum investing appears to earn persistently nonzero returns: From 1965 to 2014, the classical momentum strategy based on performance over the previous 2–12 months earned an average return of 1.57% per month (excluding microcap stocks and value-weighted returns). In the most recent 10-year period, this return was even larger—2.27%—which is much larger than in the USA. However, profitability net of transaction costs is weak because the strategy involves trading in disproportionately small stocks with high transaction costs, something that is particularly true for the loser portfolio. A strategy that concentrates only on the winner portfolio and thus avoids potential problems associated with (short) selling the costly loser portfolio appears to earn strong and persistently abnormal profits, even after transaction costs.

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In relation to the rest of the stock universe, the cross section. This implies that, in bear markets, past winners could even have negative past returns but are better off in relation to others. Asness et al. (2014) discuss this in detail.

Note the difference from seasonalities in stock markets such as the weekend effect, turn-of-the-month effect, January effect, etc., see Rozeff and Kinney (1976), French (1980), and Keim (1983). For a review, see Schwert (2003). These strategies are not investigated in this paper.

An example is the classical momentum anomaly that is not found in some Asian countries (Hameed and Kusnadi 2002), notably Japan (Liu and Lee 2001; Chui et al. 2003; Griffin et al. 2005).

Common German synonyms for momentum are “relative Stärke,” “Kontinuitätseffekt,” and “zyklische Handelsstrategie.” Common German synonyms for contrarian strategies (stock reversal) are “antizyklische Handelsstrategie” and “Gewinner-Verlierer-Effekt.”

I use transaction costs and bid–ask spreads interchangeably, given that transaction costs involve more than just the bid–ask spread; see Sect. 6.3 for details.

Studies that focus on reactions to price shocks, e.g., Atkins and Dyl (1990) for the USA and Lobe and Rieks (2011) for Germany, are not discussed, although they are somewhat related, especially to short-term contrarian strategies.

A number of studies also find that some of the mentioned strategies, notably momentum, seem to be successful for other types of securities, see, e.g., Okunev and White (2003), Erb and Harvey (2006), and Asness et al. (2013).

Moskowitz et al. (2012) document time series momentum, which differs from the price momentum discussed here: the stock’s individual performance relative to the rest of the universe (in the cross section).

See also Jegadeesh and Titman (2011) for a review of the momentum literature.

Barroso and Santa-Clara (2015) argue that these losses can be predicted and, thus, avoided.

See also Chui et al. (2003) and Griffin et al. (2005) for international evidence and exceptions.

Because of the different methodologies applied, I include only their general results, without specific numbers.

If capital markets were fully integrated and operated identically, it would not be necessary to investigate patterns outside the USA because they can be assumed to be identical around the world. However, the patterns found in the USA are not necessarily found in international capital markets due to institutional, cultural, and other differences.

Excluding the Neuer Markt from the sample is not a satisfying alternative since, after its closing in 2003, most of the stocks remained listed in the middle segment, which was combined in 2007 with the top segment to form the new top segment. Furthermore, the Neuer Markt represented a considerable amount (about 10%) of the total market capitalization of the Frankfurt Stock Exchange before its closing. See Stehle and Schmidt (2015) for details.

https://doi.org/www.sec.gov/investor/pubs/microcapstock.htm (April 15, 2015).

However, Rey and Schmid (2007)—restricted to Swiss blue-chip stocks—and Ammann et al. (2011)—restricted to stocks included in the S&P100—show that even a simple momentum strategy that is based on only one winner and one loser stock seems to be profitable, even net of transaction costs.

The plot begins in 1965—not in 1955, when the first data are available—because portfolio formation begins in 1965.

Limits in prices of December 2014 are recursively adjusted by inflation. Inflation rates are from the Federal Statistical Office as described in Stehle and Schmidt (2015). A €50 million stock in December 2014, for example, is equivalent to a €13.2 million stock as of January 1965 and €31.8 million as of January 1990.

The issues discussed are not only relevant to this study; the large number of very small stocks has implications for numerous studies that address German stocks from 1997 onward.

As a robustness check, I calculate all results in all cases for a sample excluding stocks smaller than €200 million. The results in all cases are very similar to the results when excluding microcap stocks.

“Appendix 1” additionally includes Fama–MacBeth regressions with single and/or compounded returns over certain contiguous and non-contiguous past horizons.

Alternatively, portfolio formation could be based on cumulative returns; see, e.g., Grundy and Martin (2001) for the motivation.

This limit is exceeded in about 12% of all decile portfolios for all strategies shown in Table 3.

Alternatively, the amount invested could be allocated to the remaining stocks of the portfolio or, in the event of a takeover, for example, the stock of the acquirer could be held. Due to data limitations (unknown exact event dates, monthly stock return data), these or similar approaches are not applied.

For all strategies, holding periods longer than one month, i.e., rebalancing after, e.g., 3, 6, or 12 months, were also considered. The results are not shown or discussed in detail because the returns are nearly always higher with monthly rebalancing than with longer rebalancing intervals.

All benchmark data and factor time series are from Richard Stehle’s data library, available at https://doi.org/www.wiwi.hu-berlin.de/professuren/bwl/bb/data and described in Brückner et al. (2015a). I use the dataset “ALL” with breakpoints from the top segment.

The Table 8 in “Appendix 2” contains some descriptive statistics for all strategies.

The numbers are based on Kenneth French’s data library, “10 Portfolios Formed on Momentum,” available at https://doi.org/mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html (June 24, 2015).

Based on the factor dataset by Artmann et al. (2012), the results are similar: 2.62% (value-weighted, t-statistic 2.39) and 0.83% (equal-weighted, t-statistic 1.93). Note that the factor data end in 12/2012, and thus, the calculated alphas refer only to the period from 01/2005 to 12/2012.

The momentum factor is typically calculated based on the 0.3 and 0.7 breakpoints, while the momentum strategies discussed here are based on the 0.9 and 0.1 breakpoints (the two extreme deciles).

In excess of the one-month money market rate (Monatsgeld) reported by Frankfurt banks, and after 06/2012, in excess of the one-month EURIBOR (Einmonatsgeld); see Stehle and Schmidt (2015) for details.

They are also supported by Bohl et al. (2016), who argue that long-only investors earn positive and significant returns in Germany. The authors find that the low and typically insignificant returns of the loser portfolio stem from months during market rebounds when loser stocks recover from heavy losses over preceding bear markets and exhibit large positive returns. This result, however, disappears on a risk-adjusted basis (Fama–French three-factor model).

D2 \(=\) 6.38%, D3 \(=\) 8.27%, D4 \(=\) 9.80%, D5 \(=\) 11.23%, D6 \(=\) 11.93%, D7 \(=\) 12.87%, D8 \(=\) 13.15%, D9 \(=\) 12.85%.

D2 \(=\) 7.94%, D3 \(=\) 9.85%, D4 \(=\) 10.63%, D5 \(=\) 11.03%, D6 \(=\) 11.22%, D7 \(=\) 11.85%, D8 \(=\) 11.49%, D9 \(=\) 11.77%.

Opening the position in the winner (1st time) and loser (2nd) portfolios at the beginning of the investment period and closing the position in the winner (3rd) and loser (4th) portfolios at the end of the investment period.

Gárleanu and Pedersen (2013) provide a framework of optimal trading in which the net return of a strategy such as momentum could significantly improve in the presence of transaction costs.

Data limitations allow investigating only the period from 1997 to 2014; see “Appendix 3.”

For example, at the beginning of a month, five stocks are sorted into a portfolio with each having a weight of 20%. During the month, one stock gained 30%; all others gained 0%. At the end of the month, the portfolio weights are (20 * 1.3)/(20 + 20 + 20 + 20 + (20 * 1.3)) \(=\) 26/106 \(=\) 24.53% for the stock that gained 30% during the month, and the other nine stocks have a weight of 20/(20 + 20 + 20 + 20 + (20 * 1.3)) \(=\) 20/106 \(=\) 18.87%.

Including all stocks ignores the large increase in the number of small and microcap stocks after 2000, which I stress in Sect. 3. As a consequence, the breakpoints—and thus the allocation of the stocks to the quintiles—largely change after 2000. However, for a direct comparison of the estimates obtained from the two bid–ask spread measures, this change is of minor importance.

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Title: Trading strategies based on past returns: evidence from Germany
Publication date: 26-04-2017
Published in: Financial Markets and Portfolio Management / Issue 2/2017
Print ISSN: 1934-4554
Electronic ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-017-0288-x