스타일 투자수익률의 계절적 주기성에 관한 연구 (A Study on the Style Return Seasonalityin Korea)

서지정보

· 발행기관 : 한국기업경영학회
· 수록지 정보 : 기업경영연구 / 21권 / 3호 / 93 ~ 108페이지
· 저자명 : 우원석, 이민, 최형석

초록

본 연구에서는 자산 배분의 중요한 기준점이 되는 스타일 포트폴리오 수익률의 계절적 주기성을 확인하고 이를 활용한 투자전략에 대해 알아보고자 한다. 이를 위해 1982년부터 2013년까지 기업 규모와 장부가치-시장가치 비율을 기준으로 여섯 개의 스타일 포트폴리오를 나누고 기업의 규모와 과거수익률을 기준으로 또 다른 여섯 개의 스타일 포트폴리오를 나누어 월별 수익률을 계산하였다. 그 결과 소형주로 구성된 포트폴리오는 잘 알려진 양(+)의 1월 효과뿐만 아니라 8월과 9월에 평균수익률보다 작은 음(-)의 계절적 효과가 있었다. 둘째, 횡단면적으로 스타일 포트폴리오의 수익률을 살펴본 결과 6월에 가장 작고 12월에 가장 큰 계절적 변동성의 패턴을 보여주었다. 이를 전략적으로 잘 활용할 수 있는지에 대해 조사한 결과 본 연구에서 제시한 무자본 스 타일 전환 투자전략은 연 평균 14.2%의 수익률을 내었다. 이러한 투자 수익률을 Lo and MacKinlay (1990)과 Conrad and Kaul (1998)의 방법에 따라 예측성 지수(predictability index)와 확산성지수(dispersion index)로 분해해본 결과 예측성 지수가 98.5% 가량의 투자수익률을 설명하는 것으로 분석되었다. 강건성 검증을 위해, 한국표준산업분류9차(대분류)에 따른 산업별 포트폴리오를 구성하여 무자본 산업 전환 투자전략 수익률을 구해 확인한 결과 연평균 23%에 달하는 수익을 얻었으며, 이러한 수익의 97% 이상은 예측성지수에 의해 설명되었다.

영어초록

Investors group assets into different classes based on some similarity among them. For example, stocks can be categorized into broad classes such as small versus large stocks, value versus growth stocks, prior winners versus losers, or categorized by different industry sectors. The asset classes are called “styles” and the process allocating money among styles is called “style investing” and investors must consider styles because portfolio allocation among different styles is required by law. Much of academic literature has shown that certain styles outperform other styles in the long run. In particular, small-cap (value) stocks outperformed large-cap (growth) stocks historically. However, the relative performance between these styles is not stable over time. Chan et al., (2000), for example, show that large-cap (growth) stocks outperform small-cap (value) stocks in 13 years (8 years) out of their 29 year sample period from 1970 to 1998. Style based strategy can produce long periods of poor performance. Thus, style rotation strategy, switching from one style to another, could generate additional returns when we can forecast the relative performance between styles.
In this study, we examine seasonal patterns in the cross-section of expected returns on twelve style portfolios that we composed from daily return data for stocks listed in the Korean Stock Exchange (KRX). We find that style returns exhibit substantial variations across calendar months. For example, over the sample period of January 1982 to December 2013, in January the mean return of the Small/Neutral portfolio is 6.2 percent and that of the Big/Up portfolio is only 2.3 percent. In March, however, the mean return of the Small/Neutral portfolio is -0.6 percent and that of the Big/Up portfolio is 2.5 percent. Our finding is consistent with previous literature on seasonality in stock returns which suggests the outperformance of some style against another in a specific calendar month. For example, Keim (1983), Reinganum (1983), and Roll (1983) find that small-cap stocks outperform large-cap stocks in January. Branch (1977) and Dyl (1977) suggest that tax-loss selling creates a downward price pressure on loser stocks in December and a price rebound in January. Lakonishok, Shleifer, Thaler, and Vishny (1991) find that pension funds dump prior loser stocks at the end of every quarter. However, these studies explored only the turn-of-the-year period or the end of each quarter. Our finding shows that the seasonal pattern of the style returns is not limited to January or the end of each quarter. Small stocks perform poorly in August and the Big/Down portfolios beat the market in July.
We employed the style rotation strategy suggested by Choi(2014) using the seasonal patterns among style returns. We take the long positions of styles with good performance in a specific calendar month and the short positions of styles that have done poorly in the same calendar month. For example, we rank the twelve style portfolios according to their average returns during the previous five Januaries to construct a zero investment portfolio for the next January. We repeat this for each of twelve calendar months. The strategy yields profits across all calendar months except August. Specifically, the mean profit in January alone is 5.8 percent. Overall, our seasonal strategy yields economically and statistically significant profits of 14.2 percent per year. The possible source of the profit from our strategy is seasonal autocorrelation in style returns (predictability component) or cross-sectional variation (dispersion component) in mean style returns as suggested by Lo and MacKinlay (1990) and Conrad and Kaul (1998). The decomposition of the profit shows that the main source of the profit is the predictability component. The predictability component explains more than 90 percent of the profit in every calendar month.
In order to check the robustness of seasonal regularities in style returns and the profitability of our zero-investment strategy, we extended our approach to the sector portfolios. The strategy based on the one-year historical performance in each month yields profits of 21.7 percent in January and 28 percent annually but they are not statistically significant due to the large volatility. However, when we construct the strategy with 5 or 10 year historical performance in each month, the strategy yields annul profit of higher than 20 percent, which is statistically significant. The decomposition of the profit shows that the main source of the profit is the predictability component as well. Therefore, the seasonal patterns among style returns have significant power to forecast future relative style performance, which would be inconsistent with the efficient market hypothesis.

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