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«Equity Style Investing RONG, WU How to cite: Equity Style Investing, Durham theses, Durham University. RONG, WU (2013) Available at Durham E-Theses ...»

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the value of stocks to future cash flows. The dividend discount model argues that the present value of a stock equals to the sum of the discounted expected future dividends. The parameters involved in the valuation process, namely, the expected future cash flows, the market risk premium, the market risk exposure and the term structure of interest rates share a common component, the business cycles (Dahlquist and Harvey (2001)). Hence equity style returns evolves over time, reflecting the cyclical and structural fluctuations in the business cycles. The objective of Chapter 3 is to examine the relative importance of the style driving sources that determines the differentials of style returns in the UK market. This chapter would contribute to the extant literature by explicitly examining how firmspecific characteristics and the business cycle conditions function separately to affect the dynamics of stock performance based on the size and value-growth categorisations. Specifically, it addresses a central question: what is the dominant driving force that affects the relative style performance, the firm characteristics or the business cycle risk? The empirical findings in Chapter 3 shed new light on the understanding of the source of equity style performance and add a further dimension to the current literature of anomaly versus risk compensation debate for explaining equity style premiums.

The divergence of equity style returns evolve all the time with cyclical nature. Over the time there are styles moving in and out of favour by investors according to their relative performance driven by changes of economic, financial and political conditions. There is no single style or a mix of styles dominating under all market states. Foir example, Fama and French (1992), Eleswarapu and Reinganum (1993), Dichev (1998), Chan et al. (2000), Horowitz et al. (2000a, b), Amihud (2002) and Roll (2003) and many other studies all document that the size effect cease to exist since 1980s in the U.S. markets.

Likewise, Dimson and Marsh (1999), Michou et al. (2010) report that no size effect exists in the U.K. market in later 1980s. Internationally, Barry et al. (2002) also fail to find the size effect in global emerging markets. Most recently, Fama and French (2012) show that no size premium exist in North America, Europe, Japan and Asia Pacific markets since 1990. These findings suggest that striving to one predominant style investing strategy over the entire investing horizon is by no means efficient. Furthermore, a natural question also arises whether equity style cycles do exist. Arguably, if equity style cycles exist and has long duration, smart investors could implement the active investment strategy based on style cycles by identifying the turning point of the leading styles and transitioning portfolio holding to next prevailing market segments to enhance returns.

Active investment strategies have been very popular in professional manager circles in the investment community. One objective of such strategies is to protect investors against negative effects caused by prolonged period of poor economic conditions. The fundamental idea is to follow some heuristic methods to select specific stocks or asset classes according to the changing market conditions. Motivated by the potential benefits of such active portfolio management based on the cyclicality of the relative style returns, Chapter 4 investigates a dynamic style rotation trading strategy. Prior research has confirmed the value of price-driven investment strategies at the stock level. For example, the momentum strategies of Jegadeesh and Titman (1993) and the contrarian investing of DeBont and Thaler (1985) are well documented in the literature. However, momentum strategies along the style level have not been extensively studied. Papers such as Beinstein (1995), Fan (1995), Fisher et al. (1995), Sorensen and Lazzara (1995), Kao and Shumaker (1999), Levis and Liodakis (1999), Asness et al. (2000), Ahmed and Lockwood (2002) and Lucas et al.

(2002), among others, explore the benefit of style rotations. However, as Chen and De Bondt (2004) point out, by and large these studies do not give clear details of the specific trading strategies derived from the information of equity style cycles. Chapter 4 contributes to the literature by providing valuable empirical evidence to compare with other findings in different economic and institutional environments.

The study in Chapter 4 aims to answer 2 central questions: (1) whether U.K. equity style cycles exist and hence investors can profit from the information of style cycles and (2) whether the return pattern of style momentum is distinct from price and industry momentum effects documented in the literature. The findings in this chapter could help investors better understand the ‘style effect’ in the cross-sectional expected stock returns. It also offers a practical approach for passive investors to enhance investing returns. Passive investors do not aim to ‘beat the market’ and therefore generally take indexation strategy. However, the relative fixed composition of the market index results in constant overall style exposures that is inefficient under changing market conditions. Style momentum trading strategy based on ETF (exchange traded funds) of style benchmarks can be used to enhance index returns. Since the style momentum hedge portfolios are generally market neutral they of little market risk if there is any. Style ETF generally has low transaction cost and high liquidity, as a result, the long-short style ETF momentum hedged portfolio could be designed to overlay with the underlying indexation strategy to eliminate its least efficient style exposures and generate additional alphas.

The style momentum strategy in chapter 4 is a quantitative adaptive style investing in essence. The advantage for such strategy is that its trading signal is quantitatively generated by data set and hence free of investors’ sentiment when being implemented. The strategy is selffinanced as it longs the winner style and shorts the loser style in the same time. However, while both the long and the short side of the portfolio are not limited to contain only one style, they generally take the same weight in order to satisfy the condition of self-finance. This makes style momentum strategy less attractive to some multi-style investors who have more expertise to some specific asset classes and are therefore more ambitious for their portfolio structure. Meanwhile, the construction of style momentum does not explicitly consider the underlying economic driving force that determines the relative style returns; in particular it does not account for the trade-off between style returns and risks from a mean-variance investor’s perspective.

Hence style momentum is not optimal for some specific investors.

Chapter 5 is motivated by the identified gap in the literature about the optimal multi-asset investing over the business cycles. There is substantial evidence suggesting that the distributions of expected stock returns are time-varying with predictable components derived from business cycle variables. For example, early foundation papers such as Fama and Schwert (1977), Campbell (1991), Harvey (1991), and Campbell and Ammer (1993) use dividend yield and interest rate to model stock return dynamics. The significant explanatory ability of business variables in determining stock returns can also be found in early papers like Schwert (1989). Existing literature has generally recognised the benefits of considering business cycle predictors on asset allocation process on the stock level. For instance, Kandel and Stambaugh (1996) show that research variables predicting stock returns also have significant impact on a myopic portfolio setting.

Avramov and Chordia (2006a, 2006b) demonstrate that a real-time optimising investor can benefit from incorporating business cycle information to their asset allocation between stocks and cash or investment strategies of ‘fund of mutual funds’. However, the portfolio choice implications of business cycle effect in prior studies often focus primarily on the time-varying nature of stock return distributions driven by business cycle predictors, while the role such predictors play on determining optimal multi-style allocation is less directly explored. Arguably, if a multi-style investor believes that business cycle variables can predict the conditional distributions of equity style returns, the expected style returns and the variance structure to be predicted are endogenous to the investor’s preference due to model specification. Hence, in order to capture the changing investment opportunities associated with business cycle regimes, the investor should focus primarily on identifying how the same exogenous state variable directly predicts the ultimate style investing choices, i.e. the optimal weight in the style investing portfolio.

Chapter 5 contributes to the literature by applying an optimisation framework to test several equity style investing strategies based on business cycle information and examine their ex ante in-sample and ex post out-of-sample performance. This chapter aims to answer two questions: (1) which economic variable or a combination of economic variables should track when implementing equity investing based on market segments; (2) if business cycle predictor variable X changes, should the investor invest more or less in Y style? Answers to these questions would give multi-style investors like ‘fund of hedge funds’ managers an intuitive manner to understand their asset allocation process when incorporating business cycle predictability.

1.4 Basic findings in each chapter The empirical study in Chapter 3 concludes that the underlying driving forces affecting the dynamics of relative style performance are indeed much controversial. Overall, the relative performance of small vs. large stocks and the value vs. growth characterised by price to cash-flow ratios (PC) and market to book value ratios (MTBV) are mainly driven by the cross-sectional mispricings in the context of a multifactor business cycle model. This suggests that the relative outperformance of small-cap stocks and PC- and MTBV-sorted value stocks may be driven by investors’ irrational trading behaviour that results from cognitive biases like underreaction to firm-specific news.

By contrast, the divergent returns of value and growth stocks sorted by the dividend yield are attributed to the cross-sectional differences in conditionally expected returns predicted by the business cycle model. Hence the outperformance of investing in stocks with high dividend yield is mainly captured by the predicted risk premias, and therefore should be the compensation for bearing business cycle risk.

The test results in Chapter 3 would also suggest that, while on the individual stock level the relative performance of stocks sorted on PC and MTBV are not driven by the business cycle risk, on the portfolio level the business cycle model could partly capture the time-series expected value premiums. Hence equity characteristics PC, DY and MTBV should contain information in predicting the time-variation in expected style returns. These results are consistent with findings of empirical studies regarding the time-series relations among expected returns, risk and equity characteristics (e.g. Fama and French (1993, 1996), Kothari and Shanken (1997), and Chan et al. (1998)).

The profitability of style momentum strategy documented in Chapter 4 indicates the existence of equity style cycles in the U.K. market.

Since assets behave differently during various stages of a market cycle, investing strategies to buy stocks in current in-favour (winner) styles could continue to outperform those in current out-of-favour (loser) styles for periods up to 12 months or possibly longer. Style momentum payoffs tend to increase with longer ranking periods but decrease with longer test periods, implying that the outperformance of winner styles are more persistent once more information is collected in the ranking period, while such style return differentials generally reverse at longer horizon. Consistent with the literature, style momentum effect demonstrate strong independent explanatory power for the future individual stock’s expected returns, and style momentum is distinct from the price momentum of Jegadeesh and Titman (1993) and industry momentum of Moskowitz and Grinblatt (1999) documented in the literature.

The empirical test results in Chapter 5 find that, consistent with the literature, investors tend to significantly long value stocks or smallcap stocks, and short growth stocks or large stocks in their optimal style allocation process. It is suggested that the conditional style investing incorporating business cycle effects and the unconditional style investing disregarding business cycles is very much different.

Sceptical investors disregarding business cycle predictability are generally quite conservative for their overall net equity exposures compared to the Doctrinaires who maintain strong prior beliefs about the business cycle information. The Doctrinaires are found to often take extreme weights to some styles financed by leverage, possibly because they believe the return differential of styles can be estimated using business cycle predictors and therefore extreme exposures can be reduced at bad times when expected returns are low or volatility is high.

Chapter 5 also demonstrates that business cycle predictors affect the conditional equity style returns and the optimal style investing in quite a different mechanism. For example, the role of default spread plays in the style allocation process is less significant despite of its significance in determining the expected return distributions. It is predicted that positive shocks to the short-term interest rate would induces investors to move to small-cap stocks and move away from large stocks despite the lower expected returns for small stocks and higher expected returns for large stocks are estimated by such shocks. In addition, a positive innovation to short-term interest rate would lead investors to tilt towards growth stocks, which matches their higher expected returns signalled by changes of interest rate.

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