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The purpose of this paper is to examine whether the conditionally expected return on size-based portfolios in an emerging market (EM) is determined by the country’s world risk exposure. The authors analyze the degree of financial integration of 23 emerging equity markets grouped into five size portfolios using the conditional international asset pricing model with both world and domestic market risks. The authors also compare the model’s fitness on the predictability of portfolio returns by using world and EM indices.

This study investigates whether large-cap stocks are priced globally and whether mid- and small-cap stocks are strongly influenced by domestic risk factors. The authors first examine the predictability of large-, mid-, and small-cap stock portfolio returns by using global and local variables, and next compare the model fitness by using world and EM indices on the prediction of size-based stock returns. Finally, the authors test whether the world price of covariance risk is the same for different portfolios.

The authors find that the conditional expected returns of large-cap stocks should be priced by global variables. Mid- and small-cap stocks are influenced by domestic variables rather than global variables, and their returns are priced by local residual risks. The test of the conditional asset pricing model shows that the largest stocks have the smallest mean absolute pricing errors (MAE), and their pricing errors are lower in large markets than in small markets. Third, the EM index offers more predictability for the excess returns of mid- and small-cap stocks than the world market index, but the explanatory power of this index does not increase for large-cap stocks.

EMs in the past were known as segment markets, with local risk factors more important than global risk factors, suggesting significant benefits from adding EMs to global portfolios. It would be interesting to examine whether financial integration differs for various firm sizes in the markets.

A well-documented pattern in the literature concerns the outperformance of small-cap stocks relative to their larger-cap counterparts. This paper aims to address the “small-cap versus large-cap” issue using for the first time data from the exchange traded funds (ETFs) industry.

Several raw return and risk-adjusted return metrics are estimated over the period 2012-2016.

Results are partially supportive of the “size effect”. In particular, small-cap ETFs outperform large-cap ETFs in overall raw return terms even though they fail the risk test. However, outperformance is not consistent on an annual basis. When risk-adjusted returns are taken into consideration, small-cap ETFs are inferior to their large-cap counterparts.

This research only covers the ETF market in the USA. However, given the tremendous growth of ETF markets worldwide, a similar examination of the “small vs large capitalization” issue could be conducted with data from other developed ETF markets in Europe and Asia. In such a case, useful comparisons could be made, so that we could conclude whether the findings of the current study are unique and US-specific or whether they could be generalized across the several international ETF markets.

A possible generalization of the findings would entail that profitable investment strategies could be based on the different performance and risk characteristics of small- and large-cap ETFs.

This is the first study to examine the performance of ETFs investing in large-cap stock indices vis-à-vis the performance of ETFs tracking indices comprised of small-cap stocks.

The purpose of this paper is to examine intra‐industry contagion and the following apparent violations of the efficient market hypothesis around large one‐day price decline events in individual stocks.

The paper examines daily stock returns around one‐day price declines of 10 percent or more for event stocks and their rivals. Using techniques similar to those used in Bremer and Sweeney and Cox and Peterson, the paper includes event stocks whose prices are at least $10 per share prior to the event to reduce the possible price reversal induced by bid‐ask price bounce. As is typical for the literature, the stock daily abnormal return (AR) is calculated as the difference between the actual daily stock return and the estimated stock return based on the market model estimated over a 200‐trading‐day pre‐event period [−220, −21]. Cumulative abnormal returns (CARs) for each stock are formed by aggregating the individual daily stock ARs. Denoting the large price decline event day as day 0, we examine the ARs of 41 trading days [−20,+20], the CARs for the [+1,+3] period, and the CARs for the [+4,+20] period. Cross‐sectional average ARs and CARs are calculated and tested for statistical significance. Furthermore, the paper examines whether the post‐event abnormal stock returns for the event firm and its rivals can be explained by prior event firm and industry variables.

On average, after an event, the event stock experiences a positive three‐day AR (S&P 600 stocks) followed by a 17‐day negative AR (both S&P 500 and 600 stocks). Moreover, for that 17‐day period: the rivals' stocks outperform the event firms' stocks and the event firms' returns are statistically significantly related to prior variables. The paper also finds statistically significant relationships between the prior variables and the rivals' post‐event stock returns. It provides an intra‐industry effects explanation for these results.

The paper offers insights into abnormal stock returns, for the event firm and its rivals, following the event firm's large one‐day stock price drop.

This paper examines and updates an earlier study of the liquidity of an extensive array of common stocks traded on NYSE/ASE/NML‐NASDAQ. It reports apparent variances in liquidity due to trading location and other variables. The paper suggests causes for these differences.

Market-based value style equity portfolios do not systematically outperform market-based growth style equity portfolios, despite considerable academic research that suggests that they should. This is an unresolved puzzle in the long lineage of work on this topic. The purpose of this paper is to question whether portfolio constituency rules employed by active growth and value equity investment managers might explain this puzzle.

The authors use the traditional research design and methodology of Fama and French (1993) to ensure comparability of results to prior research. Further, the authors adapt the return decomposition method of Keim (1999) to specifically answer the question in the research.

The authors find that restrictive constituency rules that omit the smallest, most illiquid stocks improve the performance of both value and growth stock portfolios. However, the authors find the impact of constituency rule restrictions on portfolio returns to be asymmetric with respect to value and growth in the small-cap investment space. Growth portfolios benefit from these changes more than value portfolios. Consistent with prior research, the authors find that value and growth style portfolios constructed from more liquid equities to be void of a statistically significant value-minus-growth return premium. The authors suggest these results might go a long way in explaining why market-based growth fund returns generally equal those of their value fund counterparts over time.

The research question central to the research, the value equity premium, has been investigated by researchers around the world over the last 20 years. The 20 year lineage of global published research on the value equity premium does, however, contain several unresolved questions. The paper specifically asks why the premium, long observed in global equity market returns, does not appear in market-based passive or active equity portfolios. This puzzle exists at the heart of the origins of the return premium itself and has serious implications for investment practitioners. If the matter cannot be reconciled, then market participants might rightly view the entire 20 year lineage of published research as irrelevant. The paper is one of few that has now extended the long lineage of research to its application in real markets.

This study aims to analyse the effects of Economic Policy Uncertainty (EPU) on the return of growth/value and small/large-cap stocks during expansionary and recessionary periods across a conditional distribution.

The authors selected a sample covering the period between 01/1995–05/2021. Quantile regressions were applied to the EPU and Russell indices. Business cycles were established following the NBER.

The results show that EPU has a negative effect on stocks with the intensity of the effect depending on the stock's profile. Small-cap and growth stocks were found to be most sensitive to EPU, especially during recessions. The negative effect is moderated by the economic cycle but is progressively diluted at the lower tail of the stock return distribution.

The findings shed more light on investment strategies for growth/value investors that pursue opportunities arising from a changing economic cycle.

This study makes the following contributions: (1) explores the impact of EPU on the return of different stocks across a conditional distribution, and (2) provides evidence on how the economic cycle influences EPU impact on growth/value stocks and small/large stocks.

:本研究擬分析跨條件分佈、以及於擴張期和衰退期,經濟政策不確定性對成長型股票/價值股和小盤股/大型股的收益的影響。

我們選擇了涵蓋1995年1月與2021年5月期間的樣本進行研究。我們於經濟政策不確定性指數和羅素指數上採用分位數迴歸法進行研究; 並跟隨著美國國家經濟研究局,建立了多個經濟週期。

研究結果顯示,經濟政策不確定性對股票是有負面影響的,而影響的強度則視乎股票的投資組合而定。我們發現,小盤股和成長型股票對經濟政策不確定性是非常敏感的,尤其是在經濟衰退期間。這負面影響會被經濟週期緩和,唯這緩和作用卻會在股票收益的低尾處逐漸減輕。

研究結果使我們更容易理解為尋找因經濟週期改變而衍生的機會的增長/價值投資者所提供的投資策略。

本研究有以下的貢獻:(一) 、 探究了經濟政策不確定性對跨條件分佈、不同的股票收益的影響; (二) 、為經濟週期會如何左右經濟政策不確定性對成長型股票/價值股和小盤股/大型股的影響,提供了證據。

The purpose of this paper is to determine if there is a significant difference in the investment risks between small-cap manufacturers that heavily depend on one or a few buyers, referred to as “dependent-buyers,” and small-cap manufacturers that have a more diversified customer base. If there is a significant difference both statistically and economically, then investors need to be aware of the dependent-buyer effect in their security selection and portfolio construction efforts.

Using large samples of firm-level data from 2000 through 2011, the authors employ standard risk estimation modeling to compute βs, idiosyncratic risks, and total risks of both dependent-buyer firms and firms with a more diversified customer base.

The authors find that the βs, idiosyncratic risks, and total risks of dependent-buyer firms are much greater than that of firms not in dependent relationships. These differences are both statistically and economically significant.

Buyer-supplier relationships can change quickly, and so a firm that has a diversified base in one period, for example, could be a dependent-buyer in the next period. Much depends on the reporting accuracy of firms and the ability of the securities exchange commission (SEC) to track the relationships.

First, the risk of individual small-cap stocks is likely to be greater than perceived from macro-level data, leading to the need for more securities if idiosyncratic risk is to be eliminated. Second, small-cap investors have the opportunity to enhance portfolio construction efficiency by referencing data published by the SEC. Third, most investors interested in small-cap manufacturing stocks should find it prudent to allocate a large percentage of their small-cap investments to an index fund. While this may sacrifice higher returns, it also reduces the probability of experiencing an unpleasant small-stock effect.

This is the first study to show that the difference in investment risks between small-cap manufacturers that depend on one or a few firms for their outputs and small-cap manufacturers that have a well-diversified customer base is statistically and economically significant, information that should be valuable to investors in their security selection and portfolio construction efforts.

This paper investigates whether style migration affects industry evolution. The study documents industry evolution in terms of market weights, returns, and risks over the sample period from 1966 to 2000. The study shows that investment styles migrate in different degrees across different industries over time. In addition, the relation between industry evolution and style migration is neither simple nor static. The paper shows that growth‐value migration has predictability about the industries' returns and changes in volatility. Furthermore, style migration in the industry is mainly driven by existing firms changing their investment styles, not by new entrants to the industry causing style shifts. Both investment theory and its application to investment management critically depend on our understanding of stock return persistence anomalies. The ability to outperform buy‐and‐hold strategies by acquiring past winning stocks and selling past losing stocks, commonly referred to as “individual stock momentum,” remains one of the most puzzling of these anomalies. Moskowitz and Grinblatt (1999) attribute the bulk of the observed momentum in individual stock returns to industry momentum—the tendency for stock return patterns at the industry level to persist. It is well known that there are hot and cold IPO markets, and hot and cold sectors of the economy. Investors may simply herd toward (away from) these hot (cold) industries and sectors, causing price pressure that could create return persistence. The recent attraction to internet stocks is perhaps the latest manifestation of such behavior, which is not unlike a similar pattern biotechnology firms and railroad firms witnessed in 1980s and 1900s, respectively. For the active portfolio manager, rotation among different industries may provide opportunities for portfolio performance enhancement. As a result, understanding both the evolution of industries and the style factors causing cyclical variation in industry returns and risk plays an important role in professional portfolio management. Given the fact that a number of researchers have found consistent differences among the returns of various equity classes, investment styles of size and growth‐value are natural candidates for studying what causes cyclical variation in industry returns and risks. Individual investment styles perform differently during various stages of a cycle of bull market and bear market. For example, small cap stocks outperformed large cap stocks in the 1970s, but large cap stocks outperformed small cap stocks in the 1980s. Growth stocks outperformed value stocks in 1998 while the opposite occurred in 1997. Although it is well documented that the cross‐sectional variation in expected returns can be captured by three factors: market, size, and book‐to‐market, it is not yet clear whether cyclical variations in style attributes, not style returns, influence cross‐sectional variation in expected returns and return variance. In the investment industry, cyclical variation in style attributes is commonly called style migration. Perez‐Quiros and Timmermann (2000) provide a rational suggestion that small firms are most strongly affected by tighter credit market conditions in a recession and thus cyclical variations in style performance result from business cycles. As certain equity classes took off and others fell out of favor, investors overreacted, thereby causing cyclical variations in returns and risks of industries where firms are similarly sensitive to the fundamental shocks. In a recent study of behavioral finance, Barberis and Shleifer (2003) argue that in the presence of switchers who can affect asset prices by moving funds across styles, a style‐level momentum strategy could be successful because good performance by a style attracts switcher flows, which then drive the prices even higher. Analyzing the extent of interaction between style migrations and industry evolution may shed light on understanding the sources of predictable components in industry returns and risk. This paper provides such a contribution to the literature. The rest of the paper is organized as follows. Section I describes the sample data and summarizes industry evolution in terms of market capitalization weights in the entire market over time. Section II analyzes style migration within each industry. Section III examines the effect of style migration on industry evolution. Section IV concludes.

The purpose of this study is to investigate explanations for the behaviour of the size premium using measures of large and small stock holdings of mutual funds.

Returns‐based style analysis is used to measure asset class exposure by regressing equity fund returns on asset class returns over the period 1965 to 2003. The coefficients estimate portfolio asset allocation indicating a fund's investment styles. The estimates from 36‐month rolling regressions of US equity fund returns on various asset classes are aggregated and used as measures of investors' exposure to small stocks. The patterns are analyzed in the context of the behaviour of the abnormal returns to small stocks.

The results indicate the importance of the 1974‐1975 bear market to the historical size premium and support an overreaction and reversal argument. Exposure to small stocks drops dramatically between 1975 and 1977, suggesting a sell‐off of small stocks. Fund exposure subsequently increases rapidly to its highest levels between 1982 and the market crash of 1987. These patterns are consistent with pricing pressure that would lead to the initial undervaluation and subsequent overvaluation driving returns to small stocks over this period.

The study introduces the application of the returns‐based style analysis methodology to studying an asset‐pricing phenomenon and demonstrates important insights that can be obtained from the use of this methodology in new contexts and at an aggregate level.

The purpose of this paper is to offer a “how to” guide for applying Merton’s (1987) valuation adjustment for incomplete information, which depends on market capitalization, idiosyncratic risk and extent of investor ownership.

The paper illustrates Bodnaruk and Ostberg’s (2009) formula for Merton’s adjustment, and presents some example empirical estimates of the adjustment for some US stocks.

The adjustment estimates are material for many example stocks, particularly volatile stocks with a low percentage of shares held by institutional funds. However, the adjustment estimates are modest for many other stocks, including some smaller cap. stocks.

Measuring the model’s inputs requires using some judgment, particularly regarding the investor ownership variable. The paper will hopefully help stimulate useful empirical research on adjustment estimates and on best practices for applying the model.

The paper may encourage more use of the incomplete-information adjustment in practice, which should lead to improved discount rate estimates in valuation analyses.

No other “bridge the gap” coverage of the incomplete-information adjustment is available in textbooks or the applied literature.