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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 study is to examine the return sensitivity of public farmland and timberland real estate investment trusts (REITs) to private-equity farmland, timberland and real estate, long-term corporate bonds and large- and small-cap stocks. The study also examines time-dependent contributions of selected asset classes to farmland and timberland REIT volatility.

The authors use a multi-factor asset pricing model under a seemingly unrelated regression framework to evaluate farmland and timberland REIT returns, and a state-space model with the Kalman filter to evaluate the time-dependent contributors of farmland and timberland REIT volatility. The authors first perform orthogonalized regressions to obtain pure independent factors, and then decompose volatility into individual asset components.

Significant loadings on financial assets are found for both farmland and timberland REITs, suggesting that they are generally driven by some common state variables. Large-cap stocks are found to be the major contributor of farmland and timberland REIT volatility, despite some differing patterns over time.

Empirical analysis of farmland REIT is very scarce. The authors compare the risk-return characteristics of farmland and timberland REITs under a state-space framework with the Kalman filter. This study can improve the understanding of the roles of farmland and timberland REITs in a multi-asset portfolio.

This study evaluates the practicability of style analysis in evaluating the risk‐adjusted performance of New Zealand's retail equity trusts. The size of the New Zealand market and the short history of data available generate doubts concerning the usefulness of style analysis under these conditions. Style analysis provides useful insight when applied to the New Zealand retail equity unit trust sector. Two prevalent styles are identified, a large cap style and a mid‐cap‐value/small cap style. Little variation in style was detected for the group of trusts that tracked the large‐cap equity index but substantial variation was indicated in relative performance versus a passive investment in their style benchmarks. Significant variation was detected, both in terms of style and relative performance of trusts that tracked a mid‐cap‐value/small‐cap index. A small number of New Zealand equity managers were able to maintain a consistent style, while meeting or beating the performance of their style benchmarks.

The authors examine the industrywise investment preferences of foreign portfolio investors (FPIs), domestic institutional investors (DIIs) and retail investors in the Indian context. They also investigate the factors influencing their preferences.

Using the quarterly shareholdings and returns data of the Indian market from March 31, 2009 to March 31, 2018, the authors employ analysis of variance to study investors' preferences and a random effect panel data model to examine the factors that influence these preferences.

FPIs hold proportionally more stocks in service-oriented industries and large-cap firms, DIIs hold proportionally large numbers of shares in paper industries and retail investors hold proportionally more shares in chemicals and textiles. FPIs prefer stocks with a high export-to-sales ratio and firms registered on a foreign stock market. Domestic investors, especially retail investors, prefer small-cap stocks and firms whose operations require local knowledge. In addition, industry heterogeneity determines investment decisions. Firm-specific and macroeconomic factors that influence investment decisions differ across industries. Finally, government policies and reforms also play a key role in attracting investors.

Policymakers can identify the key variables that influence investment, which can help direct and regulate investment in India and similar emerging markets.

This study fills a research gap by addressing how industry-level heterogeneity affects investors' preferences in terms of the industrywise preferences of different types of investors and the factors that influence their preferences.

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.

Purpose – The aggregate investment by foreign institutional investors (FIIs) in the Indian stock market is significant compared to that by domestic institutions and individual (retail) investors. The question of whether FIIs exhibit herding and positive feedback trading while investing in the Indian stock markets has not been examined so far. This study is an attempt to fill the gap and contribute to the existing evidence on foreign portfolio investment in India.

Methodology/approach – We have analyzed the daily data on purchases and sales of securities by FIIs sourced from the Securities and Exchange Board of India (SEBI), and the Bombay Stock Exchange (BSE). We have adopted the approach of Lakonishok et al. (1992), and Wermers (1999) to examine herding and positive feedback trading by foreign investors.

Findings – Our results suggest that FIIs exhibit herding and positive feedback trading during different phases of the stock market. This observed behavior is prominent in but not restricted to large cap stocks as they enjoy better liquidity.

Social implication – The herding and positive feedback trading by FIIs is a cause for concern for government of India, capital market regulator (SEBI), and the country's central bank (RBI) as it adversely affects stock prices and volatility. They are required to formulate and implement a suitable policy response given their objective of protecting the interests of small investors in the market. They may also have to monitor the purchases and sales of equities by FIIs in general and of better performing large cap stocks in particular.

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.

The purpose of this paper is to assess the performance of a contrarian investment strategy focusing on frequently traded large-cap US stocks. Previous criticisms that losers’ gains are not due to overreaction but due to their tendency to be thinly traded and smaller-sized firms than winners are addressed.

Portfolios based on past performance are constructed and it is examined whether contrarian returns exist. The Capital Asset Pricing Model (CAPM), Fama and French three-factor model and the Carhart’s (1997) momentum portfolio are used to test whether excess returns are feasible in a contrarian strategy.

The results show an asymmetric performance following portfolio formation. Although both, winners and losers portfolios, have gains during holding periods, losers outperform winners at all times, and with a differential of up to 29.2 per cent 36 months after portfolio formation. Furthermore, the loser and the winner portfolios’ alphas are significant, suggesting that the CAPM and the multifactor models are unable to explain return differentials between winners and losers. Our evidence supports two main conclusions. First, stock market overreaction still holds for a sample of large firms. Second, this is robust to the Fama and French’s (1993, 1996) three-factor model and Carhart’s (1997) momentum portfolio. Findings emphasize the relevance of a contrarian strategy when rebalancing investment portfolios.

Portfolio managers can improve stock returns by selling past winners and buying previous loser large-cap US stocks.

This paper is the first, to the authors’ knowledge, to examine frequently traded large-cap US stocks to avoid infrequent trading and size concerns.

This paper aims to investigate the intervalling effect bias in ETFs' systematic risk expressed by beta. The authors' findings reveal the existence of a significant intervalling effect on ETFs' beta obtained by the ordinary least squares method (OLS). Also investigated is the impact of ETFs' capitalization on beta. Results provide evidence that small cap ETFs have greater betas than large cap ETFs. Results also reveal that the OLS beta of all ETFs increases when the return interval is lengthened regardless of capitalization. The impact of ETFs' trading activity on systematic risk is assessed too. Findings give evidence that the OLS betas of the ETFs that trade infrequently are biased downwards while the beta of the frequently traded ETFs is biased upwards. Finally, the paper reveals a strong intervalling effect on ETFs' tracking error.

The authors employ a sample of 40 broad‐based ETFs listed on Nasdaq Stock Exchange to test whether beta estimates change when the return interval measurement changes. Their data cover a maximum period of ten years starting from September 16, 1998 using daily, weekly and monthly return data. The authors estimate beta applying three alternative methods: the market model applied with the OLS method, the Scholes and Williams model (SW beta) and the Dimson model (Dim beta).

Results indicate that the average beta of ETFs derived by the OLS method increases when the return interval increases. The differences among the daily, weekly and monthly OLS betas are statistically significant at the 1 per cent level. This finding implies a strong intervalling effect bias in ETFs' OLS beta. On the other hand, the authors did not find any statistically significant differences in daily, weekly and monthly Scholes and Williams and Dimson betas. Moreover, results show that the daily and weekly OLS and Scholes and Williams betas and weekly OLS and Dimson betas are significantly different from each other.

In this paper using a sample of 40 broad‐based ETFs listed on Nasdaq Stock Exchange, the authors have examined various issues concerning: the intervalling effect bias in ETFs' systematic risk, the relation between beta and capitalization of ETFs, the relation between beta and trading frequency of ETFs and, finally, the intervalling effect bias in ETFs' tracking error. While the literature on intervalling effect on securities' beta and the relation between systematic risk and market value and trading activity is voluminous, this is the first attempt to examine these issues with respect to ETFs.