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The purpose of this study is to offer explanations of the wide variation in the impact of market size on new market entry decisions – i.e. its positive impact lessens because of unreliable predictability of market size on post-entry profit and entry motivations other than post-entry profit.

On the basis of the two explanations, this paper builds a contingency frame that the impact of market size on new market entry depends on entry-context-specific variables. It validates the contingency frame, empirically analyzing 219 parameter estimates of the impact of market size on market entry obtained from 41 existing empirical studies.

The meta-analysis results reveal that the entry-context-specific variables used in this study – niche market entry, high-tech market entry, entry by industry incumbent firms and the year of market entry – notably moderate the impact of market size on new market entry decisions, as the research frame suggests.

This study examines the various literature and study outcomes in the areas of marketing, economics and strategy to elucidate whether and when market size is a critical driver of new market entry. In most cases, the greater the new market size, the greater is the propensity to enter the market. However, the contingency arguments stated in this paper suggest that firms may and do enter a new market even if the market size is not large at the time of entry.

This paper enhances the understanding of the relative importance of market size in market entry decisions, which depend on various entry contexts. It clarifies the direction and magnitude of the impact of such entry contexts.

Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.

Strategy Question: How do I better understand the make-up of my overall market?

Summary: Assuming that the market has been properly sized, it is important to also spend similar effort to define segments and size these appropriately. This tool basically mirrors the approach of the Bottom-up Market Sizing Tool. At this stage, emphasis turns to breaking the overall market into actionable segments. Two to three iterations again are common to improve accuracy. The tool output casts the segments as a rectangular graphic, made up of one column for each segment. Segment width is representative of its size relative to the other segments. The width of all segment columns, added together, ties back and equals the overall size of the markets. The result provides guidelines for determining strategic market segments and niches, and how to best position the firm within those segments.

Strategy Question: How do we figure out the size of our market in the absence of hard data?

Summary: This tool provides a method to estimate market size in the absence of trade association information or confidence in existing market intelligence. It is a spreadsheet-based, bottom-up approach that builds market size based on a series of logical assumptions. Each assumption is tested and validated by internal and/or external subject matter experts before moving on to the next. The tool is somewhat iterative in nature, and two to three revisions from the first pass is not uncommon. The result is a reasonably accurate assessment of the market and the key areas of the market on which to focus attention and strategy development. This provides a basis for conducting meaningful strategic market analyses.

This study examines the value relevance of recognised and disclosed revaluations of land and buildings for a large sample of Australian firms from 1993 through 1997. In contrast to prior research, we control for risk and cyclical effects and find no difference between recognised and disclosed revaluations, using yearly‐cross‐sectional and pooled regressions and using both market and non‐market dependent variables. We also find only weak evidence that revaluations of recognised and disclosed land and buildings are value relevant.

Examines differences in the level of market orientation between firms of different sizes and relates market orientation to the perceived market structure and the degree of planning control from corporate headquarters. A survey of UK firms shows that medium‐sized firms adopt a market orientation to a lesser extent than large and extra‐large firms; and there are no differences in the level of market orientation between large and extra large firms. Despite the different market orientation, there is no difference in the perceived market structure between firms of different sizes. Extra‐large firms are exposed to a higher degree of planning from the corporate headquarters than medium‐sized and large firms, but the degree does not seem to be so high as to affect their market orientation and discusses managerial implications.

The purpose of this paper is to investigate the risk factors for A‐shares listed on both Shenzhen and Shanghai Stock Exchange in China using variables from Akgun and Gibson.

The paper applies cross‐sectional regression on the orthogonal components by rearranging these risk variables into several principal components.

The results produced strong evidence that size and book‐to‐market (BM) ratio could be well explained by these alternative risk variables. Additionally, the alternative variables are better at explaining returns in terms of adjusted R‐squares.

The practical implication of the study is that investors can improve both their pricing of the investment risk and their management of the risk factors with the alternatives identified in the study.

The paper provides evidence in explaining the size and BM effects in China's stock markets.

This paper aims to confirm the existence of size, book to market (BM) and momentum effects in the New Zealand (NZ) stock market. It also aims to compare the performance of the CAPM, the Fama‐French (FF) model, and Carhart's model in explaining the variation of stock returns.

The paper adapts the Fama and French methodology using a 2×3 size‐BM ratio sort. It also forms three portfolios based on past returns to verify the momentum effect.

The paper documents significant BM and momentum effects but a relatively weaker size effect. The paper finds some improvement in explanatory power provided by the FF model relative to the CAPM but it still leaves a large part of the variation in stock returns unexplained. The FF model is also unable to explain the strong momentum effect in New Zealand.

The findings imply that: cost of capital estimates would be more accurate using Carhart's model; portfolio managers can increase returns by investing in small and high BM firms that are recent winners; performance evaluation should take into account the size, BM, and momentum effects; and the existence of size and BM return premia appear to be rewards to risk bearing.

The existing literature testing the robustness of the FF model in markets outside the USA is sparse, especially in emerging markets, with most of these studies suffering from data problems. The NZ stock market provides an interesting setting for such a study because of its unique characteristics.

This paper aims to investigate the explanatory power of the Fama-French five-factor model and compares it to the other asset pricing models. In addition, the paper examines the contributions of two additional factors: profitability and investment factor. The authors test the alternative four-factor models.

The authors use stock returns data of BSE-500 listed firms for the Indian market, an emerging market, from 1999 to 2020, thus covering the post-Asian crisis and pre- and post-financial crisis (2007–2008) periods. The authors employ 75 and 96 portfolios based on different factors. To check the performance of asset pricing models, the authors also used the GRS F-statistics and factor spanning tests.

The authors find that the five-factor model and alternative four-factor model outperform the three-factor model. Contrary to the findings for the US, but similar to the Chinese stock market, the value factor is significant for the Indian stock market. Simultaneously, the authors also find that the investment factor has no explanatory power in the presence of the profitability factor in their sample.

To the best of the authors' knowledge, this is the most comprehensive study using data more than two decades. These results are based on 75 (25 × 3) portfolios based on size, value, profitability and investment. The authors also tested these results based on 96 (32 × 3) portfolios to check robustness, and these results still hold. Furthermore, the authors find that factors based on 2 × 3 sorting have higher explanatory power than those based on 2 × 2 and 2 × 2 × 2 × 2 sorting.

The purpose of this paper is to compare recent performance of small firms with that of large firms in developed and emerging stock markets.

T‐tests as well as Wilcoxon Signed Rank test statistics are utilized to test the differences in returns between stock indices. Additionally, ANOVA and median test statistics were conducted to test differences in size premiums over years. Finally, t‐tests as well as Mann‐Whitney U test statistics were conducted to examine the differences in size premiums by market conditions and January over non‐January months.

It was found that small firms did not generate significantly different returns than large firms in recent years. More specifically, size premiums were not sensitive to market conditions and were not significantly higher in January over non‐January months. These results indicate stock markets no longer exhibit a size effect or a reverse size effect.

The paper contributes to the finance literature by examining the size effect as well as the reverse size effect in developed and emerging stock markets on recent data.