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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.

We examine the incremental cross-sectional role of several common risk factors related to size, book-to-market, and momentum in size-and-momentum-sorted portfolios. Unlike the existing literature, which focuses on the conditional mean specification only, we evaluate the common risk factors’ incremental explanatory power in the cross-sectional characterization of both average return and conditional volatility. We also investigate the role of ex-ante market risk in the cross-section. The empirical results demonstrate that the size-and-momentum-based risk factors explain a significant portion of the cross-sectional average returns and cross-sectional conditional volatility of the benchmark equity portfolios. We find that the Fama–French (1993) factors and the ex-ante market risk are priced in the cross-sectional conditional volatility. We conclude that the size-and-momentum-based factors provide a source of risk that is independent of the Fama–French factors as well as ex-post and ex-ante market risk. Our results bolster the risk-based explanation of the size and momentum effects.

This study examines the five-factor model of Fama and French (2015) on the French stock market by comparing it to the Fama and French (1993)’s base model. The new Fama and French five-factor model directed at capturing two new factors, profitability and investment in addition to the market, size and book to market premiums. The pricing models are tested using a time-series regression and the Fama and Macbeth (1973) methodology. The regularities in the factor’s behavior related to market conditions and to the sovereign debt crisis in Europe are also examined. The findings of Fama and French (2015) for the US market are confirmed on the Paris Bourse. The results show that both models help to explain some of the stock returns. However, the five-factor model is better since it has a marginal improvement over the widely used three-factor model of Fama and French (1993). In addition, the investment risk premium seems to be better priced in the French stock market than the profitability factor. The results are robust to the Fama and Macbeth (1973) methodology. Moreover, profitability and investment premiums are not affected by market conditions and the European sovereign debt crisis.

Previous studies have shown that stock returns bear a premium for downside risk versus upside potential. We develop a new risk measure which scales the traditional CAPM beta by the ratio of the upside beta to the downside beta, thereby incorporating the effects of both upside potential and downside risk. This “modified” beta has substantial explanatory power in standard asset pricing tests, outperforming existing measures, and it is robust to various alternative modeling and estimation techniques.

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

This paper explores the relationship between size, book-to-market, beta, and expected stock returns in the U.S. Information Technology sector over the July 1990–June 2001 period. Two models, the multivariate model and the three-factor model, are employed to test these relationships. The risk-return tests confirm the relationship between size, book-to-market, beta and stock returns in IT stocks is different from that in other non-financial stocks. However, the sub-period results (the periods before and after the technology crash in April 2000) show that the nature of the relationship between stock returns, size, book-to-market, and market factors, or the magnitude of the size, book-to-market, and market premiums, is on average unchanged for both sub-periods. This result suggests the technology stock crash in April 2000 was not a correction of stock prices.

Over the past 50 years, air travel in the United States has increased from approximately 33 million passengers in 1960 to over 607 million passengers in 2007 (National Transportation Statistics, 2011, Table 1–40). This is over an 18-fold increase in air travel in the past five decades. Over that same time period, the number of airports increased modestly, from 15,161 in 1980 to 19,750 in 2009. The number of those airports serving public commercial traffic is even smaller, and has declined from 730 airports in 1980 to 559 in 2009 (National Transportation Statistics, 2011, Table 1–3). Together, these two facts point to phenomenal growth among airports (measured by the number of passenger trips).

We revisit the performance of seasoned equity offerings (SEOs) in Japan against the backdrop of the Tokyo Stock Exchange's historical nine-year run up from 1980 to 1989, with the time period chosen for the purpose of comparison to previous studies. We analyze the long-run performance of 427 issues or 387 Japanese firms that conducted SEOs from 1980–1990. Initial results indicate that SEOs firms underperform standard benchmarks over subsequent 3- and 5-year periods after issuing. The results from value-weighted portfolios, however, show that SEOs outperform three out of five benchmarks. The results from the Fama-French three factor model show that all of the 16 SEO portfolios (formed by size and book-to-market quartiles) have positively significant intercepts, and most loadings are significant. The size loadings from time series three-factor model of value-weighted portfolio show that SEO sample firm returns exhibit characteristics of large firms as opposed to those of small firms under equally weighted portfolios. Our results support the arguments that (1) the returns of issuing firms are not idiosyncratic, but rather covary with the common factors of nonissuing firms and that (2) the underperformance of SEOs is sensitive to the precise test specifications.

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.