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Does Fortune magazine’s list of the 100 Fastest-Growing Companies have information content; that is, is the list a source for market-beating performance? The paper aims to discuss this issue.

Using data for 26 annual periods, 1991–2016, the paper examines the top 5, 10, 25, 50 and all 100 stocks on a return-risk basis, including an application of Modern Portfolio Theory. To generate portfolio performance metrics, the study uses conventional mean-variance analysis, which includes the estimation of returns and risks, where risk will be measured by standard deviation and β. To arrive at the performance metrics and to determine whether information content is embedded in the list, the study reviews a series of tests. Because Fortune ranks the companies from 1 to 100, the data can be used to test if information content is displayed in sub-groups, such as in the first five to ten companies, even if it does not exist in the 100-stock portfolios.

The study finds that the returns are not high enough nor are the risks low enough statistically to conclude the existence of significant information content.

As part of the authors’ efforts to move to the population of 2,600 firms as closely as possible, the authors use “delisting” returns from CRSP on 120 firms to account for missing observations, with a final sample size of 2,594 firms.

The evidence indicates that investors drawn to Fortune’s “100 Fastest-Growing Companies” should view them skeptically as a source for an effective stock selection strategy.

On the basis of the results of this study, readers will conclude that subscribers drawn to Fortune’s “100 Fastest-Growing Companies” should view them skeptically for investment recommendations. From a portfolio perspective, the study is unable to uncover information content that could lead to a market-beating performance, suggesting that the published criteria Fortune uses to select the Fastest-Growing Companies is embedded in the prices of the stocks even before Fortune publishes its list. The study notes that the selection criteria used by Fortune do involve some judgments on the part of the editorial staff (e.g. whether an announced restatement of previously reported financial data appears to have a significant impact), which means that someone who wished to anticipate the publication of the next list of the “Fastest-Growing Companies” would not only have to gather information but would also have to correctly anticipate these judgment calls.

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.

In the search for alternative income-generating assets, the paper addresses the following question, one that the literature has yet to answer: what is a reasonable allocation, if any, to asset-backed securities within a 60–40% stock-bond balanced portfolio of mutual funds?

The authors apply the Black–Litterman model of Modern Portfolio Theory to test the efficacy of adding asset-backed securities to the classic 60–40% stock-bond portfolio of mutual funds. The authors use out-of-sample tests of one, three, five, and ten years to determine a reasonable asset allocation. The data are monthly and range from January 2000 through September 2021.

The statistical evidence indicates a modest reward-risk added value from the addition of asset-backed securities, as measured by the Sharpe “reward-to-variability” ratio, in holding periods of three, five, and ten years. Based on the findings, the authors conclude that a reasonable asset allocation for income-seeking, risk-averse investors who follow the classic 60%–40% stock-bond allocation is 8%–10%.

The findings apply to a stock-bond balanced portfolio of mutual funds. Other fund combinations could produce different results.

Investors and money managers can use the findings to improve portfolio performance.

For investors seeking higher income-generating securities in the current record-low interest rate environment, the authors determine a reasonable asset allocation range on asset-backed securities. This study is the first to provide such direction to these investors.

This paper aims to assess the impact of the global financial crisis of 2007‐09 on the risk structure of S&P 500 firms by examining their market, active, and residual risks before and during the crisis.

The classic one‐factor model is estimated for each firm in the S&P 500 to decompose risk into market, active, and residual components. Five sets of regression estimates based on monthly return data are used: 2002‐06, 2003‐07, 2004‐08, 2005‐09, and 2006‐10. The estimates provide insight into the risk structure of S&P 500 firms before and during the crisis.

The average correlation coefficient between S&P 500 firms rose during the crisis from 0.20 to 0.35, an increase of 75 percent. Although the results indicate that active and residual risks are significant across the firms and across the periods, the impact of the financial crisis was mostly on market risk. The increase in risks was pronounced for financial firms, especially insurance companies, and industrial firms, especially “hard” manufacturing.

Because the study focuses on the global financial crisis of 2007‐09, researchers should be careful about generalizing the results to the post‐crisis period.

Investors should be aware that equity portfolio risk reduction during major crises can be hard to achieve because the average correlation coefficient between stock returns may rise significantly, crimping the efficacy of diversification.

It was very difficult for equity investors to shield themselves from the risk associated with the global financial crisis of 2007‐09.

The purpose of this paper is to test the efficacy of an application of modern portfolio theory (MPT) from 2000 through 2009, a period during which the annual rate of return on the S & P 500 is negative. The financial media have called this period “the lost decade” for investors.

Using monthly data, the author uses a series of annual out-of-sample tests to compare the risk-reward performances of MPT portfolios against those of the S & P 500.

The author finds that the MPT portfolios outperformed the S & P 500. During the “lost decade”. They generated a cumulative return of over 77 percent compared to a cumulative return of −9.1 percent on the S & P 500. Moreover, the MPT portfolio β’s are low, ranging from 0.45 to 1.01, suggesting above-average risk-reward performances.

The MPT portfolios are relatively small, and might not be well diversified. That said, they comprise a core set of securities that could help investors achieve a risk-reward performance that exceeds that of the S & P 500.

The results suggest that investors should not overlook the potential of MPT, despite its theoretical and practical limitations, to provide above-average returns at below-average risks.

This is the first study to show the efficacy of MPT during a period in which it was criticized at having failed investors when they needed it most.

Finance courses are major offerings in UK business schools, at various levels. Seldom do these courses move beyond theoretical modeling and textbook approaches. This is corroborated by the paltry literature on challenge-based learning (CBL) in the finance arena.

In this chapter, we describe the experience of implementing an investment fund designed by experienced members of staff and set up and run by students in one of the UK business schools in 2018. The seed capital of the Fund was donated by a variety of sources and has enabled students to use this as a jump start for their investment skills. The ethos of the Fund is not to teach students just how to invest but to put students in a real-life investment setting where they deal with the running of day-to-day activities of managing investments through a practical framework. In doing so they discover, adapt, and apply theoretical models to funds while preparing performance reports. Students have been successful in getting jobs by demonstrating their involvement, and the Fund has put them in touch with investment banks and future employers. The functioning of the Fund is analyzed in this chapter.

The chapter suggests the practical steps involved in setting up such a schema of CBL, which might aid other higher education institutions and promote entrepreneurial, creative, and team building activity.

Bond rating studies have received and continue to receive considerable attention in the literature on government finance. This study focuses on two major issues of municipal bond ratings that occupy the center-stage of these discussions: What charac-teristics does a rating institution analyze when assigning rating to a government? How significant are these characteristics in predicting the ratings given by these institutions? Using a combination of economic, financial, and demographic factors, the study reexamines these questions on a select group of cities.

Explains the rating system for US municipal bonds and its effect on borrowing costs, reviews relevant research and provides a study of the factors affecting grading by rating agencies in Virginia using 1995 data. Explains the methodology and presents the results, which identify five significant determinants of favourable ratings. Shows that net interest costs are lower when other rates of interest are low, real estate taxes are high (though not excessive), total municipal debt levels are low and credit risks are low. Confirms that bond ratings capture additional information and that a drop in ratings will raise net interest costs substantially. Considers consistency with other research and the implications of the findings for participants in the municipal bond market.

This paper aims to focus on the nexus between sustainability and desired outcomes for smart cities. The main focus is on how green leadership influences the relationship between smart and sustainable activities and stakeholder management.

The work is essentially a non-empirical review of the literature to develop a conceptual model to be tested in a subsequent study.

The findings indicate that smart cities and their sustainability activities can drive desired outcomes through green leadership. Also, green leadership has an indirect relationship with the desired outcomes of smart cities; hence, managers in the tourism and hospitality industries should cultivate their green leadership style to assist smart cities in accomplishing their goals.

This research is conceptual, and the proposed model will need to be evaluated to be more valid. Furthermore, the model is restricted to the tourist and hospitality industry, limiting the generalization and application of the findings to that area. Furthermore, because sustainability activities and smart city leadership differ by region or country, the proposed model will be suitable for more developed economies with more developed sustainability policies.

This paper makes a novel theoretical contribution by using stakeholder management as a mediating variable and green leadership as a moderating variable concurrently.

This model suggests that smart and sustainability activities of cities can lead to desired outcomes for smart cities through effective stakeholder management and green leadership.