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PurposeThe study tests risk-taking and risk-aversion capabilities while distinguishing between business group firms and stand-alone firms and considering oil price volatility. Second, this attempt to study the linkage between risk-taking during market down movements and when the firms have established themselves as product market leaders. Third, this study analyses the “sentiment” state, where it explores the reaction of corporations when the market is in the negative direction, and lastly, it explores the linkage between product market competition and risk-aversion.Design/methodology/approachThis study uses financial information for 1,273 non-financial companies and other required data from various sources. The study employs panel data and utilizes different empirical methodologies, including the generalized method of moments (GMM) estimator, to test the stated hypotheses.FindingsWe find that the business group firms have more risk-taking proficiencies compared with the stand-alone firms. Moreover, this study discovers that the corporates avoid taking risks when the market is not performing well. Also, when the market is down and crude prices are high, the management expects high earnings in the future, willingly takes risks and shows that product market leaders do not follow the risk-aversion strategy.Practical implicationsThe empirical results indicate that oil price movement can restrict management’s behaviour when choosing a risky investment project. Management should develop a robust policy that follows the group of firms. In the policy, the management should describe the level of risk that may be taken by the firm and implement it when required.Originality/valueSince we do not find any studies in this context, then there is a major and essential gap in the literature that this study should fill.

The purpose of this study was to investigate the opportunities which accrue from application of PPPs in development of down market urban housing in Kenya, hence attracting private players to partner with the public sector. The provision of down market urban housing in Kenya faces challenges like: application of outdated technologies, inadequate innovation, financing and ineffective strategies. This has led to huge housing supply backlogs, estimated to be 2 million housing units, because the annual demand is 250,000 units, but only 50,000 can be produced. Stakeholders have therefore scouted for alternative strategies, including application of PPPs, to reduce the overreliance on public sector financing of housing. Delphi method was used to gauge the likely opportunities in the application of PPPs. A sampling frame constituting of housing developers, financiers and practitioners was used, out of which a population of 88 persons in three Delphi panels was used. A sample size of 30 persons in housing financiers, 28 developers and 30 housing practitioners was used. Three rounds of Delphi iterations were used. It was found out that PPPs brings opportunities like the leveraging of assets, capacities and strengths of partners, enhanced resource mobilization, technology, effectiveness and innovation in housing delivery. It was concluded that PPPs brings many opportunities which makes private sector attracted to the concept hence assisting in housing development. The conclusion is that owing to the benefits of applying PPPs, the concept can be used for increasing the housing stock in the country, especially for low income urban households.

The issue of real-time deep down-regulation ancillary service has received increasing attention in the Northeast of China. Participation of combined heat and power plants with multitype units and flexible heating devices in the market is investigated. This paper establishes a model of feasible operating region and the coal consumption function for the combined heat and power plant. An internal optimal dispatch model and the minimum overall electric power output model for the plant satisfying a given heat load are presented. Furthermore, the models of total cost for down-regulation and average cost for deep down-regulation in two levels are established. These models are validated based on the realistic data of the plant in the Northeast of China. In case studies, the influence factors and change rules of the minimum electric power output, the total cost, and the average cost of plants are analyzed.

This paper investigates the performance of four-factor asset pricing model using Hong Kong stock returns. Our four-factor model is constructed by adding a momentum factor into the Fama and French’s (J Finance Econ 33(1):3–56, 1993 ) three-factor model. We find that the four-factor model may explain return variation using Hong Kong data. Our results show evidence that all the four factors are significant in the model and intercepts are not significant. In addition, the reasonably high values of adjusted R 2 and the insignificance of an additional explanatory variable of residual standard deviation provide supportive evidence to the model. The robustness of the model is also checked for two effects: up- and down-market conditions and seasonal behavior.

Although access to financing in Pakistan is expanding quickly, it is two to four times lower than regional benchmarks. Half of Pakistani adults, mostly women, do not engage with the financial system at all, and only 14 percent have access to formal services. Credit for small- and medium-size enterprises is rationed by the financial system. The formal microfinance sector reaches less than 2 percent of the poor, as opposed to more than 25 percent in neighboring countries. Yet it is the micro- and small businesses, along with remittances, that help families escape the poverty trap and participate in the economy. 'Bringing Finance to Pakistan's Poor' is based on a pioneering and comprehensive survey and dataset that measures the access to financial products by Pakistani households. The survey included 10,305 households in all areas of the country, excluding the tribal regions. The accompanying CD contains summary statistics. The authors develop a picture of access to and usage of financial services across the country and across different population groups, and they identify policy and regulatory priorities. Reform measures in Pakistan have been timely, but alone are not enough; financial institutions have lagged behind in adopting technology, segmenting customer bases, diversifying products, and simplifying processes and procedures. Gender bias and low levels of financial literacy remain barriers, as is geographical remoteness. However, the single strongest cause of low financial access is lack of income—not location, education, or even gender. 'Bringing Finance to Pakistan's Poor' will be of great interest to readers working in the areas of business and finance, economic policy, gender and rural development, and microfinance.

In the scientific literature business models are defined as architecture of the value creation, profit formula, key processes and key resources. For the oil industry there is a need to develop new business models that have to describe the specificity of this industry and to take into consideration the new objectives after the global oil crisis. Although crude oil price has dropped dramatically since second quarter 2014, OPEC raised crude output to the its highest value in more than three years as it pressed on with a strategy to protect market share and pressure competing producers. The objective of this article is to identify and promote new business models for state companies in the oil industry. The research methodology is based on case studies that present and analyze the business models in two of the main oil producers Iran and Iraq, where the state companies are playing an important role in this industry. The subject is relevant because the business models for state companies in the oil industry have to be modified after the oil crisis and these are not real analysed in the scientific literature. Furthermore, the aspects discussed in the current article represent the main factors that will influence investment prospects of companies in the field in the next decade.

Using Japan REITs stock data, this paper examines the risk-return relations conditional on up and down markets periods. The results show that beta is significantly and positively (negatively) related to realized returns in up (down) markets before and after controlling for extra risk factors. The same conditional results are found for unsystematic risk and total risk, providing evidence that investors do not hold well-diversified portfolios. Though skewness is significantly priced, the coefficients are unexpectedly positive (negative) in up (down) markets, indicating that investors dislike positively skewed portfolios and would ask for compensation if they are required to hold them during up markets. One possible reason is that the investors have a poor concept of skewness and or they are too aggressive during bullish markets and so they ignore the benefit of positive skewness. Subsidiary results highlight that there is no seasonal effect in the conditional relation between beta unsystematic risk total risk skewness and returns. This study is the first comprehensive study of the risk-return relations in Japan REITs market, which provides out-of-sample evidence relative to earlier tests on Asian and international stock markets. The findings give important insights and provide useful guidance on investing in Japan REITs market.

This evaluation assesses the performance of IFC’s energy efficiency finance program in China aimed at stimulating energy efficiency investments through bank guarantees and technical assistance. The difference made by the program is traced along the chain of interventions: (i) at the level of banks, the program is narrowly based on one of the two partner banks, which, with the help of the program, expanded its energy efficiency lending as a new business line; (ii) at the level of energy management companies, the program’s technical assistance improved the program participants’ access to finance; and (iii) at the end-user level, it promoted the use of energy efficiency investments that achieved reduction of greenhouse gas emissions. The utilization of IFC’s program has been rapid compared with other similar programs. The energy efficiency investments supported by the program have reduced greenhouse gas emissions by 14 million CO2 tons per year, slightly in excess of the target set at the beginning of the program. However, there is only a weak differentiation in behavior surrounding energy efficiency investment between end users supported by the program and other similar companies that were not. It is important to note that the performance of the program was heavily influenced by the government’s policy actions and the earlier efforts of other players: The Chinese government and other players such as the World Bank. The CHUEE program, relying mainly on commercial funding through IFC’s guarantees, builds on these efforts.

This chapter contains sections titled: Definition of the Yield‐to‐Call Methods for Computing the Yield‐to‐Call

Correlation is regarded as an important tool in identifying and assessing hidden risk; however, there is considerable misunderstanding about what correlation does and does not show. It often does not show what people think it does and the use of correlation in filtering investments may not provide the intended effect. This chapter takes a closer look at correlation and some of the ways it is often misinterpreted. The susceptibility of an investment to losses at the same time as equity markets and other holdings are declining is an important risk factor to consider, especially for the investments chosen to provide diversification with other portfolio holdings. Correlation is an important metric that can be used to flag this risk. However, neither does moderate to high correlation assure that this risk is present nor does low correlation assure its absence. If an investor is concerned about selecting a fund that is prone to losses when equity markets decline, correlation alone is an insufficient statistic. Instead of using only correlation for this task, investors should base their decisions on the following more comprehensive and descriptive combination of four statistics: (1) correlation, (2) Beta, (3) percentage of up months in down markets, (4) average return in down markets.