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Recent studies in the accounting literature have investigated the economic consequences of R&D capitalization. Discretionary R&D capitalization for target beating can be characterized as a firm signaling private information on its future economic benefits or as opportunistic earnings management. R&D capitalization also has an impact on a firm’s marginal costs and product market competition. The purpose of this paper is to address how firms choose R&D levels for the purpose of meeting or beating their earnings targets and how this influences sequential product market competition.

The authors study this issue in a stylized game-theoretic model where R&D choices of a firm are not only strategically made but also used to convey proprietary information to its rival. The model provides a rationale for a firm distorting its R&D level to earn more profits and meet its earnings target.

The equilibrium result indicates that before the realization of common cost shock, a firm can influence the output of its accounting system (i.e. meeting an earnings target) through adjusting its R&D choices. This firm will overinvest in R&D, and this will give an opportunity to create some reserves to be used later to earn a higher profit and reach the earnings target.

This paper contributes to the research on real earnings management in terms of how R&D capitalization affects a firm’s R&D choices by influencing the output of its accounting system through adjusting its R&D choices and the strategic impact of those choices.

The home is a substantial investment for most individual investors but the assessment of risk and return of residential real estate has not been well explored yet. The existing real estate pricing literature using a CAPM-based model generally suggests very low risk and unexplained excess returns. However, many academics suggest the residential real estate market is unique and standard asset pricing models may not fully capture the risk associated with the housing market. The purpose of this paper is to extend the asset pricing literature on residential real estate by providing improved CAPM estimates of risk and required return.

The improvements include the use of a levered β which captures the leverage risk and Lin and Vandell (2007) Time on Market risk premium which captures the additional liquidity risk of residential real estate.

In addition to presenting palatable risk and return estimates for a national real estate index, the results of this paper suggest the risk and return characteristics of multiple cities tracked by the Case Shiller Home Price Index are distinct.

The results show higher estimates of risk and required return levels than previous research, which is more consistent with the academic expectation that housing performs between stocks and bonds. In contrast to most previous studies, the authors find residential real estate underperforms based on risk, using standard financial models.

Traditional statistical methods to study the financial performance of any industry have many barriers and limitations in terms of the statistical distribution of the financial ratios, and, in particular, it considers only its positive values of it. The purpose of this paper is to estimate the financial performance of 24 Indian life insurance companies for the period from 2013 to 2016 using Grey relational analysis (GRA) proposed by Deng (1982) that accommodates the negative values in the analysis.

Financial performance of 24 Indian life insurance companies for the years from 2013–2014 to 2015–2016 is examined using a total of 14 indicators from capital adequacy ratios, liquidity ratios, operating ratios and profitability ratios (PR). The methodology used is GRA to obtain the Grey grades to rank the performance indicators, where higher relational grade shows better financial performance, and a lower score depicts the scope for improving the performance.

The results rank the insurance companies according to their financial performance in which Shriram insurance stands first with higher relational grade score, followed by the companies like IDBI Insurance, Sahara Insurance and Life Insurance Corporation of India. The main finding is that PR which have negative values are playing a crucial role in determining the financial performance of Indian life insurance companies.

This study has far-reaching practical implications in twofold: first, for the Indian life insurance industry, they have to concentrate more on PR for better financial health and, second, for any financial performance analysis, ignoring negative value ratios produce biased inference and GRA can be used for better inference.

This study is the first attempt to evaluate the financial performance of Indian life insurance using the GRA methodology. The advantage of GRA is that there is no restrictions on the statistical distribution of the data and it also accommodates the negative values, whereas all the other traditional methods insist on the statistical distribution of data, and, more importantly, they cannot handle negative values in the performance analysis.