Scaled and stable mean-variance-EVaR portfolio selection strategy with proportional transaction costs

This paper studies a portfolio optimization problem with variance and Entropic Value-at-Risk (evar) as risk measures. As the variance measures the deviation around the expected return, the introduction of evar in the mean-variance framework helps to control the downside risk of portfolio returns. This study utilized the squared l2-norm to alleviate estimation risk problems arising from the mean estimate of random returns. To adequately represent the variance-evar risk measure of the resulting portfolio, this study pursues rescaling by the capital accessible after payment of transaction costs. The results of this paper extend the classical Markowitz model to the case of proportional transaction costs and enhance the efficiency of portfolio selection by alleviating estimation risk and controlling the downside risk of portfolio returns. The model seeks to meet the requirements of regulators and fund managers as it represents a balance between short tails and variance. The practical implications of the findings of this study are that the model when applied, will increase the amount of capital for investment, lower transaction cost and minimize risk associated with the deviation around the expected return at the expense of a small additional risk in short tails

Capital Structure and Firm Value: Empirical Evidence from Ghana

Abstract This study seeks to provide evidence on the impact of capital structure on a firm &apos

A hybrid grey MCDM approach for asset allocation: evidence from China’s Shanghai Stock Exchange

Asset allocation is a critical concern for any investor in the financial market. This paper aims to prioritize five randomly selected firms from the top ten stocks by market capitalization of the Shanghai Stock Exchange (SSE) by opting for adequate financial procedures and practical criteria under uncertain conditions. Decision makers want not only the ranking order of stocks but also capital proportions to be allocated. Therefore, this study uses a hybrid multi-criteria decision-making (MCDM) approach comprising of an integrated analytic network process (ANP) and decision making trial and evaluation laboratory (DEMATEL) in a grey environment for optimal portfolio selection to provide both ranking and weighting information for decision makers. Results indicate that return, financial ratios, dividends, and risk are causal criteria group, which are the most influential determinants for obtaining high benefits with regards to stock portfolio selection in SSE. The free float of stocks is the least influencing criterion among all identified criteria of stock portfolio selection of SSE. The Industrial and Commercial Bank of China Ltd. stocks have the highest allocated proportion with the highest priority shown by investors and can be described as a suitable alternative. The practical implications of this research are that the approach, when applied, highlights how the grey system theory minimizes the uncertainties in all stages of decision-making of portfolio selection

A hybrid two-stage robustness approach to portfolio construction under uncertainty

This paper proposes a hybrid two-stage robustness approach to portfolio construction under data uncertainty. In the first stage, a stock\u27s efficiency performance from candidate stocks is assessed and selected using an integrated dynamic slack-based measure data envelopment analysis model. We discuss the stability of efficiency estimates using the leave-one-out method. In the second stage, a “robust” stable and scaled mean-variance-Entropic Value-at-Risk model is used to determine the optimal weights allocated to qualified stocks in the presence of proportional transaction costs. The proposed method reduces computational complexity, increases robustness, and provides a comprehensive evaluation of stocks under different financial decisions, thereby increasing conservatism in the investment process. We demonstrate the applicability of the proposed hybrid two-stage approach to stock data from the Shenzhen and Shanghai Stock Exchanges. Results show that with increasing required returns, the proposed method improves the capital amount for investment and lowers transaction costs at the expense of additional risk. The study concludes by comparing the computational performance of the proposed approach to that of existing methods

The economy-energy-environment Nexus in IMF’s Top 2 biggest economies: a TY approach

This paper assesses the relationship between carbon emissions, economic growth and, energy consumption, in USA and China from the perspective of Granger causality, in a multivariate framework controlling for financial development, urbanization, and trade openness. Econometric techniques employed include unit root tests, Toda and Yamamoto Granger causality, and generalized impulse response and variance decomposition analysis for the time horizon 1980–2017. Test results indicate that governments of the USA and China cannot implement sturdier strategic energy policies in the long run without inhibiting the growth of the economy because of the bidirectional causative linkage between economic growth and energy use. A causal link does not exist between carbon emissions and financial development for both countries. Nevertheless, in the USA, there exists a unidirectional Granger causality controlling from energy consumption to financial development. In both economies, urbanization Granger causes CO2 emissions and energy use but the reverse does not hold. An upsurge in energy consumption and carbon emissions will lead to a surge in trade openness but not vice versa for China. A noteworthy result is that there is a substantiation of unidirectional causality from energy consumption to carbon emissions in both countries. In the USA, impulse response and variance decomposition analysis disclosed the effect of financial development is projected to have diminutive magnitude whiles in the future, energy use, economic growth, trade openness, and urbanization would influence carbon emissions significantly. The impacts of trade openness and financial development are expected to be of little importance in China. The general findings implied that urbanization, economic growth, and energy consumption influenced CO2 emissions significantly in the USA and China. Understanding these similar and contrasting situations is essential to reaching a global agreement on climate change affecting IMF’s top 2 biggest economies. First published online 07 November 201

Modeling innovation efficiency, its micro-level drivers, and its impact on stock returns

Motivated by the COVID-19 pandemic and ensuing challenges to human and economic welfare, this research seeks to evaluate innovation efficiency, its micro-level drivers, and its impact on stock returns. This study considers innovation-related activities during two growth phases experienced by 138 listed pharmaceutical manufacturing companies in China: research and development (R&D) and marketing. A dynamic two-phase network data envelopment analysis (DEA) model measured R&D efficiency, marketing efficiency, and dynamic integrated innovation efficiency. A projection difference analysis was presented to offer a feasible solution to address inefficient companies. Then, panel regression methods were adopted to examine micro-level drivers of innovation efficiency. Additionally, a portfolio formation test was used to investigate innovation efficiency as a characteristic impacting stock returns. The results indicate that the listed companies are generally innovation inefficient. Only two are DEA innovation efficient. Low marketing and R&D efficiencies are impeding innovation efficiency amelioration, with the most losses attributed to the marketing phase. Most companies lack good conditions for R&D and the commercialization of scientific and technological breakthroughs. Institutional investors and financial analysts\u27 coverage have a positive and significant impact on innovation efficiency. Foreign institutional ownership and stock market overvaluation impact listed companies with high innovation efficiency positively. There is a negative and significant effect of size on innovation efficiency. However, growth in pharmaceutical manufacturing company size positively influences innovation efficiency at higher levels. The portfolio formation test results reveal that investors consider companies with high innovation efficiency as high-profile targets that provide high stock returns. The findings of this study offer stakeholders avenues to shape the initiation, process, and outcome of innovation

Innovation links, information diffusion, and return predictability: Evidence from China

Based on the activities of patent citation in China, a novel type of cross-firm innovation links is generated to investigate the gradual diffusion of information along the innovation chain via tests of cross-sectional return predictability. Various signals are created to represent the value of the information contained in the innovation links; these signals are demonstrated to have robust cross-predictability for stock returns in both the cross-sectional regression model and portfolio strategies. The effect of predictability is found to be stronger for stocks with high institutional ownership and analyst coverage. Considering the minimum number of steps required to establish the cross-firm linkage, innovation links are further partitioned to represent different proximity of the linked firms. It is then found that information diffuses faster across closely-linked firms than across distantly-linked firms. Sophisticated investors are found to be able to properly process the relevant information and benefit from innovation links

A dynamic SBM-DEA and portfolio formation test approach to the operating efficiency-stock returns nexus

This study assesses operating efficiency and its drivers in China\u27s inefficient publicly traded real estate companies via dynamic slack-based measure data envelopment analysis and panel regression techniques. This paper also investigates operating efficiency as a company characteristic impacting stock returns using a portfolio formation test. The results indicate that only 27 out of 106 listed real estate companies are operationally efficient. Return on assets, capital structure, higher education, and institutional ownership positively and significantly impact operating efficiency. The reward of an increase in operating efficiency is seen in its positive influence on stock returns. Listed real estate companies should improve the efficiency of their operational activities to generate higher stock returns