While the international lockdown for the COVID-19 pandemic has greatly influenced the global economy, we are still confronted with the dilemma about the economy recession when the stock market hits record highs repeatedly. As we know, since portfolio selection is often affected by many non-probabilistic factors, it is of not easiness to obtain the precise probability distributions of the return rates. Therefore, fuzzy portfolio model is proposed for solving the efficient portfolio when the economy environment remains in vagueness for the future. To manage such an investment, we revise the Chen and Tsaur’s fuzzy portfolio model by using fuzzy goal programming model. Then, two numerical examples are illustrated by the proposed model which shows that the portfolio selection can be solved by the linguistic imprecise goal of the expected return. With different linguistic descriptions for the imprecise goal of expected return for the future stock market, the optimal portfolio selection that can be solved under different investment risks which is considered better than Chen and Tsaur’s model in real world situations. The sensitivity analysis with some parameter groups can be provided for more invested risk selection in the portfolio analysis.

The focus of this study is to investigate the causal relationships between global surface temperature changes and various relevant economy-related factors and to provide a clearer regime for authorities. The study reveals that the growth rate of production-based CO₂ productivity and energy consumption in industrial, service, and transport sectors positively correlates with global surface temperature changes, aggravating the problem in the long run. However, it is evident that, on the one hand, the energy efficiency of industrial and service sectors needs to be highly scrutinized to address the mitigation issues of global surface temperature change. On the other hand, the contributions of the agricultural and transport sectors are not obvious due to their bidirectional causal relationships with respect to global surface temperature changes. Thus, improving energy efficiency and consumption in these sectors should also be a significant concern. Furthermore, the study highlights the positive causal relationship between population density and the contribution of renewable energy to global surface temperature change. Although population density aggravates the issue, the use of renewable energy confronts it. The contribution from empirical evidence presented in this study emphasizes the need for industries to improve their energy efficiency and consumption in order to mitigate global surface temperature changes.

In this paper, we adopt the nonlinear autoregressive distributed lags (NARDL) model extended by Shin et al. (2014) to investigate the relationship between the treasury yield spread and economic policy uncertainty (EPU) in Japan. This model helps us to explore the short- and long-run asymmetric reactions of explained variables through positive and negative partial sum decompositions of changes in the explanatory variable(s). In our research, the testing of the NARDL specification reveals the existence of a significant long-run asymmetric equilibrium between the yield spread and EPU in Japan. On the other hand, we find a significant positive nexus between the treasury yield spread and EPU reduction in the long run. We speculate that because of low inflation, a poor economic outlook and the low interest rate environment since 1990, financial agents are markedly sensitive to negative shocks resulting from EPU. This means that when facing a good economy, bond agents are quick to sell, especially with higher-risk long-term interest rate bonds. Meanwhile, because the Bank of Japan announced the Stock Purchasing Plan in October 2002 and from the point view of portfolio management, while the influence of a positive economic outlook dominates the negative outlook, flight from quality has no role in asset portfolio adjustment. The empirical implications are that the long history of unconventional monetary policy supports the demand for both bonds and stock markets. When taking the stock market into consideration, the correlations between the yield spread, EPU and stock market capture the full wealth effects of the low interest rate environment in Japan.

Based on the concept of high returns as the preference to low returns, this study discusses the adjustable security proportion for excess investment and shortage investment based on the selected guaranteed return rates in a fuzzy environment, in which the return rates for selected securities are characterized by fuzzy variables. We suppose some securities are for excess investment because their return rates are higher than the guaranteed return rates, and the other securities whose return rates are lower than the guaranteed return rates are considered for shortage investment. Then, we solve the proposed expected fuzzy returns by the concept of possibility theory, where fuzzy returns are quantified by possibilistic mean and risks are measured by possibilistic variance, and then we use linear programming model to maximize the expected value of a portfolio’s return under investment risk constraints. Finally, we illustrate two numerical examples to show that the expected return rate under a lower guaranteed return rate is better than a higher guaranteed return rates in different levels of investment risks. In shortage investments, the investment proportion for the selected securities are almost zero under higher investment risks, whereas the portfolio is constructed from those securities in excess investments.

This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics.