Strategies of portfolio investment with estimates of bull and bear markets

The financial market has non-linear patterns, with different return behavior in bull versus bear markets. This article uses multivariate model estimates to study portfolios in changing conditions, and develops investment strategies for portfolios in light of uncertainty about the bear or bull status of the stock market. Portfolios were optimized for the main stocks listed on the Brazilian market index Ibovespa. The portfolios proposed with estimates of changing market status outperformed others over the analyzed period, with rebalancing adjustments made either weekly or monthly.

The Impact of Consequences Awareness of Public Environment on Medicine Return Behavior: A Moderated Chain Mediation Model

With global aging trends and prosperity in the medicine market, the number of unused or expired household unused or expired medicines is increasing. Medicines which are discarded improperly result in serious pollution. From the perspective of behavioral science, the main contribution of this paper is the construction of a chain mediation model to analyze the influence mechanism between consequences awareness of the public environment and proper return behavior of unused or expired medicines. The model explores the moderating effect of personal health awareness with through observation of to the mediating effect of personal norms and return intention. Using a sample size of 366 residents from China, the proposed hypotheses are empirically tested. The results show: firstly, the direct effect of residents’ consequences awareness of public environmental awareness on the proper medicine return behavior is not significant; secondly, return intention plays a mediating role in the positive effect of consequences awareness of the public environment on proper return behavior; thirdly, personal norms and return intention play a chain mediating role in the positive impact of consequences awareness of the public environment on proper return behavior; and lastly, personal health awareness moderates the chain mediation path by strengthening the positive effect of return intention on proper return behavior.

Behavior of Stock Return ; Evidence from Indonesia and Malaysia Shariah Stock Market

This study aims to determine stock return behaviour in Indonesia and Malaysia Shariah stock market. Indonesia and Malaysia are selected based on the countries level of development and geographical factor, since both countries are emerging market with a rapid growth of Shariah stock market not only in term of listed companies but also in term of number of investor. Based on geographical proximity, both countries close to each other and have a strong bilateral relationship which makes their stock market return behaviour influence by many factors. This studies relies on two major time series investigation techniques, namely Economteric Modeling of returns; The Autoregressive model, Assumption of Linearity, Volatility Modeling of GARCH and its extension. The result showed that stock return behavior happening in Indonesia and Malaysia Shariah Stock Market.

Technological relatedness: How do firms diversify their technology?

The principle of relatedness, which helps estimate the affinity between economic activities, suggests that cities, regions, and countries are more likely to undertake new economic activities when they already perform related activities. This empirical principle has been confirmed for various dimensions---cities, regions, and countries---and their activities--- developing new technologies, products, and industries. However, the technological diversification of firms is yet unexplored. Is a firm more successful at entering a new technology when it has already accumulated related technologies? Here, we explore this issue using a unique dataset that contains firms' patent data and financial and market information. In particular, we examine Korean firms listed in the Korean stock market that published patents at the patent offices in Korea, Europe, and the United States from 1984 to 2004. We develop a technological relatedness measure to estimate whether a firm has already published patents with similar technologies. We find that firms are more likely to develop a new technology when they already have related technologies. Interestingly, technological relatedness shows an increasing return to related knowledge. We also check the robustness of this effect using propensity score matching and find that the effects of technological relatedness and its increasing return behavior remain significant when controlling for potential confounding effects. These findings extend the concept of relatedness to a firm's technological diversification and show that the development of a firm's technological knowledge is shaped by its technological relatedness.

In the Mood for a Blood Donation? Pilot Study about Momentary Mood, Satisfaction, and Return Behavior in Deferred First-Time Donors

<b><i>Introduction:</i></b> To maintain a sufficient donor pool, deferred first-time donors (FTD) should be motivated to return for blood donation. This pilot study investigates how deferral affects momentary mood, satisfaction with the donation process, and subsequent return behavior to examine their potential for motivating (deferred) FTD. <b><i>Methods:</i></b> All of the subjects (<i>n</i> = 96) completed a first questionnaire (A1) before pre-donation assessment. Deferred FTD (<i>n</i> = 22) were asked to complete a second questionnaire (A2) immediately after deferral, while non-deferred FTD (<i>n</i> = 74) filled in the second questionnaire (A3) after blood donation. The impact of deferral, momentary mood, and satisfaction with the donation process on return behavior within 12 months was tested by calculating two path analyses, controlling for sex and age. <b><i>Results:</i></b> Mood (<i>p</i> &#x3c; 0.001) and satisfaction with social aspects of the donation process (<i>p</i> = 0.01) were decreased after deferral. Deferred FTD were less likely than non-deferred FTD to return to the blood donation center within 12 months (60.8 vs. 36.4%; <i>p</i> = 0.043). However, path analyses revealed that deferral effects on mood and satisfaction were not connected to return behavior. Instead, age had a significant influence on return behavior (<i>p</i> &#x3c; 0.05) such that, overall, non-returning FTD were older than returning FTD, regardless of their deferral status. <b><i>Conclusion:</i></b> Our findings suggest that mood and satisfaction with the donation process are directly affected by deferral but not clearly responsible for low return rates. It seems promising to embed these variables in established health behavior models in further studies to increase the return rates of deferred FTD.

A Novel Consumer Purchase Behavior Recognition Method Using Ensemble Learning Algorithm

With the prosperous development of e-commerce platforms, consumer returns often occur. The issue of returns has become a stumbling block to the profitability of e-commerce companies. To protect consumers’ purchase rights, the Chinese government has introduced a 7-day unreasonable return policy. In order to use the return policy to attract consumers to buy, various e-commerce platforms have created a more relaxed and convenient return environment for consumers. On the one hand, the introduction of the return policy has increased customer trust in e-commerce platforms and stimulated purchase demand. On the other hand, the return behavior also increases the cost of the e-commerce platform. With the upgrading of consumption, customers pay more attention to personalized experience. In addition to considering price when purchasing online, the quality of services provided by e-commerce platforms will also directly affect customers’ purchasing decisions and return behavior. Therefore, under the personalized return policy of the e-commerce platform, whether consumers will make another purchase is worth studying. In order to achieve this goal, an ensemble learning method (AdaBoost-FSVM) based on fuzzy support vector machine (FSVM) is applied to predict the purchase intention of consumers. First, the grid search method is used to optimize the modeling parameters of the FSVM base classifier. Second, the AdaBoost-FSVM ensemble prediction model is constructed by using multiple base classifiers. In order to evaluate the performance of the prediction models used, logistic regression (LR), support vector machine (SVM), FSVM, random forest (RF), and XGBoost were used to construct prediction models for purchasing behavior. The experimental results demonstrate that the method used in this study has a more accurate prediction effect than the comparison algorithms. The predictive model used in this study can be used in the recommendation system of shopping websites and can also be used to guide e-commerce companies to customize various preferential policies and services, so as to quickly and accurately stimulate the purchase intention of more potential consumers.

Behaviour of Stock Returns During COVID-19 Pandemic: Evidence from Six Selected Stock Market in the World

This paper investigates the short term return behavior of six selected stock market around the world during the COVID-19 Pandemic. USA, Indonesia, India, South Korea, Saudi Arabia, and Singapore are selected based on the size of their stock market and the countries have taken a considerable amount of decision and policy to mitigate the risk of before, ongoing, and aftermath COVID-19 Pandemic. This study relies on two major time series investigation techniques, namely Econometric Modeling of returns; The Autoregressive model, Assumption of Linearity, Volatility Modeling, namely the GARCH and WBAVR Test. The results suggest that the stock return behavior in six selected countries occurs in different forms. Our findings suggest that the policymakers must understand how to shift their policy to mitigate the risk of COVID-19 in the financial sector, since we observe a strong correlation between the public health crisis and stock market performances.