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Investing in stock market is considered a high-risk monetary motion where investor may not take into version the factor of stock charge fluctuations or destroy their profits without gaining professional knowledge and investment experience. In addition, when working with stock information, people increase the importance of available and self-correcting information, a habit that runs counter to objective and reasonable investment decisions. In recent years, various methods of stock market forecasting have been proposed to enhance the quality and profitability of investor decision making. Recent machine learning models have been able to reduce the risk of stock market forecasting. However, diversity remains a major challenge in developing better learning models and in identifying intellectually valuable traits to further improve diagnosis. In this paper, we propose an optimal characteristic selection with hybrid machine learning classifier for stock market price forecasting (OFML-SMF) using social media and secondary data sources. First, we introduce a multi-objective earthworm optimization (MOEO) algorithm for optimal feature selection among multiple features i.e. called feature optimization which reduces the data dimensionality problem. Second, we develop a hybrid Quantile regression learning based deep belief neural network (HQR-DBNN) to classify the stock market price movement prediction which enhances the statistical metrics. Finally, to appraise the presentation of planned OFML-SMF model through the influential international stock market indices and the performance can be compare with the existing state-of-art models in conditions of accuracy, precision, recall and F-measure. [ABSTRACT FROM AUTHOR]

This study investigated whether overconfidence with respect to one's financial literacy affects stock market participation and retirement preparation and if so, how. Using an effective sample of 12,653 Japanese individuals, the empirical results confirm that financial literacy plays a positive role, while confidence in financial literacy also matters. For people with relatively low financial literacy, overconfidence can encourage taking financial action, while for people with high financial literacy, underconfidence can deter action. Confidence could have an effect equal to or greater than financial literacy. Moreover, it was also found that the positive effect of overconfidence is weaker for women than for men. [ABSTRACT FROM AUTHOR]

This study (a) investigates dispositional attributes regarding the risk-related personality, attitude, and behavior of college students majoring in financial engineering (FE-major group) and business (business-major group); and (b) examines whether risky behavior is differentially explained by explicit versus implicit attitude toward stock investment. The FE-major group scored consistently higher than the business-major group on measures of sensation-seeking, financial risk-taking, and computerized risk-taking behavior. This pattern was also observed in the two explicit attitudinal measures of risk, whereas no group differences were noted on the two corresponding implicit measures of risk. A newly developed implicit measure of stock investment showed incremental validity in predicting risky behavior beyond the corresponding explicit measure. [ABSTRACT FROM AUTHOR]

With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm. [ABSTRACT FROM AUTHOR]

A household may learn the importance of stock investment from its siblings. Using the logit and probit models, this paper tests empirically the learning hypothesis that a household is more likely to purchase stocks if its siblings have bought stocks, and finds evidence for the hypothesis. The estimates obtained in the analysis imply that a household's probability of stock purchases increases by about 1-3 % points if a sibling has purchased stocks. The positive results are not due to alternative explanations, such as uncontrolled correlation of age effects, financial support from siblings, common shocks, or other unobservable correlations. Moreover, the analysis does not support the objection that the positive results may be due to the effects of genes or common parenting. [ABSTRACT FROM AUTHOR]