The Impact of Investor Sentiment on Stock Market Liquidity: The Mediating Role of Investor Herding Behavior “An Empirical Study on the Egyptian Stock Exchange”

Behavioral finance is a recent approach in financial markets that have appeared because of the complexities long faced by the traditional or neoclassical finance theory. This paper investigates the influence of investor sentiment and herding behaviour on stock market liquidity using an empirical study on the Egyptian Stock Market. We examine the direct impact of Egyptian investor sentiment on the Egyptian Stock Market liquidity. As well as the indirect impact of the Egyptian investor sentiment on the Egyptian Stock Market liquidity through the Egyptian investor herding behaviour. Therefore, the major contribution is filling the gap of indirect sentiment-liquidity impact conflict. We use the monthly data of the EGX30 index from January 2004 up to December 2018 for building up investor sentiment index, investor herding behaviour, and stock market liquidity measures. Moreover, we are using two additional types of data (closed-end mutual fund discounts and the equity open-end mutual fund flows) that represent major measures which are used to build up investor sentiment index ranging through the same time-series of the previously mentioned period of this paper. Additionally, we use four control variables for stock market liquidity, namely market volatility, excess market return, term spread, and lag of the dependent variable, considering that the fourth variable is also used for investor herding behaviour. Our result shows that the investor sentiment index has both a direct and indirect impact on stock market liquidity. In addition, regarding event study analysis’ results, there are different signs of the direct and indirect impacts and different correlations between the research variables throughout the four different events that differ completely from the usual signs and correlations of the theoretical background.

Classification Models for Bank Marketing Campaign: Towards Smart Bank Marketing

In this paper, we have proposed two models of marketing classification which are Support Vector Machine (SVM) and Linear regression, these two models are the most popular and useful models of classification. In this paper, we represent how these two models are used for a case study of a bank marketing campaign, the dataset is related to a bank marketing campaign, and for Applying the machine learning models of classification, the RapidMiner software was used.

Estimating Human Mass Gathering on a Particular Time and Space Estimation by using Machine Learning

With the expanding populace, evaluating swarm thickness is a typical issue for swarm observation in Computer Vision. This issue stays a difficult assignment because of various varieties in scale issues created by various blocked uproars, changing shapes, and point of view variety. To handles these difficulties and to give a decent condition of precision we, in this way, center to gather a tremendous measure of datasets with shifting thickness levels and manufacture an Allied-CNN model. The assortment of the datasets is done from different sources like YouTube and some genuine recordings. The Allied-CNN model is worked in python and prepared on a named dataset of thousand item pictures from different points of view, for deciding thickness levels (as low thickness, medium thickness, and high thickness). Preparing results for thickness estimation show the preparation set precision arrives at 94.8%, the greatest approval exactness of just 88% is accomplished. Along these lines, this model aids in ordering a picture as low thickness, medium thickness, and high thickness. Estimations on this group datasets show that the proposed Allied-CNN performs modest outcomes contrasted with the cutting-edge strategies.