Predicting the E-Commerce Companies Stock with the Aid of Web Advertising via Search Engine and Social Media

Abstract

The consumer services market greatly depends on the consumers feedbacks. The best provided services will be increasing the rating of those services subsequently annotated with their good feedback. To give feedback one platform is social media like twitter is very suitable one. To attain consumers interest on their services, consumer markets utilizes advertisements via search engine marketing and social media platforms. The advertisements are very attractive and mind catching, people will be informed, motivated, influenced. All advertisers give advertises in form of text, picture, and video, audio and by mixing them with the aid of professional ad-makers. The search engine is a search program for finding particular sites on World Wide Web, which discovers the stuff related to keywords or characters specified by the user. In an increasingly competitive marketplace to expand and grow the business the Search engine marketing (SEM) is the effective approach. The advertisers also select video sharing platforms like YouTube-a video sharing channel, and also the search engine marketing platform to launch their advertisements to be available for consumers publicly. The public can view and share their opinion via likes/dislikes count and also comments for every video. This paper focus on attaining stock predictions from different sources and also discuss about gathering text analysis for the required stock from digital media like search engines, video channels, news feeds. The aim of this study is to consider the stock price prediction from major E-commerce consumer services companies namely Just Dial and Info edge that are publicly traded in NSE/BSE by considering web advertising and their influence on consumer services markets like Just Dial and Info edge, by adopting ensemble machine learning algorithms like Random forest, Gradient boost, XG-boost and it is observed that XG Boost outperforms the other algorithms as it exhibits least RMSE,MAE and MAPE providing the accuracy of 71.78%.

Decision Fusion for Stock Market Prediction: A Systematic Review

“…The second most popular method for fusing classification results is the tree-based method, which involves first feeding forecasts of all base learners into a tree-based algorithm, then mapping each prediction to a neighborhood in the set of dependent variables, and then returning the mean neighborhood [99]. The most commonly used tree-based methods include gradient boosting [40], [75], [96] and random forest [28], [40], [75]. It is worth noting that Barak et al [27] used five tree-based methods for decision fusion: the BF tree, decision table, decision tree, decision tree naïve Bayes (DTNB), and the LAD tree, with the decision table performing the best.…”

“…Author(s) ANN [22], [33], [41], [42], [57], [58] Decision Tree [28], [40], [75], [96] SVM [43], [68], [93] LSTM [30], [88] PNN [34] ELM [48] DBN [84]…”

“…In addition, the final forecast can be the fusion result of different types of predictions, such as the fusion of stock movement trends and external factors [56], the fusion of stock prices and external factors [51], the fusion of stock movement probability and external factors [78], and the fusion of stock volatility and external factors [50]. Binary Stock movement trend (e.g., up and down) [28], [29], [32], [39], [40], [41], [42], [43], [44], [46], [48], [62], [64], [68], [73], [75], [77], [84], [85], [88] Stock movement probability [30], [49], [78], [93], [96] Multiclass Stock movement trend (e.g., up, neutral, and down) [22], [34], [56] Stock turning indicator [57], [58] Stock risk level [27] Confidence level of stock movement [33] Effect level of external factors [56] TABLE VIII STUDIES RELATED TO REGRESSION TASKS Forecasts of Base Learners Author(s)…”

Evaluation of WeMedia Ads Marketing Based on the Demand of Internet Economy

“…In the context of Internet economy, online trading platforms provide numerous small businesses with entrepreneurial and employment opportunities and offer convenient consumption approaches and choices to all citizens [1][2][3][4][5]. As an indispensable part of the modern market economy, the WeMedia marketing channels (e.g., live broadcast, WeChat, forums, short videos, and Weibo) on Chinese online e-commerce platforms achieved a turnover of more than 100 billion yuan [6][7][8][9][10].…”