Corporate Communication Network and Stock Price Movements: Insights From Data Mining

Zhou, Pei-Yuan; Chan, Keith C. C.; Ou, Carol Xiaojuan

doi:10.1109/tcss.2018.2812703

Cited by 25 publications

(10 citation statements)

References 31 publications

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“…As the table shows the system predicted values are quite similar to actual values the average difference between actual value and predicted value is 1. Table 4 shows accuracy comparison between proposed system and paper Corporate Communication Network and Stock Price Movements [1]. It also shows the idea of the days of look-back which means discovering the patterns of communication frequency and stock price of previous year.…”

Section: Results Analysismentioning

confidence: 99%

“…A stock market or share market is the association of buyers and sellers which makes economic transactions of stocks which is also called shares, which represent ownership claims on businesses which includes securities listed on a public stock exchange as well as those who only traded privately [1]. Examples of private share trading will be shares of private companies which are bought by investors through equity or crowd funding.…”

Section: Stock Marketmentioning

confidence: 99%

“…Section I contains the introduction of stock price and stock price movement prediction and its practices, Section II contains a survey on the related work and existing frameworks, Section III contains detail description of proposed system and architecture, Section IV contains result analysis and section V concludes research work with future directions). [1] This paper states that a corporate e-mail ecosystem contains meaningful information about employees' communication network and patterns. Author has only focused on the communication frequency that is e-mail exchanged between employees.…”

Section: Predictions Stockmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Stock Price Movement based on Communication Network and Sentiment Analysis

Shiral¹,

Torvi²

2020

IJCA

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Stock market is responsible for trading the shares of public listed companies. Stock exchange facilitates stockbrokers to trade company stocks and other securities. Stock price prediction is one of the most challenging issue which is attracting researchers from many fields including economics, history, finance, and mathematics and computer science. It is difficult to apply simple time-series or regression techniques on stock market because of the volatile nature. Proposed framework attempts to predict whether a stock price sometimes in the future will be higher or lower than it is on a given day. We find a little predictive ability in the short run but definite predictive ability in the long run. Using the social communication network within company among employees, the proposed algorithm can analyze the relationship between communication context and the movements (high and low) of stock price. We have also extended the system by using sentiment analysis for email content which determines whether email context is negative or positive. System gives aggregated result from number of mail exchanged and sentiment of message body.

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Section: Results Analysismentioning

confidence: 99%

Section: Stock Marketmentioning

confidence: 99%

Section: Predictions Stockmentioning

confidence: 99%

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Predicting Stock Price Movement based on Communication Network and Sentiment Analysis

Shiral¹,

Torvi²

2020

IJCA

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“…The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor dissimilarities. It gives the result in terms of real number, and also infinite number of possibilities in prediction [9], so it is very difficult the information on the tips of fingers. There is also a more complicated reason, this regression has a "epsilon" the margin of tolerance is set in the process of conjecture to the SVM which is already formally requested.…”

Section: Support -Vector -Regressionmentioning

confidence: 99%

Stock Market Prices Prediction using Random Forest and Extra Tree Regression

Polamuri¹,

Srinivas²,

Mohan³

2019

IJRTE

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Prediction of Stock price is now a day’s an existing and interesting research area in financial and academic sectors to know the scale of economies. There did not exists any significant set of rules to estimate and predict the scale of share in the stock exchange. Many evolutionary technologies are existing such as technical, fundamental, time, statistical and series analysis which help us to attempt the prediction process, but none of the methods are proved as reliable and accurate tool to the society in the estimation of stock exchange or share market scales. Here in this paper we attempted to do innovative work through Machine Learning approach to predict or sense the behaviour tracking of the stock market sensex. Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and define the activity between the exchanges the securities between the buyers and sellers. We predicted the price of the stock based on the closing value and stock price. An algorithm with high accuracy we do the process of comparison for the accuracy of each of the model and finally is considered as better algorithm for predicting stock price. As share market is a vague domain we cannot predict the conditions occur, and also share market can never be predicted, this job can be done easily and technically through this work and the main aim of this paper is to apply algorithms in Machine Learning in predicting the stock prices.

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“…Section I contains the introduction of stock price movement prediction and its practices, Section II contains a survey on the related work and existing frameworks, Section III contains detail description of proposed system and architecture, Section IV concludes research work with future directions). [1] This paper states that a corporate e-mail ecosystem contains meaningful information about employees' communication network and patterns. Author has only focused on the communication frequency that is e-mail exchanged between employees.…”

Section: Fig 1 Prediction Processmentioning

confidence: 99%

A Detail Survey on Predicting Stock Price Movement based on Communication Network

Shiral¹,

Torvi²

2019

IJCA

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Stock price prediction is a popular topic in financial studies. Stock market is basically nonlinear in nature and predicting share price is very difficult because there are no specific set of rules to estimate the price of the share in share market. Many methods are used to predict the share price like statistical analysis, time series analysis but none of these methods are considered to be consistently acceptable prediction methods and applying traditional methods may not ensure the accuracy of prediction. Various machine learning algorithms have been used to study the highly unpredictable nature of stock market by capturing repetitive patterns. Various companies have their preferred analysis tool for stock market forecasting and the reason for preference is the accuracy with which they predict. This paper gives brief survey of well-known prediction techniques used for prediction of stock in the stock market.

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Corporate Communication Network and Stock Price Movements: Insights From Data Mining

Cited by 25 publications

References 31 publications

Predicting Stock Price Movement based on Communication Network and Sentiment Analysis

Predicting Stock Price Movement based on Communication Network and Sentiment Analysis

Stock Market Prices Prediction using Random Forest and Extra Tree Regression

A Detail Survey on Predicting Stock Price Movement based on Communication Network

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