scholarly journals Stock Price Prediction Methods based on FCM and DNN Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wennan Wang ◽  
Wenjian Liu ◽  
Linkai Zhu ◽  
Ruijie Luo ◽  
Guang Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fuzzy Clustering ◽  
Stock Price ◽  
Listed Companies ◽  
Prediction Methods ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Stock Value

With the rapid economic development and the continuous expansion of investment scale, the stock market has produced increasing amounts of transaction data and market public opinion information, making it further difficult for investors to distinguish effective investment information. With the continuous enrichment of artificial intelligence achievements, the status and influence of artificial intelligence researchers in academia and society have been greatly improved. Expert system, as an important part of artificial intelligence, has made breakthrough progress at this stage. Expert system is based on a large amount of professional knowledge and experience for a specific field. Computers of this system can be used to simulate the decision-making process of experts to provide a decision-making basis for solving some complex problems. This research mainly discusses stock price prediction methods on the basis of artificial intelligence (AI) algorithms. Fuzzy clustering is a data mining tool that has been developed in recent years and is widely used. Using this method to process super large-scale databases with various data attributes has the characteristics of high efficiency and small amount of information loss. Theoretically speaking, the use of fuzzy clustering technology and related index method can effectively reduce the massive financial fundamentals of listed companies. By analyzing the influencing factors of stock value investment, we specifically select from the financial statements of listed companies the five aspects that can reflect their profitability, development ability, shareholder profitability, solvency, and operating ability. The full text runs through a variety of AI methods that is the characteristic of the research method used in this article, which pays special attention to verifying the theoretical method model. Doing so ensures its effectiveness in practical applications. In stock value portfolio research, a portfolio optimization model, which integrates the dual objectives of portfolio risk and returns into the risk-adjusted return of capital single objective constraints and solves the portfolio, is established. The accuracy and recall of the FCM model are relatively stable, with accuracies of 0.884 and 0.001, respectively. This research can help improve the number and quality of listed companies.

scholarly journals Stock Price Prediction using Linear Regression in Machine Learning

2019 ◽  
Vol 8 (12) ◽  
pp. 1382-1385 ◽  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Past ◽  
Stock Value ◽  
The Future ◽  
The Cost

Forecast of financial exchange has been an alluring subject to the stock representatives and the specialists from different fields. Stock value forecast is dependably a dominating objective for each speculator which encourages them to realizing the future costs thinking about the past records. There have been various examinations to foresee the cost of the loads of a specific organization utilizing AI method. In this paper we would utilize straight relapse to foresee the stock cost of the organization.

A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

StockPred: a framework for stock Price prediction

2021 ◽  
Author(s):  
Marwa Sharaf ◽  
Ezz El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Nirmeen A. El-Bahnasawy
Keyword(s):  
Stock Price ◽  
Stock Price Prediction ◽  
Price Prediction
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