scholarly journals An Application of Artificial Neural Networks and Fuzzy Logic on the Stock Price Prediction Problem

2017 ◽  
Vol 1 (2) ◽  
pp. 40 ◽  
Author(s):  
Thanh Tung Khuat ◽  
My Hanh Le
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Fuzzy Logic ◽  
Stock Price ◽  
Financial Time Series ◽  
Prediction Problem ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial Time ◽  
Artificial Neural

The financial industry has been becoming more and more dependent on advanced computing technologies in order to maintain competitiveness in a global economy. Hence, the stock price prediction problem using data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. Fuzzy logic (FL) and Artificial Neural Network (ANN) present an exciting and promising technique with a wide scope for the applications of prediction. There is a growing interest in both fields of fuzzy logic computing and the financial world in the use of fuzzy logic to predict future changes in prices of stocks, exchange rates, commodities, and other financial time series. Fuzzy logic provides a way to draw definite conclusions from vague, ambiguous or imprecise information. Artificial Neural Network is one of data mining techniques being widely accepted in the business area due to its ability to learn and detect relationships among nonlinear variables. The ANN outperforms statistical regression models and also allows deeper analysis of large data sets, especially those that have the tendency to fluctuate within a short of time period. In this paper, we investigate the ability of Fuzzy logic and multilayer perceptron (MLP), which is a kind of the ANN, to tackle the financial time series stock forecasting problem. The proposed approaches were tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the comparison between those techniques is performed to examine their effectiveness.

scholarly journals Failure in Stock Price Prediction: A Comparison bettwen the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes of Existing Machine Learning Algorithms

2021 ◽  
Vol 16 (6) ◽  
Author(s):  
Ping Zhang ◽  
Jia-Yao Yang ◽  
Hao Zhu ◽  
Yue-Jie Hou ◽  
Yi Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Price ◽  
Financial Time Series ◽  
Learning Algorithms ◽  
Shape Feature ◽  
Curve Shape ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Financial Time

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

scholarly journals PERAMALAN HARGA SAHAM MENGGUNAKAN JARINGAN SYARAF TIRUAN SECARA SUPERVISED LEARNING DENGAN ALGORITMA BACKPROPAGATION

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Eko Riyanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Artificial Neural

Stock price prediction is useful for investors to see how the prospects of a company's stock investment in the future. Stock price prediction can be used to anticipate the deviation of stock prices. It can also helps investors in decision making. Artificial Neural Networks do not require mathematical models but data from problems to be solved. Information is conveyed through the data, and the Artificial Neural Network filters the information through training. Therefore, Artificial Neural Network is appropriate to solve the problem of stock price prediction.            Learning method that will be used to predict stock price is Supervised Learning with Backpropagation algorithm. With this algorithm, networks can be trained using stock price data from the previous time, classify it and adjust network link weight as new input and forecast future stock prices. By using ANN, time series prediction is more accurate. After analyzing the problem of stock price movement system, the writer can know the pattern of what variables will be taken for further insert into the stock price forecasting system.            This application can be used for stock price forecasting technique, so it will be useful for beginner investor as well as advanced investor as reference to invest in capital market. Implementing supervised learning backpropagation method will get accurate forecasting results more than 98%.Keyword - artificial neural network, stock, backpropagation.

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