scholarly journals Stock price prediction using artificial neural network integrated moving average

2020 ◽  
Vol 1641 ◽  
pp. 012028
Author(s):  
I Suryani ◽  
D C P Buani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Price ◽  
Moving Average ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Artificial Neural

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.

scholarly journals Comparison of Stock Price Prediction Models using Pre-trained Neural Networks

2021 ◽  
Vol 3 (2) ◽  
pp. 122-134
Author(s):  
C Anand
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stock Price ◽  
Linear Models ◽  
Moving Average ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Data Prediction ◽  
Comparison Results ◽  
Auto Regressive

Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures inclusive of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used in this paper for stock price prediction of an organization by using the previously available stock prices. The National Stock Exchange (NSE) of India dataset is used for training the model with day-wise closing price. Data prediction is performed for a few sample companies selected on a random basis. Based on the comparison results, it is evident that the existing models are outperformed by CNN. The network can also perform stock predictions for other stock markets despite being trained with single market data as a common inner dynamics that has been shared between certain stock markets. When compared to the existing linear models, the neural network model outperforms them in a significant manner, which can be observed from the comparison results.

scholarly journals Comparison of Financial Models for Stock Price Prediction

2020 ◽  
Vol 13 (8) ◽  
pp. 181
Author(s):  
Mohammad Rafiqul Islam ◽  
Nguyet Nguyen
Keyword(s):  
Neural Network ◽  
Stochastic Process ◽  
Predictive Models ◽  
Stock Price ◽  
Financial Models ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Neural Network

Time series analysis of daily stock data and building predictive models are complicated. This paper presents a comparative study for stock price prediction using three different methods, namely autoregressive integrated moving average, artificial neural network, and stochastic process-geometric Brownian motion. Each of the methods is used to build predictive models using historical stock data collected from Yahoo Finance. Finally, output from each of the models is compared to the actual stock price. Empirical results show that the conventional statistical model and the stochastic model provide better approximation for next-day stock price prediction compared to the neural network model.

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 Optimasi Parameter Input pada Artificial Neural Network untuk Meningkatkan Akurasi Prediksi Indeks Harga Saham

2021 ◽  
Vol 10 (2) ◽  
pp. 211-216
Author(s):  
Ignatius Wiseto Prasetyo Agung
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Price ◽  
Technology Development ◽  
Moving Average ◽  
Stock Index ◽  
Accurate Analysis ◽  
Input Parameters ◽  
Network Simulations ◽  
Artificial Neural

Stock trading is one of the businesses that has been done worldwide. In order to gain the maximum profit, accurate analysis is needed, so a trader can decide to buy and sell stock at the perfect time and price. Conventionally, two analyses are employed, namely fundamental and technical.  Technical analysis is obtained based on historical data that is processed mathematically. Along with technology development, stock price analysis and prediction can be performed with the help of computational algorithms, such as machine learning. In this research, Artificial Neural Network simulations to produce accurate stock price predictions were carried out. Experiments are performed by using various input parameters, such as moving average filters, in order to produce the best accuracy. Simulations are completed with stock index datasets that represent three continents, i.e. NYA (America, USA), GDAXI (Europe, Germany), and JKSE (Asia, Indonesia). This work proposes a new method, which is the utilization of input parameters combinations of C, O, L, H, MA-5 of C, MA-5 of O, and the average of O C prices. Furthermore, this proposed scheme is also compared to previous work done by Khorram et al, where this new work shows more accurate results.

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