PREDIKSI HARGA SAHAM MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION

In the stock market, stock price prediction is an important issue for the perpetrators of capital transactions to help making the right decision. Most traders have their own application software to predict the stock price so that it can be decided would buy the shares or sell them. By using a neural networks, prediction of stock prices can be done by using the backpropagation algorithm. Artificial neural networks can be used either to predict the level or price of the stock index, stock movement (trend), and the return earned on stocks. This study discusses the use of techniques Backpropagation Neural Network to predict the stock price closing (Close) in AKR Tbk (AKRA Corporindo) engaged in the petroleum, chemical,logistics, manufacturing and coal are simulated in Matlab. Of some testing done, the prediction results obtained are very close to the price actually with very small MSE value

Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory

STOCK MARKET PREDICTION USING ENSEMBLE OF GRAPH THEORY, MACHINE LEARNING AND DEEP LEARNING MODELS

Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random guess, they consider nothing but a proper selection of stocks and time interval in the experiments. In this project, a novel approach is proposed using graph theory. This approach leverages Spatio- temporal relationship information between different stocks by modeling the stock market as a complex network. This graph-based approach is used along with two techniques to create two hybrid models. Two different types of graphs are constructed, one from the correlation of the historical stock prices and the other is a causation-based graph constructed from the financial news mention of that stock over a period. The first hybrid model leverages deep learning convolutional neural networks and the second model leverages a traditional machine learning approach. These models are compared along with other statistical models and the advantages and disadvantages of graph-based models are discussed. Our experiments conclude that both graph-based approaches perform better than the traditional approaches since they leverage structural information while building the prediction model

Equity trend prediction with neural networks

This paper presents results of neural network based trend prediction for equity markets. Raw equity exchange data is pre-processed before being fed into a series of neural networks. The use of Self Organising Maps (SOM) is investigated as a data classification method to limit neural network inputs and training data requirements. The resulting primary simulation is a neural network that can prediction whether the next trading period will be, on average, higher or lower than the current. Combinations of pre-processing and feature extracting SOM’s are investigated to determine the more optimal system configuration