With this method, a relationship can be found between fundamental and technical indicators. CNN is also used to build market features in a hierarchical way. Experiments on dataset of Taiwan and Indonesian stock market have shown a promising result. In, CNN is used to analyze the candlestick chart of a stock market, to find the patterns inside stock market, and to predict the future movements of it. It is proved that CNN helps reduce training time and model complexity. A CNN based architecture is proposed to select features in different indicators, price, and temporal information from Borsa Istanbul 100 stocks. CNN with its advantage of feature extraction is widely used in the stock price data. In recent years, deep learning model such as Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) are increasingly used for the stock price prediction. Experiment results show that the framework can improve the rate of return and get a better prediction performance. In, a framework based on genetic algorithm is proposed which uses both feature selection and support vector machine to optimize time window of the attributes. The method they proposed has fast convergence and is efficient in stock price prediction. proposed a new method combining SVR and Grey Relation Analysis for short trend stock market prediction which takes advantage of SVR's ability in analyzing small-size and multidimensional samples. In, SVR and Hodrick–Prescott filter are combined together in order to get better result in prediction of the stock price. In, a framework is proposed to apply SVR strategy to predict the stock price. It has many applications in stock forecasting. Another important machine learning technique, Support Vector Regression (SVR), is a regression model that can show great ability in predicting future data based on historical data. In, GARCH is combined with Artificial Neural Networks (ANNs), which is far better than GARCH alone in predicting market volatility in Latin America. With the development of machine learning, some of them show great capability in stock prediction by virtue of its nonlinear mapping and strong generalization ability. Those kinds of models can achieve significant effect in the short-term prediction. Parametric statistical models, such as Autoregressive Integrated Moving Average (ARIMA) model and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), are used for stock price prediction. At early stage, the prediction of financial asset price is mainly based on the research of stock behavior. Traditional economic models and machine learning algorithms have many practical applications in stock price forecasting. These approaches consist of discovering a suitable pattern from market data and capturing the preferable moment for investment decisions. Thus, technical and quantitative methods have been utilized by investors as an attempt to forecast asset price movement. However, with the highly volatile and nonstationary nature of market, it is hard to predict the future price of stock properly. Therefore, an accurate prediction of stocks market index is very helpful for investors to get more profit. On the other hand, traders are likely to refrain from buying a stock whose value is expected to fall in the future. The investors are more likely to buy the stocks whose value is expected to increase in the future. Forecasting future price of a financial asset, such as a stock, is essential for investors as it can reduce the risk of decision-making by appropriately determining the future movement of an investment asset.

Olli stock forecast