A number of research had been carried out to forecast stock price based on technical indicators, which rely purely on historical stock price data. Nevertheless, their performance is not always satisfactory. In this paper, the effect of using hybrid market indicators of technical, fundamental indicators and experts opinion for stock price prediction is examined. Input variables extracted from th...

Changes in stock price will be influenced by many aspects of factors. When we are predicting stock price, it is difficult to build a determined mathematical model between stock prices and these complex factors. This paper first utilizes ε − SVM (ε − support vector machine) to build a stock price prediction model. By fitting the prediction error sequence, we find the law factors, which the predi...

With the development of global market and information network, international futures markets have become integrated. Price fluctuation of one futures market may cause related price fluctuation in another exchange. Many researches have shown that there is high correlation between international and domestic futures price fluctuation. However, quantitative research and the information system for a...

in general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. it is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. one could argue that these random changes act like noise which effects the deterministic variat...

The movement of stock prices is non-linear and complicated. In this study, we compared analyzed various neural network forecasting methods based on real problems related to price demand forecasting. We ultimately selected the LSTM (Long Short-Term Memory) [1] as traditional RNN’s long-term reliance improved by LSTM, which substantially enhances prediction accuracy stability. practicality method...

The application of AI techniques for stock price prediction leads to voluminous growth of wealth of investors with the advent of technology. Several prediction and estimations are coming up for almost all sectors of the market. Particularly any kind of stock price prediction is not at all possible without excessive data manipulation which can be done effectively only thru data mining. The syste...

In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squar...

Accurate and effective stock price prediction is appealing for investors due to the potential of obtaining a very high return. However, it is still a challenging task in the modern business world because of the complex, evolutionary, and nonlinear nature of stock market. Therefore, we proposed two hybrid models, which are Harmony Search (HS) based Extreme Learning Machine (ELM) that is denoted ...

A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and price spike predictions. The normal price module is a mixture of wavelet transform, linear AutoRegressive Integrated Moving Average (ARIMA) and ...