Authors present the stock price prediction algorithm by using Bayesian network. The present algorithm uses the network twice. First, the network is determined from the daily stock price and then, it is applied for predicting the daily stock price which was already observed. The prediction error is evaluated from the daily stock price and its prediction. Second, the network is determined again f...

‎Financial market modeling and prediction is a difficult problem and drastic changes of the price causes nonlinear dynamic that makes the price prediction one of the most challenging tasks for economists‎. ‎Since markets always have been interesting for traders‎, ‎many traders with various beliefs are highly active in a market‎. ‎The competition among two agents of traders‎, ‎namely trend follo...

This paper explores the question of how house prices in five different counties are affected by housing characteristics (both internally, such as number of bathrooms, bedrooms, etc. and externally, such as public schools’ scores or the walkability score of the neighborhood). Using data from sold houses listed on Zillow, Trulia and Redfin, three prominent housing websites, this paper utilizes bo...

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...