Financial time series forecasting is a popular application of machine learning methods. Previous studies report that advanced forecasting methods predict price changes in financial markets with high accuracy and that profit can be made trading on these predictions. However, financial economists point to the informational efficiency of financial markets, which questions price predictability and ...

Stock price forecasting has been mostly realized using quantitative information. However, recent studies have demonstrated that sentiment information hidden in corporate annual reports can be successfully used to predict short-run stock price returns. Soft computing methods, like neural networks and support vector regression, have shown promising results in the forecasting of stock price due to...

This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price f...

STOCK market price behavior has been studied extensively. It is influenced by a myriad of factors, including political and economic events, among others, and is a complex nonlinear time-series problem. Traditionally, stock price forecasting is performed based on technical analysis, which focuses on price action, which is the process of finding patterns in price history. More recently, research ...

Palladium is an element of PGM group that has significant physical properties. This leads to more attention to this metal. Due to vast applications of palladium in industry and its usage in jewelry, its price plays an important role in economic. Therefore, forecasting its price is crucial subject in economic and engineering design. This paper proposes the model GM(1,1) to predict the Palladium ...

Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent years, the classic estimation methods cannot correctly estimate the volatility. In order to...

Numerous research have been done to predict cryptocurrency prices since affect global economic and monetary systems. However, investigations using linear connection approaches technical analysis indicators frequently fall short of providing an explanation for changes in the pattern BitCoin pricing. This paper is proposed study time-varying parameters with long short-term memory (LSTM). The inve...