This research proposes a novel method for enhancing the accuracy of stock price prediction
by combining ensemble empirical mode decomposition (EEMD), ensemble convolutional …

It is generally known that public opinion and stock market dynamics are inextricably linked.
With the growth of social and web-based media, online platforms have emerged as a key …

Abstract Artificial Neural Network (ANN) techniques are often used for time-series data
forecasting, and Fuzzy Logic (FL) is integrated with the ANN to improve forecasting. This …

Stock market is a place where volatility is a major concern. At the same time, stock market
data is not consistent due to missing information on some trading days. Forecasting of stock …

Stock markets are a popular kind of financial markets because of the possibility of bringing
high revenues to their investors. To reduce risk factors for investors, intelligent and …

Due to external factors such as political influences, specific events and sentiment
information, stock prices exhibit randomness, high volatility and non-linear characteristics …

Time series (TS) modelling is an important area in the domain of statistics, as it enables us to
comprehend the dynamics underlying a particular phenomenon. In the spectrum of non …

Investors depend on various sources for decision-making in trading, with maximum profit
earning as the primary objective. A predictive model with experience is essential in …

In this paper a forecasting model for real estate stock returns and risks is developed and
tested with the data of German real estate companies from 1991 to 2021. In contrast to …

In this paper a forecasting model for real estate stock returns and risks is developed and
tested with the data of German real estate companies from 1991 to 2021. In contrast to …