Many researchers attempt to accurately predict stock price trends using technologies such
as machine learning and deep learning to achieve high returns in the stock market …

Real-time market prediction tool tracking public opinion in specialized newsgroups and
informative market data persuades investors of financial markets. Previous works mainly …

Abstract We propose a Multi-Period Multi-Objective Portfolio Optimization model (MPMOPO).
We used deep-learning approach to predict future behavior of stock returns. We consider …

Stock market timing is regarded as a challenging task of financial prediction. An accurate
prediction of stock trend can yield great profits for investors. At present, recurrent neural …

In this paper we present a combinatorial nonlinear technical indicator approach for the
identification of appropriate combinations of stock technical indicators as inputs in non-linear …

Movements in the volatility index of the Indian economy are influenced by global volatility
indices (fear index). This study evaluates the influence of various global implied volatility …

The main objective of this analysis is to investigate how varying the forecast horizon and the
input window length for calculating technical indicators affects the predictive performance of …

In this paper, a machine learning approach is proposed to predict the next day's stock prices.
The methodology involves comprehensive data collection and feature generation, followed …

A binary time series can be transformed into a Branch Length Similarity (BLS) entropy profile
by being mapped to a circumference called a time-circle. In this study, we explored how …

In this study, the branch-length similarity entropy profile is estimated by map** the time-
series signal to the circumference of the time circle, and the self-similarity is defined based …