Artificial neural networks in business: Two decades of research
In recent two decades, artificial neural networks have been extensively used in many
business applications. Despite the growing number of research papers, only few studies …
business applications. Despite the growing number of research papers, only few studies …
Demand forecasting for fashion products: A systematic review
Fashion is one of the most challenging categories for forecasting demand. Our study
provides a systematic literature review of the different forecasting techniques used in the …
provides a systematic literature review of the different forecasting techniques used in the …
A deep learning framework for financial time series using stacked autoencoders and long-short term memory
The application of deep learning approaches to finance has received a great deal of
attention from both investors and researchers. This study presents a novel deep learning …
attention from both investors and researchers. This study presents a novel deep learning …
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies
We offer a systematic analysis of the use of deep learning networks for stock market analysis
and prediction. Its ability to extract features from a large set of raw data without relying on …
and prediction. Its ability to extract features from a large set of raw data without relying on …
Forecasting daily stock market return using dimensionality reduction
In financial markets, it is both important and challenging to forecast the daily direction of the
stock market return. Among the few studies that focus on predicting daily stock market …
stock market return. Among the few studies that focus on predicting daily stock market …
Predicting the daily return direction of the stock market using hybrid machine learning algorithms
Big data analytic techniques associated with machine learning algorithms are playing an
increasingly important role in various application fields, including stock market investment …
increasingly important role in various application fields, including stock market investment …
Grey system theory-based models in time series prediction
Being able to forecast time series accurately has been quite a popular subject for
researchers both in the past and at present. However, the lack of ability of conventional …
researchers both in the past and at present. However, the lack of ability of conventional …
[HTML][HTML] Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder—DeepBreath
The process of continuously reallocating funds into financial assets, aiming to increase the
expected return of investment and minimizing the risk, is known as portfolio management. In …
expected return of investment and minimizing the risk, is known as portfolio management. In …
A combination of artificial neural network and random walk models for financial time series forecasting
Properly comprehending and modeling the dynamics of financial data has indispensable
practical importance. The prime goal of a financial time series model is to provide reliable …
practical importance. The prime goal of a financial time series model is to provide reliable …
Forecasting S&P 500 index using artificial neural networks and design of experiments
The main objective of this research is to forecast the daily direction of Standard & Poor's 500
(S&P 500) index using an artificial neural network (ANN). In order to select the most …
(S&P 500) index using an artificial neural network (ANN). In order to select the most …