A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising

Simulation and Assessment of Stock Market Forecasting Using Machine Learning Methodology

This paper explores the application of neural network-based machine learning methodologies for stock market forecasting, an area of significant interest due to its potential to yield high returns. The study employs deep learning models, particularly Long Short-Term Memory (LSTM) networks, recognized for their ability to process time series data and capture temporal dependencies that are crucial in understanding stock market behaviors. The methodology involves collecting extensive historical stock price data, including open, close, high, low prices, and volume traded. This data is preprocessed to normalize the values and convert them into a format suitable for LSTM networks. The neural network architecture is designed with multiple layers, including dropout layers to prevent overfitting, and is trained on a substantial dataset to predict future stock prices based on past patterns. The performance of the LSTM model is evaluated using metrics such as root mean squared error (RMSE) and mean absolute error (MAE), comparing its predictive accuracy with traditional statistical methods and simpler machine learning models. The results indicate that LSTM networks can significantly improve the accuracy of stock market forecasts, demonstrating the model's efficacy in capturing complex stock price movements and providing a reliable tool for investors and financial analysts. The study not only confirms the viability of using sophisticated machine learning techniques in financial markets but also opens avenues for further research into neural network optimizations for enhanced predictive performance

ANN in Financial Prediction

This paper focuses on the treatment of intelligent systems and their application in the financial area. Types of intelligent systems are numerous, but we will focus on those systems, which based on their ability to learn, are able to predict. The concept of inductive reasoning, how these systems learn and reason inductively, the role and their integration in financial services are some of the concepts that will be addressed. The second and the main part focuses on the application developed in the design of an artificial neural network for financial forecasts. Recognizing the need for better predictive models, not just traditional statistical model, we considered with interest the development of an application that will predict currency exchange rates, USD-ALL, given the time series of real data in years 1995-2012. We test some of the learning algorithms in our system and conclude that one of them is most suitable for this problem. This intelligent system reached to create a relational model of data, on the basis of which is able to output satisfactory results forecast. After the presentation of experimental results, the paper closes with a discussion on possible improvements that could be made in the future

ANN in Financial Prediction

This paper focuses on the treatment of intelligent systems and their application in the financial area. Types of intelligent systems are numerous, but we will focus on those systems, which based on their ability to learn, are able to predict. The concept of inductive reasoning, how these systems learn and reason inductively, the role and their integration in financial services are some of the concepts that will be addressed. The second and the main part focuses on the application developed in the design of an artificial neural network for financial forecasts. Recognizing the need for better predictive models, not just traditional statistical model, we considered with interest the development of an application that will predict currency exchange rates, USD-ALL, given the time series of real data in years 1995-2012. We test some of the learning algorithms in our system and conclude that one of them is most suitable for this problem. This intelligent system reached to create a relational model of data, on the basis of which is able to output satisfactory results forecast. After the presentation of experimental results, the paper closes with a discussion on possible improvements that could be made in the future

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks