scholarly journals Use of Artificial Neural Networks in Forecasting of Financial Time Series of High Frequencies with Stock Exchange Quotations as an Example

2010 ◽  
Vol 19 (-1) ◽  
pp. 79-97 ◽  
Author(s):  
Marcin Wasik
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Stock Exchange ◽  
Financial Time Series ◽  
High Frequencies ◽  
Financial Time ◽  
Artificial Neural

MEMORY IN FINANCIAL TIME SERIES: FROM INVESTOR BEHAVIOR TO ARTIFICIAL NEURAL NETWORKS

2012 ◽  
Author(s):  
M. TERESA SORROSAL-FORRADELLAS ◽  
M. GLÒRIA BARBERÀ-MARINÉ ◽  
LISANA B. MARTINEZ ◽  
MARÍA-JOSÉ GARBAJOSA-CABELLO
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Investor Behavior ◽  
Financial Time ◽  
Artificial Neural

scholarly journals Financial time series forecasting using Artificial Neural Networks

2019 ◽  
Vol 15 (1) ◽  
pp. 105-122
Author(s):  
Roberto Gallardo Del Ángel
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Financial Time ◽  
Artificial Neural

Este documento contiene una predicción financiera utilizando Redes Neuronales Artificiales. Hacemos nuestro análisis utilizando el algoritmo de Backpropagation tradicional y luego Backpropagation Resiliente para estimar los pesos en las redes. El uso del algorithm de Bacpropagation Resiliente permite resolver el problema de la determinación de la tasa de aprendizaje. Ambos algoritmos son bastante consistentes y arrojan predicciones similares. Analizamos seis índices principales de los mercados bursátiles de Europa, Asia y América del Norte para generar índices de aciertos que puedan compararse entre mercados. Usamos precios de cierre diarios para construir una variable de dependiente para dirigir el aprendizaje (aprendizaje supervisado) y una matriz de variables de características construidas utilizando indicadores de análisis técnico. El rango de datos de la serie de tiempo va desde Enero de 2000 a Junio de 2019, un periodo de grandes fluctuaciones debido a mejoras en la tecnología de la información y una alta movilidad de capital. En lugar de la predicción en sí misma, el objetivo científico es evaluar la importancia relativa de las variables independientes que permiten la predicción. Utilizamos dos medidas de contribución utilizadas en la literatura para evaluar la relevancia de cada variable para los seis mercados financieros analizados. Descubrimos que estas medidas no siempre son consistentes, por lo que construimos una medida de contribución simple que le da a cada peso una interpretación geométrica. Proporcionamos algunas pruebas de que la tasa de cambio (ROC) es la herramienta de predicción más útil para cuatro índices generales, con las excepciones siendo el índice Hang Sheng y EU50, en donde el fastK es el más destacado.

scholarly journals Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

FinTech ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 47-62
Author(s):  
Sanjib Kumar Nayak ◽  
Sarat Chandra Nayak ◽  
Subhranginee Das
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Optimal Parameters ◽  
Financial Time ◽  
Good Investment ◽  
Machine Learning Approach ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Artificial Neural

Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.

scholarly journals ESTIMATING STOCK RETURNS USING ARTIFICIAL NEURAL NETWORKS: AN EXPERIMENTAL DESIGN WITH AN EVIDENCE FROM IRAQ STOCK EXCHANGE

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Mohammed H Adnan ◽  
Mustafa Muneer Isma’eel
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Markets ◽  
Stock Returns ◽  
Stock Exchange ◽  
Back Propagation ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Propagation Network

The research aims to estimate stock returns using artificial neural networks and to test the performance of the Error Back Propagation network, for its effectiveness and accuracy in predicting the returns of stocks and their potential in the field of financial markets and to rationalize investor decisions. A sample of companies listed on the Iraq Stock Exchange was selected with (38) stock for a time series spanning (120) months for the years (2010_2019). The research found that there is a weakness in the network of Error Back Propagation training and the identification of data patterns of stock returns as individual inputs feeding the network due to the high fluctuation in the rates of returns leads to variation in proportions and in different directions, negatively and positively.

Sign in / Sign up

Export Citation Format

Share Document