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Brazilian Selic Rate Forecasting with Deep Neural Networks
Artificial intelligence has shortened edges in many areas, especially the economy, to support long-term and accurate forecasting of financial...
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Portfolio Optimization with Prediction-Based Return Using Long Short-Term Memory Neural Networks: Testing on Upward and Downward European Markets
In recent years, artificial intelligence has helped to improve processes and performance in many different areas: in the field of portfolio...
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The Impact of Foreign Stock Market Indices on Predictions Volatility of the WIG20 Index Rates of Return Using Neural Networks
The paper investigates the issue of volatility of stock index returns on the Warsaw Stock Exchange (WIG20 index returns volatility). The purpose of...
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GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks
This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that...
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Evaluating the Performance of Metaheuristic Based Artificial Neural Networks for Cryptocurrency Forecasting
The irregular movement of cryptocurrency market makes effective price forecasting a challenging task. Price fluctuations in cryptocurrencies often...
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Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks
Bitcoin is a volatile financial asset that runs on a decentralized peer-to-peer Blockchain network. Investors need accurate price forecasts to...
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Designing Ensemble-Based Models Using Neural Networks and Temporal Financial Profiles to Forecast Firms’ Financial Failure
Most bankruptcy prediction models that have been analyzed in the literature rely solely on variables that measure firms’ financial health over a...
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Spatiotemporal localisation patterns of technological startups: the case for recurrent neural networks in predicting urban startup clusters
More attention should be dedicated to intra-urban localisation decisions of technological startups. While the general trend of innovative companies...
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Locally-coherent multi-population mortality modelling via neural networks
This manuscript proposes an approach for large-scale mortality modelling and forecasting with the assumption of locally-coherence of the mortality...
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The response of household debt to COVID-19 using a neural networks VAR in OECD
This paper investigates responses of household debt to COVID-19-related data like confirmed cases and confirmed deaths within a neural networks panel...
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Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets
This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and...
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Applying Artificial Neural Networks and Arima Models to Analyze the Impact of ICT on the Economic Growth in Turkey
Information and communications technology (ICT) has a significant impact on economic growth. In this study, by using artificial neural networks (ANN)...
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Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products
Forecasting commodity prices is a vital issue to a wide spectrum of market participants and policy makers in the metal sector. In this work, the...
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Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks
Economic growth is crucial to improve standards of living, prosperity, and welfare. R &D and knowledge spillovers can offset the diminishing returns...
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Option Pricing Based on the Residual Neural Network
We employ an innovative deep learning method to price options quickly and accurately. Specifically, we construct the Residual Neural Network model...
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Forecasting Stock Indices: Stochastic and Artificial Neural Network Models
In recent years, there has been a bloom in the stock investors due to availability of various platforms that have provided an opportunity even for...
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The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting
Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of...
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Innovation, income, and waste disposal operations in Korea: evidence from a spectral granger causality analysis and artificial neural networks experiments
The aim of this paper is to assess the causal relationship among innovation in environment-related technologies, per capita income, and three major...
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Elitist-opposition-based artificial electric field algorithm for higher-order neural network optimization and financial time series forecasting
This study attempts to accelerate the learning ability of an artificial electric field algorithm (AEFA) by attributing it with two mechanisms:...
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Use of Econometric Predictors and Artificial Neural Networks for the Construction of Stock Market Investment Bots
The gradual replacement of human operators by investor bots in financial markets has changed the way assets are traded on stock markets. The use of...
