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Forecasting the term structure of crude oil futures prices with neural networks

Baruník, Jozef; Malinská, Barbora - 2015

The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed...

Persistent link: https://www.econbiz.de/10011378719

Measuring tanker market future risk with the use of FORESIM

Lyridis, Dimitrios; Manos, Nikolaos; Zacharioudakis, … - In: Spoudai : journal of economics and business 67 (2017) 1, pp. 38-53

Future market risk has always been a critical question in decision support processes. FORESIM is a simulation technique that models shipping markets (developed recently). In this paper we present the application of this technique in order to obtain useful information regarding future values of...

Persistent link: https://www.econbiz.de/10011661763

Predicting abnormal stock return volatility using textual analysis of news : a meta-learning approach

Myšková, Renáta; Hájek, Petr; Olej, Vladimír - In: Amfiteatru economic : an economic and business research … 20 (2018) 47, pp. 185-201

Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal...

Persistent link: https://www.econbiz.de/10011881761

Cascading logistic regression onto gradient boosted decision trees to predict stock market changes using technical analysis

Zhou, Feng; Zhang, Qun; Sornette, Didier; Jiang, Liu - 2018

In the data mining and machine learning fields, forecasting the direction of price change can be generally formulated as a supervised classfii cation. This paper attempts to predict the direction of daily changes of the Nasdaq Composite Index (NCI) and of the Standard & Poor's 500 Composite...

Persistent link: https://www.econbiz.de/10011900252

Intraday Stock Predictability Everywhere

Liu, Fred; Stentoft, Lars - 2023

With approximately 900 million observations we conduct, to our knowledge, the largest study ever of intraday stock return predictability using machine learning techniques finding consistent out-of-sample predictability across market, sector, and individual stock returns at various time horizons....

Persistent link: https://www.econbiz.de/10014349804

Unveiling the diverse efficacy of artificial neural networks and logistic regression : a comparative analysis in predicting financial distress

Sabek, Amine - In: Croatian review of economic, business and social … 9 (2023) 1, pp. 16-32

model outperformed the LR model in predicting financial distress with a classification accuracy of 100%. On the other hand …, the LR model outperformed the FFDTDNN with a classification accuracy of 83.33%. Therefore, it can be asserted that ANNs …

Persistent link: https://www.econbiz.de/10014372938

High-frequency CSI300 futures trading volume predicting through the neural network

Xu, Xiaojie; Zhang, Yun - In: Asian journal of economics and banking : AJEB 8 (2024) 1, pp. 26-53

Purpose - For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch...

Persistent link: https://www.econbiz.de/10014497016

Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting

Boubaker, Heni - 2020

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric...

Persistent link: https://www.econbiz.de/10012827248

Bankruptcy Risk Assessment in Corporate Lending Based on Hybrid Neural Networks and Fuzzy Models

Gorbatkov, Stanislav - 2020

The purpose of this article is the presentation of a novel and unconventional algorithm for bankruptcy risk management … unconventional algorithm for diagnosing corporate bankruptcy stages. This algorithm is based on the application of a system-wide law …. This algorithm has been tested on a series of experimental observations of 30 agricultural enterprises in the Sterlitamak …

Persistent link: https://www.econbiz.de/10012830011

Oil Price Forecasting Using Gene Expression Programming and Artificial Neural Networks

Mostafa, Mohamed M. - 2018

This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are...

Persistent link: https://www.econbiz.de/10012910387