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Sector-specific financial forecasting with machine learning algorithm and SHAP interaction values

Ergenç, Cansu; Aktaş, Rafet - In: Financial internet quarterly 21 (2025) 1, pp. 42-66

This study examines the application of machine learning models to predict financial performance in various sectors, using data from 21 companies listed in the BIST100 index (2013-2023). The primary objective is to assess the potential of these models in improving financial forecast accuracy and...

Persistent link: https://www.econbiz.de/10015393631

Neural Networks for Credit Risk and xVA in a Front Office Pricing Environment

Frodé, Isabelle; Sambergs, Viktor; Zhu, Shengyao - 2022

We present a data-driven proof of concept model capable of reproducing expected counterparty credit exposures from market and trade data. The model has its greatest advantages in quick single-contract exposure evaluations that could be used in front office xVA solutions. The data was generated...

Persistent link: https://www.econbiz.de/10013405380

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

Whatever it takes to understand a central banker : embedding their words using neural networks

Baumgärtner, Martin; Zahner, Johannes - 2023 - This draft: December 14, 2023

Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we...

Persistent link: https://www.econbiz.de/10014443189

CDS risk premia forecasting with multi-featured deep RNNs : an application on BR[I]CS countries

Kutuk, Yasin - In: Borsa Istanbul Review 23 (2023) 6, pp. 1380-1398

Using state-of-the-art recurrent neural network architectures, this study attempts to predict credit default swap risk premia for BR[I]CS countries as accurately as possible. In the time series setting, these recurrent neural networks are ELMAN, NARX, GRU, and LSTM RNNs, considering local and...

Persistent link: https://www.econbiz.de/10014447473

Hybridising neurofuzzy model the seasonal autoregressive models for electricity price forecasting on Germany's spot market

Paraschiv, Dorel Mihai; Bălășoiu, Narciz; Ben Amor, … - In: Amfiteatru economic : an economic and business research … 25 (2023) 63, pp. 463-478

Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have...

Persistent link: https://www.econbiz.de/10014464238

Topological tail dependence : evidence from forecasting realized volatility

Souto, Hugo Gobato - In: The Journal of finance and data science : JFDS 9 (2023), pp. 1-26

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory … behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure … of this study provide evidence that the predictions drawn from the Topological Tail Dependence Theory are correct and …

Persistent link: https://www.econbiz.de/10014514075

Intelligent Stock Prediction : A Neural Network Approach

Shahrour, Mohamad Hassan; Dekmak, Mostafa - 2022

Ever since the existence of financial markets, predicting stocks’ movement has been crucial for investors in order to increase their investment returns. Despite the plethora of research, the outstanding literature provides mixed results concerning the choice of model. Are Artificial...

Persistent link: https://www.econbiz.de/10014244977

Tourism Demand Forecasting with Different Neural Networks Models

Claveria, Oscar - 2014

This paper aims to compare the performance of different Artificial Neural Networks techniques for tourist demand forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis function and an Elman network. We also evaluate the...

Persistent link: https://www.econbiz.de/10013045968

A Multivariate Neural Network Approach to Tourism Demand Forecasting

Claveria, Oscar - 2014

This study compares the performance of different Artificial Neural Networks models for tourist demand forecasting in a multiple-output framework. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron network, a radial basis function network and an...

Persistent link: https://www.econbiz.de/10013045969