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Real-Time Macro Information and Bond Return Predictability : Does Deep Learning Help?

Feng, Guanhao; Fulop, Andras; Li, Junye - 2021

This paper examines whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, when using pure real-time macro information alone, we...

Persistent link: https://www.econbiz.de/10013250220

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

Improving the Prediction of Asset Returns With Machine Learning by Using a Custom Loss Function

Dessain, Jean - 2021

predicting asset returns, not all prediction errors are equal in terms of impact on the efficiency of the algorithm. Indeed, some … develop several custom loss functions considering the asymmetry in the objective of the algorithm. (b) We compare these custom …

Persistent link: https://www.econbiz.de/10013312657

Stock Returns Forecasting via Machine Learning with Average Windows Forecasts

Ho, Tsung-Wu; LIN, Ya-chi - 2022

Stock returns predictability has been a long-standing topic in the literature on financial economics. Developments in prediction technology have facilitated the wide use of machine learning techniques, which motivates our study of whether stock returns predictability can be improved using...

Persistent link: https://www.econbiz.de/10013313206

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

Forecasting the Market Risk Premium with Artificial Neural Networks

Oikonomikou, Leoni Eleni - 2016

This paper aims to forecast the Market Risk premium (MRP) in the US stock market by applying machine learning techniques, namely the Multilayer Perceptron Network (MLP), the Elman Network (EN) and the Higher Order Neural Network (HONN). Furthermore, Univariate ARMA and Exponential Smoothing...

Persistent link: https://www.econbiz.de/10012997285

Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns

Chen, Shiyi - 2016

Motivated by recurrent neural networks, this paper proposes a recurrent support vector regression (SVR) procedure to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent SVR based ARMA model is compared with five...

Persistent link: https://www.econbiz.de/10012997751