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Can Deep Neural Networks Outperform Fama-MacBeth Regression and Other Supervised Learning Approaches in Stock Returns Prediction With Asset-Pricing Factors?

Teng, Huei-Wen; Li, Yu-Hsien - 2022

In asset pricing, most studies focus on finding new factors such as macroeconomic factors or firm characteristics to explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in the field of finance and computer science. This paper...

Persistent link: https://www.econbiz.de/10014235825

Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns

Chen, Shiyi - 2017

Persistent link: https://www.econbiz.de/10012966267

Support vector regression based GARCH model with application to forecasting volatility of financial returns

Chen, Shiyi (contributor); Jeong, Kiho (contributor); … - 2008

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

Persistent link: https://www.econbiz.de/10003636113

Uncertainty due to infectious diseases and stock-bond correlation

Gillas, Konstantinos Gkillas; Konstantatos, Christoforos; … - In: Econometrics : open access journal 9 (2021) 2, pp. 1-18

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock-bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of...

Persistent link: https://www.econbiz.de/10012504028

Package CovRegpy : Regularised Covariance Regression and Forecasting in Python

van Jaarsveldt, Cole; Peters, Gareth; Ames, Matthew; … - 2023

This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py,...

Persistent link: https://www.econbiz.de/10014253907

Forecasting stock market realized variance with Echo State Neural Networks

Fičura, Milan - In: European Financial and Accounting Journal 12 (2017) 3, pp. 145-156

results show that asymmetric models generally outperform symmetric ones, indicating that a correlation between volatility and … returns plays an important role for volatility forecasting. Additionally, models utilizing a logarithmic transformation of the …

Persistent link: https://www.econbiz.de/10011937427

PREDICTING STOCK RETURN AND VOLATILITY WITH MACHINE LEARNING AND ECONOMETRIC MODELS : A COMPARATIVE CASE STUDY OF THE BALTIC STOCK MARKET

Nõu, Anders; Lapitskaya, Darya; Eratalay, Mustafa Hakan; … - 2022

For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to...

Persistent link: https://www.econbiz.de/10013305881

Stock Price Forecasting and Hypothesis Testing Using Neural Networks

Varaku, Kerda - 2020

In this work we use Recurrent Neural Networks and Multilayer Perceptrons, to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market...

Persistent link: https://www.econbiz.de/10012834485

A multivariate Kernel approach to forecasting the variance covariance of stock market returns

Becker, Ralf; Clements, Adam; O'Neill, Robert - In: Econometrics : open access journal 6 (2018) 1, pp. 1-27

This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of...

Persistent link: https://www.econbiz.de/10011823257

Forecasting Indian Stock Market Index Using Singular Spectrum Analysis : A Comparison with ARIMA and Artificial Neural Network

Shukla, Suwarna - 2017

In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques...

Persistent link: https://www.econbiz.de/10012947889