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A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing
Mehtab, Sidra; Sen, Jaydip - 2020
Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy...
Persistent link: https://www.econbiz.de/10014094821
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Price dividend ratio and long-run stock returns : a score driven state space model
Delle Monache, Davide; Petrella, Ivan; Venditti, Fabrizio - 2020
estimation of the state vector and of the time-varying parameters. We use this method to study the timevarying relationship …
Persistent link: https://www.econbiz.de/10012156426
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Price Dividend Ratio and Long-Run Stock Returns : A Score Driven State Space Model
Delle Monache, Davide - 2020
estimation of the state vector and of the time-varying parameters. We use this method to study the time-varying relationship …
Persistent link: https://www.econbiz.de/10012842441
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Forecasting Stock Returns with Large Dimensional Factor Models
Giovannelli, Alessandro - 2020
We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well known factor model with a static representation of the common components with a more general model...
Persistent link: https://www.econbiz.de/10012854353
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Forecasting Oil Prices With Penalized Regressions, Variance Risk Premia and Google Data
Lycheva, Maria; Mironenkov, Alexey; Kurbatskii, Alexey; … - 2023
This paper investigates whether augmenting models with the variance risk premium (VRP) and Google search data improves the quality of the forecasts for real oil prices. We considered a time sample of monthly data from 2007 to 2019 that includes several episodes of high volatility in the oil...
Persistent link: https://www.econbiz.de/10014349277
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Factor GARCH-ITO Models for High-Frequency Data with Application to Large Volatility Matrix Prediction
Kim, Donggyu - 2017
Several novel large volatility matrix estimation methods have been developed based on the high-frequency financial data … matrix and facilitates estimation of large volatility matrices. However, for predicting future volatility matrices, these … a quasi-maximum likelihood estimation method for the parameter of the factor GARCH-Ito model. We also apply it to …
Persistent link: https://www.econbiz.de/10012941598
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Sparse Multivariate Modeling for Stock Returns Predictability
Bernardi, Mauro; Bianchi, Daniele; Bianco, Nicolas - 2021
We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with …
Persistent link: https://www.econbiz.de/10013239660
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Predicting Stock Return Variance in a Large Cross Section
Wu, Liuren; Xu, Yaofei - 2022
The accuracy of variance prediction depends on both the specification and the accuracy of parameter estimation. To … pooling estimation approach that balances the need for reducing estimation errors and capturing dynamics variation both across …
Persistent link: https://www.econbiz.de/10013403955
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Big Data Approach to Realised Volatility Forecasting Using HAR Model Augmented With Limit Order Book and News
Rahimikia, Eghbal - 2020
jumps and vice versa. On the other hand, increasing the estimation sample size causes statistically significant degradation …
Persistent link: https://www.econbiz.de/10012824203
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Measuring High-Frequency Causality between Returns, Realized Volatility and Implied Volatility
Dufour, Jean-Marie - 2011
theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On …
Persistent link: https://www.econbiz.de/10013128856