Forecasting Stock Prices Volatility with Information (An ANN-GARCH Hybrid Approach)

This study compares the forecast performance of volatilities between three models for forecasting stock returns: GARCH, hybrid ANN-GARCH with only GARCH output as the ANN input, and a hybrid ANN-GARCH with information. Through the extensive evaluation, the research found out that the hybrid ANN-GARCH model with information outperforms the other two models in terms of forecasting accuracy and predictive power. This study is set to find out the improvement performance of the hybrid ANN-GARCH with information vis a vis the Univariate GARCH Keywords: Stock price forecasting, GARCH, Artificial Neural Network DOI: 10.7176/RJFA/14-17-04 Publication date:September 30th 2023

Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network

An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding opera- tions such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to im- prove the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those al- gorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assess- ment of the market ris

Machine and deep learning applications for improving the measurement of key indicators for financial institutions: stock market volatility and general insurance reserving risk

Esta tesis trata de lograr mejoras en los modelos de estimación de los riesgo financieros y actuariales a través del uso de técnicas punteras en el campo del aprendizaje automático y profundo (machine y deep learning), de manera que los modelos de riesgo generen resultados que den un mejor soporte al proceso de toma de decisiones de las instituciones financieras. Para ello, se fijan dos objetivos. En primer lugar, traer al campo financiero y actuarial los mecanismos más punteros del campo del aprendizaje automático y profundo. Los algoritmos más novedosos de este campo son de amplia aplicación en robótica, conducción autónoma o reconocimiento facial, entre otros. En segundo lugar, se busca aprovechar la gran capacidad predictiva de los algoritmos anteriormente adaptados para construir modelos de riesgo más precisos y que, por tanto, sean capaces de generar resultados que puedan dar un mejor soporte a la toma de decisiones de las instituciones financieras. Dentro del universo de modelos de riesgos financieros, esta tesis se centra en los modelos de riesgo de renta variable y reservas de siniestros. Esta tesis introduce dos modelos de riesgo de renta variable y otros dos de reservas. Por lo que se refiere a la renta variable, el primero de los modelos apila algoritmos tales como redes neuronales, bosques aleatorios o regresiones aditivas múltiples con árboles con el objetivo de mejorar la estimación de la volatilidad y, por tanto, generar modelos de riesgo más precisos. El segundo de los modelos de riesgo adapta al mundo financiero y actuarial los Transformer, un tipo de red neuronal que, debido a su alta precisión, ha apartado al resto de algoritmos en el campo del procesamiento del lenguaje natural. Adicionalmente, se propone una extensión de esta arquitectura, llamada Multi-Transformer y cuyo objetivo es mejorar el rendimiento del algoritmo inicial mediante el ensamblaje y aleatorización de los mecanismos de atención. En lo relativo a los dos modelos de reservas introducidos por esta tesis el primero de ellos trata de mejorar la estimación de reservas y generar modelos de riesgo más precisos apilando algoritmos de aprendizaje automático con modelos de reservas basados en estadística bayesiana y Chain Ladder. El segundo modelo de reservas trata de mejorar los resultados de un modelo de uso habitual, como es el modelo de Mack, a través de la aplicación de redes neuronales recurrentes y conexiones residuales

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

latent Dirichlet allocation method-based nowcasting approach for prediction of silver price

Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice