Forecasting financial time series with Boltzmann entropy through neural networks

Neural networks have recently been established as state-of-the-art in forecasting financial time series. However, many studies show how one architecture, the Long-Short Term Memory, is the most widespread in financial sectors due to its high performance over time series. Considering some stocks traded in financial markets and a crypto ticker, this paper tries to study the effectiveness of the Boltzmann entropy as a financial indicator to improve forecasting, comparing it with financial analysts’ most commonly used indicators. The results show how Boltzmann’s entropy, born from an Agent-Based Model, is an efficient indicator that can also be applied to stocks and cryptocurrencies alone and in combination with some classic indicators. This critical fact allows obtaining good results in prediction ability using Network architecture that is not excessively complex

Forecasting and modelling the VIX using Neural Networks

This study investigates the volatility forecasting ability of neural network models. In particular, we focus on the performance of Multi-layer Perceptron (MLP) and the Long Short Term (LSTM) Neural Networks in predicting the CBOE Volatility Index (VIX). The inputs into these models includes the VIX, GARCH(1,1) fitted values and various financial and macroeconomic explanatory variables, such as the S&P 500 returns and oil price. In addition, this study segments data into two sub-periods, namely a Calm and Crisis Period in the financial market. The segmentation of the periods caters for the changes in the predictive power of the aforementioned models, given the dierent market conditions. When forecasting the VIX, we show that the best performing model is found in the Calm Period. In addition, we show that the MLP has more predictive power than the LSTM

A Survey of Forex and Stock Price Prediction Using Deep Learning

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising

Pond Energy Dynamics, Evaporation Rate and Ensemble Deep Learning Evaporation Prediction: Case Study of the Thomas Pond—Brenne Natural Regional Park (France)

The energy of water masses is a first-order factor that controls the essential physicochemical dynamics of a water body. Its study allows one to understand the roots of the processes that occur at the water-mass, water-atmosphere and water-sediment interfaces. The analysis of the Thomas Pond in the Brenne region gives a valuable overview of energy stock evolution on a yearly scale. It highlights the direct impact of this evolution on thermal stratification and the potential for evaporation and exchange with the atmosphere. The study of evaporation remains challenging due to the complexity of the energy processes and factors involved. Its estimation using formulas, which are mostly empirical, is one of the most used means for studying the process. The studied pond shows a natural stratification during the summer season, however often fragile and disturbed by other climatic factors such as wind and precipitation. This disruption leads to increased exchanges between the pond and the atmosphere. The methods used to estimate pond-atmosphere exchanges, namely evaporation, vary in values ranging between 1 mm/d to &gt; 15 mm/d. Among these methods, three stand out and seem to give reasonable values. This observation is based on the noticeable drop of the pond’s water level during the period of non-communication with the outside, which corresponds to 65 mm. The energy required for this evaporation varies between 600 W/m2 and 1500 W/m2, except for the Smith model, that slightly overestimates this parameter. The regulation of ponds’ water volumes by managers, the increased duration of bungs closure and the intermittence of precipitations in recent years exacerbate the reduction of direct inputs to ponds and the aggravates the impacts of a changing climate. Under the effect of increasing air temperatures, losses by evaporation will also increase significantly. If we generalise the results obtained to all of the Brenne Park water bodies (4500 ponds of the park), losses by evaporation will lead to a significant water deficit of the Loire basin. From this study, the use of deep learning ensemble models was found to provide better short-term predictions (RMSE between 0.003 and 0.006 for all methods), thus confirming the effectiveness of these methods for similar applications.</jats:p

Utilização de deep learning para previsão dos preços das ações na bolsa de valores

Orientador: Prof. Dr. Adalto Acir Althaus JuniorMonografia (graduação) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Curso de Ciências EconômicasInclui referênciasResumo : Este trabalho teve como objetivo a criação de um algoritmo capaz de realizar previsões sobre a variação de preços futura de ações negociadas na bolsa de valores brasileira por meio da utilização da técnica de aprendizado de máquina denominada deep learning. Foram utilizados dados históricos de preço e volume para alimentar o modelo em conjunto com uma rede neural híbrida. Ao final do trabalho foi verificado que embora o modelo não tenha obtido resultados significativos para prever o valor da variação de preço no próximo dia útil para as ações testadas, um teste de investimento mostrou que o modelo pode ser útil para prever a tendência na qual os preços se movimentarão com um dia de antecedência, sobretudo quando o modelo é treinado com os dados mais recentes. Para os papéis analisados, o modelo resultou em uma estratégia de investimento com resultados superiores a de buy and hold no melhor caso e equivalente a esta mesma estratégia, no pior caso

Αξιολόγηση μοντέλων πρόβλεψης Μηχανικής μάθησης με την χρήση των Χρονοσειρών του Bitcoin και του Ethereum

Το θέμα της μεταπτυχιακής εργασίας αφορά την αξιολόγηση μεθόδων μηχανικής μάθησης για την πρόβλεψη τιμών χρονοσειρών καθώς και η σύγκριση αυτών των προβλέψεων με διαφορετικές παραμέτρους. Λόγω της ταχείας ανάπτυξης της επιστήμης και της τεχνολογίας τα τελευταία χρόνια, οι μέθοδοι μηχανικής μάθησης προσφέρουν πολλές δυνατότητες σε πολλούς τομείς, όπως η πρόβλεψη μελλοντικών τιμών χρονοσειρών. Σκοπός αυτής της εργασίας είναι τόσο η θεωρητική όσον αφορά την βαθιά μάθηση όσο και το πρακτικό μέρος αποβλέπει στην αξιολόγηση των μοντέλων πρόβλεψης των δύο τύπων τεχνητών νευρωνικών δικτύων που επιλέχτηκαν. Το πρώτο μέρος της εργασίας περιλαμβάνει το απαιτούμενο θεωρητικό υπόβαθρο, όπως τα βασικά στοιχεία της Μηχανική Μάθησης, των τεχνητών νευρωνικών δικτύων, της βαθιάς μάθησης, των χρονοσειρών και έπειτα αναφέρονται οι βασικές ιδιότητες των κρυπτονομισμάτων. Το δεύτερο και πρακτικό μέρος της εργασίας ασχολείται με την αξιολόγηση των δύο τεχνητών νευρωνικών δικτύων που χρησιμοποιήθηκαν για την πρόβλεψη των τιμών δύο ευρέως γνωστών κρυπτονομισμάτων (Bitcoin, Ethereum). Εκπαιδεύοντας και χρησιμοποιώντας τα βαθιά νευρωνικά δίκτυα (DNN) και τα δίκτυα μακροπρόθεσμης μνήμης (LSTM), προβλέφθηκαν οι μελλοντικές τιμές των χρονοσειρών του Bitcoin και του Εthereum και αυτές οι προβλέψεις συγκρίθηκαν αλλάζοντας την τοπολογία των δικτύων. Ταυτόχρονα, οι προβλέψεις συγκρίνονται μεταξύ τούς λαμβάνοντας υπόψη τις διαφορετικές μεθόδους εκπαίδευσης (διαφόριση και μη διαφόριση χρονοσειρών) που χρησιμοποιήθηκαν. Τέλος, οι συγκρίσεις σχετικά με την απόδοση των μοντέλων πρόβλεψης γίνεται με βάση συγκεκριμένες μετρικές (RMSE,MSE,MAE κλπ.) και μέσω ενός συστήματος κατάταξης (Score Card) από το οποίο προκύπτουν χρήσιμα συμπεράσματα.The subject of this postgraduate thesis is the study of machine learning methods to predict time series prices of two cryptocurrencies and compare these predictions with different parameters. The aim of this study is to evaluate the reliability and the performance in terms of predictability of two types of Deep learning Artificial Neural Networks ( DNN and LSTM) with the use of time series. The first part of this thesis includes the required theoretical background, such as the basics of machine learning, artificial neural networks, deep learning, time series, and the fundamentals of cryptocurrencies. The second part which is the practical part of this study focuses on the evaluation of the prediction models and the methodology that was applied to train the two types of the artificial neural networks (DNN and LSTM) .The numerical experiments are conducted using two time series from Cryptocurrency space (Bitcoin and Ethereum). In addition, the reliability of the predictions of each artificial Neural Network was evaluated by taking into account the different training methods that were used. Finally, the comparison of the proposed models was made based on specific metrics and through a Ranking system

Avaliação de redes neurais do tipo Long short-term memory e Multilayer perceptron para predição do valor da cotação das ações das cinco empresas mais representativas do IBOVESPA

O mercado de capitais é um mecanismo que possibilita o financiamento de empresas através de pessoas físicas poupadoras. Este instrumento assume um papel ímpar no desenvolvimento econômico de um país e possibilita a construção de patrimônio a longo prazo para os investidores. Entender e conseguir predizer o comportamento do mercado é de grande valia para investidores. O presente trabalho utilizou técnicas de Multilayer Perceptron e Long Short-Term Memory para prever o preço futuro das cinco empresas mais representativas do Ibovespa, que é principal índice do mercado brasileiro. Para construção dos algoritmos, utilizou-se a linguagem Python 3 e o ambiente em nuvem Google Collaboratory. Os modelos foram treinados e validados utilizando dados de 04 janeiro de 2010 a 05 de agosto de 2021 e testados com dados de 06 agosto até 30 de dezembro de 2021. Para encontrar o melhor conjunto de hiperparâmetros foi realizado uma otimização considerando 72 combinações distintas. Os resultados foram avaliados através do Root Mean Squared Error e demonstraram que as Multilayer Perceptron obtiveram um erro, na média, menor quando comparada as Long Short-Term Memory, e, portanto, foram consideradas a técnica mais segura para os investidores. Os resultados de RMSE obtidos para as empresas B3, Bradesco, Itaú, Petrobras e Vale, respecitvamente, foram de 0,37470; 0,44970; 0,69881; 0,64238; e 2,00160 para MLP contra 0,36930; 0,47892; 0,74098; 0, 65798; e 2,4009 para a LSTM. Por fim, os melhores resultados foram: B3 (0,36930), Bradesco (0,44970), Petrobras (0,64238), Itaú (0,69881) e Vale (2,00160).The capital market is a mechanism that makes it possible to finance companies through savings of individuals person. This instrument plays a unique role in the economic development of a country and makes it possible to build long-term equity for investors. Understand and be able to predict market behavior is of great value to any investors. The present work used Multilayer Perceptron and Long Short-Term Memory techniques to predict the future value of the five most representative companies on the Ibovespa, which is the main index of the Brazilian market. To build the algorithms, it was used Python 3 language and the Google Collaboratory cloud environment. The models were trained and validated using data from January 4, 2010 to August 5, 2021 and tested with data from August 6 to December 30, 2021. To find the best set of hyperparameters, an optimization was performed considering 72 different topologies. The results were evaluated using the Root Mean Squared Error and showed that the Multilayer Perceptron obtained an error, on average, lower when compared to the Long Short-Term Memory, and therefore were considered the safest technique for investors. The RMSE results obtained for companies B3, Bradesco, Itaú, Petrobras and Vale, respectively, were 0,37470; 0,44970; 0,69881; 0,64238; and 2,00160 for MLP versus 0,36930; 0,47892; 0,74098; 0,65798; and 2,4009 for the LSTM. Finally, the best results were: B3 (0,36930), Bradesco (0,44970), Petrobras (0,64238), Itaú (0,69881) and Vale (2,00160)