Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics

First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049

Market volatility : can machine learning methods enhance volatility forecasting?

This dissertation aims to test whether the use of machine learning (ML) techniques can improve volatility forecasting accuracy. More specifically, if it can beat the best econometric model, the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). Using S&P 500 Index data from May-2007 to August-2022, the superiority of the HAR-RV was tested and attested against competing econometric models EWMA and GARCH(1,1). Next, the performance of the ML Artificial Neural Network algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are compared to the performance of the econometric models. Five different variable sets are tested for the ML models. It is found that while both ML models are able to beat the EWMA and GARCH(1,1) models by a significant margin, the HAR-RV model still outperforms LSTM and GRU. Moreover, an analysis is conduced on the models’ predictions on the period corresponding to the Covid-19 crisis. The results did not show any evidence suggesting that ML methods have a particular advantage at predicting during high volatility events. Finally, a plausible cause that could undermine the remarkable qualities of the ML methods in the aim of volatility forecasting is discussed. It is found that the rigorous set of conditions needed to be met for the proper setup of ML models are very difficult to be met using financial data, which hinders the aptitude of ML for this purpose.Esta tese visa testar se o uso de técnicas de Machine Learning (ML) pode melhorar a precisão da previsão da volatilidade. Mais especificamente, se estes algoritmos conseguem superar o melhor modelo econométrico, o Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). Usando dados do Índice S&P 500 de Maio-2007 a Agosto-2022, a superioridade do HAR-RV perante os modelos econométricos concorrentes EWMA e GARCH(1,1), foi testada e confirmada. Em seguida, o desempenho dos algoritmos ML de redes neurais artificiais de Long Short-Term Memory (LSTM) e Gated Recurrent Unit (GRU) são comparados com o desempenho dos modelos econométricos tradicionais. Cinco conjuntos diferentes de variáveis são testados para os modelos ML. Verifica-se que enquanto ambos os modelos ML são capazes de superar os modelos EWMA e GARCH(1,1) por uma margem significante, o modelo HARRV ainda tem um desempenho superior ao LSTM e ao GRU. É ainda feita uma análise das previsões dos modelos durante o período correspondente à crise do Covid-19. Os resultados não mostram qualquer evidência que sugira que os métodos ML têm uma particular vantagem durante eventos de alta volatilidade. Finalmente, é discutida uma possível causa que poderá debilitar as sofisticadas qualidades dos métodos ML para a finalidade de previsão de volatilidade. Verifica-se que o conjunto rigoroso de condições necessárias para a correcta configuração dos modelos ML é muito difícil de se cumprir utilizando series temporais de volatilidade de mercado, o que prejudica a aptidão dos modelos ML para esta finalidade

Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction

Global Navigation Satellite Systems (GNSS) are systems that continuously acquire data and provide position time series. Many monitoring applications are based on GNSS data and their efficiency depends on the capability in the time series analysis to characterize the signal content and/or to predict incoming coordinates. In this work we propose a suitable Network Architecture, based on Long Short Term Memory Recurrent Neural Networks, to solve two main tasks in GNSS time series analysis: denoising and prediction. We carry out an analysis on a synthetic time series, then we inspect two real different case studies and evaluate the results. We develop a non-deep network that removes almost the 50% of scattering from real GNSS time series and achieves a coordinate prediction with 1.1 millimeters of Mean Squared Error

Aleatoric Uncertainty Modelling in Regression Problems using Deep Learning

[eng] Nowadays, we live in an intrinsically uncertain world from our perspective. We do not know what will happen in the future but, to infer it, we build the so-called models. These models are abstractions of the world we live which allow us to conceive how the world works and that are, essentially, validated from our previous experience and discarded if their predictions prove to be incorrect in the future. This common scientific process of inference has several non-deterministic steps. First of all, our measuring instruments could be inaccurate. That is, the information we use a priori to know what will happen may already contain some irreducible error. Besides, our past experience in building the model could be biased (and, therefore, we would incorrectly infer the future, as the models would be based on unrepresentative data). On the other hand, our model itself may be an oversimplification of the reality (which would lead us to unrealistic generalizations). Furthermore, the overall task of inferring the future may be downright non-deterministic. This often happens when the information we have a priori to infer the future is incomplete or partial for the task to be performed (i.e. it depends on factors we cannot observe at the time of prediction) and we are, consequently, obliged to consider that what we want to predict is not a deterministic value. One way to model all of these uncertainties is through a probabilistic approach that mathematically formalizes these sources of uncertainty in order to create specific methods that capture them. Accordingly, the general aim of this thesis is to define a probabilistic approach that contributes to artificial intelligence-based systems (specifically, deep learning) becoming robust and reliable systems capable of being applied to high-risk problems, where having generic good performance is not enough but also to ensure that critical errors with high costs are avoided. In particular, the thesis shows the current divergence in the literature - when it comes to dividing and naming the different types of uncertainty - by proposing a procedure to follow. In addition, based on a real problem case arising from the industrial nature of the current thesis, the importance of investigating the last type of uncertainty is emphasized, which arises from the lack of a priori information in order to infer deterministically the future, the so-called aleatoric uncertainty. The current thesis delves into different literature models in order to capture aleatoric uncertainty using deep learning and analyzes their limitations. In addition, it proposes new state-of-the-art approaches that allow to solve the limitations exposed during the thesis. As a result of applying the aleatoric uncertainty modelling in real-world problems, the uncertainty modelling of a black box systems problem arises. Generically, a Black box system is a pre-existing predictive system which originally do not model uncertainty and where no requirements or assumptions are made about its internals. Therefore, the goal is to build a new system that wrappers the black box and models the uncertainty of this original system. In this scenario, not all previously introduced aleatoric uncertainty modelling approaches can be considered and this implies that flexible methods such as Quantile Regression ones need to be modified in order to be applied in this context. Subsequently, the Quantile Regression study brings the need to solve one critical literature problem in the QR literature, the so-called crossing quantile, which motivates the proposal of new additional models to solve it. Finally, all of the above research will be summarized in visualization and evaluation methods for the predicted uncertainty to produce uncertainty-tailored methods.[cat] Estem rodejats d’incertesa. Cada decisió que prenem té una probabilitat de sortir com un espera i, en funció d’aquesta, molts cops condicionem les nostres decisions. De la mateixa manera, els sistemes autònoms han de saber interpretar aquests escenaris incerts. Tot i això, actualment, malgrat els grans avenços en el camp de la intel·ligència artificial, ens trobem en un moment on la incapacitat d'aquests sistemes per poder identificar a priori un escenari de major risc impedeix la seva inclusió com a part de solucions que podrien revolucionar la societat tal i com la coneixem. El repte és significatiu i, per això, és essencial que aquests sistemes aprenguin a modelar i gestionar totes les fonts de la incertesa. Partint d'un enfocament probabilístic, aquesta tesi proposa formalitzar els diferents tipus d'incerteses i, en particular, centra la seva recerca en un tipus anomenada com incertesa aleatòrica, ja que va ser detectada com la principal incertesa decisiva a tractar en el problema financer original que va motivar el present doctorat industrial. A partir d'aquesta investigació, la tesi proposa nous models per millorar l'estat de l'art en la modelització de la incertesa aleatòrica, així com introdueix un nou problema, a partir d’una necessitat real industrial, que apareix quan hi ha un sistema predictiu en producció que no modela la incertesa i es vol modelar la incertesa a posteriori de forma independent. Aquest problema es denotarà com la modelització de la incertesa d'un sistema de caixa negra i motivarà la proposta de nous models especialitzats en mantenir els avantatges predictius, com ara la Regressió Quantílica (RQ), adaptant-los al problema de la caixa negra. Posteriorment, la investigació en RQ motivarà la proposta de nous models per resoldre un problema fonamental de la literatura en RQ conegut com el fenomen del creuament de quantils, que apareix quan, a l’hora de predir simultàniament diferents quantils, l’ordre entre quantils no es conserva. Finalment, tota la investigació anterior es resumirà en mètodes de visualització i avaluació de la incertesa reportada per tal de produir mètodes que mitjançant aquesta informació extra prenguin decisions més robustes

Deep Dynamic Factor Models

We propose a novel deep neural net framework - that we refer to as Deep Dynamic Factor Model (D2FM) -, to encode the information available, from hundreds of macroeconomic and financial time-series into a handful of unobserved latent states. While similar in spirit to traditional dynamic factor models (DFMs), differently from those, this new class of models allows for nonlinearities between factors and observables due to the deep neural net structure. However, by design, the latent states of the model can still be interpreted as in a standard factor model. In an empirical application to the forecast and nowcast of economic conditions in the US, we show the potential of this framework in dealing with high dimensional, mixed frequencies and asynchronously published time series data. In a fully real-time out-of-sample exercise with US data, the D2FM improves over the performances of a state-of-the-art DFM

Probabilistic Load Forecasting with Deep Conformalized Quantile Regression

The establishment of smart grids and the introduction of distributed generation posed new challenges in energy analytics that can be tackled with machine learning algorithms. The latter, are able to handle a combination of weather and consumption data, grid measurements, and their historical records to compute inference and make predictions. An accurate energy load forecasting is essential to assure reliable grid operation and power provision at peak times when power consumption is high. However, most of the existing load forecasting algorithms provide only point estimates or probabilistic forecasting methods that construct prediction intervals without coverage guarantee. Nevertheless, information about uncertainty and prediction intervals is very useful to grid operators to evaluate the reliability of operations in the power network and to enable a risk-based strategy for configuring the grid over a conservative one. There are two popular statistical methods used to generate prediction intervals in regression tasks: Quantile regression is a non-parametric probabilistic forecasting technique producing prediction intervals adaptive to local variability within the data by estimating quantile functions directly from the data. However, the actual coverage of the prediction intervals obtained via quantile regression is not guaranteed to satisfy the designed coverage level for finite samples. Conformal prediction is an on-top probabilistic forecasting framework producing symmetric prediction intervals, most often with a fixed length, guaranteed to marginally satisfy the designed coverage level for finite samples. This thesis proposes a probabilistic load forecasting method for constructing marginally valid prediction intervals adaptive to local variability and suitable for data characterized by temporal dependencies. The method is applied in conjunction with recurrent neural networks, deep learning architectures for sequential data, which are mostly used to compute point forecasts rather than probabilistic forecasts. Specifically, the use of an ensemble of pinball-loss guided deep neural networks performing quantile regression is used together with conformal prediction to address the individual shortcomings of both techniques

Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting

Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function. Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study. In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure, incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems

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