Predicting Forex Currency Fluctuations Using a Novel Bio-inspired Modular Neural Network

This thesis explores the intricate interplay of rational choice theory (RCT), brain modularity, and artificial neural networks (ANNs) for modelling and forecasting hourly rate fluctuations in the foreign exchange (Forex) market. While RCT traditionally models human decision-making by emphasising self-interest and rational choices, this study extends its scope to encompass emotions, recognising their significant impact on investor decisions. Recent advances in neuro- science, particularly in understanding the cognitive and emotional processes associated with decision-making, have inspired computational methods to emulate these processes. ANNs, in particular, have shown promise in simulating neuroscience findings and translating them into effective models for financial market dynamics. However, their monolithic architectures of ANNs, characterised by fixed struc- tures, pose challenges in adaptability and flexibility when faced with data perturbations, limiting overall performance. To address these limitations, this thesis proposes a Modular Convolutional orthogonal Recurrent Neural Net- work with Monte Carlo dropout-ANN (MCoRNNMCD-ANN) inspired by recent neuroscience findings. A comprehensive literature review contextualises the challenges associated with monolithic architectures, leading to the identification of neural network structures that could enhance predictions of Forex price fluctuations, such as in the most prominently traded currencies, the EUR/GBP pairing. The proposed MCoRNNMCD-ANN is thoroughly evaluated through a detailed comparative analysis against state-of-the-art techniques, such as BiCuDNNL- STM, CNN–LSTM, LSTM–GRU, CLSTM, and ensemble modelling and single- monolithic CNN and RNN models. Results indicate that the MCoRNNMCD- ANN outperforms competitors. For instance, reducing prediction errors in test sets from 19.70% to an impressive 195.51%, measured by objective evaluation metrics like a mean square error. This innovative neurobiologically-inspired model not only capitalises on modularity but also integrates partial transfer learning to improve forecasting ac- curacy in anticipating Forex price fluctuations when less data occurs in the EUR/USD currency pair. The proposed bio-inspired modular approach, incorporating transfer learning in a similar task, brings advantages such as robust forecasts and enhanced generalisation performance, especially valuable in domains where prior knowledge guides modular learning processes. The proposed model presents a promising avenue for advancing predictive modelling in Forex predictions by incorporating transfer learning principles

DISPUTool -- A tool for the Argumentative Analysis of Political Debates

International audiencePolitical debates are the means used by political candidates to put forward and justify their positions in front of the electors with respect to the issues at stake. Argument mining is a novel research area in Artificial Intelligence, aiming at analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and automatically analyze textual data. In this paper, we present DISPUTool, a tool designed to ease the work of historians and social science scholars in analyzing the argumentative content of political speeches. More precisely, DISPUTool allows to explore and automatically identify argumentative components over the 39 political debates from the last 50 years of US presidential campaigns (1960-2016)

The SEEMPAD Dataset for Emphatic and Persuasive Argumentation

Emotions play an important role in argumentation as humans mix rational and emotional attitudes when they argue with each other to take decisions. The SEEMPAD project aims at investigating the role of emotions in human argumentation. In this paper, we present a resource resulting from two field experiments involving humans in debates, where arguments put forward during such debates are annotated with the emotions felt by the participants. In addition, in the second experiment, one of the debaters plays the role of the persuader, to convince the other participants about the goodness of her viewpoint applying different persuasion strategies. To the best of our knowledge, this is the first dataset of arguments annotated with the emotions collected from the participants of a debate, using facial emotion recognition tools.Le emozioni giocano un ruolo importante nell’argomentazione in quanto gli esseri umani uniscono atteggiamenti razionali ad atteggiamenti puramente emotivi quando discutono tra loro per prendere decisioni. Il progetto SEEMPAD si propone di studiare il ruolo delle emozioni nell’argomentazione umana. In questo articolo, presentiamo una risorsa ottenuta tramite due esperimenti empirici che coinvolgono le persone nei dibattiti. Gli argomenti presentati durante tali dibattiti sono annotati con le emozioni provate dai partecipanti nel momento in cui l’argomento viene proposto nella discussione. Inoltre, durante il secondo esperimento, uno dei partecipanti svolge il ruolo di persuasore, al fine di convincere gli altri partecipanti della bontá del suo punto di vista applicando diverse strategie di persuasione. Questa risorsa è peculiare nel suo genere, ed è l’unica a contenere argomenti annotati con le emozioni provate dai partecipanti durante un dibattito (emozioni registrate tramite strumenti di riconoscimento automatico delle emozioni facciali)