Trend prediction in financial time series: a model and a software framework.

Ghibellini, Alessandro (2021) Trend prediction in financial time series: a model and a software framework. [Laurea magistrale], Università di Bologna, Corso di Studio in Artificial intelligence [LM-DM270]
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The research has the aim to build an autonomous support for traders which in future can be translated in an Active ETF. My thesis work is characterized for a huge focus on problem formulation and an accurate analysis on the impact of the input and the length of the future horizon on the results. I will demonstrate that using financial indicators already used by professional traders every day and considering a correct length of the future horizon, it is possible to reach interesting scores in the forecast of future market states, considering both accuracy, which is around 90% in all the experiments, and confusion matrices which confirm the good accuracy scores, without an expensive Deep Learning approach. In particular, I used a 1D CNN. I also emphasize that classification appears to be the best approach to address this type of prediction in combination with proper management of unbalanced class weights. In fact, it is standard having a problem of unbalanced class weights, otherwise the model will react for inconsistent trend movements. Finally I proposed a Framework which can be used also for other fields which allows to exploit the presence of the Experts of the sector and combining this information with ML/DL approaches.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Ghibellini, Alessandro
Relatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
Time Series forecasting,Multiclass Classification,AI in Financial Market,Convolutional Neural Network,1D CNN,Deep Learning
Data di discussione della Tesi
3 Dicembre 2021

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