This thesis analyzes the predictability of financial crises, examining the macroeconomic and behavioral dynamics that lead to market instability. Initially, the theoretical foundations of Keynes's economic cycles and Minsky's Financial Instability Hypothesis, integrated with Kindleberger's five-stage model, are presented. The study then examines fundamental and technical analysis tools, including major stock indices (S&P 500 and Russell 2000) and statistical-behavioral indicators such as the VIX, the Fear & Greed Index, the RSI, and Moving Averages. This theoretical and statistical framework is subsequently applied to two contemporary case studies: the 2008 housing bubble and the 2020 COVID-19 shock. Finally, the paper discusses the inherent limitations of purely mathematical-quantitative models, illustrating the infamous collapse of the LTCM hedge fund and highlighting the role of mass irrationality in the economic landscape. In conclusion, the thesis argues that, while predicting the exact timing of a crash is impossible due to irrational variables and "false signals", adopting a hybrid approach that combines macroeconomic theory with statistical analysis allows for the identification of phases of maximum systemic vulnerability, enabling investors to effectively manage their portfolios and mitigate risk.
Il presente elaborato analizza il tema della prevedibilità delle crisi finanziarie, osservando le dinamiche macroeconomiche e comportamentali che conducono all'instabilità dei mercati. Inizialmente vengono esposti i fondamenti teorici dei cicli economici di Keynes e dell'Ipotesi di Instabilità Finanziaria di Minsky, integrata dal modello in cinque fasi di Kindleberger, per poi prendere in esame strumenti di analisi tecnica e fondamentale, tra cui i principali indici azionari (S&P 500 e Russell 2000) e indicatori statistico-comportamentali quali il VIX, il Fear & Greed Index, l'RSI e le Medie Mobili. L'apparato teorico e statistico viene successivamente applicato a due casi studio contemporanei: la bolla immobiliare del 2008 e lo shock del COVID-19 nel 2020. Infine, l'elaborato discute i limiti intrinseci dei modelli puramente matematico-quantitativi, illustrando il celebre fallimento del fondo speculativo LTCM e sottolineando il ruolo dell'irrazionalità delle masse nel mondo economico. In conclusione, la tesi sostiene che, pur essendo impossibile prevedere il momento esatto di un collasso a causa di variabili irrazionali e di "falsi segnali", l'adozione di un approccio ibrido che unisca teoria macroeconomica e analisi statistica permette di individuare le fasi di massima vulnerabilità sistemica, consentendo agli investitori di intervenire efficacemente sul proprio portafoglio per ridurne il rischio.
L'illusione delle previsioni finanziarie dei modelli statistici e l'irrazionalità dei mercati
PISTORI, TOMMASO
2025/2026
Abstract
This thesis analyzes the predictability of financial crises, examining the macroeconomic and behavioral dynamics that lead to market instability. Initially, the theoretical foundations of Keynes's economic cycles and Minsky's Financial Instability Hypothesis, integrated with Kindleberger's five-stage model, are presented. The study then examines fundamental and technical analysis tools, including major stock indices (S&P 500 and Russell 2000) and statistical-behavioral indicators such as the VIX, the Fear & Greed Index, the RSI, and Moving Averages. This theoretical and statistical framework is subsequently applied to two contemporary case studies: the 2008 housing bubble and the 2020 COVID-19 shock. Finally, the paper discusses the inherent limitations of purely mathematical-quantitative models, illustrating the infamous collapse of the LTCM hedge fund and highlighting the role of mass irrationality in the economic landscape. In conclusion, the thesis argues that, while predicting the exact timing of a crash is impossible due to irrational variables and "false signals", adopting a hybrid approach that combines macroeconomic theory with statistical analysis allows for the identification of phases of maximum systemic vulnerability, enabling investors to effectively manage their portfolios and mitigate risk.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/112206