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Doctoral thesis

Essays on financial markets predictability

    22.06.2020

277 p

Thèse de doctorat: Università della Svizzera italiana, 2020

Behavioral finance
Sentiment
Fear
Machine learning
Predictable functions
English Empirical indicators of sentiment are commonly employed in the economic literature while a precise understanding of what is sentiment is still missing. Exploring the links among the most popular proxies of sentiment, fear and uncertainty this paper aims to fill this gap. We show how fear and sentiment are specular in their predictive power in relation to the aggregate market and to cross-sectional returns. Finally, we document how sentiment and fear time cross-sectional returns: conditionally on a today’s high (low) level of fear we observe a next month high (low) return per unit of risk. The opposite holds for sentiment. After that, we propose a novel framework linking market predictability with pricing through the study of the rationale underpinning predictability. We show how the dynamics of risks and risks pricing are at the base of the predictability of behavioral and fundamental variables. This allows us to explain qualitative and quantitative differences in the dynamics of the predictability detected in bull and bear markets. Finally, we decompose the problem of market predictability into three parts: predictive models, predictors and the functions of the market uncertainty we aim at forecasting. For each of the three parts, we consider potential and challenges posed by these new approaches in the asset pricing field.
Language
  • English
Classification
Economics
License
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https://n2t.net/ark:/12658/srd1319228
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