Can Machines Explain Stock Returns?
|Author:||Mgr. Karolína Chalupová|
|Year:||2021 - winter|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Economic Theory
|Awards and prizes:|
|Abstract:||Recent research shows that neural networks predict stock returns better
than any other model. The networks’ mathematically complicated nature is
both their advantage, enabling to uncover complex patterns, and their curse,
making them less readily interpretable, which obscures their strengths and
weaknesses and complicates their usage. This thesis is one of the first attempts
at overcoming this curse in the domain of stock returns prediction. Using some
of the recently developed machine learning interpretability methods, it explains
the networks’ superior return forecasts. This gives new answers to the longstanding question of which variables explain differences in stock returns and
clarifies the unparalleled ability of networks to identify future winners and
losers among the stocks in the market. Building on 50 years of asset pricing
research, this thesis is likely the first to uncover whether neural networks
support the economic mechanisms proposed by the literature. To a finance
practitioner, the thesis offers the transparency of decomposing any prediction
into its drivers, while maintaining a state-of-the-art profitability in terms of
Sharpe ratio. Additionally, a novel metric is proposed that is particularly
suited to interpret return-predicting networks in financial practice. This thesis
offers a usable and economically explainable account of how machines make
stock return predictions.