Market efficiency analysis using AI models based on Investors’ Mood
Market efficiency analysis using AI models based on Investors’ Mood
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The Efficient Market Hypothesis assumes that stock prices in financial markets incorporate all the historical information in any of its forms (weak, semi-strong and strong). The aim of this study is to validate this hypothesis.
This study uses artificial intelligence models designed to predict IBEX trends, based on investor mood, extracting information from the big data and using natural language processing algorithms. The results of the study show that the success rate of a system that trains for only 6 months is higher than a system that uses all the available historical information. Investment strategies can also be based on the forecasts of the artificial intelligence models, which can beat the market, by setting up different trading systems for different degrees of risk, depending on the probability threshold provided by the model considered. These results imply that the Spanish financial market has a short-term memory, and does not include older information and therefore does not fulfill the efficient market hypothesis assumptions.
KEY WORDS Big data, IBEX, Bayesian Networks, investors’ mood, trading systems,
market efficiency.
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