In this paper, we examine the early predictability of the market's directional movement using intraday high‐frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an “early indicator” of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high‐frequency time series of both price and certain technical indicators. The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.

The objective of this study is to identify both micro and macroeconomic variables that allow us to analyze in advance the probabilities of business failure. The selected sample contains all the listed companies of the IPC index of Mexico, IBEX-35 of Spain and EURO STOXX50 of Europe for a time horizon of 5 years. Our contribution lies in the empirical testing of the results by two different techniques: general estimating equations (a parametric technique) and a decision tree (a non-parametric technique based on artificial intelligence). The obtained results show that the factors of liquidity, indebtedness and profitability are the ones that affect the prediction of corporatebankruptcy for listed companies, but not the macroeconomic ones, since the macroeconomic peculiarities of each country are diluted by the importance of the economic-financial structure of each company.