RT Journal Article
T1 Early prediction of Ibex 35 movements
A1 Miranda García, Isabel Marta
A1 Segovia-Vargas, M.J.
A1 Mori, U
A1 Lozano, J. A.
K1 Artificial intelligence
K1 High-frequency data
K1 Intraday pattern
K1 Price discovery
K1 Stock price prediction
K1 Trading hours
AB 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 thefirst 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.
PB John Wiley & Sons, Ltd
SN 1099-131X
YR 2022
FD 2022-11-17
LK https://hdl.handle.net/10433/22760
UL https://hdl.handle.net/10433/22760
LA en
NO Journal of Forecasting, 42(5), 1150–1166.
NO Departamento de Economía Financiera y Contabilidad. Universidad Pablo de Olavide
DS RIO
RD May 8, 2026