Abstract
In the present PhD study, the central axis will focus in the investigation of short-term technical analysis correction ways mediated by artificial intelligence.
In this framework, we search for novel architectures and methods which when fed with forecast data of different technical indicators which result in corrected forecasts (that embody the last-minute changes in fundamentals and market psychology). Characteristic of the new architectures is the fact that they can correct the technical analysis model (technical indicators). This characteristic takes advantage of the alternative methods so that their inefficiency in causal modeling and case control is minimized.
Advisory committee
Supervisor: D. Kalles, Associate Professor HOU.
V. Verykios, Professor HOU.
M. Tzagkarakis, Assistant Professor, University of Patras.