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Adaptive machine learning in stock markets is an important sytem for reaction to new information in real time, automatic prediction and completion of tasks independently, using suitable algorithms. I will talk about our several studies toward this goal. For example, Duran and Caginalp [1] propose a hybrid parameter optimization forecast algorithm including daily based learning with two streaming windows, using a semi-dynamic initial parameter vector pool having not only fixed but also most recently used successful parameter vectors from a set of grid points in a hyperbox and out-of-sample prediction. More recently, Tuncel and Duran [2] propose a new mathematical method and focus on Monte Carlo simulation to find out the effectiveness of two approaches including grid approach and random approach in hyperbox based on the experimental design for selection of initial parameter vectors in a large-scale unconstrained optimization problem. Moreover, Duran and Bommarito [3] present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio.

[1] Ahmet Duran and Gunduz Caginalp, Parameter optimization for differential equations in-asset price forecasting, Optimization Methods & Software 23 (4) (2008) 551–574, Issue: Mathematical programming in data mining and machine learning.

[2] Mehmet Tunçel and Ahmet Duran, Effectiveness of grid and random approaches for a model parameter vector optimization, Journal of Computational Science 67 (2023) 101960

[3] Ahmet Duran and Michael J. Bommarito, A profitable trading and risk management strategy despite transaction costs, Quantitative Finance, 11(6), 2011, 829-848