Lost and Found—Unfolding and Refolding Aesthetic Learning Processes

The ongoing marketisation of education is a great loss for visual arts education since explorative learning processes are marginalised in favour of more goal-oriented learning. The empirical material analysed in this research derives from the visual art portfolio of a student from an elective university course in visual arts education. Working within Deleuze and Guattari’s philosophical framework, we examine the folding, unfolding, and refolding of aesthetic learning processes, suggesting productive concepts and practices. The analysis made us aware of our own pedagogical ideals and the loss of having to disassemble them, in line with the new curricula. The student’s visual learning process showed us how to reassemble new and explorative learning processes, assigning aspects of sustainability and an ethics of care in relation to environmental and social questions. We suggest strategies for learning in the folds, where educators are called upon to prepare students for an uncertain future. This demands a creative imagination, an ethical standpoint for negotiating the curriculum in line with differentiation by forming, inventing, and fabricating new concepts and images.

An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets

CVaR-sensitive online portfolio selection (CS-OLPS) becomes increasingly important for investors because of its effectiveness to minimize conditional value at risk (CVaR) and control extreme losses. However, the non-stationary nature of financial markets makes it very difficult to address the CS-OLPS problem effectively. To address the CS-OLPS problem in non-stationary markets, we propose an effective news-driven method, named CAND, which adaptively exploits news to determine the adjustment tendency and adjustment scale for tracking the dynamic optimal portfolio with minimal CVaR in each trading round. In addition, we devise a filtering mechanism to reduce the errors caused by the noisy news for further improving CAND's effectiveness. We rigorously prove a sub-linear regret of CAND. Extensive experiments on three real-world datasets demonstrate CAND’s superiority over the state-of-the-art portfolio methods in terms of returns and risks.

Selecting Portfolios Directly Using Recurrent Reinforcement Learning (Student Abstract)

Portfolio selection has attracted increasing attention in machine learning and AI communities recently. Existing portfolio selection using recurrent reinforcement learning (RRL) heavily relies on single asset trading system to heuristically obtain the portfolio weights. In this paper, we propose a novel method, the direct portfolio selection using recurrent reinforcement learning (DPS-RRL), to select portfolios directly. Instead of trading single asset one by one to obtain portfolio weights, our method learns to quantify the asset allocation weight directly via optimizing the Sharpe ratio of financial portfolios. We empirically demonstrate the effectiveness of our method, which is able to outperform state-of-the-art portfolio selection methods.