Optimal Asset Allocation in a High Inflation Regime: a Leverage-feasible Neural Network Approach

We study the optimal multi-period asset allocation problem with leverage constraints in a persistent, high-inflation environment. Based on filtered high-inflation regimes, we discover that a portfolio containing an equal-weighted stock index partially stochastically dominates a portfolio containing a capitalization-weighted stock index. Assuming the asset prices follow the jump diffusion model during high inflation periods, we establish a closed-form solution for the optimal strategy that outperforms a passive strategy under the cumulative quadratic tracking difference (CD) objective. The closed-form solution provides insights but requires unrealistic constraints. To obtain strategies under more practical considerations, we consider a constrained optimal control problem with bounded leverage. To solve this optimal control problem, we propose a novel leverage-feasible neural network (LFNN) model that approximates the optimal control directly. The LFNN model avoids high-dimensional evaluation of the conditional expectation (common in dynamic programming (DP) approaches). We establish mathematically that the LFNN approximation can yield a solution that is arbitrarily close to the solution of the original optimal control problem with bounded leverage. Numerical experiments show that the LFNN model achieves comparable performance to the closed-form solution on simulated data. We apply the LFNN approach to a four-asset investment scenario with bootstrap resampled asset returns. The LFNN strategy consistently outperforms the passive benchmark strategy by about 200 bps (median annualized return), with a greater than 90% probability of outperforming the benchmark at the terminal date. These results suggest that during persistent inflation regimes, investors should favor short-term bonds over long-term bonds, and the equal-weighted stock index over the cap-weighted stock index

Building Trust in the Classroom: A Conceptual Model for Teachers, Scholars, and Academic Developers in Higher Education

The quality of student-teacher classroom interactions is important to learning, belonging, and success, particularly for students from groups that have been historically excluded from or marginalized in higher education. The literature commonly asserts that one necessary part of high-quality educational interactions (whether in person or online) is trust, but several scholars note that trust has not been systematically explored by scholars of higher education teaching and learning. In this paper, we propose a conceptual framework for identifying and describing teacher-initiated “trust moves” in the classroom, based on both the literature and interviews with teaching faculty in varied contexts. In doing this, we hope to provide a practical resource for teachers to reflect on possible approaches for building trust with students in their own classrooms—and for scholars and academic developers who wish to study or support these efforts. Click here to read the corresponding ISSOTL blog post.

Imaging correlates of molecular signatures in oligodendrogliomas.

Molecular subsets of oligodendroglioma behave in biologically distinct ways. Their locations in the brain, rates of growth, and responses to therapy differ with their genotypes. Retrospectively, we inquired whether allelic loss of chromosomal arms 1p and 19q, an early molecular event and favorable prognostic marker in oligodendrogliomas, were reflected in their appearance on magnetic resonance imaging. Loss of 1p and 19q was associated with an indistinct border on T(1) images and mixed intensity signal on T(1) and T(2). Loss of 1p and 19q was also associated with paramagnetic susceptibility effect and with calcification, a common histopathological finding in oligodendrogliomas. These data encourage prospective evaluation of molecular alterations and magnetic resonance imaging characteristics of glial neoplasms

Building Trust in the Classroom: A Conceptual Model for Teachers, Scholars, and Academic Developers in Higher Education

The quality of student-teacher classroom interactions is important to learning, belonging, and success, particularly for students from groups that have been historically excluded from or marginalized in higher education. The literature commonly asserts that one necessary part of high-quality educational interactions (whether in person or online) is trust, but several scholars note that trust has not been systematically explored by scholars of higher education teaching and learning. In this paper, we propose a conceptual framework for identifying and describing teacher-initiated “trust moves” in the classroom, based on both the literature and interviews with teaching faculty in varied contexts. In doing this, we hope to provide a practical resource for teachers to reflect on possible approaches for building trust with students in their own classrooms—and for scholars and academic developers who wish to study or support these efforts