Idiosyncratic Risk and Short Interest Analysis for Canadian Large Cap Stocks

While previous studies have focused on the relation between idiosyncratic risk and short interest in US stock markets, we test whether the Canadian market shows the same symptoms in costs limiting arbitrage. In order to measure arbitrage cost, we use idiosyncratic risk and use it as a proxy to determine the cost level. To prevent any ambiguity and bias in our result, we use commonly recognized indexes to measure both transaction and holding costs. Consistent with the similar study conducted in U.S., we find that high Short Interest Canadian stocks appear to have higher idiosyncratic risk that is significant enough to affect investors’ decisions

Tube-Fin Heat Exchanger Circuitry Optimization for Multiple Airflow Maldistribution Profiles

Tube-fin heat exchangers(HXs) are widely used in the HVAC&R industry. Studies have proved that by optimizing the refrigerant circuitry, heat exchanger performance can be significantly improved. Since air-to-refrigerant heat exchangers are typically confined in packaged units along with a fan, the airflow distribution on the face of the HXs is a dominant factor influencing its performance. During the operation of a heat exchanger as a part of the system, the air flow distribution changes continuously, especially as the fan speed changes during startup and shutdown cycles. This poses a design challenge as typically heat exchangers are designed using the assumption of uniform flow or for a single known flow distribution profile. For each profile and for the same flow rate, a typical circuitry optimization algorithm can generate a completely different optimal circuitry. Therefore, robust circuitry design that can always guarantee an acceptable minimum performance under various airflow distributions is required. In the field of optimization, this is referred to as robust optimization. This paper presents a robust circuitry design optimization approach. The formulation consists of an upper-level optimization problem and a lower-level finite search problem. In the lower-level problem, a finite number of typical airflow distribution profiles are imposed. These profiles are obtained from the literature, experimental measurements, and CFD simulations. The goal of the lower-level finite search problem is to obtain the worst case capacity degradation from different air flow profiles for a given circuitry. The objective of the upper-level problem is to obtain the circuitry that maximizes the worst case capacity subject to a set of operating constraints such as pressure drops and subcooling/superheat. In order to effectively obtain the optimal designs and guarantee manufacturable designs, an integer permutation based genetic algorithm (IPGA) developed in previous research is used to solve the upper-level problem. The optimized circuitry is then verified by using exhaustive search. The comparison between the solutions of the proposed approach and the optimal circuitries obtained under uniform airflow distribution shows that despite a 1.4%-3.7% decrease in capacity, robust circuitry designs are more resilient to multiple airflow maldistribution profiles. The proposed approach is applied to an A-type indoor unit which demonstrates its applicability in real-world design

Tube-Fin Heat Exchanger Circuitry Optimization Using Integer Permutation Based Genetic Algorithm

Tube-fin heat exchangers (HXs) are widely used in air-conditioning and heat pumping applications. The performance of these heat exchangers is strongly influenced by the refrigerant circuitry, i.e. the refrigerant flow path along the different tubes in the HX core. Since for a given number of tubes, the number of possible circuitries is exponentially large, neither the exhaustive search nor traditional optimization algorithms can be used to optimize the circuitry for a given application. Researchers have previously used Genetic algorithms (GA) coupled with a learning module to solve this problem, but there is no guarantee that the resulting circuitry can be manufactured in a cost-effective manner. In this paper, we present a GA-based integer permutation approach for solving the circuitry optimization problem. A finite volume heat exchanger simulation tool is used to simulate the performance of different circuitries generated by the optimizer. The crossover, mutation and individual generation genetic operators are designed such that all individuals generated by the GA are in the feasible domain. The proposed approach can explore the solution space more efficiently than a conventional GA. Exhaustive search and results from the literature are used to verify the results obtained from the proposed optimization scheme for small heat exchangers. The result shows that integer permutation based GA (IPGA) is capable of finding optimal or near-optimal refrigerant circuitry designs using a relatively low population size and iterations. Furthermore, the limits on in-tube refrigerant mass flux obtained from empirical data, are used to assist the IPGA. The manufacturability aspect is handled using a constraint-dominated sorting in the fitness assignment stage of GA with a goal of obtaining the shortest tube joints. It is shown that the proposed constraint handling technique significantly improves the manufacturability of the optimal circuits. Overall, the analyses of several test heat exchanger cases show that the constrained integer permutation based GA can generate circuitry designs with capacities superior to those obtained manually and are manufacturable. Compared to a conventional GA, it exhibits faster convergence and higher quality optimal solutions. In addition, a 3.1-8.8% increase in heat exchange capacity is obtained by IPGA compared with the conventional counter-flow circuitry

Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling

In dynamic Magnetic Resonance Imaging (MRI), k-space is typically undersampled due to limited scan time, resulting in aliasing artifacts in the image domain. Hence, dynamic MR reconstruction requires not only modeling spatial frequency components in the x and y directions of k-space but also considering temporal redundancy. Most previous works rely on image-domain regularizers (priors) to conduct MR reconstruction. In contrast, we focus on interpolating the undersampled k-space before obtaining images with Fourier transform. In this work, we connect masked image modeling with k-space interpolation and propose a novel Transformer-based k-space Global Interpolation Network, termed k-GIN. Our k-GIN learns global dependencies among low- and high-frequency components of 2D+t k-space and uses it to interpolate unsampled data. Further, we propose a novel k-space Iterative Refinement Module (k-IRM) to enhance the high-frequency components learning. We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers. Experiments show that our proposed k-space interpolation method quantitatively and qualitatively outperforms baseline methods. Importantly, the proposed approach achieves substantially higher robustness and generalizability in cases of highly-undersampled MR data