Essays in Microstructure Liquidity, Asset Pricing, and Short Selling

This thesis consists of three standalone studies in the fields of market microstructure liquidity, asset pricing, and short selling. The first study examines whether microstructure illiquidity is priced in security returns in the presence of buyers and sellers having identical preferences and facing symmetric liquidity costs. Commencing with a Lucas (1978)-type representative investor but with differing endowments, we develop a new theoretical model of counterparty trading inclusive of frictions to show that symmetric liquidity costs, which could arise either from exogenous costs or from order-flow asymmetric information, are not priced. This is because seller costs cancel out the buyer costs correctly identified in Amihud and Mendelson's (1986a) seminal theoretical model. We test our generalization of the Lucas model utilizing NYSE (US) equity market microstructure data to show that we cannot reject our main hypothesis concerning the absence of liquidity pricing effect on stock returns. We split transaction costs into their buy (upside) and sell (downside) components to find they are priced with similar magnitudes in contemporaneous returns. Based on our NYSE sample, the balanced effect of buy and sell lambda price impact does not generate a downside lambda premium in future stock returns. We further report a positive pricing effect of the bid-ask spread on future returns on the extreme quintile of lambda asymmetry. The second study examines liquidity asymmetry under variations in short selling regimes. I show a near symmetrical adverse effect of shorting flow impediments (caused by an exchange driven short-sale ban or securities lending market-driven constraints) on the buy and sell order flow price impact and liquidity supply dynamics. Overall, I find that the liquidity cost asymmetry is lower than the previously reported outcome with the US 2008 banned stocks in an extreme liquidity crisis. The differential effect is tilted towards sell-initiated order flow impact and bid side liquidity. Utilizing tick-by-tick microstructure data (including depth data) in the Hong Kong market, I conduct ordinary least squares (OLS) and regression discontinuity design (RDD) tests on the Hong Kong market to corroborate my findings. In contrast to Diamond and Verrecchia (1987), my study: a) argues for the importance of informed short sellers (as liquidity suppliers) on the bid and ask side of the market, and b) highlights the juxtaposition between the imperfect competition channel and increased adverse selection due to endogenous information acquisition under an informed short-selling ban. I further report a lower differential effect in buy versus sell under stronger mean reversion properties, a profitable setting for contrarian liquidity provisioning strategies. In the third study, I utilize a novel data panel of institutional short-sell transactions (with identification flags for hedgers and non-hedgers), equity covered put warrant data, and securities lending data based on the Taiwan market to show that put warrant derivatives hedge rebalancing raises borrowing costs (loan fees). The short-sell hedging demand is inelastic to fees. The positive fee effect with increased hedging becomes significantly strong for expensive-to-borrow stocks that have liquid at-the-the-money warrants. Traders who engage in such hedging have a solid motivation to manage downside risk due to price fluctuations and active hedge rebalancing requirements because of the sensitive delta. This risk management requirement is reflected in fees charged by lenders. My analysis provides insights into whether regulators and investors should be wary of increased bearish trading strategies in the derivatives market, which could inflate short-selling costs in the lending market. I further find that warrant hedgers’ demand is sensitive to fees before negative earnings announcements, i.e., hedgers’ short-selling demand declines with higher loan fees. This effect reflects the fact that such hedgers short when they expect higher selling pressure, i.e., they sell low. In contrast, I find that the short-selling demand of traders who are not hedgers is positively associated with costs before the negative earnings information because they feel an urgency to generate profit with overvalued stocks; in other words, they sell high when in receipt of private bad news

DietCNN: Multiplication-free Inference for Quantized CNNs

The rising demand for networked embedded systems with machine intelligence has been a catalyst for sustained attempts by the research community to implement Convolutional Neural Networks (CNN) based inferencing on embedded resource-limited devices. Redesigning a CNN by removing costly multiplication operations has already shown promising results in terms of reducing inference energy usage. This paper proposes a new method for replacing multiplications in a CNN by table look-ups. Unlike existing methods that completely modify the CNN operations, the proposed methodology preserves the semantics of the major CNN operations. Conforming to the existing mechanism of the CNN layer operations ensures that the reliability of a standard CNN is preserved. It is shown that the proposed multiplication-free CNN, based on a single activation codebook, can achieve 4.7x, 5.6x, and 3.5x reduction in energy per inference in an FPGA implementation of MNIST-LeNet-5, CIFAR10-VGG-11, and Tiny ImageNet-ResNet-18 respectively. Our results show that the DietCNN approach significantly improves the resource consumption and latency of deep inference for smaller models, often used in embedded systems. Our code is available at: https://github.com/swadeykgp/DietCNNComment: Supplementary for S. Dey, P. Dasgupta and P. P. Chakrabarti, "DietCNN: Multiplication-free Inference for Quantized CNNs," 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, 2023, pp. 1-8, doi: 10.1109/IJCNN54540.2023.1019177

Perfect powers in an alternating sum of consecutive cubes

In this paper, we consider the problem about finding out perfect powers in an alternating sum of consecutive cubes. More precisely, we completely solve the Diophantine equation (x+1)3 - (x+2)3 + ∙∙∙ - (x + 2d)3 + (x + 2d + 1)3 = zp, where p is prime and x,d,z are integers with 1 ≤ d ≤ 50

3D bioprinting for reconstituting the cancer microenvironment.

The cancer microenvironment is known for its complexity, both in its content as well as its dynamic nature, which is difficult to study using two-dimensional (2D) cell culture models. Several advances in tissue engineering have allowed more physiologically relevant three-dimensional (3D) in vitro cancer models, such as spheroid cultures, biopolymer scaffolds, and cancer-on-a-chip devices. Although these models serve as powerful tools for dissecting the roles of various biochemical and biophysical cues in carcinoma initiation and progression, they lack the ability to control the organization of multiple cell types in a complex dynamic 3D architecture. By virtue of its ability to precisely define perfusable networks and position of various cell types in a high-throughput manner, 3D bioprinting has the potential to more closely recapitulate the cancer microenvironment, relative to current methods. In this review, we discuss the applications of 3D bioprinting in mimicking cancer microenvironment, their use in immunotherapy as prescreening tools, and overview of current bioprinted cancer models

Identification of test cases for Automated Driving Systems using Bayesian optimization

With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomous driving systems are highly safety critical in nature and need to be thoroughly tested for a diverse set of conditions before being commercially deployed. Due to the huge complexities involved with Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), traditional software testing methods have well-known limitations. They also fail to cover the infinite number of adverse conditions that can occur due to a slight change in the interactions between the environment and the system. Hence, it is important to identify test conditions that push the vehicle under test to breach its safe boundaries. Hazard Based Testing (HBT) methods, inspired by Systems-Theoretic Process Analysis (STPA), identify such parameterized test conditions that can lead to system failure. However, these techniques fall short of discovering the exact parameter values that lead to the failure condition. The presented paper proposes a test case identification technique using Bayesian Optimization. For a given test scenario, the proposed method learns parameter values by observing the system's output. The identified values create test cases that drive the system to violate its safe boundaries. STPA inspired outputs (parameters and pass/fail criteria) are used as inputs to the Bayesian Optimization model. The proposed method was applied to an SAE Level-4 Low Speed Automated Driving (LSAD) system which was modelled in a driving simulator

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations