The Asymmetric Overnight Return Anomaly in the Chinese Stock Market

Traditional asset pricing theory suggests that to compensate for the uncertainty that investors bear, risky assets should generate considerably higher rates of return than the risk-free rate. However, the overnight return anomaly in the Chinese stock market, which refers to the anomaly that overnight return is significantly negative, contradicts the risk–return trade-off. We find that this anomaly is asymmetrical, as the overnight return is significantly negative after a negative daytime return, whereas the anomaly does not occur following a positive daytime return. We explain this anomaly from the perspective of investor attention. We show that the attention of individual investors behaves asymmetrically such that they draw more attention on negative daytime returns, and play an essential role in explaining the overnight return puzzle

SmartIntentNN: Towards Smart Contract Intent Detection

Researchers currently have been focusing on smart contract vulnerability detection, but we find that developers' intent to write smart contracts is a more noteworthy security concern because smart contracts with malicious intent have caused significant financial loss to users. A more unfortunate fact is that we can only rely on manual audits to check for unfriendly smart contracts. In this paper, we propose \textsc{SmartIntentNN}, Smart Contract Intent Neural Network, a deep learning-based tool that aims to automate the process of developers' intent detection in smart contracts, saving human resources and overhead. The demo video is available on \url{https://youtu.be/ho1SMtYm-wI}.Comment: 4 pages, 3 figures, conference tool track. arXiv admin note: substantial text overlap with arXiv:2211.1072

Deep Smart Contract Intent Detection

Nowadays, security activities in smart contracts concentrate on vulnerability detection. Despite early success, we find that developers' intent to write smart contracts is a more noteworthy security concern because smart contracts with malicious intent have caused significant users' financial loss. Unfortunately, current approaches to identify the aforementioned malicious smart contracts rely on smart contract security audits, which entail huge manpower consumption and financial expenditure. To resolve this issue, we propose a novel deep learning-based approach, SmartIntentNN, to conduct automated smart contract intent detection. SmartIntentNN consists of three primary parts: a pre-trained sentence encoder to generate the contextual representations of smart contracts, a K-means clustering method to highlight intent-related representations, and a bidirectional LSTM-based (long-short term memory) multi-label classification network to predict the intents in smart contracts. To evaluate the performance of SmartIntentNN, we collect more than 40,000 real smart contracts and perform a series of comparison experiments with our selected baseline approaches. The experimental results demonstrate that SmartIntentNN outperforms all baselines by up to 0.8212 in terms of the f1-score metric.Comment: 12 pages, 9 figures, conferenc

Large Norms of CNN Layers Do Not Hurt Adversarial Robustness

Since the Lipschitz properties of CNN are widely considered to be related to adversarial robustness, we theoretically characterize the $\ell_1$ norm and $\ell_\infty$ norm of 2D multi-channel convolutional layers and provide efficient methods to compute the exact $\ell_1$ norm and $\ell_\infty$ norm. Based on our theorem, we propose a novel regularization method termed norm decay, which can effectively reduce the norms of convolutional layers and fully-connected layers. Experiments show that norm-regularization methods, including norm decay, weight decay, and singular value clipping, can improve generalization of CNNs. However, they can slightly hurt adversarial robustness. Observing this unexpected phenomenon, we compute the norms of layers in the CNNs trained with three different adversarial training frameworks and surprisingly find that adversarially robust CNNs have comparable or even larger layer norms than their non-adversarially robust counterparts. Furthermore, we prove that under a mild assumption, adversarially robust classifiers can be achieved, and can have an arbitrarily large Lipschitz constant. For this reason, enforcing small norms on CNN layers may be neither necessary nor effective in achieving adversarial robustness. The code is available at https://github.com/youweiliang/norm_robustness.Comment: 15 pages, 4 figures; v5: corrected typ

Selling vertically differentiated products under one channel or two? A quality segmentation model for differentiated distribution channels

Many manufacturers, including Lenovo, Sony, Procter & Gamble, and Buckle, have adopted differentiated distribution channels to market vertically differentiated products. However, there is scant literature addressing the issue of quality differentiation in the presence of differentiated distribution channel policies. To fill this void, we examine whether (how) differentiated channel policies affect manufacturers’ quality differentiation and all parties’ performance. Specifically, we consider a manufacturer who produces two vertically differentiated products (high- and low-tier ) together, but withtwo marketing options: (1) distributing both products through one retailer (Model O, One-channel policy), or (2) providing high-quality products through one channel but low-tier products through another (Model T, Two-channel policy). Our results show that the manufacturer is more likely to decrease the level of quality differentiation in Model T than in Model O. Moreover, contrary to popular belief, we show that “quality distortion” is not limited to low-tier products but can occur with high-tier products. Among other results, we find that the one-channel policy benefits the retailer but hurts both the manufacturer and the total supply chain. To test the robustness of the results, we also comment on how the additional horizontal consumer heterogeneity affects our results and the implications of the competition at the manufacturer level

SoK: MEV Countermeasures: Theory and Practice

Blockchains offer strong security guarantees, but they cannot protect the ordering of transactions. Powerful players, such as miners, sequencers, and sophisticated bots, can reap significant profits by selectively including, excluding, or re-ordering user transactions. Such profits are called Miner/Maximal Extractable Value or MEV. MEV bears profound implications for blockchain security and decentralization. While numerous countermeasures have been proposed, there is no agreement on the best solution. Moreover, solutions developed in academic literature differ quite drastically from what is widely adopted by practitioners. For these reasons, this paper systematizes the knowledge of the theory and practice of MEV countermeasures. The contribution is twofold. First, we present a comprehensive taxonomy of 28 proposed MEV countermeasures, covering four different technical directions. Secondly, we empirically studied the most popular MEV- auction-based solution with rich blockchain and mempool data. In addition to gaining insights into MEV auction platforms' real-world operations, our study shed light on the prevalent censorship by MEV auction platforms as a result of the recent OFAC sanction, and its implication on blockchain properties

Experiments on bright field and dark field high energy electron imaging with thick target material

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained.Comment: 7pages, 7 figure