Forecasting stock market return with nonlinearity: a genetic programming approach

The issue whether return in the stock market is predictable remains ambiguous. This paper attempts to establish new return forecasting models in order to contribute on addressing this issue. In contrast to existing literatures, we first reveal that the model forecasting accuracy can be improved through better model specification without adding any new variables. Instead of having a unified return forecasting model, we argue that stock markets in different countries shall have different forecasting models. Furthermore, we adopt an evolutionary procedure called Genetic programming (GP), to develop our new models with nonlinearity. Our newly-developed forecasting models are testified to be more accurate than traditional AR-family models. More importantly, the trading strategy we propose based on our forecasting models has been verified to be highly profitable in different types of stock markets in terms of stock index futures trading

SoK: Decentralized Finance (DeFi) Attacks

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi's popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain's transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security? In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners' community. For example, few academic papers address "price oracle attacks" and "permissonless interactions", while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) SoTA bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges

SoK: Decentralized Finance (DeFi) Attacks

Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi’s popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain’s transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security? In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners’ community. For example, few academic papers address “price oracle attacks” and “permissonless interactions”, while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) SoTA bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges

The complete chloroplast genome of Silvianthus bracteatus (Carlemanniaceae) and phylogenic analysis of Lamiales

We assembled and characterized the complete chloroplast genome sequence of Silvianthus bracteatus to investigate its phylogenetic position. With a total length of 155,125 bp, the plastome comprised of a large single-copy (LSC) region of 86,054 bp, a small single-copy (SSC) region of 17,625 bp, and two inverted repeat (IR) regions of 25,723 bp. The overall percentage of GC content was 37.9. The new sequence comprised of total 136 genes, including 88 protein-coding genes, 8 ribosomal RNA genes, and 40 tRNA genes. In these genes, eight genes contained one intron and two genes contained two introns. Phylogenetic analysis showed that S. bracteatus was close to the Oleaceae

The complete chloroplast genome of Myxopyrum hainanense and phylogenic analysis of Oleaceae

We assembled and characterized the complete chloroplast genome sequence of Myxopyrum hainanense to investigate its phylogenetic position. The plastome is 156,064 bp in length, which is comprised of a large single-copy (LSC) region of 86,851 bp, a small single-copy (SSC) region of 17,837 bp, and two inverted repeat (IR) regions of 25,688 bp. The overall GC content of the plastome was 37.7. The new sequence comprised total 135 genes, including 87 protein-coding genes, 8 ribosomal RNA genes, and 40 tRNA genes. Phylogenetic analysis showed that M. hainanense was close to Nyctanthes arbor-tristis