|
on Market Microstructure |
| By: | Guo, Qi; Huang, Shao'an; Wang, Gaowang |
| Abstract: | We develop a model of government intervention with information disclosure in which a government with two private signals trades directly in financial markets to stabilize asset prices. Government intervention through informed trading stabilizes financial markets and affects market quality (market liquidity and price efficiency) through a noise channel and an information channel. Information disclosure negatively affects financial stability by deteriorating the information advantages of the government, while its final effects on market quality hinge on the relative sizes of the noise effect and the information effect. Under different information disclosure scenarios, there exist potential tradeoffs between financial stability and price efficiency. |
| Keywords: | government intervention; information disclosure; financial stability; price efficiency; market liquidity |
| JEL: | D8 G1 |
| Date: | 2024–02–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:120072&r=mst |
| By: | Erdong Chen; Mengzhong Ma; Zixin Nie |
| Abstract: | This study presents a groundbreaking Systematization of Knowledge (SoK) initiative, focusing on an in-depth exploration of the dynamics and behavior of traders on perpetual future contracts across both centralized exchanges (CEXs), and decentralized exchanges (DEXs). We have refined the existing model for investigating traders' behavior in reaction to price volatility to create a new analytical framework specifically for these contract platforms, while also highlighting the role of blockchain technology in their application. Our research includes a comparative analysis of historical data from CEXs and a more extensive examination of complete transactional data on DEXs. On DEX of Virtual Automated Market Making (VAMM) Model, open interest on short and long positions exert effect on price volatility in opposite direction, attributable to VAMM's price formation mechanism. In the DEXs with Oracle Pricing Model, we observed a distinct asymmetry in trader behavior between buyers and sellers. Such asymmetry might stem from uninformed traders reacting more strongly to positive news than to negative, leading to a tendency to accumulate long positions. This study sheds light on the potential risks and advantages of using perpetual future contracts within the DeFi space while provides mathematical basis and empirical insights based on which future theoretical works can be configurated, offering crucial insights into the rapidly evolving world of blockchain-based financial instruments. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.03953&r=mst |
| By: | Xiaorui Zuo; Yao-Tsung Chen; Wolfgang Karl H\"ardle |
| Abstract: | In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators like BTC Price and the VCRIX index. This approach may be fed into the development of trading strategies aimed at utilizing social media elements to identify and forecast market trends. Crucially, our findings suggest that strategies based on emoji sentiment can facilitate the avoidance of significant market downturns and contribute to the stabilization of returns. This research underscores the practical benefits of integrating advanced AI-driven analyses into financial strategies, offering a nuanced perspective on the interplay between digital communication and market dynamics in an academic context. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.10481&r=mst |