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on Market Microstructure |
| By: | Mayukh Mukhopadhyay; Kaushik Ghosh |
| Abstract: | Non Fungible Token (NFT) Industry has been witnessing multi-million dollar trade in recent times. With rapid innovation of the NFT market environment by technology, innovation, and decentralization, it is becoming hard to distinguish between genuine NFT from fads and scams. This article discuss the NFT market microstructure, with a focus on price formation, market structure, transparency, and applications to other financial areas. Market manipulation in NFT market with the context of wash-sale patterns has also been surveyed. The article concludes by providing pointers on due-diligence activity that can be adopted by investors to mitigate NFT trading risk. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.03172&r= |
| By: | Garg, Karan (University of Warwick) |
| Abstract: | The rise of machine learning has revolutionised finance. Institutions across the world have increasingly turned to data science and machine learning to create trading models without the need for human intervention. This has had various implications for the financial markets that they operate in, including market efficiency. This paper simulates a financial market with agent-based modelling and Monte-Carlo style simulations, to motivate a qualitative discussion about the implications of increased algorithmic trading on financial market efficiency. It finds that algorithmic traders (ATs) can seemingly increase market efficiency through better liquidity management and more complete extraction of information from prices. However, this also comes with increased instability and potential convergence to an unstable equilibrium. The Adaptive Market Hypothesis (Lo, 2004) is suggested as an alternative framework for analysing AT behaviour. |
| Keywords: | Neural Networks ; Agent-Based Modelling ; Efficient Market Hypothesis ; Stock Market Simulation ; Financial Regulation JEL Classification: C45 ; C53 ; G14 ; G17 ; G18 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wrkesp:11&r= |
| By: | Francisco Caio Lima Paiva; Leonardo Kanashiro Felizardo; Reinaldo Augusto da Costa Bianchi; Anna Helena Reali Costa |
| Abstract: | The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers. Reinforcement Learning (RL) and Natural Language Processing have gained notoriety in these single-asset trading tasks, but only a few works have explored their combination. Moreover, some issues are still not addressed, such as extracting market sentiment momentum through the explicit capture of sentiment features that reflect the market condition over time and assessing the consistency and stability of RL results in different situations. Filling this gap, we propose the Sentiment-Aware RL (SentARL) intelligent trading system that improves profit stability by leveraging market mood through an adaptive amount of past sentiment features drawn from textual news. We evaluated SentARL across twenty assets, two transaction costs, and five different periods and initializations to show its consistent effectiveness against baselines. Subsequently, this thorough assessment allowed us to identify the boundary between news coverage and market sentiment regarding the correlation of price-time series above which SentARL's effectiveness is outstanding. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2112.02095&r= |
| By: | Bruce Mizrach |
| Abstract: | Seven of the ten largest stablecoins are backed by fiat assets. The 2016 and 2017 vintages of stablecoins have failure rates of 100% and 50% respectively. More than one-third of stablecoins have failed. Tether has a 39% share of 1.77 trillion USD in 2021Q2 transactions, and USD Coin 28%. The top three stablecoins have an average velocity of 28.3. Tether transacted between 3.8 million unique addresses, 63% of the ERC-20 token network. Six of the top ten tokens have unconcentrated Herfindahl indices, but Gemini, Pax and Huobi have single holders with more than 50% of the supply. The median Tether transaction fee is similar to the cost of an ATM transaction, but they are three to four times more for Dai and USDC. Fees, which are proportional to the price of Ethereum, are rising though. Median fees for Tether rose 3,628% over the last year, and 1,897% for USD Coin. 24 hour exchange turnover in Tether is nearly $120 billion. This is comparable to the daily volume at the NYSE and almost 15 times the daily flow in money market mutual funds. Narrow bid-ask spreads and depth have attracted active HFT participation. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.01392&r= |