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on Market Microstructure |
| By: | Vittorio Astarita |
| Abstract: | This study provides a practical introduction to high-frequency trading in blockchain-based currency markets. These types of markets have some specific characteristics that differentiate them from the stock markets, such as a large number of trading exchanges (centralized and decentralized), relative simplicity in moving funds from one exchange to another, and the large number of new currencies that have very little liquidity. This study analyzes the possible risks that specifically characterize this type of trading operation, the potential opportunities, and the algorithms that are mostly used, providing information that can be useful for practitioners who intend to operate in these markets by providing (and risking) liquidity. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.08590&r=mst |
| By: | Sebastian Jaimungal; Yuri F. Saporito; Max O. Souza; Yuri Thamsten |
| Abstract: | This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interaction between these two markets, estimating the conditional dependence between variables using the concept of conditional elicitability. Furthermore, we pose an optimal execution problem where the agent hides their orders by controlling the rate at which they trade. We do so without approximating the market dynamics. The resulting dynamic programming equation is not analytically tractable, therefore, we employ the deep Galerkin method to solve it. Finally, we conduct numerical experiments and illustrate that the optimal strategy is not prone to price slippage and outperforms na\"ive strategies. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.02180&r=mst |
| By: | Davide Lauria; Yuan Hu; W. Brent Lindquist; Svetlozar T. Rachev |
| Abstract: | In this work, we introduce a discrete binary tree for pricing contingent claims with underlying securities that are characterized by discontinuity jumps. The discrete nature of financial markets means that a continuous model is unable to correctly describe the traders' expectations of future price variations. The proposed binary tree contains, as special cases, classical models of market microstructure theory. The underlying price process converges to the classical geometric Brownian motion as the time interval between trades approaches zero. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.02356&r=mst |
| By: | Soumyadip Sarkar |
| Abstract: | Reinforcement learning (RL) is a branch of machine learning that has been used in a variety of applications such as robotics, game playing, and autonomous systems. In recent years, there has been growing interest in applying RL to quantitative trading, where the goal is to make profitable trades in financial markets. This paper explores the use of RL in quantitative trading and presents a case study of a RL-based trading algorithm. The results show that RL can be a powerful tool for quantitative trading, and that it has the potential to outperform traditional trading algorithms. The use of reinforcement learning in quantitative trading represents a promising area of research that can potentially lead to the development of more sophisticated and effective trading systems. Future work could explore the use of alternative reinforcement learning algorithms, incorporate additional data sources, and test the system on different asset classes. Overall, our research demonstrates the potential of using reinforcement learning in quantitative trading and highlights the importance of continued research and development in this area. By developing more sophisticated and effective trading systems, we can potentially improve the efficiency of financial markets and generate greater returns for investors. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.06037&r=mst |
| By: | Mingyu Hao; Artem Lenskiy |
| Abstract: | As a newly emerged asset class, cryptocurrency is evidently more volatile compared to the traditional equity markets. Due to its mostly unregulated nature, and often low liquidity, the price of crypto assets can sustain a significant change within minutes that in turn might result in considerable losses. In this paper, we employ an approach for encoding market information into images and making predictions of short-term realized volatility by employing Convolutional Neural Networks. We then compare the performance of the proposed encoding and corresponding model with other benchmark models. The experimental results demonstrate that this representation of market data with a Convolutional Neural Network as a predictive model has the potential to better capture the market dynamics and a better volatility prediction. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.02472&r=mst |
| By: | Eric Budish; Peter Cramton; Albert S. Kyle; Jeongmin Lee; David Malec |
| Abstract: | We introduce and analyze a new market design for trading financial assets. The design allows traders to directly trade any user-defined linear combination of assets. Orders for such portfolios are expressed as downward-sloping piecewise-linear demand curves with quantities as flows (shares/second). Batch auctions clear all asset markets jointly in discrete time. Market-clearing prices and quantities are shown to exist, despite the wide variety of preferences that can be expressed. Calculating prices and quantities is shown to be computationally feasible. Microfoundations are provided to show that traders can implement optimal strategies using portfolio orders. We discuss several potential advantages of the new market design, arising from the combination of discrete time and continuous prices and quantities (the most widely used alternative has these reversed) and the novel approach to trading portfolios of assets. |
| JEL: | D44 D47 D53 D82 G1 G2 G23 L13 L5 |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31098&r=mst |
| By: | Jiageng Liu; Igor Makarov; Antoinette Schoar |
| Abstract: | Terra, the third largest cryptocurrency ecosystem after Bitcoin and Ethereum, collapsed in three days in May 2022 and wiped out $50 billion in valuation. At the center of the collapse was a run on a blockchain-based borrowing and lending protocol (Anchor) that promised high yields to its stablecoin (UST) depositors. Using detailed data from the Terra blockchain and trading data from exchanges, we show that the run on Terra was a complex phenomenon that happened across multiple chains and assets. It was unlikely due to concentrated market manipulation by a third party but instead was precipitated by growing concerns about the sustainability of the system. Once a few large holders of UST adjusted their positions on May 7th, 2022, other large traders followed. Blockchain technology allowed investors to monitor each other's actions and amplified the speed of the run. Wealthier and more sophisticated investors were the first to run and experienced much smaller losses. Poorer and less sophisticated investors ran later and had larger losses. The complexity of the system made it difficult even for insiders to understand the buildup of risk. Finally, we draw broader lessons about financial fragility in an environment where a regulatory safety net does not exist, pseudonymous transactions are publicly observable, and market participants are incentivized to monitor the financial health of the system. |
| JEL: | E42 E44 G21 |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31160&r=mst |