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on Market Microstructure |
| By: | Nimalendran, Mahendrarajah; Rzayev, Khaladdin; Sagade, Satchit |
| Abstract: | We investigate how high-frequency trading (HFT) in equity markets affects options market liquidity. We find that increased aggressive HFT activity in the stock market leads to wider bid–ask spreads in the options market through two main channels. First, options market makers’ quotes are exposed to sniping risk from HFTs exploiting put–call parity violations. Second, informed trading in the options market further amplifies the impact of HFT in equity markets on the liquidity of options by simultaneously increasing the options bid–ask spread and intensifying aggressive HFT activity in the underlying market. |
| Keywords: | hedging; high-frequency trading; informed trading; latency arbitrage; options liquidity |
| JEL: | G14 G12 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:124228 |
| By: | Timothy DeLise |
| Abstract: | Market making refers to a form of trading in financial markets characterized by passive orders which add liquidity to limit order books. Market makers are important for the proper functioning of financial markets worldwide. Given the importance, financial mathematics has endeavored to derive optimal strategies for placing limit orders in this context. This paper identifies a key discrepancy between popular model assumptions and the realities of real markets, specifically regarding the dynamics around limit order fills. Traditionally, market making models rely on an assumption of low-cost random fills, when in reality we observe a high-cost non-random fill behavior. Namely, limit order fills are caused by and coincide with adverse price movements, which create a drag on the market maker's profit and loss. We refer to this phenomenon as "the negative drift" associated with limit order fills. We describe a discrete market model and prove theoretically that the negative drift exists. We also provide a detailed empirical simulation using one of the most traded financial instruments in the world, the 10 Year US Treasury Bond futures, which also confirms its existence. To our knowledge, this is the first paper to describe and prove this phenomenon in such detail. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.16527 |
| By: | Marcello Monga |
| Abstract: | Automated market makers (AMMs) are a new type of trading venues which are revolutionising the way market participants interact. At present, the majority of AMMs are constant function market makers (CFMMs) where a deterministic trading function determines how markets are cleared. Within CFMMs, we focus on constant product market makers (CPMMs) which implements the concentrated liquidity (CL) feature. In this thesis we formalise and study the trading mechanism of CPMMs with CL, and we develop liquidity provision and liquidity taking strategies. Our models are motivated and tested with market data. We derive optimal strategies for liquidity takers (LTs) who trade orders of large size and execute statistical arbitrages. First, we consider an LT who trades in a CPMM with CL and uses the dynamics of prices in competing venues as market signals. We use Uniswap v3 data to study price, liquidity, and trading cost dynamics, and to motivate the model. Next, we consider an LT who trades a basket of crypto-currencies whose constituents co-move. We use market data to study lead-lag effects, spillover effects, and causality between trading venues. We derive optimal strategies for strategic liquidity providers (LPs) who provide liquidity in CPMM with CL. First, we use stochastic control tools to derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP's liquidity range is determined by the profitability of the pool, the dynamics of the LP's position, and concentration risk. Next, we use a model-free approach to solve the problem of an LP who provides liquidity in multiple CPMMs with CL. We do not specify a model for the stochastic processes observed by LPs, and use a long short-term memory (LSTM) neural network to approximate the optimal liquidity provision strategy. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.16885 |
| By: | Hongshen Yang; Avinash Malik |
| Abstract: | Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around $70 billion worth of crypto-currency is traded daily on exchanges. Trading crypto-currency is difficult due to the inherent volatility of the crypto-market. In this work, we want to test the hypothesis: "Can techniques from artificial intelligence help with algorithmically trading cryptocurrencies?". In order to address this question, we combine Reinforcement Learning (RL) with pair trading. Pair trading is a statistical arbitrage trading technique which exploits the price difference between statistically correlated assets. We train reinforcement learners to determine when and how to trade pairs of cryptocurrencies. We develop new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1-minute intervals (n = 263, 520). The traditional non-RL pair trading technique achieved an annualised profit of 8.33%, while the proposed RL-based pair trading technique achieved annualised profits from 9.94% - 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as cryptocurrencies. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.16103 |
| By: | Kogana, Shimon; Makarov, Igor; Niessnerc, Marina; Schoar, Antoinette |
| Abstract: | Trading in cryptocurrencies grew rapidly over the last decade, dominated by retail investors. Using data from eToro, we show that retail traders have different models of the underlying price dynamics of cryptocurrencies relative to other assets: They are contrarian in stocks and gold, yet these same traders follow a buy-and-hold strategy in cryptocurrencies. The differences are not explained by individual characteristics, investor composition, inattention, differences in fees, nor preference for lottery-like stocks. We conjecture that retail investors have a model where cryptocurrency price changes also affect the likelihood of future widespread adoption, which pushes prices further in the same direction. |
| Keywords: | cryptocurrencies; FinTech; retail trading; social finance; Elsevier deal |
| JEL: | G12 G14 |
| Date: | 2024–09–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:122266 |
| By: | Mignot, Sarah; Pellizzari, Paolo; Westerhoff, Frank H. |
| Abstract: | We explore the impact of fake news on asset price dynamics within the asset-pricing model of Brock and Hommes (1998). By polluting the information landscape, fake news interferes with agents' perception of the dividend process of the risky asset. Our analysis reveals that fake news decreases the steady-state price of the risky asset by making it even more risky. Moreover, fake news increases the market share of agents who use the destabilizing technical trading rule by rendering fundamental trading more difficult and costly. Instead of converging toward its steady state, the risky asset's price may thus be subject to wild fluctuations. As it turns out, these fluctuations are concentrated below the risky asset's steady-state price. We also show that fake news campaigns may allow certain agents to realize fraudulent profits. |
| Keywords: | Asset price dynamics, fake news, chartists and fundamentalists, bounded rationality and learning, stability and bifurcation analysis |
| JEL: | G12 G14 G41 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bamber:300668 |
| By: | Yacine Aït-Sahalia; Chen Xu Li; Chenxu Li |
| Abstract: | This paper relates jumps in high frequency stock prices to firm-level, industry and macroeconomic news, in the form of machine-readable releases from Thomson Reuters News Analytics. We find that most relevant news, both idiosyncratic and systematic, lead quickly to price jumps, as market efficiency suggests they should. However, in the reverse direction, the vast majority of price jumps do not have identifiable public news that can explain them, in a departure from the ideal of a fair, orderly and efficient market. Microstructure-driven variables have only limited predictive power to help distinguish between jumps with and without news. |
| JEL: | G12 G14 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32746 |
| By: | Abdul Jabbar; Syed Qaisar Jalil |
| Abstract: | This study evaluates the performance of 41 machine learning models, including 21 classifiers and 20 regressors, in predicting Bitcoin prices for algorithmic trading. By examining these models under various market conditions, we highlight their accuracy, robustness, and adaptability to the volatile cryptocurrency market. Our comprehensive analysis reveals the strengths and limitations of each model, providing critical insights for developing effective trading strategies. We employ both machine learning metrics (e.g., Mean Absolute Error, Root Mean Squared Error) and trading metrics (e.g., Profit and Loss percentage, Sharpe Ratio) to assess model performance. Our evaluation includes backtesting on historical data, forward testing on recent unseen data, and real-world trading scenarios, ensuring the robustness and practical applicability of our models. Key findings demonstrate that certain models, such as Random Forest and Stochastic Gradient Descent, outperform others in terms of profit and risk management. These insights offer valuable guidance for traders and researchers aiming to leverage machine learning for cryptocurrency trading. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.18334 |
| By: | Chong Zhang; Xinyi Liu; Mingyu Jin; Zhongmou Zhang; Lingyao Li; Zhenting Wang; Wenyue Hua; Dong Shu; Suiyuan Zhu; Xiaobo Jin; Sujian Li; Mengnan Du; Yongfeng Zhang |
| Abstract: | Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent . |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.18957 |
| By: | Jaeho Kim (Sogang University); Scott C. Linn (University of Oklahoma); Sora Chon (Inha University) |
| Abstract: | We demonstrate the superior performance of the price discovery measure recently developed by Kim and Linn (2022), termed the Long-run Forecast Share (LFS). Our examination involves a comparison of LFS with existing measures and highlights its wide applicability across various data generating processes. Recent studies, such as Shen et al. (2024) and Lautier et al. (2024), have overlooked reporting the uncertainty arising from finite sample estimation of price discovery measures. Our empirical investigation reveals that estimation uncertainty is significant in many cases, highlighting the importance of accurately quantifying this uncertainty. We introduce a novel approach for implementing the calculation of LFS based on its structural interpretation and demonstrate how our method allows quantification of the uncertainty associated with the measure. Our primary conclusions are based upon extensive simulation experiments across numerous data generating processes. We also present an in-depth investigation of price discovery in the spot and futures markets for key metal and energy commodities and find that LFS provides consistent conclusions across a variety of assumptions. |
| Keywords: | Price discovery, Futures and spot prices, Cointegration, Beveridge-Nelson decomposition |
| JEL: | C11 C32 C58 G14 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:inh:wpaper:2024-2 |