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on Market Microstructure |
| By: | Jai Pal |
| Abstract: | This study delves into the analysis of financial markets through the lens of Wyckoff Phases, a framework devised by Richard D. Wyckoff in the early 20th century. Focusing on the accumulation pattern within the Wyckoff framework, the research explores the phases of trading range and secondary test, elucidating their significance in understanding market dynamics and identifying potential trading opportunities. By dissecting the intricacies of these phases, the study sheds light on the creation of liquidity through market structure, offering insights into how traders can leverage this knowledge to anticipate price movements and make informed decisions. The effective detection and analysis of Wyckoff patterns necessitate robust computational models capable of processing complex market data, with spatial data best analyzed using Convolutional Neural Networks (CNNs) and temporal data through Long Short-Term Memory (LSTM) models. The creation of training data involves the generation of swing points, representing significant market movements, and filler points, introducing noise and enhancing model generalization. Activation functions, such as the sigmoid function, play a crucial role in determining the output behavior of neural network models. The results of the study demonstrate the remarkable efficacy of deep learning models in detecting Wyckoff patterns within financial data, underscoring their potential for enhancing pattern recognition and analysis in financial markets. In conclusion, the study highlights the transformative potential of AI-driven approaches in financial analysis and trading strategies, with the integration of AI technologies shaping the future of trading and investment practices. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.18839&r=mst |
| By: | Cheuk Yin Lee; Shen-Ning Tung; Tai-Ho Wang |
| Abstract: | Geometric mean market makers (G3Ms), such as Uniswap and Balancer, represent a widely used class of automated market makers (AMMs). These G3Ms are characterized by the following rule: the reserves of the AMM must maintain the same (weighted) geometric mean before and after each trade. This paper investigates the effects of trading fees on liquidity providers' (LP) profitability in a G3M, as well as the adverse selection faced by LPs due to arbitrage activities involving a reference market. Our work expands the model described in previous studies for G3Ms, integrating transaction fees and continuous-time arbitrage into the analysis. Within this context, we analyze G3M dynamics, characterized by stochastic storage processes, and calculate the growth rate of LP wealth. In particular, our results align with and extend the results concerning the constant product market maker, commonly referred to as Uniswap v2. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.18177&r=mst |
| By: | Francesco Bianchi; Sydney C. Ludvigson; Sai Ma |
| Abstract: | We measure the nature and severity of a variety of belief distortions in market reactions to hundreds of economic news events using a new methodology that synthesizes estimation of a structural asset pricing model with algorithmic machine learning to quantify bias. We estimate that investors systematically overreact to perceptions about multiple fundamental shocks in a macro-dynamic system, generating asymmetric compositional effects when several counteracting shocks occur simultaneously in real-world events. We show that belief overreaction to all shocks can lead the market to over- or underreact to events, amplifying or dampening volatility. |
| JEL: | G1 G12 G4 G41 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32301&r=mst |