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on Market Microstructure |
| By: | Aditya Nittur Anantha; Shashi Jain; Shivam Goyal; Dhruv Misra |
| Abstract: | Quoting algorithms are fundamental to electronic trading systems, enabling participants to post limit orders in a systematic and adaptive manner. In multi-asset or multi-contract settings, selecting the appropriate reference instrument for pricing quotes is essential to managing execution risk and minimizing trading costs. This work presents a framework for reference selection based on predictive modeling of short-term price stability. We employ multivariate Hawkes processes to model the temporal clustering and cross-excitation of order flow events, capturing the dynamics of activity at the top of the limit order book. To complement this, we introduce a Composite Liquidity Factor (CLF) that provides instantaneous estimates of slippage based on structural features of the book, such as price discontinuities and depth variation across levels. Unlike Hawkes processes, which capture temporal dependencies but not the absolute price structure of the book, the CLF offers a static snapshot of liquidity. A rolling voting mechanism is used to convert these signals into real-time reference decisions. Empirical evaluation on high-frequency market data demonstrates that forecasts derived from the Hawkes process align more closely with market-optimal quoting choices than those based on CLF. These findings highlight the complementary roles of dynamic event modeling and structural liquidity metrics in guiding quoting behavior under execution constraints. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.05749 |
| By: | Akash Deep; Chris Monico; W. Brent Lindquist; Svetlozar T. Rachev; Frank J. Fabozzi |
| Abstract: | We propose a machine learning-based extension of the classical binomial option pricing model that incorporates key market microstructure effects. Traditional models assume frictionless markets, overlooking empirical features such as bid-ask spreads, discrete price movements, and serial return correlations. Our framework augments the binomial tree with path-dependent transition probabilities estimated via Random Forest classifiers trained on high-frequency market data. This approach preserves no-arbitrage conditions while embedding real-world trading dynamics into the pricing model. Using 46, 655 minute-level observations of SPY from January to June 2025, we achieve an AUC of 88.25% in forecasting one-step price movements. Order flow imbalance is identified as the most influential predictor, contributing 43.2% to feature importance. After resolving time-scaling inconsistencies in tree construction, our model yields option prices that deviate by 13.79% from Black-Scholes benchmarks, highlighting the impact of microstructure on fair value estimation. While computational limitations restrict the model to short-term derivatives, our results offer a robust, data-driven alternative to classical pricing methods grounded in empirical market behavior. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.16701 |
| By: | W\v{e}i Zh\=ang |
| Abstract: | This paper explores neural network-based approaches for algorithmic trading in cryptocurrency markets. Our approach combines multi-timeframe trend analysis with high-frequency direction prediction networks, achieving positive risk-adjusted returns through statistical modeling and systematic market exploitation. The system integrates diverse data sources including market data, on-chain metrics, and orderbook dynamics, translating these into unified buy/sell pressure signals. We demonstrate how machine learning models can effectively capture cross-timeframe relationships, enabling sub-second trading decisions with statistical confidence. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2508.02356 |
| By: | Xinbing Kong; Cheng Liu; Bin Wu |
| Abstract: | Asynchronous trading in high-frequency financial markets introduces significant biases into econometric analysis, distorting risk estimates and leading to suboptimal portfolio decisions. Existing synchronization methods, such as the previous-tick approach, suffer from information loss and create artificial price staleness. We introduce a novel framework that recasts the data synchronization challenge as a constrained matrix completion problem. Our approach recovers the potential matrix of high-frequency price increments by minimizing its nuclear norm -- capturing the underlying low-rank factor structure -- subject to a large-scale linear system derived from observed, asynchronous price changes. Theoretically, we prove the existence and uniqueness of our estimator and establish its convergence rate. A key theoretical insight is that our method accurately and robustly leverages information from both frequently and infrequently traded assets, overcoming a critical difficulty of efficiency loss in traditional methods. Empirically, using extensive simulations and a large panel of S&P 500 stocks, we demonstrate that our method substantially outperforms established benchmarks. It not only achieves significantly lower synchronization errors, but also corrects the bias in systematic risk estimates (i.e., eigenvalues) and the estimate of betas caused by stale prices. Crucially, portfolios constructed using our synchronized data yield consistently and economically significant higher out-of-sample Sharpe ratios. Our framework provides a powerful tool for uncovering the true dynamics of asset prices, with direct implications for high-frequency risk management, algorithmic trading, and econometric inference. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.12220 |