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We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO) that evaluates split quality conditional on the pools actually used; a Full-Venue O…
We present a large-scale experimental study of quantum-computing-based molecular simulation carried out on IQM's Sirius 24-qubit superconducting processor, utilizing up to 16 operational qubits. The work employs Sample-based Quantum Diagonalization (SQD) together with the Local Unitary Cluster Jastrow (LUCJ) ansatz to estimate ground-state energies for a set of benchmark molecules, including H$_2$, LiH, BeH$_2$, H$_2…
Preface. Acknowledgments. PART ONE: BACKGROUND MATERIAL. Chapter 1. Introduction. The CAPM Model. Market Neutral Strategy. Pairs Trading. Outline. Audience. Chapter 2. Time Series. Overview. Autocorrelation. Time Series Models. Forecasting. Goodness of Fit versus Bias. Model Choice. Modeling Stock Prices. Chapter 3. Factor Models. Introduction. Arbitrage Pricing Theory. The Covariance Matrix. Application: Calculating…
In quantitative trading, stock prediction plays an important role in developing an effective trading strategy to achieve a substantial return. Prediction outcomes also are the prerequisites for active portfolio construction and optimization. However, the stock prediction is a challenging task because of the diversified factors involved such as uncertainty and instability. Most of the previous research focuses on anal…
In recent years, considerable efforts have been devoted to developing AI techniques for finance research and applications. For instance, AI techniques (e.g., machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent financial data and fi…
While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, Dr. Ernest Chan, a respected independent trader and consultant, will show you how. Whether you're an independent "retail" trader looking to start your own quantita…
Quantitative trading (QT) , which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision…
Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer interpretability but limited flexibility, while deep learning models achieve strong performance yet often underutilize financial priors. We address this gap with PRISM-VQ (PRior-Informed Stock Model with Vector Quantization), a dynamic factor framework that integrates ex…
Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a g…
An order-book market whose liquidity provision is anchored to a fundamental value carries a restoring force: the price mean-reverts to value and the book refills after a shock. We show this restoring force is a robust intrinsic stabiliser and identify it causally-dialling the anchor down removes the mean-reversion, and a leverage-driven fire-sale then self-sustains. Separately, we ask whether a stressed market transm…
The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assu…
This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury …
Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. Academic literature provides different ways to formalize these algorithms and show ho…
Prediction markets are attracting growing attention as trading volumes rise and their practical relevance increases. To ensure efficient price discovery, liquidity provision becomes ever more important. Due to the binary settlement structure in prediction markets, optimal market making leads to an optimization problem that is fundamentally different from the ones studied in classical settings. In this paper, we devel…
Automated market makers (AMMs) for prediction markets descend from market scoring rules, where a mechanism operator subsidizes a market to aggregate beliefs about uncertain events. The existing literature has focused on bounding the total worst-case loss to the subsidizer, but has not addressed how that loss is distributed across price states or over time. We use the framework of loss-versus-rebalancing (LVR) to stud…
Financial decision systems require fast surrogate models for pricing, calibration, hedging, XVA, stress testing, and portfolio optimization. Standard neural surrogates reproduce prices or risk quantities, but downstream tasks depend as much on derivatives: deltas, vegas, curve and credit-spread sensitivities, exposure and objective gradients. We formulate a derivative-informed operator-learning framework in which the…
Generating synthetic financial time series that preserve the statistical properties of real market data is essential for stress testing, risk model validation, and scenario design. Existing approaches struggle to simultaneously reproduce heavy-tailed distributions, negligible linear autocorrelation, and persistent volatility clustering. We developed a hybrid hidden Markov framework that discretized excess growth rate…
The Martian brain terrain (MBT), characterized by its unique brain-like morphology, is a potential geological archive for finding hints of paleoclimatic conditions during its formation period. The morphological similarity of MBT to self-organized patterned ground on Earth suggests a shared formation mechanism. However, the lack of quantitative descriptions and robust physical modeling of self-organized stone transpor…
Bank reserve balances reflect the quantity of reserves held by banks at the Federal Reserve and are central to understanding liquidity distribution and financial system stability.
Volatility Regime (Quant).
Quantitative Tightening Pace — The speed of balance-sheet runoff and its impact on reserves, collateral markets, and term funding.
GARCH Volatility Model — Conditional heteroskedasticity framework for forecasting volatility clusters.
Factor Momentum — Persistence in relative factor performance exploitable by systematic overlays.
Quality Factor — Exposure to profitable, stable balance-sheet companies versus junk quality.
Value Factor — Cheap versus expensive stocks — cyclical performance tied to rates and inflation.
Low Volatility Anomaly — Empirical outperformance of low-beta stocks, crowded in risk-off regimes.
Size Premium — Historical return premium for smaller capitalisation stocks with liquidity caveats.
Liquidity Premium — Compensation for holding illiquid assets and providing immediacy.
Smart Beta Strategies — Rules-based factor tilts packaged for institutional asset allocation.
Risk Parity Allocation — Equal risk contribution across asset classes, often levered to bonds in disinflation.
Target Volatility — Dynamic scaling of exposure to maintain constant portfolio volatility.
Kelly Criterion — Optimal growth bet sizing framework — fragile with estimation error.
Maximum Drawdown Control — Rules that de-risk after losses to preserve capital and investor mandates.
Tail Risk Hedging — Explicit protection against left-tail moves via options, vol, or convex instruments.
Copula Models — Dependence modeling linking marginal distributions — infamous from 2008 structured credit.
Regime Switching Model — Statistical frameworks where parameters shift between discrete market states.
Cointegration Pairs Trading — Mean-reversion on stationary spreads between related instruments.
Statistical Arbitrage — Short-horizon RV on co-moving securities using factor neutralization.
Market Impact Model — Price response to order flow used in optimal execution and capacity estimates.
Optimal Execution Algorithm — Scheduling large orders to minimize impact and timing risk.
Transaction Cost Analysis — Post-trade measurement of slippage versus benchmarks for alpha decay control.
Backtest Overfitting — False discovery from mining historical patterns that do not persist out-of-sample.
Momentum Factor (Quant).
Low Volatility Factor (Quant).
Size Factor (Quant).
Profitability Factor (Quant).
Investment Factor (Quant).
Crowding Factor (Quant).
Short Interest Factor (Quant).
Residual Momentum — Momentum on idiosyncratic returns after factor residualization.
Time Series Momentum (Quant).
Cross Sectional Momentum (Quant).
Mean Reversion Signal (Quant).
Pairs Trading (Quant).
Abnormal Return (Quant).
Active Share Measure (Quant).
Adverse Selection (Quant).
Alpha Decay 1-day — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay 1-month — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay 1-week — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay 12-month — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay 3-month — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay 6-month — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay carry — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay core — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay disinflation — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay DM — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay easing — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay EM — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay intraday — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay liquidity-crisis — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay long-short — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay overlay — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay recession — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay reflation — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay risk-off — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay risk-on — Quantitative signal, risk, or portfolio-construction building block.
Alpha Decay satellite — Quantitative signal, risk, or portfolio-construction building block.