arXiv · arXiv q-fin · 2024
In high frequency trading, accurate prediction of Order Flow Imbalance (OFI) is crucial for understanding market dynamics and maintaining liquidity. This paper introduces a hybrid predictive model that combines Vector Auto Regression (VAR) with a simple feedforward neural network (FNN) to forecast OFI and assess trading intensity. The VAR component captures linear dependencies, while residuals are fed into the FNN to…
Abdul Rahman, Neelesh Upadhye
arXiv · arXiv q-fin · 2023
This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization technique and martingale representation, we offer a solution framework for these issues and also develop a closed-form portfoli…
Weiping Wu, Yu Lin, Jianjun Gao, Ke Zhou
arXiv · arXiv q-fin · 2025
Decentralized finance (DeFi) lacks centralized oversight, often resulting in heightened volatility. In contrast, centralized finance (CeFi) offers a more stable environment with institutional safeguards. Institutional backing can play a stabilizing role in a hybrid structure (HyFi), enhancing transparency, governance, and market discipline. This study investigates whether HyFi-like cryptocurrencies, those backed by i…
Ihlas Sovbetov
arXiv · arXiv · 2025
With market capitalization exceeding USD250 billion by mid-2025, stablecoins have evolved from a crypto-focused innovation into a vital component of the global monetary structure. This paper identifies the characteristics of stablecoins from an analytical perspective and investigates the role of stablecoins in forming a hybrid monetary ecosystem where public (fiat, CBDC) and private (USDC, USDT, DAI) monies coexist. …
Hongzhe Wen, Songbai Li, R. S. M. Lau, Jamie Zhang
arXiv · arXiv · 2026
We extend a residual-learning hybrid credit scoring framework (logistic regression scorecard plus a gradient-boosting correction on its residuals, decomposed at each prediction into an interpretability ratio $ρ(x)$ that measures the share attributable to the linear branch) along three axes: an East African empirical instantiation on the Zindi Financial Inclusion in Africa data (Kenya, Rwanda, Tanzania, Uganda); a fai…
Belise Kanziga, Yaé U. Gaba, Olivier Kanamugire
arXiv · arXiv · 2026
Foundation models for time series forecasting demonstrate impressive zero-shot generalization but often underperform on specialized domains such as high-frequency finance. We present a comprehensive study of hybrid neural-classical correction for adapting frozen TimesFM (200M parameters) to stock return prediction during the volatile opening trading hour. We compare two neural correction architectures - AttnCorrect (…
Kasun Dewage, Suranadi De Silva, Shankhadeep Mondal
arXiv · arXiv · 2026
We present a convolutional variational autoencoder for cryptocurrency implied-volatility surfaces, together with a deployable predictor that combines it with a quadratic smile re-fit through a deterministic per-tenor routing rule. Trained on 6,034 fully-filled hourly Binance Options surfaces of BTC and ETH spanning May-October 2023 and parameterised on a common $6 \times 7$ tenor-delta grid, the model attains a hidde…
Sadanand Singh, Allam Reddy, Manan Chopra
arXiv · arXiv · 2026
We present a hybrid news sentiment engine that continuously learns market sentiment from paired news headlines and concurrent asset-price snapshots without requiring any neural network training or GPU compute. The system uses a three-way ensemble combining (1) a financial-domain lexicon (FinBERT-style keyword scoring), (2) an adaptive statistical TF-IDF cluster learner that organizes headlines into semantic neighborh…
Andreas Aigner
arXiv · arXiv · 2026
Hybrid quantum-classical solvers conceal how reported performance divides between quantum-processing-unit (QPU) access and other service time. We audit D-Wave's Leap service on cardinality-constrained mean-variance portfolio instances from N=10 to 640, comparing constraint-native CQMs, penalty-encoded BQMs, Gurobi MIQP, simulated annealing, and a matched-budget Tabu baseline, and we propose a four-metric operational …
Luis Lozano
arXiv · arXiv · 2026
Accurate forecasting of the Volatility-Covariance Matrix (VCV) is central to regulatory capital adequacy processes such as the Internal Capital Adequacy Assessment Process (ICAAP) and the Comprehensive Capital Analysis and Review (CCAR). Traditional econometric models, including GARCH-family and Exponentially Weighted Moving Average (EWMA) approaches, suffer from parametric rigidity, distributional assumptions, and n…
Ujjwala Vadrevu
arXiv · arXiv · 2026
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…
Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee, Alok Shukla, Sai Shankar P.
arXiv · arXiv · 2026
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…
Abdulrahman Alswaidan, Jeffrey D. Varner
arXiv · arXiv · 2026
Portfolio optimization under strict cardinality constraints is a combinatorial challenge that defies classical convex optimization techniques, particularly in the context of "Direct Indexing" and ESG-constrained mandates. In the Noisy Intermediate-Scale Quantum (NISQ) era, the Quantum Approximate Optimization Algorithm (QAOA) offers a promising hybrid approach. However, standard QAOA implementations utilizing transve…
Javier Mancilla, Theodoros D. Bouloumis, Frederic Goguikian
arXiv · arXiv · 2026
The intricate behavior patterns of financial markets are influenced by fundamental, technical, and psychological factors. During times of high volatility and regime shifts causes many traditional strategies like trend-following or mean-reversion to fail. This paper proposes a hybrid AI-based trading strategy that combines (1) trend-following and directional momentum capture via EMA and MACD, (2) detection of price no…
Varun Narayan Kannan Pillai, Akshay Ajith, Sumesh K J
arXiv · arXiv · 2025
Accurate volatility forecasting is essential in banking, investment, and risk management, because expectations about future market movements directly influence current decisions. This study proposes a hybrid modelling framework that integrates a Stochastic Volatility model with a Long Short Term Memory neural network. The SV model improves statistical precision and captures latent volatility dynamics, especially in r…
Anna Perekhodko, Robert Ślepaczuk
arXiv · arXiv · 2025
This paper introduces a hybrid framework for portfolio optimization that fuses Long Short-Term Memory (LSTM) forecasting with a Proximal Policy Optimization (PPO) reinforcement learning strategy. The proposed system leverages the predictive power of deep recurrent networks to capture temporal dependencies, while the PPO agent adaptively refines portfolio allocations in continuous action spaces, allowing the system to…
Jun Kevin, Pujianto Yugopuspito
arXiv · arXiv · 2025
We address finance-native collateral optimization under ISDA Credit Support Annexes (CSAs), where integer lots, Schedule A haircuts, RA/MTA gating, and issuer/currency/class caps create rugged, legally bounded search spaces. We introduce a certifiable hybrid pipeline purpose-built for this domain: (i) an evidence-gated LLM that extracts CSA terms to a normalized JSON (abstain-by-default, span-cited); (ii) a quantum-i…
Tao Jin, Stuart Florescu, Heyu, Jin
arXiv · arXiv · 2025
Covariance matrices estimated from short, noisy, and non-Gaussian financial time series are notoriously unstable. Empirical evidence suggests that such covariance structures often exhibit power-law scaling, reflecting complex, hierarchical interactions among assets. Motivated by this observation, we introduce a power-law covariance model to characterize collective market dynamics and propose a hybrid estimator that i…
Andres Garcia-Medina