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Results for “EM” · papers 18 · wiki 36
Academic Papers · 18arXiv q-fin live 0 · desk corpus 933
arXiv · arXiv · 2025

Is attention truly all we need? An empirical study of asset pricing in pretrained RNN sparse and global attention models

This study investigates the pre-trained RNN attention models with the mainstream attention mechanisms, such as additive attention, Luong's three attentions, global self-attention and sliding window sparse attention, for the empirical asset pricing research on the top 420 large-cap US stocks. This is the first paper on the large-scale state-of-the-art (SOTA) attention mechanisms applied in the asset pricing context. T…

Shanyan Lai
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arXiv · arXiv · 2025

Increasing Systemic Resilience to Socioeconomic Challenges: Modeling the Dynamics of Liquidity Flows and Systemic Risks Using Navier-Stokes Equations

Modern economic systems face unprecedented socioeconomic challenges, making systemic resilience and effective liquidity flow management essential. Traditional models such as CAPM, VaR, and GARCH often fail to reflect real market fluctuations and extreme events. This study develops and validates an innovative mathematical model based on the Navier-Stokes equations, aimed at the quantitative assessment, forecasting, an…

Davit Gondauri
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OpenAlex · 2009 · cites 352

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems

Acknowledgments. Chapter 1 Introduction. Chapter 2 Evolution of High-Frequency Trading. Financial Markets And Technological Innovation. Evolution Of Trading Methodology. Chapter 3 Overview of the Business of High-Frequency Trading. Comparison With Traditional Approaches to Trading. Market Participants. Operating Model. Economics. Capitalizing a High-Frequency Trading Business. Conclusion. Chapter 4 Financial Markets …

Irene Aldridge
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OpenAlex · The Lancet Neurology · 2021 · cites 8082

Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND: Regularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at globa…

Valery L. Feigin, Benjamin Stark, Catherine O. Johnson, Gregory A. Roth, Catherine Bisignano
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arXiv · arXiv · 2021

Liquidity Stress Testing in Asset Management -- Part 3. Managing the Asset-Liability Liquidity Risk

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers the modeling of the liability liquidity risk (or funding liquidity), the second dimension is dedicated to the modeling of the asset liquidity risk (or market liquidity), whereas the third dimension considers the management of the asset-liability liquidi…

Thierry Roncalli
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arXiv · arXiv · 2021

Liquidity Stress Testing in Asset Management -- Part 2. Modeling the Asset Liquidity Risk

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability…

Thierry Roncalli, Amina Cherief, Fatma Karray-Meziou, Margaux Regnault
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arXiv · arXiv · 2021

Liquidity Stress Testing in Asset Management -- Part 1. Modeling the Liability Liquidity Risk

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers asset-liability liquidity risk management (or asset-liability matching). The…

Thierry Roncalli, Fatma Karray-Meziou, François Pan, Margaux Regnault
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arXiv · arXiv · 2019

Systemic liquidity contagion in the European interbank market

Systemic liquidity risk, defined by the IMF as "the risk of simultaneous liquidity difficulties at multiple financial institutions", is a key topic in macroprudential policy and financial stress analysis. Specialized models to simulate funding liquidity risk and contagion are available but they require not only banks' bilateral exposures data but also balance sheet data with sufficient granularity, which are hardly a…

V. Macchiati, G. Brandi, G. Cimini, G. Caldarelli, D. Paolotti
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OpenAlex · Proceedings of the AAAI Conference on Artificial Intelligence · 2020 · cites 136

Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach

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…

Yang Liu, Qi Liu, Hongke Zhao, Pan Zhen, Chuanren Liu
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arXiv · arXiv · 2026

Harvesting the Volatility Risk Premium: A Learning-to-Rank Approach

This paper develops the first end-to-end application of cross-sectional learning-to-rank to the S&P 500 weekly options (SPXW) zero-day-to-expiration surface, integrated with margin-aware position sizing, an abstention rule driven by model uncertainty, and a strict out-of-time integrity check. A LightGBM LambdaRank ranker scores a daily nine-strategy cross-section composed of eight delta-targeted short-put positions a…

Maciej Wysocki
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arXiv · arXiv · 2026

Energy Market and Carbon Emission Spillovers in Critical Minerals Investment: A Dynamic Connectedness Approach

Design/methodology/approach A time-varying parameter vector autoregression (TVP-VAR) model is employed to quantify dynamic connectedness and directional volatility spillovers using daily data from May 1, 2013, to May 2, 2023. The study isolates the impact of extreme events by splitting the data into pre- and post-COVID-19 samples based on the February 2020 stock market crash. Purpose This paper examines the daily fin…

Haibo Wang, Lutfu Sua, Jaime Ortiz, Jun Huang, Bahram Alidaee
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arXiv · arXiv · 2026

Forecasting of volatility and risk premia in electricity markets

We study forecasting of the realized covariation in electricity markets. The realized covariation in this context is a matrix-valued representation of the latent infinite-dimensional covariance operator and a parsimonious matrix-HAR type model is constructed to facilitate estimation. We test the model on one-week ahead forecasts of the weekly realized covariation and find that the inclusion of longer time horizons an…

Thomas K. Kloster, Fred Espen Benth
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arXiv · arXiv · 2026

Regime-Based Portfolio Allocation Using Hidden Markov Models and Reinforcement Learning

This study develops a regime-aware portfolio allocation framework that integrates Markov switching models with Reinforcement Learning (RL) to dynamically allocate across equities (SPY), long-term Treasuries (TLT), and gold (GLD). Using daily ETF data from 2004-2025, we first characterize market behavior through a discrete Markov chain and then estimate a three-state Gaussian Hidden Markov Model (HMM) selected by the …

Ajay Kumar Verma, Nunik Srikandi Putri, Neo Paul Lesupi
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arXiv · arXiv · 2026

Towards Chemically Accurate and Scalable Quantum Simulations on IQM Quantum Hardware: A Quantum-HPC Hybrid Approach

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.
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arXiv · arXiv · 2026

Beyond Prompting: An Autonomous Framework for Systematic Factor Investing via Agentic AI

This paper develops an autonomous framework for systematic factor investing via agentic AI. Rather than relying on sequential manual prompts, our approach operationalizes the model as a self-directed engine that endogenously formulates interpretable trading signals. To mitigate data snooping biases, this closed-loop system imposes strict empirical discipline through out-of-sample validation and economic rationale req…

Allen Yikuan Huang, Zheqi Fan
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arXiv · arXiv · 2026

P vs NP Problem in Portfolio Optimization: Integrating the Markowitz-CAPM Framework with Cardinality Constraints and Black-Scholes Derivative Pricing

This paper makes the Millennium Prize problem P vs NP operational in quantitative finance by studying cardinality-constrained portfolio selection. Starting from the convex Markowitz mean-variance program with CAPM-based expected returns (Rf plus beta times ERP), we impose a hard sparsity rule that limits the portfolio to K assets out of approximately 94 industry portfolios (Damodaran). The constraint couples discrete…

Davit Gondauri
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arXiv · arXiv · 2026

The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector

This paper evaluates the causal impact of Generative Artificial Intelligence (GenAI) adoption on productivity and systemic risk in the U.S. banking sector. Using a novel dataset linking SEC 10-Q filings to Federal Reserve regulatory data for 809 financial institutions over 2018--2025, we employ two complementary identification strategies: Dynamic Spatial Durbin Models (DSDM) to capture network spillovers and Syntheti…

Tatsuru Kikuchi
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Wiki Entities · 36
AI Systems

Activation Function

An activation function is the pointwise nonlinearity between linear layers. Without it, stacked layers collapse to a single linear map.

AI Systems

Adam Optimizer

Adam is an adaptive first-order optimizer that keeps exponential moving averages of the gradient and its square, giving per-parameter step sizes.

AI Systems

Agent Workflow

An agent workflow is a structured loop that plans, calls tools or models, observes results, and repeats until a stop condition — a pipeline with memory, contracts, and failure handling rather than a single completion.

AI Systems

Attention Mechanism

Attention builds a weighted average of values, with weights from a compatibility function of queries and keys. It lets a model focus on relevant parts of a context instead of a single fixed vector.

AI Systems

Autoencoder

An autoencoder learns to reconstruct its input through a bottleneck, producing a compressed latent that can be used for denoising, retrieval, or as a generative seed.

AI Systems

Backpropagation

Backpropagation computes gradients of a scalar loss with respect to every weight by applying the chain rule backwards through the computational graph.

AI Systems

Batch Normalization

Batch normalization re-centers and re-scales layer inputs using mini-batch statistics, then learns a scale and shift, reducing internal covariate shift and allowing higher learning rates.

AI Systems

Beam Search

Beam search is a heuristic decoder that keeps the k best partial sequences at each step instead of greedily taking only the top token — the classic seq2seq inference method.

AI Systems

BERT

BERT is a bidirectional Transformer encoder trained with masked language modeling and next-sentence prediction, then fine-tuned on downstream NLP tasks.

AI Systems

Byte Pair Encoding

BPE grows a vocabulary by repeatedly merging the most frequent adjacent pairs, starting from characters or bytes, until a target vocab size is reached.

AI Systems

Chain of Thought

Chain-of-thought prompting asks the model to emit intermediate reasoning steps before the answer, which reliably lifts arithmetic, symbolic, and multi-hop tasks.

AI Systems

CLIP

CLIP jointly trains an image encoder and a text encoder so matched image–caption pairs are close in a shared space, enabling zero-shot visual classification by text prompts.

AI Systems

Contrastive Learning

Contrastive learning pulls representations of related pairs together and pushes unrelated pairs apart. It is the pretraining idea behind SimCLR, CLIP, and many embedding models.

AI Systems

Convolutional Neural Network

A CNN shares a local kernel across spatial (or temporal) positions, building translation-equivariant features. It is the inductive bias that cracked modern computer vision.

AI Systems

Cross-Entropy Loss

Cross-entropy measures how well a predicted distribution q matches a target distribution p. For one-hot labels it reduces to −log q(y), the usual classification and language-model loss.

AI Systems

Deep Q-Network

DQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step.

AI Systems

Diffusion Model

A diffusion model learns to reverse a gradual noising process. Sampling starts from noise and iteratively denoises toward the data distribution.

AI Systems

Dropout

Dropout randomly zeroes hidden units during training so the net cannot rely on any single co-adaptation, then scales weights at test time (or uses inverted dropout).

AI Systems

Early Stopping

Early stopping treats training time as a capacity knob: halt when a validation metric stops improving so the model does not wander into overfit.

AI Systems

Embedding

An embedding is a learned dense vector for an object (token, sentence, image, user) such that geometry supports retrieval, clustering, or as input to a downstream model.

AI Systems

Fine-Tuning

Fine-tuning continues training a pretrained model on a narrower distribution so the same weights specialize — classification heads, instruction following, or a desk domain.

AI Systems

Flash Attention

FlashAttention computes exact attention with tiling that keeps softmax stats in SRAM, cutting HBM traffic and unlocking longer contexts at the same FLOP count.

AI Systems

Gated Recurrent Unit

GRU is a lighter gated RNN with reset and update gates, often matching LSTM quality at lower cost on medium-length sequences.

AI Systems

Generative Adversarial Network

A GAN trains a generator and a discriminator against each other: the generator maps noise to fake samples, the discriminator learns real vs fake, and the equilibrium is a generator whose samples match the data distribution.

AI Systems

GPT

GPT is a decoder-only Transformer trained to predict the next token. Scale plus this objective produced in-context learning and the current foundation-model product line.

AI Systems

Gradient Descent

Gradient descent updates parameters against the gradient of a loss: θ ← θ − η ∇_θ L. Stochastic and mini-batch variants make the method tractable on large datasets.

AI Systems

Graph Neural Network

A GNN updates each node from its neighbors. Message passing lets the model use relational structure — markets, molecules, citation graphs — instead of forcing a grid.

AI Systems

Hallucination

Hallucination is fluent generation that is not supported by the source or the world — a likelihood-trained model completing a pattern, not a database lookup.

AI Systems

In-Context Learning

In-context learning is when a frozen language model improves at a task from examples placed in the prompt, without weight updates.

AI Systems

Knowledge Distillation

Knowledge distillation trains a smaller student to match a teacher’s output distribution (soft labels), transferring behavior without copying every weight.

AI Systems

Layer Normalization

Layer normalization standardizes activations across features for each example, not across the batch — the stabilizer that made Transformers trainable.

AI Systems

Learning Rate Schedule

A learning-rate schedule is the planned path of η_t — warmup, cosine, step decay — that often matters more than the architecture headline on a given run.

AI Systems

Long Short-Term Memory

LSTM is a gated RNN whose cell state can carry information across many steps, with input, forget, and output gates trained by gradient descent.

AI Systems

LoRA

LoRA fine-tunes a frozen model by learning low-rank adapters on selected weight matrices, cutting trainable parameters and storage versus full fine-tunes.

AI Systems

Mixture of Experts

MoE routes each token (or example) to a sparse subset of specialist feed-forward experts, raising parameter count without paying dense FLOPs on every token.

AI Systems

Neural Network

A neural network is a layered function approximator: units compute a weighted sum, apply a nonlinearity, and pass the result forward so the whole stack can learn a mapping from inputs to outputs.

Option Blackboard · 3
vol-structure

IV Rank & Surface Filter

Vol-structure note for classifying rich vs cheap ATM IV and surface dispersion before choosing credit or debit templates.

code

Put/Call Flow Watch

Scanner code fragment for put/call ratio and open-interest activity on the ATM chain window.

strategy

Defined-Risk Bridge Playbook

Strategy bridge from Blackboard scanner cards into Options Lab templates without implying live broker execution.

Encyclopedia · 24
AI Systems · Foundations

Activation Function

An activation function is the pointwise nonlinearity between linear layers. Without it, stacked layers collapse to a single linear map.

AI Systems · Foundations

Adam Optimizer

Adam is an adaptive first-order optimizer that keeps exponential moving averages of the gradient and its square, giving per-parameter step sizes.

AI Systems · Foundations

Agent Workflow

An agent workflow is a structured loop that plans, calls tools or models, observes results, and repeats until a stop condition — a pipeline with memory, contracts, and failure handling rather than a single completion.

Systems · Foundations

Alpha Decay

Alpha Decay — Speed at which a signal loses predictive power as capital competes for it.

Strategies · Foundations

Asset Growth Effect

Short high asset-growth firms and long low/negative growth — the investment/empire-building anomaly.

CTA · Foundations

ATR Trailing-Stop Trend

Enter on a trend signal, then trail a stop at k × ATR behind the favorable extreme — Wilder volatility as the exit engine.

AI Systems · Foundations

Attention Mechanism

Attention builds a weighted average of values, with weights from a compatibility function of queries and keys. It lets a model focus on relevant parts of a context instead of a single fixed vector.

AI Systems · Foundations

Autoencoder

An autoencoder learns to reconstruct its input through a bottleneck, producing a compressed latent that can be used for denoising, retrieval, or as a generative seed.

AI Systems · Foundations

Backpropagation

Backpropagation computes gradients of a scalar loss with respect to every weight by applying the chain rule backwards through the computational graph.

Commodities · Foundations

Baltic Dry Index

Baltic Dry Index tracks shipping rates for dry bulk commodities and offers a real-economy signal on trade flows, freight conditions, and industrial demand.

Banking · Foundations

Bank CDS Index

Bank CDS Index tracks the cost of insuring major bank credit risk and serves as a real-time indicator of banking-system stress and confidence.

Liquidity · Foundations

Bank Reserve Balances

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.

Liquidity · Foundations

Bank Term Funding Program Usage

BTFP usage tracks how much funding banks obtain through the Bank Term Funding Program, offering insight into balance-sheet stress and demand for official liquidity backstops.

AI Systems · Foundations

Batch Normalization

Batch normalization re-centers and re-scales layer inputs using mini-batch statistics, then learns a scale and shift, reducing internal covariate shift and allowing higher learning rates.

Mathematics · Foundations

Bayesian Inference

Bayesian inference updates a prior distribution over parameters with data via Bayes’ rule to get a posterior — beliefs as probabilities, not just a point estimate.

AI Systems · Foundations

Beam Search

Beam search is a heuristic decoder that keeps the k best partial sequences at each step instead of greedily taking only the top token — the classic seq2seq inference method.

Desk Slang · Foundations

Bear Steepener

A bear steepener is a curve move where long yields rise more than front yields (or fronts fall less) as the market prices more term premium, more deficit, or less faith in long-run restraint — and duration loses.

CTA · Foundations

Behavioral CTA

A systematic book that targets documented investor behaviors — stops, anchoring, month-end flows — rather than a generic trend equation.

AI Systems · Foundations

BERT

BERT is a bidirectional Transformer encoder trained with masked language modeling and next-sentence prediction, then fine-tuned on downstream NLP tasks.

Economy · Foundations

Beveridge Curve

Beveridge Curve — Vacancy-unemployment relationship signaling matching efficiency and structural labor shifts.

Crypto · Foundations

Blockchain

A blockchain is an append-only replicated ledger with a consensus rule — a database with an incentive system, not a price target.

Mathematics · Foundations

Brownian Motion

Brownian motion (Wiener process) is the continuous-time random walk with independent Gaussian increments — the backbone of Black–Scholes, Ito calculus, and most diffusion models.

Emerging Markets · Foundations

BTP-Bund Spread

BTP-Bund spread measures the yield difference between Italian and German government bonds and is a key indicator of euro-area sovereign stress and fragmentation risk.

AI Systems · Foundations

Byte Pair Encoding

BPE grows a vocabulary by repeatedly merging the most frequent adjacent pairs, starting from characters or bytes, until a target vocab size is reached.

Cards · 5
StrategyCard

USD Funding Stress as Equity Vol Trigger

A funding-stress monitor can warn before equity volatility reprices.

SignalCard

Corporate Yield Spreads and Bond Liquidity

A liquidity premium monitor can separate default risk from liquidity-driven spread widening.

MethodCard

Paper to Strategy Transfer

A reusable card for converting research papers into buildable strategy or monitoring objects.

RiskCard

Funding Stress Monitor

A card for tracking cross-currency basis, repo dislocations, and balance-sheet stress as early warning signals.

StrategyCard

Dual-Blade Entry Filter

A serial decision card combining direction filter and time-window filter before execution.

Local Modules · 1
TopicsRadar

Topics

Theme clusters.

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