Search

Search

Papers, wiki, Option Blackboard, encyclopedia, and cards.

Results for “EM” · papers 7 · wiki 36
Academic Papers · 7arXiv q-fin live 0 · desk corpus 7
arXiv · arXiv · 2026

Data-Driven Duration Management -- Term Structure Forecasting Using Machine Learning

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 …

Tobias Lausser, Joao Eduardo Vuolo, Rudi Zagst
Open in CodexSource →PDF
arXiv · arXiv · 2018

Are multi-factor Gaussian term structure models still useful? An empirical analysis on Italian BTPs

In this paper, we empirically study models for pricing Italian sovereign bonds under a reduced form framework, by assuming different dynamics for the short-rate process. We analyze classical Cox-Ingersoll-Ross and Vasicek multi-factor models, with a focus on optimization algorithms applied in the calibration exercise. The Kalman filter algorithm together with a maximum likelihood estimation method are considered to f…

Michele Leonardo Bianchi
Open in CodexSource →PDF
arXiv · arXiv · 2026

Early-Buyer Groups and Fast-Wallet Teams on a Bonding-Curve Launchpad: A Prospective Measurement Study of pump.fun on Solana

We study coordinated groups of wallets on a bonding-curve launchpad (pump.fun on Solana), using a complete record of 200\,443 token launches, 15\,854\,121 curve trades and 51\,025\,958 SOL transfers over 88.3 hours. A prospective, multi-channel discovery procedure (co-buying, shared funding, joint signing, transfers, consolidation) yields 23\,710 wallet groups. Without ground-truth labels we validate them by out-of-s…

Fail Gafarov
Open in CodexSource →PDF
arXiv · arXiv · 2024

Investigating the price determinants of the European Emission Trading System: a non-parametric approach

The European carbon market plays a pivotal role in the European Union's ambitious target of achieving carbon neutrality by 2050. Understanding the intricacies of factors influencing European Union Emission Trading System (EU ETS) market prices is paramount for effective policy making and strategy implementation. We propose the use of the Information Imbalance, a recently introduced non-parametric measure quantifying …

Cristiano Salvagnin, Aldo Glielmo, Maria Elena De Giuli, Antonietta Mira
Open in CodexSource →PDF
arXiv · arXiv · 2023

Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose…

Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
Open in CodexSource →PDF
arXiv · arXiv · 2026

Learning AI Agents, Market Makers, and Stress in Automated Markets

Automated markets increasingly route order flow from learning trading agents through automated market makers (AMMs). We model trader archetypes as AI agents defined by their reward functions and analyze the feedback they create on a constant-product AMM. Herd-dominated and arbitrage-dominated markets of equal size have identical return distributions and turnover, so no rule based on volatility, value-at-risk, stress …

Irene Aldridge
Open in CodexSource →PDF
arXiv · arXiv · 2026

Reading the Count: Prediction Markets and Brazil's 2026 Presidential Election

We rebuild the public count of Brazil's 2026 presidential runoff minute by minute from the electoral court's section-level records and compare it with every trade on Kalshi and Polymarket. A projection published live on the night gave the eventual winner, Lula, a probability of 0.998 from 19:15, with 1.6% of sections counted. The markets reached 0.9 at 20:24, with 46% counted. Read as the projection's central estimat…

Raphael Coelho
Open in CodexSource →PDF
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.

← Back to Codex