REST framework predicts stock trends by considering stock-specific and related-stock events.
problem Predicting stock trends using event information from news, social media, and discussion boards.
method REST framework addresses two main shortcomings of existing event-driven methods: stock-specific event influence and related-stock event influence.
result REST framework achieves higher investment returns compared to baselines.
Event-driven features improve forex price prediction accuracy.
problem Inaccurate predictions in forex due to market volatility.
method Developed event-driven features and used LSTM, BiLSTM, GRU models.
result Improved prediction system with minimal risk.
Paper uncovers causal structures in Hawkes processes with latent subprocesses.
problem Tackles latent subprocesses in Hawkes processes with complex event-driven interactions.
method Proposes a two-phase iterative algorithm that infers causal relationships and identifies latent subprocesses.
result Successfully recovers causal structures in datasets with latent subprocesses.
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, designed for global optimization of expensive functions where concurrent function …
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackl…
Most automation in machine learning focuses on model selection and hyper parameter tuning, and many overlook the challenge of automatically defining predictive tasks. We still heavily rely on human experts to define prediction tasks, and generate labels by aggregating raw data. In this paper, we tackle the challenge of…
New asynchronous algorithms improve speed in decentralized optimization networks.
problem Hard convergence analysis for asynchronous decentralized optimization.
method Continuized framework to analyze heterogeneous delays in event-driven updates.
result Achieves asynchronous speedup with convergence rate controlled by eigengap weighted by local delays.
PredictionMarketBench benchmarks trading agents on prediction markets.
problem Evaluating trading agents on prediction markets with realistic conditions.
method Deterministic replay of historical data, execution-realistic simulator, agent interface.
result Fee-aware algorithmic strategies outperform naive agents in volatile episodes.
Paper proposes a new trading strategy using corporate event detection from news articles.
problem Predicting stock movements based on corporate events from news articles.
method Bi-level event detection model: low-level for token-level event identification, high-level for article-level event identification.
result The proposed strategy outperforms existing models in stock prediction metrics.
Proposes a neural LOB model for market-making.
problem Capturing dynamic LOB events in financial markets.
method Neural Hawkes process for modeling LOB events.
result Model captures real market price fluctuations.
The paper challenges the assumption of a unique global time in financial markets, highlighting market incompleteness.
problem The assumption of a unique global time in financial markets is challenged.
method The paper contrasts event-time, renewal, point-process, and order-flow descriptions of financial markets.
result Non-uniqueness of time leads to a more foundational form of market incompleteness.
We revisit and demonstrate the Epps effect using two well-known non-parametric covariance estimators; the Malliavin and Mancino (MM), and Hayashi and Yoshida (HY) estimators. We show the existence of the Epps effect in the top 10 stocks from the Johannesburg Stock Exchange (JSE) by various methods of aggregating Trade …
Exact simulation method for market impact estimation under various execution strategies.
problem Estimating market impact from observed price trajectories under different execution strategies.
method Conditional simulation of point processes under perturbed intensities.
result Exact, event-driven algorithm for reconstructing counterfactual paths.
Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the even…
Summarizes financial news for better investment decisions.
problem Information overload from financial news hinders timely investment decisions.
method Personalized Chain-of-Thought summarization framework integrating user-specified keywords.
result Personalized summaries highlight relevant market signals, improving investment narratives.
Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc. The main flavors of neural networks which are used commonly are convolutional (CNN) and recurrent (RNN). In spite of rap…
The study uses financial events to predict stock market movements.
problem Predicting stock market movements using financial events.
method Combined event extraction method, BERT/ALBERT enhanced event representation, and extended hierarchical attention network.
result Significantly better accuracies and higher simulated returns compared to state-of-the-art models.
The event-driven and elastic nature of serverless runtimes makes them a very efficient and cost-effective alternative for scaling up computations. So far, they have mostly been used for stateless, data parallel and ephemeral computations. In this work, we propose using serverless runtimes to solve generic, large-scale …
This paper models short rates with jumps using PDEs.
problem Capturing jumps and spikes in interest rates.
method PDE approach for pricing interest rate derivatives.
result Established Feynman-Kač representation and derived solutions.
Optimizes search times by resetting agents when a threshold is reached.
problem Improving search efficiency in systems with thresholds.
method Develops a framework for correlated stochastic processes with threshold resetting.
result Optimal resetting can prevent larger losses and is applicable to various stochastic systems.
The team predicts foreign exchange rates using clustering and attention models.
problem Complexity and unexpected events in foreign exchange markets.
method Clustering and attention models applied to historical data.
result Improved event-driven price prediction for oversold scenarios.
This work optimizes DNN inference for energy-harvesting devices by compressing and selectively executing neural network exits.
problem Inference delays and energy inefficiency in energy-harvesting devices.
method Developed a power trace-aware and exit-guided network compression algorithm for multi-exit neural networks.
result Superior accuracy and reduced latency compared to state-of-the-art techniques.
Spiking neuronal networks are usually simulated with three main simulation schemes: the classical time-driven and event-driven schemes, and the more recent hybrid scheme. All three schemes evolve the state of a neuron through a series of checkpoints: equally spaced in the first scheme and determined neuron-wise by spik…
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing demands for computing and power resources has become inevitable. Spiking neural networks (SNNs) have attracted widespread interest as the third-g…
Framework detects tipping points in complex systems using ML.
problem Detecting tipping points in complex, emergent systems.
method Combining manifold learning, neural networks, and Gaussian processes.
result Reduced-order models for mesoscopic and mean-field dynamics.
Spiking Neural Networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be levera…
We propose reinforcement learning on simple networks consisting of random connections of spiking neurons (both recurrent and feed-forward) that can learn complex tasks with very little trainable parameters. Such sparse and randomly interconnected recurrent spiking networks exhibit highly non-linear dynamics that transf…
Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be levera…
Spiking neural networks (SNNs) offer a promising alternative to current artificial neural networks to enable low-power event-driven neuromorphic hardware. Spike-based neuromorphic applications require processing and extracting meaningful information from spatio-temporal data, represented as series of spike trains over …
Fan tokens surged before World Cup matches, but declined during them, revealing cognitive biases.
problem Analyzing the impact of FIFA World Cup matches on fan tokens.
method Event study and intraday analysis of blockchain-based fan tokens.
result Fan tokens experienced a surge in returns six months before the World Cup, followed by a decline during the matches, revealing asymmetries in performance.
We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronol…
NeuroMem-FHP framework estimates FHP parameters efficiently.
problem Estimating parameters of fractional Hawkes process (FHP) with long-range dependence.
method Developed LSTM and Transformer neural architectures to estimate FHP parameters directly from inter-arrival times.
result Transformer achieves highest estimation accuracy (MSE = 0.1634) compared to classical MLE (MSE = 2.8032).
Study quantifies systemic risk in DeFi using network analysis.
problem Systemic risk in decentralized finance (DeFi) ecosystem.
method Network-based fragility analysis of TVL dynamics.
result Developed CFI and RCS to quantify structural fragility and risk contribution.
LOBDIF predicts limit order book events using a diffusion model.
problem Predicting the timing and type of events in a dynamic market system.
method LOBDIF uses a diffusion model to learn the complex time-event distribution in limit order book streams.
result LOBDIF significantly outperforms existing methods in real-world data experiments.
A simple learning agent learns to trade in an agent-based market model.
problem Optimal execution of trades in an agent-based financial market model.
method Asynchronous trading through a matching engine, varying initial order sizes and state spaces, calibration of empirical stylized facts and price impact curves.
result Smaller state space agents converge faster in learning and can trade intuitively using spread and volume states.
Paper uses machine learning to analyze stock market anomalies, predicting drift direction and portfolio performance.
problem Capturing dynamics of Post-Earnings-Announcement Drift (PEAD) using machine learning.
method Uses Extreme Gradient Boosting (XGBoost) with genetic algorithm optimization to analyze PEAD dynamics.
result Demonstrates how PEAD dynamics are influenced by different factors across sectors and quarters.
There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discret…
This paper measures financial market resilience in China and identifies key uncertainties.
problem Measuring financial market resilience in China.
method Quantitative analysis of total financial market and sub-markets, Diebold-Yilmaz connectedness approach.
result Financial market resilience in China is event-driven and influenced by geopolitical risks, economic and trade policy uncertainty, and U.S.-China tensions.
Study analyzes market co-movements in critical mineral investments using change point detection and cross-sectional analysis.
problem Market dynamics in critical mineral investments during significant global events.
method Combines change-point detection (PELT algorithm) with cross-sectional analysis on ESG-ranked ETFs.
result Investors herded during market downturns and shifted to anti-herding after positive news and geopolitical shocks.
SNNs optimize cross-market portfolios with neuromorphic computing, reducing computational overhead and improving returns.
problem Complex cross-market portfolio optimization with high-frequency, multi-dimensional datasets.
method Leaky Integrate-and-Fire neuron dynamics, adaptive thresholding, spike-timing-dependent plasticity, lateral inhibition, hierarchical clustering, population-based spike encoding, multiple decoding strategies.
result SNNs deliver superior risk-adjusted returns and reduced volatility compared to ANN benchmarks, with improved computational efficiency.
Optimal threshold resetting reduces search time for multiple diffusive searchers.
problem Optimizing search time for multiple diffusive searchers in a one-dimensional space.
method Threshold resetting (TR) is introduced as an event-driven optimization strategy, coupling resetting to the internal dynamics of searchers.
result Optimal threshold distance u significantly reduces mean first-passage time for N≥2 searchers, with a minimum at Nopt(u). Model predicts stationary equilibrium in investment decisions of firms in fluctuating markets.
problem Investment decisions in fluctuating markets with varying volatility and commodity prices.
method Mean-field model with Gaussian productivity shocks and two-state Markov chain for macroeconomic events.
result Existence, uniqueness, and characterization of stationary mean-field equilibrium with barrier-type investment strategy.
This paper introduces a new learning rule for probabilistic SNNs that improves log-likelihood, accuracy, and calibration.
problem Training and inference of deterministic SNNs are constrained by their inability to generate multiple independent outputs.
method Introduces a generalized expectation-maximization (GEM) learning rule for probabilistic SNNs.
result The GEM-SNN learning rule leads to significant improvements in log-likelihood, accuracy, and calibration.
This paper proposes a new geometric framework for asset pricing.
problem The asymmetry between risk-neutral and physical measures in asset pricing.
method Information geometry, focusing on the relativity of probabilistic reference frames.
result Unified explanation for price fluctuations, event-driven behavior, and risk premia.
AIF improves physical AI agents' performance in dynamic environments.
problem Physical AI agents are less capable than biological agents in open-ended real-world environments.
method Developed from probability theory, Bayesian machine learning, variational inference, and Active Inference (AIF), grounded in the Free Energy Principle.
result AIF minimizes variational free energy and is well-suited to physical constraints.
Spiking Neural Networks (SNNs) are brain-inspired, event-driven machine learning algorithms that have been widely recognized in producing ultra-high-energy-efficient hardware. Among existing SNNs, unsupervised SNNs based on synaptic plasticity, especially Spike-Timing-Dependent Plasticity (STDP), are considered to have…
Adaptive framework predicts stock prices better during volatile periods.
problem Inability of standard prediction models to handle regime-dependent stock market behavior.
method Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control.
result 0.59% MAPE with adaptive system, compared to 0.80% for baseline.
DCT-SNN uses DCT to reduce inference latency in SNNs.
problem High inference latency in SNNs.
method Proposes a time-based encoding scheme using DCT to reduce timesteps.
result Achieves top-1 accuracy comparable to standard deep learning while reducing inference latency.