The paper develops a mathematical model for strategic shifts.
problem Finding optimal moments for strategy changes in market dynamics.
method Explicit strategy formulation using fluctuation theory.
result Analytical results predict optimal strategy shifts.
In a financial market, for agents with long investment horizons or at times of severe market stress, it is often changes in the asset price that act as the trigger for transactions or shifts in investment position. This suggests the use of price thresholds to simulate agent behavior over much longer timescales than are…
Over-the-counter markets are at the center of the postcrisis global reform of the financial system. We show how the size and structure of such markets can undergo rapid and extensive changes when participants engage in portfolio compression, a post-trade netting technology. Tightly-knit and concentrated trading structu…
New method detects TC imagery patterns for rapid intensity change.
problem Detecting upcoming rapid intensity changes in TC satellite imagery.
method Nonparametric test of association between images and event labels using neural networks and bootstrap.
result Identifies archetypes of infrared imagery associated with elevated rapid intensification risk.
DeltaGrad rapidly retrain models with minimal data changes.
problem Rapid retraining of machine learning models with minimal data changes.
method DeltaGrad algorithm based on cached training information.
result DeltaGrad compares favorably to state-of-the-art methods.
This study analyzes cryptocurrency market crashes using complex network analysis.
problem Identifying and understanding dynamics of cryptocurrency market crashes.
method Complex network analysis of cryptocurrency market during pre-crash, crash, and post-crash periods.
result Network density and clustering coefficient spike during crashes, indicating uninformed panic sell-off.
ReCAP adapts to dynamic financial markets by segmenting and combining policy vectors.
problem Inefficient traditional PM approaches in non-stationary financial markets.
method Integrates continual learning into PM, segmenting regimes and adapting policies.
result Consistently outperforms baselines in real-world financial datasets.
Proposes a method to incorporate current market conditions in VaR and stress testing.
problem Inaccurate VaR and stress testing under changing market conditions.
method Clusters market conditions using Variational Inference (VI) and historical data weighting.
result Proposed approach provides more accurate insights into portfolio risk under near-term market changes.
Graph auto-encoders predict stock market instability by measuring graph structure changes.
problem Forecasting stock market instability and volatility.
method Use graph auto-encoders to reconstruct graph structure and measure changes.
result Higher GAE reconstruction error correlates with higher volatility.
Study on rapid policy changes in reinforcement learning.
problem Rapid change of greedy policy in reinforcement learning.
method Empirical study and ablation analysis.
result Policy churn is a beneficial form of implicit exploration.
MacroHFT uses memory and context-aware reinforcement learning to improve HFT performance.
problem Overfitting and biased decisions in HFT due to rapid market changes.
method Memory Augmented Context-aware Reinforcement Learning (MacroHFT) that trains multiple sub-agents and a hyper-agent.
result MacroHFT achieves state-of-the-art performance on minute-level trading tasks.
Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and …
A new Python-C++ framework for agent-based simulation.
problem Understanding market dynamics and effects of delays.
method User-friendly Python API with efficient C++ implementation, message-driven architecture.
result Investigated the role of order processing delay in financial markets.
Improved trading strategy using deep learning and changepoint detection for market changes.
problem Traditional momentum strategies struggle with rapid market changes, especially after trend reversals.
method Inserted an online changepoint detection module into a Deep Momentum Network (DMN) pipeline.
result Improvement in Sharpe ratio by one-third over 1995-2020 period, especially beneficial in nonstationary periods.
Study validates capital structure theories in Indian public sector banks.
problem Understanding the impact of capital structure on financial performance in Indian banks.
method Developed theoretical framework from capital structure theories, tested hypotheses using statistical techniques.
result Established relation between debt component and financial performance variables.
Analyzes changes in cryptocurrency market structure.
problem Understanding shifts in cryptocurrency market dynamics.
method Structural change analysis techniques.
result Identifies key structural changes in the market.
Article examines NFT market microstructure and trading risks.
problem Difficulty in distinguishing genuine NFTs from fads and scams.
method Analyzes price formation, market structure, and transparency.
result Provides due-diligence pointers to mitigate NFT trading risk.
This paper explores how RL enhances HFT strategies in volatile markets.
problem Adapting to changing market dynamics in HFT.
method Deep Q-Learning applied to statistical arbitrage strategies.
result RL improves adaptability and profitability in HFT.
This paper optimizes portfolios using TDA and financial news sentiment.
problem Effective portfolio diversification through understanding asset similarity.
method Integrates TDA with FinBERT sentiment scores for dynamic rebalancing.
result Outperforms traditional methods in returns and risk-adjusted performance.
In this paper we address the problem of understanding the success of algorithms that organize patches according to graph-based metrics. Algorithms that analyze patches extracted from images or time series have led to state-of-the art techniques for classification, denoising, and the study of nonlinear dynamics. The mai…
Study compares price limit and circuit breaker effects in stock markets.
problem Preventing rapid and steep price drops in stock exchanges.
method Agent-based model for financial market simulation.
result Price limit and circuit breaker have similar effects under same conditions, but price limit less effective with shorter limit time range.
The paper analyzes how stock market dimensionality changes impact portfolio performance.
problem Impact of dimensional changes on portfolio performance in a changing market.
method Development of self-financing stock portfolios in a stochastic portfolio theory framework with dimensional jumps.
result Quantification of how listing or delisting events and market shocks affect portfolio return.
Innovation is to organizations what evolution is to organisms: it is how organisations adapt to changes in the environment and improve. Governments, institutions and firms that innovate are more likely to prosper and stand the test of time; those that fail to do so fall behind their competitors and succumb to market an…
This paper fine-tunes BERT for stock market sentiment analysis and improves trading performance.
problem Improving trading performance in non-strongly efficient markets.
method Fine-tuning BERT on annotated data, combining with Alpha191 model for regression and prediction.
result Emotional factors significantly improve trading performance, increasing return rates by 73.8% compared to baseline.
Detects changes in global financial networks before crashes.
problem Financial contagion and crashes across global markets.
method Sequential change point detection in dynamic networks.
result Can detect changes in network behavior before stock market crashes.
This paper investigates the effects of a price limit change on the volatility of the Korean stock market's (KRX) intraday stock price process. Based on the most recent transaction data from the KRX, which experienced a change in the price limit on June 15, 2015, we examine the change in realized variance after the pric…
In a market with a rough or Markovian mean-reverting stochastic volatility there is no perfect hedge. Here it is shown how various delta-type hedging strategies perform and can be evaluated in such markets in the case of European options. A precise characterization of the hedging cost, the replication cost caused by th…
MAML's success is due to feature reuse, not rapid learning.
problem Understanding the effectiveness of MAML in few-shot learning.
method Ablation studies and analysis of latent representations.
result Feature reuse is the dominant factor in MAML's success.
Body-worn video (BWV) cameras are increasingly utilized by police departments to provide a record of police-public interactions. However, large-scale BWV deployment produces terabytes of data per week, necessitating the development of effective computational methods to identify salient changes in video. In work carried…
Sharp changes in time series representing market dynamics are studied by means of the self--similar analysis suggested earlier by the authors. These sharp changes are market booms and crashes. Such crises phenomena in markets are analogous to critical phenomena in physics. A simple classification of the market crisis p…
Method detects and visualizes changes in financial markets' asset relationships.
problem Detecting and explaining changes in financial markets' asset relationships.
method Construct co-occurrence networks, calculate Graph-Based Entropy, apply Differential Network.
result Visualization of changes in financial markets with high interpretability.
This study investigates empirically whether the degree of stock market efficiency is related to the prediction power of future price change using the indices of twenty seven stock markets. Efficiency refers to weak-form efficient market hypothesis (EMH) in terms of the information of past price changes. The prediction …
Study shows changes in information sharing between Bitcoin markets during 2017 crash.
problem Understanding information dynamics in Bitcoin markets during the 2017 crash.
method Analysis of high-frequency market-microstructure observables using information theoretic measures.
result Temporal changes in information sharing across markets, including predictability, memory, and synchronous coupling.
We propose an artificial market model based on deterministic agents. The agents modify their ask/bid price depending on past price changes. The temporal development of market price fluctuations is calculated numerically. A probability density function of market price changes has power law tails. Autocorrelation coeffic…
Study high-frequency trading patterns in cryptocurrencies.
problem Understanding automated trading algorithms in cryptocurrency markets.
method Analyzes intraday trading data of cryptocurrencies, focusing on returns, volumes, and volatility.
result Provides insights into predictability of economic value in cryptocurrency markets.
Paper proposes a fast method to detect changes in audiometric function.
problem Detecting changes in psychometric function estimation quickly and accurately.
method Bayesian active differential selection for pure-tone audiogram testing.
result Fewer tones can detect changes in audiometric function with high confidence.
A new index CRIX for cryptocurrencies is proposed to track market changes.
problem Tracking the volatile and frequently changing cryptocurrency market.
method AIC-based method to select index constituents and assign optimal weights to altcoins.
result CRIX index improves tracking performance of cryptocurrency portfolios.
Behavioral finance has become an increasingly important subfield of finance. However the main parts of behavioral finance, prospect theory included, understand financial markets through individual investment behavior. Behavioral finance thereby ignores any interaction between participants. We introduce a socio-financia…
SimStock learns stock similarities for better investment management.
problem Challenges in identifying similar stocks due to non-stationary financial markets.
method Temporal self-supervised learning framework combining SSL and temporal domain generalization.
result SimStock outperforms existing methods in finding similar stocks.
Paper optimizes a big data and ML risk monitoring system for financial markets.
problem Traditional risk monitoring methods are inadequate for modern financial markets due to data complexity and volume.
method Four-layer architecture integrating big data and advanced ML algorithms (LSTM, RF, GB).
result Significantly enhances efficiency and accuracy in risk management, especially in market crash risk detection.
We discovered that past changes in the market correlation structure are significantly related with future changes in the market volatility. By using correlation-based information filtering networks we device a new tool for forecasting the market volatility changes. In particular, we introduce a new measure, the "correl…
Study uses AI to predict changes in international public finances based on US markets.
problem Understanding correlations between US and international public finances.
method Artificial intelligence and neural networks to model and predict changes.
result Neural network model achieved MSE of 2.79, indicating significant correlation and impact of US market volatility on international markets.
The paper examines spillovers between agriculture, crude oil, carbon, and climate markets.
problem Understanding dynamic spillovers between agriculture, crude oil, carbon emission, and climate markets.
method A novel R2 decomposed connectedness approach. result Overall spillovers are mainly contemporaneous, not lagged; climate change significantly impacts others; agricultural markets have heterogeneous effects; corn is a major risk contributor.
Volatility, fitting with first order Landau expansion, stationarity, and causality of the Taiwan stock market (TAIEX) are investigated based on daily records. Instead of consensuses that consider stock market index change as a random time series we propose the market change as a dual time series consists of the index a…
Market structure changed dramatically in US during COVID-19, mirroring 2008 crisis.
problem Impact of COVID-19 on market structure.
method Observation of market structure changes during the outbreak.
result Market structure resembles 2008 crisis but may evolve into a new structure.
We investigate financial market correlations using random matrix theory and principal component analysis. We use random matrix theory to demonstrate that correlation matrices of asset price changes contain structure that is incompatible with uncorrelated random price changes. We then identify the principal components o…
We present a model of financial markets originally proposed for a turbulent flow, as a dynamic basis of its intermittent behavior. Time evolution of the price change is assumed to be described by Brownian motion in a power-law potential, where the `temperature' fluctuates slowly. The model generally yields a fat-tailed…
This review analyzes deep learning methods for electricity price forecasting across different markets.
problem Insufficient analysis of deep learning methods in electricity price forecasting.
method Unified taxonomy of deep learning components, analysis of trends across markets.
result Shift toward probabilistic, microstructure-centric, and market-aware designs.