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.
The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted…
Enhances trading signals using image analysis and weighted moving averages.
problem Improving price trend trading strategies in financial markets.
method Image-induced importance weights applied to weighted moving averages of trading signals.
result Significant enhancement of price trend trading signals with improved portfolio selection.
New bases constructed for KBSM of lens spaces.
problem Computing Kauffman bracket skein modules of lens spaces.
method Using a fibered torus basis, new bases constructed for L(p,2) and L(4k,2k+1). result New generating sets found for KBSM of L(0,1). Study shows publicly available news impacts financial markets.
problem Impact of publicly available news on financial markets.
method Extracted news from Common Crawl, identified relevant companies, used sentiment analysis and information theory.
result Publicly available news has significant impact on financial markets.
Study finds market inefficiencies vary by time scale, with news uncertainty key.
problem Evaluating scale-dependent informational efficiency of stock markets.
method Tensor-eigenvalue-based Financial Chaos Index, Granger causality, network analysis.
result Semi-strong form of EMH rejected at daily frequency, but not at monthly.
New bases found for Kauffman bracket skein module of fibered torus.
problem Computing Kauffman bracket skein module of fibered 3-manifolds.
method Constructed bases for Kauffman bracket skein module of product annulus and circle, then applied to (β,2)-fibered torus. result Found a new basis for KBSM of (β,2)-fibered torus. We study association between macroeconomic news and stock market returns using the statistical theory of copulas, and a new comprehensive measure of news based on the indexing of news wires. We find the impact of economic news on equity returns to be nonlinear and asymmetric. In particular, controlling for economic con…
News embeddings improve volatility forecasts.
problem Improving volatility forecasting accuracy.
method Transformed news text into embeddings, evaluated standalone and combined with benchmarks.
result News contains useful predictive information, especially for stock-related content.
We give new explicit formulas for the representations of the mapping class group of a genus one surface with one boundary component which arise from Integral TQFT. Our formulas allow one to compute the h-adic expansion of the TQFT-matrix associated to a mapping class in a straightforward way. Truncating the h-adic expa…
This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive po…
New model uses financial news to predict stock returns.
problem Predicting stock returns based on financial news.
method Derive company embedding vectors from news, select basis assets, and use statistical methods.
result NEUS model outperforms Fama-French 5-factor model.
In this survey paper we present results about link diagrams in Seifert manifolds using arrow diagrams, starting with link diagrams in F×S1 and N×^S1, where F is an orientable and N an unorientable surface. Reidemeister moves for such arrow diagrams make the study of link invariants possible. T…
A new base distribution for normalizing flows allows modeling complex distributions without sacrificing invertibility.
problem Normalizing flows struggle with complex, non-trivial distributions.
method Learned rejection sampling for base distribution, combined with optimization of log-likelihood and Kullback-Leibler divergence.
result The method effectively models complicated distributions without sacrificing invertibility.
The paper proves conditions for minimal surfaces to be holomorphic and stable.
problem Conditions for stable minimal surfaces to be holomorphic.
method Developed a method of constructing variations to prove the equivalence.
result Holomorphicity and stability conditions for minimal surfaces.
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting …
This paper predicts stock prices using LLMs and news embeddings.
problem Predicting stock prices with high accuracy and relevance.
method Integrates LLMs with stock name embeddings and attention mechanisms for news filtering.
result Reduces MAE by 7.11% compared to baseline.
This work explains crises in markets without external news using bounded rational agents.
problem Inability to model out-of-equilibrium dynamics in economic markets.
method Modeling bounded rational strategic reasoning in multi-agent market games.
result Bounded rational strategic reasoning can lead to endogenously emerging crises.
A new WNN framework selects wavelet bases for efficient learning.
problem Challenges in constructing accurate wavelet bases and high computational costs in WNN.
method Introduces a constructive WNN that selects initial bases and trains functions by introducing new bases for predefined accuracy while reducing computational costs.
result Significantly improves computational efficiency through a frequency estimator and wavelet-basis increase mechanism.
The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic environment, decision-makers across the public and private sectors require accurate forecasts of the economic outlook. Existing predictive f…
In this paper we present two new bases, BH2′ and BH2, for the Kauffman bracket skein module of the handlebody of genus 2 H2, KBSM(H2). We start from the well-known Przytycki-basis of KBSM(H2), BH2, and using the technique of parting we present elements in BH2 in open b…
IEN speeds up T-Rex+GVS for fast, efficient GWAS.
problem Efficiently selecting groups of genetic variants in large-scale genomics studies.
method Informed Elastic Net (IEN) as a faster base selector for T-Rex+GVS.
result IEN reduces computation time while maintaining high TPR and FDR control.
News sentiment in U.S. economic newspapers has become more persistent over 45 years.
problem Understanding the temporal dynamics of U.S. economic news sentiment over time.
method Daily economic news sentiment index from 1980-2025, analyzed using sentiment indexes.
result News sentiment states have become more persistent, with longer residence times in optimistic or pessimistic regimes.
This paper examines how regional trade agreements affect global trade relationships.
problem The relationship between regional trade agreements and global trade purity.
method Defined and decomposed synthesized trade resistance, separated natural and artificial factors, used expectation maximization algorithm to optimize parameters, and quantified trade purity indicator.
result Regional trade agreements contribute to the relative prosperity of EU and NAFTA countries, but weaken the role of trade unions and accelerate multilateral trade liberalization.
Paper predicts international trade flows using machine learning and factorization models.
problem Predicting international bilateral trade flows with PTAs.
method Two-stage approach combining SHAP Explainer and Factorization Machine models.
result Enhanced predictive accuracy and deeper insights into trade dynamics.
Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into k classes using the k-shell decomposition m…
Analysis shows preference for Chinese yuan in global trade network.
problem Analyzing the preference of countries to trade in US dollars or Chinese yuan.
method Mathematical analysis of world trade network using Ising spin interactions.
result Majority of countries prefer trading in Chinese yuan due to network structure.
Model predicts trading strategies based on latent demand and price impact.
problem Predicting strategic trading behavior of investors with private targets.
method Equilibrium model of dynamic trading, learning, and pricing by strategic investors.
result Trading strategies are a combination of target following, liquidity provision, and front-running based on latent demand and price pressure.
Here, we present the World Trade Atlas 1870-2013, a collection of annual world trade maps in which distance combines economic size and the different dimensions that affect international trade beyond mere geography. Trade distances, which are based on a gravity model predicting the existence of significant trade channel…
A dynamic herding model with interactions of trading volumes is introduced. At time t, an agent trades with a probability, which depends on the ratio of the total trading volume at time t−1 to its own trading volume at its last trade. The price return is determined by the volume imbalance and number of trades. The …
This paper conducts an empirically study on the trade package composed of a sequence of consecutive purchases or sales of 23 stocks in Chinese stock market. We investigate the probability distributions of the execution time, the number of trades and the total trading volume of trade packages, and analyze the possible s…
Automated trading system with preprocessing and reinforcement learning.
problem Efficient portfolio trading for individual investors.
method Feature preprocessing, recurrent reinforcement learning, automated trading algorithm.
result System outperforms other strategies in profit and drawdown.
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
problem The profitability of technical trading rules in stock markets is controversial.
method Proves the upper bound of cumulative return and investigates the profitability of technical trading rules using bootstrap methodology.
result Technical trading rules are not better than random trading and less profitable than the market.
The paper classifies trades into types based on proximity and measures their impact on stock prices.
problem Understanding the impact of high-frequency trades on stock prices and their predictability.
method Classifies trades into five types based on proximity, measures conditional order imbalance (COI), and develops trading strategies.
result Strong positive correlations between contemporaneous returns and COIs, and positive associations with future returns for isolated trades.
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.
Developed Forex trading heuristics with high profit potential.
problem Reduced trade opportunities due to technical indicator values.
method Machine learning simulation of 10 years of Forex data.
result Optimized trade parameters for 118 pips daily profit.
Paper examines costs of using wrong price impact models in trading.
problem Misspecifying price impact models in trading predictions.
method Derives formulas for misspecification costs and applies to trading data.
result Misspecification costs are asymmetric, affecting profits and losses.
This paper surveys cryptocurrency trading research, covering various aspects.
problem Understanding the unique nature and behavior of cryptocurrencies as assets.
method Comprehensive review of 146 research papers on cryptocurrency trading.
result Identifies promising open opportunities in cryptocurrency trading.
We extend the framework of trading strategies of Gatheral [2010] from single stocks to a pair of stocks. Our trading strategy with the executions of two round-trip trades can be described by the trading rates of the paired stocks and the ratio of their trading periods. By minimizing the potential cost arising from cros…
Brazil proposes a new BRICS trade currency to dominate international trade.
problem Dominance of BRICS currency in international trade.
method Mathematical model of influence battle between three currencies, using trade flows and weights in global trade.
result By 2012, about 58% of countries preferred trading with the BRICS currency.
Trade finance history traced from medieval origins to modern markets.
problem Evolution and standardization of trade finance products.
method Historical analysis of market structures and regulatory changes.
result Global trade finance market evolved from local to centralized, then decentralized.
CREDIT learns to master pair trading with risk-aware RL, outperforming existing methods.
problem Challenges in applying RL to pair trading due to temporal correlations and risk considerations.
method Risk-aware recurrent reinforcement learning (RL) with bidirectional GRU and temporal attention.
result CREDIT achieves significant profit in pair trading over five years of U.S. stock data.
Study reveals inefficiencies in EU carbon trading market.
problem Inefficiencies in carbon trading market undermine emission reduction goals.
method Analysis of granular transaction data from 2005-2020.
result 40% of firms never trade in a given year, and many trade only during high-price months.
Using the United Nations COMTRADE database \cite{comtrade} we construct the Google matrix G of multiproduct world trade between the UN countries and analyze the properties of trade flows on this network for years 1962 - 2010. This construction, based on Markov chains, treats all countries on equal democratic grounds …
Unified market-based description of returns and variances of trades.
problem Market-based variance of trades and market portfolio.
method Unified market-based approach to describe returns and variances of trades and market portfolio.
result Market-based variance accounts for random volumes of trades and differs from Markowitz's portfolio variance.
Automated trading systems on developed and emerging capital markets are studied in this paper. The standard for developed market is automated trading system with 40-days simple moving average. We tested it for the index SIX Industrial for 1000 and 730 trading days of the slovak emerging capital market. The Buy and Hold…
Optimal transport and neural networks improve trade modeling accuracy.
problem Capturing subtler drivers of trade beyond supply and demand.
method Employing optimal transport and deep neural networks to learn a time-dependent cost function from data.
result Consistently outperforms traditional gravity models in accuracy.
Paper examines trade/no trade patterns in illiquid stocks, highlighting effects of varying zero returns probabilities.
problem Detecting long-run trade/no trade effects in illiquid stocks with varying zero returns probabilities.
method Proposes a framework considering constant and time-varying zero returns probabilities, analyzing trade/no trade categorical sequences.
result Long-run trade/no trade effects may be spuriously detected in presence of non-constant zero returns probabilities.