The study extracts market direction from transaction data.
problem Extracting market direction from transaction data.
method Dynamic equation with time scale selection from past transactions.
result Automatic determination of time scale for price calculation.
This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, …
Study compares ML algorithms for predicting stock market directional bias.
problem Predicting the direction of stock market movements.
method Examined and contrasted logistic regression, decision tree, random forest, and a deep neural network.
result All models consistently reach above 50% in directional bias forecasting.
Market activity scales near a constant of 0.632 in intrinsic time.
problem Understanding the stability of market scaling laws.
method Modeling market directional changes as a memoryless exponential hazard process and identifying the intrinsic time scaling constant.
result The intrinsic time scaling constant is 1−1/e=0.632. The paper introduces a machine learning method to forecast market direction using efficient frontier coefficients.
problem Improving asset return estimation for portfolio optimization.
method Monthly directional market forecast using an online decision tree trained on efficient frontier coefficients.
result The method outperforms baseline portfolios and other feature sets.
New pricing algorithm learns demand curves and optimizes prices in dynamic markets.
problem Dynamic pricing in markets with incomplete demand information and shifting conditions.
method Actor-Critic Information-Directed Pricing (ACIDP) using IDS algorithms and auditing procedures.
result ACIDP outperforms UCB and TS in market environment shifts.
This paper analyzes the direction of the causality between crude oil, gold and stock markets for the largest economy in the world with respect to such markets, the US. To do so, we apply non-linear Granger causality tests. We find a nonlinear causal relationship among the three markets considered, with the causality go…
Machine learning models predict EUR/USD currency direction with 58.52% accuracy.
problem Predicting the directional movement of EUR/USD in the Foreign Exchange market.
method Comparative analysis of machine learning models, including decorrelated and non-decorrelated feature sets, and meta-estimators.
result 58.52% accuracy for one-day ahead forecasts.
Novel TM-vector model predicts stock market direction using Twitter and market data.
problem Challenging stock market forecasting with equal or ignored user effects.
method TM-vector trained with Twitter features and market information, using IndRNN.
result Significant accuracy in predicting stock market direction, especially for Apple.
Privacy subsidy found in market trading with noisy direction signals.
problem Analyzing welfare and bid-ask spread in a market with privacy mechanisms.
method Closed-form derivation of bid-ask spread and welfare under flip-noise direction observation.
result Privacy subsidy of μηΔ from liquidity pool to traders, robust across models. We find the explicit expression for the equilibrium wealth distribution of the Directed Random Market process, recently introduced by Martínez-Martínez and López-Ruiz, which turns out to be a Gamma distribution with shape parameter 21. We also prove the convergence of the discrete-time process describing the…
The paper extends option pricing theory for markets with informed traders.
problem Discontinuity in option pricing for markets with informed traders.
method New models for option pricing in complete markets considering informed traders' information on stock price direction and return mean.
result The discontinuity puzzle in option pricing is resolved using continuous diffusion price processes.
Paper uses bipartite graph to forecast cross-market returns, revealing asymmetry.
problem Cross-market return predictability and asymmetry between U.S. and Chinese markets.
method Directed bipartite graph capturing time-ordered linkages, hypothesis testing for edge selection, regularized and ensemble machine learning models.
result U.S. returns predict Chinese intraday returns, but not vice versa, revealing asymmetry.
A first attempt at obtaining market--directional information from a non--stationary solution of the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented. We demonstrate that the concept of price impact is poorly applicable to market dynamics. Inste…
Study of Polymarket's prediction market microstructure using tick-level order book data.
problem Understanding the microstructure of decentralized prediction markets.
method Analysis of a continuous tick-level order book feed and on-chain trade records.
result Trade direction inferred from Polymarket's public order-book feed disagrees with on-chain data in ~59% of cases.
The study finds no evidence of stochastic arbitrage opportunities in S&P 500 index options.
problem Identifying arbitrage opportunities in S&P 500 index options.
method Developed linear and mixed-integer linear programs to compute the maximum option premium.
result No evidence of systematic stochastic arbitrage opportunities in S&P 500 index options.
Transient market impact explained via Nash equilibrium in a game.
problem Understanding the transient nature of market impact.
method Analyzing a game between a trader and an arbitrageur, deriving decay kernels.
result Implied transient impact can be derived from trader behavior at Nash equilibrium.
We present an empirical study of the intertwined behaviour of members in a financial market. Exploiting a database where the broker that initiates an order book event can be identified, we decompose the correlation and response functions into contributions coming from different market participants and study how their b…
Private credit markets have expanded significantly, offering unique lending technology to private equity firms.
problem Understanding the growth and characteristics of private credit markets.
method Systematic survey of academic literature, development of integrated theoretical framework, empirical evidence.
result Private credit markets offer a distinct lending technology with higher spreads over syndicated loans.
Polymarket-v1 Database tracks 1.2B trades across 1.3M markets with 100% ground-truth direction.
problem Lack of ground-truth data in prediction markets archives.
method Ground-truth archive of 1.2B trades from Polymarket's CTF Exchange.
result Ground-truth data reveals systematic errors in microstructure metrics.
In terms of transfer entropy, we investigated the strength and the direction of information transfer in the US stock market. Through the directionality of the information transfer, the more influential company between the correlated ones can be found and also the market leading companies are selected. Our entropy analy…
Filters on order flow improve short-term market directionality.
problem Improving directional signals from order flow in financial markets.
method Structural filters on order lifetime, modification count, and timing applied to BankNifty index futures.
result Filters on parent orders of executed trades show stronger directional association with returns.
We investigate the strength and the direction of information transfer in the U.S. stock market between the composite stock price index of stock market and prices of individual stocks using the transfer entropy. Through the directionality of the information transfer, we find that individual stocks are influenced by the …
A new approach to obtaining market--directional information, based on a non-stationary solution to the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented. In our previous work[2], we established that it is the share execution flow (I=dV/dt) and…
Uses news sentiment scores for direct reinforcement trading in financial markets.
problem Incorporating news data into quantitative trading remains challenging.
method Directly uses news sentiment scores and raw data as inputs for reinforcement learning, processed by sequence models.
result Achieves superior performance compared to market benchmarks.
Study finds users mostly use recent market and decision information to guess market direction.
problem Limited ability to model and predict human decision-making in stock markets.
method Used networks inference with stochastic block models (SBM) to find most predictive model of unobserved decisions.
result Users mostly use recent information to guess market direction, and their decision-making strategies are analogous to behaviors in other contexts.
Cross-sectional "Information Coefficient" (IC) is a widely and deeply accepted measure in portfolio management. The paper gives an insight into IC in view of high-dimensional directional statistics: IC is a linear operator on the components of a centralizing-unitizing standardized random vector of next-period cross-sec…
Study explores financial market linkages between Japan and US markets.
problem Inconsistency in empirical studies regarding financial market causal linkages.
method Causal discovery methods including VAR-LiNGAM and LPCMCI with domain knowledge.
result VAR-LiNGAM reveals causal influences among financial markets, while LPCMCI identifies potential latent confounders.
Unified framework predicts S&P500 index direction using transfer learning and causal graph.
problem Predicting the movement of financial indices like S&P500.
method Transfer learning, causal graph, multidisciplinary knowledge, VAE network.
result 74.3% accuracy, 67% F1-score, 0.42 Matthew correlation on 12 years test period.
A new trading system learns to minimize risk and maximize returns in real markets.
problem Optimizing trading strategies under risk constraints in financial markets.
method Direct Reinforcement Learning with Conditional Value-at-Risk as the risk measure.
result The proposed algorithm outperforms traditional methods in real-world financial markets, demonstrating robustness and profitability.
Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originat…
Research examines how foreign direct investment in Vietnam affects stock returns.
problem Impact of foreign direct investment on stock returns in Vietnam.
method Analyzes data from 1995 to 2015 focusing on M&A deals.
result Foreign direct investment in Vietnam, particularly M&A, influences abnormal stock returns.
Order-flow entropy predicts price magnitude without directionality.
problem Predicting price magnitude in financial markets.
method Real-time order-flow entropy computed from a 15-state Markov transition matrix.
result Order-flow entropy predicts the magnitude of intraday returns with high accuracy.
In this paper we extend the market-making models with inventory constraints of Avellaneda and Stoikov ("High-frequency trading in a limit-order book", Quantitative Finance Vol.8 No.3 2008) and Gueant, Lehalle and Fernandez-Tapia ("Dealing with inventory risk", Preprint 2011) to the case of a rather general class of mid…
Paper predicts high-frequency futures return directions using mean-uncertainty methods.
problem Data imbalance in short-term price movements of futures markets.
method Employed mean-uncertainty logistic regression and support vector machines under sublinear expectation framework.
result Mean-uncertainty approaches outperform conventional methods in classification metrics and average returns.
StakeBench evaluates language understanding by linking comments to market commitments, improving model alignment with real-world outcomes.
problem Existing financial NLP benchmarks measure perceived language rather than market commitments.
method StakeBench uses observable market behavior to supervise models, testing their ability to detect commitments, identify sides, and project odds.
result Models partially recover position-side signals but struggle with later tasks, highlighting structural failures.
Study finds option volume imbalance predicts equity market returns.
problem Predicting equity market returns using option volume imbalance.
method Nonlinear analysis of option volumes decomposed into five market participant classes.
result Strong signals of predictability of excess market returns from Market-Maker volumes.
The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However…
Survey on recent deep learning models for stock market prediction.
problem Building effective prediction models for stock market.
method Review of various data sources, neural network structures, and evaluation metrics.
result Highlighting recent progress and future research directions.
Crowding is most likely an important factor in the deterioration of strategy performance, the increase of trading costs and the development of systemic risk. We study the imprints of \emph{crowding} on both anonymous market data and a large database of metaorders from institutional investors in the U.S. equity market. …
Survey on deep learning methods for stock market prediction.
problem Lack of comprehensive survey on deep learning methods for stock market prediction.
method Propose a novel taxonomy summarizing state-of-the-art models based on deep neural networks.
result Provide detailed statistics on datasets and evaluation metrics.
Study examines how EU's Value at Risk constraints affect insurance oligopolies.
problem Impact of EU's Value at Risk constraints on insurance oligopolies.
method Bertrand model with profit-maximizing companies facing Value at Risk constraints.
result Value at Risk constraints can lead to monopolistic premiums or market failure.
Unified framework maps financial market dynamics using TE and KM, revealing directional information flow.
problem Challenges in traditional correlation analysis of financial markets, especially during crises.
method Combines Transfer Entropy (TE) and Kramers-Moyal (KM) expansion to analyze dynamic interactions among major indices.
result Increased directional information flow during crises, highlighting gold-dollar and oil-equity linkages.
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 define a methodology to quantify market activity on a 24 hour basis by defining a scale, the so-called scale of market quakes (SMQ). The SMQ is designed within a framework where we analyse the dynamics of excess price moves from one directional change of price to the next. We use the SMQ to quantify the FX market an…
This research predicts stock market movements using Vision-Language models.
problem Predicting future stock market direction using historical data.
method Utilizing image and byte-based representations of stock data processed with Vision-Language models.
result The proposed approach significantly outperforms deep learning baselines.
In this paper we complete and extend our previous work on stochastic control applied to high frequency market-making with inventory constraints and directional bets. Our new model admits several state variables (e.g. market spread, stochastic volatility and intensities of market orders) provided the full system is Mark…
Using virtual stock markets with artificial interacting software investors, aka agent-based models (ABMs), we present a method to reverse engineer real-world financial time series. We model financial markets as made of a large number of interacting boundedly rational agents. By optimizing the similarity between the act…