We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a p…
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48 results for “Mid Price Movement”
Paper proposes new features and deep learning models for mid-price prediction.
problem Challenging task of predicting mid-price movement based on LOB data.
method Developed new handcrafted features and deep learning models.
result Deep learning models can predict mid-price movement and time to change.
Paper uses ML to predict stock price movements from order book data.
problem Forecasting stock price movements in financial markets.
method Combines handcrafted and ML features for three classifiers, evaluated on two setups.
result Machine Learning shows promise for this task, suggesting future research.
In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti…
Deep learning predicts cryptocurrency price movements with 78% accuracy.
problem Predicting price formation in cryptocurrency markets with high volatility and illiquidity.
method Applied deep learning to predict mid-price changes on live tick-level cryptocurrency data.
result Achieved 78% accuracy in predicting mid-price movement of Bitcoin vs USD.
Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicatorsq-fin.ST
Study predicts stock mid-prices using machine learning with selected features.
problem Predicting mid-price movements in stock markets.
method Wrapper feature selection using entropy, LMS, LDA, and adaptive logistic regression.
result Best performance achieved with a small set of advanced features.
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Marketsecon.EM
Bayesian model predicts mid-price dynamics in financial markets.
problem Challenges in predicting financial markets using traditional methods.
method Bayesian bilinear neural network with temporal attention.
result Feasibility and advantages of Bayesian deep-learning approach.
Deep learning models predict price movements using stationary features from limit order books.
problem Challenges in applying deep learning to financial data due to its non-stationary nature.
method Proposed a method to create stationary features allowing DL models to be effectively applied.
result A combined model outperforms individual LSTM and CNN models in predicting price movements.
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 …
This paper presents a method to estimate mid-prices of European corporate bonds using real-time dealer information.
problem Estimating mid-prices in illiquid markets where direct market prices are not available.
method Bayesian approach using particle filtering and sequential Monte Carlo.
result A new method for real-time mid-price estimation of corporate bonds.
ALPE improves mid-price forecasting in HFT with real-time data.
problem Real-time mid-price forecasting in high-frequency trading.
method Adaptive Learning Policy Engine (ALPE) using RL and adaptive epsilon decay.
result ALPE outperforms other models in mid-price forecasting.
Study of Hawkes processes in limit order books for price volatility analysis.
problem Understanding price volatility in limit order books.
method Construct and analyze general compound Hawkes processes.
result Established Law of Large Numbers and Functional Central Limit Theorems for specific variations.
In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average time series between January 1997 and August 2008. Modeling the index movements and log-index movements as stationary Gaussian processes, we find a total of 116 and 119 statistically stationary segments respect…
The study introduces a new stickiness parameter for stock prices using a non-linear model.
problem Understanding how closely individual stocks follow a stock index's price movements.
method Developed a non-linear pricing model inspired by tectonic plate movements to measure stickiness.
result Defined a stickiness parameter for stock price returns using a novel model.
The study models market price movement based on investors' expectations.
problem Understanding the dynamics of investors' expectations and market price movement.
method Developed a non-linear evolutionary equation linking investors' expectations and market asset price movement.
result Model predictions co-integrated with asset time series, suggesting potential for price movement forecasting.
This work's purpose is to understand the dynamics of limit order books in order-driven markets. We try to illustrate a dynamical trading mechanism attached to the microstructure of limit order markets. We capture the iterative nature of trading processes, which is critical in the dynamics of bid-ask pairs and the switc…
Taureau uses Twitter sentiment analysis to predict stock market movement.
problem Predicting stock market movement using public opinion on Twitter.
method Obtained historical tweets, filtered and labeled, generated word embeddings, assessed sentiment scores, correlated with stock price movement, designed and evaluated predictive model.
result Taureau can predict stock price movement from lagged sentiment scores.
The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity pri…
Mid-LSTM improves midterm stock prediction accuracy.
problem Large cumulative errors in short-term deep learning models for midterm stock predictions.
method Mid-LSTM incorporates market trend as hidden states, using ARMA and LSTM.
result Mid-LSTM achieves 2-4% improvement in prediction accuracy on S&P 500 stocks.
Beyond the Bid-Ask: Strategic Insights into Spread Prediction and the Global Mid-Price Phenomenonq-fin.TR
Study examines new financial metrics and their implications for trading and risk management.
problem Liquidity and price dynamics in financial markets.
method High-frequency trading data, ARMA(1,1)-GARCH(1,1) model, normal inverse Gaussian distribution, option pricing model, Rachev ratio.
result New financial metrics (TMOBBAS, GMP) have heavy-tailed distributions and significant deviations from normality.
Paper examines GCHP for mid-price prediction in financial data.
problem Predicting mid-price in financial data with stochastic models.
method General Compound Hawkes Process (GCHP) for mid-price prediction.
result GCHP models show potential for predicting mid-price direction and volatility.
In this study, we present a simple stochastic order-book model for investors' swarm behaviors seen in the continuous double auction mechanism, which is employed by major global exchanges. Our study shows a characteristic called "fat tail" is seen in the data obtained from our model that incorporates the investors' swar…
Study applies Hawkes volatility to mid-price process for real-time risk management.
problem Lack of studies on Hawkes volatility for tick-level price dynamics.
method Derived variance formula for unmarked and marked Hawkes models, applied to mid-price process.
result Reliable results and high predictive power of intraday Hawkes volatility.
Study shows how order flow at multiple price levels affects stock prices.
problem Understanding how order flow at different price levels influences stock prices.
method Fit a linear relationship between multi-level order-flow imbalance (MLOFI) and mid-price changes using high-quality data.
result The inclusion of more price levels in MLOFI improves the fit with mid-price changes.
Paper uses CNN to predict stock price movement as an image classification problem.
problem Predicting stock price movement using machine learning.
method CNN-based model for classifying stock price movement based on the first hour of trading.
result The algorithm effectively separated between stock price movement classes and outperformed other strategies.
Proposes a stochastic model for limit order book dynamics.
problem Captures the dynamics of limit order books in financial markets.
method Develops a stochastic partial differential equation (SPDE) model with multiplicative noise.
result Shows efficient estimation and computation methods for the model.
The paper models battery valuation in intraday electricity markets, incorporating liquidity costs.
problem Valuing batteries in intraday electricity markets considering liquidity costs.
method Stochastic model for mid-prices combined with a deterministic model for liquidity costs, using dynamic programming for optimization.
result Liquidity costs significantly impact battery valuation, especially with multiple batteries.
LLT improves cryptocurrency price movement prediction accuracy.
problem Predicting intraday price movements of cryptocurrencies.
method Linear law-based feature space transformation (LLT) applied to cryptocurrency price data.
result LLT significantly enhances prediction accuracy for all cryptocurrencies.
Study identifies key trades predicting market movements.
problem Predicting future market price movements.
method Optimized neural network predictor to identify influential trades.
result Trades with specific characteristics significantly impact future price predictions.
The Time Function of Stock Priceq-fin.ST
The paper defines the time function of stock prices using a mathematical model.
problem Understanding the movement and predictability of stock prices over time.
method Empirical evidence and mathematical modeling of white noise.
result Derives auto-correlation function, displacement formula, and power spectral density of stock price movement.
Optimal execution comparison across risks and dynamics, with solutions for displaced diffusionsq-fin.TR
We solve a version of the optimal trade execution problem when the mid asset price follows a displaced diffusion. Optimal strategies in the adapted class under various risk criteria, namely value-at-risk, expected shortfall and a new criterion called "squared asset expectation" (SAE), related to a version of the cost v…
Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the t…
Proposes deep mixture models for probabilistic price movement forecasting in high-frequency trading.
problem Probabilistic forecasting of price movements in high-frequency trading.
method Deep recurrent neural networks with probabilistic mixture models.
result Outperforms benchmark models in both metric-based and simulated trading scenarios.
Framework analyzes stock price co-movement with fundamentals using big data.
problem Understanding complex relationships between stock price co-movements and fundamental characteristics.
method Advanced big data techniques, four regression models.
result Identifies leading co-movement stocks and their influencing factors.
Hybrid model predicts stock prices using ML, DL, and NLP.
problem Improving prediction accuracy of stock price movement.
method Machine learning, deep learning, natural language processing, sentiment analysis.
result LSTM model outperforms traditional machine learning models.
Why do a market's prices move up or down? Claims about causes are made without actual information, and accepted or dismissed based upon poor or non-existent evidence. Here we investigate the price movements that ended with Apple stock closing at \$500.00 on January 18, 2013. There is a ready explanation for this price …
Unified model for market dynamics, linking price and order flow.
problem Modeling market dynamics and order flow in a unified framework.
method Markovian market model driven by a hidden Brownian efficient price, signal-driven and queue-reactive models.
result Stability of mid-price around efficient price at macroscopic scale, behavior as diffusion.
The paper introduces a new price model based on entropy that better fits high-frequency market data.
problem Understanding fair prices in high-frequency markets with bid-ask imbalance.
method A parametrized family of prices derived from the Maximum Entropy Principle, minimizing bias given volume imbalance.
result The model can generate higher kurtosis and heavy-tailed distributions compared to standard models.
Predict stock price movements using financial data and news articles with LLMs.
problem Predicting stock price movements using financial data and news articles.
method Combining financial data and news articles, employing pre-trained LLMs, and using retrieval augmentation techniques.
result Predicted stock price movements with a weighted F1-score of 58.5% and 59.1%.
HLOB predicts mid-price changes in L.O.Bs using deep learning.
problem Forecasting mid-price changes in Limit Order Books.
method HLOB uses a deep learning model with an Information Filtering Network and Homological Convolutional Neural Networks.
result HLOB outperforms state-of-the-art models in real-world datasets.
Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evide…
Study shows news from various topics impacts Nifty 50 index.
problem Lack of analysis on news impact on Nifty 50 index.
method Analyzed Nifty 50 index movement with sentiments from diverse news topics.
result Sentiment scores from different topics significantly impact Nifty 50 index.
Research finds correlations between Bitcoin online discourse and price/volume movements.
problem Mapping sentiment to Bitcoin price and volume movements.
method Collected and analyzed data from Bitcointalk.org, news sources, and Reddit communities.
result Weak to moderate correlations between online sentiment and Bitcoin price/volume movements.
Analyzes transaction costs for corporate bonds using a new analytical methodology.
problem Challenges in assessing the quality of corporate bond executions via Transaction Cost Analysis.
method Analyzes TRACE Enhanced dataset to estimate initiator, bid-ask spread, and mid-price dynamics; applies regularized regression models and transient impact models.
result Identifies price impact asymmetry between customer-buy and consumer-sell orders.
Mechanisms of information communication and market price movements. The case of SP 500 marketq-fin.ST
The paper analyzes how market prices respond to information processing and non-linear dynamics.
problem Understanding how market prices change in response to information.
method Logistic Continuous Wavelet Transformation method applied to SP 500 market data.
result Identifies patterns in market dynamics and describes them using a new theory of reflexive communication.
At the ultra high frequency level, the notion of price of an asset is very ambiguous. Indeed, many different prices can be defined (last traded price, best bid price, mid price,...). Thus, in practice, market participants face the problem of choosing a price when implementing their strategies. In this work, we propose …
Learning the Spoofability of Limit Order Books With Interpretable Probabilistic Neural Networksq-fin.TR
Real-time detection of spoofing in cryptocurrency exchanges using neural networks.
problem Detecting and mitigating spoofing activity in limit order books.
method Novel order flow variables based on multi-scale Hawkes processes and a probabilistic market manipulation gain model.
result 31% of large orders could spoof the market, highlighting the importance of posting distance in price formation.
GCNET predicts stock price movements using graph convolutional networks.
problem Predicting stock price movements using interrelated stocks data.
method GCNET models stock relations as an influence network, uses graph convolutional networks for prediction.
result GCNET significantly improves prediction accuracy and MCC measures.