Study evaluates and compares traditional and causal machine learning methods for estimating direct price effects of environmental amenities.
problem Estimating direct price effects of environmental amenities in housing markets.
method Empirical Monte Carlo simulation to compare traditional regression and causal machine learning approaches.
result Causal Machine Learning (CML) methods, particularly causal forest DID, perform comparably to generalized DID in most scenarios.
New method for personalized pricing using invalid instrumental variables.
problem Personalized pricing under endogeneity with limited standard methods.
method PRINT method for continuous treatment, solving conditional moment restrictions.
result Established optimal pricing strategy under endogeneity with invalid instrumental variables.
A new model prices assets considering market microstructure effects.
problem Including market microstructure effects in dynamic asset pricing.
method Discrete binary tree model with history-dependent underlying security prices.
result The model preserves historical price dynamics and is market-complete, arbitrage-free.
Study asset price bubbles using random matching and stochastic factors.
problem Understanding and modeling asset price bubbles through investor contagion.
method Developed a stochastic model of liquidity-based asset price bubbles using random matching mechanism.
result Derived conditions for arbitrage-free financial market models.
We employ a 2x3 factorial experiment to study two central factors in the design of prediction markets (PMs) for idea evaluation: the overall design of the PM, and the elasticity of market prices set by a market maker. The results show that 'multi-market designs' on which each contract is traded on a separate PM lead to…
In this paper a simple, effective adaptation of Alternating Direction Implicit (ADI) time discretization schemes is proposed for the numerical pricing of American-style options under the Heston model via a partial differential complementarity problem. The stability and convergence of the new methods are extensively inv…
DBNs predict cryptocurrency price directions by uncovering causal relationships.
problem Predicting cryptocurrency price movements due to volatility and external factors.
method Dynamic Bayesian Networks (DBN) approach to identify causal relationships among features.
result DBN significantly outperforms baseline models in predicting cryptocurrency prices.
Study uses deep learning to predict asset prices, finds complex target processes lead to meaningless predictions.
problem Complexity of successful price prediction models hinders understanding.
method Deep learning models for high-frequency price prediction, focusing on volatility and directional prediction.
result Inadequately defined target price process renders predictions meaningless.
Coronavirus impacts oil prices through volatility and direct effects.
problem Impact of coronavirus on oil prices and volatility.
method ARDL estimation controlling for financial volatility and US economic policy uncertainty.
result COVID-19 daily infections have a negative long-term impact on oil prices.
We explore nature of price formation in financial markets and develop a theory of bid and ask price dynamics in which the two prices form due to quantum-chaotic interaction between buy and sell orders. In this model bid and ask prices are represented by eigenvalues of a 2x2 price operator corresponding to 'bid' and 'as…
This paper performs the numerical analysis and the computation of a Spread option in a market with imperfect liquidity. The number of shares traded in the stock market has a direct impact on the stock's price. Thus, we consider a full-feedback model in which price impact is fully incorporated into the model. The price …
Model predicts stock price direction with high accuracy using analyst ratings and technical indicators.
problem Rejecting the Efficient Market Hypothesis by generating excess returns on the stock market.
method Leveraged technical and fundamental indicators, used various classification models, and applied feature ranking.
result Overall accuracy of 83.62%, precision of 85% for buy signals, and recall of 100% for sell signals.
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.
Understanding the statistical properties of recurrence intervals of extreme events is crucial to risk assessment and management of complex systems. The probability distributions and correlations of recurrence intervals for many systems have been extensively investigated. However, the impacts of microscopic rules of a c…
The study uses LSTM and random forests to forecast stock price movements for intraday trading.
problem Forecasting directional movements of stock prices for intraday trading.
method Employed random forests and LSTM networks to analyze S&P 500 constituent stocks.
result Multi-feature setting provided higher daily returns (0.64% using LSTM, 0.54% using random forests) compared to single-feature setting.
Study liquidity impact on spread option pricing.
problem Imperfect liquidity in stock markets affects option pricing.
method Developed partial-impact and full-impact models to analyze European spread options.
result Full impact model leads to higher option prices due to increased stock buying.
Bayesian model improves asset price forecasting using realized volatility.
problem Improving asset price forecasting accuracy.
method Integrates dynamic gamma process with DLMs for price and realized volatility.
result Significant improvements in asset price forecasting compared to standard models.
Researchers develop a method to measure treatment effects in settings with shared states.
problem Measuring treatment effects in settings with shared states like prices, recommendations, or social signals.
method Double machine learning (DML) theorem with conditions for efficient inference under shared-state interference.
result Efficient estimation of average direct effect (ADE) and global average treatment effect (GATE) in various models.
Trading styles affect long-run variance of asset prices, increasing under trend-following and decreasing under mean-reverting.
problem Understanding how different trading styles impact the long-run variance of asset prices.
method Probabilistic models designed to capture the direction of trading were used.
result Trading styles increase long-run variance under trend-following and decrease it under mean-reverting conditions.
We generalise the description of the dynamics of the order book of financial markets in terms of a Brownian particle embedded in a fluid of incoming, exiting and annihilating particles by presenting a model of the velocity on each side (buy and sell) independently. The improved model builds on the time-averaged number …
Bitcoin's price direction is better predicted without additional drivers during high volatility.
problem Predicting Bitcoin's price direction using various determinants.
method Continuous local transfer entropy for feature selection and deep learning classification model.
result Bitcoin's price direction can be better predicted without additional drivers during high volatility.
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 presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is…
New model shows negative resilience can improve trading efficiency.
problem Optimal trade execution in limit order books with negative resilience.
method Stochastic order book model with negative resilience.
result Negative resilience can lead to more efficient trading.
Develops numerical methods for pricing exchange options in a market with limited liquidity.
problem Pricing European style exchange options in a market with finite liquidity.
method Integrates price impact into the dynamics of correlated assets using a controlled variate approach.
result Numerical pricing methods for exchange options are developed and validated.
Faced with the growing research towards crude oil price fluctuations influential factors following the accelerated development of Internet technology, accessible data such as Google search volume index are increasingly quantified and incorporated into forecasting approaches. In this paper, we apply multi-scale data tha…
This study compares direct and indirect methods for estimating own funds in life insurance, finding indirect methods more effective under realistic asset-liability coupling.
problem Computing own funds for life insurers using direct and indirect methods in a risk-neutral pricing framework.
method Introduced a novel family of mixed estimators including both direct and indirect methods, integrated into a control variate framework for variance reduction.
result The indirect method is more effective under realistic asset-liability coupling, but neither method is universally superior.
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…
This paper uses GAN and ERMSE to improve stock price movement prediction accuracy.
problem Predicting stock price movement direction is challenging due to complex, incomplete, and fuzzy information.
method The paper proposes a deep learning model using GAN and ERMSE to forecast stock market trends.
result The GAN model outperformed LSTM in predicting stock price movement direction with a 4.35% improvement.
GSNE improves house price predictions by embedding geo-spatial context.
problem Lack of contextual information in house price prediction models.
method Geo-Spatial Network Embedding (GSNE) using graph neural networks.
result GSNE embeddings consistently improve house price prediction performance.
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.
Bitcoin option prices reflect both market maker supply and trader demand, especially from those with insider information.
problem Understanding how market prices of bitcoin options are influenced by both market makers and informed traders.
method Analysis of Deribit options tick-level data to identify supply and demand effects.
result At-the-money option prices are driven by volatility traders, while out-of-the-money options are influenced by both volatility traders and those with insider information.
CryptoGAT improves cryptocurrency price prediction by treating it as a graph problem.
problem Cryptocurrency price prediction challenges due to extreme volatility.
method CryptoGAT, a Graph Attention Network, redefines cryptocurrency prediction as a cross-asset graph problem.
result CryptoGAT outperforms state-of-the-art methods in cryptocurrency price prediction.
Model predicts and optimizes trading of electricity price spreads across multiple zones.
problem Forecasting and optimizing day-ahead versus real-time price spreads in U.S. electricity markets.
method Unified statistical model for positive and negative spikes, structural price impact model based on bid stacks.
result Optimal trading strategy improves risk-return profile and highlights market heterogeneity.
Paper develops methods for fair insurance pricing without direct access to sensitive attributes.
problem Fairness in insurance pricing with restricted access to sensitive attributes.
method Develops statistical methods for estimating discrimination-free premiums using privatized sensitive attributes.
result The proposed methods enable fair insurance pricing while respecting privacy and regulatory constraints.
The calibration of volatility models from observable option prices is a fundamental problem in quantitative finance. The most common approach among industry practitioners is based on the celebrated Dupire's formula [6], which requires the knowledge of vanilla option prices for a continuum of strikes and maturities that…
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…
Study predicts stock price direction on earnings announcement days using multi-modal deep learning.
problem Predicting stock price movements during earnings announcements is challenging due to market noise and discontinuities.
method Constructed a multi-modal feature space combining fundamental metrics, technical indicators, and sentiment scores from financial news articles. Evaluated LSTM and Transformer models against a baseline.
result Transformer model outperforms LSTM in identifying volatile movements, achieving higher macro F1-score.
There are several researches that deal with the behavior of SEs and their relationships with different economical factors. These range from papers dealing with this subject through econometrical procedures to statistical methods known as copula. This article considers the impact of oil and gold price on Tehran Stock Ex…
CMTF improves financial market forecasting by fusing multiple data types.
problem Lack of effective integration of diverse financial data sources.
method Transformer-based deep learning framework with tensor interpretation and auto-training.
result CMTF outperforms classical and deep learning models in price direction classification.
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.
Grid-scale batteries' bid patterns in price uncertainty markets
problem Interpreting bids from grid-scale batteries in wholesale electricity markets under price uncertainty
method Developing an asset-level model of a price-taking battery
result Empirical results deliver insights into withholding behavior, uncertainty effects, and risk management reshaping bid curves
New statistical factors improve portfolio risk estimation.
problem Improving estimation of portfolio risk using new statistical factors.
method Matrix factor models and statistical methods (partial F test, double selection LASSO).
result New statistical factors add explanatory power in asset pricing.
High-frequency traders can act as either small informed traders or round-trippers, affecting price discovery and liquidity.
problem Effects of high-frequency trading on price discovery and liquidity.
method Extended Kyle's model with interactions between large informed traders and high-frequency traders.
result High-frequency traders can act as Small-IT or Round-Tripper, impacting price discovery and liquidity.
Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose deep spatio-temporal models and extreme value theo…
In this paper we consider a new mathematical extension of the Black-Scholes model in which the stochastic time and stock share price evolution is described by two independent random processes. The parent process is Brownian, and the directing process is inverse to the totally skewed, strictly α-stable process. The subo…
Estimates price elasticity from autocorrelated time series using causal graphs.
problem Inconsistent IV estimators in autocorrelated time series data.
method Model equilibrium with unobserved confounders, derive DAG, and use graphical inference for valid IV estimators.
result Valid IV estimators improve understanding of economic dynamics.
IMM uses imitation learning and predictive representation learning to improve market making strategies.
problem Challenges in training RL agents for multi-price level market making strategies.
method IMM combines RL and imitation learning, introducing effective state and action representations and a representation learning unit.
result IMM outperforms existing RL-based market making strategies in financial criteria.