Triangle fees adjust fees based on trade size and price movement, improving price accuracy and revenue.
problem Price staleness and low fee revenue in AMMs.
method Decreasing marginal fees proportional to price movement, creating incentives for price accuracy.
result Triangle fees strictly improve the Pareto frontier of price accuracy versus losses.
Deep learning models predict stock prices with high accuracy.
problem Accurate prediction of stock prices despite market unpredictability.
method CNN and LSTM-based deep learning models trained on historical stock data.
result Models achieve high accuracy in forecasting future stock prices.
Price and return predictions are limited by economic complexity, not just volatility.
problem Limited accuracy of price and return probability forecasts by Gaussian distributions.
method Analyzes economic reasons behind limitations in predicting price and return statistical moments.
result Predictions of price and return probabilities by Gaussian distributions are inaccurate due to economic complexity.
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.
For a given level of accuracy in option prices, the paper considers the problem of deciding when exactly, as one or more of the pricing parameters change, a barrier option degenerates into a simpler type of option. This problem is meaningful in the real world where option prices are always determined within a certain l…
This paper evaluates forecast quality in electricity markets beyond traditional accuracy measures.
problem Traditional accuracy measures fail to reflect the economic value of electricity price forecasts.
method Investigates four quality dimensions: accuracy, dispersion, association, and extremum identification.
result Dispersion- and association-based measures better capture forecast economic value.
Extensive neural networks eliminate the need for SABR pricing formulas.
problem Lack of exact pricing formulas for the SABR model.
method Used a GPU-based simulation and an extensive neural network to learn implied volatilities.
result Neural networks achieve high accuracy and efficiency comparable to Monte-Carlo simulations.
THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.
problem Improving accuracy in predicting day-ahead electricity prices.
method Temporal hierarchy forecasting (THieF) reconciling hourly and block forecasts.
result THieF significantly improves accuracy (up to 13%) at all levels of prediction.
Quantum algorithms improve stock price prediction accuracy.
problem Improving stock price prediction accuracy using quantum techniques.
method Extracted stock price indicators, used QA and PCA for feature selection and dimensionality reduction, trained QSVM for binary classification.
result Quantum Support Vector Machine (QSVM) outperformed classical models in stock price prediction accuracy.
The study examines how market trade randomness influences price and return volatility.
problem The accuracy of predicting market-based volatilities and macroeconomic variables is limited.
method Analyzes time series of trade values and volumes, and develops econometric methodologies for predicting volatilities.
result Current macroeconomic models underestimate the accuracy of predicting market-based volatilities and macroeconomic variables.
Uniswap V3 struggles with price accuracy during sudden market drops.
problem Price inaccuracies on Uniswap V3 during abrupt price drops.
method Empirical study of Uniswap V3's performance during market shocks.
result Liquidity providers lack agility and incentives in volatile conditions.
Neural jump model improves option pricing accuracy.
problem Jump risk in option pricing.
method Neural jump stochastic differential equation model with Gumbel-Softmax gradient learning.
result Neural jump components significantly improve option pricing accuracy.
Deep learning improves option pricing for a non-martingale asset model.
problem Computing call option prices for the Barndorff-Nielsen and Shephard model with infinite jumps.
method Developed a supervised deep-learning scheme using Monte Carlo teaching data and a Black-Scholes-derived variable.
result Significant improvement in accuracy of option pricing.
Paper uses evidence theory to improve stock price forecasting accuracy.
problem Inaccurate stock price predictions due to time series limitations.
method Applies evidence theory's confidence functions and Dempster combination rule to stock price forecasting.
result Improved accuracy in stock price predictions compared to classic methods.
Informer improves option pricing accuracy in volatile markets.
problem Challenges in accurate option pricing due to market volatility and traditional model limitations.
method Applying Informer, a Transformer-based neural network, for option pricing.
result Informer outperforms traditional models in option pricing accuracy.
We discuss the pricing methodology for Bonus Certificates and Barrier Reverse-Convertible Structured Products. Pricing for a European barrier condition is straightforward for products of both types and depends on an efficient interpolation of observed market option pricing. Pricing products We discuss the pricing metho…
The study challenges the reliability of VaR due to market randomness.
problem Reliability and accuracy of VaR predictions are compromised by market randomness.
method Introduces market-based probabilities of price and return, dependent on trade values and volumes.
result Market-based price volatility is more accurate than frequency-based VaR predictions.
Paper predicts stock prices using ML and human intelligence.
problem Uncertainty in stock price prediction.
method ML models (LSTM, ARIMA, CNN-LSTM, GRU, LSTM-GRU) augmented with Superforecasters predictions.
result Superforecasters' predictions improve stock price prediction accuracy.
ARHNN method improves electricity price forecasting accuracy.
problem Improving accuracy in electricity price forecasting.
method Combines Autoregressive Hybrid Nearest Neighbors (ARHNN) method with calibration sample selection and forecast combination.
result ARHNN method outperforms benchmarks by up to 10% in German, Spanish, and New England markets.
Quantum methods improve option pricing accuracy.
problem Pricing financial derivatives using Monte Carlo integration.
method Hybrid classical-quantum methods using Fourier series and QML.
result Quantum methods achieve remarkable accuracy in option pricing.
Improved path integral method for financial derivatives pricing.
problem Analytical intractability of financial derivative pricing models.
method Generalized semi-classical path integral approach to time-dependent Hamiltonians.
result Accuracy and computational efficiency of the path integral approach for derivatives pricing.
Deep learning predicts crop prices with improved accuracy.
problem Accurate prediction of agricultural crop prices for better decision-making.
method Innovative deep learning approach using GNNs and CNN models.
result At least 20% better performance than previous literature.
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
problem Improving stock price prediction accuracy for Apple Inc. using technical indicators.
method Evaluation of 123 technical indicators and 10 regression models on 13 years of Apple Inc. data.
result Combining feature selection with regression models significantly improves prediction accuracy.
A new two-step LSMC method improves game option pricing accuracy.
problem Improving game option pricing accuracy using Monte Carlo methods.
method Proposed a two-step Longstaff Schwartz Monte Carlo approach with two regression models fitted at each time step.
result Our method produces more reliable results compared to the original LSMC.
Deep neural network improves Heston model calibration accuracy and speed.
problem Calibrating the Heston model with numerical stability issues.
method Gradient-based deep learning framework (DDN) to learn Heston model and its derivatives.
result DDN significantly outperforms non-differential neural networks in calibration accuracy and speed.
MNN improves American call option pricing accuracy.
problem Inaccurate valuation of American call options.
method Modular Neural Network (MNN) model.
result MNN model outperforms traditional models and FNN.
The paper presents an approximate formula for European mortgage options pricing.
problem Pricing European mortgage options with accuracy and efficiency.
method Approximation of the underlying price distribution using lognormal distributions and matching moments.
result The proposed formula provides a good approximation with high accuracy compared to Monte Carlo simulations.
Enhances option pricing with fractional order Black-Scholes-Merton model.
problem Improving precision and authenticity of option pricing.
method Integrates fractional order Black-Scholes-Merton with neural networks.
result Improves accuracy in capturing complex diffusion dynamics and memory effects.
Predict stock prices using HMMs trained on fractional price changes and intraday highs/ lows.
problem Forecasting stock prices considering time dependency and volatility.
method Hidden Markov Models (HMMs) trained on fractional price changes and intraday highs/ lows.
result The MAP estimate of stock prices for the next day was produced using the trained HMM.
Paper introduces benchmark-neutral pricing for long-term contracts.
problem High prices of long-term contracts under risk-neutral pricing.
method Uses growth optimal portfolio as numeraire and new pricing measure.
result Identifies minimal possible prices for contingent claims.
LSTM networks improve stock price prediction accuracy.
problem Enhancing stock price forecasting accuracy.
method LSTM networks with hyperparameter tuning and feature selection.
result 53% improvement in predictive accuracy.
Paper explores balancing market dynamics and interpretable forecasting models for energy prices.
problem Tackles the challenge of accurately predicting mFRR price and understanding market dynamics.
method Compares XGBoost and EBM for forecasting mFRR activation price in the balancing market.
result EBM provides comparable forecasting accuracy to XGBoost but with higher interpretability.
A new multi-factor model improves commodity pricing accuracy.
problem Enhancing accuracy in commodity pricing by integrating multiple risk factors.
method A four-factor model using Kalman filter for simultaneous estimation and state variable filtering.
result The four-factor model outperforms existing models in capturing futures term structures and crude oil pricing.
Recent progress in the development of efficient computational algorithms to price financial derivatives is summarized. A first algorithm is based on a path integral approach to option pricing, while a second algorithm makes use of a neural network parameterization of option prices. The accuracy of the two methods is es…
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.
Hybrid approach improves crude oil price forecasting using multi-scale data.
problem Forecasting crude oil prices with multi-scale data.
method Hybrid approach combining K-means, KPCA, and KELM.
result Hybrid approach outperforms traditional methods in both level and directional forecasting accuracy.
Combining various data types predicts S&P 500 stock prices with high accuracy.
problem Predicting S&P 500 stock prices with high accuracy.
method Combined technical, fundamental, and text data with machine learning models like Random Forest and LSTM.
result Achieved 66.18% accuracy in S&P 500 index prediction and 62.09% in individual stock prediction.
This study improves stock price prediction using multimodal data.
problem Improving financial asset price forecasting accuracy.
method Combining candlestick time series and textual news flow data using LSTM and pre-trained models.
result Textual modality reduces MAPE by 55%.
Fine-tuning a time series model improves financial price prediction accuracy.
problem Improving accuracy in predicting financial market prices using large models.
method Continual pre-training of a time series foundation model on financial data to fine-tune its performance for price prediction.
result The fine-tuned model outperforms the baseline in various financial metrics.
Machine learning improves American option pricing accuracy.
problem Complexities of American options and traditional models' limitations.
method Monte Carlo simulations combined with machine learning algorithms (Least Square Method, LSTM, GRU).
result GRU model outperforms LSTM in predicting bid prices, enhancing accuracy and stability.
This paper compares LSTM, GRU, and Transformer models for stock price prediction.
problem Improving stock price prediction accuracy in fast-paced financial markets.
method Training models on Tesla stock data from 2015 to 2024, comparing LSTM, GRU, and Transformer.
result LSTM model achieved 94% accuracy in predicting stock prices.
This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A…
Deep learning calibrates HJM forward curves for commodity options pricing.
problem Calibrating HJM forward curves for accurate option pricing in commodity markets.
method Introduced a neural network to approximate true option prices from model parameters, calibrated using observed option prices.
result Neural network calibration yields high accuracy in recovering option prices, even with model parameter approximation loss.
Study improves stock index prediction accuracy using TPE-GRNN models.
problem Enhancing prediction of stock index prices in volatile markets.
method Gated recurrent neural networks (LSTM, GRU) combined with TPE Bayesian optimization.
result TPE-LSTM method shows lowest MAPE (best accuracy) for NIFTY 50 index prediction.
This study improves stock price prediction by incorporating anticipated macroeconomic policy changes.
problem Improving accuracy in stock price prediction.
method Incorporates future expected macroeconomic policy changes and historical stock prices.
result Our method outperforms conventional approaches with an RMSE of 1.61 compared to 1.75.
Bond prices are a reflection of extremely complex market interactions and policies, making prediction of future prices difficult. This task becomes even more challenging due to the dearth of relevant information, and accuracy is not the only consideration--in trading situations, time is of the essence. Thus, machine le…
We propose an efficient lattice procedure which permits to obtain European and American option prices under the Black and Scholes model for digital options with barrier features. Numerical results show the accuracy of the proposed method.
A novel multi-objective optimization framework improves insurance pricing fairness.
problem Exacerbated trade-offs between competing fairness criteria in insurance pricing using machine learning.
method Proposes a novel multi-objective optimization framework using NSGA-II to jointly optimize accuracy and fairness criteria.
result Consistently achieves a balanced compromise between accuracy and fairness, outperforming single-model approaches.