Improved fuzzy support vector machine for stock price trend forecasting.
problem Weak performance of traditional support vector machines in handling fuzzy and noisy data.
method Proposed a novel advanced fuzzy support vector machine (NA-FSVM) to improve precision.
result Improved model precision in predicting stock price trends.
Model predicts EU carbon prices using market and political factors.
problem Predict future carbon prices for EU market management.
method Support vector regression with grid search and cross validation.
result Model predicts carbon prices accurately for 2030.
The paper factors long-term affine pricing kernels into two components.
problem Understanding long-term behavior of affine pricing kernels.
method Long-term factorization into discounting rate and martingale component.
result Explicit identification of long bond volatility and martingale component volatility.
The study explains economic recession through equilibrium models.
problem Understanding economic recession through equilibrium models.
method Developed theorems to describe equilibrium structure and applied to European economies.
result Characterized equilibrium states leading to economic recession.
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.
Defines certainty equivalent and utility indifference pricing for incomplete preferences.
problem Incomplete preferences represented by multiple priors and utility functions.
method Defines certainty equivalent and utility buy/sell prices as set-valued functions of claims, proves monotonicity and convexity properties, approximates bounds via convex vector optimization.
result Certainty equivalent and indifference price bounds can be computed or approximated by convex vector optimization.
Efficiently calibrates Bergomi models to VIX derivatives using vector quantization.
problem Calibrating Bergomi models to VIX derivatives for accurate pricing.
method Applied vector quantization in mixed Bergomi models for fast and efficient option pricing.
result Calibration of Bergomi models to VIX derivatives is feasible and accurate over daily data.
Support vector machines predict cryptocurrency price movements with high accuracy.
problem Predicting short-term price movements in cryptocurrencies.
method Developed technical indicators, tested various classification methods, including SVM.
result Support vector machines yield the most profitable trading strategies.
A fast model estimates future prices from orderbook data.
problem Estimating future prices from orderbook data.
method Hyperdimensional vector Tsetlin machine framework for fast estimation.
result Demonstrated robust estimate of future prices.
The participants of the electricity market concern very much the market price evolution. Various technologies have been developed for price forecast. SVM (Support Vector Machine) has shown its good performance in market price forecast. Two approaches for forming the market bidding strategies based on SVM are proposed. …
A method for pricing and superhedging European options under proportional transaction costs based on linear vector optimisation and geometric duality developed by Lohne & Rudloff (2014) is compared to a special case of the algorithms for American type derivatives due to Roux & Zastawniak (2014). An equivalence between …
This paper improves stock price forecasting using grey correlation analysis and feature-weighted SVR.
problem Improving accuracy of stock price forecasting.
method Divided factors affecting stock price movement into behavioral and technical. Used grey correlation analysis to measure relationships and transform into characteristic weights. Applied feature-weighted SVR.
result Significantly improved forecast accuracy compared to unmodified data.
No arbitrage holds if a Pareto solution exists for vector-valued utility maximization.
problem Existence of no arbitrage in markets with transaction costs and multiple assets.
method Prove no arbitrage condition equivalent to Pareto solution for vector-valued utility maximization.
result A consistent price process can be constructed from the Pareto maximizer.
Study collective pricing and hedging with admissible risk exchanges forming a finitely generated convex cone.
problem Collective pricing and hedging with exchanges forming a finitely generated convex cone.
method Extend collective First Fundamental Theorem of Asset Pricing and pricing-hedging duality.
result No collective arbitrage implies the closedness of the aggregate feasibility cone.
We derive asset pricing formula for markets with incomplete information and subjective views.
problem Asset pricing in markets with informational imperfections and subjective investor beliefs.
method Closed-form market equilibrium formula based on Merton's model, non-linear system of equations, conditional posterior distribution.
result Derivation of market reference model for excess returns under random shadow-costs.
Measures incompleteness of financial markets using asset rank and acceptance set dimension.
problem Measuring incompleteness of incomplete financial markets.
method Introduce rank of vector price process and dimension of acceptance set.
result Rank and dimension of acceptance set are equal.
Firm optimizes prices for products with varying feature values to maximize revenue.
problem Maximizing revenue from products with changing feature values and unknown parameters.
method Projected Stochastic Gradient Descent (PSGD) for dynamic pricing.
result Regret bounds for PSGD pricing policy in two settings: antagonistic and stochastic feature models.
The paper models asset prices with random volatility to match option prices.
problem Matching asset price dynamics with observed option prices.
method Uses a mixture of diffusion processes with random volatility.
result Derives explicit pricing formulas for derivatives.
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.
Study uses neural networks to improve option pricing accuracy.
problem Reducing variance in Monte Carlo estimators for option pricing.
method Characterizes neural networks' universal approximation property and applies it to sampling measures.
result Sampling measures generated by neural networks can approximate optimal measures arbitrarily well.
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.
Paper proposes a new hybrid model for forecasting house prices.
problem Forecasting sudden house price drops to prevent financial crises.
method Combines EEMD signal processing with SVR machine learning.
result Proposed model outperforms other models with half the error.
Neural networks predict stock prices better than traditional methods.
problem Predicting stock prices in volatile financial markets.
method Compared five neural network models (BP, RBF, GRNN, SVMR, LS-SVMR) on three stocks.
result BP neural network outperformed other models in accuracy.
Machine learning predicts Bitcoin price with high accuracy.
problem Uncertainty in Bitcoin price prediction for investors.
method Used machine learning techniques with technical indicators.
result Stacking ensemble model with random forest and GLM is optimal.
We model the logarithm of the price (log-price) of a financial asset as a random variable obtained by projecting an operator stable random vector with a scaling index matrix E ‾ ‾ \underline{\underline{E}} E onto a non-random vector. The scaling index E ‾ ‾ \underline{\underline{E}} E models prices of the individual financial asse…
In this paper we analyse financial implications of exchangeability and similar properties of finite dimensional random vectors. We show how these properties are reflected in prices of some basket options in view of the well-known put-call symmetry property and the duality principle in option pricing. A particular atten…
Pricing and hedging rainbow options using Bayesian MS-VAR process.
problem Pricing and hedging rainbow options under varying economic conditions.
method Bayesian Markov-Switching Vector Autoregressive (MS-VAR) process to model regime-switching economic variables.
result Model provides a simpler and more economic variable-dependent approach for rainbow options pricing and hedging.
The study identifies and predicts extreme stock price fluctuations using HHT and SVM.
problem Sporadic large stock price fluctuations due to various factors.
method Hilbert-Huang Transformation (HHT) for identifying extreme events (EEs) and Support Vector Regression (SVR) for forecasting.
result High instantaneous energy concentration in stock price during both positive and negative extreme events.
Fast-vollib offers high-performance option pricing and IV computation.
problem Efficiently pricing and computing implied volatility for financial models.
method Open-source Python library with PyTorch, JAX, and CUDA backends, implementing Halley and LBR algorithms.
result High-performance option pricing and IV computation with vectorized implementations.
Walraswap solves batch auction pricing by finding optimal AMM swaps.
problem Executing all trade orders with optimal automated market makers (AMMs).
method Uses Brouwer's fixed-point theorem to find equilibrium prices.
result A solution to batch auction pricing problems in blockchain.
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%.
This study analyzes relationships between factor endowments and commodity outputs in a trade model.
problem Analyzing factor endowment-commodity output relationships in a trade model.
method Developed a method to estimate the position of the EWS-ratio vector and derived sufficient conditions for specific sign patterns.
result Derived sufficient conditions for extreme factors to be complements and for specific Stolper-Samuelson sign patterns.
Abstract framework for no-arbitrage concepts in topological vector lattices.
problem Generalization of no-arbitrage concepts in topological vector lattices.
method Imposing a structural condition on trading strategies and deriving abstract FTAP.
result NUPBR, NAA 1 _1 1 , and NA 1 _1 1 may not be equivalent in general setting. This paper examines the problem of pricing spread options under some models with jumps driven by Compound Poisson Processes and stochastic volatilities in the form of Cox-Ingersoll-Ross(CIR) processes. We derive the characteristic function for two market models featuring joint normally distributed jumps, stochastic vol…
Geometric analysis of nonlinear dynamics applied to financial time series.
problem Understanding dynamic properties of financial time series.
method Nonparametric filtering method to estimate vector fields and their derivatives from nonlinear oscillation models.
result Vector fields and their derivatives provide insights into the dynamic properties of financial time series.
Deep learning models predict option prices from 3D tensor data.
problem Predicting option prices for risk management and trading.
method 3D tensor representation of financial data, deep learning models (2D tensors in 3 channels).
result Proposed models outperform traditional methods like B-S model and vector-based LSTM.
Improved bounds on the copula of a bivariate random vector are computed when partial information is available, such as the values of the copula on a given subset of [ 0 , 1 ] 2 [0,1]^2 [ 0 , 1 ] 2 , or the value of a functional of the copula, monotone with respect to the concordance order. These results are then used to compute model-free bo…
Model earnings call transcripts for better stock price prediction.
problem Predicting future stock price movements using earnings call transcripts.
method Deep learning framework with an attention mechanism to encode text data into vectors for predicting stock price movements.
result The proposed model outperforms traditional machine learning methods in stock price prediction.
A system predicts stock prices and recommends investment portions.
problem Optimizing stock investment decisions based on predicted prices and risk tolerance.
method Support Vector Regression for price prediction, Markowitz portfolio theory and fuzzy logic for investment recommendations.
result Experimental results on NYSE show the system's effectiveness.
The study examines statistical properties of market price and liquidity responses.
problem Understanding the statistical properties of market price and liquidity responses.
method Utilized singular value decomposition to analyze interconnections and statistical characteristics of responses.
result Traded volumes play a critical role in price changes induced by liquidity changes.
Dynamic pricing algorithms can work with covariates without i.i.d. assumptions.
problem Dynamic pricing with covariates under a generalized linear demand model.
method UCB and Thompson sampling-based pricing algorithms.
result Achieves an O ( d T log T ) O(d\sqrt{T}\log T) O ( d T log T ) regret upper bound without i.i.d. covariates assumption. This paper speeds up PDV model calibration by learning SPX and VIX prices.
problem Slow calibration of the 4-factor PDV model due to expensive outer simulation.
method Learning SPX and VIX prices with neural networks to reduce outer simulation time.
result Calibration times reduced to just a few seconds.
Paper predicts Airbnb prices using machine learning and customer reviews.
problem Predicting optimal Airbnb prices with limited property information.
method Uses machine learning, sentiment analysis, and various models.
result Develops a model to help both property owners and customers with price evaluation.
Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.
problem Uncertainty in crypto coin values.
method Long Short Term Memory, Support Vector Machine, Polynomial Regression models.
result Support Vector Machine with linear kernel had the smallest mean square error.
Bayesian MS-VAR process improves option pricing models.
problem Improving option pricing models for better accuracy.
method Bayesian Markov-Switching Vector Autoregressive (MS-BVAR) process with risk-neutral valuation.
result Derived pricing formulas for various options.
Study provides error estimates for approximating game options with diffusion asset prices.
problem Approximating fair prices of game options with diffusion asset prices.
method Error estimates for discrete approximations of diffusion processes, applied to game options.
result Effective tool for computing fair prices of game options in multi-asset markets.
We propose a mathematical procedure for finding informed trader activities in European-style options and their underlying asset. The regression model (9) with moving average component was written. Being added to it ARMA-process for log-price differences of underlying asset, the generalized model is written as Vector AR…
Deep models predict intraday electricity prices accurately.
problem Accurately forecasting intraday electricity prices.
method Two deep time series probabilistic models using ESNs with stochastic disturbances and copulas.
result Deep distributional models provide accurate short-term probabilistic price forecasts.