Historical returns depend on historical closing prices and distributions. We describe how to compute adjusted closing prices from closing price/distribution data with an emphasis on spreadsheet implementation. Then the growth of a security from one date to another (1 + total return) is just the ratio of the correspondi…
A new GNN model predicts stock trends by learning historical and future correlations.
problem Limited improvement in stock trend prediction models due to ignoring future patterns.
method DishFT-GNN framework that trains a teacher and student model to capture historical and future data correlations.
result State-of-the-art performance on real-world datasets.
Proposes dynamic borrowing method for historical data in clinical trials.
problem Insufficient statistical power in rare and pediatric disease clinical trials.
method Dynamic borrowing method based on frequentist approach using similarity measures.
result Demonstrates usefulness of dynamic borrowing in reanalyzing clinical trial data.
Two prediction models improve supply-demand forecasting for autonomous vehicles.
problem Improving accuracy and stability of supply-demand predictions for autonomous vehicles.
method Two prediction models based on residual network, LSTM, attention mechanism, and multi-attention mechanism.
result Our frameworks provide more accurate and stable prediction results than existing methods.
This work improves task specification learning from demonstrations using maximum causal entropy.
problem Lack of guarantees for safe task composition and historical dependencies in learning from demonstrations.
method Adapting maximum causal entropy inverse reinforcement learning to estimate task specifications using reduced ordered binary decision diagrams.
result Polynomial time algorithm for estimating task specifications from demonstrations.
Deep learning predicts path-dependent processes from historical data.
problem Predicting path-dependent processes using historical data.
method Nonparametric regression with deep neural networks.
result Deep learning method converges to theoretical predictions as observation frequency increases.
Algometrics analyzes how predictive models affect their own forecasts in algorithmic markets.
problem How predictive models affect their own forecasts in algorithmic markets.
method Introduces algometrics, a framework for time series with feedback, proving three results on deployment risk.
result Deployment risk cannot be identified from passive historical data alone, and historical rankings can invert under crowding.
We undertake a systematic comparison between implied volatility, as represented by VIX (new methodology) and VXO (old methodology), and realized volatility. We compare visually and statistically distributions of realized and implied variance (volatility squared) and study the distribution of their ratio. We find that t…
Analyzes multi-day stock returns, showing linear volatility and mean dependence.
problem Linear dependence of volatility and mean in accumulated stock returns.
method Modified Jones-Faddy skew t-distribution analysis.
result Linear dependence of volatility and mean on the number of days of accumulation.
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
problem Challenges in predicting stock prices of aerospace companies due to market uncertainty and complexity.
method Combination of Principal Component Analysis (PCA) and Recurrent Neural Networks (RNN).
result PCA improves both accuracy and efficiency of stock price prediction.
Time-related features improve time series forecasting models.
problem Lack of explicit time-related encoding in current forecasting models limits their ability to capture cyclical and seasonal trends.
method Introducing Time Stamp Forecaster (TimeSter) to encode time-related features and integrating it with a linear backbone.
result TimeLinear model reduces MSE by 23% on benchmark datasets, improving performance with exceptional efficiency.
NeuTSFlow models continuous functions behind time series forecasting.
problem Forecasting treats time series as discrete sequences, ignoring their continuous nature.
method NeuTSFlow uses Neural Operators to learn the transition between historical and future function families.
result NeuTSFlow outperforms traditional methods in forecasting accuracy and robustness.
Unified HS and related methods with explicit modeling assumptions.
problem Lack of clear assumptions in HS methods for Value-at-Risk.
method Explicitly defined parametric model for asset returns and extraction of innovation process.
result HS and related methods require more assumptions than commonly acknowledged.
We propose the new Top-Dog-Index to quantify the historic deviation of the supply data of many small branches for a commodity group from sales data. On the one hand, the common parametric assumptions on the customer demand distribution in the literature could not at all be supported in our real-world data set. On the o…
The paper models rating transitions and calibrates them to market data for XVA calculations.
problem Calibrating rating models to both historical and market data for accurate XVA calculations.
method Modeling rating transitions as a Markov chain, calibrating to historical and market data, proposing a novel calibration procedure.
result Improved XVA scheme through better calibration of rating models.
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
problem Incomplete user profiles and dynamic user preferences degrade recommender quality.
method A cross-network time-aware recommender that learns from multiple source networks and develops current user models.
result The proposed solution achieves superior performance in accuracy, novelty, and diversity.
The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography (EEG) and facial electromyography (EMG), we apply historical function-on-function regression models to EEG and EMG data that were simultaneously r…
The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst exponent H>0.5, is exploited in order to predict future BTC/USD price. A Monte Carlo simulation with 104 geometric fractional Brownian motion realisations is performed as extensions of historical data. The accuracy of statistical inferen…
Proposes a new fairness definition based on equity for machine learning classification.
problem Machine learning systems can perpetuate societal biases.
method Formalizes a new fairness definition based on equity, operationalizes it for classification, and evaluates its effectiveness.
result Demonstrates the effectiveness of the new fairness definition for equitable classification.
ArtificialReplay improves data efficiency in bandits using historical data.
problem Data inefficiency in warm-starting bandit algorithms.
method ArtificialReplay, a meta-algorithm for incorporating historical data into any bandit algorithm.
result ArtificialReplay uses only a fraction of historical data compared to a full warm-start approach, achieving identical regret.
Spotify improves content mix using contextual bandits.
problem Skewed historical data and varying user preferences across contexts.
method Contextual bandits to dynamically learn optimal content type distribution.
result Improved precision and user engagement with under-represented content types.
The Local Volatility model is a well-known extension of the Black-Scholes constant volatility model whereby the volatility is dependent on both time and the underlying asset. This model can be calibrated to provide a perfect fit to a wide range of implied volatility surfaces. The model is easy to calibrate and still ve…
ADR helps LLMs find and use historical analogies for foresight analysis.
problem LLMs struggle to find relevant historical analogies due to surface-level matching.
method Proposes CANA framework with mechanism alignment and cross-analogy confirmation.
result CANA improves historical analogy generation by up to 10%.
Data describing historical economic growth are analysed. Included in the analysis is the world and regional economic growth. The analysis demonstrates that historical economic growth had a natural tendency to follow hyperbolic distributions. Parameters describing hyperbolic distributions have been determined. A search …
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.
This study reviews techniques to estimate volatility and price Variance Swaps.
problem Estimating historical volatility and pricing Variance Swaps.
method Review of existing techniques.
result Discussion of various methods to estimate volatility and price Variance Swaps.
Drawdowns measuring the decline in value from the historical running maxima over a given period of time, are considered as extremal events from the standpoint of risk management. To date, research on the topic has mainly focus on the side of severity by studying the first drawdown over certain pre-specified size. In th…
Non-linear shrinkage isn't optimal for portfolio optimization, especially when asset dependence is non-stationary.
problem Optimizing portfolios with non-stationary asset dependence structures.
method Derived and compared non-linear shrinkage with an optimal target for covariance matrix estimation.
result Non-linear shrinkage can be significantly improved for portfolio optimization.
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…
Proposes SAHP for better Hawkes process modeling.
problem Predicting occurrence patterns of event sequences.
method Leverages self-attention to modify Hawkes process intensity function.
result Demonstrates effectiveness on real-world datasets.
Pre-trained LLM adapted with LoRA improves offline RL for quantitative trading.
problem Challenges in offline RL for quantitative trading due to complex temporal dependencies and overfitting.
method Integrates pre-trained GPT-2 weights and LoRA for efficient fine-tuning of a Decision Transformer.
result Outperforms existing offline RL methods in certain trading scenarios.
Improved particle pricing methods for path-dependent options.
problem Efficient simulation of spot price and volatility for path-dependent options.
method Sequential Monte Carlo with branching and resampling.
result Branching algorithms improve pricing performance for path-dependent options.
Generative diffusion models forecast implied vol surfaces without arbitrage issues.
problem Forecasting arbitrage-free implied volatility surfaces using historical data with path-dependent dynamics.
method Generative diffusion model (DDPM) with conditional training on market variables, including EWMAs and returns. Dynamic penalty scheme based on SNR to enforce arbitrage-free surfaces.
result Superior performance in volatility forecasting compared to existing methods.
Historical (Stressed-) Value-at-Risk ((S)VAR), and Expected Shortfall (ES), are widely used risk measures in regulatory capital and Initial Margin, i.e. funding, computations. However, whilst the definitions of VAR and ES are unambiguous, they depend on input distributions that are data-cleaning- and Data-Model-depende…
New control theory for self-path-dependent problems solves unique constraints.
problem Optimal control with self-path-dependent constraints in stochastic systems.
method Introduces new HJB equations for variational inequalities with historical maximum controls.
result Value functions are viscosity solutions to HJB equations under Lipschitz conditions.
Improved Bayesian inference using power priors with historical data.
problem Improving Bayesian inference with historical data.
method Generalized power priors that adapt to the α parameter of Amari's α-divergence. result Improved performance through appropriate choices of the α parameter. Develops algorithms to exploit historical and pre-clustered arm information in bandit problems.
problem Optimizing decision-making in multi-armed bandit and contextual bandit problems with historical observations and pre-clustered arms.
method META algorithm that combines historical observations and pre-clustering information, deriving regret bounds for various scenarios.
result META algorithm effectively balances between using historical observations and clustering, outperforming the other in different scenarios.
AI mirrors modern math's autonomous development, raising interpretive challenges.
problem AI's effectiveness in math mirrors historical autonomy of math.
method Analyzes historical evolution of modern mathematics and AI's role.
result AI's affinity with math's historical autonomy suggests interpretive limits.
Efficiently optimize GPs by reusing candidate solutions multiple times.
problem High computational cost of Gaussian process optimization due to unique historical points.
method Sticking to a candidate solution for multiple evaluation steps and limiting switches.
result Improved efficiency and practicality of Gaussian process optimization algorithms.
The current research on credit risk is primarily focused on modeling default probabilities. Recovery rates are often treated as an afterthought; they are modeled independently, in many cases they are even assumed constant. This is despite of their pronounced effect on the tail of the loss distribution. Here, we take a …
Paper addresses OPE for dependent bandit samples using MDS and batch updates.
problem Evaluating policies from non-i.i.d. historical data in contextual bandits.
method Constructs an MDS-based estimator for dependent samples, solves batch update and deficient support issues.
result Derives an asymptotically normal estimator for evaluation policy value.
New model incorporates long-range dependence in mortality rates for better valuation and risk management.
problem Lack of appropriate models for valuing and managing mortality securities with long-range dependence.
method Proposes a novel class of Volterra mortality models that incorporate LRD, derived in closed-form solution.
result Models provide flexibility and tractability for valuing and hedging mortality-related products.
Combines experimental and historical data for robust policy evaluation.
problem Policy evaluation with mixed data sources, especially experimental vs historical.
method Linear integration of estimators from experimental and historical data, optimized for MSE minimization.
result Proposed estimators outperform traditional methods in ridesharing company data.
The paper uses GRU and self-attention for SPY option pricing.
problem Precise prediction of SPY option prices for better investment decisions.
method Partitioned dataset, built four models, used SHAP for interpretation.
result Self-attention GRU model outperforms traditional models.
We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock pr…
New algorithm for bandits with delayed action effects, reducing regret.
problem Delayed impact of actions in multi-armed bandits.
method Formulated a new bandit setting with delayed action effects, proposed an algorithm with regret bound.
result Achieved a regret of ildeO(KT2/3) and showed a matching lower bound. In this paper we look at the efficacy of different risk measures on energy markets and across several different stock market indices. We use both the Value at Risk and the Tail Conditional Expectation on each of these data sets. We also consider several different durations and levels for historical risk measures. Throu…
The paper studies estimation of parameters of diffusion market models from historical data. The standard definition of implied volatility for these models presents its value as an implicit function of several parameters, including the risk-free interest rate. In reality, the risk free interest rate is unknown and need …