A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
ADR helps LLMs find and use historical analogies for foresight analysis.
New method interprets machine learning forecasts as historical analogies.
The Analog Ensemble (AnEn) method tries to estimate the probability distribution of the future state of the atmosphere with a set of past observations that correspond to the best analogs of a deterministic Numerical Weather Prediction (NWP). This model post-processing method has been successfully used to improve the fo…
The paper develops a method to forecast financial risk multiple steps ahead using quantile time series and historical simulation.
We propose a 4-factor model for overnight returns and give explicit definitions of our 4 factors. Long horizon fundamental factors such as value and growth lack predictive power for overnight (or similar short horizon) returns and are not included. All 4 factors are constructed based on intraday price and volume data a…
In Lorentzian manifolds of any dimension the concept of causal tensors is introduced. Causal tensors have positivity properties analogous to the so-called ``dominant energy condition''. Further, it is shown how to build, from ANY given tensor , a new tensor quadratic in and ``positive'', in the sense that it is …
Unified HS and related methods with explicit modeling assumptions.
Residual networks' depth is mathematically equivalent to expanding an implicit ensemble size.
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…
ArtificialReplay improves data efficiency in bandits using historical data.
Proposes dynamic borrowing method for historical data in clinical trials.
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 …
This study reviews techniques to estimate volatility and price Variance Swaps.
The process of liquidity provision in financial markets can result in prolonged exposure to illiquid instruments for market makers. In this case, where a proprietary position is not desired, pro-actively targeting the right client who is likely to be interested can be an effective means to offset this position, rather …
The use of sequential Monte Carlo within simulation for path-dependent option pricing is proposed and evaluated. Recently, it was shown that explicit solutions and importance sampling are valuable for efficient simulation of spot price and volatility, especially for purposes of path-dependent option pricing. The result…
This paper is an expansion of my lecture for David Epstein's birthday, which traced a logical progression from ideas of Euclid on subdividing polygons to some recent research on invariants of hyperbolic 3-manifolds. This `logical progression' makes a good story but distorts history a bit: the ultimate aims of the chara…
Improved Bayesian inference using power priors with historical data.
Develops SPT with price impact, deriving formulas for wealth and arbitrage conditions.
In the area of traditional physics the atomic nucleus belongs to the most complex systems. It involves essentially all elements that characterize complexity including the most distinctive one whose essence is a permanent coexistence of coherent patterns and of randomness. From a more interdisciplinary perspective, thes…
Combines experimental and historical data for robust policy evaluation.
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…
Analogator learns to make analogies by example.
Conventional economic analysis of stringent climate change mitigation policy generally concludes various levels of economic slowdown as a result of substantial spending on low carbon technology. Equilibrium economics however could not explain or predict the current economic crisis, which is of financial nature. Meanwhi…
Calibrates historical and implied correlations in energy markets.
Econophysics embodies the recent upsurge of interest by physicists into financial economics, driven by the availability of large amount of data, job shortage in physics and the possibility of applying many-body techniques developed in statistical and theoretical physics to the understanding of the self-organizing econo…
Contextualizing financial news improves stock price predictions.
LLM forecasting benchmarks suffer from information leakage, which confounds model performance.
Analog forecasting uses local dynamics to predict chaotic systems.
Typically flat filling, linear or polynomial interpolation methods to generate missing historical data. We introduce a novel optimal method for recreating data generated by a diffusion process. The results are then applied to recreate historical data for stocks.
A new GNN model predicts stock trends by learning historical and future correlations.
Analog methods improve forecast accuracy in complex models.
Study compares VaR models and finds GARCH-FHS superior.
The paper evaluates the probability distributions of analog-to-target distances for multiple analogs.
Quantum effects improve stock option pricing model.
New algorithm reduces online learning regret by exploiting historical invariances.
Industry datasets used for text classification are rarely created for that purpose. In most cases, the data and target predictions are a by-product of accumulated historical data, typically fraught with noise, present in both the text-based document, as well as in the targeted labels. In this work, we address the quest…
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…
RL improves market making with historical data time travel.
We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms. The historical observations can contain any number of instances for each arm, and the pre-clustering information is a fixed clustering of arms provided as part of the input. We de…
Identifying the type of font (e.g., Roman, Blackletter) used in historical documents can help optical character recognition (OCR) systems produce more accurate text transcriptions. Towards this end, we present an active-learning strategy that can significantly reduce the number of labeled samples needed to train a font…
ContraSim learns financial headline similarities for market forecasting.
Surveying nonparametric inference with shape constraints, past and future.
Explains historical connections between vector bundle splitting and Riemann-Hilbert problems.
Improves trial efficiency by adjusting for historical prognostic scores.
Explains isometric immersions and their applications.
FGD reduces noisy gradient variance in SGD for neural networks.
EHNA learns node embeddings from historical network neighborhoods.
The study uses historical revenue data to forecast music catalog cashflows and multipliers.