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.
Algorithm selects variables and bandwidths for geographically weighted regression.
GWRBoost improves GWR for better spatial relationship quantification.
GeoConformal predicts spatial uncertainty without relying on specific models.
We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means of higher-dimensional embeddings (Schoenberg transformations). Geographical flow …
This paper begins to explore the determinants of the topological properties of the international - trade network (ITN). We fit bilateral-trade flows using a standard gravity equation to build a "residual" ITN where trade-link weights are depurated from geographical distance, size, border effects, trade agreements, and …
SXL embeds spatial autocorrelation into neural networks for better geographic data learning.
The paper predicts an Efficient Market Property for the equity market, where stocks, when denominated in units of the growth optimal portfolio (GP), have zero instantaneous expected returns. Well-diversified equity portfolios are shown to approximate the GP, which explains the well-observed good performance of equally …
SX-GeoTree improves spatially coherent explanations in geospatial regression trees.
New core inflation measure predicts future headline inflation.
Spatially weighted conformal prediction improves uncertainty quantification in house price models.
This paper explores the relationships between migration and trade using a complex-network approach. We show that: (i) both weighted and binary versions of the networks of international migration and trade are strongly correlated; (ii) such correlations can be mostly explained by country economic/demographic size and ge…
In this paper, we consider the problem of predicting demographics of geographic units given geotagged Tweets that are composed within these units. Traditional survey methods that offer demographics estimates are usually limited in terms of geographic resolution, geographic boundaries, and time intervals. Thus, it would…
Mathematical analysis shows Delisle-Euler map methods are optimal.
Study improves motor insurance claim prediction using geographic data.
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore different geographical feature representations in the context of predicting colorectal cancer survival curves for patients in the state of Iowa, sp…
Study analyzes global public sentiment on DeFi from 2012-2022.
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner. Here we present a novel method for ambulance demand prediction using Gaussian process regression (GPR) in time and geographic space. The method exhibits superior accuracy to MEDIC…
Cost overruns in transport infrastructure projects know no geographical limits, overruns are a global phenomenon. Nevertheless, the size of cost overruns varies with location. In the Netherlands, cost overruns appear to be smaller compared to the rest of the world. This paper tests whether Dutch projects perform signif…
Paper presents a method for geographic ratemaking using spatial embeddings.
SE-KGE embeds spatial data into KGs for better spatial reasoning.
Image compression techniques reveal network structure for shipping box optimization.
WBCP improves conformal prediction for distribution shifts using weighted Dirichlet posteriors.
A scalable Bayesian linear regression framework for spatial data.
STICC clusters geographic objects considering both spatial contiguity and attributes.
Meta-data from photo-sharing websites such as Flickr can be used to obtain rich bag-of-words descriptions of geographic locations, which have proven valuable, among others, for modelling and predicting ecological features. One important insight from previous work is that the descriptions obtained from Flickr tend to be…
We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications typically there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression ove…
A new SBM for non-negative zero-inflated edge weights in networks.
Study uses trajectory embedding to measure place function similarity at fine spatial granularity.
We develop a cross-sectional research design to identify causal effects in the presence of unobservable heterogeneity without instruments. When units are dense in physical space, it may be sufficient to regress the "spatial first differences" (SFD) of the outcome on the treatment and omit all covariates. The identifyin…
In the multiple linear regression setting, we propose a general framework, termed weighted orthogonal components regression (WOCR), which encompasses many known methods as special cases, including ridge regression and principal components regression. WOCR makes use of the monotonicity inherent in orthogonal components …
Enhances linear regression with Kalman filter for loss minimization.
Improved RTM uses integer weights to reduce computation and increase interpretability.
Gradient boosting for spatial regression models improves prediction accuracy.
Adversarial training makes logistic regression weight loss landscapes sharper.
Gradient descent with random weights in linear regression analyzed for various noise types.
Geography effect is investigated for the Chinese stock market including the Shanghai and Shenzhen stock markets, based on the daily data of individual stocks. The Shanghai city and the Guangdong province can be identified in the stock geographical sector. By investigating a geographical correlation on a geographical pa…
The effects of weather on agriculture in recent years have become a major global concern. Hence, the need for an effective weather risk management tool (i.e., weather derivatives) that can hedge crop yields against weather uncertainties. However, most smallholder farmers and agricultural stakeholders are unwilling to p…
Study reveals centralization in Bitcoin transactions involving retail users.
Proposes a new method to improve regression models with reweighted samples.
New method generates geolocated synthetic populations from real data.
Novel characterization of augmented balancing weights combining outcome and weighting models.
The objective of this paper is to fill a gap in the literature on internationalization, in relation to the absence of objective and measurable performance indicators on the process of how firms sequentially enter external markets. To that end, this research develops a quantitative tool that can be used as a performance…
Proposes FSM-IRL to learn invariant network representations considering feature and structural shifts.
In the economic literature, geographic distances are considered fundamental factors to be included in any theoretical model whose aim is the quantification of the trade between countries. Quantitatively, distances enter into the so-called gravity models that successfully predict the weight of non-zero trade flows. Howe…
We analyze optimal weighted ridge regression in overparameterized linear models.
Unified approach to linear regression using covariance fitting for optimal weights.
Lagrange's map construction ideas influenced later mathematicians.
Real estate appraisal is a complex and important task, that can be made more precise and faster with the help of automated valuation tools. Usually the value of some property is determined by taking into account both structural and geographical characteristics. However, while geographical information is easily found, o…