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.
Study on frequencies of non-simple curves in surfaces of large genus.
Addressing the ongoing examination of high-frequency trading practices in financial markets, we report the results of an extensive empirical study estimating the maximum possible profitability of the most aggressive such practices, and arrive at figures that are surprisingly modest. By "aggressive" we mean any trading …
Bayesian econometrics improves nowcasting during pandemics.
We present a novel procedure for scaling relatively high frequency tail probability and quantile estimates for the conditional distribution of returns.
This paper presents a model of the dynamics of the wage income distribution.
Time delay neural networks (TDNNs) are an effective acoustic model for large vocabulary speech recognition. The strength of the model can be attributed to its ability to effectively model long temporal contexts. However, current TDNN models are relatively shallow, which limits the modelling capability. This paper propo…
We analyze total, asymmetric and frequency connectedness between oil and forex markets using high-frequency, intra-day data over the period 2007 -- 2017. By employing variance decompositions and their spectral representation in combination with realized semivariances to account for asymmetric and frequency connectednes…
Estimates for -capacities on symmetric manifolds.
Paper introduces a new IV regression method for mixed-frequency data.
Why deep neural networks (DNNs) capable of overfitting often generalize well in practice is a mystery [#zhang2016understanding]. To find a potential mechanism, we focus on the study of implicit biases underlying the training process of DNNs. In this work, for both real and synthetic datasets, we empirically find that a…
Granger-causality in the frequency domain is an emerging tool to analyze the causal relationship between two time series. We propose a bootstrap test on unconditional and conditional Granger-causality spectra, as well as on their difference, to catch particularly prominent causality cycles in relative terms. In particu…
Model assesses systemic risk in crude oil and gasoline futures markets.
A new high-frequency market making strategy using Deep Hawkes process.
Bayesian CART models improve insurance claims frequency prediction and interpretation.
Paper tackles joint community detection and phase synchronization in stochastic block models.
System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems. The performance of commonly used unsupervised anomaly detection methods may suffer in that setting, because they use frequency as a proxy for anomaly. We propose a novel concept for anomaly dete…
Study on singularities of area-minimizing currents, focusing on frequency and branch points.
Modeling high-frequency speculative markets as auction search processes.
Study improves Cox model for predicting stock trading signs using Japanese market data.
Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This pr…
Study analyzes data breach reporting patterns and frequency across U.S. states, finding increasing trends after 2020.
This paper extends Kelly Criterion to include rebalancing frequency for optimal portfolio selection.
New principle reveals how neural networks learn complex interactions.
Deep learning predicts uncertainty to optimize Eurodollar futures trading.
Study on HFTs' interactions with a large trader using mean field game theory.
Paper proposes a risk index combining frequency and severity of abnormal driving patterns.
New method models complex dynamics using a base variable.
Here we present a novel approach to statistical analysis of financial time series. The approach is based on -grams frequency dictionaries derived from the quantized market data. Such dictionaries are studied by evaluating their information capacity using relative entropy. A specific quantization of (originally conti…
Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our procedure …
FIBS extracts relevant features from IBTSs for classification.
We study the statistical regularities of opening call auction using the ultra-high-frequency data of 22 liquid stocks traded on the Shenzhen Stock Exchange in 2003. The distribution of the relative price, defined as the relative difference between the order price in opening call auction and the closing price of last tr…
Tests factor models by decomposing market into body and tail legs, revealing inconsistent results.
Study analyzes risks and opportunities in blockchain currency markets.
New estimator reveals intraday betas mainly driven by correlations.
We adapt the Higher Criticism (HC) goodness-of-fit test to measure the closeness between word-frequency tables. We apply this measure to authorship attribution challenges, where the goal is to identify the author of a document using other documents whose authorship is known. The method is simple yet performs well witho…
We demonstrate that minority mechanisms arise in the dynamics of markets because of effects of price impact; accordingly the relative importance of minority and delayed majority mechanisms depends on the frequency of trading. We then use minority games to illustrate that a vanishing price return auto-correlation functi…
Using ultra-high-frequency data extracted from the order flows of 23 stocks traded on the Shenzhen Stock Exchange, we study the empirical regularities of order placement in the opening call auction, cool period and continuous auction. The distributions of relative logarithmic prices against reference prices in the thre…
Investigate the evolving structure of cryptocurrency interactions using high-frequency returns.
The Epps effect helps distinguish between continuous and discrete financial tick data.
SKI speeds up Toeplitz Neural Networks by avoiding explicit decay bias and using frequency response.
We develop a framework especially suited to the autocorrelation properties observed in financial times series, by borrowing from the physical picture of turbulence. The success of our approach as applied to high frequency foreign exchange data is demonstrated by the overlap of the curves in Figure (1), since we are abl…
Three methods for tuning HMC diagonal scale matrices compared.
An efficient adaptive direct numerical integration (DNI) algorithm is developed for computing high quantiles and conditional Value at Risk (CVaR) of compound distributions using characteristic functions. A key innovation of the numerical scheme is an effective tail integration approximation that reduces the truncation …
A Hawkes process model with a time-varying background rate is developed for analyzing the high-frequency financial data. In our model, the logarithm of the background rate is modeled by a linear model with a relatively large number of variable-width basis functions, and the parameters are estimated by a Bayesian method…
Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of …
A Lissajous knot is one that can be parameterized by a single cosine function in each coordinate. Lissajous knots are highly symmetric, and for this reason, not all knots are Lissajous. We prove several theorems which allow us to place bounds on the number of Lissajous knot types with given frequencies and to efficient…
Paper develops new spot regression estimators using candlesticks for asset pricing.
Paper analyzes systematic jump risk around the clock using news narratives.