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.
Corrects technical error in change of measure for HTB models.
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
Study uniform consistency in nonparametric mixture models and mixed regression.
We consider the minimum error entropy (MEE) criterion and an empirical risk minimization learning algorithm in a regression setting. A learning theory approach is presented for this MEE algorithm and explicit error bounds are provided in terms of the approximation ability and capacity of the involved hypothesis space w…
This technical note extends recent results on the computational complexity of globally minimizing the error of piecewise-affine models to the related problem of minimizing the error of switching linear regression models. In particular, we show that, on the one hand the problem is NP-hard, but on the other hand, it admi…
The purpose of this research is to apply technical analysis of Sutte Indicator in stock trading which will assist in the investment decision making process i.e. buying or selling shares. This research takes data of "A" on the Indonesia Stock Exchange(IDX or BEI) 29 November 2006 until 20 September 2016 period. To see t…
Transformer model with mixed-frequency data improves stock volatility prediction.
VTA combines verbal and latent reasoning for accurate stock time-series forecasts.
New decision-theoretic calibration error metric improves prediction reliability.
Robust estimators for Gaussian sparse tasks with optimal error under contamination.
The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machin…
Traditional error detection approaches require user-defined parameters and rules. Thus, the user has to know both the error detection system and the data. However, we can also formulate error detection as a semi-supervised classification problem that only requires domain expertise. The challenges for such an approach a…
New AI error correctors improve classifier performance with provable guarantees.
Efficiently estimates private least squares with linear error growth.
Constructs ε-splitting maps for geodesic balls with non-negative Ricci curvature.
Artificial Intelligence (AI) systems sometimes make errors and will make errors in the future, from time to time. These errors are usually unexpected, and can lead to dramatic consequences. Intensive development of AI and its practical applications makes the problem of errors more important. Total re-engineering of the…
We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well a…
In this paper, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings. A two-stage non-convex implementation is developed based on sparse tensor decomposition and thresholded gradient descent, which ensures exact recovery in the noiseless case and stable recovery in the noisy cas…
Proposes a new model to handle noisy data in scientific research.
We show how to estimate a model's test error from unlabeled data, on distributions very different from the training distribution, while assuming only that certain conditional independencies are preserved between train and test. We do not need to assume that the optimal predictor is the same between train and test, or t…
The paper models financial returns data with measurement error.
This work explores efficient reinforcement learning with density features in low-rank MDPs.
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
The paper analyzes the generalization error of min-norm interpolators in transfer learning with limited test samples.
Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian an…
New approach uses Gaussian processes to learn and track complex systems with guaranteed accuracy.
A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statistical challenges, including the design of statistical methods that enable the coherent propagation of probabilities through a (possibly dete…
Diffusion approximation provides weak approximation for stochastic gradient descent algorithms in a finite time horizon. In this paper, we introduce new tools motivated by the backward error analysis of numerical stochastic differential equations into the theoretical framework of diffusion approximation, extending the …
Improved TD learning reduces variance and bias errors.
The paper analyzes LOCV for high-dimensional risk estimation, proving error bounds.
Abstract: A new approach to technical indicators without lag.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Polynomial-time algorithm for learning halfspaces with Gaussian-distributed data and adversarial noise.
Neural networks can approximate rectifiable measures with small error.
Paper establishes bounds for RNN-TPPs, showing four-layer networks can achieve vanishing errors.
Technical trading rules have a long history of being used by practitioners in financial markets. Their profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousands traditional technical trading rules on the Shanghai Securities Co…
Revisits life insurance surplus models with new technical bases.
We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for contr…
The paper proves exponential mixing for hyperbolic manifolds, with applications to geodesic holonomy.
New algorithm learns changing discrete distributions with minimal drift error.
Robust machine learning models improve DNA regulatory sequence prediction under various shifts.
Near-optimal algorithms for mean estimation and linear regression with Gaussian covariates and Huber contamination.
The paper decomposes unsupervised learning's generalization error into model, data, and variance components.
Proposes ridge regression on Riemannian manifolds for time-series prediction.
Weak form of the Efficiency Market Hypothesis (EMH) excludes predictions of future market movements from historical data and makes the technical analysis (TA) out of law. However the technical analysis is widely used by traders and speculators who steadely refuse to consider the market as a "fair game" and survive with…
The errors-in-variables (EIV) regression model, being more realistic by accounting for measurement errors in both the dependent and the independent variables, is widely adopted in applied sciences. The traditional EIV model estimators, however, can be highly biased by outliers and other departures from the underlying a…
New method predicts and optimizes matrix recovery from noisy measurements.
We present a numerical approach for approximating unknown Hamiltonian systems using observation data. A distinct feature of the proposed method is that it is structure-preserving, in the sense that it enforces conservation of the reconstructed Hamiltonian. This is achieved by directly approximating the underlying unkno…