Multi-step-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multi-step-ahead…
On-device research index
arXiv research
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.
168,695 papers · 148 categories
Trend · papers per month
48 results for “predictable strategies”
Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterat…
Predictive modelling and supervised learning are central to modern data science. With predictions from an ever-expanding number of supervised black-box strategies - e.g., kernel methods, random forests, deep learning aka neural networks - being employed as a basis for decision making processes, it is crucial to underst…
We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies' past performance. Based on the chosen trading strategy they determine their prediction of the movement for the following time period of…
Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimizationq-fin.ST
Study uses RNN for real-time crypto price prediction and trading optimization.
problem High volatility in cryptocurrency markets makes traditional forecasting models unreliable.
method Data collection, preprocessing, model refinement, and backtesting.
result Improved accuracy in real-time crypto price prediction and optimized trading strategies.
A new stock selection strategy uses combined machine learning with dynamic weighting methods.
problem Improving stock selection accuracy and performance.
method Combined machine learning algorithms with static and dynamic weighting methods.
result IC-based dynamic weighting outperforms static evaluation metrics in backtested returns and predictive performance.
Enhanced portfolio selection using sentiment data and LSTM.
problem Improving portfolio selection through sentiment analysis and price prediction.
method Semantic Attention Model for sentiment prediction, LSTM for price prediction, mean-variance strategy for portfolio optimization.
result Sentiment-aware portfolio strategies outperform non-sentiment aware models on average.
Study efficient rebalancing strategies for portfolio tracking error.
problem Optimizing portfolio rebalancing under high-frequency asset price models.
method Discrete-time rebalancing strategies derived from continuous model.
result Asymptotically efficient sequence of simple strategies.
We present a deep long short-term memory (LSTM)-based neural network for predicting asset prices, together with a successful trading strategy for generating profits based on the model's predictions. Our work is motivated by the fact that the effectiveness of any prediction model is inherently coupled to the trading str…
New trading strategy uses deep neural networks for future stock price predictions.
problem Traditional backtesting of trading strategies is unreliable for future trades.
method Developed a deep neural network to predict stock prices and select optimal trading strategies.
result Neural network predictions improve trading performance metrics.
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
New method combines model forecasts and real-time observations for hourly wind speed predictions.
problem Filling the six-hour gap between weather model runs for accurate hourly wind speed forecasts.
method Combines quasi-real-time observed wind speed and weather model predictions using a novel Ensemble Model Output Statistics (EMOS) strategy.
result Successfully improved wind speed predictions compared to observed data from SYNOP stations.
Parallel neural network training yields better long-term prediction accuracy.
problem Choosing the right training strategy for neural networks in dynamical systems.
method Comparison of parallel and series-parallel training strategies on five neural network architectures and two examples.
result Parallel training consistently outperforms series-parallel training in long-term prediction accuracy.
New method learns optimal prediction strategies in adversarial games.
problem Learning optimal prediction procedures in uncertain data environments.
method Adversarial Monte Carlo approach with neural network architecture.
result Optimal strategy is equivariant and invariant to various transformations.
This work introduces COLA, a strategy to aggregate conformal prediction sets efficiently.
problem Efficiently combining multiple conformity scores to reduce prediction set size.
method Introduces COnfidence-Level Allocation (COLA) to optimally allocate confidence levels across sets.
result COLA achieves smaller prediction sets than state-of-the-art methods while maintaining valid coverage.
One of the current challenges in machine learning is how to deal with data coming at increasing rates in data streams. New predictive learning strategies are needed to cope with the high throughput data and concept drift. One of the data stream mining tasks where new learning strategies are needed is multi-target regre…
New method detects heuristics in complex game strategies.
problem Understanding decision-making in games with infinite strategy spaces.
method Introducing decoupled strategies to detect convergence towards Nash equilibria.
result Predictive measure ΔD reveals participants' actions with high success rate.
Paper uses conformal prediction for solar power forecasting in electricity markets.
problem Enhancing participation in electricity markets through accurate day-ahead PV power predictions.
method Combines machine learning for point predictions and conformal prediction for uncertainty quantification.
result CP with k-nearest neighbors and Mondrian binning outperforms linear quantile regressors in predicting PV power.
Estimates reliability of nuclear fuel using advanced modeling techniques.
problem Determining the reliability of TRISO-coated particle fuel, which has small failure probabilities and expensive computational models.
method Coupled active learning, multifidelity modeling, and subset simulation.
result Multifidelity modeling strategies consistently reduce the number of high-fidelity model calls.
Selective classification improves trading strategies by abstaining from predictions.
problem Designing effective trading strategies using selective classification.
method Extends binary or multi-class classifiers to allow abstaining from predictions, evaluates across different feature sets and classifiers.
result Selective classifiers can improve trading performance by avoiding poor predictions.
The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.
problem Online selective conformal prediction's exchangeability issues and false coverage rate control problems.
method Evaluation and correction of existing calibration selection strategies, proposing new ones that preserve exchangeability.
result Novel calibration selection strategies ensure both selection-conditional coverage and FCR control.
Hierarchical graph learning for calendar spread strategies in commodity futures markets
problem Developing machine-learning methods for calendar spread strategies in commodity futures markets
method Proposing a hierarchical graph learning approach
result Outperforming benchmark models in both prediction and trading performance
Develops a new bidding system to maximize advertiser profit.
problem Inaccurate prediction of ad lift-effect due to biased log data.
method Unbiased Lift-based Bidding System that predicts lift-effect from biased log data.
result Demonstrates superior and practical high-performing lift-based bidding strategy.
Optimizes forecast distributions for financial risk management.
problem Improving risk management through better forecast distributions.
method Optimizes forecast distributions using scoring rules relevant to financial risk management.
result Tail-focused predictive distributions yield better outcomes in hedging strategies involving VIX futures.
We introduce and study the notion of sure profit via flash strategy, consisting of a high-frequency limit of buy-and-hold trading strategies. In a fully general setting, without imposing any semimartingale restriction, we prove that there are no sure profits via flash strategies if and only if asset prices do not exhib…
Despite the superiority of convolutional neural networks demonstrated in time series modeling and forecasting, it has not been fully explored on the design of the neural network architecture and the tuning of the hyper-parameters. Inspired by the incremental construction strategy for building a random multilayer percep…
Two strategies extend multi-label chaining for imprecise probability estimates.
problem Handling imprecise probability estimates in multi-label classification.
method Adapting multi-label chaining to use convex sets of distributions (credal sets).
result Adapted approaches produce relevant cautiousness on hard-to-predict instances.
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
problem Ambiguous outcome labels in real-world datasets hinder accurate multi-class classification.
method Information-theoretic classification accuracy (ITCA) and search strategies (greedy, breadth-first) guide label combination.
result ITCA improves prediction accuracy and identifies ambiguous labels across diverse applications.
Two approaches improve conformal Bayes for label shift, one post-hoc and one in-training.
problem Improving prediction sets for target domain under label shift.
method Two complementary approaches: post-hoc calibration and in-training adaptation.
result In-training adaptation achieves up to 43% width reduction at unchanged coverage.
Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variantsstat.ML
Theoretical and empirical study on SMOTE rebalancing strategy for imbalanced data.
problem Handling imbalanced tabular data sets using SMOTE and its variants.
method Derive non-asymptotic upper bounds on SMOTE density, adapt SMOTE based on theoretical findings.
result SMOTE tends to copy original minority samples asymptotically and vanishes near minority class boundaries.
We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions. Our games map the input context to outcomes by first condensing the input into private player types that specify the utilities, weighted interactions…
Unified framework for set-valued classification tackles ambiguous multi-class datasets.
problem Ambiguous multi-class datasets in modern statistics.
method Unified statistical framework encompassing various set-valued classification formulations.
result Infinite sample optimal strategies and plug-in principle for data-driven algorithms.
Optimal trading strategy adapts to signals in markets with price impact.
problem Optimal liquidation in markets with linear price impact and predictive signals.
method Formulated as a stochastic control problem, solved using probabilistic and convex analytic techniques.
result Explicit solution for optimal trading strategy in terms of SDEs.
TuneUp improves GNN training by focusing on hard-to-learn nodes.
problem Sub-optimal training of GNNs on all nodes equally.
method Two-stage training: base GNN + tail node improvement.
result Significant improvement in tail node prediction performance.
Bayesian spatial predictive synthesis improves spatial data predictions.
problem Model misspecification and heterogeneity in spatial data.
method Bayesian ensemble methodology capturing spatially-varying model uncertainty and performance heterogeneity.
result Synthesized predictions outperform standard methods in accuracy and uncertainty quantification.
We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized maximum likelihood (SNML) coincide and are optimal if and only if the latter is exchangeable, and if and only if the opti…
LLMs outperform human analysts in predicting earnings direction.
problem Evaluating financial statements without narrative or industry-specific information.
method Trained GPT4 on standardized, anonymous financial statements and instructed to predict earnings direction.
result LLMs predict earnings directionally with accuracy comparable to narrowly trained ML models.
Study improves healthcare time series imputation by considering structured missingness.
problem Structured missingness in clinical data impacts time series imputation models.
method Analysis of different masking strategies on imputation methods using PhysioNet Challenge 2012 dataset.
result Masking choices significantly affect imputation accuracy and clinical prediction.
In this paper an approach based on expectation maximization (EM) clustering to find the climate regions and a support vector machine to build a predictive model for each of these regions is proposed. To minimize the biases in the estimations a ten cross fold validation is adopted both for obtaining clusters and buildin…
We consider the problem of hedging a European contingent claim in a Bachelier model with transient price impact as proposed by Almgren and Chriss. Following the approach of Rogers and Singh and Naujokat and Westray, the hedging problem can be regarded as a cost optimal tracking problem of the frictionless hedging strat…
We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, , and at each time step invest a particular fraction, , of their budget. The return on investment (RoI), , is characterized by a periodic function with different types and leve…
Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed in a supervised setting, where the accuracy of each base classifier can be empirically measured and this information is incorporated in the…
New methods improve neural directed link prediction across all sub-tasks.
problem Directed link prediction requires handling edge directionality and bidirectionality, not just edge existence.
method Proposes three strategies: Multi-Class Framework, Multi-Objective, and Scalarized approaches.
result Improved performance across all three sub-tasks of directed link prediction.
New method corrects active learning for distribution shifts and outliers.
problem Conventional active learning methods fail to account for test-time distribution.
method JEPIG, a hybrid of BALD and EPIG, maximizes expected predictive information gain.
result JEPIG outperforms conventional methods in active learning with distribution shifts.
We have developed a strategy for the analysis of newly available binary data to improve outcome predictions based on existing data (binary or non-binary). Our strategy involves two modeling approaches for the newly available data, one combining binary covariate selection via LASSO with logistic regression and one based…
A Generalized Online Algorithm for Translation and Scale Invariant Prediction with Expert Advicecs.LG
Generalized algorithm for translation and scale-invariant prediction.
problem Sequential prediction with expert advice, focusing on translation and scale invariance.
method Designing a generalized online algorithm using the universal prediction perspective to compete against a generic class of expert selection strategies.
result No preliminary knowledge of loss sequences is required; performance bounds are stable under arbitrary scalings and translations.
This study proposes a framework for identifying profitable trading opportunities based on volatility and causal relationships.
problem Identifying profitable trading opportunities in financial markets.
method A combination of Gaussian Mixture Model (GMM), Granger Causality Test (GCT), Peter-Clark Momentary Conditional Independence (PCMCI) test, Dynamic Time Warping (DTW), and K-Nearest Neighbours (KNN) for identifying and executing trades.
result The proposed volatility-based trading strategy outperformed a Buy-and-Hold strategy, yielding a total return of 15.38%.
This paper deals with the explicit design of strategy formulations to make the best strategic choices from a conventional matrix form of representing strategic choices. The explicit strategy formulation is an analytical model which is targeted to provide a mathematical strategy framework to find the best moment for str…