This paper tackles efficient testing strategies for COVID-19 by using a partially observable MDP approach.
problem Greedy testing strategies miss dormant virus areas, leading to inefficient use of testing resources.
method Develops efficient learning strategies based on policy iteration and look-ahead rules for a sequential learning-based resource allocation problem.
result Shows that the testing problem can be effectively managed using a partially observable MDP approach.
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…
We propose procedures for testing whether stock price processes are martingales based on limit order type betting strategies. We first show that the null hypothesis of martingale property of a stock price process can be tested based on the capital process of a betting strategy. In particular with high frequency Markov …
This study optimizes trading strategy parameters using walk-forward techniques and finds robust performance.
problem Optimizing trading strategy performance through parameter optimization.
method Walk-forward optimization with varying window lengths, tested on Bitcoin, Binance Coin, and Ethereum.
result The strategy outperforms Buy-and-Hold with lower drawdown and higher Information Ratio.
Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively. We outline several strategies for testing the correctness of MCMC algorithms. Specifically, we advocate writing code in a modular way, w…
Novel pairs trading strategy for cointegrated cryptocurrencies using copulas.
problem Identifying profitable trading opportunities in cointegrated cryptocurrency pairs.
method Linear and non-linear cointegration tests, correlation coefficient, copula families, back-testing.
result The strategy outperforms buy-and-hold trading strategies in profitability and risk-adjusted returns.
Optimizes group testing for COVID-19 to reduce test numbers.
problem Minimizing tests for accurate infection detection.
method Bayesian approach with genetic algorithms and sub-modularity.
result Greedy-adaptive method provides theoretical guarantees.
We presented Bayesian portfolio selection strategy, via the k factor asset pricing model. If the market is information efficient, the proposed strategy will mimic the market; otherwise, the strategy will outperform the market. The strategy depends on the selection of a portfolio via Bayesian multiple testing methodol…
Sequential tests for two-sample and independence testing using betting strategies.
problem Testing sequential data for two-sample and independence without kernel selection issues.
method Prediction-based betting strategies that adaptively determine distribution and joint distribution.
result Prediction-based tests outperform kernel-based approaches in high-dimensional or structured data settings.
Evology models US equity mutual funds interactions for investment strategies.
problem Understanding complex interactions in financial markets.
method Agent-based model (ABM) of US stock market participants and their strategies.
result Trading strategies interact with other market participants and conditions.
Recent studies have shown that online portfolio selection strategies that exploit the mean reversion property can achieve excess return from equity markets. This paper empirically investigates the performance of state-of-the-art mean reversion strategies on real market data. The aims of the study are twofold. The first…
Improved active output selection reduces calibration time by 10% or more.
problem Efficiently calibrate models with noisy data.
method Improved active output selection strategy considering noise estimate.
result At least 10% fewer measurements needed compared to existing strategies.
The paper develops diverse risk models for US stock portfolios.
problem Maximizing profits while minimizing risk in stock markets.
method Various high-dimensional risk models and investment strategies tested.
result Out-of-sample tests show improved portfolio performance.
Optimizing news headlines is important for publishers and media sites. A compelling headline will increase readership, user engagement and social shares. At Yahoo Front Page, headline testing is carried out using a test-rollout strategy: we first allocate equal proportion of the traffic to each headline variation for a…
This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.
problem Improving profitability and risk management in cryptocurrency trading.
method Engle-Granger, KSS, Johansen tests; optimal look-back window; mean-reversion speed calibration; microstructure limitations consideration.
result The strategy outperforms naive buy-and-hold in Bitmex exchange with low maximum drawdown.
Neyman-Pearson testing improves goodness of fit in detecting new physics.
problem Detecting small anomalies in data distributions.
method Employing Neyman-Pearson strategy with a rich parametrized family of models.
result Neyman-Pearson testing is more sensitive to small departures and unbiased towards specific anomalies.
The paper improves A/B testing for non-Gaussian data, ensuring reliable results with large sample sizes.
problem Inaccurate A/B testing results due to non-normal data and unequal sample sizes.
method Derives explicit formulas for minimum sample size and introduces an Edgeworth-based correction.
result Corrected method improves reliability of A/B testing in real-world conditions.
Adaptive sequential testing optimizes epidemic control by learning optimal test strategies.
problem Optimizing test allocation in epidemics with network and temporal dependence.
method Adaptive sequential design with Online Super Learner for optimal test strategies.
result Superior performance in simulated university COVID-19 pandemic.
This work explores test-time scaling strategies for LLMs, improving sample efficiency and expressiveness.
problem Understanding the sample efficiency and expressiveness of test-time scaling strategies for LLMs.
method Established separation and expressiveness results for self-consistency, best-of-n, and self-correction strategies. result Self-correction enables Transformers to simulate online learning over multiple tasks without prior knowledge.
In this paper we propose strategies for estimating performance of a classifier when labels cannot be obtained for the whole test set. The number of test instances which can be labeled is very small compared to the whole test data size. The goal then is to obtain a precise estimate of classifier performance using as lit…
Study compares data-driven vs model-based MRS quantification strategies, focusing on resilience to out-of-distribution effects.
problem Resilience to out-of-distribution effects in data-driven MRS quantification.
method Compared three data-driven strategies (supervised regression, self-supervised learning, test-time adaptation) against model-based fitting tools.
result Test-time adaptation proved most resilient to out-of-distribution effects, while self-supervised learning achieved intermediate performance.
Optimal strategy for A/B/n testing with control in stratified populations.
problem Discovering which arms have higher weighted expectation than the control in stratified populations.
method Sequentially choosing one arm per time step based on weighted subpopulation means.
result The strategy is asymptotically optimal, ensuring correct answers with high probability.
Testing-by-betting strategies almost surely go bankrupt under null hypotheses.
problem Understanding the behavior of betting strategies under null hypotheses.
method Analyzed the asymptotics of betting strategies under null distributions, focusing on the almost sure divergence of sums.
result Testing-by-betting strategies go bankrupt with probability one under any non-degenerate null distribution.
We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion. It avoids GLOS's complex CDF estimation process,hence resists the "Butterfly Effect" …
Significant pattern mining, the problem of finding itemsets that are significantly enriched in one class of objects, is statistically challenging, as the large space of candidate patterns leads to an enormous multiple testing problem. Recently, the concept of testability was proposed as one approach to correct for mult…
This work constructs a hypothesis test for detecting whether an data-generating function h:Rp→R belongs to a specific reproducing kernel Hilbert space H0 , where the structure of H0 is only partially known. Utilizing the theory of reproducing kernels, we reduce this hypothesis …
We consider a scenario where an agent has multiple available strategies to explore an unknown environment. For each new interaction with the environment, the agent must select which exploration strategy to use. We provide a new strategy-agnostic method that treat the situation as a Multi-Armed Bandits problem where the…
The problem of finding itemsets that are statistically significantly enriched in a class of transactions is complicated by the need to correct for multiple hypothesis testing. Pruning untestable hypotheses was recently proposed as a strategy for this task of significant itemset mining. It was shown to lead to greater s…
Deep RL learns optimal trading strategies.
problem Optimizing trading strategies using deep reinforcement learning.
method Deep deterministic policy gradient algorithm applied to simple trading environments.
result Deep RL can recover optimal trading strategies and achieve close-to-optimal rewards.
Single tree outperforms random forest in testing accuracy.
problem The challenge of improving single decision tree performance.
method Gradient-based entire tree optimization framework, scaled sigmoid approximation, numerical stability algorithm, subtree polish strategy.
result Optimized single tree outperforms classic random forest by 2.03% on average.
In this paper we present a hybrid active sampling strategy for pairwise preference aggregation, which aims at recovering the underlying rating of the test candidates from sparse and noisy pairwise labelling. Our method employs Bayesian optimization framework and Bradley-Terry model to construct the utility function, th…
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.
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. This work provides a solution to hardening DNNs under adversarial attacks through…
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.
HATT improves online decision tree ensembles by using a more eager splitting strategy.
problem Improving the efficiency of online decision tree ensembles.
method Replacing Hoeffding Tree's split strategy with HATT, which uses the Hoeffding Test for candidate splits.
result HATT outperforms Hoeffding Tree in online bagging and boosting ensembles, as shown by significant performance improvements in various testbenches.
The study uses machine learning to predict cryptocurrency market trends and design profitable trading strategies.
problem Predicting cryptocurrency market trends for profitable trading.
method Applied k-Nearest Neighbours, eXtreme Gradient Boosting, and Random Forest classifiers to detect trends.
result High profit factor of 1.60 for unseen data, showing promising results.
New method learns adaptive exploration strategies for dynamic tasks.
problem Learning effective exploration strategies in changing environments.
method Informed policy regularization to reduce sample complexity of RNN-based policies.
result Method learns efficient exploration strategies balancing information gathering and reward maximization.
Diversified risk parity strategies outperform equally-weighted portfolios in various asset universes.
problem Finding optimal portfolio allocations that balance risk and reward.
method Integrates various reward-risk measures and generic allocation rules into diversified risk parity.
result Diversified reward-risk parity strategies exhibit higher average returns, Sharpe ratios, and Calmar ratios compared to equally-weighted risk portfolios.
Cluster analysis is an unsupervised learning strategy that can be employed to identify subgroups of observations in data sets of unknown structure. This strategy is particularly useful for analyzing high-dimensional data such as microarray gene expression data. Many clustering methods are available, but it is challengi…
In incomplete financial markets not every contingent claim can be replicated by a self-financing strategy. The risk of the resulting shortfall can be measured by convex risk measures, recently introduced by Föllmer, Schied (2002). The dynamic optimization problem of finding a self-financing strategy that minimizes the …
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%.
Hybrid LSTM-ARIMA model outperforms other algorithms in algorithmic investment strategies.
problem Developing an optimal algorithmic investment strategy.
method Combines LSTM and ARIMA models, using LSTM for final predictions and ARIMA residuals for boosting.
result LSTM-ARIMA algorithm outperforms all other algorithms across multiple equity indices.
Paper proposes a novel trading strategy combining clustering and reinforcement learning for multi-period portfolio management.
problem Developing an effective trading strategy for multi-period portfolio management.
method The paper integrates clustering techniques with reinforcement learning to categorize and manage stocks across multiple trading periods.
result The proposed strategy outperforms conventional techniques in various metrics, achieving an average return of 151% over 360 trading periods.
Study examines if LLMs' trading styles match real market behavior.
problem Lack of behavioral consistency in LLMs' trading strategies.
method Year-long simulations with LLMs, operationalizing behavioral finance drivers, and comparing with financial theory.
result LLMs' strategy switching is only partially consistent with behavioral finance theories.
E-C2ST uses E-values for high-dimensional data two-sample tests.
problem Statistical testing for high-dimensional data.
method Combines split likelihood ratio tests and predictive independence tests, using E-values for anytime-valid sequential tests.
result E-C2ST achieves enhanced statistical power by partitioning datasets into multiple batches.
Generative Adversarial Networks simulate realistic market interactions.
problem Lack of agent-level historical data limits market simulation realism.
method Conditional Generative Adversarial Networks (CGANs) trained on real data.
result CGAN-based synthetic market generator outperforms previous methods in market responsiveness and realism.
Study develops a multi-pair trading strategy using graph clustering and machine learning.
problem Improving risk-adjusted returns and reducing transaction costs in US equities market.
method Statistical arbitrage, graph clustering algorithms, Kelly criterion, machine learning classifiers.
result Optimal signal detection and risk management techniques outperformed benchmarks.
Explains classic quantitative strategies and their workings.
problem Debunking the magic behind black-box quantitative strategies.
method Derives from simple intuitions and mathematics, addressing both formal and informal aspects.
result Provides a deeper understanding of quantitative strategies.