Study minimizes market inefficiency in systemic economies.
problem Minimizing deviations of market prices from fundamental values.
method Characterized market inefficiency and developed a matrix of holdings to minimize it.
result Portfolio holdings should deviate more from diversification if banks have similar systemic significance.
We study the effects of introducing information inefficiency in a model for a random linear economy with a representative consumer. This is done by considering statistical, instead of classical, economic general equilibria. Employing two different approaches we show that inefficiency increases the consumption set of a …
Study measures irreversibility in crypto trends using Kullback-Leibler divergence.
problem Assessing irreversibility in cryptocurrency trends.
method Defined irreversibility index using Kullback-Leibler divergence between uptrend and downtrend distributions.
result Strong irreversibility in all analyzed cryptocurrencies, with trends evolving over time.
In this paper we examine inefficiencies and information disparity in the Japanese stock market. By carefully analysing information publicly available on the internet, an `outsider' to conventional statistical arbitrage strategies--which are based on market microstructure, company releases, or analyst reports--can never…
Adapts auxiliary losses using gradient similarity to improve neural network performance.
problem Statistical inefficiency in neural networks and difficulty in selecting helpful auxiliary tasks.
method Uses cosine similarity between gradients of tasks to adaptively weight auxiliary losses.
result Guaranteed convergence to critical points of the main task and practical usefulness across domains.
The optimal (`equilibrium') macroscopic properties of an economy with N industries endowed with different technologies, P commodities and one consumer are derived in the limit N→∞ with n=N/P fixed using the replica method. When technologies are strictly inefficient, a phase transition occurs upon increas…
Study finds cryptoasset markets inefficient due to capital reallocation frictions.
problem Inefficiency in cryptoasset markets due to capital reallocation frictions.
method Examined investments with dominant and secondary risk factors, derived equilibrium restrictions, and tested empirically.
result Empirical results strongly reject necessary equilibrium restrictions, indicating market inefficiency.
The paper analyzes data markets with multiple data aggregators, showing non-uniqueness of equilibria and social inefficiency.
problem Non-uniqueness of equilibria and social inefficiency in data markets with multiple data aggregators.
method Characterization of generalized Nash equilibria and analysis of necessary and sufficient conditions for social inefficiency.
result There are either infinitely many or no generalized Nash equilibria, leading to social inefficiency.
Algorithm beats sports betting markets, showing inefficiencies.
problem Inefficiencies in sports betting markets.
method Created a betting algorithm using a novel dataset and win probability model.
result Above market returns for various sports betting markets.
We perform detrending moving average analysis (DMA) and detrended fluctuation analysis (DFA) of the WTI crude oil futures prices (1983-2012) to investigate its efficiency. We further put forward a strict statistical test in the spirit of bootstrapping to verify the weak-form market efficiency hypothesis by employing th…
Study finds many stocks in S&P 500 are inefficient, suggesting financial analysts outperform blindfolded monkeys.
problem Degree of inefficiency in U.S. stock market performance.
method Confidence intervals for proportions to assess inefficiency in S&P 500 components.
result Proportion of inefficient stocks in the S&P 500 index estimated to be between 12.13% and 27.87%
Proposes a new normalization method using convolutional neural networks.
problem Slow and inefficient training of deep neural networks.
method Uses depth-wise convolutional neural networks to approximate statistics.
result Learned coefficients improve the approximation of statistics.
Efficiently handles contextual bandits with diffusion models.
problem Challenges in online decision-making with contextual bandits.
method Leverage pre-trained diffusion models as priors to capture action dependencies.
result Developed an algorithm for efficient posterior approximation.
Study finds inefficiency in Brazilian stock market through correlations and fat-tailed returns.
problem Inefficiency of the Brazilian stock market, particularly the IBOVESPA future contracts.
method Analysis of cross-correlations with foreign markets, examination of log-return distribution, and neural network forecasting.
result Strong dependence on foreign markets and fat-tailed returns indicate inefficiency.
Value functions struggle to represent transition dynamics, impacting statistical efficiency.
problem Limited representational power of value functions in capturing transition dynamics.
method Case studies of various reinforcement learning problems to explore the limitations of value-based methods.
result Value-based methods can be as efficient as model-based ones in some cases but severely underperform in others due to information loss.
Study reveals inefficiencies in EU carbon trading market.
problem Inefficiencies in carbon trading market undermine emission reduction goals.
method Analysis of granular transaction data from 2005-2020.
result 40% of firms never trade in a given year, and many trade only during high-price months.
We propose a geometric algorithm for topic learning and inference that is built on the convex geometry of topics arising from the Latent Dirichlet Allocation (LDA) model and its nonparametric extensions. To this end we study the optimization of a geometric loss function, which is a surrogate to the LDA's likelihood. Ou…
Social learning can make financial markets inefficient, but individual learning can fix this.
problem Inefficiencies in financial markets due to social learning.
method Study of the Minority Game model with social and individual learning mechanisms.
result Individual learning can rescue a population from the inefficiencies caused by social learning.
Unified platform for statistical and machine learning in bioinformatics.
problem Workflow inefficiencies in using multiple tools for data analysis.
method Automated hyperparameter optimization, feature importance analysis, statistical tests.
result Accelerates biological discovery workflows with methodological soundness.
Hamiltonian Monte Carlo on ReLU networks is inefficient due to large local error.
problem Inefficiency of Hamiltonian Monte Carlo on ReLU neural networks.
method Analysis of Hamiltonian Monte Carlo with leapfrog integrator for Bayesian neural network inference.
result Leapfrog HMC for ReLU networks has a large local error rate of Ω(ε), leading to inefficiency. New spectral algorithm estimates random graph parameters robustly against corrupted nodes.
problem Estimating the parameter of an Erdős-Rényi random graph with adversarial corruption.
method Spectral algorithm designed for computational efficiency, with an inefficient but information-theoretic alternative.
result Achieves optimal error rate up to logarithmic factors, matching statistical lower bounds.
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…
Investors trade based on shifting prices, leading to market inefficiencies.
problem Market inefficiencies due to trading behavior and financial dynamics.
method Analyzes trading strategies and market dynamics.
result Arbitrage opportunities arise from concentrated ownership and low collateral requirements.
This paper shows how diverse tasks can make inefficient exploration in MTRL efficient.
problem The challenge of efficient exploration in Multitask Reinforcement Learning.
method A generic policy-sharing algorithm with myopic exploration design trained on diverse tasks.
result A generic policy-sharing algorithm with myopic exploration design can be sample-efficient in MTRL.
I summarize the recent work on market (in)efficiency, highlighting key elements why financial markets will never be made efficient. My approach is not by adding more empirical evidence, but giving plausible reasons as to where inefficiency arises and why it's not rational to arbitrage it away.
Study on deleting user data in linear regression models to maintain limited memory.
problem Deleting user data in a limited time frame for statistical models.
method Proposed FIFD-OLS and FIFD-Adaptive Ridge algorithms for low-dimensional and online settings.
result Demonstrated effectiveness of FIFD-Adaptive Ridge in maintaining statistical efficiency.
Improved CG force-field learning from all-atom data.
problem Training accurate coarse-grained models from all-atom simulations is challenging.
method Optimized force mapping to improve statistical efficiency of force-field learning.
result Substantially improved CG force-fields can be learned from the same simulation data.
Market inefficiencies arise from density-dependent returns in a noisy environment.
problem Market inefficiencies and excess volatility.
method Developed a market model using ecological concepts.
result Market dynamics are density-dependent, leading to inefficiencies.
Study finds inefficiencies in US equity markets, especially with Dow 30 stocks.
problem Inefficiencies in US equity markets, particularly with Dow 30 stocks.
method Analyzed all quotes and trades associated with Dow 30 stocks in 2016 using comprehensive data.
result Fragmentation and dislocation of information feeds led to inefficiencies and opportunity costs.
Many studies have shown that there are good reasons to claim very low predictability of currency nevertheless, the deviations from true randomness exist which have potential predictive and prognostic power [J.James, Quantitative finance 3 (2003) C75-C77]. We analyze the local trends which are of the main focus of the t…
DDSME outperforms SME in estimating multimodal distributions.
problem Efficiency of score matching in multimodal distributions.
method Diffusion-based denoising score matching (DDSME) compared to vanilla score matching (SME).
result DDSME avoids the error bound deterioration of SME with increasing mode separation.
Study quantifies inefficiencies in U.S. equity markets, identifying open/close periods and affected stocks.
problem Inefficiencies in U.S. equity markets, particularly near open and close times.
method Comprehensive dataset analysis of trading activity, focusing on quote dislocations.
result Around 23% of trades occur during quote dislocations, leading to estimated $2 billion USD in opportunity costs.
The paper improves asymmetric causality tests by addressing inefficiencies and statistical significance issues.
problem Inefficiencies and statistical significance issues in asymmetric causality tests.
method Improved asymmetric causality tests via partial cumulative sums for positive and negative components, explicitly testing differences between causal parameters.
result Efficiently tested hypotheses on asymmetric causal interaction between financial markets.
Paper evaluates and improves private feature selection methods.
problem Feature selection in high-dimensional datasets with privacy constraints.
method Correlations-based order statistic privatized for feature selection.
result Our method significantly outperforms established baseline for private feature selection.
Iterative procedures for parameter estimation based on stochastic gradient descent allow the estimation to scale to massive data sets. However, in both theory and practice, they suffer from numerical instability. Moreover, they are statistically inefficient as estimators of the true parameter value. To address these tw…
Study finds Bitcoin market efficient, no exploitable inefficiencies with neural networks.
problem Investigating market inefficiencies in Bitcoin using neural networks.
method Used a feedforward neural network with various asset-related input features.
result Adding more features does not improve prediction accuracy, and one feature set outperforms a buy-and-hold strategy.
Cryptocurrencies are ranked for efficiency using a new Complexity-Entropy Plane.
problem Evaluating the efficiency of cryptocurrencies using traditional financial metrics.
method Developed a Binary Complexity-Entropy Plane (BiCEP) to analyze daily price fluctuations of major cryptocurrencies.
result Only Shiba Inu (SHIB) is significantly inefficient, while most cryptocurrencies operate in close-to-efficient conditions.
Market inefficiencies persist in DEXes, especially during high volatility.
problem Inefficiencies in decentralized exchanges during high volatility.
method Analyzed 30% of trades, tracked price inaccuracies, and observed market adaptability.
result Market inefficiencies persist, especially during high volatility.
Efficiently learns private models using public data.
problem Improving private learning performance with public data.
method Proves computationally efficient algorithms for private learning with public data.
result First computationally efficient algorithms for private learning with public data.
We discuss the stationary states of a model economy in which N heterogeneous adaptive consumers purchase commodity bundles repeatedly from P sellers. The system undergoes a transition from an inefficient to an efficient state as the number of consumers increases. In the latter phase, however, price fluctuations may…
Detecting adversarial examples is as hard as classifying them.
problem The difficulty of detecting adversarial examples in machine learning models.
method Proved a general hardness reduction between detection and classification of adversarial examples.
result The hardness reduction implies that detecting adversarial examples is computationally infeasible.
New approach improves AI's handling of incomplete data.
problem Improving AI's ability to work with incomplete data.
method Proposes a new likelihood-free EM algorithm for faster, more efficient inference.
result More statistically efficient than masking approach and faster than conventional EM.
p-index approach shows efficient-contrarian strategy outperforms others in low-sentiment periods
problem Evaluating investment strategies in stock markets
method Using p-index risk measure with European put option
result Efficient-contrarian strategy outperforms others in low-sentiment periods
Study analyzes EU ETS carbon market dynamics, revealing inefficiencies and anomalies.
problem Inefficiencies and anomalies in EU ETS trading and pricing mechanisms.
method Empirical analysis using AR-GARCH model and weighted network analysis.
result Heterogeneous and sometimes counter-intuitive elasticities in price-volume relationships.
This paper examines Bitcoin's price predictability, finding inefficiencies under certain conditions.
problem Predictability of Bitcoin's price movements.
method Theoretical reviews, empirical analyses, machine learning approaches, time series modeling.
result Bitcoin's market tends toward efficiency but shows exploitable inefficiencies under specific conditions.
LLMs show potential for predicting financial returns, contrary to common belief.
problem Common belief that LLMs are unsuitable for financial market returns prediction.
method Chronos model from Ansari et al. (2024) tested on largest American single stocks.
result LLMs can predict time series that are nearly random, generating alpha.
Wasserstein Autoencoders improve model efficiency and interpretability for low-dimensional data.
problem Limited statistical guarantees for WAEs in low-dimensional data.
method Proper network architecture selection and analysis of expected excess risk convergence rates.
result WAEs can learn data distributions efficiently when intrinsic dimension is considered.
Paper proposes DSP to accelerate deep learning model training by addressing locking and straggler problems.
problem Training deep neural networks is slow and inefficient due to locking and straggler issues.
method Layer-wise Staleness and DSP algorithm to handle locking and straggler problems.
result DSP achieves significant training speedup with stronger robustness than compared methods.