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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.

169,291 papers · 148 categories

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17335066 · Jun 202019922001200920182026
48 results for biased coin

Study shows most people make poor decisions when betting on a biased coin.

problem People's decision-making under uncertainty is poor, even among trained individuals.
method 61 quantitatively trained participants played a game with a biased coin.
result 30% of participants lost their entire stake, indicating poor decision-making.

This paper tracks coin circulation in Bitcoin to identify miners and analyze mining pool structures.

problem Identifying and understanding Bitcoin miners and their profit distribution schemes.
method Constructs fresh coin circulation networks and uses a heuristic algorithm to compare networks from different mining pools.
result Infers common profit distribution schemes of Bitcoin mining pools and observes an increasing trend in miner numbers.

The Kelly Criterion is applied to prediction markets to analyze risk and return.

problem Mean beliefs in prediction markets often differ from actual prices.
method Logarithmic utility and Kullback-Leibler divergence are used to study risk and return adjustments.
result Misjudgment of bias and investment fraction affect portfolio growth rate.

This paper explores using nonlinear control for robust logarithmic growth in coin flipping games.

problem Tackles the use of nonlinear control in recursive betting games with logarithmic growth.
method Formulates a robust nonlinear control problem for a simple coin flipping game, considering a probability range for the coin's bias.
result Provides a closed-form description of the optimal robust nonlinear controller, which outperforms linear controllers.

Cryptocurrency market capitalizations follow power-law distributions with distinct exponents.

problem Characterizing the dynamics of cryptocurrency coins and tokens.
method Proportional growth model applied to coin and token distributions.
result The power-law exponents for coins and tokens are distinct and converge to 1 for tokens in the future.

Optimal adaptive algorithm estimates coin mixture fractions with tight sample complexity bounds.

problem Estimating the fraction of positive coins in a mixture with unknown biases.
method Fully-adaptive algorithm with tight sample complexity bounds of Θ(ρ/ε²Δ² log(1/δ)).
result Upper and lower bounds of Θ(ρ/ε²Δ² log(1/δ)) samples for 1-δ probability of success.

Study designs for estimating treatment effects in adaptive experiments.

problem Estimating treatment effects under adaptive treatment assignment.
method Propose and analyze IPW and AIPW estimators, establish CLTs under design stability.
result Central limit theorems for IPW and AIPW estimators under design stability.

Study reveals widespread manipulation of meme coins, leading to significant economic losses.

problem Widespread manipulation of meme coins leading to economic losses.
method Cross-chain analysis of 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base.
result 82.8% of high-return tokens show evidence of artificial growth strategies.

Paper proposes a new coin betting method for training deep networks without learning rates.

problem Deep learning requires tuning many hyperparameters, especially learning rates.
method Reduces deep network training to a coin betting game, eliminating learning rates.
result Empirical and theoretical evidence shows the new method outperforms existing stochastic gradient algorithms.

Examines various types of cryptocurrencies and their economic properties.

problem Understanding the economic characteristics of different cryptocurrencies.
method Characterization and analysis of different classes of cryptocurrencies using balance sheet operations.
result Different types of cryptocurrencies have distinct economic properties, ranging from commodities to liabilities of central banks.

The paper proposes a method to estimate treatment effects using CAR designs with additional covariates.

problem Estimating distributional treatment effects in CAR designs with additional covariates.
method Flexible distribution regression framework that incorporates additional covariates using machine learning methods.
result The proposed estimator attains the semiparametric efficiency bound for distributional treatment effects under CAR.

Paper examines how crypto-assets affect corporate governance of SMEs and public companies.

problem Impact of crypto-assets on corporate governance of SMEs and public companies.
method Analyzes various use cases of DLT technology and their effects on corporate governance.
result New stakeholders (crypto-assets holders) change governance of SMEs and public companies.

Predicts cryptocurrency pump probability using sequence-based neural networks.

problem Detecting pump-and-dump schemes in cryptocurrency markets.
method Developed a sequence-based neural network (SNN) that encodes historical P&D events into sequences for prediction.
result SNN improves prediction accuracy by leveraging positional attention to extract useful information.

The paper develops inference methods for high-dimensional multi-task regression with row-sparse coefficients.

problem Inference for high-dimensional multi-task regression with unknown coefficient matrix under row-sparsity.
method Proposes chi-square and normal inference methodologies using MT Lasso with de-biasing scheme and interaction matrix.
result Derives asymptotic normal and chi-square distribution results for valid confidence intervals and ellipsoids.

A coin-flipping game paradox illustrates how conditional probability estimation can distort risk assessment.

problem Distortion of risk assessment due to incorrect conditional probability estimation.
method A coin-flipping game to illustrate the paradox of conditional probability estimation.
result Incorrect conditional probability estimation can lead to excessive risk bearing.

CPDOs can't achieve a Cash-In event in finite tosses, mirroring Zeno's Paradox.

problem The impossibility of achieving a Cash-In event in CPDOs, mirroring Zeno's Paradox.
method Coin-tossing model and analysis of infinite geometric series.
result CPDOs can't achieve a Cash-In event in a finite lifetime, mirroring Zeno's Paradox.

Predicting alt-coin prices with Twitter sentiment analysis.

problem Predicting price fluctuations of alt-coins using social media sentiment.
method Extracted hourly tweets, classified sentiment, created sentiment indices, trained a Gradient Boosting Tree Model.
result Model predictions correlated 0.81 with historical data, statistically significant.

Optimizes cryptocurrency trading pairs for efficiency and decentralization.

problem Finding optimal trading pairs among many cryptocurrencies without direct volume data.
method Two-stage process: 1) Fill missing values using eigenvalue decomposition with regularization, 2) Optimize pairs using branch and bound with pruning.
result Optimal trading pairs lead to more decentralized markets and better liquidity.

New algorithms learn latent variable models without tuning, outperforming existing methods.

problem Learning latent variable models without manual tuning.
method Two particle-based algorithms using free energy minimization and coin betting.
result Learning algorithms are entirely tuning-free and competitive with existing methods.

Study sets a nontrivial upper limit on return forecasting accuracy.

problem Establishing a practical upper limit for return forecasting accuracy.
method Defined a coin-flip oracle model to theoretically outperform practical models and used its RextOOS2R^2_{ ext{OOS}} as an upper bound.
result Theoretical upper bound on RextOOS2R^2_{ ext{OOS}} is a quadratic function of directional accuracy.

New algorithms for sampling in constrained domains without learning rates.

problem Sampling in constrained domains with fairness constraints and post-selection inference.
method Coin betting ideas from convex optimisation and a unifying framework for constrained sampling.
result Our algorithms achieve competitive performance without hyperparameter tuning.

C2P2 predicts cryptocurrency price movements considering similarities among coins.

problem Predicting cryptocurrency price movements using historical and sentiment data.
method Collective classification using similarity metrics for 21 cryptocurrencies.
result C2P2 outperforms existing methods by 5.1-83% on 21 cryptocurrencies.

Study replicability in high-dimensional statistics, resolving open problems.

problem Ensuring consistent results in high-dimensional statistical tasks.
method Introduced replicable learning algorithms and established computational and statistical equivalence with high-dimensional isoperimetric tilings.
result Matching sample complexity upper and lower bounds for replicable mean estimation and coin problem.