A novel algorithm for actively trading stocks is presented. While traditional expert advice and "universal" algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirica…
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169,341 papers · 148 categories
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48 results for “market beating”
Python models predict stock sentiment for market-beating returns.
problem Predicting public sentiment for stock trading.
method Crowd-sourced labeled data, trained and evaluated various models.
result Best models predict market-beating returns from public sentiment.
Wave-wavelet trading strategy wins and beats the market.
problem Solving buy low sell high, market beating, and GBM prediction problems.
method Trading strategies based on wave and wavelet movements.
result Trading strategies outperform market, surprising result.
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.
Paper optimizes portfolio selection with ICX order constraints.
problem Minimizing portfolio variance with ICX order constraints.
method Optimal and efficient portfolios are derived in closed form.
result Closed-form solutions for optimal and efficient portfolios.
This study compares two neural models for financial forecasting, showing their superiority.
problem Improving financial market trend predictions using neural networks.
method Systematic comparison of N-HiTS and N-BEATS with conventional models.
result N-HiTS and N-BEATS enhance forecast accuracy and robustness in financial time series data.
A strategy to beat benchmarks by investing in heavily shorted but fundamentally sound securities.
problem Overcoming behavioral biases in investing, particularly the 'rebound effect'.
method Quantitative metrics, historical data, and securities lending modeling.
result The Bounce Basket strategy can outperform market returns during market downturns.
A simple framework uses daily prices and volumes to beat the market.
problem Optimizing portfolio performance using only observable data.
method Three matrices derived from price history: return correlations, monthly ranking Markov chains.
result Market-beating portfolio with high Sharpe ratios.
Fundamental portfolio beats market portfolio under certain conditions.
problem Empirical evidence of fundamental portfolio outperformance.
method Theoretical foundation based on stock price reversion to fundamental values.
result Fundamental portfolio outperforms market portfolio under strong reversion conditions.
Develops a new method for benchmark portfolios and market outperformance strategies.
problem Creating effective benchmark portfolios for market outperformance.
method Explicit formulaic algorithm and multifactor risk model tailored for long-only portfolios.
result Explicit positive weights for benchmarks without principal components or iterations.
Study shows how margin loan interest rates converge to a choke price, limiting long-term advantage in the broker call money market.
problem Long-term dynamics of margin loan interest rates and their impact on retail clients' advantage in the broker call money market.
method Analyzes the broker call money market dynamics, assuming perfect inelastic supply and continuous reinvestment, to show convergence of relative size and margin loan interest rates.
result Margin loan interest rates converge to a choke price, limiting the long-term advantage of retail clients over the market.
Our goal is to resolve a problem proposed by Fernholz and Karatzas [On optimal arbitrage (2008) Columbia Univ.]: to characterize the minimum amount of initial capital with which an investor can beat the market portfolio with a certain probability, as a function of the market configuration and time to maturity. We show …
Obtaining more accurate equity value estimates is the starting point for stock selection, value-based indexing in a noisy market, and beating benchmark indices through tactical style rotation. Unfortunately, discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estim…
The paper limits the profitability of technical trading rules and finds they are not better than random trading.
problem The profitability of technical trading rules in stock markets is controversial.
method Proves the upper bound of cumulative return and investigates the profitability of technical trading rules using bootstrap methodology.
result Technical trading rules are not better than random trading and less profitable than the market.
New trading strategy beats traditional grid in crypto markets.
problem Low expected return of traditional grid trading strategy.
method Dynamic Grid Trading (DGT) strategy that adapts to market conditions.
result DGT strategy outperforms traditional grid and buy-and-hold strategies.
Derives formulas for capital asset performance in continuous time.
problem No stochastic assumptions, no investor beliefs or preferences.
method Game-theoretic approach to efficient market hypothesis.
result Formula resembling classical CAPM for security or portfolio returns.
Equally weighted S&P 500 outperforms market cap weighted portfolio.
problem Finding better portfolio weighting methods than market cap weighting.
method Empirical study comparing equally weighted S&P 500 to market cap weighted S&P 500, and introducing MaxMedian rule.
result MaxMedian rule outperforms equally weighted S&P 500 over 1958-2016 horizon.
Researchers use VAEs to create understandable heart beat representations.
problem Lack of explainable models for ECG beat classification.
method Variational Auto-Encoders (VAEs) with linear dense networks.
result Interpretable ECG beat space generated.
We investigate triangular arbitrage within the spot foreign exchange market using high-frequency executable prices. We show that triangular arbitrage opportunities do exist, but that most have short durations and small magnitudes. We find intra-day variations in the number and length of arbitrage opportunities, with la…
We present results on simulations of a stock market with heterogeneous, cumulative information setup. We find a non-monotonic behaviour of traders' returns as a function of their information level. Particularly, the average informed agents underperform random traders; only the most informed agents are able to beat the …
N-BEATS-MOE improves time series forecasting by adapting to series characteristics.
problem Forecasting heterogeneous time series with varying characteristics.
method Mixture-of-Experts layer with dynamic block weighting.
result Consistent improvements across 12 benchmark datasets, especially for heterogeneous series.
The value of information in a multi-agent market modelphysics.soc-ph
We present an experimental and simulated model of a multi-agent stock market driven by a double auction order matching mechanism. Studying the effect of cumulative information on the performance of traders, we find a non monotonic relationship of net returns of traders as a function of information levels, both in the e…
Algorithm beats best constant rebalancing portfolio in long-term investment.
problem Poor performance of learning algorithms in online portfolio optimization.
method Leverages serial dependence in asset returns without distributional assumptions.
result Strategy asymptotically grows to highest rate among all strategies.
The Limits of Complexity: Why Feature Engineering Beats Deep Learning in Investor Flow Predictionq-fin.CP
Simple feature engineering beats complex models in financial prediction.
problem Understanding when complex models outperform simple alternatives in financial prediction.
method Independent Component Analysis (ICA), Wavelet Coherence, Long Short-Term Memory (LSTM) networks with attention mechanisms.
result A simple linear model using normalized flows achieves superior returns compared to complex models.
Generative adversarial network system improves ECG arrhythmia classification.
problem Improving automatic ECG arrhythmia classification accuracy.
method Generative adversarial network with patient-specific normal beats and generated abnormal beats.
result Superior overall classification performance for SVEB and VEB on MIT-BIH arrhythmia database.
A financial market comprising of a certain number of distinct companies is considered, and the following statement is proved: either a specific agent will surely beat the whole market unconditionally in the long run, or (and this "or" is not exclusive) all the capital of the market will accumulate in one company. Thus,…
Investors trade too much in experimental markets, harming their wealth.
problem Investors overtrade in artificial markets, leading to poorer outcomes.
method Experimental asset markets with explicit market impact.
result Excessive trading by subjects leads to poorer wealth outcomes.
Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learningq-fin.ST
AI models predict stock trends using historical data and public sentiment.
problem Improving stock market prediction accuracy using AI.
method Employed regression and classification ML algorithms for technical and fundamental analysis respectively.
result Median performance suggests AI is not yet superior to stock markets.
Outperformance Portfolio Optimization via the Equivalence of Pure and Randomized Hypothesis Testingq-fin.PM
We study the portfolio problem of maximizing the outperformance probability over a random benchmark through dynamic trading with a fixed initial capital. Under a general incomplete market framework, this stochastic control problem can be formulated as a composite pure hypothesis testing problem. We analyze the connecti…
Conformal Kelly: Conformal Prediction Intervals as the Scale in Fractional Kelly Position Sizingq-fin.PM
Combines conformal prediction intervals with Kelly strategy to optimize portfolio growth.
problem Optimizing portfolio growth using conformal prediction intervals.
method Combines conformal prediction intervals with fractional Kelly strategy to size portfolio positions.
result Compounds at 28.5% annualised net log growth with a Sharpe ratio of 1.34.
DL-FUMI learns heartbeat patterns from BCG signals for precise heart rate estimation.
problem Estimating precise heart rates from ballistocardiogram signals with uncertainty.
method Multiple instance dictionary learning to learn heartbeat concepts from BCG signals.
result DL-FUMI's heartbeat concept achieves superior performance over comparison algorithms.
The condition for stationary increments, not scaling, detemines long time pair autocorrelations. An incorrect assumption of stationary increments generates spurious stylized facts, fat tails and a Hurst exponent H_s=1/2, when the increments are nonstationary, as they are in FX markets. The nonstationarity arises from s…
We discuss martingales, detrending data, and the efficient market hypothesis for stochastic processes x(t) with arbitrary diffusion coefficients D(x,t). Beginning with x-independent drift coefficients R(t) we show that Martingale stochastic processes generate uncorrelated, generally nonstationary increments. Generally,…
We present a universal algorithm for online trading in Stock Market which performs asymptotically at least as good as any stationary trading strategy that computes the investment at each step using a fixed function of the side information that belongs to a given RKHS (Reproducing Kernel Hilbert Space). Using a universa…
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%
Adaptive volatility method improves probabilistic financial forecasting.
problem Probabilistic forecasting in financial markets.
method Adapts classical time-varying volatility models with online stochastic optimization.
result Ranked 5th in M6 financial forecasting competition.
Sparse portfolio strategy from mutual funds' favorite stocks in China A share market.
problem Building a sparse portfolio from mutual funds' favorite stocks in a market with limited fund information.
method Analyzed mutual fund favorite stocks, used portfolio optimizer with constraints, and compared different methods.
result Sparse portfolios consistently outperform the benchmark index 930950.CSI.
New method finds profitable investment opportunities by considering additional financial variables.
problem Finding trading strategies that outperform the market with high probability.
method Generalizing functionally generated portfolios to include continuous-path semimartingales.
result Inclusion of additional processes can reduce time horizons for profitable arbitrage opportunities.
The MRS-GARCH model outperforms single-regime GARCH models in crude oil volatility forecasting.
problem Forecasting crude oil market volatility accurately.
method Evaluation of single-regime GARCH models and two-regime MRS-GARCH model at different data frequencies and time horizons.
result The two-regime MRS-GARCH model provides more accurate volatility forecasts for daily data but not for weekly and monthly data.
Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selectionq-fin.CP
MarketSenseAI uses AI to select stocks with 10-30% excess alpha.
problem Selecting profitable stocks in financial markets.
method Integrates GPT-4 for analyzing diverse data and decision-making.
result Demonstrated exceptional performance with up to 72% cumulative return.
Paper tackles market making in corporate bonds using deep reinforcement learning.
problem Optimizing bid and ask quotes for a large universe of bonds in OTC markets.
method Discrete-time actor-critic algorithm with deep neural networks.
result Approximates optimal bid and ask quotes over a large universe of bonds.
Adversarial policies beat superhuman Go AI systems.
problem Vulnerability of superhuman AI systems to adversarial attacks.
method Training adversarial policies to trick KataGo into making blunders.
result Adversarial policies achieve >97% win rate against KataGo at superhuman settings.
RNN beats Lee-Carter in forecasting mortality rates.
problem Forecasting mortality rates across different demographics.
method Long Short-Term Memory (LSTM) recurrent neural network trained on multiple countries, ages, and sexes.
result RNN model outperforms the Lee-Carter model in mortality rate forecasting.
Study improves forecast accuracy of daily volatility to enhance portfolio performance.
problem Improving predictability of realized variance from market views.
method High-dimensional machine learning models and low-dimensional factor models used to forecast firm-level volatility.
result Marginal improvements in forecast error lead to significant gains in portfolio performance.
IBPF algorithm tackles high-dimensional parameter learning for complex systems.
problem Learning high-dimensional parameters in complex, partially observed, and nonlinear systems.
method Iterated Block Particle Filter (IBPF) for graphical state space models.
result IBPF algorithm consistently beats the curse of dimensionality across various experiments.
N-BEATS(P) efficiently forecasts millions of time series with reduced memory and time.
problem Efficiently forecasting millions of time series with high accuracy.
method Global parallel variant of N-BEATS model designed for multi-step time series forecasting.
result Significant reduction in training time and memory usage with comparable accuracy.
K-expectiles clusteringstat.ME
Proposes a new clustering method based on expectiles for non-spherical clusters.
problem Inability of -means to handle non-spherical clusters.
method Uses expectiles to define cluster centers and searches for clusters via a greedy algorithm.
result Outperforms -means and spectral clustering on asymmetric shaped clusters.
This paper uses Tsallis relative entropy to optimize stock portfolios, showing better consistency in risk-return profiles.
problem Optimizing stock portfolios with consistent risk-return profiles.
method Constructing portfolios by binning risk values and allocating stocks based on risk values, comparing with four risk measures.
result Tsallis relative entropy yields more consistent risk-excess return profiles compared to other measures.