101 trading formulas for short-term alpha strategies.
problem Developing and validating trading strategies with short holding periods.
method Explicit formulas and computer code for 101 alphas, empirical analysis of correlations and volatilities.
result Alphas have low pairwise correlation and strong volatility dependence, turnover has little explanatory power.
Study finds short-term trading signals can enhance alpha in U.S. S&P 500 portfolios.
problem Traditional factor investing misses real-time market dislocations.
method Double-selection LASSO framework to control for fundamental factors and isolate trading signals.
result 17 distinct trading signals capture significant risk premiums and enhance portfolio diversification.
Paper proposes a reinforcement learning method for trading using expert trajectories.
problem Inability of existing methods to handle long-term goals and delayed rewards in futures trading.
method Modeling futures trading as MDP, using reinforcement learning with expert trajectories and multiple short-term alpha factors.
result The proposed method outperforms traditional and deep learning methods in trading performance.
StockGPT predicts stock returns using AI, outperforming traditional strategies.
problem Making accurate stock predictions and trading decisions.
method Trains an autoregressive model on historical stock returns, using attention mechanisms to learn patterns.
result StockGPT's portfolios outperform traditional strategies, yielding significant alphas.
A new deep learning model improves asset pricing predictions.
problem Improving asset pricing models for better predictions.
method Pseudo-Siamese Network (SNAP) for conditional asset pricing.
result The SNAP model outperforms benchmarks in out-of-sample prediction and Sharpe ratio.
The study finds cash productivity predicts stock performance in a specific subset of firms.
problem Predicting future stock performance using cash productivity.
method Using financial and market data, calculated cash returns as a proxy for operational efficiency, and tested a long-only strategy on Nasdaq-listed non-financial firms.
result Cash productivity has significant predictive power in a handpicked portfolio but limited across the broader Nasdaq universe.
Predicts S&P 500 trends using machine learning models.
problem Market trend prediction for S&P 500 index.
method Feature engineering, machine learning models (Logistic Regression, Decision Trees, Random Forests, Neural Networks, KNN, XGBoost), data preprocessing, hyperparameter tuning, SMOTE.
result KNN for short-term predictions, XGBoost for long-term forecasts.
Alpha2 discovers logical formulaic alphas using deep reinforcement learning.
problem Discovering interpretable formulaic alphas for better trading strategies.
method Formulating alpha discovery as program construction, using deep reinforcement learning to navigate the search space.
result Empirical experiments show Alpha2 identifies diverse, logical, and effective alphas improving trading strategy performance.
New method extracts stock returns from alphas without combining them, saving costs.
problem Costly alpha combos in hedge funds.
method Explicit algorithm and source code for extracting expected returns from alphas.
result Substantial cost savings (3% vs 10%) and avoidance of noise.
Alpha-GPT mines new trading signals with human-AI interaction.
problem Mining new alphas for effective trading signals.
method Human-AI interaction and prompt engineering algorithmic framework.
result Demonstrates Alpha-GPT's effectiveness in generating creative, insightful, and effective alphas.
Study uses deep learning to predict stock trends with superior performance.
problem Predicting short-term equity trends with high accuracy.
method Dual-task multilayer perceptron (MLP) integrating technical signals and deep learning.
result Deep learning model outperforms linear baselines in multi-factor stock selection.
Paper proposes a new framework to mine synergistic formulaic alphas for better stock trend forecasting.
problem Mining alphas separately ignores their combined performance, leading to suboptimal models.
method Proposes a reinforcement learning-based framework that optimizes the mining of synergistic formulaic alpha sets.
result Demonstrates higher returns in stock trend forecasting compared to previous approaches.
Alpha-R1 uses LLMs to reason about economic factors and news for better alpha screening.
problem Challenges in data-driven investment strategies due to signal decay and regime shifts.
method Reinforcement learning trained on 8B parameters to evaluate alpha relevance under changing market conditions.
result Empirically outperforms benchmark strategies and shows improved robustness to alpha decay.
Extract risk factors from dead alphas to improve tradable alpha performance.
problem Improving performance of tradable alphas by identifying non-profitable stock return directions.
method Explicit algorithm and source code for extracting risk factors from dead alphas.
result Dead alphas reveal non-profitable stock return directions, enhancing tradable alpha performance.
We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor …
We compute the analytic expression of the probability distributions F{FTSE100,+} and F{FTSE100,-} of the normalized positive and negative FTSE100 (UK) index daily returns r(t). Furthermore, we define the alpha re-scaled FTSE100 daily index positive returns r(t)^alpha and negative returns (-r(t))^alpha that we call, aft…
In terms of the stock exchange returns, we compute the analytic expression of the probability distributions F{DAX,+} and F{DAX,-} of the normalized positive and negative DAX (Germany) index daily returns r(t). Furthermore, we define the alpha re-scaled DAX daily index positive returns r(t)^alpha and negative returns (-…
Alpha-GPT 2.0 integrates human insights into AI-driven investment research.
problem Efficiency and precision in quantitative investment research.
method Iterative Human-AI interaction using large language models.
result Enhanced efficiency and precision in quantitative investment research.
It is well known that combining multiple hedge fund alpha streams yields diversification benefits to the resultant portfolio. Additionally, crossing trades between different alpha streams reduces transaction costs. As the number of alpha streams increases, the relative turnover of the portfolio decreases as more trades…
RiskMiner discovers formulaic alphas using MCTS for better performance.
problem Mining formulaic alphas without considering structural information and alpha correlations.
method Formulates alpha mining as an MDP and solves it with a risk-seeking MCTS.
result Our method outperforms state-of-the-art benchmarks and achieves the most profitable results.
Internal crossing of trades between multiple alpha streams results in portfolio turnover reduction. Turnover reduction can be modeled using the correlation structure of the alpha streams. As more and more alphas are added, generally turnover reduces. In this note we use a factor model approach to address the question o…
Enhances genetic programming for stock alpha discovery with warm start and structural constraints.
problem Overwhelming search space and computational burden in traditional genetic programming for alpha factor discovery.
method Proposes a new GP framework with warm start and structural constraints to enhance search performance and interpretability.
result Superior out-of-sample prediction results and higher portfolio returns compared to benchmarks.
New calibration methods improve fitting of weak variance-alpha-gamma process.
problem Improving fitting of a multivariate Lévy process.
method Comparison of three calibration methods: method of moments, maximum likelihood estimation, and digital moment estimation.
result Maximum likelihood estimation produces a better fit when a specific condition holds, while digital moment estimation produces a better fit when the condition is violated.
We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications typically there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression ove…
Unified convergence analysis of alpha-SVRG under strong convexity.
problem Analyzing the convergence of alpha-SVRG in strongly convex environments.
method Unified convergence rate expression for alpha-SVRG under fixed learning rate, demonstrating faster convergence than SGD and SVRG.
result alpha-SVRG has a faster convergence rate compared to SGD and SVRG under suitable choice of alpha.
AlphaEval evaluates alpha mining models efficiently and comprehensively.
problem Lack of systematic evaluation for alpha mining models.
method Unified, parallelizable evaluation framework assessing predictive power, stability, robustness, financial logic, and diversity.
result AlphaEval achieves evaluation consistency comparable to comprehensive backtesting, providing more comprehensive insights and higher efficiency.
New methods for tuning alpha in Gibbs posteriors improve speed and accuracy.
problem Inconsistency in Bayesian inference and lack of fast tuning methods for alpha.
method Proposed two data-driven methods: sample-splitting and bootstrapping. Formulated alpha-posteriors for three models.
result Sample-splitting outperforms SafeBayes in speed and accuracy, especially in complex models.
An explicit expression is obtained for the sectional curvature in the plane spanned by two stationary flows, cos(k, x) and cos(l, x). It is shown that for certain values of the wave vectors k and l the curvature becomes positive for alpha > alpha_0, where 0 < alpha_0 < 1 is of the order 1/k. This suggests that the flow…
Researchers calculate alpha invariant for certain complex projective spaces.
problem Computing alpha invariant for specific types of complex projective spaces.
method Computed alpha invariant using total degree and Pontryagin classes.
result Alpha invariant depends only on total degree and Pontryagin classes.
We give a simple explicit formula for turnover reduction when a large number of alphas are traded on the same execution platform and trades are crossed internally. We model turnover reduction via alpha correlations. Then, for a large number of alphas, turnover reduction is related to the largest eigenvalue and the corr…
AlphaSAGE mines diverse alphas via GFlowNets, overcoming RL issues.
problem Reward sparsity, inadequate sequential representations, and single optimal mode issues in RL for alphas.
method Structure-aware encoder (RGCN), GFlowNets, dense reward structure.
result Empirically outperforms existing baselines in mining diverse alphas.
PPO optimizes LLM-generated alpha weights for better trading performance.
problem Adapting LLM-generated alphas for varying market conditions.
method Proximal Policy Optimization (PPO) for dynamic alpha weight adjustment.
result PPO-optimized strategy achieves higher Sharpe ratios and smaller drawdowns.
The paper presents methods to improve uncertainty calibration in Bayesian Neural Networks.
problem Uncalibrated Bayesian Neural Networks often lead to overconfidence.
method The paper uses alpha-divergences from Information Geometry for calibration.
result Calibration using alpha-divergences provides better uncertainty estimates and is more efficient.
AlphaForge mines and dynamically combines alpha factors for better investment performance.
problem Inconsistency and inflexibility of fixed factor weights in alpha factor mining.
method Generative-predictive neural network for factor generation and dynamic weight adjustment.
result Demonstrated superior performance in formulaic alpha factor mining and portfolio returns.
AutoAlpha efficiently discovers effective alpha factors for quantitative investment.
problem Mining effective alpha factors for successful quantitative investment models.
method Hierarchical evolutionary algorithm with PCA-QD search, warm start, and replacement methods.
result AutoAlpha discovers and generates effective formulaic alphas for portfolio optimization.
In these notes we discuss investment allocation to multiple alpha streams traded on the same execution platform, including when trades are crossed internally resulting in turnover reduction. We discuss approaches to alpha weight optimization where one maximizes P&L subject to bounds on volatility (or Sharpe ratio). The…
Mannheim curves are defined for immersed curves in 3-dimensional sphere S^3 . The definition is given by considering the geodesics of S^3. First, two special geodesics, called principal normal geodesic and binormal geodesic, of S^3 are defined by using Frenet vectors of a curve immersed in S^3. Later, the curve alpha i…
AlphaCFG discovers alpha factors using grammar-guided search.
problem Discovering formulaic alpha factors in finance.
method AlphaCFG uses a grammar-based framework to define and discover alpha factors with syntactic and semantic constraints.
result AlphaCFG outperforms state-of-the-art methods in trading profitability and efficiency.
Fano manifolds with specific alpha invariant are K-stable.
problem Characterizing Fano manifolds with a specific alpha invariant.
method Analyzing alpha invariant and proving K-stability.
result Fano manifolds with α(X)=n/(n+1) and n≥2 are K-stable. The paper evaluates biased methods for alpha-divergence minimization.
problem The impact of bias on solutions found for alpha-divergence minimization.
method Empirical evaluation of biased methods for alpha-divergence minimization, focusing on bias effects and dimensionality.
result Solutions are biased towards KL-divergence minimizers and require impractical computation in high dimensions to minimize alpha-divergence.
New algorithm separates audio sources better using alpha-stable distributions.
problem Improving audio source separation using complex distributions.
method Estimating mixtures of alpha-stable distributions using characteristic function matching.
result Better separation performance than Gaussian-based methods.
Alpha-norm regularization simplifies marketing demand forecasting.
problem Ultra high-dimensional problems in demand estimation and forecasting.
method Nonconvex alpha-norm objective with coordinate descent and proximal operators.
result Alpha-norm regularization provides accurate out-of-sample estimates for promotion effects.
Given a smooth, closed, oriented 4-manifold X and alpha in H_2(X,Z) such that alpha.alpha > 0, a closed 2-form w is constructed, Poincare dual to alpha, which is symplectic on the complement of a finite set of unknotted circles. The number of circles, counted with sign, is given by d = (c_1(s)^2 -3sigma(X) -2chi(X))/4,…
Unbiased methods for alpha-divergence minimization struggle in high dimensions.
problem The difficulty of unbiased alpha-divergence minimization in high dimensions.
method Signal-to-Noise Ratio (SNR) analysis of gradient estimators.
result The SNR of the gradient estimator worsens exponentially with dimensionality.
Paper proposes a new REINFORCE algorithm for mining formulaic alpha factors with reduced variance.
problem Mining formulaic alpha factors with interpretability and robustness in volatile markets.
method Developed a novel REINFORCE algorithm with a dedicated baseline and reward shaping.
result Boosts correlation with returns by 3.83% and enhances excess returns compared to existing methods.
We present conditions under which positive alpha exists in the realm of active portfolio management- in contrast to the controversial result in Jarrow (2010, pg. 20) which implicates delegated portfolio management by surmising that positive alphas are illusionary. Specifically, we show that the critical assumption used…
In this paper, the geometric meaning of (alpha,beta)-norms is made clear. On this basis, we introduce a new class of Finsler metrics called general (alpha,beta)-metrics, which are defined by a Riemannian metric and an 1-form. These metrics not only generalize original (alpha,beta)-metrics naturally, but also include so…
Hybrid ML ensemble predicts market risk and generates alpha.
problem Forecasting short-horizon market risk and generating alpha.
method Neural networks and tree-based voting models integrated for trading signal.
result Sharpe ratio of 2.51 and annualized CAPM alpha of +0.28.