Investigates the use of Information Coefficient as a stock selection model performance measure.
problem The adequacy and effectiveness of Information Coefficient (IC) for evaluating stock selection models is unclear.
method Simulation and simple statistical modeling to examine IC behavior statically and dynamically.
result Proposes two practical procedures for IC-based ongoing performance monitoring of stock selection models.
Stock selection improved with a novel neural model capturing continuous stock dynamics.
problem Lack of continuous stock dynamics prediction and implicit cross-domain dependencies.
method StockODE, a latent variable model with NRODEs and hierarchical hypergraph for continuous stock volatility and inter-domain dependencies.
result Significantly outperforms baselines, improving Sharpe Ratio by up to 18.57%.
Study finds stock selection ability of Chinese mutual funds is better than asset allocation ability.
problem Evaluating the performance of actively managed mutual funds in China.
method Developed performance measures for asset allocation and selection using holding-based models and compared them with Fama-French and Treynor-Mazuy models.
result Stock selection ability from holding-based models is positively correlated with Fama-French model, while industry allocation is positively correlated with Treynor-Mazuy model.
ChatGPT selects stocks for investment portfolios, but optimization models improve results.
problem Using AI for investment advice due to model inaccuracies.
method Used ChatGPT to generate a stock universe, then compared various portfolio optimization strategies.
result Combining AI-generated stock selection with advanced optimization models yields better investment outcomes.
A machine learning approach for dynamic stock recommendation outperforms traditional strategies.
problem Lack of time for analysts to check all S&P 500 stocks and the need for a reliable stock selection strategy.
method Selecting representative stock indicators, using five machine learning methods, and choosing the model with the lowest Mean Square Error to rank stocks.
result The proposed scheme outperforms the long-only strategy on the S&P 500 index in terms of Sharpe ratio and cumulative returns.
A fuzzy expert system selects stocks for BSE using AI techniques.
problem Selecting stocks for investment allocation is challenging due to many influencing factors.
method Dempster-Shafer (DS) evidence theory for rule base generation, portfolio optimization model with ACO algorithm.
result The model's performance is satisfactory for short-term investment.
3S-Trader uses LLMs to optimize stock portfolios by scoring, strategizing, and selecting stocks.
problem Lack of multi-LLM frameworks for adaptive stock scoring, strategy, and selection in portfolio optimization.
method 3S-Trader incorporates scoring, strategy, and selection modules for stock portfolio construction, using historical strategies and market conditions to generate optimized selections.
result 3S-Trader achieves the highest accumulated return of 131.83% on DJIA constituents with a Sharpe ratio of 0.31 and Calmar ratio of 11.84.
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
problem Improving stock price prediction accuracy for Apple Inc. using technical indicators.
method Evaluation of 123 technical indicators and 10 regression models on 13 years of Apple Inc. data.
result Combining feature selection with regression models significantly improves prediction accuracy.
A new stock selection strategy uses combined machine learning with dynamic weighting methods.
problem Improving stock selection accuracy and performance.
method Combined machine learning algorithms with static and dynamic weighting methods.
result IC-based dynamic weighting outperforms static evaluation metrics in backtested returns and predictive performance.
H-GAT improves stock selection by capturing complex higher-order stock relations and integrating both technical and fundamental analysis.
problem Stock selection difficulty and lack of comprehensive analysis.
method Higher-order Graph Attention Network (H-GAT) that incorporates both technical and fundamental analysis.
result H-GAT outperforms existing methods in stock selection metrics.
Proposes a new method for big portfolio selection using graph-based conditional moments.
problem Challenges in selecting portfolios for thousands of stocks.
method Graph-based Conditional Moments (GRACE) method: learns quantiles, means, variances, skewness, and kurtosis of stock returns.
result Shows superior performance compared to competitors, especially in measures of conditional variance, skewness, and kurtosis.
Hybrid model uses TOPSIS, EMD, and ELM for stock selection.
problem Difficult to predict stock market due to political and economic factors.
method Combines TOPSIS, EMD, and ELM for stock selection.
result Hybrid model increases profit percentage compared to random selection.
This paper demonstrates how to apply machine learning algorithms to distinguish good stocks from the bad stocks. To this end, we construct 244 technical and fundamental features to characterize each stock, and label stocks according to their ranking with respect to the return-to-volatility ratio. Algorithms ranging fro…
This paper evaluates various loss functions for Transformer models in stock ranking.
problem Evaluating loss functions for Transformer models in stock ranking.
method Systematic evaluation of advanced loss functions (pointwise, pairwise, listwise) on S&P 500 data.
result Different loss functions impact a model's ability to discern profitable relative orderings among assets.
The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.
problem Sparse portfolio selection in financial markets.
method Topological data analysis (TDA) for clustering stock price movements.
result The TDA-based clustering strategy significantly enhances sparse portfolio performance.
Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial rati…
Study finds long memory in some emerging Asian stocks but not in developed markets.
problem Evaluating stock market efficiency in emerging vs developed markets.
method Improved wavelet estimator of long range dependence.
result Emerging Asian markets show more long memory in stock returns than developed markets.
LMoE uses LLMs to improve stock trading by selecting experts based on textual and price data.
problem Traditional neural network-based router selection in MoE models is suboptimal and ignores textual data.
method Proposes LLMoE, using LLMs as routers to select experts based on historical price data and stock news.
result LLoM outperforms state-of-the-art MoE models and other deep neural network approaches.
Development of stock networks is an important approach to explore the relationship between different stocks in the era of big-data. Although a number of methods have been designed to construct the stock correlation networks, it is still a challenge to balance the selection of prominent correlations and connectivity of …
TDA improves stock portfolio selection by analyzing data structure.
problem Traditional portfolio selection methods fail to handle stock market data complexities.
method Two-stage method involving time series generation and clustering with TDA features.
result TDA-based portfolio outperforms other methods consistently over different time frames.
Paper uses machine learning for stock prediction using fundamental data.
problem Predicting stock prices using fundamental data.
method Used three machine learning algorithms (FNN, RF, ANFIS) and feature selection for stock prediction.
result Random Forest (RF) model achieved the best prediction results.
Improved stock trading model using feature selection and ensemble learning.
problem Challenges in making profit in the US stock market.
method Feature selection from 148 to 30, dynamic selection of top 25 features, ensemble learning with four classifiers.
result Best model generated 54.35% profit over 18 months.
Study automates feature selection and clustering for HFT stock price forecasting.
problem Manual feature selection and clustering for high-frequency trading (HFT) stock price forecasting.
method Dual competitive feature importance mechanism and clustering via shallow neural network topology.
result Enhanced forecasting ability of the RBFNN regressor through automated feature selection and clustering.
Study finds no evidence dual-class stocks are effective predictors.
problem Investment efficiency of dual-class stocks.
method In-depth analysis of stock price divergence, innovative LSTM model training set selection.
result No compelling evidence dual-class stocks are effective predictors.
Clusters of highly correlated stocks are identified for better asset selection.
problem Identifying a small set of stocks to approximate the diversification of the whole stock universe.
method Data-driven correlation blockmodel clustering approach.
result The algorithm effectively detects clusters of highly correlated stocks.
This paper explores how combining quantitative factors and news from LLMs improves stock return prediction.
problem Improving stock return prediction using quantitative factors and news.
method Introduces a fusion learning framework to learn unified representations from factors and LLM-generated newsflow, comparing combination, summation, and attentive methods. Explores mixture models and decoupled training approaches.
result Effective multimodal modeling of factors and news improves stock return prediction and selection.
The study examines stock splits and their effects on companies, managers, and shareholders.
problem Misunderstandings and confounding factors around stock splits and their impacts.
method Selected database analysis of nine recent events, examining market impact, trading volume, and shareholder base.
result Stock splits enhance trading volume, increase shareholder base, and improve market liquidity.
New model uses financial news to predict stock returns.
problem Predicting stock returns based on financial news.
method Derive company embedding vectors from news, select basis assets, and use statistical methods.
result NEUS model outperforms Fama-French 5-factor model.
ARIMA and LSTM models predict stock prices with varying accuracy.
problem Predicting stock prices accurately.
method ARIMA and LSTM models trained on historical stock data.
result LSTM model performs better in predicting stock price changes.
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.
Investigates optimal portfolio selection with regime-switching-induced stock price shocks.
problem Mean-variance portfolio selection with regime-switching and stock price jumps.
method Modeling regime-switching and stock price jumps, deriving optimal portfolio strategy and efficient frontier using ODEs.
result Added complexity due to regime-switching-induced stock price shocks, leading to nonlinear ODEs.
Long-term investors, different from short-term traders, focus on examining the underlying forces that affect the well-being of a company. They rely on fundamental analysis which attempts to measure the intrinsic value an equity. Quantitative investment researchers have identified some value factors to determine the cos…
Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use …
MambaStock predicts stock prices with high accuracy using a state space model.
problem Inaccurate stock price predictions due to nonlinearity in stock market data.
method Mamba-based state space model with selection mechanism and scan module.
result MambaStock outperforms previous methods in stock price prediction accuracy.
GARCH models predict stock volatility in Indian sectors.
problem Designing accurate models for future stock volatility.
method GARCH framework applied to ten Indian stocks.
result Asymmetric GARCH models outperform in volatility forecasting.
Stockformer uses wavelet transform and multi-task learning to predict stock returns and trends.
problem Challenges in predicting market dynamics due to policy uncertainty and economic events.
method Integrates wavelet transformation and multitask self-attention networks to capture market trends and fluctuations.
result Stockformer outperforms existing models on multiple real stock market datasets, demonstrating exceptional stability and reliability.
We select n stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the k trading days of our database from the stock price time series. We analyze each ensemble of stock returns by extracting its first four central moments. We observe that these moments are fl…
We empirically test predictability on asset price by using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directio…
Pairs Trading is carried out in the financial market to earn huge profits from known equilibrium relation between pairs of stock. In financial markets, seldom it is seen that stock pairs are correlated at particular lead or lag. This lead-lag relationship has been empirically studied in various financial markets. Earli…
We report the results of fifteen sets of portfolio selection simulations using stocks in the ASX200 index for the period May 2000 to December 2013. We investigated five portfolio selection methods, randomly and from within industrial groups, and three based on neighbor-Net phylogenetic networks. We report that using ra…
Research develops a DSS for stock selection and asset allocation using fundamental data.
problem Complex financial markets and limited use of fundamental data analysis.
method Data gathering, cleaning, and modeling of fundamental data; integration with macroeconomic conditions.
result Enhanced predictive model for mid- to long-term stock returns.
HRT uses bi-level reinforcement learning to optimize stock selection and execution in multi-asset equity markets.
problem Optimizing automated equity trading decisions under risk, turnover, and transaction costs.
method Hierarchical Reinforced Trader (HRT) framework that separates selection and execution decisions.
result HRT outperforms other methods in learning-based return-risk-cost trade-offs, improving Sharpe ratio and reducing turnover.
Study improves Cox model for predicting stock trading signs using Japanese market data.
problem Improving Cox model for predicting stock trading signs using Japanese market data.
method Added new covariates and used high-frequency trading data for 222 Nikkei 225 stocks.
result Cox-type model performs well in Japanese market and identifies key factors for accurate estimation.
Training a practical and effective model for stock selection has been a greatly concerned problem in the field of artificial intelligence. Even though some of the models from previous works have achieved good performance in the U.S. market by using low-frequency data and features, training a suitable model with high-fr…
Quantum algorithms improve stock price prediction accuracy.
problem Improving stock price prediction accuracy using quantum techniques.
method Extracted stock price indicators, used QA and PCA for feature selection and dimensionality reduction, trained QSVM for binary classification.
result Quantum Support Vector Machine (QSVM) outperformed classical models in stock price prediction accuracy.
Investigates quantum vs classical portfolio optimization of 60 stocks.
problem Optimizing risk vs return portfolios of 60 stocks using quantum and classical methods.
method Classical and quantum annealing approaches applied to historical data.
result Quantum and classical methods yield similar optimal portfolios.
Generative AI models enhance sector-based investment portfolios, but performance varies by market conditions.
problem Improving investment performance through better stock selection in volatile markets.
method Applied LLMs from OpenAI, Google, Anthropic, DeepSeek, and xAI to select and weight stocks within S&P 500 sectors.
result LLM-weighted portfolios outperform sector indices in stable markets but underperform in volatile ones.
Trading system uses NP-hard optimization to select stocks for high Sharpe ratio trading.
problem Finding profitable, uncorrelated stocks for high Sharpe ratio trading.
method NP-hard combinatorial optimization using Ising machine and simulated bifurcation algorithm.
result Trading strategy with FPGA-based system achieves 164 μs response latency.