Investment managers face harder choices in green stocks due to reduced performance variability.
problem Difficulty in deploying talent in green stocks due to reduced performance variability.
method Analysis of S&P 500 firms' greenhouse gas emission levels and peer performance ratios.
result Performance variability has decreased in green stocks, making it harder for managers to choose.
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.
Human decision making by professionals trading daily in the stock market can be a daunting task. It includes decisions on whether to keep on investing or to exit a market subject to huge price swings, and how to price in news or rumors attributed to a specific stock. The question then arises how professional traders, w…
This paper uses CNN-LSTM to predict stock market performance.
problem Predicting stock market performance is challenging due to changing prices and lack of advanced libraries.
method Developed a CNN-LSTM Neural Network model to track stock data patterns and predict future performance.
result The CNN-LSTM model outperformed other models in predicting stock market performance.
The p-index improves investment performance for NYSE stocks but not for SSE stocks.
problem Improving investment performance for stocks using the p-index.
method Comparing different p-ratio strategies and empirical efficient frontiers for SSE and NYSE stocks.
result The p-index enhances investment performance for NYSE stocks but not for SSE stocks.
StonkBERT predicts stock price movements using company text data.
problem Can language models predict medium-run stock price movements?
method Fine-tuning transformer-based language models (BERT) on company text data (news articles, blogs, annual reports) for stock price performance classification.
result StonkBERT shows substantial improvement in predictive accuracy compared to traditional models, with news articles providing the best results.
Study shows tweets about COVID-19 can predict stock market performance.
problem Understanding the impact of COVID-19 on stock markets.
method Text sentiment analysis of Twitter data to correlate tweets about COVID-19 with stock market performance.
result Strong relationship between COVID-19 sentiment and stock market performance can be predicted.
Decision trees improve intraday trading strategies for NIFTY50 stocks.
problem Creating optimal trading rules for individual stocks.
method Using decision trees to generate unique trading rules for each stock based on technical indicators.
result Decision tree strategies outperform simple buy-and-hold for many stocks.
Deep learning models improve stock portfolio performance.
problem Improving stock portfolio allocation strategies.
method Used MLP, CNN, LSTM, and Transformer models to predict stock returns.
result Deep learning models enhance long-short stock portfolio performance.
Study examines how social media sentiment impacts biotech stocks.
problem Understanding the impact of social media on biotech stock prices.
method VADER sentiment analysis, ARIMA, and VAR models were used to forecast stock market performance.
result Complex interplay between tweet sentiment and stock market performance was identified.
LSTM model predicts stock prices with high accuracy in stable sectors but struggles with volatile ones.
problem Predicting stock prices in emerging markets with limited data.
method Developed and evaluated an LSTM network on historical OHLCV data and technical indicators.
result Strong predictive performance (R2>0.87) for stable sectors, but challenges for volatile ones. The study examines network analysis for predicting stock market performance.
problem Understanding lead-lag relationships in the NYSE.
method Network analysis of the NYSE to identify lead-lag effects.
result Network analysis reveals valuable insights for investors and analysts.
Project predicts stock performance and builds an efficient portfolio for six Indian sectors.
problem Predicting stock prices accurately for optimal portfolio design.
method Analysis of time series, machine learning, and deep learning models; Modern Portfolio Theory; minimum variance and optimal risk portfolio optimization.
result Built and tested an efficient portfolio for six Indian sectors using historical stock prices.
This study evaluates different portfolio designs for Indian stocks.
problem Optimizing portfolio weights for risk and return in volatile stock markets.
method Three portfolio design approaches: risk minimization, risk optimization, and equal weighting. Historical data from 2017-2022 used.
result Equal-weight portfolios outperformed other designs in most sectors.
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.
This study uses NLP to predict stock performance based on analyst reports.
problem Predicting stock performance using textual information from analyst reports.
method Natural language processing (NLP) and a customized BERT deep learning model for Chinese text.
result Strong positive sentiment in analyst reports increases excess return and intraday volatility, while strong negative sentiment increases volatility and trading volume but decreases excess return.
Analyzes ESG impact on stock market performance using social media and news data.
problem Understanding the impact of ESG news on stock market performance.
method Summarized live ESG data from social media and news, created sentiment index, calculated stock price changes, and compared sentiment to performance.
result ESG sentiment correlates with stock price changes, indicating its impact on market performance.
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.
A flexible calendar rebalancing approach for Indian stock portfolios.
problem Optimizing stock portfolio performance in the Indian stock market.
method Calendar rebalancing of sector-specific portfolios based on historical stock prices.
result The proposed calendar rebalancing approach improves portfolio performance over the test period.
Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolu…
Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.
problem Inherently difficult stock price prediction due to low signal-to-noise ratio.
method Wavelet transform convolution, channel attention, and LSTM integration.
result Robust performance in post-pandemic market conditions.
Study analyzes Disney stock market performance using machine learning.
problem Forecasting stock market performance of Disney.
method Exploratory data analysis, feature engineering, model selection (linear regression).
result Linear regression model performed best.
Nostradamus links climate and stock market performance.
problem Understanding the impact of climate on stock prices.
method Analyzing historical data, climate indicators, and natural disasters.
result Significant correlation between climate and stock price fluctuations.
PriceSeer benchmarks LLMs in real-time stock prediction.
problem Evaluating LLMs' stock prediction accuracy and robustness.
method Real-time benchmark with 110 U.S. stocks, internal and external information expansion.
result LLMs perform suboptimally in long-term predictions due to fake news and specific industries.
The study predicts stock volatility using LSTM and GARCH models.
problem Accurately predicting stock price volatility is challenging.
method Multiple volatility models (GARCH, GJR-GARCH, EGARCH, LSTM) applied to three sectors.
result LSTM outperformed other models in pharma sector volatility prediction.
Quantum neural network and tensor network models outperform classical models in Japanese stock market predictions.
problem Improving stock return predictions using quantum and quantum-inspired machine learning.
method Evaluation of quantum neural network and tensor network models against classical models like linear and neural networks.
result Tensor network model outperforms classical models in Japanese stock market, including linear and neural network models.
The paper analyzes how stock market dimensionality changes impact portfolio performance.
problem Impact of dimensional changes on portfolio performance in a changing market.
method Development of self-financing stock portfolios in a stochastic portfolio theory framework with dimensional jumps.
result Quantification of how listing or delisting events and market shocks affect portfolio return.
We review a resent {\em time-dependent} performance measure for economical time series -- the (optimal) investment horizon approach. For stock indices, the approach shows a pronounced gain-loss asymmetry that is {\em not} observed for the individual stocks that comprise the index. This difference may hint towards an sy…
Deep RL applied for Indian stock trading strategies.
problem Designing profitable trading strategies for Indian stock markets.
method Applied deep reinforcement learning to ten Indian stock datasets.
result Models' performance compared and evaluated.
This study compares three portfolio optimization methods on Indian stocks.
problem Comparing portfolio optimization methods on Indian stocks.
method Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning approaches.
result Reinforcement Learning outperformed other methods in terms of Sharpe ratio.
This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-speci…
MiM-StocR combines momentum indicators and adaptive ranking loss for better stock recommendation.
problem Lack of simultaneous short-term trend and ranking prediction in stock recommendation models.
method Integrates momentum indicators and proposes Adaptive-k ApproxNDCG for ranking optimization.
result MiM-StocR outperforms state-of-the-art MTL baselines in stock recommendation.
GAT-AGNN learns stock trends using graph and attention mechanisms.
problem Predicting dynamic stock trends in a complex market.
method Sequential graph structure with attention mechanisms.
result GAT-AGNN outperforms state-of-the-art methods in stock trend prediction.
Transformer pre-training improves stock return prediction accuracy.
problem Improving stock price prediction accuracy for better investment decisions.
method Pre-trained transformer models on TSX index, fine-tuned for individual stocks, compared to LSTM and XGBoost.
result Transformer model achieved lower mean squared error than benchmarks.
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.
GCNET predicts stock price movements using graph convolutional networks.
problem Predicting stock price movements using interrelated stocks data.
method GCNET models stock relations as an influence network, uses graph convolutional networks for prediction.
result GCNET significantly improves prediction accuracy and MCC measures.
Model earnings call transcripts for better stock price prediction.
problem Predicting future stock price movements using earnings call transcripts.
method Deep learning framework with an attention mechanism to encode text data into vectors for predicting stock price movements.
result The proposed model outperforms traditional machine learning methods in stock price prediction.
Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the even…
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper im…
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.
Study evaluates stock price forecasting models during the pandemic.
problem Forecasting stock prices during the Covid-19 pandemic.
method Four models (Long-Short Term Memory, XGBoost, Autoregression, Last Value) were tested on stock prices of Facebook, Amazon, Tesla, Google, and Apple.
result Autoregression and Last Value models outperform other models due to strong correlation between prices.
Paper proposes TDQN, a DRL strategy for optimal stock trading.
problem Optimal trading position determination in stock markets.
method Deep reinforcement learning (DRL) with Trading Deep Q-Network (TDQN) algorithm.
result TDQN strategy significantly improves Sharpe ratio performance.
A new DRL system using LSTM improves stock trading performance.
problem Adapting DRL to financial data with low signal-to-noise ratios.
method Cascaded LSTM networks for feature extraction and reinforcement learning.
result Our model outperforms previous models in cumulative returns and Sharp ratio.
Study compares three portfolio optimization methods on Indian stocks.
problem Optimizing portfolios for the Indian stock market.
method Three portfolio optimization methods (MVP, HRP, HERC) applied to 15 sectors.
result Identified portfolios with highest cumulative return, lowest volatility, and best Sharpe Ratio.
This paper compares three portfolio designs for Indian stocks.
problem Designing an optimum portfolio that balances return and risk.
method Three approaches: minimum risk, optimum risk, and Eigen portfolios.
result Optimum risk portfolios and Eigen portfolios identified for each sector.
A deep learning strategy outperforms traditional methods in stocks portfolio management.
problem Optimizing stock portfolio performance using machine learning.
method Deep Deterministic Policy Gradient framework with neural networks.
result Compound annual return rate of 14.12% compared to 7 other strategies.
Forecasting US stock market indices during COVID-19 using machine learning models.
problem Predicting stock market behavior during the pandemic.
method Used Random Forest and LSTM models on historical stock prices.
result Improved accuracy in forecasting stock market returns.
Study finds super-efficiency correlates more strongly with stock market valuation than ROA in Chinese banks.
problem Investigating the relationship between bank efficiency and stock market valuation.
method Employed a non-radial, non-oriented slack-based super-efficiency Data Envelopment Analysis (Super-SBM-UND-VRS) model, treating NPLs as undesired output.
result Super-efficiency is more strongly correlated with stock market valuation than ROA, as measured by Tobin's Q.