Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
problem Robust stock index forecasting
method Modified Transformer architecture with Shifted Data Augmentation
result Best performance on benchmark datasets
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48 results for “Stock Index Forecasting”
Deep learning predicts S&P 500 index direction.
problem Accurate stock price prediction remains challenging.
method Convolutional neural network model for S&P 500 index forecasting.
result Model achieves over 55% accuracy in predicting index direction.
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short an…
Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the…
The paper uses PCA and HMM to forecast stock returns outperforming buy-and-hold.
problem Predicting stock returns accurately.
method Applied PCA to covariance matrix of S&P 500 stocks, used HMM on principal components, and forecasted stock returns.
result The model outperforms buy-and-hold strategy in terms of annualized Sharpe ratio.
Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Daysq-fin.GN
Model forecasts global stock market volatility using dynamic graphs and all trading days.
problem Enhance forecasting accuracy and practical utility in global stock market volatility.
method Spatial-temporal graph neural network architecture to capture volatility spillover effect.
result Forecasting performance surpasses baseline models in all scenarios.
Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange of India, over a period of four …
Study introduces TeMoP model for better stock market predictions.
problem Decreasing prediction errors and robustness across datasets in machine learning models.
method Probabilistic multiple lag order model based on trend encoding.
result TeMoP model outperforms machine learning models in accuracy and stability across different stock indexes.
EXAMM evolves RNNs for stock return prediction and portfolio trading.
problem Predicting stock returns for optimal portfolio trading.
method Evolutionary Neural Architecture Search (EXAMM) for evolving RNNs.
result Evolving RNNs outperform traditional benchmarks in stock trading.
Research uses SWT and BDLSTM to forecast stock and oil prices amid COVID-19.
problem Impact of COVID-19 on stock and oil prices forecasting.
method Integrates Stationary Wavelet Transform and Bidirectional Long Short-Term Memory networks.
result BDLSTM+WT-ADA achieved satisfactory results in Crude Oil price forecasting.
This paper presents deep learning models for NIFTY 50 stock price prediction.
problem Accurately predicting stock prices using historical data.
method Used CNN and LSTM-based deep learning models on NIFTY 50 historical data.
result Univariate encoder-decoder convolutional LSTM model is the most accurate.
Graph Neural Network improves volatility forecasting for 500 S&P stocks.
problem Forecasting short-term realized volatility in a multivariate setting.
method Graph Transformer Network for Volatility Forecasting.
result Our model outperforms benchmarks on 500 S&P stocks.
Forecasting stock market decline and recovery post-COVID-19.
problem Analyzing exogenous risk's impact on stock markets.
method Two case studies using historical data and stochastic fluctuations.
result 85% accuracy in predicting S&P500 index decline and recovery.
Paper finds significant impact of stock market swings on equity risk premium predictability.
problem Predicting equity risk premium based on stock market behavior changes.
method Introduced Bullish Index and used FDMAA for returns analysis; considered 28 indicators.
result Positive shocks in Bullish Index correlate with strong equity risk premium predictability for up to six months, while negative shocks correlate for up to nine months.
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.
This paper analyses how Time Series Analysis techniques can be applied to capture movement of an exchange traded index in a stock market. Specifically, Seasonal Auto Regressive Integrated Moving Average (SARIMA) class of models is applied to capture the movement of Nifty 50 index which is one of the most actively excha…
By adopting Multifractal detrended fluctuation (MF-DFA) analysis methods, the multifractal nature is revealed in the high-frequency data of two typical indexes, the Shanghai Stock Exchange Composite 180 Index (SH180) and the Shenzhen Stock Exchange Composite Index (SZCI). The characteristics of the corresponding multif…
The Stochastic Volatility (SV) model and its variants are widely used in the financial sector while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of Deep Learning. Our article combines these two methods in a non-trivial way and proposes a model, which we call th…
The study evaluates nine machine learning regressors for predicting NASDAQ stock opening prices.
problem Predicting stock market opening prices for profitable trading strategies.
method Nine different machine learning regressors were applied to NASDAQ stock market data.
result The study found that certain regressors outperform others in predicting stock opening prices.
Machine learning predicts US stock market crashes.
problem Early detection of stock market crises.
method Random Forest and Extreme Gradient Boosting models.
result Extreme Gradient Boosting outperforms other models.
Improved genetic algorithm optimizes SVR for robust long-term stock index forecasting.
problem Inaccurate long-term stock price predictions.
method Adaptive Weighted Genetic Algorithm-Optimized SVR (IGA-SVR).
result Reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR.
This study compares deep learning and statistical models for stock price forecasting.
problem Accurate stock price prediction is challenging due to market volatility.
method Used deep learning (LSTM, RNN, CNN, FULL CNN) and statistical models (ARIMA, Moving Averages) on S&P 500 data.
result LSTM model showed the lowest Mean Absolute Error (MAE), indicating highest accuracy.
With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper aims to improve the accuracy of stock price forecasting. It improv…
Forecasting stock market direction is always an amazing but challenging problem in finance. Although many popular shallow computational methods (such as Backpropagation Network and Support Vector Machine) have extensively been proposed, most algorithms have not yet attained a desirable level of applicability. In this p…
Cubic predicts stock market indices by fusing stock latent embeddings and converting to binary classification.
problem Challenges in predicting stock market indices due to isolated time series treatment and simple regression.
method Fusion of stock latent embeddings, binary encoding classification, and confidence-guided prediction.
result Cubic outperforms state-of-the-art baselines in stock index prediction tasks.
An original method, assuming potential and kinetic energy for prices and conservation of their sum is developed for forecasting exchanges. Connections with power law are shown. Semiempirical applications on S&P500, DJIA, and NASDAQ predict a coming recession in them. An emerging market, Istanbul Stock Exchange index IS…
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.
Novel TM-vector model predicts stock market direction using Twitter and market data.
problem Challenging stock market forecasting with equal or ignored user effects.
method TM-vector trained with Twitter features and market information, using IndRNN.
result Significant accuracy in predicting stock market direction, especially for Apple.
Paper uses financial news for stock trend forecasting using deep multiple instance learning.
problem Forecasting stock trends from financial news articles.
method Developed a flexible and adaptive multi-instance learning model for bags of instances (financial news articles) on trading days.
result Outstanding trend prediction accuracy compared to state-of-the-art approaches.
The paper predicts financial markets using news text and semantic network analysis.
problem Predicting financial markets with news data.
method Semantic network analysis of news text to assess economic keywords' importance.
result The index captures financial market phases and predicts returns and volatilities.
New optimizers improve stock market forecasting accuracy.
problem Forecasting S&P 500 Index returns with MambaStock model.
method Evaluation of various optimizers (Adam, RMSProp, Lion, Roaree).
result Roaree optimizers combine faster training with reduced oscillations.
The validity of the Efficient Market Hypothesis has been under severe scrutiny since several decades. However, the evidence against it is not conclusive. Artificial Neural Networks provide a model-free means to analize the prediction power of past returns on current returns. This chapter analizes the predictability in …
We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which th…
The study aims to explore the strength of causal relationship between stock price search interest and real stock market outcomes on worldwide equity market indices. Such a phenomenon could also be mediated by investor behavior and extent of news coverage. The stock-specific internet search trends data and corresponding…
Time series analysis and forecasting of stock market prices has been a very active area of research over the last two decades. Availability of extremely fast and parallel architecture of computing and sophisticated algorithms has made it possible to extract, store, process and analyze high volume stock market time seri…
Uncertainty in the financial market and application to forecastabnormal financial fluctuationsq-fin.RM
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
New method evaluates financial graphs for stock trend forecasting.
problem Lack of dynamic stock relationship graphs and evaluation methods.
method SPNews dataset and novel evaluation methods independent of downstream tasks.
result Evaluation methods can differentiate between various financial relationship graphs.
Stress index strategy enhanced with financial news sentiment analysis for the equity marketsq-fin.ST
Enhanced stock market strategy using stress index and financial news sentiment analysis.
problem Improving risk assessment and prediction in equity markets.
method Combines financial stress indicator with sentiment analysis of financial news.
result Improved performance with higher Sharpe ratio and reduced drawdowns.
This research predicts stock market movements using Vision-Language models.
problem Predicting future stock market direction using historical data.
method Utilizing image and byte-based representations of stock data processed with Vision-Language models.
result The proposed approach significantly outperforms deep learning baselines.
Financial Times Series such as stock price and exchange rates are, often, non-linear and non-stationary. Use of decomposition models has been found to improve the accuracy of predictive models. The paper proposes a hybrid approach integrating the advantages of both decomposition model (namely, Maximal Overlap Discrete …
The Financial Chaos Index models stock market volatility across three regimes based on mutual price fluctuations.
problem Capturing regime-dependent volatility in stock markets.
method Developed a regime-switching framework using the Financial Chaos Index (FCIX) and elastic net regression.
result Identified three market regimes: low-chaos, intermediate-chaos, and high-chaos, each with distinct volatility characteristics.
Paper introduces COBRA variations for multivariate time series forecasting.
problem Multivariate time series forecasting challenges.
method Innovative COBRA variations, data preprocessing, Bayesian optimisation vs. grid search.
result Proposed methodologies outperform state-of-the-art models.
A new framework forecasts stock trends by mining shared information from concepts.
problem Forecasting stock trends using static concept information limits accuracy.
method Proposes a graph-based framework that mines concept-oriented shared information from both predefined and hidden concepts.
result Improves stock trend forecasting performance through dynamic concept relevance and hidden concept information.
The paper uses news headlines to predict stock prices using embeddings.
problem Predicting stock prices using news headlines.
method Using OpenAI-based text embedding models and PCA to create vector encodings of news headlines, then training machine learning models on financial data.
result Headline data embeddings improve stock price prediction by at least 40%.
This paper uses GAN and ERMSE to improve stock price movement prediction accuracy.
problem Predicting stock price movement direction is challenging due to complex, incomplete, and fuzzy information.
method The paper proposes a deep learning model using GAN and ERMSE to forecast stock market trends.
result The GAN model outperformed LSTM in predicting stock price movement direction with a 4.35% improvement.
Random investment strategies outperform sensible ones, even with forecasts.
problem The usefulness of investment strategies based on forecasts is questioned.
method Investigated the performance of sensible and nonsensical investment strategies, including forecasts.
result There is no substantial difference between the performances of ``best'' and ``trivial'' forecasts.
Optimal stock price prediction model using recurrent neural networks with RMSprop optimizer.
problem Stock price prediction using neural networks.
method Comparison of fully connected, convolutional, and recurrent architectures; inclusion of three optimization techniques.
result Single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test MAE of 0.0150 and 0.0148 respectively.
Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either th…