In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Mult…
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48 results for “technical data”
How to reconcile Market Efficiency and Technical Analysiscond-mat.stat-mech
Weak form of the Efficiency Market Hypothesis (EMH) excludes predictions of future market movements from historical data and makes the technical analysis (TA) out of law. However the technical analysis is widely used by traders and speculators who steadely refuse to consider the market as a "fair game" and survive with…
Study finds similar companies in Dhaka Stock Exchange using technical data.
problem Analyzing all companies in Dhaka Stock Exchange is impractical.
method Used technical data to identify companies moving together.
result Technical data can reveal company relationships without fundamental data.
In this survey, a short introduction in the recent discovery of log-normally distributed market-technical trend data will be given. The results of the statistical evaluation of typical market-technical trend variables will be presented. It will be shown that the log-normal assumption fits better to empirical trend data…
We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for contr…
Transformer model predicts stock trends using technical data and sentiment analysis.
problem Lack of accurate long-term stock trend prediction using traditional models.
method Developed a Transformer-based model integrating technical stock data and sentiment analysis.
result Transformer model shows significant improvement in directional accuracy over RNNs, especially for longer sequence lengths.
Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data-driven approach to predict intraday stock jumps using the in…
Scalable GPLVM reduces complexity in scRNA-seq data, accounting for technical and biological confounders.
problem Complexity and confounders in scRNA-seq data hamper interpretation.
method Extended Gaussian process latent variable model (GPLVM) to handle large datasets.
result Framework reconstructs latent signatures and captures disease-specific gene expression.
Much of modern practice in financial forecasting relies on technicals, an umbrella term for several heuristics applying visual pattern recognition to price charts. Despite its ubiquity in financial media, the reliability of its signals remains a contentious and highly subjective form of 'domain knowledge'. We investiga…
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
Study improves cryptocurrency price prediction using neural networks and technical indicators.
problem Improving cryptocurrency price prediction accuracy.
method Integrates technical indicators, Transformer neural network, and BiLSTM.
result Demonstrates superior performance in predicting cryptocurrency prices.
Technical trading rules have a long history of being used by practitioners in financial markets. Their profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousands traditional technical trading rules on the Shanghai Securities Co…
TINs use neural networks to interpret technical indicators for trading.
problem Lack of interpretable neural architectures for technical indicators in trading.
method Introduced TINs, a neural architecture that reformulates technical indicators into trainable modules.
result Improved risk-adjusted performance compared to traditional indicator-based strategies.
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.
CNN model predicts financial market movement with better performance.
problem Difficult to predict financial markets due to complex dynamics.
method Proposes a novel one-dimensional CNN model for financial market prediction.
result CNN model achieves more robust and profitable performance than previous approaches.
Technical trading rules have been widely used by practitioners in financial markets for a long time. The profitability remains controversial and few consider the stationarity of technical indicators used in trading rules. We convert MA, KDJ and Bollinger bands into stationary processes and investigate the profitability…
This paper tackles hidden technical debts in fair ML systems for Fintech.
problem Building fair machine learning systems in financial services.
method Examining key stages of ML system development and deployment.
result Technical debts exist in deploying fair ML systems in Fintech.
Predicts short-term futures contract direction using neural networks and order flow data.
problem Challenges in predicting short-term directional movement of futures contracts.
method Engineering features from technical analysis, order flow, and order-book data; training a Tabnet neural network.
result Achieved an accuracy of 0.601 in predicting directional change on the Silver Futures Contract.
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.
VTA combines verbal and latent reasoning for accurate stock time-series forecasts.
problem Challenges in combining textual analysis with time-series data for financial forecasting.
method Converts stock price data into textual annotations, optimizes reasoning trace using inverse MSE, conditions time-series model outputs on reasoning attributes.
result VTA achieves state-of-the-art forecasting accuracy and interpretable reasoning traces.
TF-MoDISco (Transcription Factor Motif Discovery from Importance Scores) is an algorithm for identifying motifs from basepair-level importance scores computed on genomic sequence data. This technical note focuses on version v0.5.6.5. The implementation is available at https://github.com/kundajelab/tfmodisco/tree/v0.5.6…
Predict stock trends using news sentiment and technical indicators in Spark.
problem Predicting the stock market trend is challenging due to multiple influencing factors.
method Created a machine learning classification problem with features from technical indicators and news sentiment scores.
result Random Forest model achieved 63.58% test accuracy in Spark.
Study finds traditional technical indicators underperform in high-frequency trading, suggesting risk management over prediction.
problem Inadequately explored effectiveness of technical indicators in high-frequency trading, particularly at minute-level frequency.
method Evaluation of random forest models with traditional technical indicators on minute-level SPY data.
result In-sample performance is superior to out-of-sample, with risk-adjusted metrics not outperforming a simple buy-and-hold strategy.
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.
Combining various data types predicts S&P 500 stock prices with high accuracy.
problem Predicting S&P 500 stock prices with high accuracy.
method Combined technical, fundamental, and text data with machine learning models like Random Forest and LSTM.
result Achieved 66.18% accuracy in S&P 500 index prediction and 62.09% in individual stock prediction.
Technical analysis (TA) has been used for a long time before the availability of more sophisticated instruments for financial forecasting in order to suggest decisions on the basis of the occurrence of data patterns. Many mathematical and statistical tools for quantitative analysis of financial markets have experienced…
Abstract: A new approach to technical indicators without lag.
problem Defining classical technical indicators as bounded operators for lag-free trading.
method Using linear algebra to redefine technical indicators as bounded operators in space.
result Demonstrated the no-lag versions of technical indicators are simpler and more effective.
NEAT algorithm optimizes stock trading with reduced risk.
problem Maximizing earnings while minimizing risk in stock trading.
method Applied NEAT algorithm to stock trading with multiple technical indicators, using progressive training data and a multi-objective fitness function.
result NEAT model achieved similar returns to Buy & Hold but with lower risk and stability.
In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
Study investigates how machine learning models degrade over time, leading to patient safety issues.
problem Overtime degradation of machine learning models in clinical settings.
method Used MIMIC-IV dataset to train models replicating commercial approaches, observing and analyzing degradation over a decade.
result An RNN model built on Epic features degrades from 0.729 AUC to 0.525 AUC over a decade, highlighting technical and clinical drift as root causes.
Revisits life insurance surplus models with new technical bases.
problem Classifying and extending life insurance surplus models.
method Using Markov models and classifying technical bases in Thiele's equation.
result Introduces a `canonical' model with three technical bases.
Machine learning models show intermarket data can predict stock market performance better than expected.
problem Evaluating the semi-strong form of the Efficient Market Hypothesis.
method Used machine learning techniques on various intermarket data sets to predict stock market performance.
result Intermarket data significantly outperforms baselines in predicting stock market movement, contradicting the semi-strong EMH.
Improved NTL detection using human-in-the-loop approach with explainability.
problem Challenges in detecting NTL due to biased data and black-box models.
method Human-in-the-loop approach with explanatory methods to guide model training.
result Improved accuracy, interpretability, robustness, and flexibility of the prediction model.
Study improves cryptocurrency price prediction using deep learning with trading and social media indicators.
problem Predicting price movements of cryptocurrencies using deep learning.
method Used deep learning algorithms (MLP, CNN, LSTM, ALSTM) on hourly and daily data of Bitcoin and Ethereum.
result Unrestricted model with trading and social media indicators outperforms restricted model.
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…
The paper explores new risk models for autonomous driving.
problem Risk management and actuarial modeling for autonomous vehicles.
method Examines technical difficulties and proposes a novel risk model.
result The new model better reflects real-world driving safety.
Study shows HPO improves stock return forecasting models.
problem Improving accuracy of stock return forecasting models.
method Used deep neural networks with hyperparameter optimization (HPO) and regularization techniques.
result Model with technical indicators and dropout regularization outperformed other models by 0.53% in-sample and 1.11% out-of-sample.
GA-MSSR optimizes forex trading rules for higher returns and reduced risk.
problem Noisy market data affects the consistency and profitability of trading algorithms.
method Optimized trading rules derived from technical indicators using a Genetic Algorithm.
result GA-MSSR achieved superior performance with significant positive returns and reduced risk factors.
We present a solution to an optimal stopping problem for a process with a wide-class of novel dynamics. The dynamics model the support/resistance line concept from financial technical analysis.
High-dimensional data acquired from biological experiments such as next generation sequencing are subject to a number of confounding effects. These effects include both technical effects, such as variation across batches from instrument noise or sample processing, or institution-specific differences in sample acquisiti…
Advanced ML/DL models predict stock prices using technical analysis.
problem Accurately predicting stock prices in a complex market.
method Use of deep learning models for stock price prediction.
result Deep learning models can predict stock prices with high accuracy.
Machine learning models outperform traditional technical analysis in Bitcoin trading.
problem Maximizing profits in the Bitcoin market using trading signals.
method Comparison of machine learning models (LightGBM, LSTM) and technical analysis strategies (EMA, MACD+ADX).
result LSTM model achieved a 65.23% cumulative return over a year, significantly outperforming other strategies.
This paper provides the technical details of gradient flow construction and related problems, which are essential for our construction of Lagrangian torus fibrations for Calabi-Yau hypersurfaces.
Survey on LSTM-based anomaly detection for technical systems.
problem Detect anomalies in technical systems due to complex dynamics.
method Use LSTM networks and other AI techniques to detect anomalies considering temporal and contextual characteristics.
result Demonstrates the potential of LSTM networks and graph-based approaches for anomaly detection.
Lie groups applied to tech progress in economic growth.
problem Understanding the impact of technical progress on economic growth.
method Application of Lie theory and economic modeling.
result Estimation of GDP function for Viet Nam, highlighting tech progress impact.
This paper predicts stock prices using BERT for sentiment analysis and GAN for technical indicators.
problem Predicting stock prices using advanced techniques.
method BERT for sentiment analysis, GAN for technical indicators, comparison with baseline models.
result GAN outperformed baseline models in predicting stock prices.
We propose a new indicator for technical analysis. The indicator emphasizes maximums and minimums in price series with inherent smoothing and has a potential to be useful in both mechanical trading rules and chart pattern analysis.
This paper evaluates LLMs for technical market analysis, finding GPT-4 Turbo and FinGPT outperform passive benchmarks.
problem Evaluating LLMs for technical market analysis in financial markets.
method Structured evaluation of five LLMs (GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, FinGPT) on four tasks: candlestick pattern recognition, directional signal generation, backtesting, and financial report comprehension.
result GPT-4 Turbo and FinGPT outperform passive benchmarks in simulated backtesting, with GPT-4 Turbo achieving the highest annualized return and Sharpe ratio.