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Improving stock trading decisions based on pattern recognition using machine learning technology

https://doi.org/10.1371/journal.pone.0255558

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Encyclopedia of Candlestick Charts

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Stock price prediction based on deep neural networks

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Using percentage accuracy to measure neural network predictions in Stock Market movements

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A formal approach to candlestick pattern classification in financial time series

inferredcites →https://doi.org/10.1016/j.asoc.2019.105747openalex-snapshot

Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices

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Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches

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Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

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Deep learning with long short-term memory networks for financial market predictions

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An effective application of decision tree to stock trading

inferredcites →https://doi.org/10.1016/j.eswa.2008.11.028openalex-snapshot

Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network

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Using support vector machine with a hybrid feature selection method to the stock trend prediction

inferredcites →https://doi.org/10.1016/j.eswa.2013.12.009openalex-snapshot

Automated trading with performance weighted random forests and seasonality

inferredcites →https://doi.org/10.1016/j.eswa.2014.07.040openalex-snapshot

Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques

inferredcites →https://doi.org/10.1016/j.eswa.2014.10.031openalex-snapshot

Predicting stock market index using fusion of machine learning techniques

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Literature review: Machine learning techniques applied to financial market prediction

inferredcites →https://doi.org/10.1016/j.jbankfin.2005.08.001openalex-snapshot

Candlestick technical trading strategies: Can they create value for investors?

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Trend definition or holding strategy: What determines the profitability of candlestick charting?

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Stock market index prediction using artificial neural network

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A hybrid ARIMA and support vector machines model in stock price forecasting

inferredcites →https://doi.org/10.1016/j.omega.2011.07.008openalex-snapshot

Stock index forecasting based on a hybrid model

inferredcites →https://doi.org/10.1016/j.pacfin.2013.10.006openalex-snapshot

The profitability of candlestick charting in the Taiwan stock market

inferredcites →https://doi.org/10.1016/j.pacfin.2015.10.007openalex-snapshot

Are candlestick trading strategies effective in certain stocks with distinct features?

inferredcites →https://doi.org/10.1016/j.physa.2016.03.081openalex-snapshot

The predictive power of Japanese candlestick charting in Chinese stock market

inferredcites →https://doi.org/10.1016/j.physa.2017.02.072openalex-snapshot

Multidimensional k -nearest neighbor model based on EEMD for financial time series forecasting

inferredcites →https://doi.org/10.1016/s0305-0548(02)00037-0openalex-snapshot

Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index

inferredcites →https://doi.org/10.1016/s0925-2312(03)00372-2openalex-snapshot

Financial time series forecasting using support vector machines

inferredcites →https://doi.org/10.1016/s0957-4174(00)00027-0openalex-snapshot

Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1080/00207543.2016.1237785openalex-snapshot

Bayesian regularisation neural network based on artificial intelligence optimisation

inferredcites →https://doi.org/10.1080/135048698334637openalex-snapshot

The predictive power of price patterns

inferredcites →https://doi.org/10.1080/14697688.2013.768774openalex-snapshot

The intra-day performance of market timing strategies and trading systems based on Japanese candlesticks

inferredcites →https://doi.org/10.1109/access.2018.2868970openalex-snapshot

Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network

inferredcites →https://doi.org/10.1109/ijcnn.2017.7966014openalex-snapshot

Restricted Boltzmann machine based stock market trend prediction

inferredcites →https://doi.org/10.1111/j.1467-6419.2007.00519.xopenalex-snapshot

WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS?

inferredcites →https://doi.org/10.1111/j.1540-6261.1970.tb00518.xopenalex-snapshot

EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*

inferredcites →https://doi.org/10.1371/journal.pone.0180944openalex-snapshot

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

inferredcites →https://doi.org/10.1371/journal.pone.0211402openalex-snapshot

Financial time series forecasting using twin support vector regression

inferredcites →https://doi.org/10.1371/journal.pone.0227222openalex-snapshot

Forecasting stock prices with long-short term memory neural network based on attention mechanism

inferredcites →https://doi.org/10.2307/3666289openalex-snapshot

Market Efficiency and the Returns to Technical Analysis

inferredcites →https://doi.org/10.3905/jod.2005.580514openalex-snapshot

Performance of Candlestick Analysis on Intraday Futures Data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Futuretesting Quantitative Strategies

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Creating a Customized Dataset for Financial Pattern Recognition in Deep Learning

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Analysis of Frequent Trading Effects of Various Machine Learning Models

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CPC-SAX: Data mining of financial chart patterns with symbolic aggregate approXimation and instance-based multilabel classification

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Predictive Analysis of Stock Market Trends Using Support Vector Machines and Neural Networks

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Stock Trend Analysis using Time Series Techniques & Deep Neural Network with Data Visualization

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Enhancing Financial Trading Strategies with Pattern Recognition: A Systematic Literature Review of Methods, Trend and Challenge

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Stock Market Forecasting: An Overview of Multi-Factor Approach Combining Technical Indicators, Support-Resistance Levels, and Chart Patterns

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Deep Ensemble Model for Stock Price Forecasting Using Technical Indicators and Bayesian Optimization

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Financial Pattern Recognition Using Feature Extraction and Classification

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Quantitative study of candlestick pattern & identifying candlestick patterns using deep learning for the Indian stock market

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