A hybrid stock trading framework integrating technical analysis with machine learning techniques

Dash, Rajashree; Dash, P.K.

doi:10.1016/j.jfds.2016.03.002

Cited by 165 publications

(84 citation statements)

References 19 publications

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“…Los primeros trabajan aprendizaje inductivo de descripciones simbóli-cas, mientras que los segundos se centran en los métodos de reconocimiento de patrones o en la estadística. En los últimos años, el uso del aprendizaje máquina se ha extendido con rapidez [38], se ven aplicaciones en dominios como detección de fraudes, sistemas de recomendación [39], detección de spam [40], predicciones financieras [41], comercio y mercadeo [42], [43], entre otros.…”

Section: Machine Learningunclassified

Big Data: una exploración de investigaciones, tecnologías y casos de aplicación

Hernandez-Leal

Méndez

Cadavid

2017

TecnoL.

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Big Data se ha convertido en una tendencia a nivel mundial y aunque aún no cuenta con un concepto científico o académico consensuado, se augura cada día mayor crecimiento del mercado que lo envuelve y de las áreas de investigación asociadas. En este artículo se reporta una exploración de literatura sobre Big Data, que comprende un estado del arte de las técnicas y tecnologías asociadas a Big Data, las cuales abarcan captura, procesamiento, análisis y visualización de datos. Se exploran también las características, fortalezas, debilidades y oportunidades de algunas aplicaciones y modelos que incluyen Big Data, principalmente para el soporte al modelado de datos, análisis y minería de datos. Asimismo, se introducen algunas de las tendencias futuras para el desarrollo de Big Data por medio de la definición de aspectos básicos, alcance e importancia de cada una. La metodología empleada para la exploración incluye la aplicación de dos estrategias, una primera corresponde a un análisis cienciométrico; y la segunda, una categorización de documentos por medio de una herramienta web de apoyo a los procesos de revisión literaria. Como resultados se obtiene una síntesis y conclusiones en torno a la temática y se plantean posibles escenarios para trabajos investigativos en el campo de dominio.

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Section: Machine Learningunclassified

Big Data: una exploración de investigaciones, tecnologías y casos de aplicación

Hernandez-Leal

Méndez

Cadavid

2017

TecnoL.

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“…To interpolate more accurately than traditional methods such as linear interpolation, there is an approach based on machine learning [3][4][5][6][7][8][9]. e studies on time series data using machine learning have been largely conducted for forecasting weather and stock price [10][11][12][13][14]. ere are several prior studies related to the various applications of atmospheric data which commonly use machine learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Kim

Park²,

Choi³

et al. 2020

Computational Intelligence and Neuroscience

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We propose three quality control (QC) techniques using machine learning that depend on the type of input data used for training. These include QC based on time series of a single weather element, QC based on time series in conjunction with other weather elements, and QC using spatiotemporal characteristics. We performed machine learning-based QC on each weather element of atmospheric data, such as temperature, acquired from seven types of IoT sensors and applied machine learning algorithms, such as support vector regression, on data with errors to make meaningful estimates from them. By using the root mean squared error (RMSE), we evaluated the performance of the proposed techniques. As a result, the QC done in conjunction with other weather elements had 0.14% lower RMSE on average than QC conducted with only a single weather element. In the case of QC with spatiotemporal characteristic considerations, the QC done via training with AWS data showed performance with 17% lower RMSE than QC done with only raw data.

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“…Moreover, in conjunction with price charts, TIs can provide leading indications of future price movements, offering additional explanatory power to models forecasting financial time series. Numerous studies, such as [12][13][14], have extensively demonstrated the explanatory power of TIs across stock markets. Given that technicals result from universally exhibited behavioural biases among investors, such studies motivate our examination of TIs with the DAM.…”

Section: Introductionmentioning

confidence: 99%

Introducing Technical Indicators to Electricity Price Forecasting: A Feature Engineering Study for Linear, Ensemble, and Deep Machine Learning Models

et al. 2019

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Day-ahead electricity market (DAM) volatility and price forecast errors have grown in recent years. Changing market conditions, epitomised by increasing renewable energy production and rising intraday market trading, have spurred this growth. If forecast accuracies of DAM prices are to improve, new features capable of capturing the effects of technical or fundamental price drivers must be identified. In this paper, we focus on identifying/engineering technical features capable of capturing the behavioural biases of DAM traders. Technical indicators (TIs), such as Bollinger Bands, Momentum indicators, or exponential moving averages, are widely used across financial markets to identify behavioural biases. To date, TIs have never been applied to the forecasting of DAM prices. We demonstrate how the simple inclusion of TI features in DAM forecasting can significantly boost the regression accuracies of machine learning models; reducing the root mean squared errors of linear, ensemble, and deep model forecasts by up to 4.50%, 5.42%, and 4.09%, respectively. Moreover, tailored TIs are identified for each of these models, highlighting the added explanatory power offered by technical features.

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A hybrid stock trading framework integrating technical analysis with machine learning techniques

Cited by 165 publications

References 19 publications

Big Data: una exploración de investigaciones, tecnologías y casos de aplicación

Big Data: una exploración de investigaciones, tecnologías y casos de aplicación

Spatiotemporal Approaches for Quality Control and Error Correction of Atmospheric Data through Machine Learning

Introducing Technical Indicators to Electricity Price Forecasting: A Feature Engineering Study for Linear, Ensemble, and Deep Machine Learning Models

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