Classification of intraday S&amp;P500 returns with a Random Forest

Lohrmann, Christoph; Luukka, Pasi

doi:10.1016/j.ijforecast.2018.08.004

Cited by 60 publications

(32 citation statements)

References 55 publications

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“…For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. These results are in agreement with other research that shows RFs to have a high stock price predictive accuracy (Ballings et al 2015;Basak et al 2019;Lohrmann and Luukka 2019;Weng et al 2018;Ampomah et al 2020). The positive predictive values and negative predictive values indicate that there is little asymmetry between the up and down prediction classifications.…”

Section: Discussionsupporting

confidence: 93%

“…The research in this paper shows that RFs produce more accurate clean energy stock price direction forecasts than logit models. These results add to a growing body of research that shows machine learning methods like RFs have considerable stock price direction predictive performance (Ballings et al 2015;Basak et al 2019;Lohrmann and Luukka 2019;Weng et al 2018;Ampomah et al 2020). None of these studies, however, consider clean energy stock prices.…”

Section: Discussionmentioning

confidence: 65%

“…More recently, momentum and economic or psychology behavior factors have been identified as possible sources of stock price predictability (Gray and Vogel 2016;Lo et al 2000;Christoffersen and Diebold 2006;Moskowitz et al 2012). In addition, the existing literature on stock price predictability shows that predicting stock price direction is more successful than predicting actual stock prices (Basak et al 2019;Leung et al 2000;Nyberg 2011;Nyberg and Pönkä 2016;Pönkä 2016;Ballings et al 2015;Lohrmann and Luukka 2019).…”

Section: Introductionmentioning

confidence: 99%

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A Random Forests Approach to Predicting Clean Energy Stock Prices

Sadorsky

2021

JRFM

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Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

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A Random Forests Approach to Predicting Clean Energy Stock Prices

Sadorsky

2021

JRFM

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“…However, both centralized and decentralized management have a number of advantages and disadvantages [10][11][12][13]. This is one of the main contradictions of cryptocurrencies: the openness of the system, its supranationality and a high level of automation imply the absence of centralized control over its issue and movement of digital currency from any state [18][19][20][21][22].…”

Section: Literature Reviewmentioning

confidence: 99%

Cryptocurrency Open Innovation Payment System: Comparative Analysis of Existing Cryptocurrencies

Титов

Uandykova

Litvishko

et al. 2021

Journal of Open Innovation: Technology, Market, and Complexity

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This study was conducted to evaluate the implementation of an open innovation cryptocurrency financial system using a statistical approach. The data array reflects the actual speed of the cryptocurrency system, expressed in transactions per second (TPS), taken as the average annual speed. The article offers a comprehensive approach for choosing the optimal cryptocurrency financial system. The final analysis shows that the reasons for the adoption of the cryptocurrency financial system are practicality and convenience, as well as efficient transaction time, faster payment, and simplicity of the payment process. The impact of social factors, expected efforts, and conditions of assistance on the attitude to the cryptocurrency financial system were evaluated. In addition, social factors that have a significant impact on the implementation of the cryptocurrency financial system were identified.

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“…Many machine techniques have already been applied to forecast the stock market. For example, logistic regression (LR) and Neural Network (NNs) [21] [22], deep neural networks (DNN) [23], decision trees (DTs) [24][25] [26], support vector machines (SVM) [27], k-nearest neighbors (KNN) [28], random forests (RFs) [29] [30] and long shortterm memory networks (LSTMs) [31] [32] have been used to predict the stock market. Moreover, many authors try to improve the prediction ability by combining machine learning models with other methods.…”

Section: Introductionmentioning

confidence: 99%

Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

et al. 2021

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Stock market forecasting is a knotty challenging task due to the highly noisy, nonparametric, complex and chaotic nature of the stock price time series. With a simple eight-trigram feature engineering scheme of the inter-day candlestick patterns, we construct a novel ensemble machine learning framework for daily stock pattern prediction, combining traditional candlestick charting with the latest artificial intelligence methods. Several machine learning techniques, including deep learning methods, are applied to stock data to predict the direction of the closing price. This framework can give a suitable machine learning prediction method for each pattern based on the trained results. The investment strategy is constructed according to the ensemble machine learning techniques. Empirical results for the period of 2000 to 2017 of China's stock market confirm that our feature engineering has effective predictive power, with a prediction accuracy of more than 60% for some trend patterns. Various measures such as big data, feature standardization, and elimination of abnormal data can effectively solve the problem of data noise. An investment strategy based on our forecasting framework excels in both individual stock and portfolio performance theoretically. Transaction costs have a significant impact on investment. Additional technical indicators can improve the forecast accuracy to varying degrees. Technical indicators, especially the momentum indicators, can improve forecasting accuracy in most cases.

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Classification of intraday S&P500 returns with a Random Forest

Cited by 60 publications

References 55 publications

A Random Forests Approach to Predicting Clean Energy Stock Prices

A Random Forests Approach to Predicting Clean Energy Stock Prices

Cryptocurrency Open Innovation Payment System: Comparative Analysis of Existing Cryptocurrencies

Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme

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