Stock Trading with Random Forests, Trend Detection Tests and Force Index Volume Indicators

Ładyżyński, Piotr; Żbikowski, Kamil; Grzegorzewski, Przemysław

doi:10.1007/978-3-642-38610-7_41

Cited by 27 publications

(11 citation statements)

References 6 publications

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“…The F1 of XGB is significantly greater than that of all other trading algorithms. 1.0000 1.0000 1.0000 0.9999 GRU 1.0000 1.0000 1.0000 1.0000 0.9975 CART 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 NB 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 RF 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0270 0.0000 LR 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5428 SVM 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3125 0.0000 0.0000 0.0000 XGB 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0002 0.3954 0.0000 (5) Through the hypothesis test analysis of H5a and H5b, we can obtain p value<2.2e-16. So, there are statistically significant differences between the AUC of all trading algorithms.…”

Section: Comparative Analysis Of Performance Of Different Trading Strmentioning

confidence: 99%

“…Over the years, traditional ML methods have shown strong ability in trend prediction of stock prices [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In recent years, artificial intelligence computing methods represented by DNN have made a series of major breakthroughs in the fields of Natural Language Processing, image classification, voice translation, and so on.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

Yuan

et al. 2019

Mathematical Problems in Engineering

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According to the forecast of stock price trends, investors trade stocks. In recent years, many researchers focus on adopting machine learning (ML) algorithms to predict stock price trends. However, their studies were carried out on small stock datasets with limited features, short backtesting period, and no consideration of transaction cost. And their experimental results lack statistical significance test. In this paper, on large-scale stock datasets, we synthetically evaluate various ML algorithms and observe the daily trading performance of stocks under transaction cost and no transaction cost. Particularly, we use two large datasets of 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) from 2010 to 2017 and compare six traditional ML algorithms and six advanced deep neural network (DNN) models on these two datasets, respectively. The experimental results demonstrate that traditional ML algorithms have a better performance in most of the directional evaluation indicators. Unexpectedly, the performance of some traditional ML algorithms is not much worse than that of the best DNN models without considering the transaction cost. Moreover, the trading performance of all ML algorithms is sensitive to the changes of transaction cost. Compared with the traditional ML algorithms, DNN models have better performance considering transaction cost. Meanwhile, the impact of transparent transaction cost and implicit transaction cost on trading performance are different. Our conclusions are significant to choose the best algorithm for stock trading in different markets.

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Section: Comparative Analysis Of Performance Of Different Trading Strmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

Yuan

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Our analysis is dominated by whole market prediction [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] with a number of instances of individual equity forecasts [19][20][21][22][23][24][25][26][27]. The two have so much in common that analytically they belong in the same category (price series forecasting), even though from investment perspective they do not (allocation into specific market(s) is very different from individual stock picking).…”

Section: Thematic Reviewmentioning

confidence: 99%

A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life

Buczynski

Cuzzolin

Sahakian

2021

Int J Data Sci Anal

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The numerical nature of financial markets makes market forecasting and portfolio construction a good use case for machine learning (ML), a branch of artificial intelligence (AI). Over the past two decades, a number of academics worldwide (mostly from the field of computer science) produced a sizeable body of experimental research. Many publications claim highly accurate forecasts or highly profitable investment strategies. At the same time, the picture of real-world AI-driven investments is ambiguous and conspicuously lacking in high-profile success cases (while it is not lacking in high-profile failures). We conducted a literature review of 27 academic experiments spanning over two decades and contrasted them with real-life examples of machine learning-driven funds to try to explain this apparent contradiction. The specific contributions our article will make are as follows: (1) A comprehensive, thematic review (quantitative and qualitative) of multiple academic experiments from the investment management perspective. (2) A critical evaluation of running multiple versions of the same models in parallel and disclosing the best-performing ones only (“cherry-picking”). (3) Recommendations on how to approach future experiments so that their outcomes are unambiguously measurable and useful for the investment industry. (4) An in-depth comparison of real-life cases of ML-driven funds versus academic experiments. We will discuss whether present-day ML algorithms could make feasible and profitable investments in the equity markets.

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“…Moreover, the NN follows a nonparametric approach, as it reduces the complexity of the training. Other models such as support vector machines (SVMs) with handcrafted features [4,5], random forests [6,7], ensemble of the same classifiers [8], integration of different classifiers [9], and the most advanced and recent one is the deep learning networks [10,11] are the evidence of the progress in stock price forecasting problem. In continuation, the other recent works Chong et al [12], Gudelek et al [13], Hiransha et al [14], and Barra et al [15], depict the research focuses on exploring various network framework and methods in stock market domain.…”

Section: Introductionmentioning

confidence: 99%

A Repairing Artificial Neural Network Model-Based Stock Price Prediction

Bai¹,

Thanabal²

2021

IJCIS

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Predicting the stock price movements based on quantitative market data modeling is an open problem ever. In stock price prediction, simultaneous achievement of higher accuracy and the fastest prediction becomes a challenging problem due to the hidden information found in raw data. Various prediction models based on machine learning algorithms have been proposed in the literature. In general, these models start with the training phase followed by the testing phase. In the training phase, the past stock market data are used to learn the patterns toward building a model that would then use to predict future stock prices. The performance of such learning algorithms heavily depends on the quality of the data as well as optimal learning parameters. Among the conventional prediction methods, the use of neural network has greatest research interest because of their advantages of self-organizing, distributed processing, and self-learning behaviors. In this work, dynamic nature of the data is mainly focused. In conventional models the retraining has to be carried out for two cases: the data used for training has higher noise and outliers or model trained without preprocessing; the learned data has to update dynamically for recent changes. In this sense, propose a self-repairing dynamic model called repairing artificial neural network (RANN) that correct such errors effectively. The repairing includes adjusting the prediction model from noise, outliers, removing a data sample, and adjusting an attribute value. Hence, the total reconstruction of the prediction model could be avoided while saving training time. The proposed model is validated with five different real-time stock market data and the results are quantified to analyze its performance.

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Stock Trading with Random Forests, Trend Detection Tests and Force Index Volume Indicators

Cited by 27 publications

References 6 publications

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life

A Repairing Artificial Neural Network Model-Based Stock Price Prediction

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