Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange

Ismail, Mohd Sabri; Noorani, Mohd Salmi Md; Ismail, Munira; Razak, Fatimah Abdul; Alias, Mohd Almie

doi:10.1016/j.asoc.2020.106422

Cited by 44 publications

(21 citation statements)

References 60 publications

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“…Similarly, Sattarov – (2020) obtain 0.62 accuracy when predicting BTC price using sentiment analysis. Overall, the superiority of the SVM forecast is in line with previous studies which support SVM’s, such as Ismail et al (2020), Rouhani and Abedin (2020) and Mallqui and Fernandes (2019).…”

Section: Resultssupporting

confidence: 88%

“…Relatively few studies used ML to predict the direction of financial assets. A comparative study by Ismail et al (2020) found that SVM produces the best results in predicting the direction on Malaysian stock market. This is also backed by Mallqui and Fernandes (2019), who found that SVM obtained the best results when forecasting BTC's exchange rates, compared to artificial neural networks and recurrent neural networks.…”

Section: Sentiment Analysis Cryptocurrency and Machine Learningmentioning

confidence: 99%

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Predicting bitcoin price movements using sentiment analysis: a machine learning approach

Gurrib

Kamalov

2021

SEF

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Purpose Cryptocurrencies such as Bitcoin (BTC) attracted a lot of attention in recent months due to their unprecedented price fluctuations. This paper aims to propose a new method for predicting the direction of BTC price using linear discriminant analysis (LDA) together with sentiment analysis. Design/methodology/approach Concretely, the authors train an LDA-based classifier that uses the current BTC price information and BTC news announcements headlines to forecast the next-day direction of BTC prices. The authors compare the results with a Support Vector Machine (SVM) model and random guess approach. The use of BTC price information and news announcements related to crypto enables us to value the importance of these different sources and types of information. Findings Relative to the LDA results, the SVM model was more accurate in predicting BTC next day’s price movement. All models yielded better forecasts of an increase in tomorrow’s BTC price compared to forecasting a decrease in the crypto price. The inclusion of news sentiment resulted in the highest forecast accuracy of 0.585 on the test data, which is superior to a random guess. The LDA (SVM) model with asset specific (news sentiment and asset specific) input features ranked first within their respective model classifiers, suggesting both BTC news sentiment and asset specific are prized factors in predicting tomorrow’s price direction. Originality/value To the best of the authors’ knowledge, this is the first study to analyze the potential effect of crypto-related sentiment and BTC specific news on BTC’s price using LDA and sentiment analysis.

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Section: Resultssupporting

confidence: 88%

Section: Sentiment Analysis Cryptocurrency and Machine Learningmentioning

confidence: 99%

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

Gurrib

Kamalov

2021

SEF

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“…Additionally, PH is applied with machine learning to predict the movement of financial data. Such a task has been done in Ismail et al [40] by using PH and machine learning methods (logistic regression, neural network, support vector machine, and random forest) to predict the next-day direction of the Kuala Lumpur Composite Index (KLCI). Moreover, Baitinger and Flegel [41] also introduced PH to produce microstructural predictors, where these predictors are combined with machine learning and statistical factor extraction methods to predict asset returns.…”

Section: Literature Reviewmentioning

confidence: 99%

Early Warning Signals of Financial Crises Using Persistent Homology and Critical Slowing Down: Evidence From Different Correlation Tests

Ismail

Noorani²,

Ismail³

et al. 2022

Front. Appl. Math. Stat.

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In this study, a new market representation from persistence homology, known as the L1-norm time series, is used and applied independently with three critical slowing down indicators [autocorrelation function at lag 1, variance, and mean for power spectrum (MPS)] to examine two historical financial crises (Dotcom crash and Lehman Brothers bankruptcy) in the US market. The captured signal is the rising trend in the indicator time series, which can be determined by Kendall's tau correlation test. Furthermore, we examined Pearson's and Spearman's rho correlation tests as potential substitutes for Kendall's tau correlation. After that, we determined a correlation threshold and predicted the whole available date. The point of comparison between these correlation tests is to determine which test is significant and consistent in classifying the rising trend. The results of such a comparison will suggest the best test that can classify the observed rising trend and detect early warning signals (EWSs) of impending financial crises. Our outcome shows that the L1-norm time series is more likely to increase before the two financial crises. Kendall's tau, Pearson's, and Spearman's rho correlation tests consistently indicate a significant rising trend in the MPS time series before the two financial crises. Based on the two evaluation scores (the probability of successful anticipation and probability of erroneous anticipation), by using the L1-norm time series with MPS, our result in the whole prediction demonstrated that Spearman's rho correlation (46.15 and 53.85%) obtains the best score as compared to Kendall's tau (42.31 and 57.69%) and Pearson's (40 and 60%) correlations. Therefore, by using Spearman's rho correlation test, L1-norm time series with MPS is shown to be a better way to detect EWSs of US financial crises.

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“…To name a few, some of those include modelling topological features of complex swarm behavior in space and time [64], the discovery of a subgroup of breast cancers [65], the detection and quantification of periodic patterns in chaotic data [66,67] and the understanding of patterns of chaotic attractors in phase space [68]. In financial data analysis, besides what have been mentioned in the introduction, PH is also currently started to be applied in predicting next day direction of stock price movement using machine learning methods [69] and in financial decisions [70]. These demonstrate that PH has huge potential to analyze complex, high dimensional and noisy data, which includes financial data.…”

Section: Given a Rips Complexes Filtration Such Asmentioning

confidence: 99%

Detecting Early Warning Signals of Major Financial Crashes in Bitcoin Using Persistent Homology

2020

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This study explores persistent homology to detect early warning signals of the 2017 and 2019 major financial crashes in Bitcoin. Sliding window is used to obtain point cloud datasets from a multidimensional time series (Bitcoin, Ethereum, Litecoin and Ripple). We apply persistent homology to quantify transient loops that appear in multiscale topological spaces, which associated on each point cloud dataset and encode the quantified information in a persistence landscape. Temporal changes in persistence landscapes are measured via their 1-norms. Consequently, a new representative is attained, called 1-norms time series. The 1-norms is associated with indicators: autocorrelation function at lag 1, variance and mean power spectrum at low frequencies to detect the signals. By using Kendall's tau correlation and significance test, significant rising trend events that occur before major financial crashes in Bitcoin are defined as the signals. A threshold is determined to scan entire data and record all the significant rising trend events. Lastly, we compare 1-norms with residuals time series, which is another representative obtained from de-trending approach. Our result portrays that autocorrelation function at lag 1 and variance of the 1-norms successfully detect early warning signals before the 2017 and 2019 major financial crashes. However, variance of the 1norms is better since it able to signal another 2018 major financial crash. For the residuals, no early warning signals are detected. Hence, persistent homology provides a better representative than de-trending approach. Overall, persistent homology is a promising method to detect early warning signals of major financial crashes in Bitcoin. INDEX TERMS Cryptocurrencies, bitcoin, persistent homology, critical transition, early warning signals.

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Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange

Cited by 44 publications

References 60 publications

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

Predicting bitcoin price movements using sentiment analysis: a machine learning approach

Early Warning Signals of Financial Crises Using Persistent Homology and Critical Slowing Down: Evidence From Different Correlation Tests

Detecting Early Warning Signals of Major Financial Crashes in Bitcoin Using Persistent Homology

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