Stock Market Trading Rules Discovery Based on Biclustering Method

Xue, Yun; Liu, Zhiwen; Luo, Jie; Ma, Zhihao; Zhang, Meizhen; Hu, Xiaohui; Kuang, Qiuhua

doi:10.1155/2015/849286

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…Iyer et al (2015) have proposed a stock market prediction device using digital signal processing models. Xue et al (2015) have introduced stock market trading rules based on the biclustering method. Guo et al (2015) have proposed a stock market forecasting model combining a two-directional two-dimensional principal component analysis and a radial basis function neural network.…”

Section: Review Of Literaturementioning

confidence: 99%

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

2019

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This paper explores the power of stock trading trend using an integrated New Threshold Fuzzy Cognitive Maps (NTFCMs) Markov chain model. This new model captures the positive as well as the negative jumps and predicts the trend for different stocks over 4 months which follow an uptrend, downtrend and a mixed trend. The mean absolute per cent error (MAPE) tolerance limits, the root mean square error (RMSE) tolerance limits are determined for various stock indices over a multi-timeframe period and observed for the existing methods lying within the defined limits. The results show for every 'n' number of predictions made, the predicted close value of the day's stock price was within tolerance limit with 0 % error and with 100% accuracy in predicting the future trend.

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Section: Review Of Literaturementioning

confidence: 99%

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

2019

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“…Xue use a biclustering algorithm to find local patterns in the quantized historical data. A Biclustering-Based Intelligent System could find different patterns which contain a subset of technical indicators with different periodic parameters [31].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Visual Analysis for Bank Non-Performing Loans and Government Debt Distress

Liu

Kim

2019

Sustainability

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This article visualizes bank non-performing loans (NPLs) and government debt distress data integration and an outcome classification after the outbreak of European sovereign debt. Linear and functional principal component analysis (FPCA) and biclustering are used to show the clustering pattern of NPLs and government debt for 25 EU and BRICS countries (Brazil, Russia, India, China and South Africa) during the period of 2006 to 2017 through high-dimensional visualizations. The results demonstrate that the government debt markets of EU countries experienced a similar trend in terms of NPLs, with a similar size of NPLs across debt markets. Through visualization, we find that the government debt and NPLs of EU and BRICS countries increased drastically after the crisis, and crisis countries are contagious. However, the impact of the Greek debt crisis is lower for non-crisis countries, because the debt markets of these countries are decoupled from the Greek market. We also find that sovereign debtors in the EU countries have much closer fiscal linkages than BRICS countries. The level of crisis in the EU countries will be higher than that in the BRICS countries if crisis is driven by the common shocks of macroeconomic fundamentals.

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“…Huang () applied a biclustering algorithm to explore comovement in a small set of currencies across non‐consecutive time periods. Xue et al () used a biclustering method on daily stock prices for learning trading rules. To be effective in practice, it is most useful to bicluster stocks within a moving time window and to utilize interdependencies within and between the time windows for financial applications.…”

Section: Introductionmentioning

confidence: 99%

Multiple day biclustering of high‐frequency financial time series

Liu

Zou

Равишанкер

2018

Stat

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With recent technological advances, high-frequency transaction-by-transaction data are widely available to investors and researchers. To explore the microstructure of variability of stock prices on transaction-level intra-day data and to dynamically study patterns of comovement over multiple trading days, we propose a multiple day time series biclustering algorithm (CC-MDTSB) that extends the time series biclustering algorithm (CC-TSB). For identifying biclusters within each trading day, our algorithm provides a faster alternative to the random replacement method in the CC-TSB algorithm. Moreover, our algorithm does not require prespecification of the number of biclusters for each trading day. Instead, we set a threshold on the number of stocks within the biclusters to yield an adaptive stopping criterion for multiple day analysis. An analysis of the biclusters determined over multiple trading days enables us to study the dynamic behaviour of stocks over time. We effectively estimate the comovement probability of each m-tuple of stocks conditional on the other stocks within the dynamic biclusters and propose a method to forecast comovement days using a nonparametric double exponential smoothing procedure.Step 4. Identify the next bicluster: Eliminate the stocks in A 4 from A 1 and repeat steps 2-3. Let A 5 denote the next identified bicluster.Step 5. Stop search for biclusters: Let`D 1, : : : , L denote the number of identified biclusters for a trading day. Let S`denote the number of stocks in the`th bicluster within the trading day. Let U denote the total number of stocks (out of the S stocks) that belong in these L biclusters. If U Äˇ, continue to repeat steps 1-4. Otherwise, stop identifying biclusters for that trading day. We repeat this process for each of the T trading days by repeating steps 1-5.Parameter selection. The user-defined threshold˛is the tolerance of insertion or deletion of the rows or columns. When˛is large (small), the algorithm tends to give large (small) biclusters. We have used˛D 1.2 in our analysis, for which the distributions of the number and sizes of the biclusters are provided in Section 6.1. Â, the threshold Stat 2018; 7: e176 Comovement probability. Our CC-MDTSB algorithm calculates the comovement probability for any m-tuple of stocks. Table I shows the top 15 pairs of stocks with the largest comovement probability, that is, the probability of trading days for which the two stocks are in the same bicluster (i.e., are comoving) in 2013. For example, the estimated comovement probability of two stocks in the (oil) energy sector, CVX (Chevron) and XOM (Exxon Mobil), Stat 2018; 7: e176 9 of 17The 1-minute averaged log returns incorporate the volatility in the stock prices. The volatility-adjusted log returns represent the risk-adjusted asset log returns relative to the amount of risk the investment has taken over a given period Stat 2018; 7: e176 (wileyonlinelibrary.com) https://doi.

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Stock Market Trading Rules Discovery Based on Biclustering Method

Cited by 7 publications

References 17 publications

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

Sustainable Visual Analysis for Bank Non-Performing Loans and Government Debt Distress

Multiple day biclustering of high‐frequency financial time series

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