Stock market prediction using Hidden Markov Model

Somani, Poonam; Talele, Shreyas; Sawant, Suraj

doi:10.1109/itaic.2014.7065011

Cited by 27 publications

(14 citation statements)

References 5 publications

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“…We introduce now a brief overview of the Markov model theory needed to understand the proposed enhanced of the LSTM prediction system. As reported in Reference [15], the Markov Model based approaches for financial time-series forecasting shows promising results. In Reference [15], for instance, the authors described a predicting method based on usage of Hidden Markov Model in order to improve the accuracy and a comparison of the existing techniques based on Machine and Deep learning.…”

Section: The Lstms Forecasting Framework: Descriptionmentioning

confidence: 87%

“…As reported in Reference [15], the Markov Model based approaches for financial time-series forecasting shows promising results. In Reference [15], for instance, the authors described a predicting method based on usage of Hidden Markov Model in order to improve the accuracy and a comparison of the existing techniques based on Machine and Deep learning. As is well known, a stochastic process can In Figure 4, the authors report an overall representation of the proposed pipeline (the part devoted to the stock trend and price prediction) in order to show the processing flow we have designed to reach the desired targets: An efficient stock price and trend prediction algorithm.…”

Section: The Lstms Forecasting Framework: Descriptionmentioning

confidence: 87%

“…The financial research area confirmed the fundamental role of probability theory and stochastic analysis in the share price formation. As reported in Reference [15], in the context of stochastic analysis the Markov theory (Markov Chain, Hidden Markov Mode, Markov propriety, etc.) shows very promising results in addressing financial time series forecasting problem.…”

Section: The Lstms Forecasting Framework: Descriptionmentioning

confidence: 99%

“…where θ is heuristic parameter to manage the level of profit, we defined as 0.5 in our experiments. Clearly, in (15) we report the net profit of the trade according to the broker applied commissions. After above evaluation, if all described constraints are satisfied, the proposed algorithm decides to open the trade and then to perform monitoring of the current stock price dynamic.…”

Section: The Trading System Algorithm Blockmentioning

confidence: 99%

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Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System

et al. 2019

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Stock market prediction and trading has attracted the effort of many researchers in several scientific areas because it is a challenging task due to the high complexity of the market. More investors put their effort to the development of a systematic approach, i.e., the so called “Trading System (TS)” for stocks pricing and trend prediction. The introduction of the Trading On-Line (TOL) has significantly improved the overall number of daily transactions on the stock market with the consequent increasing of the market complexity and liquidity. One of the most main consequence of the TOL is the “automatic trading”, i.e., an ad-hoc algorithmic robot able to automatically analyze a lot of financial data with target to open/close several trading operations in such reduced time for increasing the profitability of the trading system. When the number of such automatic operations increase significantly, the trading approach is known as High Frequency Trading (HFT). In this context, recently, the usage of machine learning has improved the robustness of the trading systems including HFT sector. The authors propose an innovative approach based on usage of ad-hoc machine learning approach, starting from historical data analysis, is able to perform careful stock price prediction. The stock price prediction accuracy is further improved by using adaptive correction based on the hypothesis that stock price formation is regulated by Markov stochastic propriety. The validation results applied to such shares and financial instruments confirms the robustness and effectiveness of the proposed automatic trading algorithm.

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Section: The Lstms Forecasting Framework: Descriptionmentioning

confidence: 87%

Section: The Lstms Forecasting Framework: Descriptionmentioning

confidence: 87%

Section: The Lstms Forecasting Framework: Descriptionmentioning

confidence: 99%

Section: The Trading System Algorithm Blockmentioning

confidence: 99%

See 2 more Smart Citations

Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System

et al. 2019

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“…The ANN prediction model was trained using a dataset of Microsoft Corporation for year 2011 [6], which contains open, high, low, adjacent close and volume as input parameters and close price as output parameter (predict value). Hidden Markov Models (HMM)-based solution has been used to predict the stock market fluctuation [7], the implemented algorithm was tested on three different stock indices ICICI, SBI, IDBI. Mean Absolute Percentage Error was used as a metric to evaluate the performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Stock Market using C-means Clustering and Particle Filter

Darwish¹,

Hilal²

2017

IJCA

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In this article, Particle Filter and C-means are used to predict a value of a point in a time series. Similar data in a time-series are grouped using C-means algorithm. Afterward, a number of particle filters are used as sub-predictors. These sub-predictors start from different points, which are the centers of clusters resulted from clustering algorithm. Outputs from all filters were used to obtain Final prediction result. A weighted average method is used to aggregate the outputs of the filters. Particle filters are used in here to model non-Gaussian time series. Benchmark datasets were used to evaluate the proposed algorithm. To measure its prediction performance, the results derived from the proposed model were compared with those of other algorithms. The comparison proved the effectiveness and accuracy of the proposed method.

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