A fusion model of HMM, ANN and GA for stock market forecasting

Hassan, Md. Rafiul; Nath, Baikunth; Kirley, Michael

doi:10.1016/j.eswa.2006.04.007

Cited by 289 publications

(106 citation statements)

References 20 publications

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“…The survey of the relevant literature showed that there have been many studies for stock market prediction, Many research papers have appeared in the literature using evolutionary computing tools such as genetic algorithm (GA) [2], particle swarm optimization (PSO) [3], and bacterial foraging optimization (BFO) [4] in developing forecasting models. In [5], Hassan et al described a novel time series forecasting tool, their fusion model combines a Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to forecast financial market behavior.…”

Section: Introductionmentioning

confidence: 99%

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A New Particle Swarm Optimization Based Stock Market Prediction Technique

Seidy¹

2016

ijacsa

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Abstract-Over the last years, the average person's interest in the stock market has grown dramatically. This demand has doubled with the advancement of technology that has opened in the International stock market, so that nowadays anybody can own stocks, and use many types of software to perform the aspired profit with minimum risk. Consequently, the analysis and prediction of future values and trends of the financial markets have got more attention, and due to large applications in different business transactions, stock market prediction has become a critical topic of research. In this paper, our earlier presented particle swarm optimization with center of mass technique (PSOCoM) is applied to the task of training an adaptive linear combiner to form a new stock market prediction model. This prediction model is used with some common indicators to maximize the return and minimize the risk for the stock market. The experimental results show that the proposed technique is superior than the other PSO based models according to the prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

“…In [5], Hassan et al described a novel time series forecasting tool, their fusion model combines a Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to forecast financial market behavior.…”

Section: Introductionmentioning

confidence: 99%

A New Particle Swarm Optimization Based Stock Market Prediction Technique

Seidy¹

2016

ijacsa

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“…These techniques are implemented for classification and prediction in the financial sector. Since the 1990s, researchers from the computer science sector have applied AI techniques such as Artificial Neural Network (ANN) (Lawrence, 1997;Naeini et al, 2010), Expert Systems (ES) (Lee and Jo, 1999), Support Vector Machine (SVM) (Kim, 2003), Hidden Markov Model (HMM) (Hassan and Nath, 2005) and Genetic Algorithms (GA) (Hassan et al, 2007) to learn trend patterns for prediction, and have proven the usefulness of these techniques in learning the trends patterns. In this paper, we combine Dynamic Time Warping (DTW) with other methods to predict the trend of currency exchange rates using one technical indicator Linear Regression Line (LRL), which is novel to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Forex prediction engine: framework, modelling techniques and implementations

Tiong

Ngo

Lee

2016

IJCSE

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This paper presents foreign exchange (Forex) prediction engine that included framework, modelling techniques and implementations, to support the needs of financial organizations or individual investors. In the financial sector, Forex prediction is considered to be a complex field, based on the noise of exchange rates as the major challenge. However, there are some financial instruments that are available to guide the individual for future investments. We propose a promising Forex prediction engine using historical Forex data, to extract a pattern movement over a period of time series using Linear Regression Line (LRL) technique and the proposed segmentation algorithm. Subsequently, Artificial Neural Network (ANN) algorithm is applied to classify unique groups of uptrend and downtrend patterns. Dynamic Time Warping (DTW) algorithm is implemented through brute force to identify the similarity trend and the matched result that is used to predict the trend movement for next day. This research reveals the structure of Forex prediction engine by the description of an integrated framework using technical analysis method and machine learning algorithm. The experimental results of AUD -USD and EUR -USD currencies demonstrated 71% of accuracy and its predictions are reliable.

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“…The importance of further developments in soft computing led to several papers devoted to forecasting stock indexes using techniques such as support vector machines (e.g., Chiu & Chen, 2009;Huang, Nakamori, & Wang, 2005;Kim, 2003;Pai & Lin, 2005;Wen et al, 2010), fuzzy systems (e.g., Chang & Liu, 2008;Chang, Wang, & Liu, 2007;Huang & Yu, 2005;Wang, 2003), genetic algorithms (e.g., Chen et al, 2009;Oh, Kim, & Min, 2005;Oh, Kim, Min, & Lee, 2006;Potvin, Soriano, & Vallee, 2004) and mixed methods (e.g., Armano, Marchesi, & Murru, 2005;Armano, Murru, & Roli, 2002;Hassan, Nath, & Kirley, 2007;Kwon & Moon, 2007;Leigh, Purvis, & Ragusa, 2002).…”

Section: Introductionmentioning

confidence: 99%

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

Rodríguez-González

García-Crespo

Colomo‐Palacios

et al. 2011

Expert Systems with Applications

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a b s t r a c tStock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For short term predictions, the technical indicator relative strength indicator (RSI) has been published in many papers and used worldwide.CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using arti ficial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two sce narios. In the first, it will predict a market in our case, the Spanish IBEX 35 stock market. In the second, it will predict single company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index.

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A fusion model of HMM, ANN and GA for stock market forecasting

Cited by 289 publications

References 20 publications

A New Particle Swarm Optimization Based Stock Market Prediction Technique

A New Particle Swarm Optimization Based Stock Market Prediction Technique

Forex prediction engine: framework, modelling techniques and implementations

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

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