Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection

Göçken, Mustafa; Özçalıcı, Mehmet; Boru, Aslı; Dosdoğru, Ayşe Tuğba

doi:10.1007/s00521-017-3089-2

Cited by 42 publications

(20 citation statements)

References 34 publications

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“…The overall related work is described in Table 1. Most of the work considered stock price prediction [25], [18], [56], [31], [46]. There is limited work on stock crisis prediction.…”

Section: Related Workmentioning

confidence: 99%

Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market

Naik

Mohan

2021

IEEE Access

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A stock market crash is a drop in stock prices more than 10% across the major indices. Stock crisis prediction is a difficult task due to more volatility in the stock market. Stock price sell-offs are due to various reasons such as company earnings, geopolitical tension, financial crisis, and pandemic situations. Crisis prediction is a challenging task for researchers and investors. We proposed a stock crisis prediction model based on the Hybrid Feature Selection (HFS) technique. First, we proposed the HFS algorithm to removes the irrelevant financial parameters features of stock. The second is the Naive Bayes method is considered to classify the strong fundamental stock. The third is we have used the Relative Strength Index (RSI) method to find a bubble in stock price. The fourth is we have used moving average statistics to identify the crisis point in stock prices. The fifth is stock crisis prediction based on Extreme Gradient Boosting (XGBoost) and Deep Neural Network(DNN) regression method. The performance of the model is evaluated based on Mean Squared Error(MSE), Mean Absolute Error(MAE), and Root Mean Square Error(RMSE). HFS based XGBoost method was performed better than HFS based DNN method for predicting the stock crisis. The experiments considered the Indian datasets to carried out the task. In the future, the researchers can explore other technical indicators to predict the crisis point. There is more scope to improve and finetune the XGBoost method with a different optimizer.

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“…The overall related work is described in Table 1. Most of the work considered stock price prediction [25], [18], [56], [31], [46]. There is limited work on stock crisis prediction.…”

Section: Related Workmentioning

confidence: 99%

Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market

Naik

Mohan

2021

IEEE Access

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“…Compared to supervised/unsupervised learning approaches, the difference of our approach is that we generate a trading strategy rather than only stock price prediction as in existing research studies [10,19]. Stock price prediction definitely is a very important task, but eventually we need to build a strategy to decide what to buy and what to sell in the market that requires a further research step.…”

Section: Our Contributionmentioning

confidence: 99%

Reinforcement Learning in Stock Trading

Dang

2019

Advances in Intelligent Systems and Computing

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Using machine learning techniques in financial markets, particularly in stock trading, attracts a lot of attention from both academia and practitioners in recent years. Researchers have studied different supervised and unsupervised learning techniques to either predict stock price movement or make decisions in the market. In this paper we study the usage of reinforcement learning techniques in stock trading. We evaluate the approach on real-world stock dataset. We compare the deep reinforcement learning approach with state-of-the-art supervised deep learning prediction in real-world data. Given the nature of the market where the true parameters will never be revealed, we believe that the reinforcement learning has a lot of potential in decision-making for stock trading.

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“…The goal of this linear regression is to explore the relation between the input feature with that of the target Value and give us a continuous Valued output for the given unknown data. Machine Learning means teaching the machine how to find patterns in data [5]. To extract those and manipulate them.…”

Section: A Linear Regression Fig 1 General Data Flow Diagram Of a Lmentioning

confidence: 99%

Stock Market Prices Prediction using Random Forest and Extra Tree Regression

Polamuri¹,

Srinivas²,

Mohan³

2019

IJRTE

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Prediction of Stock price is now a day’s an existing and interesting research area in financial and academic sectors to know the scale of economies. There did not exists any significant set of rules to estimate and predict the scale of share in the stock exchange. Many evolutionary technologies are existing such as technical, fundamental, time, statistical and series analysis which help us to attempt the prediction process, but none of the methods are proved as reliable and accurate tool to the society in the estimation of stock exchange or share market scales. Here in this paper we attempted to do innovative work through Machine Learning approach to predict or sense the behaviour tracking of the stock market sensex. Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and define the activity between the exchanges the securities between the buyers and sellers. We predicted the price of the stock based on the closing value and stock price. An algorithm with high accuracy we do the process of comparison for the accuracy of each of the model and finally is considered as better algorithm for predicting stock price. As share market is a vague domain we cannot predict the conditions occur, and also share market can never be predicted, this job can be done easily and technically through this work and the main aim of this paper is to apply algorithms in Machine Learning in predicting the stock prices.

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Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection

Cited by 42 publications

References 34 publications

Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market

Novel Stock Crisis Prediction Technique—A Study on Indian Stock Market

Reinforcement Learning in Stock Trading

Stock Market Prices Prediction using Random Forest and Extra Tree Regression

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