A Historical Data based Ensemble System for Efficient Stock Price Prediction

2019 ◽  
Vol 12 ◽  
Author(s):  
Vijay Kumar Dwivedi ◽  
Manoj Madhava Gore
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Historical Data ◽  
Stock Exchange ◽  
Primary Objective ◽  
Lasso Regression ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Consistent Manner ◽  
Selection Operator

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing least absolute shrinkage and selection operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

Analysis of Gradient Descent Optimization Techniques with Gated Recurrent Unit for Stock Price Prediction: A Case Study on Banking Sector of Nepal Stock Exchange

2019 ◽  
Vol 24 (2) ◽  
pp. 17-21
Author(s):  
Arjun Singh Saud ◽  
Subarna Shakya
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Gradient Descent ◽  
Banking Sector ◽  
Stock Exchange ◽  
Optimization Techniques ◽  
Future Price ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Cost

The stock price is the cost of purchasing a security or stock in a stock exchange. The stock price prediction has been the aim of investors since the beginning of the stock market. It is the act of forecasting the future price of a company's stock. Nowadays, deep learning techniques are widely used for identifying the stock trends from large amounts of past data. This research has experimented two big and robust commercial banks listed in the Nepal Stock Exchange (NEPSE) and compared stock price prediction performance of GRU with three widely used gradient descent optimization techniques: Momentum, RMSProp, and Adam. GRU with Adam is more accurate and consistent approach for predicting stock prices from the present study.

scholarly journals A Robust Predictive Model for Stock Price Forecasting

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Tamal Datta Chaudhuri
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Work ◽  
Efficient Market Hypothesis ◽  
Price Forecasting ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Daily Data ◽  
Functional Forms

Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted accurately. On the other hand, there are propositions that have shown that, if appropriately modelled, stock prices can be predicted fairly accurately. The latter have focused on choice of variables, appropriate functional forms and techniques of forecasting. This work proposes a granular approach to stock price prediction by combining statistical and machine learning methods with some concepts that have been advanced in the literature on technical analysis. The objective of our work is to take 5 minute daily data on stock prices from the National Stock Exchange (NSE) in India and develop a forecasting framework for stock prices. Our contention is that such a granular approach can model the inherent dynamics and can be fine-tuned for immediate forecasting. Six different techniques including three regression-based approaches and three classification-based approaches are applied to model and predict stock price movement of two stocks listed in NSE - Tata Steel and Hero Moto. Extensive results have been provided on the performance of these forecasting techniques for both the stocks.

Evaluating and understanding text-based stock price prediction models

2014 ◽  
Vol 50 (2) ◽  
pp. 426-441 ◽  
Author(s):  
Enric Junqué de Fortuny ◽  
Tom De Smedt ◽  
David Martens ◽  
Walter Daelemans
Keyword(s):  
Stock Price ◽  
Prediction Models ◽  
Stock Price Prediction ◽  
Price Prediction

scholarly journals Analysis of L2 Regularization Hyper Parameter for Stock Price Prediction

2021 ◽  
Vol 26 (1) ◽  
pp. 83-88
Author(s):  
Arjun Singh Saud ◽  
Subarna Shakya
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Work ◽  
Learning Models ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Machine Learning Model ◽  
Regularization Techniques ◽  
Machine Learning Models

Nowadays stock price prediction is an active area of research among machine learning researchers. One of the main problems with machine learning models is overfitting. Regularization techniques are widely used approaches to avoid over-fitted models. L2 regularization is one of the most popular and widely used regularization techniques. Regularization hyperparameter (ʎ) is one key parameter to be optimized for a well-generalized machine learning model. Hyperparameters can’t be learned by machine learning models during the learning process. We need to find their optimal value through experiments. This research work analyzed the L2 regularization hyperparameter used with a gated recurrent unit (GRU) network for stock price prediction. We experimented with five stocks from the Nepal Stock Exchange (NEPSE) and observed that stock price can be predicted with lower mean squared errors (MSEs) when the value of ʎ was around 0.0005. Therefore, this research paper recommended using ʎ=0.0005 with L2 regularization for stock price prediction.

scholarly journals Importance of Event Binary Features in Stock Price Prediction

2020 ◽  
Vol 10 (5) ◽  
pp. 1597 ◽  
Author(s):  
Yoojeong Song ◽  
Jongwoo Lee
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Models ◽  
Training Data ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Stock Investment ◽  
Binary Features

In Korea, because of the high interest in stock investment, many researchers have attempted to predict stock prices using deep learning. Studies to predict stock prices have been continuously conducted. However, the type of stock data that is suitable for deep learning has not been established, and it has not been confirmed that the developed stock prediction model can actually result in a profit. To date, designing a good deep learning model depends on how well the user can extract the features that represent all the characteristics of the training data. Among the various available features for training and test data, we determined that the use of event binary features can make stock price prediction models perform better. An event binary feature refers to a 0 or 1 value describing whether an indicator is satisfied (1) or not (0) for any given day and stock. We proposed and compared a stock price prediction model with three different feature combinations to verify the importance of binary features. As a result, we derived a prediction model that defeated the market (KOSPI and KODAQ (KOSPI (Korea Composite Stock Price Index) and KOSDAQ (Korean Securities Dealers Automated Quotations) is Korean stock indices)). The results suggest that deep learning is suitable for stock price prediction.

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