scholarly journals Evaluation of Weight Decay Regularization Techniques for Stock Price Prediction using Gated Recurrent Unit Network

2021 ◽  
Vol 19 (2) ◽  
pp. 9-15
Author(s):  
Arjun Singh Saud ◽  
Subarna Shakya
Keyword(s):  
Stock Price ◽  
Research Work ◽  
Research Field ◽  
Price Forecasting ◽  
Weight Decay ◽  
Stock Price Forecasting ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
L1 Regularization ◽  
Regularization Techniques

Stock price forecasting in the field of interest for many stock investors to earn more profit from stock trading. Nowadays, machine learning researchers are also involved in this research field so that fast, accurate and automatic stock price forecasting can be achieved. This research paper evaluated GRU network’s performance with weight decay reg-ularization techniques for predicting price of stocks listed NEPSE. Three weight decay regularization technique analyzed in this research work were (1) L1 regularization (2) L2 regularization and (3) L1_L2 regularization. In this research work, six randomly selected stocks from NEPSE were experimented. From the experimental results, we observed that L2 regularization could outperform L1 and L1_L2 reg-ularization techniques for all six stocks. The average MSE obtained with L2 regularization was 4.12% to 33.52% lower than the average MSE obtained with L1 regularization, and it was 10.92% to 37.1% lower than the average MSE obtained with L1_L2 regularization. Thus, we concluded that the L2 regularization is best choice among weight regularization for stock price prediction.

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.

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 PERAMALAN HARGA SAHAM MENGGUNAKAN JARINGAN SYARAF TIRUAN SECARA SUPERVISED LEARNING DENGAN ALGORITMA BACKPROPAGATION

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Eko Riyanto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Artificial Neural

Stock price prediction is useful for investors to see how the prospects of a company's stock investment in the future. Stock price prediction can be used to anticipate the deviation of stock prices. It can also helps investors in decision making. Artificial Neural Networks do not require mathematical models but data from problems to be solved. Information is conveyed through the data, and the Artificial Neural Network filters the information through training. Therefore, Artificial Neural Network is appropriate to solve the problem of stock price prediction.            Learning method that will be used to predict stock price is Supervised Learning with Backpropagation algorithm. With this algorithm, networks can be trained using stock price data from the previous time, classify it and adjust network link weight as new input and forecast future stock prices. By using ANN, time series prediction is more accurate. After analyzing the problem of stock price movement system, the writer can know the pattern of what variables will be taken for further insert into the stock price forecasting system.            This application can be used for stock price forecasting technique, so it will be useful for beginner investor as well as advanced investor as reference to invest in capital market. Implementing supervised learning backpropagation method will get accurate forecasting results more than 98%.Keyword - artificial neural network, stock, backpropagation.

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.

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a 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, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

scholarly journals Stock Price Prediction Using Deep Learning Models

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab ◽  
Gourab Nath
Keyword(s):  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
The Other ◽  
Learning Models ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Other Hand

Prediction of future movement of stock prices has been a 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, on the other hand, there are propositions illustrating that, if appropriately modeled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using five deep learning-based regression models. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of December 29, 2014 to July 31, 2020. Based on the NIFTY data during December 29, 2014 to December 28, 2018, we build two regression models using <i>convolutional neural networks</i> (CNNs), and three regression models using <i>long-and-short-term memory</i> (LSTM) networks for predicting the <i>open</i> values of the NIFTY 50 index records for the period December 31, 2018 to July 31, 2020. We adopted a multi-step prediction technique with <i>walk-forward validation</i>. The parameters of the five deep learning models are optimized using the grid-search technique so that the validation losses of the models stabilize with an increasing number of epochs in the model training, and the training and validation accuracies converge. Extensive results are presented on various metrics for all the proposed regression models. The results indicate that while both CNN and LSTM-based regression models are very accurate in forecasting the NIFTY 50 <i>open</i> values, the CNN model that previous one week’s data as the input is the fastest in its execution. On the other hand, the encoder-decoder convolutional LSTM model uses the previous two weeks’ data as the input is found to be the most accurate in its forecasting results.

scholarly journals Stock Price Prediction Based on XGBoost and LightGBM

2021 ◽  
Vol 275 ◽  
pp. 01040
Author(s):  
Yue Yang ◽  
Yang Wu ◽  
Peikun Wang ◽  
Xu Jiali
Keyword(s):  
Stock Price ◽  
Original Data ◽  
Mean Value ◽  
Price Forecasting ◽  
Combined Model ◽  
Data Set ◽  
Missing Value ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Given

Stock trading, as a kind of high frequency trading, generally seeks profits in extremely short market changes. And effective stock price forecasting can help investors obtain higher returns. Based on the data set provided by Jane Street, this paper makes use of XGBoost model and LightGBM model to realize the prediction of stock price. Since the given training set has a large amount of data and includes abnormal data such as missing value, we first carry out feature engineering processing on the original data and take the mean value of the missing value, so as to obtain the preprocessed data that can be used in modeling. The experimental results show that the combined model of XGBoost and LightGBM has better prediction performance than the single model and neural network.

scholarly journals Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253217
Author(s):  
Rohitash Chandra ◽  
Yixuan He
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Uncertainty Quantification ◽  
Stock Markets ◽  
Stock Price ◽  
Model Parameters ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Bayesian Neural Networks ◽  
Stock Price Prediction

Recently, there has been much attention in the use of machine learning methods, particularly deep learning for stock price prediction. A major limitation of conventional deep learning is uncertainty quantification in predictions which affect investor confidence. Bayesian neural networks feature Bayesian inference for providing inference (training) of model parameters that provides a rigorous methodology for uncertainty quantification in predictions. Markov Chain Monte Carlo (MCMC) sampling methods have been prominent in implementing inference of Bayesian neural networks; however certain limitations existed due to a large number of parameters and the need for better computational resources. Recently, there has been much progress in the area of Bayesian neural networks given the use of Langevin gradients with parallel tempering MCMC that can be implemented in a parallel computing environment. The COVID-19 pandemic had a drastic impact in the world economy and stock markets given different levels of lockdowns due to rise and fall of daily infections. It is important to investigate the performance of related forecasting models during the COVID-19 pandemic given the volatility in stock markets. In this paper, we use novel Bayesian neural networks for multi-step-ahead stock price forecasting before and during COVID-19. We also investigate if the pre-COVID-19 datasets are useful of modelling stock price forecasting during COVID-19. Our results indicate due to high volatility in the stock-price during COVID-19, it is more challenging to provide forecasting. However, we found that Bayesian neural networks could provide reasonable predictions with uncertainty quantification despite high market volatility during the first peak of the COVID-19 pandemic.

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a 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, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

scholarly journals Stock Price Prediction Using Deep Learning Models

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab ◽  
Gourab Nath
Keyword(s):  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
The Other ◽  
Learning Models ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Other Hand

Prediction of future movement of stock prices has been a 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, on the other hand, there are propositions illustrating that, if appropriately modeled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using five deep learning-based regression models. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of December 29, 2014 to July 31, 2020. Based on the NIFTY data during December 29, 2014 to December 28, 2018, we build two regression models using <i>convolutional neural networks</i> (CNNs), and three regression models using <i>long-and-short-term memory</i> (LSTM) networks for predicting the <i>open</i> values of the NIFTY 50 index records for the period December 31, 2018 to July 31, 2020. We adopted a multi-step prediction technique with <i>walk-forward validation</i>. The parameters of the five deep learning models are optimized using the grid-search technique so that the validation losses of the models stabilize with an increasing number of epochs in the model training, and the training and validation accuracies converge. Extensive results are presented on various metrics for all the proposed regression models. The results indicate that while both CNN and LSTM-based regression models are very accurate in forecasting the NIFTY 50 <i>open</i> values, the CNN model that previous one week’s data as the input is the fastest in its execution. On the other hand, the encoder-decoder convolutional LSTM model uses the previous two weeks’ data as the input is found to be the most accurate in its forecasting results.

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