A time series analysis-based stock price prediction using machine learning and deep learning models

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

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

10.36227/techrxiv.15088734 ◽

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.

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Stock Price Prediction Using Deep Learning Models

10.36227/techrxiv.16640197 ◽

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 convolutional neural networks (CNNs), and three regression models using long-and-short-term memory (LSTM) networks for predicting the open 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 walk-forward validation. 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 open 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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Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Intelligent Systems in Accounting Finance & Management ◽

10.1002/isaf.1459 ◽

2019 ◽

Vol 26 (4) ◽

pp. 164-174 ◽

Cited By ~ 3

Author(s):

Mahla Nikou ◽

Gholamreza Mansourfar ◽

Jamshid Bagherzadeh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Stock Price ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Stock Price Prediction ◽

Price Prediction ◽

Deep Learning Algorithm

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Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models

10.36227/techrxiv.15103602 ◽

2021 ◽

Author(s):

Jaydip Sen ◽

Sidra Mehtab ◽

Abhishek Dutta

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Stock Prices ◽

Stock Price ◽

Regression Models ◽

Short Term Memory ◽

Efficient Market Hypothesis ◽

Training Data ◽

Stock Price Prediction ◽

Price Prediction

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. Researchers have also worked on technical analysis of stocks with a goal of identifying patterns in the stock price movements using advanced data mining techniques. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records from December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. Using the grid-searching technique, the hyperparameters of the LSTM models are optimized so that it is ensured that validation losses stabilize with the increasing number of epochs, and the convergence of the validation accuracy is achieved. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week's open value of the NIFTY 50 time series is the most accurate model.

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Failure in Stock Price Prediction: A Comparison bettwen the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes of Existing Machine Learning Algorithms

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2021.6.4549 ◽

2021 ◽

Vol 16 (6) ◽

Author(s):

Ping Zhang ◽

Jia-Yao Yang ◽

Hao Zhu ◽

Yue-Jie Hou ◽

Yi Liu ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Stock Price ◽

Financial Time Series ◽

Learning Algorithms ◽

Shape Feature ◽

Curve Shape ◽

Stock Price Prediction ◽

Price Prediction ◽

Financial Time

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

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