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Title
Abstract
Figures
Introduction
Related Works
Background
Experimental Settings and Results
Dataset Description
Base CNN for Our Dataset
Evaluation Methodology
Results
Discussion
Conclusion
References
All Topics
Computer Science
Machine Learning

U-CNNpred: A Universal CNN-based Predictor for Stock Markets

Profile image of Ehsan HoseinzadeEhsan Hoseinzade

2019, arXive

Abstract

The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extracting such general features in stock market prediction domain and show how it can improve the performance of financial market prediction. We present a framework called U-CNNpred, that uses a CNN-based structure. A base model is trained in a specially designed layer-wise training procedure over a pool of historical data from many financial markets, in order to extract the common patterns from different markets. Our experiments, in which we have used hundreds of stocks in S\&P 500 as well as 14 famous indices around the world, show that this model can outperform baseline algorithms when predicting the directional movement of the markets for which it has been trained for. We also show that the base model can be fine-tuned for predicting new markets and achieve a better performance compared to the state of the art baseline algorithms that focus on constructing market-specific models from scratch.

Figures (14)
layers. This happens through Eq 10. The last step is updating weights of the CNN using calculated gradient
layers. This happens through Eq 10. The last step is updating weights of the CNN using calculated gradient
that fits the problem.
that fits the problem.
called the base predictor. 4.3. Transfer learning
called the base predictor. 4.3. Transfer learning
a graphical visualization of what we described just now. Figure 1: Graphical Visualization of 2D-CNNpred (Hoseinzade & Haratizadeh, 2019)
a graphical visualization of what we described just now. Figure 1: Graphical Visualization of 2D-CNNpred (Hoseinzade & Haratizadeh, 2019)
Table 3: Description of used algorithms
Table 3: Description of used algorithms
Table 4: F-measure of some of the stocks in S&P 500 index
Table 4: F-measure of some of the stocks in S&P 500 index
Figure 2: Performance of algorithms in having the best F-measure for predicting each of the 458 stocks
Figure 2: Performance of algorithms in having the best F-measure for predicting each of the 458 stocks
Table 5: F-measure of base predictor with different layers
Table 5: F-measure of base predictor with different layers
Table 6: F-measure of algorithms in prediction of U.S. indices
Table 6: F-measure of algorithms in prediction of U.S. indices
Table 7: F-measure of algorithms in prediction of world's famous indices
Table 7: F-measure of algorithms in prediction of world's famous indices
The list of features that were used in this research is represented in Table 8:
The list of features that were used in this research is represented in Table 8:
References \hmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M. A.. Mahmoodi, A., & Jam, P. A.
References \hmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M. A.. Mahmoodi, A., & Jam, P. A.

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Jaydip Sen

Third National Conference on Machine Learning and Artificial Intelligence (NCMLAI), 2020, Delhi, India, 2020

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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A review of stock market predictions using deep learning models
Amol Barwal

Journal of University of Shanghai for Science and Technology, 2021

Stock trading has always been an appealing option in the world of financial market to make significant profits. As a popular saying goes, “Trading is not gambling”. It is true for the case of stock trading also. Successful traders always do a lot of research and analysis before buying and selling their stocks. This analysis usually includes looking at past record of a stock and finding patterns for future predictions. As machines are very good at processing huge amount of data and finding patterns from the data, we can see why the use of deep learning models can be beneficial in this case. In this paper, we are going to study various deep learning models used by researchers in the past to predict stock prices.

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A stock market trading framework based on deep learning architectures
ATHARVA SHAH

Multimedia Tools and Applications, 2022

Market prediction has been a key interest for professionals around the world. Numerous modern technologies have been applied in addition to statistical models over the years. Among the modern technologies, machine learning and in general artificial intelligence have been at the core of numerous market prediction models. Deep learning techniques in particular have been successful in modeling the market movements. It is seen that automatic feature extraction models and time series forecasting techniques have been investigated separately however a stacked framework with a variety of inputs is not explored in detail. In the present article, we suggest a framework based on a convolutional neural network (CNN) paired with long-short term memory (LSTM) to predict the closing price of the Nifty 50 stock market index. A CNN-LSTM framework extracts features from a rich feature set and applies time series modeling with a look-up period of 20 trading days to predict the movement of the next day. Feature sets include raw price data of target index as well as foreign indices, technical indicators, currency exchange rates, commodities price data which are all chosen by similarities and well-known trade setups across the industry. The model is able to capture the information based on these features to predict the target variable i.e. closing price with a mean absolute percentage error of 2.54% across 10 years of data. The suggested framework shows a huge improvement on return than the traditional buy and hold method.

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Enhancing Stock Market Prediction: A Robust LSTM-DNN Model Analysis on 26 Real-Life Datasets
Khorshed Alam

IEEE Access , 2024

Predicting the closing price of the stock market with accuracy is highly uncertain and volatile. Deep learning (DL) can analyze vast amounts of historical stock data to identify patterns and correlations, aiding in predictive modeling. By learning from past market behavior, deep learning algorithms can potentially forecast future price movements with some degree of accuracy. This research presents a hybrid Long Short-Term Memory (LSTM) and Deep Neural Network (DNN) model designed to tackle the complexities of stock market prediction, including market volatility and intricate patterns. Initially the proposed model is being trained on Bajaj's stock dataset. Getting noteworthy performance in single dataset does not prove robustness of the model. The research validates the model's robustness and scalability through rigorous comparative analysis on 26 company's stock datasets, achieving an average R-squared (R2) score of 0.98606, a Mean Absolute Error (MAE) of 0.0210, and a Mean Squared Error (MSE) of 0.00111. To assess the contribution of our proposed model, we retrained previously used deep learning models alongside our new approach, utilizing a shared dataset for validation. Additionally, we provide ablation study of LSTM-DNN model which provides insights into the individual contributions of each component towards detecting closing price of stocks, offering valuable information for optimizing future stock market prediction models. The model's exceptional performance sets a new standard in stock market prediction, offering promising implications for investors, traders, and financial analysts. Making our work available open-source on https://github.com/codewithkhurshed/SMP-IUB can enhance its accessibility and promote future research opportunities in stock market price prediction.

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Pattern Learning Via Artificial Neural Networks for Financial Market Predictions
Andreas Gabler

SSRN Electronic Journal, 2018

Convolutional neural networks (CNN) and long short-term memory (LSTM) networks have become a staple of sequence learning. Due to the well-established fact that financial time series data exhibit exceptionally noisy characteristics, capital market anomalies are virtually impossible to detect. We deploy CNN networks for predicting out-of-sample stock movements for 200 high-volume European stocks from 1994 until 2014, and compare its overall performance with a modified LSTM model as in Fischer, Krauss (2017). Specifically, we compare empirical training and validation accuracies of both model architectures and reveal portfolio performance characteristics in terms of return and risk metrics for different portfolio sizes, trying to derive common patterns within the top and flop stocks. Thus, we unveil sources of long-term profitability and demonstrate, that both LSTM and CNN networks are able to extract meaningful information from such noisy financial time series.

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