Proceedings ArticleDOI
Stock market's price movement prediction with LSTM neural networks
David M. Q. Nelson,Adriano C. M. Pereira,Renato Arantes de Oliveira +2 more
- pp 1419-1426
TLDR
This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators, and the results are promising.Abstract:
Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. For that goal, a prediction model was built, and a series of experiments were executed and theirs results analyzed against a number of metrics to assess if this type of algorithm presents and improvements when compared to other Machine Learning methods and investment strategies. The results that were obtained are promising, getting up to an average of 55.9% of accuracy when predicting if the price of a particular stock is going to go up or not in the near future.read more
Citations
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Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
TL;DR: A comprehensive literature review on DL studies for financial time series forecasting implementations and grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM).
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Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
TL;DR: In this paper, a Long Short-Term Memory (LSTM) neural network model was used for flood forecasting, where the daily discharge and rainfall were used as input data, and characteristics of the data sets which may influence the model performance were also of interest.
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CNNpred: CNN-based stock market prediction using a diverse set of variables
TL;DR: A CNN-based framework is suggested, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets.
A Random Walk Down Wall Street
TL;DR: The a random walk down wall street is universally compatible with any devices to read, allowing you to get the most less latency time to download any of the authors' books like this one.
Journal ArticleDOI
ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module
Yujin Baek,Hayoung Kim +1 more
TL;DR: ModAugNet-c yields a lower test error than the comparative model (SingleNet) in which an overfitting prevention LSTM module is absent, and its applicability in various instances where it is challenging to artificially augment data, such as medical data analysis and financial time-series modeling is found.
References
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