A review of stock market predictions using deep learning models

2021 ◽  
Vol 23 (07) ◽  
pp. 1335-1341
Author(s):  
Amol Barwal ◽  
◽  
Nishi Gupta ◽  
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Financial Market ◽  
Stock Prices ◽  
Learning Models ◽  
Huge Amount ◽  
Stock Trading ◽  
The Past ◽  
The World

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.

scholarly journals Deep Malaria Parasite Detection in Thin Blood Smear Microscopic Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2284
Author(s):  
Asma Maqsood ◽  
Muhammad Shahid Farid ◽  
Muhammad Hassan Khan ◽  
Marcin Grzegorzek
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Red Blood Cells ◽  
Convolutional Neural Networks ◽  
Blood Cells ◽  
Superior Performance ◽  
Learning Models ◽  
Infected Female ◽  
The World ◽  
Augmentation Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

scholarly journals Prediksi Harga Saham Dalam Berinvestasi Pada Perusahaan Manufaktur

2021 ◽  
Vol 9 (2) ◽  
pp. 101-114
Author(s):  
Fauziyah Fauziyah
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Manufacturing Industry ◽  
Stock Exchange ◽  
Manufacturing Sector ◽  
Prediction Method ◽  
Manufacturing Companies ◽  
Upward Trend ◽  
Stock Trading ◽  
The Past

Abstract Indonesia Stock Exchange (IDX) is a term that is well known in the world of stocks in Indonesia. One of the company sectors listed on the IDX is manufacturing. The contribution of the manufacturing sector to Gross Domestic Product (GDP) was recorded to be the largest compared to other sectors. In this research, the manufacturing companies that will be used as the object of research to predict their stock prices are manufacturing companies listed in LQ45. In stock trading, prices fluctuate up or down. Stock conditions that fluctuate every day make investors who are going to invest in the Manufacturing industry must observe and study the past company data before investing. This data is important for investors to find out what might happen to a company's stock price. Thus, predicting stock prices in the manufacturing industry for the future is needed as a stage in deciding which manufacturing companies are good to investing in. The prediction method in this research uses ARIMA. The results obtained are the stock prices of companies GGRM, HMSP, ICBP, INDF, INTP and UNVR following a downward trend, so that the actions taken by investor in these companies are selling stocks, while for the stock prices of companies ASII, CPIN, INKP, JPFA, SMGR, TKIM, following an upward trend, so that the actions taken by investors in these companies are buying stocks.Keywords: Prediction, ARIMA, Investment  BEI merupakan istilah yang terkenal pada dunia saham di Indonesia. Sektor perusahaan yang terdapat di BEI salah satunya adalah manufaktur. Kontribusi sektor manufaktur dalam Produk Domestik Bruto (PDB) tercatat yang paling besar dibandingkan sektor lainnya. Di dalam penelitian ini, perusahaan manufaktur yang akan dijadikan objek penelitian untuk diramalkan harga sahamnya yaitu perusahaan manufaktur yang terdaftar di LQ45.  Pada perdagangan saham, harga mengalami fluktuasi naik maupun turun.  Keadaan saham yang fluktuasi setiap hari menjadikan investor yang akan berinvestasi di industri Manufaktur harus mengamati dan mempelajari data perusahaan dimasa lalu sebelum melakukan investasi. Data tersebut penting bagi investor untuk mengetahui kemungkinan yang terjadi pada harga saham suatu perusahaan. sehingga, meramal harga saham pada industri manufaktur untuk masa yang akan datang sangat dibutuhkan sebagai tahapan dalam memutuskan perusahaan Manufaktur yang baik dalam melakukan investasi. Metode Prediksi dalam penelitian ini menggunakan ARIMA. Hasil yang didapat yaitu harga saham perusahaan GGRM, HMSP, ICBP, INDF, INTP dan UNVR mengikuti tren turun, sehingga langkah yang diambil untuk investor pada perusahaan tersebut adalah menjualnya sedangkan untuk harga saham perusahaan ASII, CPIN, INKP, JPFA, SMGR, TKIM, mengikuti tren naik, sehingga langkah yang diambil untuk investor pada perusahaan tersebut adalah membeli saham.Kata Kunci: Prediksi, ARIMA, Investasi

scholarly journals Understanding the Interdependency between the Nifty 50 Future Index and the Advanced Future Stock Market through Econometrics

2019 ◽  
Vol 8 (4) ◽  
pp. 3660-3664
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Granger Causality ◽  
Foreign Exchange ◽  
Financial Market ◽  
Stock Prices ◽  
Economic Condition ◽  
The Future ◽  
Bidirectional Movement ◽  
High Degree

In recent times the stock market is accepted as a tool to measure the economic condition of a nation. It is found that the Indian financial market as highly volatile due to the lower value of rupees in foreign exchange with the dollar. This motivated the researchers to measure the interdependencies of [Nifty 50 future (India), Nikkei 225(Japan), NASDAQ 100 Futures (USA), Dow Jones 30 (USA), SSEC (China), Hang Seng Future (Hong Kong), and FTSE 100 (London)]. The analysis covers monthly stock prices for a period of 10years from April 2008 to March 2018. The measurement of interdependencies is studied through granger causality and correlation after the confirmation of the non-normality of data and stationary of data. The result shows a high degree of correlation between NASDAQ and Dow Jones shows 98.76% followed by 96.89% between Nifty 50 future and NASDAQ. The co-movement result of Nifty 50 future through granger causality states Nifty 50 future can explain the future stock market of Nikkei (Japan) and SSEC (China) and the Hang Seng future (Hong Kong) has a bidirectional movement with Nifty 50 futures. The study is useful for the investors to identify the interdependencies of the indices and understand the movement in a significant manner.

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.

A Review of Recent Deep Learning Models in COVID-19 Diagnosis

2021 ◽  
Vol 6 (5) ◽  
pp. 10-15
Author(s):  
Ela Bhattacharya ◽  
D. Bhattacharya
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Test Results ◽  
Learning Models ◽  
Future Directions ◽  
Human Contact ◽  
The World ◽  
Short Span

COVID-19 has emerged as the latest worrisome pandemic, which is reported to have its outbreak in Wuhan, China. The infection spreads by means of human contact, as a result, it has caused massive infections across 200 countries around the world. Artificial intelligence has likewise contributed to managing the COVID-19 pandemic in various aspects within a short span of time. Deep Neural Networks that are explored in this paper have contributed to the detection of COVID-19 from imaging sources. The datasets, pre-processing, segmentation, feature extraction, classification and test results which can be useful for discovering future directions in the domain of automatic diagnosis of the disease, utilizing artificial intelligence-based frameworks, have been investigated in this paper.

Smart Farming Using Internet of Things

2021 ◽  
pp. 236-250
Author(s):  
Rishabh Verma ◽  
Latika Kharb
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Water System ◽  
Field Conditions ◽  
Learning Models ◽  
System Framework ◽  
Smart Farming ◽  
The World ◽  
Crop Types ◽  
Smart Agriculture

Smart farming through IoT technology could empower farmers to upgrade profitability going from the amount of manure to be used to the quantity of water for irrigating their fields and also help them to decrease waste. Through IoT, sensors could be used for assisting farmers in the harvest field to check for light, moistness, temperature, soil dampness, etc., and robotizing the water system framework. Moreover, the farmers can screen the field conditions from anyplace and overcome the burden and fatigue to visit farms to confront problems in the fields. For example, farmers are confronting inconvenience while utilizing right quantity and time to use manures and pesticides in their fields as per the crop types. In this chapter, the authors have introduced a model where farmers can classify damaged crops and healthy crops with the help of different sensors and deep learning models. (i.e., The idea of implementing IoT concepts for the benefit of farmers and moving the world towards smart agriculture is presented.)

scholarly journals An Empirical Analysis of the Efficient Market Hypothesis in China’s Stock Market

2021 ◽  
Vol 4 (3) ◽  
pp. 1-5
Author(s):  
Jiaxuan Xu
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Empirical Research ◽  
Empirical Analysis ◽  
Stock Prices ◽  
Efficient Market Hypothesis ◽  
Security Market ◽  
Stock Trading ◽  
Efficient Market ◽  
Basic Framework

The efficient market hypothesis is one of the most important theories in finance. According to this hypothesis, in a stock market with sound laws, good functions, high transparencies, and extensive competitions, all valuable information is timely, accurately, and fully reflected in the trend of stock prices including the current and future values of enterprises. Unless there are market manipulations, it would be impossible for investors to gain more above the average profits in the market by analyzing former prices. Since the efficient market hypothesis has been introduced, it has become an interest in the empirical research of the security market. It is one of the most controversial investment theories and there are many evidences supporting and also opposing this hypothesis. Nevertheless, this hypothesis still holds an important status in the basic framework of mainstream theories in modern financial markets. By analyzing simulated investment transactions in regard to stock trading of three different enterprises, this paper verified that the efficient market hypothesis is partially valid.

scholarly journals Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

2021 ◽  
Vol 40 ◽  
pp. 03030
Author(s):  
Mehdi Surani ◽  
Ramchandra Mangrulkar
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Freedom Of Speech ◽  
Learning Approaches ◽  
Learning Models ◽  
Media Usage ◽  
The Past ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Machine Learning Models

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.

scholarly journals Diagnosis of COVID-19 using CT scan images and deep learning techniques

2020 ◽  
Author(s):  
Vruddhi Shah ◽  
Rinkal Keniya ◽  
Akanksha Shridharani ◽  
Manav Punjabi ◽  
Jainam Shah ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Ct Scan ◽  
Efficient Method ◽  
Learning Models ◽  
Automated Diagnosis ◽  
Learning Techniques ◽  
The World

Early diagnosis of the coronavirus disease in 2019 (COVID-19) is essential for controlling this pandemic. COVID-19 has been spreading rapidly all over the world. There is no vaccine available for this virus yet. Fast and accurate COVID-19 screening is possible using computed tomography (CT) scan images. The deep learning techniques used in the proposed method was based on a convolutional neural network (CNN). Our manuscript focuses on differentiating the CT scan images of COVID-19 and non-COVID 19 CT using different deep learning techniques. A self developed model named CTnet-10 was designed for the COVID-19 diagnosis, having an accuracy of 82.1 %. Also, other models that we tested are DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19. The VGG-19 proved to be superior with an accuracy of 94.52 % as compared to all other deep learning models. Automated diagnosis of COVID-19 from the CT scan pictures can be used by the doctors as a quick and efficient method for COVID-19 screening.

scholarly journals Quantifying Seagrass Distribution in Coastal Water with Deep Learning Models

2020 ◽  
Vol 12 (10) ◽  
pp. 1581 ◽  
Author(s):  
Daniel Perez ◽  
Kazi Islam ◽  
Victoria Hill ◽  
Richard Zimmerman ◽  
Blake Schaeffer ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Satellite Images ◽  
Support Vector ◽  
Learning Approach ◽  
Learning Models ◽  
Learning Techniques ◽  
The World ◽  
New Locations

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations.

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