scholarly journals Research on the Correlation between Hot News and Stock Index Volatility Based on Deep Learning

2020 ◽  
Keyword(s):  
Deep Learning ◽  
Stock Index

Improved big data stock index prediction using deep learning with CNN and GRU

2020 ◽  
Vol 7 (4) ◽  
pp. 202
Author(s):  
Jithin Eapen ◽  
Abhishek Verma ◽  
Doina Bein
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Stock Index

Improved big data stock index prediction using deep learning with CNN and GRU

2020 ◽  
Vol 7 (4) ◽  
pp. 202
Author(s):  
Doina Bein ◽  
Abhishek Verma ◽  
Jithin Eapen
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Stock Index

scholarly journals Research on Market Stock Index Prediction Based on Network Security and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jiake Li
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Network Security ◽  
Stock Market ◽  
Financial Management ◽  
Information Sources ◽  
Information Source ◽  
Investor Sentiment ◽  
Stock Index ◽  
Forecasting Models

As one of the most popular financial management methods, stocks have attracted more and more investors to participate. The risks of stock investment are relatively high. How to reduce risks and increase profits has become the most concerned issue for investors. Traditional stock forecasting models use forecasting models based on stock time series analysis, but time series models cannot consider the influence of investor sentiment on stock market changes. In order to use investor sentiment information to make more accurate stock market forecasts, this paper establishes a stock index forecast and network security model based on time series and deep learning. Based on the time series model, it is proposed to use CNN to extract in-depth emotional information to replace the basic emotional features of the emotional extraction level. At the data source level, other information sources, such as basic features, are introduced to further improve the predictive performance of the model. The results show that the algorithm is feasible and effective and can better predict the changes in the market stock index. This also proves that multiple information sources can improve the accuracy of model prediction more effectively than a single information source.

scholarly journals Deep Learning Models for Predicting Monthly TAIEX to Support Making Decisions in Index Futures Trading

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3268
Author(s):  
Duy-An Ha ◽  
Chia-Hung Liao ◽  
Kai-Shien Tan ◽  
Shyan-Ming Yuan
Keyword(s):  
Deep Learning ◽  
Prediction Models ◽  
Stock Index ◽  
Gradient Boosting ◽  
Learning Models ◽  
Futures Trading ◽  
Convolutional Network ◽  
Stock Market Forecasting ◽  
Extreme Gradient Boosting ◽  
Stop Loss

Futures markets offer investors many attractive advantages, including high leverage, high liquidity, fair, and fast returns. Highly leveraged positions and big contract sizes, on the other hand, expose investors to the risk of massive losses from even minor market changes. Among the numerous stock market forecasting tools, deep learning has recently emerged as a favorite tool in the research community. This study presents an approach for applying deep learning models to predict the monthly average of the Taiwan Capitalization Weighted Stock Index (TAIEX) to support decision-making in trading Mini-TAIEX futures (MTX). We inspected many global financial and economic factors to find the most valuable predictor variables for the TAIEX, and we examined three different deep learning architectures for building prediction models. A simulation on trading MTX was then performed with a simple trading strategy and two different stop-loss strategies to show the effectiveness of the models. We found that the Temporal Convolutional Network (TCN) performed better than other models, including the two baselines, i.e., linear regression and extreme gradient boosting. Moreover, stop-loss strategies are necessary, and a simple one could be sufficient to reduce a severe loss effectively.

scholarly journals Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fang Jia ◽  
Boli Yang
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Short Term Memory ◽  
Test Sample ◽  
Stock Index ◽  
Financial Assets ◽  
Learning Models ◽  
Long Short Term Memory ◽  
Forecasting Volatility ◽  
Deep Learning Model

Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.

scholarly journals Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Devinder Kumar ◽  
Graham W. Taylor ◽  
Alexander Wong
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Stock Index ◽  
Decision Making Process ◽  
Financial Industry ◽  
Stock Market Prediction ◽  
Regulatory Processes ◽  
Black Boxes ◽  
Wide Range ◽  
Major Shortcoming

Deep learning has been shown to outperform traditional machinelearning algorithms across a wide range of problem domains. However,current deep learning algorithms have been criticized as uninterpretable"black-boxes" which cannot explain their decision makingprocesses. This is a major shortcoming that prevents the widespreadapplication of deep learning to domains with regulatoryprocesses such as finance. As such, industries such as financehave to rely on traditional models like decision trees that are muchmore interpretable but less effective than deep learning for complexproblems. In this paper, we propose CLEAR-Trade, a novelfinancial AI visualization framework for deep learning-driven stockmarket prediction that mitigates the interpretability issue of deeplearning methods. In particular, CLEAR-Trade provides a effectiveway to visualize and explain decisions made by deep stock marketprediction models. We show the efficacy of CLEAR-Trade in enhancingthe interpretability of stock market prediction by conductingexperiments based on S&P 500 stock index prediction. The resultsdemonstrate that CLEAR-Trade can provide significant insightinto the decision-making process of deep learning-driven financialmodels, particularly for regulatory processes, thus improving theirpotential uptake in the financial industry.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
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