Figure - available from: Algorithms
This content is subject to copyright.
The distribution of daily S&P 500 index returns from 1963–2016. The mean return is 0.00031 and the standard deviation is 0.01. The skewness and kurtosis are −0.62 and 20.68, respectively.
Source publication
We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index, but not strongly enough to reject market efficie...
Similar publications
Les tourbières ont stocké un tiers du carbone organique des sols mondiaux (C) malgré une superficie ne représentant que 3% de la surface terrestre. Cependant, en réponse aux changements globaux, les tourbières boréales et tempérées, majoritairement dominées par des sphaignes, peuvent être envahies par des plantes vasculaires susceptibles de modifie...
Citations
... This includes NN which can be broadly categorized into; recurrent neural networks (RNN), deep neural networks (DNN), and convolution neural networks (CNN). Among RNN and DNN, long-short term memory (LSTM) and multilayer perceptron (MLP) are very popular and show good accuracy [3][4][5][6][7][8]. On the other hand, CNN is not frequently used, due to dimensional input structure, complexity, cost, or response time but its effectiveness in extracting patterns as shown in a few studies is comparable to other NN architectures [9][10][11]. ...
... It incorporates single and multiple hidden layers containing m the number of hidden neurons, while the input layer accommodates n neurons, corresponding to the number of input values in an input vector [29]. In a study [5], to examine S&P-500, a feed-forward MLP is trained using three hidden layers to predict closing prices from 10 to 30 days ahead with nominal accuracy. Another MLP implementation [3] uses simple TIs on Bitcoin-US Dollar (USD) to forecast returns, and this architecture has been found to give good results, however, this study uses a smaller feature set which can be potentially improved by adding extra features. ...
Cryptocurrency has become a popular trading asset due to its security, anonymity, and decentralization. However, predicting the direction of the financial market can be challenging, leading to difficult financial decisions and potential losses. The purpose of this study is to gain insights into the impact of Fibonacci technical indicator (TI) and multi-class classification based on trend direction and price-strength (trend-strength) to improve the performance and profitability of artificial intelligence (AI) models, particularly hybrid convolutional neural network (CNN) incorporating long short-term memory (LSTM), and to modify it to reduce its complexity. The main contribution of this paper lies in its introduction of Fibonacci TI, demonstrating its impact on financial prediction, and incorporation of a multi-classification technique focusing on trend strength, thereby enhancing the depth and accuracy of predictions. Lastly, profitability analysis sheds light on the tangible benefits of utilizing Fibonacci and multi-classification. The research methodology employed to carry out profitability analysis is based on a hybrid investment strategy—direction and strength by employing a six-stage predictive system: data collection, preprocessing, sampling, training and prediction, investment simulation, and evaluation. Empirical findings show that the Fibonacci TI has improved its performance (44% configurations) and profitability (68% configurations) of AI models. Hybrid CNNs showed most performance improvements particularly the C-LSTM model for trend (binary-0.0023) and trend-strength (4 class-0.0020) and 6 class-0.0099). Hybrid CNNs showed improved profitability, particularly in CLSTM, and performance in CLSTM mod. Trend-strength prediction showed max improvements in long strategy ROI (6.89%) and average ROIs for long-short strategy. Regarding the choice between hybrid CNNs, the C-LSTM mod is a viable option for trend-strength prediction at 4-class and 6-class due to better performance and profitability.
... The research employed a main constituent investigation (PCA), including a constrained Boltzmann computer, plus a 3-DNN to forecast fund revenues in the Korean stock market at a pace of 5 min. Das et al. [200] analyze the S & P 500 Index's predictability by employing previous revenues of every fund in the directory by DNN employed. To envisage the path of the concluding worth against five to thirty times in advance, the researcher tutors a feed-deep learning system with three concealed tiers of 2 hundred knots individually. ...
Cryptocurrency has grown outstandingly in recent years. Additional events throughout the planet have acknowledged the significance of embracing numeral benefits virtually with rapid advances seen in these directions. In today's financial market, the decision to buy or sell cryptocurrency is an interesting challenge faced by day traders. Over the year, it has reached unprecedented highs leading to thoughts explaining the trend in its growth. The idea of whether the movement of financial assets can be predicted has kept investors, economists, and researchers very engaged in recent years. Therefore, the paper used machine learning to construct a model for the Stock and Cryptocurrency price prediction using technical indicators that are most important for market trend study. This study learns how to adapt Long Short-Term Memory (LSTM) to build the cryptocurrency price prediction model. The key factors used are available price, close price, high price, low price, volume and market cap with the interdependencies amid some cryptocurrencies thus centers on measuring vital features that influence the trade’s unpredictability by applying the model to increase the effectiveness of the process. Nonetheless, the cryptocurrency market lacks firm regulatory structures and is unpredictable, making forecasting prices more difficult and complex. From the analysis, it was established that machine learning models provide better performance in predicting cryptocurrency price. The LSTM model outperformed other models in terms of Bitcoin, Ether and Litecoin cryptocurrencies. The proposed model is found to be efficient for cryptocurrency price prediction when compared to similar models with 67.43% accuracy.
... Besides, Chong et al. [63] found out applying covariance-based market structure analysis to the predictive network remarkably increases the covariance estimation. Das et al. [60] used DNN to predict the future trends of the S&P 500 Index. Their results show that their model can poorly forecast the underlying stocks' behavior in the S&P 500 index. ...
... CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82]. ...
This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
... Besides, Chong et al. [63] found out applying covariance-based market structure analysis to the predictive network remarkably increases the covariance estimation. Das et al. [60] used DNN to predict the future trends of the S&P 500 Index. Their results show that their model can poorly forecast the underlying stocks' behavior in the S&P 500 index. ...
... CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82]. ...
This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
... Besides, Chong et al. [63] found out applying covariance-based market structure analysis to the predictive network remarkably increases the covariance estimation. Das et al. [60] used DNN to predict the future trends of the S&P 500 Index. Their results show that their model can poorly forecast the underlying stocks' behavior in the S&P 500 index. ...
... CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82]. ...
This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.
... Besides, Chong et al. [63] found out applying covariance-based market structure analysis to the predictive network remarkably increases the covariance estimation. Das et al. [60] used DNN to predict the future trends of the S&P 500 Index. Their results show that their model can poorly forecast the underlying stocks' behavior in the S&P 500 index. ...
... CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82]. ...
This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
... Besides, Chong et al. [63] found out applying covariance-based market structure analysis to the predictive network remarkably increases the covariance estimation. Das et al. [60] used DNN to predict the future trends of the S&P 500 Index. Their results show that their model can poorly forecast the underlying stocks' behavior in the S&P 500 index. ...
... CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82]. ...
This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
... Besides, Chong et al. [63] found out applying covariance-based market structure analysis to the predictive network remarkably increases the covariance estimation. Das et al. [60] used DNN to predict the future trends of the S&P 500 Index. Their results show that their model can poorly forecast the underlying stocks' behavior in the S&P 500 index. ...
... CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82]. ...
This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
... Besides, Chong et al. [63] found out applying covariance-based market structure analysis to the predictive network remarkably increases the covariance estimation. Das et al. [60] used DNN to predict the future trends of the S&P 500 Index. Their results show that their model can poorly forecast the underlying stocks' behavior in the S&P 500 index. ...
... CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82]. ...
This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
... Besides, Chong et al. [63] found out applying covariance-based market structure analysis to the predictive network remarkably increases the covariance estimation. Das et al. [60] used DNN to predict the future trends of the S&P 500 Index. Their results show that their model can poorly forecast the underlying stocks' behavior in the S&P 500 index. ...
... CNN algorithm is also used for analyzing social media data for sentiment analysis [58]. The DNN algorithm, likewise the LSTM, is only used to analyze financial time series data to predict stock prices [46,49,63] and the S&P 500 Index trend prediction [60]. The GRU algorithm, which is another DL model, is applied in the e-commerce section to analyze financial time series [80] and customer time series [82]. ...
This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.
