Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market

Saini, Ashwini Kumar; Sharma, Anoop

doi:10.1007/s40745-019-00230-7

Cited by 30 publications

(12 citation statements)

References 8 publications

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“…Using formula (7), F cg can be determined similar to the sum of gravities G in formula (5). e fitness value F(x i ) for maximum optimization has been given in formula (8), while formula ( 9) is used for minimum optimization:…”

Section: The Proposed Techniquementioning

confidence: 99%

“…e stock exchange is a preferred choice for investors because it brings them high profits. However, forecasting in the stock exchange is a complex process because stock prices depend on several factors and are greatly affected by rumors [5].…”

Section: Introductionmentioning

confidence: 99%

“…(1) We design a generic model that can be used to select the best values of hyperparameters for an ANN (2) We find the best configuration for ANN in terms of NHL, NNPHL, ACTFUN, and LR using the proposed PSOCoG for ANN (3) e proposed model can be utilized to estimate the closing price using different historical data of stock market indices with high accuracy and minimum error (4) We compare the proposed model with traditional ANN, the standard PSO (SPSO), and ANN with standard PSO models (5) We study the efficiency of the proposed model under the effect of COVID-19 e rest of the paper is organized as follows. Section 2 introduces the background of standard particle swarm optimization and the stock market.…”

Section: Introductionmentioning

confidence: 99%

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A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

Jamous

ALRahhal

El-Darieby

2021

Scientific Programming

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Since the declaration of COVID-19 as a pandemic, the world stock markets have suffered huge losses prompting investors to limit or avoid these losses. The stock market was one of the businesses that were affected the most. At the same time, artificial neural networks (ANNs) have already been used for the prediction of the closing prices in stock markets. However, standalone ANN has several limitations, resulting in the lower accuracy of the prediction results. Such limitation is resolved using hybrid models. Therefore, a combination of artificial intelligence networks and particle swarm optimization for efficient stock market prediction was reported in the literature. This method predicted the closing prices of the shares traded on the stock market, allowing for the largest profit with the minimum risk. Nevertheless, the results were not that satisfactory. In order to achieve prediction with a high degree of accuracy in a short time, a new improved method called PSOCoG has been proposed in this paper. To design the neural network to minimize processing time and search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSOCoG has been employed to select the best hyperparameters in order to construct the best neural network. The created network was able to predict the closing price with high accuracy, and the proposed model ANN-PSOCoG showed that it could predict closing price values with an infinitesimal error, outperforming existing models in terms of error ratio and processing time. Using S&P 500 dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 13%, SPSOCOG by approximately 17%, SPSO by approximately 20%, and ANN by approximately 25%. While using DJIA dataset, ANN-PSOCoG outperformed ANN-SPSO in terms of prediction accuracy by approximately 18%, SPSOCOG by approximately 24%, SPSO by approximately 33%, and ANN by approximately 42%. Besides, the proposed model is evaluated under the effect of COVID-19. The results proved the ability of the proposed model to predict the closing price with high accuracy where the values of MAPE, MAE, and RE were very small for S&P 500, GOLD, NASDAQ-100, and CANUSD datasets.

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Section: The Proposed Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

Jamous

ALRahhal

El-Darieby

2021

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…In contrast, stock market prediction is a complex task to achieve with the help of artificial intelligence. It is due to stock prices depend on many factors, including trends and news in the market [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction

2020

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Prediction of financial time series is a great challenge for statistical models. In general, the stock market times series present high volatility due to its sensitivity to economic and political factors. Furthermore, recently, the covid-19 pandemic has caused a drastic change in the stock exchange times series. In this challenging context, several computational techniques have been proposed to improve the performance of predicting such times series. The main goal of this article is to compare the prediction performance of five neural network architectures in predicting the six most traded stocks of the official Brazilian stock exchange B3 from March 2019 to April 2020. We trained the models to predict the closing price of the next day using as inputs its own previous values. We compared the predictive performance of multiple linear regression, Elman, Jordan, radial basis function, and multilayer perceptron architectures based on the root of the mean square error. We trained all models using the training set while hyper-parameters such as the number of input variables and hidden layers were selected using the testing set. Moreover, we used the trimmed average of 100 bootstrap samples as our prediction. Thus, our approach allows us to measure the uncertainty associate with the predicted values. The results showed that for all times series, considered all architectures, except the radial basis function, the networks tunning provide suitable fit, reasonable predictions, and confidence intervals.

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“…[ 1 – 4 ]). These algorithms include evolutionary computation through the genetic algorithm to support vector machine or various neural network-based approach [ 5 , 6 ], deep learning methods such as deep belief net coupled with multi-linear perceptron [ 7 – 10 ]. However, these models do not deal with the dependent stream of data.…”

Section: Introductionmentioning

confidence: 99%

Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network

Dey

Gouda

2020

Ann. Data. Sci.

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In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions to select the optimal block length while performing the bootstrapping of the sample. We also propose a benchmark to compare the confidence interval measured through different bootstrap strategies. We illustrate the experimental results through some stock price data set.

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Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market

Cited by 30 publications

References 8 publications

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

A New ANN-Particle Swarm Optimization with Center of Gravity (ANN-PSOCoG) Prediction Model for the Stock Market under the Effect of COVID-19

Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction

Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network

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