Stock Index Forecasting Research Articles

Artificial Intelligence in Islamic Finance: Forecasting Stock Indices with Neural Prophet

Ensuring financial system stability is paramount, especially in markets guided by Sharia principles, where investor confidence and adherence to ethical standards play critical roles. The ability to accurately forecast stock movements within this framework not only supports informed investment decisions but also strengthens the overall stability of financial markets. This research employs the innovative Neural Prophet model to predict Islamic stock indices in Indonesia with remarkable accuracy and depth. The model demonstrates its capability not only in accurately forecasting trends but also in detecting subtle fluctuations within three Islamic stock indices: the Jakarta Islamic Index (JII), the Jakarta Islamic Index 70 (JII70), and the Indonesia Sharia Stock Index (ISSI). Visual representations highlight the model's adaptability and advanced foresight, surpassing traditional models. The significance of this research lies in its potential to enhance the precision of stock index predictions, particularly for Islamic stocks, offering stakeholders deeper insights. The model's effectiveness spans both stable and volatile market conditions, making it a valuable tool for informed financial decision-making. Accurate forecasts aid in risk management and support well-informed investment decisions in fluctuating markets, thereby contributing to financial system stability.

Daily and Weekly Geometric Brownian Motion Stock Index Forecasts

In this manuscript, daily and weekly geometric Brownian motion forecasts are obtained and tested for reliability for three indexes, DJIA, NASDAQ and S&P 500. A twenty-year rolling window is used to estimate the drift and diffusion components, and applied to obtain one-period-ahead geometric Brownian motion index values and associated probabilities. Expected values are estimated by totaling up the product of the index value and its associated probabilities, and test for reliability. The results indicate that geometric Brownian-simulated expected index values estimated using one thousand simulations can be reliable forecasts of the actual index values. Expected values estimated using one or ten simulations are not as reliable, while those obtained using at least one hundred simulations could be useful.

Forecasting Stock Indices: Stochastic and Artificial Neural Network Models

Forecasting Stock Indices: Stochastic and Artificial Neural Network Models

Navigating Inflation Challenges: AI-Based Portfolio Management Insights

After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios.

Daily and Weekly Geometric Brownian Motion Stock Index Forecasts

Daily and Weekly Geometric Brownian Motion Stock Index Forecasts

Forecasting stock index return and volatility based on GAVMD- Carbon-BiLSTM: How important is carbon emission trading?

This paper proposes a new forecast method of stock price return and volatility by GAVMD-Carbon-BiLSTM. First, based on the data from eight carbon markets in China, the logarithmic rate of return of carbon emission rights price is constructed. Second, the TVP-VAR model is used to investigate the impact effect of carbon emission rights trading, stock price return, and volatility. It is used as a predictor of return and volatility rationality. Third, the genetic algorithm is used to optimize the parameters of the variational mode decomposition. Fourth, the return and volatility of stock price are decomposed into multiple intrinsic modes, effectively reducing the data’s complexity. Finally, GAVMD is combined with BiLSTM, and the logarithmic return of carbon emission trading price is used as input to predict the stock price return and volatility. The results show that carbon emissions trading impacts the rate of return and volatility at different lead times and time points. Therefore, using it to predict stock price return and volatility can improve prediction accuracy. At the same time, combining GAVMD with BiLSTM, the prediction performance of carbon emission trading price logarithmic return rate as input is much better than other machine learning models.

Forecasting stock indices with the COVID-19 infection rate as an exogenous variable.

Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. In this study, the effect of covariates has been analyzed for three stock indices: S & P 500, Morgan Stanley Capital International (MSCI) world stock index, and the Chicago Board Options Exchange (CBOE) Volatility Index (VIX). Results show that parametric approaches can be good forecasting models for the S & P 500 index and the VIX index. On the other hand, a random walk model can be adopted to forecast the MSCI index. Moreover, among the three random walk forecasting methods for the MSCI index, the naïve method provides the best forecasting model.

Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks

Modeling and forecasting of stock indexes is an important and challenging work in the field of financial research, which is of great significance for investors to reduce risk on investment and improve investment returns. However, the basic data of the stock index includes five indicators, namely, opening price, highest price, lowest price, closing price and trading volume (COHLV), each of which contains some information related to the future trend. And these indicators are affected by different factors such as politics, economy, psychology and so on, so they have nonlinear, high noise and other complex characteristics. These reasons make the existing methods cannot effectively improve the accuracy of stock index forecasting. In order to solve this problem, we propose a hybrid stock index forecasting model named MEMD-TCN, which is based on multivariate empirical mode decomposition (MEMD) and temporal convolutional networks (TCN). The novelty of the proposed model focuses on decomposition of multivariate time series and using only fully convolutional layers to forecast stock index. The proposed model first uses MEMD to decompose the data of COHLV to obtain subsequences of different fluctuation frequencies of each time series, then inputs subsequences of the same frequency into TCN to predict the subsequences of closing price in the next period. Ultimately, the forecasted values for the closing price are obtained by reconstructing the prediction results of all the subsequences of closing price. To evaluate the performance of the proposed MEMD-TCN model, stock index of several countries that can reflect the overall changes of the market are used. The experimental results verify the notable effectiveness and necessity of the decomposition of multivariate time series. Meanwhile, the results demonstrate the significant superiority of the proposed MEMD-TCN model on accuracy and stability.

Forecasting Stock Market Indices Using the Recurrent Neural Network Based Hybrid Models: CNN-LSTM, GRU-CNN, and Ensemble Models

Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared error (MSE) and mean absolute error (MAE), respectively, in the case of one-time-step forecasting and 81.5% of the cases in terms of the MSE and MAE in the case of multi-time-step forecasting.

Conducting stock market index prediction via the localized spatial–temporal convolutional network

The stock index movement prediction is a fundamental research issue in financial investment. The high-noise and dynamic of stock data make this problem challenging. Efficiently modeling the underlying spatial–temporal correlations between indices is a potential study area for raising the prediction performance. Existing methods usually use separate modules to capture spatial and temporal correlations, which lose sight of local spatial–temporal correlation. In this paper, we propose a brand-new prediction model named the localized graph convolutional network (LoGCN) for stock index forecasting. This model can represent the intricate local spatial–temporal connections via a well-designed convolution mechanism. First, the localized graph construction method is proposed to model the local spatial–temporal correlations. Then, the localized graph convolutional module is presented to fuse the impact of different indices. Finally, the fully connected network is put forward to transform the features into the expected index trends. 42 Chinese stock market indices were selected to conduct extensive experiments. Three evaluation metrics including the increase rate regression, further trends classification, and stock market back-testing were adopted to the experimental comparison. The experiments show that our method is 4.2%, 3.1%, and 40% better than conventional methods under these three metrics.

Quantum-enhanced Versus Classical Support Vector Machine: An Application to Stock Index Forecasting

Quantum-enhanced Versus Classical Support Vector Machine: An Application to Stock Index Forecasting

Do European, Middle-East and Asian Stock Markets Impact on Indian Stock Market? A Case Study Based on NIFTY Stock Index Forecasting

This paper estimates NIFTY index from Indian stock market by considering a cluster of MSCI European, Middle East and Asian stock market indices. In the forecasting process, we obtain group of independent variables to test its relative impact over dependent variable (NIFTY) considering a sample size of daily observations from January 2000 to December 2021 abstracted from Bloomberg. We run OLS regression, Quantile estimations with additional parameter of VIF and BKW. We found significant impact association with China (Asian index) and Saudi Arabia (Middle East index) during the forecasting process compared to rest of sample indices that exceed unexpectedly out of VIF limits. Further, we recorded strong association of independent variables despite of statistical significance (<1%) in OLS regression estimation.

Stock Market Forecasting Using the Random Forest and Deep Neural Network Models Before and During the COVID-19 Period

Stock market forecasting is considered the most challenging problem to solve for analysts. In the past 2 years, Covid-19 has severely affected stock markets globally, which, in turn, created a great problem for investors. The prime objective of this study is to use a machine learning model to effectively forecast stock index prices in three time frames: the whole period, the pre-Covid-19 period, and the Covid-19 period. The model accuracy testing results of mean absolute error, root mean square error, mean absolute percentage error, and r2 suggest that the proposed machine learning models autoregressive deep neural network (AR-DNN(1, 3, 10)), autoregressive deep neural network (AR-DNN(3, 3, 10)), and autoregressive random forest (AR-RF(1)) are the best forecasting models for stock index price forecasting for the whole period, for the pre-Covid-19 period, and during the Covid-19 period, respectively, under high stock price fluctuations compared to traditional time-series forecasting models such as autoregressive moving average models. In particular, AR-DNN(1, 3, 10) is suggested when the number of observations is large, whereas AR-RF(1) is suggested for a series with a low number of observations. Our study has a practical implication as they can be used by investors and policy makers in their investment decisions and in formulating financial decisions and policies, respectively.

StockNet—GRU based stock index prediction

Predicting financial trends of stock indexes is important for investors to reduce risk on investment and efficient decision making if the prediction is made accurately. Researchers, in recent times have applied deep learning approaches in this field which have essentially beaten conventional machine learning approaches. To overcome the issue of overfitting we presented a new data augmentation approach in our GRU based StockNet model consisting of two modules. Injection module to prohibit overfitting and Investigation module for stock index forecasting. The proposed approach has been validated on Indian stock market (CNX-Nifty). Proposed StockNet-c model produces 65.59%, 27.30% and 14.89 % less test loss in terms of RMSE, MAE and MAPE respectively, in comparison to TargetNet model where overfitting prohibition injection module is missing.

Malaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithms

Nowadays, Machine Learning (ML) plays a significant role in the economy, especially in the stock trading strategy. However, there is an inadequate extensive data analysis using various ML methods. Previous findings usually focus on the forecasting stock index or selecting a limited number of stocks with restricted features. Therefore, the contribution of this paper focused on evaluating different supervised learning algorithms, namely Logistic Regression (LR), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), on a big dataset from 28 stocks in Bursa Malaysia. By setting their parameter along and using Walk-Forward Analysis (WFA) method, the trading signal was evaluated based on Accuracy Rate, Precision Rate, Recall Rate, and F1 Score. For stock trading strategies in Malaysia in particular, the findings of this study show that SVM has a better performance compared to LR and XGB in time series forecasting. The ML algorithms have values ranging from 53% to 66% for Accuracy Rate (AR), Recall Rate (RR), and F1 Score (F1). In addition, SVM has the highest Precision Rate (PR) of 73% among the ML algorithms.

Malaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithms

Nowadays, Machine Learning (ML) plays a significant role in the economy, especially in the stock trading strategy. However, there is an inadequate extensive data analysis using various ML methods. Previous findings usually focus on the forecasting stock index or selecting a limited number of stocks with restricted features. Therefore, the contribution of this paper focused on evaluating different supervised learning algorithms, namely Logistic Regression (LR), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), on a big dataset from 28 stocks in Bursa Malaysia. By setting their parameter along and using Walk-Forward Analysis (WFA) method, the trading signal was evaluated based on Accuracy Rate, Precision Rate, Recall Rate, and F1 Score. For stock trading strategies in Malaysia in particular, the findings of this study show that SVM has a better performance compared to LR and XGB in time series forecasting. The ML algorithms have values ranging from 53% to 66% for Accuracy Rate (AR), Recall Rate (RR), and F1 Score (F1). In addition, SVM has the highest Precision Rate (PR) of 73% among the ML algorithms.

Research on stock index forecast based on the forgetting-genetic network model

The stock market plays an important role in national economic development. Predicting its trend, to ensure market stability, efficiency, and safety, is one of the hottest topics of current research. Some algorithms have been proposed to use in this study, however, the drawback in forecasting accuracy and applicability of the model and other issues limit its application in practice. In this paper, we designed a forgotten-genetic network model to predict the stock index. Finally, a comparison between existing algorithms to verify the validity and applicability of the proposed method in this study.

Deep Nonlinear Ensemble Framework for Stock Index Forecasting and Uncertainty Analysis

Deep Nonlinear Ensemble Framework for Stock Index Forecasting and Uncertainty Analysis

An Optimal Weighted Combined Model Coupled with Feature Reconstruction and Deep Learning for Multivariate Stock Index Forecasting

Stock index prediction plays an important role in the creation of better investment strategies. However, prediction can be difficult due to the random fluctuation of financial time series. In pursuit of improved stock index prediction, a hybrid prediction model is proposed in this paper, which contains two-step data pretreatment, double prediction models, and smart optimization. In the data pretreatment stage, in order to carry more information about the prediction target, multidimensional explanatory variables are selected by the Granger causality test, and to eliminate data redundancy, feature extraction is inserted with the help of principal component analysis; both of these can provide a higher-quality dataset. Bi-directional long short-term memory and bi-directional gated recurrent unit network, as the concurrent prediction models, can improve not only the precision, but also the robustness of results. In the last stage, the proposed model integrates the weight optimization of the cuckoo search of the two prediction results to take advantage of both. For the model performance test, four main global stock indices are used. The experimental results show that our model performs better than other benchmark models, which indicates the potential of the proposed model for wide application.

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

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.