Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification.

The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability.

Examining media's coverage of COVID-19 vaccines and social media sentiments on vaccine manufacturers' stock prices.

Introduction: The COVID-19 pandemic caused a widespread public health and financial crisis. The rapid vaccine development generated extensive discussions in both mainstream and social media, sparking optimism in the global financial markets. This study aims to explore the key themes from mainstream media's coverage of COVID-19 vaccines on Facebook and examine how public interactions and responses on Facebook to mainstream media's posts are associated with daily stock prices and trade volume of major vaccine manufacturers. Methods: We obtained mainstream media's coverage of COVID-19 vaccines and major vaccine manufacturers on Facebook from CrowdTangle, a public insights tool owned and operated by Facebook, as well as the corresponding trade volume and daily closing prices from January 2020 to December 2021. Structural topic modelling was used to analyze social media posts while regression analysis was conducted to determine the impact of Facebook reactions on stock prices and trade volume. Results: 10 diverse topics ranging from vaccine trials and their politicization (note: check that we use American spelling throughout), to stock market discussions were found to evolve over the pandemic. Although Facebook reactions were not consistently associated with vaccine manufacturers' stock prices, 'Haha' and 'Angry' reactions showed the strongest association with stock price fluctuations. In comparison, social media reactions had little observable impact on trading volume. Discussion: Topics generated reflect both actual events during vaccine development as well as its political and economic impact. The topics generated in this study reflect both the actual events surrounding vaccine development and its broader political and economic impact. While we anticipated a stronger correlation, our findings suggest a limited relationship between emotional reactions on Facebook and vaccine manufacturers' stock prices and trading volume. We also discussed potential technical enhancements for future studies, including the integration of large language models.

When does digital merger and acquisition create shareholder value? An empirical investigation in the Chinese context.

In China, acquiring firms are increasingly focused on the immediate financial returns that digital mergers and acquisitions (DM&A) can help them achieve in the stock market, but there is little literature that examines which acquiring firms achieve greater returns. Based on signaling theory, this study conceptualizes DM&A announcements as signals released by the acquiring firms to the stock market and explores the factors influencing the Chinese stock market's reaction to such signals. This research empirically investigates potential influencing factors using a short-term event methodology together with regression analysis based on the data collected in China's Shanghai and Shenzhen stock markets during 2012-2021. The research finds that the Chinese stock market reacts more positively to DM&A announcements for acquiring firms with high executive shareholdings, high executive openness, strong digital innovation capabilities, and in regions with higher levels of investor protection. This study is the first attempt to explore the factors influencing the stock market's response to DM&A in the Chinese context.

Does US monetary policy uncertainty affect returns of Asian Developed, emerging, and frontier equity markets? Empirical evidence by using the quantile-on-quantile approach.

This paper examines the impact of the Monetary Policy Uncertainty (MPU) of the United States on Asian developed, emerging, and frontier stock markets using a Quantile-on-Quantile (QQR) approach by using monthly data from January 2006 to December 2022 of 14 Asian countries. The study finds that US monetary policy significantly negatively influences Asian stock markets. This is primarily due to the widespread use of the US dollar as a universal currency, resulting in substantial ripple effects on other nations through trade relationships. In Asian developed markets, MPU is negatively related to Australia and New Zealand. At the same time, it has a positive relationship with Hong Kong and Japan at the upper quantiles. Among Asian emerging markets, MPU negatively impacts Taiwan's, India's, and China's returns, increasing this negative relationship at higher MPU quantiles. Additionally, MPU has a significant negative relationship with Thailand, Indonesia, Korea, and Malaysia returns. In contrast, higher quantiles of MPU have no discernible impact on the Philippines stock returns. In Asian frontier markets, MPU negatively impacts Pakistan's and Sri Lanka's returns. The implications of these findings are twofold: for investors, this study provides valuable insights for hedging activities, allowing for more informed decisions based on the MPU of other countries to identify profitable stocks. For policymakers, this research aids in formulating effective monetary policy strategies. Furthermore, future studies can build upon these results by exploring other markets and comparing their outcomes with the findings presented in this study.

Study on the Stability of Complex Networks in the Stock Markets of Key Industries in China.

Investigating the significant "roles" within financial complex networks and their stability is of great importance for preventing financial risks. On one hand, this paper initially constructs a complex network model of the stock market based on mutual information theory and threshold methods, combined with the closing price returns of stocks. It then analyzes the basic topological characteristics of this network and examines its stability under random and targeted attacks by varying the threshold values. On the other hand, using systemic risk entropy as a metric to quantify the stability of the stock market, this paper validates the impact of the COVID-19 pandemic as a widespread, unexpected event on network stability. The research results indicate that this complex network exhibits small-world characteristics but cannot be strictly classified as a scale-free network. In this network, key roles are played by the industrial sector, media and information services, pharmaceuticals and healthcare, transportation, and utilities. Upon reducing the threshold, the network's resilience to random attacks is correspondingly strengthened. Dynamically, from 2000 to 2022, systemic risk in significant industrial share markets significantly increased. From a static perspective, the period around 2019, affected by the COVID-19 pandemic, experienced the most drastic fluctuations. Compared to the year 2000, systemic risk entropy in 2022 increased nearly sixtyfold, further indicating an increasing instability within this complex network.

How does stock market react to environmental penalty announcements?

Environmental penalty announcement (EPA) has received increasing attention for its potential to convey valuable information and affect capital market performance. Using data on listed companies in China, this paper examines stock market reaction to environmental penalty announcements, the behavior of different types of investors, and the moderating factors of these responses. The findings show that (1) disclosure of EPA by listed companies results in negative abnormal returns, but this negative market reaction is not sustained. (2) Heavy polluters and non-state-owned enterprises are exposed to more negative abnormal returns when they disclose EPA. (3) Environmental reputation can mitigate the negative stock market reaction to EPA, while the participation of green investors will intensify this reaction. (4) Retail investors tend to sell stocks of companies that disclose EPA as media attention increases, while institutional investors increase their shareholding especially in companies that already have high holdings, high ESG scores, and in regions with low levels of green finance development. This paper serves as a reference for governments, firms, and stakeholders on stock market reaction to environmental information disclosures.

Impact of sentimental factors on stock portfolio returns -an empirical analysis.

This study aims to introduce an integrated model for understanding the influence of various sentimental factors in conjunction with macroeconomic factors on portfolio returns across ten industry sectors within the US market. These sentimental factors are categorized into market-wide, consumer, and individual stock market factors to assess their impact on industry portfolio returns. Employing the Autoregressive Distributed Lag (ARDL) model, the study evaluates the effects of macroeconomic and sentimental factors on stock market portfolio returns. The findings reveal a negative relationship between short-term interest rates and portfolio returns in specific industry sectors like manufacturing, telecom, and wholesale/retail. The study finds a positive relationship between the Hi-tech sector's risk spread and portfolio returns. Market sentimental factors positively influence portfolio returns of durable, non-durable, utility, and other sectors. Individual sentimental factors negatively impact portfolio returns in hi-tech, utility, durable, energy, and other sectors. The stock market-related individual, sentimental factor of the number of IPOs has a positive impact on portfolio returns in the energy sector and a negative impact on portfolio returns in other sectors. Consumer sentimental factors are significant positive determinants for portfolio returns in durable, energy, telecom, health, and other sectors. Discounts on closed-end funds may provide vital fundamental information regarding lower future earnings for stocks in the durable and energy sectors. The study provides valuable insights for investors to optimize their portfolio strategies in response to macroeconomic and sentimental factors within specific industry sectors.

Herding during different types of crises: The COVID-19 health crisis and Russia-Ukraine political crisis.

We analysed herding behaviour in the recent pandemic and conflict. We employed the cross-sectional dispersion of daily stock returns to estimate herding's intensity in the Saudi stock market. We conducted all analyses for the entire sample and four sub-samples. Additionally, we investigate the existence of the asymmetry in the investors' responses; whether there are differences between up and down markets and between high-volatility and low-volatility days. We found that herding did not occur in the pre-COVID-19, occurred in the during-COVID-19, disappeared in the post-COVID-19 and did not occur during the Russia-Ukraine conflict. Robustness checks confirm our finding that herding manifested in the during-COVID-19 period. Additionally, no difference exists between bearish and bullish or high-and low-volatility days, pushing aside the asymmetry in the investors' responses. This study may raise investors' awareness of their cognitive bias's influence on their decisions, improving market efficiency by increasing the rationality of investors' decisions.

Chinese stock market integration with developed world: A portfolio diversification analysis.

This study investigates integration dynamics between the Chinese stock market and major developed counterparts-Australia, Germany, Japan, the UK, and the US-focusing on portfolio diversification. Using a comprehensive analytical approach from 2012 to 2022, encompassing events like the Belt and Road Initiative, the Shanghai market crash, US-China trade tensions, and the COVID-19 pandemic, the research employs descriptive statistics, unit root tests, cointegration analysis, and VECM-based Granger Causality Tests. Findings indicate modest integration, endorsing diversified portfolios for developed country investors due to higher returns in China with acceptable risk. Unit root analysis confirms cointegration with developed indices, indicating relatively low integration. Granger Causality Tests reveal bidirectional causality, emphasizing mutual influence. Notably, no causal link exists between the US and China, possibly due to regulatory disparities and the trade war. The study enhances understanding of Chinese stock market dynamics, supporting global economic intertwining and urging further openness of China's domestic shares for economic growth.

Regional and periodic asymmetries in the effect of Russia-Ukraine war on global stock markets.

This study aims to investigate regional and periodic asymmetries in the impact of the outbreak of the Russia-Ukraine war on global equity markets. Employing the event study methodology, the current study examines global stock market reactions within a 61-day window centred around the event day, i.e., February 24, 2022. MSCI equity indices of 47 sample countries have been utilized to ensure uniformity in the index development methodology. They provide broader coverage of global equity markets by including large and mid-cap companies, representing approximately 85% of the free float-adjusted market capitalization for each sampled country. The study extends the event window to 61 days to assess the enduring effects of the war over a relatively longer period. The research delineates regional and periodic asymmetries and posits that the impact of the war on a market is contingent upon its geographical proximity and trade relations with Russia and Ukraine. Additionally, the impact is stronger during a shorter window surrounding the event date but diminishes over the extended period.

LEET: stock market forecast with long-term emotional change enhanced temporal model.

The stock market serves as a macroeconomic indicator, and stock price forecasting aids investors in analysing market trends and industry dynamics. Several deep learning network models have been proposed and extensively applied for stock price prediction and trading scenarios in recent times. Although numerous studies have indicated a significant correlation between market sentiment and stock prices, the majority of stock price predictions rely solely on historical indicator data, with minimal effort to incorporate sentiment analysis into stock price forecasting. Additionally, many deep learning models struggle with handling the long-distance dependencies of large datasets. This can cause them to overlook unexpected stock price fluctuations that may arise from long-term market sentiment, making it challenging to effectively utilise long-term market sentiment information. To address the aforementioned issues, this investigation suggests implementing a new technique called Long-term Sentiment Change Enhanced Temporal Analysis (LEET) which effectively incorporates long-term market sentiment and enhances the precision of stock price forecasts. The LEET method proposes two market sentiment index estimation methods: Exponential Weighted Sentiment Analysis (EWSA) and Weighted Average Sentiment Analysis (WASA). These methods are utilized to extract the market sentiment index. Additionally, the study proposes a Transformer architecture based on ProbAttention with rotational position encoding for enhanced positional information capture of long-term emotions. The LEET methodology underwent validation using the Standard & Poor's 500 (SP500) and FTSE 100 indices. These indices accurately reflect the state of the US and UK equity markets, respectively. The experimental results obtained from a genuine dataset demonstrate that this method is superior to the majority of deep learning network architectures when it comes to predicting stock prices.

Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities.

Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets' prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks' data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters' optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets' data. Profitable and timely trading strategies can be formulated based on our proposed prediction model.

A systematic literature survey on recent trends in stock market prediction.

Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions.

A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN.

Time series, including noise, non-linearity, and non-stationary properties, are frequently used in prediction problems. Due to these inherent characteristics of time series data, forecasting based on this data type is a highly challenging problem. In many studies within the literature, high-frequency components are commonly excluded from time series data. However, these high-frequency components can contain valuable information, and their removal may adversely impact the prediction performance of models. In this study, a novel method called Two-Level Entropy Ratio-Based Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (2LE-CEEMDAN) is proposed for the first time to effectively denoise time series data. Financial time series with high noise levels are utilized to validate the effectiveness of the proposed method. The 2LE-CEEMDAN-LSTM-SVR model is introduced to predict the next day's closing value of stock market indices within the scope of financial time series. This model comprises two main components: denoising and forecasting. In the denoising section, the proposed 2LE-CEEMDAN method eliminates noise in financial time series, resulting in denoised intrinsic mode functions (IMFs). In the forecasting part, the next-day value of the indices is estimated by training on the denoised IMFs obtained. Two different artificial intelligence methods, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), are utilized during the training process. The IMF, characterized by more linear characteristics than the denoised IMFs, is trained using the SVR, while the others are trained using the LSTM method. The final prediction result of the 2LE-CEEMDAN-LSTM-SVR model is obtained by integrating the prediction results of each IMF. Experimental results demonstrate that the proposed 2LE-CEEMDAN denoising method positively influences the model's prediction performance, and the 2LE-CEEMDAN-LSTM-SVR model outperforms other prediction models in the existing literature.

How effective is machine learning in stock market predictions?

In this study, it is aimed to compare the performances of the algorithms by predicting the movement directions of stock market indexes in developed countries by employing machine learning algorithms (MLMs) and determining the best estimation algorithm. For this purpose, the movement directions of indexes such as the NYSE 100 (the USA), NIKKEI 225 (Japan), FTSE 100 (the UK), CAC 40 (France), DAX 30 (Germany), FTSE MIB (Italy), and TSX (Canada) were estimated by employing the decision tree, random forest k-nearest neighbor, naive Bayes, logistic regression, support vector machines and artificial neural network algorithms. According to the results obtained, artificial neural networks were found to be the best algorithm for NYSE 100, FTSE 100, DAX 30 and FTSE MIB indices, while logistic regression was determined to be the best algorithm for the NIKKEI 225, CAC 40, and TSX indices. The artificial neural networks, which exhibited the highest average prediction performance, have been determined as the best prediction algorithm for the stock market indices of developed countries. It was also noted that artificial neural networks, logistic regression, and support vector machines algorithms were capable of predicting the directional movements of all indices with an accuracy rate of over 70 %.

Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers.

This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as "#Borsaistanbul, #Bist, #Bist30, #Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches.

Exploring the interwoven relationship: Property rights, financial freedom, government regulation, and stock market fluctuations in emerging economies - A novel system GMM perspective.

This study delves into the impact of formal institutions on stock market volatility within a selection of emerging economies. Specifically, it examines the role that formal institutions play in shaping this volatility. To accomplish our goal, we analyze panel data from 46 emerging nations spanning the years 2000-2019, utilizing system generalized method of moments (GMM), as well as random and fixed effect models for our estimations. The findings of this research validate the existence of a significant association between formal institutions and stock market volatility. Likewise, through dynamic panel estimation, we discover that formal institutions such as property rights, financial freedom, and government regulations have a notable negative effect on stock market volatility. Consequently, this study implies that formal institutions play a crucial role in reducing stock market volatility in emerging economies, fostering their development. The insights gained from this research encourage policymakers to view formal institutions as key influencers of stock market volatility. These results offer valuable guidance for emerging nations.

Indifferent to CSR performance? Market liberalization brings about changes.

The relationship between stock market performance and corporate social responsibility (CSR) activities indicates the extent of acceptance by investors of such activities. Based on a sample of Chinese-listed companies and a quasi-natural experiment, we explore the impact of an important form of market liberalization-the Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect-on the extent of investors' acceptance of CSR using a difference-in-differences approach. The results show that Stock Connect reinforces the favorable correlation between CSR performance and the market response to CSR reports, as well as the positive association between CSR performance and Tobin's Q. This effect is particularly pronounced for nonstate-owned enterprises,firms with lower agency costs, and firms with a lower possibility of using CSR reports for impression management. Thus, market liberalization results in the improved short- and long-term stock market performance of companies with strong CSR records. Further research shows that the benefits of the program are more prominent for companies without Qualified Foreign Institutional Investors (QFII) shareholdings, and the impact of significant foreign ownership through the Stock Connect program on the stock market performance related to CSR engagement is incremental over QFII ownership. The findings support the continuous liberalization of China's capital market.

The dynamic causality between Chinese and ASEAN stock markets.

COVID-19 has caused severe shocks to the Chinese and ASEAN stock markets. This paper investigates the relationship between the Chinese and ASEAN stock markets using the bootstrap rolling-window causality test. The results show that there is a bidirectional Granger causality relationship between the Chinese and ASEAN stock markets with time-varying characteristics. Before the COVID-19 outbreak, the interaction between the Chinese and ASEAN stock markets was mainly positive. After the COVID-19 outbreak, during the off-peak period, the interaction between the Chinese and ASEAN stock markets was positive or negative at different periods; during the peak period of the epidemic, the ASEAN stock markets had negative impacts on the Chinese stock market. In addition, the relationship between the Chinese and ASEAN stock markets was enhanced during COVID-19. According to the interaction mechanism, economic and political factors would affect the relationship between the Chinese and ASEAN stock markets, but major events such as COVID-19 have a greater impact. Therefore, macroeconomic policy should play a positive role in the stock market.

The Impact of the COVID-19 Pandemic on Stock Market Performance in G20 Countries: Evidence from Long Short-Term Memory with a Recurrent Neural Network Approach.

In light of developing and industrialized nations, the G20 economies account for a whopping two-thirds of the world's population and are the largest economies globally. Public emergencies have occasionally arisen due to the rapid spread of COVID-19 globally, impacting many people's lives, especially in G20 countries. Thus, this study is written to investigate the impact of the COVID-19 pandemic on stock market performance in G20 countries. This study uses daily stock market data of G20 countries from January 1, 2019 to June 30, 2020. The stock market data were divided into G7 countries and non-G7 countries. The data were analyzed using Long Short-Term Memory with a Recurrent Neural Network (LSTM-RNN) approach. The result indicated a gap between the actual stock market index and a forecasted time series that would have happened without COVID-19. Owing to movement restrictions, this study found that stock markets in six countries, including Argentina, China, South Africa, Turkey, Saudi Arabia, and the United States, are affected negatively. Besides that, movement restrictions in the G7 countries, excluding the United States, and the non-G20 countries, excluding Argentina, China, South Africa, Turkey, and Saudi, significantly impact the stock market performance. Generally, LSTM prediction estimates relative terms, except for stock market performance in the United Kingdom, the Republic of Korea, South Africa, and Spain. The stock market performance in the United Kingdom and Spain countries has significantly reduced during and after the occurrence of COVID-19. It indicates that the COVID-19 pandemic considerably influenced the stock markets of 14 G20 countries, whereas less severely impacting 6 remaining countries. In conclusion, our empirical evidence showed that the pandemic had restricted effects on the stock market performance in G20 countries.