Actual Stock Price Research Articles

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Stock price prediction using combined GARCH-AI models

The non-linear and non-stationary nature of financial time series data poses significant challenges for standalone statistical and neural network methods. While predictive modeling in finance often focuses on volatility, there is a notable lack of research on predicting actual stock prices, particularly in the African market. This study addresses this gap by utilizing Airtel stock data from Yahoo Finance, spanning June 28, 2019, to May 8, 2024. The research employs the GARCH model to extract statistical properties, which are then combined with historical prices and fed into LSTM, GRU, and Transformer models leading to GARCH-LSTM, GARCH-GRU, GARCH-Transfomer hybrid models. These hybrid models are benchmarked against standalone LSTM, GRU and Transfomer models using RMSE, MAE, MAPE, and R-squared metrics. Results indicate that hybrid models, especially GARCH-LSTM, significantly outperform standalone models. This integration of GARCH with advanced AI models offers a more robust framework for stock price prediction, enhancing accuracy and reliability in forecasting future prices.

PMANet: a time series forecasting model for Chinese stock price prediction

Forecasting stock movements is a crucial research endeavor in finance, aiding traders in making informed decisions for enhanced profitability. Utilizing actual stock prices and correlating factors from the Wind platform presents a potent yet intricate forecasting approach. While previous methodologies have explored this avenue, they encounter challenges including limited comprehension of interrelations among stock data elements, diminished accuracy in extensive series, and struggles with anomaly points. This paper introduces an advanced hybrid model for stock price prediction, termed PMANet. PMANet is founded on Multi-scale Timing Feature Attention, amalgamating Multi-scale Timing Feature Convolution and Ant Particle Swarm Optimization. The model elevates the understanding of dependencies and interrelations within stock data sequences through Probabilistic Positional Attention. Furthermore, the Encoder incorporates Multi-scale Timing Feature Convolution, augmenting the model's capacity to discern multi-scale and significant features while adeptly managing lengthy input sequences. Additionally, the model's proficiency in addressing anomaly points in stock sequences is enhanced by substituting the optimizer with Ant Particle Swarm Optimization. To ascertain the model’s efficacy and applicability, we conducted an empirical study using stocks from four pivotal industries in China. The experimental outcomes demonstrate that PMANet is both feasible and versatile in its predictive capability, yielding forecasts closely aligned with actual values, thereby fulfilling application requirements more effectively.

An Effective Composite Investment Quantitative Strategy with Machine Learning Method

In recent years, significant breakthroughs in AI and machine learning have garnered international attention. One such advancement is the development of key algorithms, including "machine self-learning and self-coding algorithms." These algorithms enable machines to continually learn from data and adjust parameters autonomously, without human intervention. Additionally, machines engage in "reinforcement learning," where algorithms achieve set goals through constant trial and error in specific environments. This has allowed current AI systems to adapt to more complex settings and learn more intricate content. In terms of data processing, AI has also made significant strides, particularly with "Recurrent Neural Networks" enabling current AI to handle sequential data more effectively like neural networks, natural language texts, and speech. Consequently, applying these technologies in the financial investment sector has become a hot topic in the industry. This paper outlines the limitations of traditional methods and proposes a new quantitative composite investment strategy. Our main approach involves designing an intelligent investment advisory model capable of self-learning, continuous self-improvement, and market adaptation. Our process includes analyzing industries, collecting extensive stock and securities data, and eliminating noise data. We then construct a model and assess its stability. Finally, accuracy is determined through an indicator. The results of our method show a high degree of similarity between the predicted stock prices and actual stock prices, indicating the effectiveness of our approach. Thus, our method offers a valuable reference for future applications.

A Comparative study of the Envisaged and Definite Stock Prices of BSE SMEs Using RNN during the COVID-19 Pandemic

The stock market is unstable, but the use of machine learning algorithms allows to predict its future dynamics before spending. The most popular area of scientific research right nowadays is machine learning, which involves enabling computers to perform tasks that often require human intelligence. The purpose of this paper is to construct a model using a network of Long-Short Term Memory model (LSTM) to forecast future stock market values. The paper presents the advantages and disadvantages of machine learning for assessing and forecasting the stock market. A review of literature on the application of machine learning models in key areas of finance using methodological model assessment and data manipulation is also available. This paper focuses on the losses of the SME sector due to COVID-19 by doing a comparative study using secondary data collection between the predicted closed stock prices and actual stock prices of the BSE SME IPO index for the period from 1 January 2018 to 30 April 2021.The LSTM network of Recurrent Neural Networks (RNNs) most effective deep learning model, is used to predict stock prices. The study provides insight and direction on where lockdown has a massive impact on the stock prices of BSE SME IPOs. The authors developed a model for predicting the future value of stock in the market, the application of which gave some positive results, demonstrating the need for machine learning and how it can change the world of finance. The novelty of the study is that in India, machine learning and deep learning methods in the field of finance are used much less often than in other countries.

Incorporating Stock Prices and Social Media Sentiment for Stock Market Prediction: A Case of Indonesian Banking Company

Forecasting the stock market is one of the most popular topics to be discussed in many fields. Many studies, especially in information technology have been conducted machine learning algorithms to achieve a more accurate prediction of the stock market. This research aims to find the effectiveness in predicting stock market performance by utilizing social media sentiment in combination with historical data. In addition, this research uses a machine learning algorithm to train a model to predict the stock price of each bank and training the model on a dataset that included the historical stock prices of the bank, as well as the sentiment scores of the social media posts about the bank and evaluate the performance of the model by comparing the predicted stock prices to the actual stock prices. The research shows that the R2 and RMSE score model that has been built with its historical data has slightly better performance than the model that has been built with the combination of historical data and social media sentiment. The finding indicates that the research method is closely correlated and affected to the performance of the stock market prediction.

Stock Value Analysis Based on the DDM Model: An Example of Company COSCO

The stock market is unpredictable and there are many ways and means to analyze stocks, DDM is still worth to be analyzed and discussed as one of the less common but very basic and important analytical means. This paper, will focus on GGM in DDM and apply it to COSCO Shipping Holding to find out whether DDM is applicable to this stock and whether this stock is worth investing in. In this paper, it obtains the relevant data from the company's annual report as well as the financial data website, calculate and derive the final estimate by using the DDM formula. The estimated value will be compared with the actual value to determine whether the stock is worth investing in or not. In the end, the estimated value calculated by DDM is much larger than the actual stock price, which means that the stock is seriously undervalued and may bring great returns according to the principle of DDM. Combining the actual situation and objective factors, this paper concludes that the principle and formula of DDM are very important, but it is not applicable to all situations. The necessary conditions are more demanding, and they need to be combined with many other theories to analyze many aspects.

External Event Crises on The Stock Market: Taking Japan’s Sea Discharge Incident as An Example

On August 22, 2023, the Japanese government announced that it would start discharging nuclear wastewater into the sea from August 24. This decision was widely questioned and opposed by the international community and caused strong concerns in Japan. This article aims to take the Japanese nuclear sewage discharge incident into the sea as an example to explore the impact of social crisis on Japan's domestic stock market. The main research method of this article is based on stock return analysis, using the ARIMA model to predict the Japanese stock price trend after excluding the impact of this event, and then comparing it with real data. The results of this study provide evidence that the predicted stock prices are close to the actual stock prices, indicating that as of mid-September, there was no short-term negative impact on the Japanese stock market due to the event, but from a long-term perspective, the predictions remain It is uncertain whether stock prices in Japan's financial markets will fall because of this incident. The main conclusion drawn from this study is that external events may not have a large impact on stock prices in the short term. Different from previous literature, this study uses ARIMA analysis on daily data and weekly data to obtain the relationship between them. Recommendations include organizing a team of experts; hiring a legal team; establishing a complete procedure to handle social media information, and having a dedicated public relations team may be helpful.

Google Stocks Prediction by Machine Learning of RNN and LSTM Techniques

The objective of this study is to utilize a combined model of two algorithms, namely Long Short-Term Memory network and Recurrent Neural Network, to forecast the stock price of Google. Using Google stock price data from 2010 to 2022 as the training set and performed data preprocessing and feature engineering. This then build a deep neural network model consisting of multiple LSTM and RNN layers and train it by the backpropagation algorithm. During training, this paper employs an appropriate loss function and optimizer to minimize the prediction error. In conclusion, the performance of the model was assessed by employing Google stock price data from 2023 as a test set. By comparing the error between the actual stock price and the predicted value of the model, it can evaluate the accuracy and stability of the model. The experimental results show that the superposition model using LSTM and RNN algorithms can effectively predict the Google stock price with high accuracy and stability. This research presents a practical approach that can enhance the predictive capabilities of investors, financial institutions, and other related domains, enabling them to make well-informed investment decisions in the stock market.

Unraveling the Impact of the Federal Reserve Rate Hike Policy on Amazons Stock Price Based on ARIMA Model

At the beginning of 2022, the inflation rate of the US economy was high. Consequently, the Federal Reserve initiated a series of 11 consecutive interest rate hikes on March 16, 2022. In this macroeconomic environment of rising interest rates, this article aims to use the ARIMA model to predict Amazons stock price after the interest rate hike, based on its stock price before the interest rate hike. It also seeks to compare the difference between the predicted stock price and the actual stock price to assess the impact of the successive rate hike policies on Amazons stock price. The study found that the Feds continuous rate hike policy would significantly reduce Amazons stock price, with its long-term relative decline even exceeding 30 percent. In comparison to previous articles that focused on the stock market or the Federal Reserves interest rate hike policy, this article no longer concentrates solely on theoretical analysis or macroeconomic market changes. Instead, it utilizes quantitative methods to place a greater emphasis on Amazon, a representative American internet company. This more specific perspective and more specific data enable investors to gain a clearer understanding of the direction and magnitude of the impact of interest rate hike policies on individual company stock prices. Based on the research results, this paper suggests that policymakers should carefully consider the policy of raising interest rates to avoid causing panic in the stock market. Entrepreneurs should strive to create more positive news, and investors should be cautious about holding stocks in an environment of rising interest rates.

Research on the Option Pricing of Tesla’s Stock Based on B-S Model and Binomial Tree Method

With the announcement of price drop of Tesla’s multiple vehicles during 2022 and 2023, its stock also entered a very unstable age which cause many investors meet significant loss, so did its option prices. In this article, B-S model, Binomial Tree, and Monte Carlo Simulation are implemented to do research on Tesla’s stock price and its call options. The results shows that B-S model has a much lower value of option prices when Binomial Tree does not have multiple division of time period. On the other hand, Monte Carlo Simulation indicates that it would be hard to predict the actual price trend since actual price went to a very low percentile among all simulations. The meaning of this research is to prove Binomial Tree is complementary to B-S model with introduce the concept of time whereas B-S model only uses actual stock prices and calculate the average. Monte Carlo Simulation also warns the investors to continue to pay attention to the news and incidents happened around the companies they invested to.

Research on momentum strategy and contrarian strategy in AI stock prediction

The emergence of ChatGPT has significantly enhanced the recognition and acceptance of artificial intelligence concept stocks within the Chinese stock market. Nevertheless, the short- and long-term fluctuations in the prices of AI companies remain uncertain. Therefore, the purpose of this research is to determine optimal strategy for evaluating the suitability of the contrarian strategy versus the momentum strategy in predicting the stock prices of AI concept stocks in the Chinese stock market. Based on a cross-comparison of the Chinese financial data sources iFinD and Wind Economic Database (EDB), this study collects the price data of AI concept stocks over the past six months, starting from the date of ChatGPT's publication. This study employ Python to model stock price movements for both the momentum and reversal strategies. The goodness of fit is evaluated by comparing the modeled stock prices with the actual stock prices. This study demonstrates that the momentum strategy exhibits greater explanatory power than the contrarian strategy, accurately predicting 84.21% of artificial intelligence concept stocks. However, other studies suggest that while AI concept stocks continue to rise, momentum strategies remain effective, whereas when market sentiment cools down, contrarian strategies become more suitable for Chinese AI concept stocks. Hence, in China, the effectiveness of these strategies may vary depending on the prevailing market conditions.

The Impact of the Opening Policy of the COVID-19 on Catering, Tourism and Hotel Industries

On December 7, 2022, China officially released management of the COVID-19, and the economic level of various industries began to gradually rebound, and people's consumption levels also gradually improved. In the consumer industry, the recovery of the catering, tourism, and hotel industries was first perceived by the market. In order to explore the impact of the COVID-19 liberalization policy on these three industries, this paper used the ARIMA model to analyze the stock closing price data of the industries before the COVID-19 liberalization, simulated the stock price trend without the implementation of the COVID-19 liberalization policy, and compared it with the actual stock price. Through the analysis, it was found that the policy of comprehensive COVID-19 control has promoted the economic development of China's catering, tourism, and hotel industry. The economic situation of all three industries has rebounded to a certain extent. Especially for the catering industry, the promotion effect is most obvious, followed by the hotel industry, and finally the tourism industry. The research in this article can help the government better respond to the current economic recovery situation, propose corresponding incentive policies, and promote the healthy development of China's catering, tourism, and hotel industries.

The Impact of China's Pandemic Deregulation Policy on SSEC and SZSE Indexes

In early stages of the COVID-19 outbreak, Chinese stock market experienced a sharp decline as investors became increasingly concerned about the economic consequences of the pandemic. As the pandemic continued to spread globally, the Chinese government implemented strict measures to control its spread, which included lockdowns, travel restrictions, and other forms of social distancing. Although these measures worked, economy was drastically affected with many stores closing down and consumer expenditures significantly declining. This, meanwhile, led to a decrease in corporate profits and a reduction in investor confidence. However, on December 8, 2022, the Chinese government issued a pandemic deregulation policy identifying people would return to their normal life. In this paper, prices of the Shanghai Stock Exchange Composite (SSEC) index and Shenzhen Securities Component (SZSE) index were retrieved and ARIMA method was adopted to predict the stock prices for a period after the pandemic. The author compared forecast prices with the actual stock prices and then analyzed the implications of the deregulation policy on the stock market. These two indexes are only a snapshot of the Chinese economy, and certain informative feedback can be obtained through this study, which is helpful to relevant investors and policy makers.

The Analysis of the Stock Price of AAPL Based on the Comparison Between the Intrinsic Stock Price and Actual Stock Price

As Apple becomes a world-leading technology company, it is no doubt that its stock has brought a huge amount of profit to its investors. Many investors rely on financial modules to estimate the change in stock values and make the final decision. They contribute significantly to the decision-making of investors. This paper intends to use the composition of the models to evaluate the monthly stock price of AAPL in the past five years with the composition of the models and compare it with the real open stock price. It turns out that the real value was overvalued in the first four years yet undervalued in the fifth year.

STOCK PRICE PREDICTION USING ARTIFICIAL NEURAL NETWORKS WITH THE ADALINE METHOD

Prediction of stock prices has an important role in making investment decisions in financial markets. In this study, researchers propose the use of artificial neural networks with the Adaline method to predict stock prices. The Adaline method is a learning algorithm that iteratively adapts network weights to minimize prediction errors. In the early stages, researchers collect relevant stock price historical data and process it into a training dataset. Next, build an artificial neural network with one input layer, one output layer, and use a linear activation function. The network weights are initialized randomly, and the training process begins. In network training, the researcher applies the Adaline algorithm with the descending gradient method to optimize the objective function. The researcher updates the network weights based on the difference between the actual stock price and the stock price predicted by the network. This process is repeated iteratively until it converges or reaches the specified stopping criteria. After the training is complete, the researcher evaluates the network performance using validation data that was not used in the training. Researchers used evaluation metrics, such as the mean absolute error (MAE) and root mean square error (RMSE), to measure the accuracy of network predictions. In addition, researchers also compared the performance of the Adaline network with other prediction methods, such as linear regression. The experimental results show that the artificial neural network with the Adaline method is able to provide accurate stock price predictions. Researchers observed a significant reduction in prediction error compared to the linear regression method. In addition, the Adaline network also shows good adaptability to changes in market trends. This research makes an important contribution to the development of stock price prediction methods using artificial neural networks. The results can be used as a guide for investors and stock traders to make more informed and effective investment decisions. Keywords : stock price prediction, artificial neural network, Adaline method, learning algorithm, performance evaluation.

Stock Price Correlation Analysis with Twitter Sentiment Analysis Using The CNN-LSTM Method

The intricate interplay between stock prices, reflecting a company's intrinsic value, and multifaceted factors like economic conditions, corporate performance, and market sentiment, constitutes a vital research domain. Grounded in sentiment analysis, our study deciphers public opinions from vast textual data to gauge sentiment, leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. We focus on Bank Central Asia (BBCA), a prominent Indonesian banking institution, aiming to forecast stock price fluctuations by analyzing sentiment trends extracted from social media, especially Twitter. Meticulous experimentation, encompassing data segmentation, feature extraction, augmentation, and model refinement, yields significant enhancements in prediction accuracy. The CNN-LSTM model's performance improves from 73.41% to a robust 77.75% accuracy, with F1-scores rising from 73.00% to 75.42%. Importantly, strong correlations emerge between sentiment predictions and actual stock price movements, validated by Spearman correlation coefficients. Positive sentiment exhibits a substantial correlation of 0.745 with stock price changes, while negative sentiment exerts notable influence with a correlation coefficient of 0.691. In summary, our study advances the field of sentiment-driven stock price prediction, showcasing deep learning's effectiveness in extracting sentiment from social media narratives. The implications extend to understanding market dynamics and potentially integrating sentiment-aware strategies into financial decision-making. Future research directions could explore model transferability across financial contexts, real-time sentiment data integration, and interpretability techniques for enhanced practicality in sentiment-driven predictions.

Online portfolio selection of integrating expert strategies based on mean reversion and trading volume

In this paper, we propose an effective online portfolio selection strategy by integrating expert opinions, which are obtained based on mean reversion and trading volume. Existing studies have found that mean reversion and high volume premium exist in the stock market in the short term. Some online portfolio strategies have been proposed that rely upon mean reversion, but it is rare to consider both mean reversion and high volume premium, which is what we will do in this paper. First, a portfolio based on mean reversion and high volume premium is constructed using recent window data. Second, a pool of portfolios, also known as a pool of expert strategies or expert opinions, is established by changing the window size. Finally, the online gradient update algorithm is adopted to integrate a pool of expert strategies, and the MRvol strategy in this paper is proposed. Theoretically, we prove that the regret of MRvol is bounded. Empirically, we use the actual stock price and trading volume data from different markets to test the performance of MRvol. The results show that MRvol performs better than other online strategies in terms of final cumulative wealth and risk-adjusted return metrics in most cases, among which the annualized percentage yield on all datasets is 16%–37%, and the average values of Sharpe ratio, Calmar ratio and Information ratio are 1.113, 0.9216 and 0.0299, respectively. Additionally, MRvol has linear computational time complexity, and the running time of test results is all less than 1 s. Furthermore, it maintains strong robustness under different parameters and can bear reasonable transaction costs.

Stock Analysis Based on Valuation Model Application-Taking APPL Company as an Example

This article mainly studies the application of value evaluation model in the securities market. At the same time, this article also applies the discounted free cash flow method (DCF) to the equity value evaluation of APPL. By constructing a financial forecasting model, the future free cash flow of the company is calculated, and then the equity value and per share value of the company are obtained on the basis of appropriate discount rate and growth rate. Finally, through comparison with the actual stock price, the investment value and risk of the company are analyzed. At the same time, by combining professional knowledge with value evaluation methods, researchers can make scientific, systematic, and comprehensive stock analysis to help them make correct investment decisions. This article mainly introduces the basic theory and application methods of value evaluation and provides a detailed analysis through examples. The empirical results show that using this method to value securities can provide more accurate and comprehensive valuation, which helps investors understand the value and risk of securities.

The Impact Caused by COVID-19 Pandemic on Pfizer Inc. Stock Price: Based on a Counterfactual Framework

As the coronavirus pandemic spreads worldwide, many nations have implemented social distance regulations and quarantine measures to stop the spread of the COVID-19 epidemic. During this period, people's consumption levels dropped all over the world, resulting in a decrease in the capacity of the entire society to produce goods and services. As a result, thousands of workers lost their jobs and numerous manufacturing plants, entertainment companies struggled financially until going out of business. However, the value of some biopharmaceutical firms' shares has climbed rather than fallen due to some of these businesses made great contribution on preventing the spread of the pandemic by developing vaccines and useful medications. In this article, an American multinational pharmaceutical and biotechnology corporation, Pfizer Inc. was selected and daily stock data from February 2009 to December 2022 was extracted. The ARIMA model was applied to forecast the stock price of Pfizer Inc. without the Covid-19 pandemic. This study focuses on comparing the forecasted stock price with actual stock price and explores the possible reasons for this difference.