Apple Stocks Research Articles

The Effect of Monetary Policies in COVID and Post-COVID on the Apple Stock Price and Beta

Abstract: Central bank interventions, such as monetary policies, influence market activities in many ways. For example, the stock price and beta value. Monetary policy is a mechanism to stabilize the market by adjusting the interest rate. Theoretically, the interest rate negatively relates to stock price and beta value. However, based on other previous research, the relationship can vary across industries and countries. Other determinants can also co-influence the stock activities, including business policies. Thus, each company experienced various results from the monetary policies during the COVID and post-COVID periods. Specifically, how did Apple Inc.s stock price and beta value react to the monetary policies released by FOMC? This paper explores Apple Inc. stock price and beta under the influence of monetary policies in the COVID and post-COVID period. Additionally, this paper finds that there is a significant negative relationship between the interest rates and stock price in COVID, and an insignificant negative relationship in post-COVID; There is an insignificant negative relationship between the interests rates and stock beta in COVID, and an insignificant positive relationship in post-COVID. Hence, stock price and beta are largely influenced by market dynamics and extrinsic factors. The contradictory result can be caused by the policies Apple Inc. released and the imprecise calculation.

Effectiveness of AI-based Machine Learning Algorithms in Predicting Global Market Movements

The challenging yet traditional field of stock market forecasting has garnered significant interest from data scientists and economists. Given the high risks associated with trading, where investors can lose part or all of their initial capital, there is a growing need for advanced methods to support investment decisions. By leveraging the capability to predict future stock performance patterns by machine learning or AI, the return on investment for short-term trading can potentially be increased. This research reviews the structures of various AI-based machine learning techniques with a case study of Apple stock data spanning from 2010 to 2021 and the effectiveness of the algorithms in terms of various real-time factors. Among several machine learning algorithms, the SVM algorithm is shown to be the most accurate, with an accuracy rate of 76% for the case study. However, the effectiveness of machine learning systems in predicting stock prices is influenced by several factors. The inherent volatility and unpredictability of financial markets make it challenging for models to anticipate sudden, unforeseen events. Overfitting is a common issue where models that perform well on historical data fail to generalize to new, unseen data because they capture noise instead of genuine market trends. Data quality issues, such as noisy or incomplete data, further complicate predictions. Additionally, the complexity of machine learning models can hinder interpretability, making it easier for analysts to trust and utilize their outcomes. This research underscores the necessity for careful and ongoing refinement of ML techniques in stock price forecasting.

Analysis of Apple Stock - Based on R

Analysis of Apple Stock - Based on R

Predicting the stock opening price of Apple company

As the stock market plays a crucial role in the world economy, researchers have used multiple mathematical and statistical models such as Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks model to forecast the fluctuation in stock price despite their unpredictability as the stock market, being a stochastic process, would be easily affected by an abundance of factors such as governmental policies, industrial news, and natural calamities. Therefore, based on the previous studies, this paper attempts to forecast the stock opening price of Apple Inc., one of the world-leading companies in the technology industry, utilizing the Autoregressive Integrated Moving Average (ARIMA) model. In order to minimize the impact on the stock market brought by the COVID-19 pandemic, this paper will analyze separately the opening price of Apple stock before and after the epidemic outbreak and will compare the difference the pandemic made in the stock market, as well as the forecasting models.

Pendekatan Metode Monte Carlo untuk Simulasi Pergerakan Harga Saham

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

Evolution of the SPX gene family and its role in the response mechanism to low phosphorus stress in self-rooted apple stock

BackgroundPhosphorus plays a key role in plant adaptation to adversity and plays a positive role in the yield and quality formation of apples. Genes of the SPX domain-containing family are widely involved in the regulation of phosphorus signalling networks. However, the mechanisms controlling phosphorus deficiency are not completely understood in self-rooted apple stock.ResultsIn this study, 26 members of the apple SPX gene family were identified by genome-wide analysis, and further divided into four subfamilies (SPX, SPX-MFS, SPX-EXS, and SPX-RING) based on their structural features. The chromosome distribution and gene duplications of MdSPXs were also examined. The promoter regions of MdSPXs were enriched for multiple biotic/abiotic stresses, hormone responses and typical P1BS-related elements. Analysis of the expression levels of 26 MdSPXs showed that some members were remarkably induced when subjected to low phosphate (Pi) stress, and in particular MdSPX2, MdSPX3, and MdPHO1.5 exhibited an intense response to low Pi stress. MdSPX2 and MdSPX3 showed significantly divergent expression levels in low Pi sensitive and insensitive apple species. Protein interaction networks were predicted for 26 MdSPX proteins. The interaction of MdPHR1 with MdSPX2, MdSPX3, MdSPX4, and MdSPX6 was demonstrated by yeast two-hybrid assay, suggesting that these proteins might be involved in the Pi-signaling pathway by interacting with MdPHR1.ConclusionThis research improved the understanding of the apple SPX gene family and contribute to future biological studies of MdSPX genes in self-rooted apple stock.

Research on Apple’s Stock Price Trend Forecasting

As the world's largest company by market capitalization, Apple has attracted the attention of many investors. Many investors have developed a strong interest in Apple stocks. However, it is not easy to study the trend of Apple stocks. Because there are many factors affecting Apple's stock price changes, it is very complicated and difficult to review the details of all these factors. Predicting and analyzing stock prices can provide investors with practical tools to raise funds and reduce investment risks. This paper starts with the time series research method of stock prices, which has proved to be a relatively effective method. Through this method, this paper can have an in-depth understanding of the fluctuation of stock prices, so as to better grasp market trends and make more informed investment decisions. By using CNN and LSTM models for prediction, the author finds that it rose by an average of 4% per trading day, and its MSE value was basically below 10, The MSE of other stocks has been optimized from more than 1900 shares predicted in less than 10 a share. This paper splits and analyzes the adjusted data to obtain more reasonable and accurate prediction results. Finally, this article aims to provide valuable reference for those who are interested in stock forecasting or studying the trend of Apple's stock price.

Stock Price Prediction with Denoising Autoencoder and Transformers

Predicting stock market price movement has been a challenging problem for time series forecasting due to its inherent volatility. The introduction of machine learning techniques, such as the use of a recurrent neural network (RNN), have since dominated the field for stock trend forecasting and stock price prediction. RNNs have inherent model limitations that are solved with the introduction of the transformer model, which has since been used in many sequential classification and generative tasks. This study demonstrates the viability of a transformer-based model on the field of stock price prediction by comparing the predictions of a transformer-based model with some RNN-based baseline models on the hourly closing price of Apple (AAPL) stock prices. A dimension reducing simple autoencoder was also incorporated into the transformer model to increase performance. Although RNN-based models are shown to outperform the transformer model in accuracy, the transformer model leads in directional stock accuracy. This demonstrates that the transformer is applicable to compete with RNN based models within the field, along with the incorporation of an autoencoder to further integrate the transformer’s power to this field.

The prediction of Apple stock price based on linear regression model and random forest model

In the financial market, due to various factors, the stock price fluctuation is universal. Therefore, the directional prediction of stock market price based on technical analysis is very important in stock investment. This paper conducted a regression analysis and forecasted the future trends in the close price of Apple stock through recent five years between 2018 and 2023. For the purpose of this specific study, this paper did descriptive statistical analysis of the dataset, and made graphs and analyses of regression and predictions relied on the techniques of the Linear Regression Model and Random Forest Model. Based on the three indices: MSE, RMSE, and MAE, the paper compared the advantages and disadvantages of the two machine learning methods. The result of the experiments indicated that the regression generated through employment of the Linear Regression Model outperforms the result of the Random Forest Model, leading to the conclusion that Linear Regression Model is a more effective method to forecast in this dataset.

L’action Apple, une opportunité de suivi de tendance: Zoom sur les ratios financiers et l’évolution boursière de 2019 à 2023

This article aims to evaluate the performance and profitability of Apple Stock, as well as its trend following strategy, based on different financial ratios, over a period of five years, from 2019 to 2023. For this, it relies on the concept of trend following, a trading strategy that consists of following the direction of the market, without trying to anticipate changes. After a presentation of the literature review on the subject, we will present Indicators and tools to monitor a trend, then we will analyze the financial data of Apple, and its stock market behavior, over a period of five years. We will conclude our article by giving recommendations on whether or not to invest in Apple stock.

The comparison stateless and stateful LSTM architectures for short-term stock price forecasting

Deep learning techniques are making significant contributions to the rapid advancements in forecasting. A standout algorithm known for its ability to produce accurate forecasts by recognizing temporal autocorrelation within the data is the Long Short-Term Memory (LSTM) algorithm, a component of Recurrent Neural Networks (RNN). The LSTM method employs both stateless and stateful architecture approaches, providing versatility in its application. This research aims to compare stateful and stateless algorithms in LSTM models, focusing on forecasting stock prices, such as those of Apple Inc. This comparative analysis is crucial, taking into account various characteristics of time series data, including the benefits and drawbacks of temporal autocorrelation. The comparison results reveal that, despite the stateful algorithm requiring more computational time, it achieves greater accuracy than the stateless approach. The forecast indicates a potential upward trend in share prices for the period of January to December 2024, according to the projected outlook for Apple's stock value. However, it is essential to exercise prudence in interpreting these results, considering that share price fluctuations are influenced by a significant number of variables.

Comparison of Decision Tree Regression with Linear Regression Based on Prediction of Apple Stock Price

Machine learning has been increasingly used in stock price prediction with outstanding success. Decision tree regression models and linear regression models are both important models for predicting stock prices. The paper use decision tree regression and linear regression models to predict the opening price, closing price, high price and low price of Apple's stock price data respectively. The prediction effects of the two models are evaluated by the indicators of goodness of fit, mean square error, root mean square error and mean absolute error, and the prediction effects of the two models are compared. This experimental concludes that the decision tree regression model has better and more advantageous prediction results compared to the linear regression model. This study has guiding significance for machine learning in predicting stock prices when choosing a basic model or a combination of models for prediction.

Stock Price Prediction for Technology Company

Individuals aim to develop accurate models for stock prices to make informed decisions as investors, so they can determine opportune moments to purchase and sell stocks for maximizing profits. This paper select Apple stock from yahoo finance range from Aug 1st 2013 to Aug 1st 2023, and then forecasting its future 30 days stock price. This study contain four models, which are XGboost, linear regression, K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM). Those models are all fit the train and test data and then draw a visualization plot. For selecting the best model, this paper use root mean squared error(RMSE) metrics and mean absolute percentage error(MAPE) and got the best model are Linear Regression and LSTM. Depending on the mechanism of four models, LSTM can treated as the best one to predict future stock price. In future study, researchers can use LSTM model more to predict stock price for other companies in order to get the best result.

The Influence of Federal Reserves Interest Rates Hike Cycle on Transnational Corporations

The monetary policy of the United States has a broad and profound impact on the world economy and financial markets. Due to the critical position of the US dollar in the world economy, the US monetary policy will directly affect countries and economies worldwide, and the liquidity of the US dollar is also the foundation of global liquidity. The series of interest rate increases implemented by the Federal Reserve starting in 2022 has led to the appreciation of the US dollar index, which has significantly impacted the import costs and overseas sales of domestic and multinational companies in the United States. This article selects the global multinational technology company Apple and extracts daily closing data from June 2022 to April 2023 and the USD index for the same period. This study applies the VAR model to assess the correlation between both and uses the ARMA-GARCH-X model to analyze the impact of USD index on the volatility of Apple stocks. This study analyzes the impact of interest rate hikes on multinational corporations and provides management and investment advice to their stakeholders.

Transcriptional regulation of MdPIN7 by MdARF19 during gravity-induced formation of adventitious root GSA in self-rooted apple stock

Self-rooted apple stock is widely used for apple production. However, the shallowness of the adventitious roots in self-rooted apple stock leads to poor performance in the barren orchards of China. This is because of the considerable difference in the development of a gravitropic set-point angle (GSA) between self-rooted apple stock and seedling rootstock. Therefore, it is crucial to study the molecular mechanism of adventitious root GSA in self-rooted apple stock for breeding self-rooted and deep-rooted apple rootstock cultivars. An apple auxin response factor MdARF19 functioned to establish the adventitious root GSA of self-rooted apple stock in response to gravity and auxin signals. MdARF19 bound directly to the MdPIN7 promoter, activating its transcriptional expression and thus regulating the formation of the adventitious root GSA in 12-2 self-rooted apple stock. However, MdARF19 influenced the expression of auxin efflux carriers (MdPIN3 and MdPIN10) and the establishment of adventitious root GSA of self-rooted apple stock in response to gravity signals by direct activation of MdFLP. Our findings provide new information on the transcriptional regulation of MdPIN7 by auxin response factor MdARF19 in the regulation of the adventitious root GSA of self-rooted apple stock in response to gravity and auxin signals.

Research on Financial Position of Apple

Today, with the rapid development of economy and technology, the new products and new technologies launched by Apple, which has been innovating, have changed people's way of life, so it stands out from many technology companies and is widely recognized around the world. And many investors are also turning their investment winds to technology companies, so it will be necessary to further analyze Apple's development through stock value and financial information. This paper mainly determines whether Apple is worth investing by analyzing its financial information and stock growth trend. This paper is based on previous literature and comparative study. It is concluded that Apple's stock is on the rise in general and has high profitability. However, its liquidity and credit are poor, but apple is still a company worth investing in.

Attention based hybrid parametric and neural network models for non‐stationary time series prediction

AbstractThis paper investigates non‐stationary time series analysis and forecasting techniques for financial datasets. We focus on the use of a popular non‐stationary parametric model namely GARCH and neural network model LSTM, with an attention mechanism to capture the complex temporal dynamics and dependencies in the data. We propose a hybrid GARCH‐ATT‐LSTM model where the GARCH model is employed for volatility forecasting, attention mechanism is applied to capture the more important parts of the data sequence and enhance the interpretability of the model, and the LSTM model is used for price forecasting. Our experiments are conducted on real‐world financial datasets, that is, Apple stock price, Dow Jones index, and gold futures price. We compare the performance of GARCH‐ATT‐LSTM against the sole LSTM model, ATT‐LSTM model, and LSTM‐GARCH model. Our results show that GARCH‐ATT‐LSTM outperforms the baseline methods and achieves high accuracy in price forecasting. It implies the effectiveness of the attention mechanism in improving the interpretability and stability of the model and the success of combining parametric models with neural network models. The findings suggest that GARCH‐ATT‐LSTM can be a valuable tool for non‐stationary time series analysis and forecasting in financial applications.

A Study on the Causes of Apomixis in Malus shizongensis

Apomixis is a unique reproductive process that produces fertile offspring without the combination of sperm and egg cells. This process perfectly reproduces maternal DNA, making it possible to fix heterosis during reproduction. Malus shizongensis is a newly discovered species that is closely related to Malus hupehensis Rehd. After de-male bagging, it was found that the fruit set rate reached 78.7%. Preliminary analysis indicated that M. shizongensis have apomictic reproductive characteristics. In this work, we employed paraffin sectioning and electron scanning microscopy to explore apomixis in M. shizongensis during the development of male–female gametes and embryo sacs. Stigma fluorescence assays showed that pollen germination was normal, but less pollen entered the ovaries. Additionally, analysis of anthers indicated the presence of dysplasia and paraffin sectioning revealed that the pollen mother cells were aborted due to abnormal disintegration of the tapetum layer. Taken together, our results indicate that the primary causes of apomixis in M. shizongensis are anther dysplasia and male gamete development failure, resulting in reduced pollen tube entry into ovaries and reduced reproduction of female gametes. In conclusion, this study provide a theoretical basis and technical supports for apple stock breeding and apple industry development.

Apple's Financial Outlook: A Comprehensive Analysis of its Stock Performance

As traders, investors, and analysts try to decide whether to buy, sell, or hold Apple Inc. shares, Apple Stock Prediction is a crucial component of the financial market. It is difficult to forecast the future value of Apple's stock due to market volatility and a variety of unknown events, including shifts in customer tastes, political unpredictability, and global economic trends. Therefore, it is essential to use a variety of techniques to find the most effective strategy for forecasting Apple's stock price. To ascertain the intrinsic value of a stock, fundamental analysis examines financial statements, market patterns, and economic conditions. This method looks at the sales, profit margins, and cash flow of Apple Inc. as well as its overall financial performance. On the other hand, technical analysis examines past market data, such as price and volume, to spot patterns and trends that can predict future price movements. Charts, graphs, and other visual aids are used in this strategy to pinpoint potential entry and exit positions for trading Apple's stock. Multi-Layer Perceptron is a kind of artificial neural network that mimics the actions of the human brain and has been successfully used to analyze large amounts of complex data. In contrast, XGBoost is a machine learning algorithm that makes predictions using previous data, making it perfect for predicting the future movement of Apple's stock.

A Comparative Analysis of Amazon, Microsoft, and Apple's Stock Investment Value

This paper provides a comparative analysis of the stock investment value of Amazon, Microsoft, and Apple, three global companies that investors may consider when investing in stocks. The author analyzes each company's business diversity, financial situation, and industry competition. Amazon is a leading e-commerce company with strong domestic and international competitors in the field of e-commerce and cloud computing. Microsoft is a stable company with a dominant position in the personal computer operating system market and a growing market share in the field of cloud computing. Apple has a diverse product line, including hardware and software products, and faces competition from many competitors, but maintains its competitiveness in the market by investing in technological innovation. The paper concludes that the stock market is full of opportunities and challenges. And investors need to have a certain risk awareness and long-term investment vision. The analysis provided in this paper can help different investors to select stocks that suit their preferences and investment objectives. The paper uses data from Yahoo Finance and company websites to provide an overview of each company's performance, net sales, and stock prices. And compare the calculation results. Then select the stocks that investors with different preferences prefer.