Apple Stocks Research Articles

Effects of Interval Flooding Stress on Physiological Characteristics of Apple Leaves

As a result of the continuous global warming in recent years, the average annual number of rain days in China has been on the decline, while the number of rainstorm days has gradually increased. These conditions make it extremely easy to form a waterlogging environment, which has an adverse impact on plant growth and development. In many apple-producing areas in China, apples are subject to severe flooding during planting. In this study, two-year-old apple rootstock M9T337 was used to explore the effects of interval water stress on the morphological and physiological parameters of apple leaves. The purpose was to determine the plant’s adaptability to waterlogged environments and provide theoretical reference for management and maintenance after waterlogging. The results showed that the effect on flooded (T2) on apple stock was greater than that of waterlogged (T1), Short-term (7 d) waterlogging (T1) did not affect the growth of seedlings but was conducive to the accumulation of dry matter. Furthermore, the initial stress was be imprinted on the plants, which could directly affect their response to later stress. The results of principal component analysis (PCA) revealed that PC1, PC2, and PC3 explained 26.92%, 17.46%, and 13.03% of the physiological changes under water stress, respectively. By calculating the weight of each indicator, we concluded that high-frequency resistance r, relative chlorophyll content (SPAD) and maximum photochemical efficiency Fv/Fm are important parameters for apple rootstocks affected by water stress.

Minimizing post-shock forecasting error through aggregation of outside information

We develop a forecasting methodology for providing credible forecasts for time series that have recently undergone a shock. We achieve this by borrowing knowledge from other time series that have undergone similar shocks for which post-shock outcomes are observed. Three shock effect estimators are motivated with the aim of minimizing average forecast risk. We propose risk-reduction propositions that provide conditions that establish when our methodology works. Bootstrap and leave-one-out cross-validation procedures are provided to prospectively assess the performance of our methodology. Several simulated data examples and two real data examples of forecasting Conoco Phillips and Apple stock price are provided for verification and illustration.

INFLUENCE OF THE STERILIZATION METHOD AND THE TIME OF INTRODUCTION INTO IN VITRO CULTURE ON THE VIABILITY OF EXPLANTS OF THE CLONAL APPLE STOCK 54-118

The stage of introducing explants into a sterile culture is difficult in the technology of clonal micropropagation of plants. The article shows the possible ways of sterilization and the introduction terms of explants of the clonal apple stocks 54-118 into in vitro culture in order to reduce planting infection and increase the yield of sterile viable explants. The best time for introducing clonal apple stocks 54-118 into sterile in vitro culture is the period of active shoot growth. Sterilization of explants with ethyl alcohol (70,0 %, 1 min) in combination with hydrogen peroxide (33,0 %) for 7 minutes and ethyl alcohol (70,0 %, 1 min) in combination with diacide (0,1 %) within 6 minutes contributed to the production of 63,0 % and 60,0 % of viable sterile explants.

Perspective apple clonal rootstocks for intensive orchards

Breeding work in the Michurinsk State Agrarian University (MSAU) is based on one of the eff ective strategies for producing high-quality planting material for establishment of intensive perennial fruit plantations – the use of apple varieties and clone stocks that are highly resistant to extreme factors and immune to diseases. As a result of many years of breeding work, the MSAU is the originator of 24 forms of clonal stocks (out of 46 forms allowed for cultivation in the Russian Federation) and the patent holder of 8 stocks. In 2018, 4 apple clonal rootstocks (70-20-20, 70-20-21, 71-7-22, 75- 1-62) were included in the State Register of Breeding Achievements admitted to cultivation on the territory of the Russian Federation. The main requirements for apple stocks for intensive orchards are high resistance to major environmental factors; suitability for intensive types of orchards; increased resistance to pathogens; good ability to vegetative propagation; lack of root shoots in the orchard; increase of productivity, early fruited varieties, quality of fruits. The advantages of planting material of apple trees on weak clonal rootstocks of the MSAU breeding are established: high compatibility of rootstocks with the best zoned and promising apple varieties; high frost resistance and winter hardiness of the root system, withstanding up to -16 °С – there are no foreign analogues on this basis; high resistance of stocks to the bacterial burn (unlike most foreign stocks); accelerated entry into fruiting, the onset of the period of commodity bearing fruit in the orchard on the 3rd ... 5th year; high productivity of commercial apple plantings in the conditions of Central Russia – an average of 25 to 40-50 tons / ha. For widespread introduction into production, the most studied clonal stocks of the MSAU breeding are recommended in Russia and in many countries of the world: the Paradizka Budagovsky (dwarf), 62-396 (half-dwarf), 54-118 (medium-sized). To increase the productivity and the resistance of plants and the eff ectiveness of Russian horticulture as a whole, it is necessary to create mother nurseries of apple rootstocks, free from harmful viral infections (repositories).

Stock Prices Prediction with Recurrent Neural Networks

Data and Information is the base for making investment choices. Stock market is typically a place where shares of certain companies trying to raise their capital, are traded. With the availability of large amount of data and refinement methods, investors nowadays, are able to make rational investment decisions. Advancement in computational intelligence, use of AI in the form of Neural Networks has created a new basis for predicting stock prices. In this work, we have employed Recurrent Neural Networks to implement time series prediction. The Long Term Short Memory Architecture has been used as the network architecture to perform prediction on Apple Stock Prices. The implementation is done on Keras platform.

Stock Prediction Analysis by using Linear Regression Machine Learning Algorithm

Stock market is varying day to day. Many factors such as government policies, industry performance, market sentiment etc are the main cause of up and downs in stock market. To invest money in stock market, study and analysis of stock market is essential. This type of analysis can be done by using Machine learning algorithms. The main objective of this paper is to predict the stock market future values by using linear regression machine learn algorithms based on past values. The methodology is developed and implemented in python on APPLE and TSLA stock.

Agrobiological evaluation of new apple clonal rootstocks of the Michurinsk State Agrarian University selection using different breeding methods

The results of many years of research on propagation of promising forms of clonal stocks of apple trees of the Michurinsk State Agrarian University selection in the mother plantation by stooling, by herbaceous cuttings and by clonal micropropagation are presented. According to the complex of economic and biological features, promising clonal rootstocks of an apple tree with high shoot-forming capacity in the mother plant were identified: dwarf apple trees 2-3-49, 2-15-2, 2-15-15, 3-4-7, 5-21-27; semi dwarf apple trees 2-3-14, 2-9-49, 2-9-102, 2-12-10, 5-24-1, 5-27-1. The rootstocks that are promising for propagation by herbaceous cuttings – 2-3-14, 2-3-49, 2-12-10, 2-15-15, 3-4-7, 5-21-93; at the same time, treatment with exogenous promoters such as IBA and NAA at a concentration of 50 mg/l increases the yield and quality of stock material. A modified MSO nutrient medium with the addition of 0.5-1.0 mg/l of 6-BAP and 0.1-0.2 mg/l of IBA is selected for the clonal micropropagation of apple stocks.

Stocks Trading Using Relative Strength Index, Moving Average and Reinforcement Learning Techniques: A Case Study of Apple Stock Index

With a tremendous increase of the usage of machine learning (ML) in recent years, a method called reinforcement learning (RL) which is a branch of ML has gained a huge attraction, as it has addressed the problem of learning automation of decisions making over time. In case of the financial trading, many approaches such as descriptive, fundamental, and technical analysis are used to make decision on stocks investment. Another approach that this paper aims to explore is the Deep Q-Learning which is also a suitable method to deal with the much more practical problem of financial trading. This paper applies the listed methods of analysis (Descriptive, technical and the Deep Q-Learning) on apple stocks index (AAPL). The paper founds that these techniques can be beneficial to traders and can also help making both long-term and short-term trading investment.

Transcriptional regulation of MdPIN3 and MdPIN10 by MdFLP during apple self-rooted stock adventitious root gravitropism

BackgroundThe close planting of dwarfing self-rooted rootstocks is currently a widely used method for apple production; however, self-rooted rootstocks are weak with shallow roots and poor grounding. Therefore, understanding the molecular mechanisms that establish the gravitropic set-point angles (GSAs) of the adventitious roots of self-rooted apple stocks is important for developing self-rooted apple rootstock cultivars with deep roots.ResultsWe report that the apple FOUR LIPS (MdFLP), an R2R3-MYB transcription factor (TF), functions in establishing the GSA of the adventitious roots of self-rooted apple stocks in response to gravity. Biochemical analyses demonstrate that MdFLP directly binds to the promoters of two auxin efflux carriers, MdPIN3 and MdPIN10, that are involved in auxin transport, activates their transcriptional expression, and thereby promotes the development of adventitious roots in self-rooted apple stocks. Additionally, the apple auxin response factor MdARF19 influences the expression of those auxin efflux carriers and the establishment of the GSA of adventitious roots of apple in response to gravity by directly activating the expression of MdFLP.ConclusionOur findings provide new insights into the transcriptional regulation of MdFLP by the auxin response factor MdARF19 in the regulation of the GSA of adventitious roots of self-rooted apple stocks in response to gravity.

Generalized value at risk forecasting

In this paper, using estimating function approach, a new optimal volatility estimator is introduced and based on the recursive form of the estimator a data-driven generalized EWMA model for value at risk (VaR) forecast is proposed. An appropriate data-driven model for volatility is identified by the relationship between absolute deviation and standard deviation for symmetric distributions with finite variance. It is shown that the asymptotic variance of the proposed volatility estimator is smaller than that of conventional estimators and is more appropriate for financial data with larger kurtosis. For IBM, Microsoft, Apple stocks and SP 500 index the proposed method is used to identify the model, estimate the volatility, and obtain minimum mean square error(MMSE) forecasts of VaR.

The health condition of Steve Jobs and the value of Apple’s shares

This paper aims to analyze the influence of news about Steve Jobs’ health on the price of Apple stock during the period from 10/16/2003 to 10/05/2011. Seven major events were identified for which significant market reaction was detected within five days after the release. The results indicate a trend dampening market reactions over time, suggesting that after the first news, investors gradually included in their predictions the frequent absences of the ceo. The mistaken announcement of Steve Jobs’ death on August 28th, and his appearance showing signs of physical weakness on September 9th 2008, in the acute phase of the financial crisis, were decisive factors in influencing the value of Apple’s shares. Furthermore, the news about Steve Jobs’ health did not cause significant impact on the price of the shares after he returned to the presidency of Apple. This indicates that the market had already picked up signs of a probable permanent absence.

Forecasting Apple Stock Closure Prices Using Artificial neural networks

Forecasting Apple Stock Closure Prices Using Artificial neural networks

Evaluación del riesgo y rendimiento individual de las acciones de Apple, Inc. y de Microsoft Corporation

La evaluación del riesgo y rendimiento de cualquier activo es fundamental en el proceso de toma de decisiones de inversión, por lo cual, el objetivo de este trabajo fue evaluar el riesgo y rendimiento individual de las acciones de las compañías Apple Inc. y Microsoft Corporation, a partir de los precios de las cotizaciones de las acciones de dichas compañías, durante el período comprendido del 1 de marzo de 2013 al 1 de marzo de 2018. Para determinar el riesgo y rendimiento, se procedió a utilizar diversas técnicas estadísticas tales como la media o promedio, rango, varianza, desviación estándar y el coeficiente de variación. Los resultados obtenidos indicaron que el rendimiento promedio de las acciones de Apple fue de 2.40% con una desviación estándar de 6.36%, mientras que para Microsoft fue de 2.38% y 6.08%. Se determinó a través del índice de desempeño que las acciones de Microsoft obtienen mayor rendimiento por cada unidad de riesgo asumido.

A path integral based model for stocks and order dynamics

We present a model for the short-term dynamics of financial assets based on an application to finance of quantum gauge theory, developing ideas of Ilinski. We present a numerical algorithm for the computation of the probability distribution of prices and compare the results with APPLE stocks prices and the S&P500 index.

Generalized Value at Risk Forecasting

In this paper, using estimating function approach, a new optimal volatility estimator is introduced, and, based on the recursive form of the estimator, a data-driven generalized EWMA model for VaR forecast is proposed. An appropriate data-driven model for volatility is identified by the relationship between the absolute deviation and the standard deviation for symmetric distributions with finite variance. It is shown that the asymptotic variance of the proposed volatility estimator is smaller than that of conventional estimators and is more appropriate for financial data with larger kurtosis. For IBM, Microsoft, and Apple stocks the proposed method is used to identify the model, estimate the volatility, and obtain VaR forecasts.

AN EFFICIENT HYBRID MACHINE LEARNING METHOD FOR TIME SERIES STOCK MARKET FORECASTING

Time series forecasting like stock price predicting is one of the most important complications in the financial area with non-stationary and highly-noisy variables, which are affected by many factors. This study applies a hybrid method of Genetic Algorithm (GA) and Artificial Neural Network (ANN) technique to develop a method for predicting stock price and time series. In the GA method,the output values are further fed to a developed ANN algorithm to fix the error on exact point. Our analysis suggests that the GA and ANN can increase the accuracy in less iteration.We analyzed the 200-day main index as well as five of the companies listed on the NASDAQ. By applying the proposed method to the Apple stock dataset, based on a hybrid model of GA and Back Propagation (BP) algorithms; we reach to improvement in SSE and timeimprovement to traditional methods. These results show these performances and the speed and the accuracy of our proposed approach.

PENENTUAN HARGA OPSI DENGAN MODEL BLACK-SCHOLES MENGGUNAKAN METODE BEDA HINGGA CENTER TIME CENTER SPACE (CTCS)

Stock options is defined as a contract between two parties or two person. The first parties is a buyer the contract has a right to buy or sell some stocks to the second parties. The contract contains price of selling and buying and a certain period of time when the transaction will be done. To generate a profit, investor have to calculate the fear price from the options how the options price can be bought or sold. A model of Black-Scholes is one of modelscan be used for calculating the option price. The model is partial differential equation form. One of methodsto find a solution of the model is a finite difference of Centre Time Centre Space (CTCS). This research aims to establish the option pricing formula with Black-Scholes Models with the solution using the CTCS finite difference method.After that, it is applied to determine the option price of Apple(AAPL) stocks from the American stock exchange (NASDAQ).The results is obtained bought optionprice and sold option price at 28 July 2017 are $5.2558 and $ 0.9734. The price of bought option in the market is $5.67 (>$5.2558), so investor should to sell bought option. Whereas the price of sold option in the market is $1.32 (>$0.9734), so investor should to sell sold option.

Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach

Stock market prediction is the challenging area for the investors to yield profits in the financial markets. The investors need to understand the financial markets which are more volatile and affected by many external factors. This paper proposes a subtractive clustering based adaptive neuro fuzzy approach for predicting apple stock data prices. The research data used in this study is from 3rd Jan 2005 to 30th Jan 2015. Four technical indicators are proposed in this study. They are Simple moving average for 1 week, simple moving average for 2 weeks, 14 days Disparity and Larry Williams R%. These variables are used as inputs to the neuro fuzzy system to predict the daily apple stock prices. Also, this study compares the proposed work with the ANFIS training method and subtractive clustering method etc. The performance of all these models is analyzed. The measures like training error, testing error, number of rules and number of parameters are calculated and compared for analysis. From the simulation results, the average performance of subtractive clustering based neuro fuzzy approach was found considerably better than the other networks.

A Comparison of Pricing and Hedging Performances of Equity Derivatives Models

This paper investigates the pricing/hedging conundrum, i.e. the observation of a mismatch between derivatives models’ pricing and hedging performances, that has so far been under-emphasized as the literature tends to focus on increasingly complicated option pricing models, without adequately addressing hedging performance. Hence, we analyze the ability of the Black-Scholes, Practitioner Black-Scholes, Heston-Nandi and Heston models to Deltahedge a set of call options on the S&P500 index and Apple stock. We extend earlier studies in that we consider the impact of asset dynamics, apply a stringent payoff replication strategy, look at the impact of moneyness at maturity and test for the robustness to the parameters’ calibration frequency and Delta-Vega hedging. The study shows that adding risk factors to a model, such as stochastic volatility, should only be considered in light of the data dynamics. Even then, however, more complicated models generally fare poorly for hedging purposes. Hence, a better fit of a model to option prices is not a good indicator of its hedging performance, and so of its ability to describe the underlying dynamics. This can be understood for reasons of over-fitting. Those findings hint to a potentially appealing hedging-based calibration of models’ parameters, rather than the standard pricing-based one.

A comparison of pricing and hedging performances of equity derivatives models

ABSTRACTThis article investigates the pricing/hedging conundrum, i.e. the observation of a mismatch between derivatives models’ pricing and hedging performances, that has so far been under-emphasized as the literature tends to focus on increasingly complicated option pricing models, without adequately addressing hedging performance. Hence, we analyse the ability of the Black–Scholes, Practitioner Black–Scholes, Heston–Nandi and Heston models to Delta-hedge a set of call options on the S&P500 index and Apple stock. We extend earlier studies in that we consider the impact of asset dynamics, apply a stringent payoff replication strategy, look at the impact of moneyness at maturity and test for the robustness to the parameters’ calibration frequency and Delta-Vega hedging. The study shows that adding risk factors to a model, as stochastic volatility, should only be considered in light of the data dynamics. Even then, however, more complicated models generally fare poorly for hedging purposes. Hence, a better fit of a model to option prices is not a good indicator of its hedging performance, and so of its ability to describe the underlying dynamics. This can be understood for reasons of over-fitting. Those findings hint to a potentially appealing hedging-based calibration of models’ parameters, rather than the standard pricing-based one.