scholarly journals Evaluation of development of Apple Inc. stock price time series

2022 ◽  
Vol 132 ◽  
pp. 01012
Author(s):  
Jakub Horák ◽  
Dominik Kaisler
Keyword(s):  
Time Series ◽  
Economic Crisis ◽  
Stock Prices ◽  
Stock Price ◽  
Common Factor ◽  
Global Market ◽  
Global Pandemic ◽  
Daily Data ◽  
Difficult Times ◽  
Analysis Of Time Series

The paper deals with the the development of a specific company’s stock price time series. The aim of the paper is to use the time series method for a detailed analysis and evaluation of the development of Apple Inc. stock prices. Daily data from 2000 to 2020, daily data from the period of the economic crisis between 2007 and 2009 and daily data from the Covid-19 pandemic period from March 2020 to the end of the same year are used. The data, from the period of 2000 - 2020 show a gradual increase in Apple’s stock prices. The most common factor leading to the increase in stock prices is the launch of a new product or service on the global market. On the contrary, the reason for the decline in stock prices is customer dissatisfaction, the excess of demand over supply, or the political situation. The analysis of time series for the period of the economic crisis points to the fact that thanks to the development, innovation and constant introduction of new products into the market, the company was not significantly affected by the crisis and neither were stock prices. Naturally, there were some fluctuations in prices, but at the end of 2009, the company even reached the highest stock prices in its history to date. The analysis of time series during the global pandemic of Covid-19 shows a steady rise in stock prices. Currently, the company sells more and more products and introduces new services that help us work, study or entertain ourselves in these difficult times, in the safety of our homes.

scholarly journals Deterministic chaos and forecasting in Amazon’s share prices

Equilibrium ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 253-273
Author(s):  
Michael Hanias ◽  
Stefanos Tsakonas ◽  
Lykourgos Magafas ◽  
Eleftherios I. Thalassinos ◽  
Loukas Zachilas
Keyword(s):  
Time Series ◽  
Chaos Theory ◽  
Stock Prices ◽  
Stock Price ◽  
Chaotic Behavior ◽  
Financial Time Series ◽  
Deterministic Chaos ◽  
Value Added ◽  
Share Prices ◽  
Daily Data

Research background: The application of non-linear analysis and chaos theory modelling on financial time series in the discipline of Econophysics. Purpose of the article: The main aim of the article is to identify the deterministic chaotic behavior of stock prices with reference to Amazon using daily data from Nasdaq-100. Methods: The paper uses nonlinear methods, in particular chaos theory modelling, in a case study exploring and forecasting the daily Amazon stock price. Findings & Value added: The results suggest that the Amazon stock price time series is a deterministic chaotic series with a lot of noise. We calculated the invariant parameters such as the maxi-mum Lyapunov exponent as well as the correlation dimension, managed a two-days-ahead forecast through phase space reconstruction and a grouped data handling method.

scholarly journals BLACK SWAN GLOBAL MARKET: ANALYSIS OF THE EFFECT OF THE COVID-19 DEATH RATE ON THE VOLATILITY OF EUROPEAN FOOTBALL CLUB STOCK PRICES (CASE STUDY OF JUVENTUS F.C., MANCHESTER UNITED, AJAX AMSTERDAM AND BORUSSIA DORTMUND)

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Franco Benony Limba ◽  
Jacobus Cliff Diky Rijoly ◽  
Margreath I Tarangi
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Global Market ◽  
Bad News ◽  
Series Data ◽  
Black Swan ◽  
Financial Industry ◽  
Market Effect ◽  
European Football

Abstract: The Covid-19 pandemic that hit the world also directly affected financial markets and global stock markets; this condition in economic terminology is known as the Black Swann Global Market Effect. Black Swan Global Market Effect is also experienced by sports industries in the financial industry, the football industry. The purpose of this paper is to see whether there is an influence between the Covid-19 pandemic conditions on the share value of several major European football clubs, namely Ajax Amsterdam, Borussia Dortmund, Juventus F.C., and Manchester United, as a result of the Black Swan Global Market Effect. The data used in this paper is time-series data from March 2020 to August 2020. Meanwhile, to answer the black swan effect phenomenon, the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) method is used. The results showed that stocks that were the object of research (Ajax, Borussia Dortmund, Juventus, and Machester United) showed a large response to bad News (an increase in deaths due to covid-19). Abstrak:Pandemic covid-19 yang mengantam dunia juga secara langsung mempengaruhi pasar keuangan serta pasar saham global, kondisi ini dalam terminology ekonomi dikenal sebagai Black Swann Global Markert Effect. Black Swan Global Market Effect hal ini juga dialami industry-industri olahraga yang berada dalam industry keuangan tersebut salah satunya industry sepakbola.Tujuan penulisan ini adalah untuk melihat apakah terdapat pengaruh antara kondisi pandemic covid-19 terhadap nilai saham beberapa klub sepakbola besar eropa yaitu Ajax Amsterdam, Borussia Dortmund, Juventus FC, dan Manchester United sebagai akibat dari Black Swan Global Market Effect.Data yang digunakan dalam penulisan ini adalah data time series dari bulan maret 2020 hingga agustus 2020. Sementara untuk menjawab fenomoena black swan effect ini digunakan metode Threshold Generalized Autoregressive Conditional Heteroskedacity (TGARCH). Hasil Penelitian menunjukkan bahwa, saham-saham yang menjadi objek penelitian (Ajax, Borussia Dortmund, Juventus, dan Machester United) menunjukan respons yang besar terhadap bad news (peningkatan jumlah kematian akibat covid-19). Black Swan Global Market, Pandemi Covid-19, TGARCH Models

EMPIRICAL LAWS OF A STOCK PRICE INDEX AND A STOCHASTIC MODEL

2003 ◽  
Vol 06 (03) ◽  
pp. 303-312 ◽  
Author(s):  
TAISEI KAIZOJI ◽  
MICHIYO KAIZOJI
Keyword(s):  
Stochastic Model ◽  
Power Law ◽  
Stock Prices ◽  
Stock Price ◽  
Price Index ◽  
Probability Function ◽  
The Other ◽  
Power Law Distribution ◽  
Exponential Distributions ◽  
Daily Data

Recent works by econo-physicists [5,8,15,19] have shown that the probability function of the share returns and the volatility satisfies a power law with an exponent close to 4. On the other hand, we investigated quantitatively the return and the volatility of the daily data of the Nikkei 225 index from 1990 to 2003, and we found that the distributions of the returns and the volatility can be accurately described by the exponential distributions [11]. We then propose a stochastic model of stock markets that can reproduce these empirical laws. In our model the fluctuations of stock prices are caused by interactions among traders. We indicate that the model can reproduce the empirical facts mentioned above. In particular, we show that the interaction strengths among traders are a key variable that can distinguish the emergence of the exponential distribution or the power-law distribution.

scholarly journals FUNDAMENTALS OF INTEGRATED COMMERCIAL BANK IN MACROECONOMIC AND SHARIA PERSPECTIVE IN INDONESIA

2018 ◽  
Vol 3 (2) ◽  
pp. 349-387
Author(s):  
Kumara Jati ◽  
Aziza Rahmaniar Salam
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Commercial Bank ◽  
Stock Prices ◽  
Stock Price ◽  
Vector Autoregression ◽  
Share Price ◽  
Fasting Period ◽  
Goods And Services ◽  
Bank Stock

This research analyses the fundamentals of integrated commercial bank in macroeconomic and sharia perspective in Indonesia. Based on the calculation of Vector Autoregression (VAR), the impact of macroeconomic variables (Jakarta Stock Islamic Index / JKSII, Indonesian Stock Price Composite Index / JKSE, Crude Oil Price, and Exchange Rate)  on stock prices of commercial banks vary. These shocks indicate an indirect price transmission through exchange rate channels and economic growth. From the Structrural Time Series Model (STSM), JKSII, JKSE, and commercial bank share price prediction will generally increase at the end of 2017 and 2018. This will generate hope and benefit for policy maker and business actors in the banking, finance and sharia sectors. In general, the ARMA-ARCH/GARCH model with dummy variables found negative impact of “Fasting Period and Eid Al-Fitr” on return of JKSII, JKSE, and commercial bank stock price. This indicates a cycle of stock price decline that occurs when consumers spend more money to purchase goods and services. However, this cycle of stock price declines is only temporary because the recovery of the world economy and the increase in demand for goods and services in the future can be a pull factor for stock prices (demand factor). Policy makers and stakeholders related to the financial system, banking and capital markets, especially the sharia sector need to see the movement of conventional bank stocks and “Fasting Period and Eid Al-Fitr” as they move in the opposite direction for a certain period.   Keywords: Stock Price of Commercial Bank, Macroeconomic and Sharia Perspective, Vector Autoregression (VAR), Structural Time-Series Models (STSM), ARMA-ARCH/GARCH   JEL Classification Codes: F31, F47, G15, G21

scholarly journals Revisiting spillover effect: An empirical evidence from GARCH-ARMA approach

Industrija ◽  
2021 ◽  
Vol 49 (1) ◽  
pp. 67-80
Author(s):  
Huruta Dolfriandra ◽  
Andreas Hananto ◽  
Roberto Forestal ◽  
Anboli Elangovan ◽  
John Diaz
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Empirical Evidence ◽  
Stock Prices ◽  
Stock Price ◽  
Spillover Effect ◽  
Commodity Markets ◽  
Commodity Exchange ◽  
Daily Data ◽  
Positive Spillover

This study analyzes the spillover effect of markets' commodity, exchange rate, and stock price. Starting from July 1, 2009, the daily data to December 31, 2019, are conducted in our study. The GARCH-ARMA approach has been undertaken in this study. The results show that four pairs experience the unidirectional (positive) spillover effect of return. Yet, the spillover effect of volatility shows a two-way relationship (both positive and negative) between commodity markets, stock prices, and exchange rates. To conclude, both stock prices and gold are volatility's net transmitters to other markets, while the EURUSD market is some markets' net receiver of volatility.

scholarly journals Implementation of Long Short-Term Memory and Gated Recurrent Units on Grouped Time-Series Data to Predict Stock Prices Accurately

2021 ◽  
Author(s):  
Armin Lawi ◽  
Hendra Mesra ◽  
Supri Amir
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Stocks are an attractive investment option since they can generate large profits compared to other businesses. The movement of stock price patterns on the stock market is very dynamic; thus it requires accurate data modeling to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to accurately predict stock price movements using time-series data, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. However, several previous implementation studies have not been able to obtain convincing accuracy results. This paper proposes the implementation of the forecasting method by classifying the movement of time-series data on company stock prices into three groups using LSTM and GRU. The accuracy of the built model is evaluated using loss functions of Rooted Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results showed that the performance evaluation of both architectures is accurate in which GRU is always superior to LSTM. The highest validation for GRU was 98.73% (RMSE) and 98.54% (MAPE), while the LSTM validation was 98.26% (RMSE) and 97.71% (MAPE).

scholarly journals Quantifying the Multiscale Predictability of Financial Time Series by an Information-Theoretic Approach

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 684 ◽  
Author(s):  
Xiaojun Zhao ◽  
Chenxu Liang ◽  
Na Zhang ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Stock Price ◽  
Financial Time Series ◽  
High Frequency Data ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Information Theoretic ◽  
Daily Data ◽  
Wide Range

Making predictions on the dynamics of time series of a system is a very interesting topic. A fundamental prerequisite of this work is to evaluate the predictability of the system over a wide range of time. In this paper, we propose an information-theoretic tool, multiscale entropy difference (MED), to evaluate the predictability of nonlinear financial time series on multiple time scales. We discuss the predictability of the isolated system and open systems, respectively. Evidence from the analysis of the logistic map, Hénon map, and the Lorenz system manifests that the MED method is accurate, robust, and has a wide range of applications. We apply the new method to five-minute high-frequency data and the daily data of Chinese stock markets. Results show that the logarithmic change of stock price (logarithmic return) has a lower possibility of being predicted than the volatility. The logarithmic change of trading volume contributes significantly to the prediction of the logarithmic change of stock price on multiple time scales. The daily data are found to have a larger possibility of being predicted than the five-minute high-frequency data. This indicates that the arbitrage opportunity exists in the Chinese stock markets, which thus cannot be approximated by the effective market hypothesis (EMH).

scholarly journals A Hybrid Prediction Method for Stock Price Using LSTM and Ensemble EMD

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yang Yujun ◽  
Yang Yimei ◽  
Xiao Jianhua
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Prediction Method ◽  
Reference Value ◽  
Prediction Methods ◽  
Hybrid Prediction ◽  
Original Stock ◽  
Stock Price Prediction ◽  
Comparison Results

The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

scholarly journals Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company

2019 ◽  
Vol 61 ◽  
pp. 01006 ◽  
Author(s):  
Jakub Horák ◽  
Tomáš Krulický
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Stock Prices ◽  
Stock Price ◽  
Exponential Time ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Artificial Neural ◽  
Time Series Alignment

Accurate stock price prediction is very difficult in today's economy. Accurate prediction plays an important role in helping investors improve return on equity. As a result, a number of new approaches and technologies have logically evolved in recent years to predict stock prices. One is also the method of artificial neural networks, which have many advantages over conventional methods. The aim of this paper is to compare a method of exponential time series alignment and time series alignment using artificial neural networks as tools for predicting future stock price developments on the example of the company Unipetrol. Time series alignment is performed using artificial neural networks, exponential alignment of time series, and then a comparison of time series of predictions of future stock price trends predicted using the most successful neural network and price prediction calculated by exponential time series alignment is performed. Predictions for 62 business days were obtained. The realistic picture of further possible development is surprisingly given based on the exponential alignment of time series.

scholarly journals A Robust Predictive Model for Stock Price Forecasting

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Tamal Datta Chaudhuri
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Research Work ◽  
Efficient Market Hypothesis ◽  
Price Forecasting ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Daily Data ◽  
Functional Forms

Prediction of future movement of stock prices has been the subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted accurately. On the other hand, there are propositions that have shown that, if appropriately modelled, stock prices can be predicted fairly accurately. The latter have focused on choice of variables, appropriate functional forms and techniques of forecasting. This work proposes a granular approach to stock price prediction by combining statistical and machine learning methods with some concepts that have been advanced in the literature on technical analysis. The objective of our work is to take 5 minute daily data on stock prices from the National Stock Exchange (NSE) in India and develop a forecasting framework for stock prices. Our contention is that such a granular approach can model the inherent dynamics and can be fine-tuned for immediate forecasting. Six different techniques including three regression-based approaches and three classification-based approaches are applied to model and predict stock price movement of two stocks listed in NSE - Tata Steel and Hero Moto. Extensive results have been provided on the performance of these forecasting techniques for both the stocks.

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