scholarly journals EFFECT OF PANDEMIC ON STOCK PRICE BEHAVIOUR WITH SPECIAL REFERENCE TO S&P BSE HEALTHCARE INDEX

2020 ◽  
pp. 47-56
Author(s):  
Sucheesmita Dash ◽  
Sanjay Kumar Satapathy
Keyword(s):  
Stock Market ◽  
Special Reference ◽  
Stock Price ◽  
T Test ◽  
Stock Exchanges ◽  
Positive Deviation ◽  
Market Behavior ◽  
National Crisis ◽  
Closing Price ◽  
Epidemic Risk

The outburst of pandemic has caused widespread disorder in the country and the stock market is not also free from its grip. Despite the decrement in the performance of sectoral indices, the pharma sector has outperformed in both the NSE and BSE stock exchanges. The present study focuses on the market behavior of S&P BSE Healthcare Index and its top ten constituent companies’ pre and post pandemic outbreak. The market behavior is measured in terms of the closing price of the index and its constituents which is being studied by using t-test. The results of the study indicated that there is no deviation in the performance of S&P BSE Healthcare Index after the outburst of pandemic. While eight of its constituent companies showed deviation in their performance post pandemic outbreak. Out of which most of the companies showed positive deviation post pandemic outburst. Therefore, it would be beneficial for the investors to invest in the pharma sector stocks amid the national crisis. KEYWORDS: Pandemic, Epidemic, Risk, Pharma sector.

Pengujian Anomali January Effect Terhadap Saham Pada Perusahaan Yang Terdaftar Di Jakarta Islamic Index

Media Ekonomi ◽  
2020 ◽  
pp. 6
Author(s):  
Ika Yustina Rahmawati ◽  
Tiara Pandansari
Keyword(s):  
Event Study ◽  
Stock Price ◽  
Abnormal Return ◽  
T Test ◽  
January Effect ◽  
Paired Sample ◽  
Price Data ◽  
Closing Price

Tujuan dari penelitian ini adalah untuk untuk menguji perbedaan return dan abnormal return pada bulan Januari dan selain bulan Januari pada indeks JII. Sampel yang digunakan adalah saham perusahaan yang termasuk pada indeks JII, penelitian ini merupakan event study sehingga pada periode pengamatan akan melihat reaksi pada sebelum, saat, dan sesudah event. Pada penelitian ini, periode yang digunakan adalah H- 10 (sebelum event), H0 (saat event) dan H+10 (sesudah event). Sumber data diperoleh dari yahoo finance, sahamok.com dan IDX. Jenis data yang digunakan dalam penelitian ini adalah data sekunder, diantaranya adalah harga penutupan saham yang sudah disesuaikan (adjusted closing price) dan harga penutupan IHSG. Data berupa harga saham harian (daily stock price). Data-data tersebut kemudian dianalisis dengan menggunakan metode analisis paired sample t-test. Hasil penelitian menunjukkan bahwa jika dilihat return saham (Rit) pada tahun 2014 dan 2015 menunjukkan adanya perbedaan return dan memberikan adanya signal adanya January Effect sedangkan untuk tahun 2016 hasilnya berbeda dengan tahun sebelumnya karena hasilnya tidak menunjukkan perbedaan return dan dipastikan tidak ada indikasi January effect. Untuk abnormal return (AR) untuk semua tahun penelitian (2014, 2015, 2016) menunjukkan adanya perbedaan, yang menunjukkan adanya perbedaan AR dan memberikan signal adanya January Effect sehingga memengaruhi para pelaku pasar dalam mengambil keputusan.

scholarly journals Rede de unidades recorrentes chaveadas e transformadas discretas de ondaletas à previsão e operação no mercado financeiro

2021 ◽  
Author(s):  
◽  
V. Biazon
Keyword(s):  
Wavelet Transform ◽  
Stock Market ◽  
Stock Price ◽  
Time Windows ◽  
Training Data ◽  
Discrete Wavelet ◽  
Time Step ◽  
Financial Return ◽  
Proposed Model ◽  
Closing Price

Trading in the stock market always comes with the challenge of choosing the best decision to take on each time step. The problem is intensified by the theory that it is not possible to predict stock market time series as all information related to the stock price is already contained in it, which theory is known as Efficient Market Hypotesis. Although the market, in general, has no distinguishable tendencies, thus being consistent to the EMH, there are several time windows where there is some predictability, to some extent in the data, if we consider the use of technical indicators. In this work, a novel model is proposed to seek benefit from said periods operating to choose its actions and waiting for the best moment to execute them. This model, called Discrete Wavelet Transform Gated Recurrent Unit Network) (DWT-GRU), is divided in three modules, them being, the preprocessing of the data by the wavelet transform, the training and prediction of the closing price two days in the future and the decision making based on the evaluation of the gradient of the closing price. The proposed model was compared to other RNN architectures, with and without the use of wavelet preprocessing, and the "buy-and-hold" strategy. The results shown that the proposed model surpassed all the statistical metrics of accuracy, precision, recall, F1 and financial return of all the estabilshed comparisson models in the analysed stocks of the Brazilian Stock Market. The analysed stocks as the base for the study were the blue-chips of the IBOVESPA index, them being, PETR4, VALE3, ITUB4, ABEV3, and the ETF that mirrors the index itself, BOVA11. As training data the analysed period was since 2001 for the stocks and 2008 for the Fundo de Índice Negociado em Bolsa - ExchangeTraded Fund (ETF) BOVA11. At last, it is presented the financial results of the application of the algorithm in real time swing-trade operations validating its efficiency and winning over the buy-and-hold strategy

scholarly journals FUNDAMENTAL AND TECHNICAL ANALYSIS OF THE STOCK PRICE

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Denis Spahija ◽  
Seadin Xhaferi
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Stock Exchanges ◽  
Full Time ◽  
Equal Opportunities ◽  
Factors Affecting ◽  
The Past ◽  
Real Value

Trading with stocks in developed market conditions for some is fun, for others it is a way to preserve the real value of the asset, while for the most is a challenge to gain bigger profits quickly and easily. Dreams on stock market alchemy rely on the development and upgrading of special systems whose ultimate goal is to uncover stock price secrets and their changes. What are the chances of this happening? Chances are minimal, according to experiences from the world’s leading stock exchanges in the past. The stock market complexity, the number and unpredictability of factors affecting stock prices and unexpected changes or stability do not give much hope to those who know what’s going to happen in the future. In such endeavors there are equal opportunities for both stock exchange experts and full-time amateurs. For all this, if the stock market cannot be defeated or deceived, then it is better to join it. So this means: to create a diversified portfolio of securities that provides a safe income, slightly higher than annual inflation, minimizing the risk.

scholarly journals A Stock Closing Price Prediction Model Based on CNN-BiSLSTM

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Haiyao Wang ◽  
Jianxuan Wang ◽  
Lihui Cao ◽  
Yifan Li ◽  
Qiuhong Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Short Term ◽  
Term Memory ◽  
Closing Price ◽  
Long Short Term Memory

As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory (BiSLSTM) improved on bidirectional long short-term memory (BiLSTM) adds 1 − tanh(x) function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network (CNN), and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error (MAE), root-mean-squared error (RMSE), and R-square (R2) evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks.

scholarly journals Pengujian Anomali January Effect Terhadap Saham Pada Perusahaan Yang Terdaftar Di Jakarta Islamic Index

Media Ekonomi ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 6
Author(s):  
Ika Yustina Rahmawati ◽  
Tiara Pandansari
Keyword(s):  
Event Study ◽  
Stock Price ◽  
Abnormal Return ◽  
T Test ◽  
January Effect ◽  
Paired Sample ◽  
Price Data ◽  
Closing Price

Tujuan dari penelitian ini adalah untuk untuk menguji perbedaan return dan abnormal return pada bulan Januari dan selain bulan Januari pada indeks JII. Sampel yang digunakan adalah saham perusahaan yang termasuk pada indeks JII, penelitian ini merupakan event study sehingga pada periode pengamatan akan melihat reaksi pada sebelum, saat, dan sesudah event. Pada penelitian ini, periode yang digunakan adalah H- 10 (sebelum event), H0 (saat event) dan H+10 (sesudah event). Sumber data diperoleh dari yahoo finance, sahamok.com dan IDX. Jenis data yang digunakan dalam penelitian ini adalah data sekunder, diantaranya adalah harga penutupan saham yang sudah disesuaikan (adjusted closing price) dan harga penutupan IHSG. Data berupa harga saham harian (daily stock price). Data-data tersebut kemudian dianalisis dengan menggunakan metode analisis paired sample t-test. Hasil penelitian menunjukkan bahwa jika dilihat return saham (Rit) pada tahun 2014 dan 2015 menunjukkan adanya perbedaan return dan memberikan adanya signal adanya January Effect sedangkan untuk tahun 2016 hasilnya berbeda dengan tahun sebelumnya karena hasilnya tidak menunjukkan perbedaan return dan dipastikan tidak ada indikasi January effect. Untuk abnormal return (AR) untuk semua tahun penelitian (2014, 2015, 2016) menunjukkan adanya perbedaan, yang menunjukkan adanya perbedaan AR dan memberikan signal adanya January Effect sehingga memengaruhi para pelaku pasar dalam mengambil keputusan.

Macroeconomic Factors and Pakistani Equity Market”

2004 ◽  
Vol 43 (4II) ◽  
pp. 619-637 ◽  
Author(s):  
Muhammad Nishat ◽  
Rozina Shaheen
Keyword(s):  
Stock Market ◽  
Industrial Production ◽  
Stock Prices ◽  
Stock Price ◽  
Consumer Price Index ◽  
Reverse Causality ◽  
Macroeconomic Factors ◽  
Macroeconomic Variables ◽  
Equilibrium Relationships

This paper analyzes long-term equilibrium relationships between a group of macroeconomic variables and the Karachi Stock Exchange Index. The macroeconomic variables are represented by the industrial production index, the consumer price index, M1, and the value of an investment earning the money market rate. We employ a vector error correction model to explore such relationships during 1973:1 to 2004:4. We found that these five variables are cointegrated and two long-term equilibrium relationships exist among these variables. Our results indicated a "causal" relationship between the stock market and the economy. Analysis of our results indicates that industrial production is the largest positive determinant of Pakistani stock prices, while inflation is the largest negative determinant of stock prices in Pakistan. We found that while macroeconomic variables Granger-caused stock price movements, the reverse causality was observed in case of industrial production and stock prices. Furthermore, we found that statistically significant lag lengths between fluctuations in the stock market and changes in the real economy are relatively short.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

scholarly journals Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

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