Query: forecasting stocks covid

Candidate papers:

abstract: During the pandemic, Infineon's supply chain impacts were largely defined by governmental decisions that affected transit times. Later, one of the major impacts came from demand reductions for semiconductors. Therefore, a System Dynamics (SD) Model that investigates the interdependence between infections and governmental strictness in restrictions using an extended SEIR model was developed in AnyLogic. For the quantification of governmental measures, the Oxford Blavatnik University coronavirus government response tracker was used. Based on this conceptual output, the resulting transit time increases have been connected with the strictness of these measures. The model links reductions in semiconductor demand to the duration of lockdowns as indicated by the current measures. The findings show that demand shocks and transit time delays can be buffered using flexible capacities and safety stocks.
author_names: B Feng

Sin-Bom Kim

Sanja Lazarova-Molnar

Z Zheng

T Roeder

Renee M Thiesing
title: FORECASTING SUPPLY CHAIN IMPACT BY PREDICTING GOVERNMENTAL DECISIONS IN THE COVID 19 PANDEMIC
year: 2020

abstract: With the heightened volatility in stock prices during the Covid 19 pandemic, the need for price forecasting has become more critical. We investigated the forecast performance of four models including Long Short Term Memory, XGBoost, Autoregression, and Last Value on stock prices of Facebook, Amazon, Tesla, Google, and Apple in COVID 19 pandemic time to understand the accuracy and predictability of the models in this highly volatile time region. To train the models, the data of all stocks are split into train and test datasets. The test dataset starts from January 2020 to April 2021 which covers the COVID 19 pandemic period. The results show that the Autoregression and Last value models have higher accuracy in predicting the stock prices because of the strong correlation between the previous day and the next day's price value. Additionally, the results suggest that the machine learning models (Long Short Term Memory and XGBoost) are not performing as well as Autoregression models when the market experiences high volatility.
author_names: Navid Mottaghi

Sara Farhangdoost
title: Stock Price Forecasting in Presence of Covid 19 Pandemic and Evaluating Performances of Machine Learning Models for Time Series Forecasting
year: 2021

abstract: Starting with the differences between forecasting and prediction and going deeper into prediction, a knowledge based model is presented The evolution of the stocks markets are analyzed, as well as how the epidemics and pandemics prior to the stock markets have affected them and how it is currently being affected by covid 19 The defined model is applied to a use case using Case Based Reasoning (CBR) it makes an analogy between the 2008 crisis with the covid 19 crisis in 2020 to predict whether the stock markets will take more or less time to recover
author_names: Antonio Lorenzo Sanchez

Jose Angel Olivas
title: Intelligent data analysis of the influence of COVID 19 on the stock market using Case Based Reasoning
venue: J. Comput. Sci. Technol.
year: 2020

abstract: Currently the world is facing a global problem in the form of the COVID 19 pandemic. The spread of the COVID 19 outbreak continues to move significantly, especially in Indonesia. Since it was announced by President Joko Widodo in early March until now, the number of positive cases of COVID 19 has reached 418.375 cases. The impact of COVID 19 is a serious threat to the global economy. COVID 19 attacks the movement of stocks on global exchanges. The Composite Stock Price Index (IHSG) has touched its lowest level in history due to COVID 19. COVID 19 can be considered as an event that occurs out of control, so of course it will affect various sectors, particularly the economic sector, by spreading fear for investors and creating uncertainty in the global economy. The purpose of this study is to determine the impact of COVID 19 on the IHSG and to build a model that can be used for forecasting. The analytical method used is intervention analysis. The intervention model is used to model data that contains shocks. In this case, the suspected shock is the COVID 19. The intervention function used is the step function. Furthermore, risk measurement will be carried out so that risk is at a controllable level. Measurable risk can reduce the chance of loss that may be borne by investors. The method used is Value at Risk (VaR) The risk measurement is applied to the outcome forecasting data from the intervention model. Based on the results analysis, intervention model shows that COVID 19 has had an impact and a harsh slap on IHSG. Forecasting results from the intervention model have a good accuracy, with MAPE from in sample is 0,76% Then the forecasting results from the intervention model have a Value at Risk (VaR) which ranges from 0,45% to 1,96% with a confidence level of 95% Keywords IHSG, Intervention Analysis, Value at Risk
author_names: Andrea Tri Rian Dani

Alifta Ainurrochmah

Narita Yuri Adrianingsih
title: Analysis for Evaluating the Impact of COVID 19 on the Indonesian Composite Stock Price Index
year: 2020

abstract: Starting with the differences between forecasting and prediction and going deeper into prediction, a knowledge based model is presented. The evolution of the stocks markets are analyzed, as well as how the epidemics and pandemics prior to the stock markets have affected them and how it is currently being affected by covid 19. The defined model is applied to a use case using Case Based Reasoning (CBR) it makes an analogy between the 2008 crisis with the covid 19 crisis in 2020 to predict whether the stock markets will take more or less time to recover.
author_names: Antonio T Lorenzo

Jose Angel Olivas
title: Intelligent Data Analysis of the Influence of COVID 19 on the Stock Market using Case Based Reasoning Analisis Inteligente de los Datos de la Influencia de la COVID 19 sobre los Mercados de Valores
year: 2020

abstract: The stock market is an organized body where public companies offer their stocks through initial public offerings and traders buy/sell these stocks so as to obtain profits. It is dynamic and volatile in nature which makes the task of stock market trend prediction a complex problem. In recent times, the COVID 19 pandemic has made this task even harder. With the rising number of COVID 19 cases across the globe, the market has never been more volatile. This has resulted in the poor performance of various traditional trend prediction algorithms because these algorithms do not account for the impact of the pandemic on the stock market trends. The proposed work aims to enhance the stock market prediction ability of various common prediction models by taking into account the factors related to COVID 19. The forecasting techniques analysed are Decision Tree Regressor, Random Forest Regressor and Support Vector Regressor (SVR) Currently the most affected countries by COVID 19 are the United States of America, India and Russia. Therefore we have analysed the prediction performance of various approaches discussed in this paper on S&P 500 Index, Nifty50 Index and RTS Index using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) Results obtained showcase that all the techniques used perform better when the COVID 19 features were included.
author_names: Rajni Jindal

Nikhil Bansal

Nitin Chawla

Sanskriti Singhal
title: Improving Traditional Stock Market Prediction Algorithms using Covid 19 Analysis
venue: 2021 International Conference on Emerging Smart Computing and Informatics (ESCI)
year: 2021

abstract: The affliction caused by the COVID 19 Pandemic is diverse from other disasters seen so far. Supply chain industries are facing unique challenges in fulfilling the essential needs of the people. The objective of the paper is to analyze the supply and demand of essentials during pre pandemic and post pandemic lockdowns using machine learning algorithms. This helps for supply chain industries in forecasting and managing the supply and demand of essential stocks for the future. Data are analyzed using prediction algorithms to check the actual and predicted values. The clustering algorithm along with rolling mean is used for half yearly data of 2019 and 2020 to identify the sales of different categories of essential commodities. This paper aims at applying intelligence in predicting various categories of sales by providing timely information for B2B Industries during the time of disasters.
author_names: P Anitha

Malini M Patil

Rekha B Venkatapur
title: COVID 19 Effect on Supply and Demand of Essential Commodities using Unsupervised Learning Method
venue: Journal of The Institution of Engineers (India) Series B
year: 2021

abstract: The impact of Covid 19 has triggered the current global economic downturn affecting all aspects of the economy including the Islamic stock index. This study aims to determine the model for forecasting the index. The Islamic stock index is used in six countries through adopting the Autoregressive Conditional Heteroscedasticity Generalized Autoregressive Conditional Heteroscedasticity (ARCH GARCH) method on daily data over the period January 2020 to October 2020. The risk level of each index was found to be influenced by the residual value from the previous day. The forecasting revealed the tendency of all stock prices to decline. These are associated with impacts of the Covid 19 pandemic on current and future economic performance. Investors need to assess the sector's fundamentals and the individual stocks in question, that are potential winners with propensity to recover and grow well once the market rebounds. They also need to continuously track the development of the pandemic in tandem with the economic sector and thus make the necessary adjustments at every step of the investment process. (c) 2021 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.
author_names: Faizul Mubarok

Mohammad Arif
title: Pandemic Attack and Islamic Stocks Index: A Cross Country Analysis
year: 2021

abstract: The stock market is volatile and complicated, especially in 2020. Because of a series of global and regional "black swans" such as the COVID 19 pandemic, the U.S. stock market triggered the circuit breaker three times within one week of March 9 to 16, which is unprecedented throughout the history. Affected by the whole circumstance, the stock prices of individual corporations also plummeted by rates that were never predicted by any pre developed forecasting models. It reveals that there was a lack of satisfactory models that could predict the changes of stocks prices when catastrophic, highly unlikely events occur. To fill the void of such models and to help prevent investors from heavy losses during uncertain times, this paper aims to capture the movement pattern of stock prices under anomalous circumstances. First, we detect outliers in sequential stock prices by fitting a standard ARIMA model and identifying the points where predictions deviate significantly from actual values. With the selected data points, we train ARIMA and LSTM models at the single stock level, industry level, and the general market level, respectively. Since the public moods affect the stock market tremendously, a sentiment analysis is also incorporated into the models in the form of sentiment scores, which are converted from comments about specific stocks on Reddit. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98% which can be used to optimize existing prediction methodologies.
author_names: Jinlong Ruan

Wei Wu

Jiebo Luo
title: Stock Price Prediction Under Anomalous Circumstances
venue: 2020 IEEE International Conference on Big Data (Big Data)
year: 2020

abstract: Considerable theoretical and empirical evidence links price comovements with the behavior of retail investors. Nevertheless, when predicting stock return correlations, research has focused on the leverage effect. We propose a new model of realized covariances that allows exogenous predictors to influence the correlation dynamics while ensuring the predicted matrices' positive definiteness. Using this model, we provide evidence for the predictive power of sentiment and attention measures obtained from social media and web search query data for the correlations of 35 Dow Jones stocks. At the one day forecasting horizon, these findings are confirmed by value at risk forecasts.
author_names: Daniele Ballinari
title: Retail Investors' Trading Activity and the Predictability of Stock Return Correlations
year: 2020