scholarly journals Predictability of Stock Price Fluctuations Based on Business Relationships: A Comparison of Normal and the COVID-19 Pandemic Periods in Japan

2021 ◽  
Vol 13 (18) ◽  
pp. 10146
Author(s):  
Shoma Sakamoto ◽  
Shintaro Sengoku
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Factor Model ◽  
Customer Relationships ◽  
Business Relationships ◽  
Monthly Basis ◽  
Limited Region ◽  
Stock Price Prediction ◽  
Available Information ◽  
The Impact

The stock prices of a company are significantly influenced by changes of its business relationships. However, the effectiveness of stock price prediction based on such inter-firm business relationships has been partially confirmed in limited region and/or timeframe cases. In particular, it has not been verified under highly volatile market conditions such as those caused by the COVID-19 pandemic. To address these issues, we analyzed the impact of supplier–customer relationships on stock prices in the case of the Japanese stock market using The Fama-French three-factor model and publicly available information of business relationships. The subjects were classified into two conditions—normal and COVID-19—and the stock price predictability associated with changes of stock prices of related companies for both short and long holding periods. As a result, the significance of stock price predictability was confirmed on a daily and monthly basis in the given region. In addition, specific factors including a volatile event caused by a customer company, a stock price downturn, and the company size of a customer particularly improved stock price predictability in the pandemic.

scholarly journals Stock Price Prediction Using LSTM on Indian Share Market

10.29007/qgcz ◽  
2019 ◽  
Author(s):  
Achyut Ghosh ◽  
Soumik Bose ◽  
Giridhar Maji ◽  
Narayan Debnath ◽  
Soumya Sen
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Investor Sentiment ◽  
Spot Price ◽  
Physical Factors ◽  
Stock Price Prediction ◽  
The Impact ◽  
A Company

Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long Short- Term Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.

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.

scholarly journals Optimal feedback control of stock prices under credit risk dynamics

2021 ◽  
Author(s):  
Jinghai Shao ◽  
Sovan Mitra ◽  
Andreas Karathanasopoulos
Keyword(s):  
Credit Risk ◽  
Stock Prices ◽  
Stock Price ◽  
Global Financial Crisis ◽  
Optimal Feedback Control ◽  
Dynamic Programming Principle ◽  
Price Model ◽  
Managerial Control ◽  
Optimal Feedback ◽  
The Impact

AbstractIn this paper we provide a stock price model that explicitly incorporates credit risk, under a stochastic optimal control system. The stock price model also incorporates the managerial control of credit risk through a control policy in the stochastic system. We provide explicit conditions on the existence of optimal feedback controls for the stock price model with credit risk. We prove the continuity of the value function, and then prove the dynamic programming principle for our system. Finally, we prove the Viscosity Solution of the Hamilton–Jacobi–Bellman equation. This paper is particularly relevant to industry, as the impact of credit risk upon stock prices has been prominent since the commencement of the Global Financial Crisis.

scholarly journals The Impact of COVID-19 on the Stock Price of Socially Responsible Enterprises: An Empirical Study in Taiwan Stock Market

2021 ◽  
Vol 18 (4) ◽  
pp. 1398
Author(s):  
Kuo-Jung Lee ◽  
Su-Lien Lu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
Recovery Phase ◽  
Socially Responsible ◽  
Cumulative Impact ◽  
Study Approach ◽  
Price Performance ◽  
The Impact

This study examines the impact of the COVID-19 outbreak on the Taiwan stock market and investigates whether companies with a commitment to corporate social responsibility (CSR) were less affected. This study uses a selection of companies provided by CommonWealth magazine to classify the listed companies in Taiwan as CSR and non-CSR companies. The event study approach is applied to examine the change in the stock prices of CSR companies after the first COVID-19 outbreak in Taiwan. The empirical results indicate that the stock prices of all companies generated significantly negative abnormal returns and negative cumulative abnormal returns after the outbreak. Compared with all companies and with non-CSR companies, CSR companies were less affected by the outbreak; their stock prices were relatively resistant to the fall and they recovered faster. In addition, the cumulative impact of the COVID-19 on the stock prices of CSR companies is smaller than that of non-CSR companies on both short- and long-term bases. However, the stock price performance of non-CSR companies was not weaker than that of CSR companies during times when the impact of the pandemic was lower or during the price recovery phase.

Market reactions to the inclusion of people with disabilities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Claudia Araceli Hernández González
Keyword(s):  
Event Study ◽  
Stock Prices ◽  
Stock Price ◽  
Abnormal Returns ◽  
People With Disabilities ◽  
Market Reactions ◽  
Content Type ◽  
Study Methodology ◽  
Research Advance ◽  
The Impact

PurposeThis study aims to provide evidence of market reactions to organizations' inclusion of people with disabilities. Cases from financial journals in 1989–2014 were used to analyze the impact of actions taken by organizations to include or discriminate people with disabilities in terms of the companies' stock prices.Design/methodology/approachThis research is conducted as an event study where the disclosure of information on an organization's actions toward people with disabilities is expected to impact the organization's stock price. The window of the event was set as (−1, +1) days. Stock prices were analyzed to detect abnormal returns during this period.FindingsResults support the hypotheses that investors value inclusion and reject discrimination. Furthermore, the impact of negative actions is immediate, whereas the impact of positive actions requires at least an additional day to influence the firm's stock price. Some differences among the categories were found; for instance, employment and customer events were significantly more important to a firm's stock price than philanthropic actions. It was observed that philanthropic events produce negative abnormal returns on average.Originality/valueThe event study methodology provides a different perspective to practices in organizations regarding people with disabilities. Moreover, the findings in this research advance the literature by highlighting that organizations should consider policies and practices that include people with disabilities.

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a 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, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

scholarly journals Impact of Financial Ratios on Stock Price: Evidence from Indian Listed Banks on NSE

2021 ◽  
Vol 11 (4) ◽  
pp. 5132-5144
Author(s):  
Nitish Rane ◽  
Pooja Gupta
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Banking Sector ◽  
Study Data ◽  
Capital Adequacy ◽  
Financial Ratios ◽  
Annual Reports ◽  
Earnings Per Share ◽  
Net Interest Margin ◽  
The Impact

This study aims to examine the impact of financial ratios on the stock prices of companies listed on NIFTY Bank. Nifty Bank is a sub-index of NIFTY 50 and has various listed banks included based on the criteria given by NSE. This study data has been taken from the period 2010-2019 and taken from the company annual reports. The analysis is done using panel data regression and other tests to verify the best model for the dataset. The results obtained from this study show that the capital adequacy ratio and the dividend payout ratio do not impact the stock price. In contrast, earnings per share, net NPA ratio, and basic earnings per share, net profit margin, and net interest margin exhibited a relationship with the stock price. In the Indian context, there is less research available on this topic, and the idea chosen for the study is original. Along with this, the data collected for the study and the code used for analysis is original work. New investors can use the results of this study in the Indian stock market to analyze a stock and take proper investment decisions. Another practical usage of this study is that banking sector companies can improve their ratios to attract new investors.

scholarly journals The effects of dividend policy to stock prices of listed companies in Vietnam stock market

2020 ◽  
Author(s):  
Thị Lam Hồ ◽  
Thùy Phương Trâm Hồ
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Dividend Policy ◽  
Corporate Value ◽  
Business Managers ◽  
Dividend Payment ◽  
Share Prices ◽  
Finance Management ◽  
The Impact

Dividend policy is one of the most important policies in corporate finance management. Understanding the impact of dividend policy on the distribution of profits, corporate value and thus on the stock price is important for business managers to make policies and for investors to make investment decisions. This study is conducted to evaluate the impact of dividend policy on share prices for companies listed on Vietnam’s stock market in the period from 2010 to 2018, based on the availability of continuous dividend payment data. Using the FGLS method with panel data of 100 companies listed on the HoSE and HNX, we find evidence of the impact of dividend policy on stock prices, supporting supports the bird in the hand and the signal detection theories. The findings of this study help to suggest a few recommendations for business managers and investors.

scholarly journals Comparison of ARIMA, ANN and LSTM for Stock Price Prediction

2020 ◽  
Vol 218 ◽  
pp. 01026
Author(s):  
Qihang Ma
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Moving Average ◽  
Arima Model ◽  
Predictive Ability ◽  
Research Direction ◽  
Ann Model ◽  
Advantages And Disadvantages ◽  
Stock Price Prediction

The prediction of stock prices has always been a hot topic of research. However, the autoregressive integrated moving average (ARIMA) model commonly used and artificial neural networks (ANN) still have their own advantages and disadvantages. The use of long short-term memory (LSTM) networks model for prediction also shows interesting possibilities. This article compares three models specifically through the analysis of the principles of the three models and the prediction results. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing. The ANN model performs better than that of the ARIMA model. The combination of time series and external factors may be a worthy research direction.

scholarly journals Stock Price Prediction Using Deep Learning Models

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab ◽  
Gourab Nath
Keyword(s):  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
The Other ◽  
Learning Models ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Other Hand

Prediction of future movement of stock prices has been a 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, on the other hand, there are propositions illustrating that, if appropriately modeled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using five deep learning-based regression models. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of December 29, 2014 to July 31, 2020. Based on the NIFTY data during December 29, 2014 to December 28, 2018, we build two regression models using <i>convolutional neural networks</i> (CNNs), and three regression models using <i>long-and-short-term memory</i> (LSTM) networks for predicting the <i>open</i> values of the NIFTY 50 index records for the period December 31, 2018 to July 31, 2020. We adopted a multi-step prediction technique with <i>walk-forward validation</i>. The parameters of the five deep learning models are optimized using the grid-search technique so that the validation losses of the models stabilize with an increasing number of epochs in the model training, and the training and validation accuracies converge. Extensive results are presented on various metrics for all the proposed regression models. The results indicate that while both CNN and LSTM-based regression models are very accurate in forecasting the NIFTY 50 <i>open</i> values, the CNN model that previous one week’s data as the input is the fastest in its execution. On the other hand, the encoder-decoder convolutional LSTM model uses the previous two weeks’ data as the input is found to be the most accurate in its forecasting results.

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