The Behaviour of Banking Stocks During the Financial Crisis and Recessions. Evidence from Changes‐in‐Changes Panel Data Estimations

Asteriou, Dimitrios; Pılbeam, Keith; Sarantidis, Antonios

doi:10.1111/sjpe.12191

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…In this regard, this study identifies the changes in private education expenditure at three quantiles, such as the 25th, 50th, and 75th percentiles, during the three economic crises. Several previous studies have also employed CIC to analyse heterogeneous effects (Kottelenberg and Lehrer 2017; Asteriou et al 2019; Valente 2019; Roller and Steinberg 2020). Lastly, this study utilises the STATA user command published by Melly and Santangelo (2015).…”

Section: Methodsmentioning

confidence: 99%

Does an economic crisis deflate education bubble and inequality? Lessons from South Korea 1997–2020

2024

Asian-Pac Economic Lit

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Rapid education expansion has been a main driver of the remarkable economic growth in South Korea for last decades. However, in recent times, its excessive education credentialism is considered a hurdle against further developments. This study examined whether education bubble and inequality decreased during the Asian Financial Crisis 1997–98, the Global Financial Crisis 2008–09, and the COVID‐19 pandemic 2020. It tracked quarterly distributional changes in private education expenditure of Korean households with Changes‐in‐Changes. The findings indicate that Korean households postponed private education expenditure cut in the first quarter of the crises to prevent their children from falling behind in severe education competition. Then, they temporarily downsized it in the second quarter. During the pandemic, vulnerable students experienced higher fluctuations in private education expenditure than they did in previous crises closely related to disproportionate effects of the pandemic on household income and consumption expenditure. Therefore, this study suggests more expansionary measures for disadvantaged students to recover from a learning loss and improving the public education system as a fundamental measure to mitigate severe private education dependency.

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Section: Methodsmentioning

confidence: 99%

Does an economic crisis deflate education bubble and inequality? Lessons from South Korea 1997–2020

2024

Asian-Pac Economic Lit

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“…Predicting stock prices accurately, especially during periods of heightened market uncertainty, presents a unique challenge for existing prediction models. Notably, stock-price volatility experiences a substantial fluctuation during economic downturns [142] which, in turn, adds complexity to accurate forecasting. Trade volume serves as a more effective predictor of panic-induced volatility than the traditional inputs commonly used in the literature (e.g., [143]).…”

Section: Problem Definition and Formulationmentioning

confidence: 99%

Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques

Zolfagharinia,

Najafi,

Rizvi

et al. 2024

Algorithms

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Price prediction tools play a significant role in small investors’ behavior. As such, this study aims to propose a method to more effectively predict stock prices in North America. Chiefly, the study addresses crucial questions related to the relevance of news and tweets in stock-price prediction and highlights the potential value of considering such parameters in algorithmic trading strategies—particularly during times of market panic. To this end, we develop innovative multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to investigate the influence of Twitter count (TC), and news count (NC) variables on stock-price prediction under both normal and market-panic conditions. To capture the impact of these variables, we integrate technical variables with TC and NC and evaluate the prediction accuracy across different model types. We use Bloomberg Twitter count and news publication count variables in North American stock-price prediction and integrate them into MLP and LSTM neural networks to evaluate their impact during the market pandemic. The results showcase improved prediction accuracy, promising significant benefits for traders and investors. This strategic integration reflects a nuanced understanding of the market sentiment derived from public opinion on platforms like Twitter.

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“…The reason why investigating the accuracy of price prediction models during times of panic may result in useful insights is that stock prices drastically increase in volatility during periods of recession (Asteriou, Pilbeam, Sarantidis, 2019). This increased volatility will result in greater difficulty in predicting prices accurately, potentially rendering the models useless.…”

Section: Chapter 3 Problem Definitionmentioning

confidence: 99%

The Impact Of Twitter And News Count Variables On Stock Price Prediction Via Neural Networks

Rizvi¹

2023

Preprint

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<p> This study examines how Twitter and News Count variables generated by Bloomberg L.P. when utilized as inputs impact the stock price prediction accuracy of two distinct neural network types. The neural network types that are examined are Multi-Layer Perceptron neural networks and Long Short-Term Memory neural networks. Besides, all models were tested on two distinct periods, one without any market panic, the other including a prolonged period of market panic. The results suggest that the inclusion of Twitter and News Count variables significantly improve Multi-Layer Perceptron networks, but no significant improvement occurred for Long-Short Term Memory networks. Regarding periods of panic and no panic, the inclusion of the variables improved stock price prediction via neural networks in both scenarios. </p>

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The Behaviour of Banking Stocks During the Financial Crisis and Recessions. Evidence from Changes‐in‐Changes Panel Data Estimations

Cited by 10 publications

References 24 publications

Does an economic crisis deflate education bubble and inequality? Lessons from South Korea 1997–2020

Does an economic crisis deflate education bubble and inequality? Lessons from South Korea 1997–2020

Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques

The Impact Of Twitter And News Count Variables On Stock Price Prediction Via Neural Networks

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