Journal of Risk and Financial Management

Research

28 pages, 1643 KiB

Open AccessArticle

Examining Market Quality on the Egyptian Exchange (EGX): An Intraday Liquidity Analysis

by Ahmed Rushdy and Nagwa Samak

J. Risk Financial Manag. 2025, 18(1), 32; https://doi.org/10.3390/jrfm18010032 - 15 Jan 2025

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This study examines the intraday dynamics of liquidity and trading activity on the Egyptian Exchange (EGX) to assess its market quality. Using reconstructed five-minute limit order book data, this study measures liquidity dimensions and explores anomalies through interval-of-day and day-of-week models. Key findings [...] Read more.

This study examines the intraday dynamics of liquidity and trading activity on the Egyptian Exchange (EGX) to assess its market quality. Using reconstructed five-minute limit order book data, this study measures liquidity dimensions and explores anomalies through interval-of-day and day-of-week models. Key findings reveal an inverted J-shaped pattern in spreads due to information asymmetry, a U-shaped pattern in total depth, and a J-shaped market depth pattern. Additionally, significant day-of-week effects are observed, with Sundays showing the lowest liquidity and Thursdays the highest trading activity. These patterns highlight the impact of the EGX’s unique microstructure, including tick sizes and a preference for limit orders. This study underscores the influence of market structure on liquidity, trading efficiency, and cost, emphasizing the need for tailored regulatory and trading strategies. It provides valuable insights for investors optimizing trading strategies and policymakers seeking to enhance market integrity. Concluding, this research offers a foundation for understanding intraday liquidity patterns in emerging markets like the EGX and proposes future exploration of how information flows and trading mechanisms affect price discovery and market efficiency. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

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19 pages, 317 KiB

Open AccessArticle

A Collection of Wisdom in Predicting Sector Returns: The Use of Google Search Volume Index

by Hsiu-lang Chen and Jolana Stejskalova

J. Risk Financial Manag. 2024, 17(10), 452; https://doi.org/10.3390/jrfm17100452 - 5 Oct 2024

Viewed by 1144

Abstract

This study investigates whether the aggregate investor information demand for all stocks in a sector demonstrated in the Google search volume index (SVI) can predict the sector’s performance. The evidence shows that a sector’s abnormal SVI can predict the sector’s performance next month, [...] Read more.

This study investigates whether the aggregate investor information demand for all stocks in a sector demonstrated in the Google search volume index (SVI) can predict the sector’s performance. The evidence shows that a sector’s abnormal SVI can predict the sector’s performance next month, even after controlling for the sector’s contemporaneous standardized unexpected earnings and lagged returns on both the market and the sector. Also found is a partial reversal in the sector’s long-run performance that is not completely consistent with the price pressure hypothesis. This indicates that some fundamental information about a sector can be captured by the sector’s abnormal SVI on a timely basis. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

20 pages, 1347 KiB

Open AccessArticle

Assessing the Impact of the ECB’s Unconventional Monetary Policy on the European Stock Markets

by Carlos J. Rincon and Anastasiia V. Petrova

J. Risk Financial Manag. 2024, 17(9), 425; https://doi.org/10.3390/jrfm17090425 - 23 Sep 2024

Viewed by 957

Abstract

This study assesses the effects of the European Central Bank’s (ECB) unconventional monetary policy (UMP) on the prices of selected European stock market indices during the European sovereign debt (2010–2012) and the COVID-19 pandemic (2020–2022) crises interventions. This research employs the instrumental variables [...] Read more.

This study assesses the effects of the European Central Bank’s (ECB) unconventional monetary policy (UMP) on the prices of selected European stock market indices during the European sovereign debt (2010–2012) and the COVID-19 pandemic (2020–2022) crises interventions. This research employs the instrumental variables (IV) two-stage least squares (2SLS) model approach to evaluate the effects of changes in the size of the ECB’s balance sheet on the pricing of key equity market indices in Europe. The results of this study suggest that the ECB’s asset value expansion had the opposite statistically significant effects on the European stock market indices’ prices between the interventions. That is, an increase in the ECB’s balance sheet size was associated with a decrease in the prices of the indices during the sovereign debt crisis and with a rise during the COVID-19 pandemic. This research pinpoints the price sensitivity of each of the European equity indices to the ECB’s UMP and determines the different outcomes of the ECB’s quantitative easing policy between the interventions. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

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18 pages, 3076 KiB

Open AccessArticle

Neural Network-Based Predictive Models for Stock Market Index Forecasting

by Karime Chahuán-Jiménez

J. Risk Financial Manag. 2024, 17(6), 242; https://doi.org/10.3390/jrfm17060242 - 11 Jun 2024

Cited by 1 | Viewed by 4788

Abstract

The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&P500 index, recognising that a critical component of financial decision making is market volatility. The [...] Read more.

The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&P500 index, recognising that a critical component of financial decision making is market volatility. The research examines neural network models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU), taking into account their individual characteristics of pattern recognition, sequential data processing, and handling of nonlinear relationships. These models are analysed using key performance indicators such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy, a metric considered essential for prediction in both the training and testing phases of this research. The results show that although each model has its own advantages, the GRU and CNN models perform particularly well according to these metrics. GRU has the lowest error metrics, indicating its robustness in accurate prediction, while CNN has the highest directional accuracy in testing, indicating its efficiency in data processing. This study highlights the potential of combining metrics for neural network models for consideration when making decisions due to the changing dynamics of the stock market. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

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9 pages, 281 KiB

Open AccessArticle

Risk Characterization of Firms with ESG Attributes Using a Supervised Machine Learning Method

by Prodosh Eugene Simlai

J. Risk Financial Manag. 2024, 17(5), 211; https://doi.org/10.3390/jrfm17050211 - 19 May 2024

Viewed by 1017

Abstract

We examine the risk–return tradeoff of a portfolio of firms that have tangible environmental, social, and governance (ESG) attributes. We introduce a new type of penalized regression using the Mahalanobis distance-based method and show its usefulness using our sample of ESG firms. Our [...] Read more.

We examine the risk–return tradeoff of a portfolio of firms that have tangible environmental, social, and governance (ESG) attributes. We introduce a new type of penalized regression using the Mahalanobis distance-based method and show its usefulness using our sample of ESG firms. Our results show that ESG companies are exposed to financial state variables that capture the changes in investment opportunities. However, we find that there is no economically significant difference between the risk-adjusted returns of various ESG-rating-based portfolios and that the risk associated with a poor ESG rating portfolio is not significantly different than that of a good ESG rating portfolio. Although investors require return compensation for holding ESG stocks, the fact that the risk of a poor ESG rating portfolio is comparable to that of a good ESG rating portfolio suggests risk dimensions that go beyond ESG attributes. We further show that the new covariance-adjusted penalized regression improves the out-of-sample cross-sectional predictions of the ESG portfolio’s expected returns. Overall, our approach is pragmatic and based on the ease of an empirical appeal. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

17 pages, 1325 KiB

Open AccessArticle

Asymmetric Effects of Uncertainty and Commodity Markets on Sustainable Stock in Seven Emerging Markets

by Pitipat Nittayakamolphun, Thanchanok Bejrananda and Panjamapon Pholkerd

J. Risk Financial Manag. 2024, 17(4), 155; https://doi.org/10.3390/jrfm17040155 - 12 Apr 2024

Viewed by 2080

Abstract

The increase in global economic policy uncertainty (EPU), volatility or stock market uncertainty (VIX), and geopolitical risk (GPR) has affected gold prices (GD), crude oil prices (WTI), and stock markets, which present challenges for investors. Sustainable stock investments in emerging markets may minimize [...] Read more.

The increase in global economic policy uncertainty (EPU), volatility or stock market uncertainty (VIX), and geopolitical risk (GPR) has affected gold prices (GD), crude oil prices (WTI), and stock markets, which present challenges for investors. Sustainable stock investments in emerging markets may minimize and diversify investor risk. We applied the non-linear autoregressive distributed lag (NARDL) model to examine the effects of EPU, VIX, GPR, GD, and WTI on sustainable stocks in seven emerging markets (Thailand, Malaysia, Indonesia, Brazil, South Africa, Taiwan, and South Korea) from January 2012 to June 2023. EPU, VIX, GPR, GD, and WTI showed non-linear cointegration with sustainable stocks in seven emerging markets and possessed different asymmetric effects in the short and long run. Change in EPU increases the return of Thailand’s sustainable stock in the long run. The long-run GPR only affects the return of Indonesian sustainable stock. All sustainable stocks are negatively affected by the VIX and positively affected by GD in the short and long run. Additionally, long-run WTI negatively affects the return of Indonesia’s sustainable stocks. Our findings contribute to rational investment decisions on sustainable stocks, including gold and crude oil prices, to hedge the asymmetric effect of uncertainty. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

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9 pages, 734 KiB

Open AccessArticle

Empirical Distribution of the U.S. Housing Market during the Great Recession: Nonlinear Scaling Behavior after a Major Crash

by Fotios M. Siokis

J. Risk Financial Manag. 2024, 17(3), 130; https://doi.org/10.3390/jrfm17030130 - 21 Mar 2024

Viewed by 1587

Abstract

This study focuses on the real estate bubble burst in the US housing market during 2007–2008. We analyze the dynamics of the housing market crash and the after-crash sequence during the Great Recession. When a complex system deviates away from its typical path [...] Read more.

This study focuses on the real estate bubble burst in the US housing market during 2007–2008. We analyze the dynamics of the housing market crash and the after-crash sequence during the Great Recession. When a complex system deviates away from its typical path by the occurrence of an extreme event, its behavior is strongly characterized as nonstationary with higher volatility. With the utilization of a robust method, we present the characteristics of the aftershock period and provide useful information about the spatial distribution and the decay process of the aftershock sequence in terms of time. The returns of the housing price indices are well approximated by the empirics of a power law. Although we deal with low-frequency data, a time power-law relaxation pattern is identified. Our findings align with those in geophysics, indicating that the value of the relaxation parameter typically hovers around one and varies across different thresholds. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

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13 pages, 896 KiB

Open AccessArticle

On Comparing and Assessing Robustness of Some Popular Non-Stationary BINAR(1) Models

by Yuvraj Sunecher and Naushad Mamode Khan

J. Risk Financial Manag. 2024, 17(3), 100; https://doi.org/10.3390/jrfm17030100 - 28 Feb 2024

Viewed by 1428

Abstract

Intra-day transactions of stocks from competing firms in the financial markets are known to exhibit significant volatility and over-dispersion. This paper proposes some bivariate integer-valued auto-regressive models of order 1 (BINAR(1)) that are useful to analyze such financial series. These models were constructed [...] Read more.

Intra-day transactions of stocks from competing firms in the financial markets are known to exhibit significant volatility and over-dispersion. This paper proposes some bivariate integer-valued auto-regressive models of order 1 (BINAR(1)) that are useful to analyze such financial series. These models were constructed under both time-variant and time-invariant conditions to capture features such as over-dispersion and non-stationarity in time series of counts. However, the quest for the most robust BINAR(1) models is still on. This paper considers specifically the family of BINAR(1)s with a non-diagonal cross-correlation structure and with unpaired innovation series. These assumptions relax the number of parameters to be estimated. Simulation experiments are performed to assess both the consistency of the estimators and the robust behavior of the BINAR(1)s under mis-specified innovation distribution specifications. The proposed BINAR(1)s are applied to analyze the intra-day transaction series of AstraZeneca and Ericsson. Diagnostic measures such as the root mean square errors (RMSEs) and Akaike information criteria (AICs) are also considered. The paper concludes that the BINAR(1)s with negative binomial and COM–Poisson innovations are among the most suitable models to analyze over-dispersed intra-day transaction series of stocks. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

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14 pages, 1387 KiB

Open AccessArticle

Predicting the Profitability of Directional Changes Using Machine Learning: Evidence from European Countries

by Nicholas D. Belesis, Georgios A. Papanastasopoulos and Antonios M. Vasilatos

J. Risk Financial Manag. 2023, 16(12), 520; https://doi.org/10.3390/jrfm16120520 - 18 Dec 2023

Cited by 3 | Viewed by 2245

Abstract

In this paper, we follow the suggestions of past literature to further explore the prediction of the profitability direction by employing different machine learning algorithms, extending the research in the European setting and examining the effect of profits mean reversion for the prediction [...] Read more.

In this paper, we follow the suggestions of past literature to further explore the prediction of the profitability direction by employing different machine learning algorithms, extending the research in the European setting and examining the effect of profits mean reversion for the prediction of profitability. We provide evidence that simple algorithms like LDA can outperform classification trees if the data used are preprocessed correctly. Moreover, we use nested cross-validation and show that sample predictions can be obtained without using the classic train–test split. Overall, our prediction results are in line with previous studies, and we also found that cash flow-based measures like Free Cash Flow and Operating Cash Flow can be predicted more accurately compared to accrual-based measures like return on assets or return on equity. Full article

(This article belongs to the Special Issue Financial Valuation and Econometrics)

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