Financial Crises, Macroeconomic Variables, and Long-Run Risk: An Econometric Analysis of Stock Returns Correlations (2000 to 2019)

Tronzano, Marco

doi:10.3390/jrfm14030127

Cited by 18 publications

(25 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Each LSTM unit contains one memory unit and three gating units, and the specific structure is illustrated in Figure 2. Memory unit is mainly used to record the state of each neuron; input and output gates are mainly used to solve the data reception and correct parameter and output; the forgetting gate mainly controls how much the neural unit state is forgotten [12][13][14][15][16][17].…”

Section: Basic Structurementioning

confidence: 99%

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Yang

Jia

Liu

2022

Security and Communication Networks

View full text Add to dashboard Cite

In view of the financial risks faced by listed enterprises, how to accurately predict the risks is an important work. However, the traditional LSTM financial diagnosis model has the disadvantage of low accuracy; the specific reason is that the LSTM model has the problems of overfitting and gradient disappearance in risk diagnosis. Therefore, Dropout is adopted to solve the overfitting problem in the process of premodel prediction, and the BN algorithm is used to solve the gradient disappearance problem in the process of iteration. In order to verify the feasibility of above improvements, the financial data of China’s A-share listed enterprises from 2017 to 2020 are taken as samples to analyze the financial data of listed enterprises through single-step dimension and multistep dimension. The experimental results show that under the analysis of two dimensions, the financial prediction accuracy of the improved LSTM for T-2∼T-3 years can reach 83.96% and 91.19%, respectively, which indicates that through the above improvements, the model can be improved and has certain reference value.

show abstract

Section: Basic Structurementioning

confidence: 99%

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Yang

Jia

Liu

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Gold is a well-known type of precious metal, which is widely used as jewelry and valuable property because it is soft and malleable. Furthermore, gold has been a popular and trusted investment instrument over time (Hillier et al, 2006;Blose, 2010;Baur and Lucey, 2010;Baur and McDermott, 2010;Hood and Malik, 2013;Reboredo, 2013;Ciner et al, 2013;Areal et al, 2015;Beckmann et al, 2015;O'Connor et al, 2015;Baur and McDermott, 2016;Hoang et al, 2016;Iqbal, 2017;Bekiros et al, 2017;Junttila et al, 2018;Tronzano, 2021). This is consistent with the discovery of Makala and Li (2021) that aside from being a valuable asset, gold is an investment instrument capable of protecting wealth because its value tends to be higher than other precious metals, such as platinum and palladium.…”

Section: Introductionmentioning

confidence: 66%

Analysis of Precious Metal Price Movements Using Long Memory Model and Fuzzy Time Series Markov Chain

Arif

Devianto

Yollanda

et al. 2022

IJEEP

View full text Add to dashboard Cite

Precious metals occur naturally and have a high resistance to corrosion or oxidation. These natural resources are used as investment instruments to protect wealth values, such as gold, silver, and palladium. Price movements need to be understood when investing, and it is achieved through a time series model that predicts future prices. Also, autoregressive fractional integrated moving average (ARFIMA) is used to model price movements with long memory effects, while fuzzy time series Markov chain (FTSMC) is employed for performing numerical approach. It was observed that gold price movement has a long memory effect; therefore, it is eligible to be formed into the ARFIMA model. However, the silver and palladium prices do not contain a long memory effect, which means their movements are only formed through the FTSMC numerical model. The ARFIMA modeling results show that the gold price long memory model has the best accuracy with the smallest error value and also demonstrates excellent goodness of fit. Furthermore, the gold price long memory model movement has long-term stability compared to other precious metals. This provides an investment advantage because it is a stable asset, easy to liquidate in cash, free of interest, has an emergency fund role, and protects wealth’s value.

show abstract

“…Macroeconomic variables and stock prices Throughout the past few decades, academics, economists, policymakers and practitioners (Mukherjee and Naka, 1995;Maysami and Koh, 2000;Kwon andShin, 1999, Gjerde andSaettem, 1999;Chinzara, 2011;Debata and Mahakud, 2018); have investigated the link between macroeconomic variables and stock return. According to the findings of a few studies (Czapkiewicz et al, 2018;Tronzano, 2021;Park et al, 2019), the return on stocks is largely dependent on macroeconomic factors such as GDP, interest rate (TB), CPI rate and industrial production (IP). Existing theories propose a variety of models that can be used to investigate the relationship between stock return and macroeconomic variables.…”

Section: Literature Reviewmentioning

confidence: 99%

The dynamic relationship between economic crisis, macroeconomic variables and stock prices in Sri Lanka

Madurapperuma

2022

JMB

View full text Add to dashboard Cite

PurposeThis study aims to examine the short- and long-term equilibrium relationship between All share price index (ASPI), macroeconomic variables and the economic crisis in Sri Lanka.Design/methodology/approachMonthly time series data for inflation (CPI), industrial production (IP), an exchange rate (EX), an interest rate (TB), short-term interest rate (CD) and economic crisis were used from 2010 to 2021. The ADF test, the bound testing approach, the CUSUM test and the CUSUMQ test were used in this study.FindingsThe findings show a long-run stable relationship between stock price, macroeconomic variables and political crisis (i.e., CPI, IP, ER, TB, CD and economic crisis). The results of the Johansen cointegration test suggest that there is at least one cointegrating equation, indicating that there is a long-run equilibrium relationship between macroeconomic variables and stock prices in Sri Lanka.Research limitations/implicationsThe vector error correction estimates show that the coefficient of the error correction term is significant with a negative sign, indicating that a long-run dynamic relationship exists between macroeconomic variables and stock prices. In the short term, economic crisis has had a big effect on stock prices suggesting that Sri Lanka’s domestic financial markets are linked to the stability of the country.Originality/valueThis research establishes the links between stock returns, macroeconomic variables and economic crisis. So far, research has been unable to establish the empirical nature of such links. The authors believe that this paper fills that gap.

show abstract

Financial Crises, Macroeconomic Variables, and Long-Run Risk: An Econometric Analysis of Stock Returns Correlations (2000 to 2019)

Cited by 18 publications

References 42 publications

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Analysis of Precious Metal Price Movements Using Long Memory Model and Fuzzy Time Series Markov Chain

The dynamic relationship between economic crisis, macroeconomic variables and stock prices in Sri Lanka

Contact Info

Product

Resources

About