scholarly journals The Use of Accounting and Stock Market Data to Predict Bank Financial Distress: The Case of East Asian Banks

2008 ◽  
Author(s):  
Isabelle Distinguin ◽  
Amine Tarazi ◽  
Jocelyn Trinidad
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
East Asian ◽  
Market Data

scholarly journals Market Discipline and the Use of Stock Market Data to Predict Bank Financial Distress

2006 ◽  
Vol 30 (2) ◽  
pp. 151-176 ◽  
Author(s):  
Isabelle Distinguin ◽  
Philippe Rous ◽  
Amine Tarazi
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
Market Discipline ◽  
Market Data

scholarly journals Light a lamp and look at the stock market

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Radeef Chundakkadan
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
Investor Sentiment ◽  
Downside Risk ◽  
Bond Yields ◽  
Risky Assets ◽  
Market Participants ◽  
Market Return ◽  
The Impact

AbstractIn this study, we investigate the impact of the light-a-lamp event that occurred in India during the COVID-19 lockdown. This event happened across the country, and millions of people participated in it. We link this event to the stock market through investor sentiment and misattribution bias. We find a 9% hike in the market return on the post-event day. The effect is heterogeneous in terms of beta, downside risk, volatility, and financial distress. We also find an increase (decrease) in long-term bond yields (price), which together suggests that market participants demanded risky assets in the post-event day.

scholarly journals Financial distress prediction of listed companies – empirical evidence on the Vietnamese stock market

2020 ◽  
Vol 17 (2) ◽  
pp. 377-388
Author(s):  
Tran Quoc Thinh ◽  
Dang Anh Tuan ◽  
Nguyen Thanh Huy ◽  
Tran Ngoc Anh Thu
Keyword(s):  
Stock Market ◽  
Financial Distress ◽  
Financial Statements ◽  
Listed Companies ◽  
Research Model ◽  
Z Score ◽  
Economic Decisions ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
High Level

Financial distress is a matter of concern in the recent period as Vietnam gradually enters global markets. This paper aims to examine the factors of Altman Z-score to detect the financial distress of Vietnamese listed companies. The authors use a sample of 30 delisted companies due to financial problems and 30 listed companies on the Vietnamese stock market from 2015 to 2018. They employ Independence Samples T-test to test the research model. It is found that there are significant differences in the factors of Altman Z-score between the group of listed companies and the group of delisted companies. Further analyses using subsamples of delisted companies show that the factors of Altman Z-score are also statistically different between companies with a low level of financial distress and those with a high level of financial distress. Based on the results, there are some suggestions to assist practitioners and the State Securities Commission in detecting, preventing, and strictly controlling financially distressed businesses. These results also enable users of financial statements to make more rational economic decisions accordingly.

Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading

2022 ◽  
Vol 16 (4) ◽  
pp. 1-22
Author(s):  
Chang Liu ◽  
Jie Yan ◽  
Feiyue Guo ◽  
Min Guo
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Mean Square Error ◽  
Short Term Memory ◽  
The State ◽  
Machine Learning Algorithms ◽  
Future Market ◽  
Mean Square ◽  
Market Data ◽  
Market Trends

Although machine learning (ML) algorithms have been widely used in forecasting the trend of stock market indices, they failed to consider the following crucial aspects for market forecasting: (1) that investors’ emotions and attitudes toward future market trends have material impacts on market trend forecasting (2) the length of past market data should be dynamically adjusted according to the market status and (3) the transition of market statutes should be considered when forecasting market trends. In this study, we proposed an innovative ML method to forecast China's stock market trends by addressing the three issues above. Specifically, sentimental factors (see Appendix [1] for full trans) were first collected to measure investors’ emotions and attitudes. Then, a non-stationary Markov chain (NMC) model was used to capture dynamic transitions of market statutes. We choose the state-of-the-art (SOTA) method, namely, Bidirectional Encoder Representations from Transformers ( BERT ), to predict the state of the market at time t , and a long short-term memory ( LSTM ) model was used to estimate the varying length of past market data in market trend prediction, where the input of LSTM (the state of the market at time t ) was the output of BERT and probabilities for opening and closing of the gates in the LSTM model were based on outputs of the NMC model. Finally, the optimum parameters of the proposed algorithm were calculated using a reinforced learning-based deep Q-Network. Compared to existing forecasting methods, the proposed algorithm achieves better results with a forecasting accuracy of 61.77%, annualized return of 29.25%, and maximum losses of −8.29%. Furthermore, the proposed model achieved the lowest forecasting error: mean square error (0.095), root mean square error (0.0739), mean absolute error (0.104), and mean absolute percent error (15.1%). As a result, the proposed market forecasting model can help investors obtain more accurate market forecast information.

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