scholarly journals Optimizing Credit Gaps for Predicting Financial Crises: Modelling Choices and Tradeoffs

2021 ◽  
Vol 2021 (1307) ◽  
pp. 1-40
Author(s):  
Daniel O. Beltran ◽  
◽  
Mohammad R. Jahan-Parvar ◽  
Fiona A. Paine ◽  
◽  
...  
Keyword(s):  
Financial Crises ◽  
Decomposition Methods ◽  
Cycle Decomposition ◽  
Common Trend ◽  
New Information ◽  
Early Warning Indicators ◽  
Out Of Sample ◽  
Credit Gap ◽  
Out Of Sample Prediction ◽  
Warning Indicators

Credit gaps are good predictors for financial crises, and banking regulators recommend using them to inform countercyclical capital buffers for banks. Researchers typically create credit gap measures using trend-cycle decomposition methods, which require many modelling choices, such as the method used, and the smoothness of the underlying trend. Other choices hinge on the tradeoffs implicit in how gaps are used as early warning indicators (EWIs) for predicting crises, such as the preference over false positives and false negatives. We evaluate how the performance of credit-gap-based EWIs for predicting crises is influenced by these modelling choices. For the most common trend-cycle decomposition methods used to recover credit gaps, we find that optimally smoothing the trend enhances out-of-sample prediction. We also show that out-of sample performance improves further when we consider a preference for robustness of the credit gap estimates to the arrival of new information, which is important as any EWI should work in real-time. We offer several practical implications.

scholarly journals News-based Sentiment Indicators

2019 ◽  
Vol 19 (273) ◽  
Author(s):  
Chengyu Huang ◽  
Sean Simpson ◽  
Daria Ulybina ◽  
Agustin Roitman
Keyword(s):  
Early Warning ◽  
Text Analysis ◽  
Financial Crises ◽  
Specific Language ◽  
Risk Hedging ◽  
Early Warning Indicators ◽  
Warning Indicators

We construct sentiment indices for 20 countries from 1980 to 2019. Relying on computational text analysis, we capture specific language like “fear”, “risk”, “hedging”, “opinion”, and, “crisis”, as well as “positive” and “negative” sentiments, in news articles from the Financial Times. We assess the performance of our sentiment indices as “news-based” early warning indicators (EWIs) for financial crises. We find that sentiment indices spike and/or trend up ahead of financial crises.

PHM Features for Large Circuit Boards to Be Implemented Into Electric Drivetrain Applications

2021 ◽  
Author(s):  
Alexander Otto ◽  
Eberhard Kaulfersch ◽  
Prashant Kumar Singh ◽  
Claudio Romano ◽  
Marcus Hildebrandt ◽  
...  
Keyword(s):  
Printed Circuit Boards ◽  
Experimental Methods ◽  
Accelerated Ageing ◽  
Deformation Analysis ◽  
Electronic Control ◽  
Circuit Boards ◽  
Electronic Components ◽  
Printed Circuit ◽  
Early Warning Indicators ◽  
Warning Indicators

Abstract Canary structures being used as early warning indicators represent an important tool for condition and health monitoring of electronic components and systems. In this paper, printed circuit boards with canary structures based on SMD 2512 ceramic chip resistors with reduced solder pad sizes were studied. Focus of these investigations was set on thermo-mechanical and mechanical stresses caused by passive thermal cycling as well as by vibrational loads. For this purpose, experimental methods such as deformation analysis and accelerated ageing tests as well as finite element based methods were applied. In addition, an outlook on the implementation of these canary structures into dual inverter electronic control boards for electrical powertrain applications will be given.

Predicting the trend of stock index based on feature engineering and CatBoost model

2021 ◽  
pp. 2150027
Author(s):  
Renzhe Xu ◽  
Yudong Chen ◽  
Tenglong Xiao ◽  
Jingli Wang ◽  
Xiong Wang
Keyword(s):  
Time Scale ◽  
Stock Index ◽  
Feature Engineering ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Back Testing ◽  
Price Series ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Sharp Ratio

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.

scholarly journals Forecasting Cattle Prices in the Presence of Structural Change

1992 ◽  
Vol 24 (2) ◽  
pp. 11-22 ◽  
Author(s):  
Barry K. Goodwin
Keyword(s):  
Structural Change ◽  
A Priori ◽  
Arima Model ◽  
Var Model ◽  
Impulse Response Functions ◽  
Cattle Industry ◽  
Vector Autoregressive ◽  
Economic Relationships ◽  
New Information ◽  
Out Of Sample

AbstractRecent empirical research and developments in the cattle industry suggest several reasons to suspect structural change in economic relationships determining cattle prices. Standard forecasting models may ignore structural change and may produce biased and misleading forecasts. Vector autoregressive (VAR) models that allow parameters to vary with time are used to forecast quarterly cattle prices. The VAR procedures are flexible in that they allow the identification of structural change that begins at an a priori unknown point and occurs gradually. The results indicate that the lowest RMSE for out-of-sample forecasts of cattle prices is obtained using a gradually switching VAR model. However, differences between the gradually switching VAR model and a univariate ARIMA model are not strongly significant. Impulse response functions indicate that adjustments of cattle prices to new information have become faster in recent years.

Customizing persuasive messages; the value of operative measures

2018 ◽  
Vol 35 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Maurits Kaptein
Keyword(s):  
Interactive Media ◽  
Database Systems ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Persuasive Messages ◽  
Psychological Traits ◽  
Content Type ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Operative Measures

Purpose This paper aims to examine whether estimates of psychological traits obtained using meta-judgmental measures (as commonly present in customer relationship management database systems) or operative measures are most useful in predicting customer behavior. Design/methodology/approach Using an online experiment (N = 283), the study collects meta-judgmental and operative measures of customers. Subsequently, it compares the out-of-sample prediction error of responses to persuasive messages. Findings The study shows that operative measures – derived directly from measures of customer behavior – are more informative than meta-judgmental measures. Practical implications Using interactive media, it is possible to actively elicit operative measures. This study shows that practitioners seeking to customize their marketing communication should focus on obtaining such psychographic observations. Originality/value While currently both meta-judgmental measures and operative measures are used for customization in interactive marketing, this study directly compares their utility for the prediction of future responses to persuasive messages.

scholarly journals Determinants of The Application of Macro Prudential Instruments

2017 ◽  
Vol 20 (3) ◽  
pp. 117-136 ◽  
Author(s):  
Firano Zakaria ◽  
Filali A. Fatine
Keyword(s):  
Financial Stability ◽  
Central Banks ◽  
Economic Cycle ◽  
Bank Profitability ◽  
Economic Cycles ◽  
Financial Expertise ◽  
Early Warning Indicators ◽  
The Stability ◽  
Intermediate Variables ◽  
Warning Indicators

The use of macro prudential instruments today gives rise to a major debate within the walls of central banks and other authorities in charge of financial stability. Contrary to micro prudential instruments, whose effects remain limited, macro prudential instruments are different in nature and can affect the stability of the financial system. By influencing the financial cycle and the financial structure of financial institutions, the use of such instruments should be conducted with great vigilance as well as macroeconomic and financial expertise. But the experiences of central banks in this area are sketchy, and only some emerging countries have experience using these types of instruments in different ways. This paper presents an analysis of instruments of macro prudential policy and attempts to empirically demonstrate that these instruments should be used only in specific economic and financial situations. Indeed, the results obtained, using modeling bivariate panel, confirm that these instruments are more effective when used to mitigate the euphoria of financial and economic cycles. In this sense, the output gap, describing the economic cycle, and the Z-score are the intermediate variables for the activation of capital instruments. Moreover, the liquidity ratio and changes in bank profitability are the two early warning indicators for activation of liquidity instruments.

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