{"title":"An Early Warning System for Identifying Financial Instability","authors":"Erindi Allaj, S. Sanfelici","doi":"10.2139/ssrn.3738936","DOIUrl":"https://doi.org/10.2139/ssrn.3738936","url":null,"abstract":"Financial crises prediction is an essential topic in finance. Designing an efficient Early Warning System (EWS) can help prevent catastrophic losses resulting from financial crises. We propose an EWS for predicting potential market instability conditions under which perturbations in the price level may evolve in large price declines or changes in general. Our system is based on the so called price-volatility feedback rate, which is supposed to describe the ease of the market in absorbing small price perturbations. A logit regression EWS is employed to predict future large price losses and Early Warning Indicators (EWIs) based on the realized variance (RV) and on the price-volatility feedback rate are considered. Our study reveals that, while the RV may sometimes fail in predicting future price losses in a given time series, the EWI employing the price-volatility feedback rate is always an important predictor of financial instability.","PeriodicalId":222384,"journal":{"name":"DecisionSciRN: Other Forecasting (Sub-Topic)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132297186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Confirmation Bias in Analysts’ Response to Consensus Forecasts","authors":"Huan Cai, Tong Yao, Xiaodi Zhang","doi":"10.2139/ssrn.4211686","DOIUrl":"https://doi.org/10.2139/ssrn.4211686","url":null,"abstract":"This paper provides evidence of confirmation bias by sell-side analysts in their earnings forecasts. We show that analysts tend to put higher weight on public information when the current forecast consensus is more consistent with their previous forecasts. Our results further suggest that the effect of confirmation bias on analyst forecasts is distinct from that of conservatism, self-attribution bias, or overconfidence. We find that analysts with better forecasting performance, shorter experience following a firm, providing earlier forecasts, or facing more dispersion in peer forecasts, tend to be less subject to confirmation bias, consistent with existing cognitive and social psychology theories.","PeriodicalId":222384,"journal":{"name":"DecisionSciRN: Other Forecasting (Sub-Topic)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117276164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Kalman Filter Estimation of the KNW Model","authors":"A. Pelsser","doi":"10.2139/ssrn.3885556","DOIUrl":"https://doi.org/10.2139/ssrn.3885556","url":null,"abstract":"This technical note gives implementation notes for estimating the Koijen-Nijman-Werker model from historical data based on a Kalman filter. We provide an independent derivation of the KNW model. We propose a different implementation of the state-space formulation of the KNW model and we test the impact of two different specifications for the initialisation of the Kalman filter maximum-likelihood estimation. By doing so, we provide an independent verification of the parameter estimations provided by DNB for the Committee Parameters. We find that the parameter estimates reported by DNB and our own parameter estimates are very similar.","PeriodicalId":222384,"journal":{"name":"DecisionSciRN: Other Forecasting (Sub-Topic)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121906079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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