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How Likely Is an Economic Depression? 经济萧条的可能性有多大?
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-03-19 DOI: 10.2139/ssrn.3549671
C. Sarmiento
{"title":"How Likely Is an Economic Depression?","authors":"C. Sarmiento","doi":"10.2139/ssrn.3549671","DOIUrl":"https://doi.org/10.2139/ssrn.3549671","url":null,"abstract":"This paper uses a panel vector-autoregressive (VAR) process with different distributional assumptions to forecast GDP contraction severities and identify the likelihood of a depression threshold event across main Latin American countries. We compare these results to similar hypothetical events for U.S., U.K., France, and Canada.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81895156","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}
引用次数: 0
Sentiment about Stock Prices 对股价的看法
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-02-13 DOI: 10.2139/ssrn.3614431
P. Kuang, Li Tang
{"title":"Sentiment about Stock Prices","authors":"P. Kuang, Li Tang","doi":"10.2139/ssrn.3614431","DOIUrl":"https://doi.org/10.2139/ssrn.3614431","url":null,"abstract":"The paper develops and estimates a stock pricing model with sentiment shocks to stock price forecasts and learning about stock prices by investors which replicates several survey evidence on stock price forecasts along with a standard set of asset pricing facts for the United States. A unique feature is that stock price forecasts in the model are not anchored by (or not co-integrated with) forecasts of fundamentals as in survey data. The model suggests about two-thirds of the fluctuations of stock price-dividend ratios are driven by shifting investors’ expectations as a result of the dynamic interaction between the sentiment shocks and investors’ learning.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74136624","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}
引用次数: 0
Advanced Quantitative Equity Portfolio Management System 先进的定量股票投资组合管理系统
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-02-09 DOI: 10.2139/ssrn.3535271
Dominic Clermont
{"title":"Advanced Quantitative Equity Portfolio Management System","authors":"Dominic Clermont","doi":"10.2139/ssrn.3535271","DOIUrl":"https://doi.org/10.2139/ssrn.3535271","url":null,"abstract":"Portfolio management is both complex and simple. It involves many steps and processes that leads to intended results: delivering excess returns within some risk budget. Each steps and processes is important and one needs to look at and improve each sub-system. Such Quantitative Equity Portfolio Management Systems require an extensive factor database, alpha forecasting system, risk modeling system, portfolio construction/optimization, risk monitoring, performance attribution, transaction cost modelling, and backtesting.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81423818","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}
引用次数: 0
Corporate Bankruptcy Prediction Model for Internet Startup Companies 互联网创业公司破产预测模型
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-02-01 DOI: 10.2139/ssrn.3529795
B. Bae, C. C. Lee
{"title":"Corporate Bankruptcy Prediction Model for Internet Startup Companies","authors":"B. Bae, C. C. Lee","doi":"10.2139/ssrn.3529795","DOIUrl":"https://doi.org/10.2139/ssrn.3529795","url":null,"abstract":"The purpose of this paper is to present a new approach to developing a financial distress prediction model that analyzes factors affecting success or failure of dot-com companies. In a new model, both demand side and supply side categories account for the performance of firms following IPOs. Huyghebaert et al. (2000) and Lewis et al. (2000) serve as a framework for the new model. This research uses a multiple discriminant analysis to build the proposed model. The demand side category includes an underwriter reputation factor and a market condition factor, while the supply side category includes a funds flow factor. The statistical results show that independent variables such as Gross Profit Margins, Cash Flows, Receivables, Accounts Payables, and Market Value are significant whereas Stock Holders’ Equities, Dividends, Capital Expenditures, and Inventories are insignificant.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88110416","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}
引用次数: 1
The R Package sentometrics to Compute, Aggregate and Predict with Textual Sentiment 基于文本情感计算、聚合和预测的R包计量学
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-01-16 DOI: 10.18637/jss.v099.i02
David Ardia, Keven Bluteau, S. Borms, Kris Boudt
{"title":"The R Package sentometrics to Compute, Aggregate and Predict with Textual Sentiment","authors":"David Ardia, Keven Bluteau, S. Borms, Kris Boudt","doi":"10.18637/jss.v099.i02","DOIUrl":"https://doi.org/10.18637/jss.v099.i02","url":null,"abstract":"We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78256158","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}
引用次数: 19
Frequency-Domain Information for Active Portfolio Management 主动投资组合管理的频域信息
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-01-09 DOI: 10.2139/ssrn.3527688
Gonçalo Faria, Fabio Verona
{"title":"Frequency-Domain Information for Active Portfolio Management","authors":"Gonçalo Faria, Fabio Verona","doi":"10.2139/ssrn.3527688","DOIUrl":"https://doi.org/10.2139/ssrn.3527688","url":null,"abstract":"We assess the benefits of using frequency-domain information for active portfolio management. To do so, we forecast the bond risk premium and equity risk premium using a methodology that isolates frequencies (of the predictors) with the highest predictive power. The resulting forecasts are more accurate than those of traditional forecasting methods for both asset classes. When used in the context of active portfolio management, the forecasts based on frequency-domain information lead to better portfolio performances than when using the original time series of the predictors. It produces higher information ratio (0.57 vs 0.45), higher CER gains (1.12% vs 0.81%), and lower maximum drawdown (19.1% vs 19.6%).","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85956514","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}
引用次数: 0
A Better Method of Applying OLS to the CAPM, Prediction, and Forecasting 一种将OLS应用于CAPM、预测和预测的更好方法
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-01-07 DOI: 10.2139/ssrn.3162767
J. Bell
{"title":"A Better Method of Applying OLS to the CAPM, Prediction, and Forecasting","authors":"J. Bell","doi":"10.2139/ssrn.3162767","DOIUrl":"https://doi.org/10.2139/ssrn.3162767","url":null,"abstract":"This paper formulates a weighting function from conventional least squares (LS) and combines it with estimation theory to provide the statistical estimate, expected value, and variance of any point on the polynomial constructed for fitting a set of existing data. This solves the problem of the missing variance at arbitrary points on the polynomial from LS derived by Gauss. The method includes three LS tricks: (a) Reframing LS from creating a polynomial for fitting existing data to estimating an already existing polynomial corrupted with statistically described sampling errors. (b) Restructuring LS processing from approximating polynomial coefficients to creating a weighting function for estimating the independent variable at any point on the LS polynomial. (c) Averaging the statistical deviations from the estimated LS polynomial to estimate the variance of sampling errors. The method is based on two cold hard CAPM data sets: (a) samples of the asset corrupted by statistically described errors and (b) deterministic samples of the corresponding market. Hand-waving arguments about market forces and investor behavior apply only after LS processing, not before or during. An example of the technique applied to fictitious sales as a function of GNP shows the technique applies to virtually any problem addressed by polynomial LS.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82846761","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}
引用次数: 1
Machine Learning Treasury Yields 机器学习国债收益率
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-01-06 DOI: 10.2139/ssrn.3514832
Zurab Kakushadze, Willie Yu
{"title":"Machine Learning Treasury Yields","authors":"Zurab Kakushadze, Willie Yu","doi":"10.2139/ssrn.3514832","DOIUrl":"https://doi.org/10.2139/ssrn.3514832","url":null,"abstract":"We give explicit algorithms and source code for extracting factors underlying Treasury yields using (unsupervised) machine learning (ML) techniques, such as nonnegative matrix factorization (NMF) and (statistically deterministic) clustering. NMF is a popular ML algorithm (used in computer vision, bioinformatics/computational biology, document classification, etc.), but is often misconstrued and misused. We discuss how to properly apply NMF to Treasury yields. We analyze the factors based on NMF and clustering and their interpretation. We discuss their implications for forecasting Treasury yields in the context of out-of-sample ML stability issues.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"5 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79501830","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}
引用次数: 0
Real-Time Forecasting Using Mixed-Frequency Vars with Time-Varying Parameters 具有时变参数的混频变量实时预测
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-01-01 DOI: 10.2139/ssrn.3529010
Markus Heinrich, Magnus Reif
{"title":"Real-Time Forecasting Using Mixed-Frequency Vars with Time-Varying Parameters","authors":"Markus Heinrich, Magnus Reif","doi":"10.2139/ssrn.3529010","DOIUrl":"https://doi.org/10.2139/ssrn.3529010","url":null,"abstract":"This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying parameter VAR with stochastic volatility (MF-TVP-SV-VAR). Overall, the MF-TVP-SV-VAR delivers accurate now- and forecasts and, on average, outperforms its competitors. We assess the models’ accuracy relative to expert forecasts and show that the MF-TVP-SV-VAR delivers better inflation nowcasts in this regard. Using an optimal prediction pool, we moreover demonstrate that the MF-TVP-SV-VAR has gained importance since the Great Recession.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81032760","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}
引用次数: 0
Stock Market Return Predictability Before and After the Dodd-Frank Act 多德-弗兰克法案前后股市回报的可预测性
Econometric Modeling: Capital Markets - Forecasting eJournal Pub Date : 2020-01-01 DOI: 10.2139/ssrn.3620062
Isabel Casas, Xiuping Mao, H. Veiga
{"title":"Stock Market Return Predictability Before and After the Dodd-Frank Act","authors":"Isabel Casas, Xiuping Mao, H. Veiga","doi":"10.2139/ssrn.3620062","DOIUrl":"https://doi.org/10.2139/ssrn.3620062","url":null,"abstract":"We analyze the stock market return predictability for three different periods. We evaluate the conditional variance (CV) and the variance risk premium (VRP) as predictors of stock market returns for which we are using well-established versions of the heterogeneous auto-regressive (HAR) model and propose two new semi-parametric extensions. Results show that the CV and VRP are predictors of future stock market returns in the period before the global financial crisis (GFC). However, these variables lose predictive power after the Dodd-Frank Act (DFA) and change sign, indicating that investors are willing to pay a risk premium for \"good uncertainty\".","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"12 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91508410","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}
引用次数: 0
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