In this paper, we compare two fundamentally different judgmental demand forecasting approaches used to estimate demand and their corresponding demand distributions. In the first approach, parameters are obtained from a linear regression and maximum likelihood estimation (MLE) based on team forecasts and dispersion within the judgmental forecasts. The second approach ignores dispersion and instead estimates the demand distribution based on the mean demand forecast and the historic relative forecast errors as measured by A/F ratios — that is, the ratio of actual to forecast outcomes. We show that accounting for forecast dispersion (as a timely indicator of anticipated demand risk) explains demand uncertainty sublinearly whereas the mean demand forecast most often explains demand uncertainty as being more than linear. We use actual company data from an online retailer to show that the A/F ratio approach dominates the MLE approach in terms of de-biasing the mean demand forecast, predicting total season demand, predicting the percentage of demand actually served at a target service level, and maximizing realized gross profit. However, the MLE approach more closely follows the assumed standard normally distributed demand and hence yields better-fitting demand distributions. Product segmentation can further improve the forecast accuracy of both approaches. In the application case study described here, we fit the data and analyze accuracy of forecasts. The results indicate that, in order to maximize accuracy, demand forecasts should always employ product segmentation and should favor the A/F ratio approach for order quantities “close” to the mean; otherwise, the MLE approach is preferred.
{"title":"Estimating Demand Uncertainty Using Dispersion of Team Forecasts or Distributions of Forecast Errors","authors":"Christoph Diermann, Arnd Huchzermeier","doi":"10.2139/ssrn.2782402","DOIUrl":"https://doi.org/10.2139/ssrn.2782402","url":null,"abstract":"In this paper, we compare two fundamentally different judgmental demand forecasting approaches used to estimate demand and their corresponding demand distributions. In the first approach, parameters are obtained from a linear regression and maximum likelihood estimation (MLE) based on team forecasts and dispersion within the judgmental forecasts. The second approach ignores dispersion and instead estimates the demand distribution based on the mean demand forecast and the historic relative forecast errors as measured by A/F ratios — that is, the ratio of actual to forecast outcomes. We show that accounting for forecast dispersion (as a timely indicator of anticipated demand risk) explains demand uncertainty sublinearly whereas the mean demand forecast most often explains demand uncertainty as being more than linear. We use actual company data from an online retailer to show that the A/F ratio approach dominates the MLE approach in terms of de-biasing the mean demand forecast, predicting total season demand, predicting the percentage of demand actually served at a target service level, and maximizing realized gross profit. However, the MLE approach more closely follows the assumed standard normally distributed demand and hence yields better-fitting demand distributions. Product segmentation can further improve the forecast accuracy of both approaches. In the application case study described here, we fit the data and analyze accuracy of forecasts. The results indicate that, in order to maximize accuracy, demand forecasts should always employ product segmentation and should favor the A/F ratio approach for order quantities “close” to the mean; otherwise, the MLE approach is preferred.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127461709","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}
We consider the problem of forecasting realized variance measures. These measures are highly persistent, but also noisy estimates of the underlying integrated variance. Recently, Bollerslev, Patton and Quaedvlieg (2016, Journal of Econometrics, 192, 1-18) exploited this fact to extend the commonly used Heterogeneous Autoregressive (HAR) by letting the model parameters vary over time depending on estimated measurement errors. We propose an alternative specification that allows the autoregressive parameter of the HAR model for volatilities to be driven by a latent Gaussian autoregressive process that may depend on the estimated measurement error. The model is estimated using the Kalman filter. Our analysis considers realized volatilities of 40 stocks from the S&P 500 for three different observation frequencies. Our preferred model provides a better model fit and generates superior forecasts. It consistently outperforms the competing models in terms of different loss functions and for various subsamples of the forecasting period.
我们考虑预测已实现方差的问题。这些措施是高度持久的，但也是对潜在综合方差的嘈杂估计。最近，Bollerslev, Patton和Quaedvlieg (2016, Journal of Econometrics, 192, 1-18)利用这一事实，通过让模型参数随估计测量误差随时间变化来扩展常用的异质性自回归(HAR)。我们提出了另一种规范，允许波动性的HAR模型的自回归参数由潜在的高斯自回归过程驱动，该过程可能取决于估计的测量误差。利用卡尔曼滤波对模型进行估计。我们的分析考虑了标准普尔500指数中40只股票在三种不同观察频率下的已实现波动率。我们的首选模型提供了更好的模型拟合，并产生了更好的预测。在不同的损失函数和预测周期的各种子样本方面，它始终优于竞争模型。
{"title":"Improved Forecasting of Realized Variance Measures","authors":"Jeremias Bekierman, H. Manner","doi":"10.2139/ssrn.2812586","DOIUrl":"https://doi.org/10.2139/ssrn.2812586","url":null,"abstract":"We consider the problem of forecasting realized variance measures. These measures are highly persistent, but also noisy estimates of the underlying integrated variance. Recently, Bollerslev, Patton and Quaedvlieg (2016, Journal of Econometrics, 192, 1-18) exploited this fact to extend the commonly used Heterogeneous Autoregressive (HAR) by letting the model parameters vary over time depending on estimated measurement errors. We propose an alternative specification that allows the autoregressive parameter of the HAR model for volatilities to be driven by a latent Gaussian autoregressive process that may depend on the estimated measurement error. The model is estimated using the Kalman filter. Our analysis considers realized volatilities of 40 stocks from the S&P 500 for three different observation frequencies. Our preferred model provides a better model fit and generates superior forecasts. It consistently outperforms the competing models in terms of different loss functions and for various subsamples of the forecasting period.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131157625","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}
This paper analyses leading indicator properties of a broad set of credit spreads, compiled on the basis of information from both corporate bonds and bank loans for forecasting of real activity, unemployment, inflation and lending volumes in the euro area and in five major European economies. It also introduces a set of indicators for excess bond premia, adjusting corporate bond spreads for credit risk of the issuer and the term, coupon and liquidity premia. I find that the majority of macroeconomic indicators can be better predicted by the excess bond premia compared to non-adjusted indices; the rating-adjustment and time-varying parameter estimates seem to be particularly important. Although the predictive power of lending spreads is inferior to the predictive power of the excess bond premia, the forecasting performance of models which use the information from both lending and corporate bond spreads is always superior to models using only information from one source of external funding. JEL Classification: G12, C21, C22, E37, E44
{"title":"Leading Indicator Properties of Corporate Bond Spreads, Excess Bond Premia and Lending Spreads in the Euro Area","authors":"E. Krylova","doi":"10.2139/ssrn.2797178","DOIUrl":"https://doi.org/10.2139/ssrn.2797178","url":null,"abstract":"This paper analyses leading indicator properties of a broad set of credit spreads, compiled on the basis of information from both corporate bonds and bank loans for forecasting of real activity, unemployment, inflation and lending volumes in the euro area and in five major European economies. It also introduces a set of indicators for excess bond premia, adjusting corporate bond spreads for credit risk of the issuer and the term, coupon and liquidity premia. I find that the majority of macroeconomic indicators can be better predicted by the excess bond premia compared to non-adjusted indices; the rating-adjustment and time-varying parameter estimates seem to be particularly important. Although the predictive power of lending spreads is inferior to the predictive power of the excess bond premia, the forecasting performance of models which use the information from both lending and corporate bond spreads is always superior to models using only information from one source of external funding. JEL Classification: G12, C21, C22, E37, E44","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130777177","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}
The Treasury has published two reports on the economic consequences of a decision by the UK to vote to leave the European Union in the Referendum on 23 June. Together, the reports predict that each household in the UK will be worse off (in terms of a lower gross domestic product) by £4,300 or more by 2030. This prediction is grossly exaggerated for two main reasons. First, the Treasury assumes that the government will not respond to what it calls the ‘extreme shock’ of leaving the EU – a shock that is assumed to last for two years, which is longer than that caused by the Global Financial Crisis – and so will stand by while the economy dives into a recession with GDP falling by up to 6% over the next two years (relative to where the economy would be if the UK remained in the EU) – equivalent to losing 50% of our trade with the EU, even though we will still be in the Single Market during this period. This is simply not credible – had the government responded in the same way during the GFC, the consequences for the economy would have been catastrophic.Second, it assumes that the UK, the fifth largest economy in the world, will be unable to negotiate more favorable trading arrangements than currently exist with either the EU or the rest of the world – which has three times the GDP of the EU and nine times its population and is growing much faster than the stagnant EU economy. As a result of this assumption, GDP is predicted to be lower by up to 7.5% p.a. by 2030. This prediction comes from combining the outcome from a short-term model (called a vector autoregressive (VAR) model) which is used for the first two years after leaving with a long-term model (called a gravity model) which is used to project GDP between 2018 and 2030. The reason that the models are switched in 2018 is because this is the maximum time allowed to negotiate an exit from the EU under Article 50 of the Treaty on European Union. The specific gravity model used by the Treasury is centred on the EU: this model predicts that the UK would actually be better off not only staying in the EU but actually joining the euro – although the Treasury does not acknowledge this. Had the Treasury used a different gravity model centred on the rest of the world – which it certainly should have considered – it might well have found that the UK would be better off leaving the EU. Most of the other economic models that have examined the economic consequences of Brexit – and which have been entirely ignored by the Treasury – find that it will make little difference to the UK’s economy whether the UK stays in or leaves the EU. This is consistent with both Greenland’s experience of leaving the EU in 1985 and Ireland’s experience of ending currency union with the UK in 1979 – neither of which is considered in the Treasury reports.
{"title":"Measurement Without Theory: On the Extraordinary Abuse of Economic Models in the EU Referendum Debate","authors":"D. Blake","doi":"10.2139/SSRN.2819954","DOIUrl":"https://doi.org/10.2139/SSRN.2819954","url":null,"abstract":"The Treasury has published two reports on the economic consequences of a decision by the UK to vote to leave the European Union in the Referendum on 23 June. Together, the reports predict that each household in the UK will be worse off (in terms of a lower gross domestic product) by £4,300 or more by 2030. This prediction is grossly exaggerated for two main reasons. First, the Treasury assumes that the government will not respond to what it calls the ‘extreme shock’ of leaving the EU – a shock that is assumed to last for two years, which is longer than that caused by the Global Financial Crisis – and so will stand by while the economy dives into a recession with GDP falling by up to 6% over the next two years (relative to where the economy would be if the UK remained in the EU) – equivalent to losing 50% of our trade with the EU, even though we will still be in the Single Market during this period. This is simply not credible – had the government responded in the same way during the GFC, the consequences for the economy would have been catastrophic.Second, it assumes that the UK, the fifth largest economy in the world, will be unable to negotiate more favorable trading arrangements than currently exist with either the EU or the rest of the world – which has three times the GDP of the EU and nine times its population and is growing much faster than the stagnant EU economy. As a result of this assumption, GDP is predicted to be lower by up to 7.5% p.a. by 2030. This prediction comes from combining the outcome from a short-term model (called a vector autoregressive (VAR) model) which is used for the first two years after leaving with a long-term model (called a gravity model) which is used to project GDP between 2018 and 2030. The reason that the models are switched in 2018 is because this is the maximum time allowed to negotiate an exit from the EU under Article 50 of the Treaty on European Union. The specific gravity model used by the Treasury is centred on the EU: this model predicts that the UK would actually be better off not only staying in the EU but actually joining the euro – although the Treasury does not acknowledge this. Had the Treasury used a different gravity model centred on the rest of the world – which it certainly should have considered – it might well have found that the UK would be better off leaving the EU. Most of the other economic models that have examined the economic consequences of Brexit – and which have been entirely ignored by the Treasury – find that it will make little difference to the UK’s economy whether the UK stays in or leaves the EU. This is consistent with both Greenland’s experience of leaving the EU in 1985 and Ireland’s experience of ending currency union with the UK in 1979 – neither of which is considered in the Treasury reports.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"89 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134005690","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}
Monetary policy is forward looking and, in its pursuit of transparency, it communicates its economic outlook to the public at large. As a result, there is great interest in the FOMC's projections and its determinants. Indeed, do these projections converge to the actual values and at what pace? To what extent predictions for a given year are determined jointly with predictions for other years? To what extent FOMC participants differ in their outlook? Are their differences related to the state of the economy? To the Chair of the FOMC? What information is being used for revising these projections and is it possible to anticipate what the FOMC will anticipate? Is it possible to extract a narrative about the functioning of the economy? And is that narrative consistent with existing theories? To address these questions, we assemble FOMC forecasts from 2008 to 2015, examine their statistical properties, and assess the extent to which these forecasts can be predicted using publicly available data at the time the forecasts are made.
{"title":"The Future of Illusions or the Illusions of the Future: FOMC Economic Projections 2008-2015","authors":"Sebastian Herrador, Jaime R. Marquez","doi":"10.2139/ssrn.2769817","DOIUrl":"https://doi.org/10.2139/ssrn.2769817","url":null,"abstract":"Monetary policy is forward looking and, in its pursuit of transparency, it communicates its economic outlook to the public at large. As a result, there is great interest in the FOMC's projections and its determinants. Indeed, do these projections converge to the actual values and at what pace? To what extent predictions for a given year are determined jointly with predictions for other years? To what extent FOMC participants differ in their outlook? Are their differences related to the state of the economy? To the Chair of the FOMC? What information is being used for revising these projections and is it possible to anticipate what the FOMC will anticipate? Is it possible to extract a narrative about the functioning of the economy? And is that narrative consistent with existing theories? To address these questions, we assemble FOMC forecasts from 2008 to 2015, examine their statistical properties, and assess the extent to which these forecasts can be predicted using publicly available data at the time the forecasts are made.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133137136","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}
This paper will provide information on topographic finance and how it can be used in econometric and financial analysis. First we will cover what topographic finance means. Secondly, a discussion of what problems can be visualized will be but forth. Thirdly, a high level description of the concept of a surface will be advanced. Then a discussion on the theoretical framework will be articulated using chaos theory and emergence concepts. Fifthly, a mythological history of Poseidon will be explored and coupled with the development of the Poseidon software that is development by my company called Reykjavik. Finally, the future development in topographical finance will be proposed.
{"title":"Topographic Finance","authors":"P. Cottrell","doi":"10.2139/ssrn.2769250","DOIUrl":"https://doi.org/10.2139/ssrn.2769250","url":null,"abstract":"This paper will provide information on topographic finance and how it can be used in econometric and financial analysis. First we will cover what topographic finance means. Secondly, a discussion of what problems can be visualized will be but forth. Thirdly, a high level description of the concept of a surface will be advanced. Then a discussion on the theoretical framework will be articulated using chaos theory and emergence concepts. Fifthly, a mythological history of Poseidon will be explored and coupled with the development of the Poseidon software that is development by my company called Reykjavik. Finally, the future development in topographical finance will be proposed.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128599673","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}
In this paper, we employ machine learning techniques to analyze fifteen seasons of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was characterized by 44 predictor variables and one binary response variable taking on a value of "TRUE" if a team had made the playoffs, and value of "FALSE" if a team had missed the playoffs. After fitting an initial classification tree to this problem, this tree was then pruned which decrease the test error rate. Further to this, a random forest of classification trees was grown which provided a very accurate model from which a variable importance plot was generated to determine which predictor variables had the greatest influence on the response variable. The result of this work was the conclusion that the most important factors in characterizing a team’s playoff eligibility are the opponent field goal percentage and the opponent points per game. This seems to suggest that defensive factors as opposed to offensive factors are the most important characteristics shared among NBA playoff teams.
{"title":"Finding Common Characteristics Among NBA Playoff and Championship Teams: A Machine Learning Approach","authors":"I. S. Kohli","doi":"10.2139/ssrn.2764396","DOIUrl":"https://doi.org/10.2139/ssrn.2764396","url":null,"abstract":"In this paper, we employ machine learning techniques to analyze fifteen seasons of NBA regular season data from every team to determine the common characteristics among NBA playoff teams. Each team was characterized by 44 predictor variables and one binary response variable taking on a value of \"TRUE\" if a team had made the playoffs, and value of \"FALSE\" if a team had missed the playoffs. After fitting an initial classification tree to this problem, this tree was then pruned which decrease the test error rate. Further to this, a random forest of classification trees was grown which provided a very accurate model from which a variable importance plot was generated to determine which predictor variables had the greatest influence on the response variable. The result of this work was the conclusion that the most important factors in characterizing a team’s playoff eligibility are the opponent field goal percentage and the opponent points per game. This seems to suggest that defensive factors as opposed to offensive factors are the most important characteristics shared among NBA playoff teams.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127009108","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}
Xiang Yu, G. Mitra, Cristiano Arbex-Valle, Tilman Sayer
We investigate how “news sentiment” in general and the “impact of news” in particular can be utilized in designing equity trading strategies. News is an event that moves the market in a small way or a big way. We have introduced a derived measure of news impact score which takes into consideration news flow and decay of sentiment. Since asset behavior is characterized by return, volatility and liquidity we first consider a predictive analytic model in which market data and impact scores are the inputs and also the independent variables of the model. We finally describe the trading strategies which take into consideration the three important characteristics of an asset, namely, return, volatility and liquidity. The minute-bar market data as well as intraday news sentiment metadata have been provided by Thomson Reuters.
{"title":"An Impact Measure for News: Its Use in (Daily) Trading Strategies","authors":"Xiang Yu, G. Mitra, Cristiano Arbex-Valle, Tilman Sayer","doi":"10.2139/ssrn.3706827","DOIUrl":"https://doi.org/10.2139/ssrn.3706827","url":null,"abstract":"We investigate how “news sentiment” in general and the “impact of news” in particular can be utilized in designing equity trading strategies. News is an event that moves the market in a small way or a big way. We have introduced a derived measure of news impact score which takes into consideration news flow and decay of sentiment. Since asset behavior is characterized by return, volatility and liquidity we first consider a predictive analytic model in which market data and impact scores are the inputs and also the independent variables of the model. We finally describe the trading strategies which take into consideration the three important characteristics of an asset, namely, return, volatility and liquidity. The minute-bar market data as well as intraday news sentiment metadata have been provided by Thomson Reuters.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115358272","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}
Mikhail Pevzner, S. Radhakrishnan, Chandra Seethamraju
We examine whether the properties of earnings forecasts – bias and dispersion are different across periods when macroeconomic forecasts are optimistic than non-optimistic, and whether this difference in analyst forecast optimism is stronger during recessionary periods. We find that the long-horizon earnings forecasts are more optimistically biased in periods when the macroeconomic forecasts are optimistically biased as well, and the bias is more pronounced during periods of recession. We also find that the long-horizon earnings forecast dispersion is lower in periods when the long-horizon macroeconomic forecasts are optimistic than in other periods. These results suggest that firms that meet or beat earnings forecasts when there is no recession and macroeconomic forecast is optimistic are likely to have opportunistically biased their long-term forecasts and walked them down, i.e. opportunistic; and that firms that meet or beat earnings forecasts when there is recession and macroeconomic forecast is optimistic are likely to be the ones that are positioned to perform well when the economy recovers. Consistent with this we find that premium for meeting or beating the analysts’ earnings forecasts is highest in periods when there is recession and macroeconomic forecasts are optimistic; and there is no premium when there is no recession and macroeconomic forecast is optimistic. Collectively, the results show the interaction between the macroeconomic outlook and firm-level forecast properties.
{"title":"Analysts’ Long-Horizon Earnings Forecast Properties and Long-Horizon Macroeconomic Forecast Optimism","authors":"Mikhail Pevzner, S. Radhakrishnan, Chandra Seethamraju","doi":"10.2139/ssrn.2744069","DOIUrl":"https://doi.org/10.2139/ssrn.2744069","url":null,"abstract":"We examine whether the properties of earnings forecasts – bias and dispersion are different across periods when macroeconomic forecasts are optimistic than non-optimistic, and whether this difference in analyst forecast optimism is stronger during recessionary periods. We find that the long-horizon earnings forecasts are more optimistically biased in periods when the macroeconomic forecasts are optimistically biased as well, and the bias is more pronounced during periods of recession. We also find that the long-horizon earnings forecast dispersion is lower in periods when the long-horizon macroeconomic forecasts are optimistic than in other periods. These results suggest that firms that meet or beat earnings forecasts when there is no recession and macroeconomic forecast is optimistic are likely to have opportunistically biased their long-term forecasts and walked them down, i.e. opportunistic; and that firms that meet or beat earnings forecasts when there is recession and macroeconomic forecast is optimistic are likely to be the ones that are positioned to perform well when the economy recovers. Consistent with this we find that premium for meeting or beating the analysts’ earnings forecasts is highest in periods when there is recession and macroeconomic forecasts are optimistic; and there is no premium when there is no recession and macroeconomic forecast is optimistic. Collectively, the results show the interaction between the macroeconomic outlook and firm-level forecast properties.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115768740","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}
This brief note builds on Sabol (2015) by describing ways to account for forecasting errors made about the expected path of short-term interest rates in a model of expected bond returns. I consider the Cieslak and Povala (2014) model of monetary policy expectations frictions as one such measure of unexpected returns. I conduct a real time out-of-sample forecasting exercise and provide figures to easily show the validity of these models. Adding the predictable changes in Fed Policy, or the monetary policy risk premium, to measures of expected returns leads to improved forecasts. Much of this gain accrues to forecasts of shorter duration bonds.
{"title":"The Monetary Policy Risk Premium and Expected Bond Returns","authors":"Steven Sabol","doi":"10.2139/ssrn.2708336","DOIUrl":"https://doi.org/10.2139/ssrn.2708336","url":null,"abstract":"This brief note builds on Sabol (2015) by describing ways to account for forecasting errors made about the expected path of short-term interest rates in a model of expected bond returns. I consider the Cieslak and Povala (2014) model of monetary policy expectations frictions as one such measure of unexpected returns. I conduct a real time out-of-sample forecasting exercise and provide figures to easily show the validity of these models. Adding the predictable changes in Fed Policy, or the monetary policy risk premium, to measures of expected returns leads to improved forecasts. Much of this gain accrues to forecasts of shorter duration bonds.","PeriodicalId":308524,"journal":{"name":"ERN: Other Econometrics: Applied Econometric Modeling in Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130394699","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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