In this paper, we provide an exact finite sample analysis of predictive regressions with overlapping long-horizon returns. This analysis allows us to evaluate the reliability of various asymptotic theories for predictive regressions in finite samples. In addition, our finite sample analysis sheds lights on the long outstanding question of whether a predictive regression with short or long-horizon returns is more powerful in detecting return predictability. Finally, we provide a simple bias-adjusted estimator of the slope coefficient as well as its estimated standard error for predictive regression with long-horizon returns. The resulting t-ratio of our bias-adjusted estimator has excellent size properties and dominates existing alternatives in the literature.
{"title":"Finite Sample Analysis of Predictive Regressions with Long-Horizon Returns","authors":"Raymond Kan, Jiening Pan","doi":"10.2139/ssrn.3790052","DOIUrl":"https://doi.org/10.2139/ssrn.3790052","url":null,"abstract":"In this paper, we provide an exact finite sample analysis of predictive regressions with overlapping long-horizon returns. This analysis allows us to evaluate the reliability of various asymptotic theories for predictive regressions in finite samples. In addition, our finite sample analysis sheds lights on the long outstanding question of whether a predictive regression with short or long-horizon returns is more powerful in detecting return predictability. Finally, we provide a simple bias-adjusted estimator of the slope coefficient as well as its estimated standard error for predictive regression with long-horizon returns. The resulting t-ratio of our bias-adjusted estimator has excellent size properties and dominates existing alternatives in the literature.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"220 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90238152","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}
Pub Date : 2021-07-25DOI: 10.31014/AIOR.1992.04.03.371
Md. Tuhin Ahmed, N. Naher
Modelling volatility has become increasingly important in recent times for its diverse implications. The main purpose of this paper is to examine the performance of volatility modelling using different models and their forecasting accuracy for the returns of Dhaka Stock Exchange (DSE) under different error distribution assumptions. Using the daily closing price of DSE from the period 27 January 2013 to 06 November 2017, this analysis has been done using Generalized Autoregressive Conditional Heteroscedastic (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedastic (APARCH), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH), Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) and Integrated Generalized Autoregressive Conditional Heteroscedastic (IGARCH) models under both normal and student’s t error distribution. The study finds that ARMA (1,1)- TGARCH (1,1) is the most appropriate model for in-sample estimation accuracy under student’s t error distribution. The asymmetric effect captured by the parameter of ARMA (1,1) with TGARCH (1,1), APARCH (1,1) and EGARCH (1,1) models shows that negative shocks or bad news create more volatility than positive shocks or good news. The study also provides evidence that student’s t distribution for errors improves forecasting accuracy. With such an error distribution assumption, ARMA (1,1)-IGARCH (1,1) is considered the best for out-of-sample volatility forecasting.
{"title":"Modelling & Forecasting Volatility of Daily Stock Returns Using GARCH Models: Evidence from Dhaka Stock Exchange","authors":"Md. Tuhin Ahmed, N. Naher","doi":"10.31014/AIOR.1992.04.03.371","DOIUrl":"https://doi.org/10.31014/AIOR.1992.04.03.371","url":null,"abstract":"Modelling volatility has become increasingly important in recent times for its diverse implications. The main purpose of this paper is to examine the performance of volatility modelling using different models and their forecasting accuracy for the returns of Dhaka Stock Exchange (DSE) under different error distribution assumptions. Using the daily closing price of DSE from the period 27 January 2013 to 06 November 2017, this analysis has been done using Generalized Autoregressive Conditional Heteroscedastic (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedastic (APARCH), Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH), Threshold Generalized Autoregressive Conditional Heteroscedastic (TGARCH) and Integrated Generalized Autoregressive Conditional Heteroscedastic (IGARCH) models under both normal and student’s t error distribution. The study finds that ARMA (1,1)- TGARCH (1,1) is the most appropriate model for in-sample estimation accuracy under student’s t error distribution. The asymmetric effect captured by the parameter of ARMA (1,1) with TGARCH (1,1), APARCH (1,1) and EGARCH (1,1) models shows that negative shocks or bad news create more volatility than positive shocks or good news. The study also provides evidence that student’s t distribution for errors improves forecasting accuracy. With such an error distribution assumption, ARMA (1,1)-IGARCH (1,1) is considered the best for out-of-sample volatility forecasting.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87787894","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}
I find that returns are predictably negative for several months after the onset of recessions, and only become high thereafter. I identify business-cycle turning points by estimating a state-space model using macroeconomic data. Conditioning on the business cycle further reveals that returns exhibit momentum in recessions, whereas in expansions they display the mild reversals expected from discount rate changes. A market timing strategy that optimally exploits this business-cycle pattern produces a 60% increase in the buy-and-hold Sharpe ratio. I find that a subset of hedge funds add value for their clients in part by avoiding stock market crashes during recessions.
{"title":"Late to Recessions: Stocks and the Business Cycle","authors":"Roberto Gomez Cram","doi":"10.2139/ssrn.3671346","DOIUrl":"https://doi.org/10.2139/ssrn.3671346","url":null,"abstract":"I find that returns are predictably negative for several months after the onset of recessions, and only become high thereafter. I identify business-cycle turning points by estimating a state-space model using macroeconomic data. Conditioning on the business cycle further reveals that returns exhibit momentum in recessions, whereas in expansions they display the mild reversals expected from discount rate changes. A market timing strategy that optimally exploits this business-cycle pattern produces a 60% increase in the buy-and-hold Sharpe ratio. I find that a subset of hedge funds add value for their clients in part by avoiding stock market crashes during recessions.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78839507","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 finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a Fama-Macbeth-type predictive performance evaluation. In addition to robust predictability evidence, there are four main findings. First of all, we find the lagged corporate bond market return as the most important predictor, suggesting a short-term market reversal story. Second, this paper concludes that equity information is conditionally redundant for similar public and private company bond performance. Third, a model-forecast-implied long-short strategy delivers 1.48% monthly returns and 1.4% alpha during the last two decades, which substantially drops if we do not consider private company bonds. Finally, the return predictability is mainly due to the cash flow component instead of the discount rate component.
{"title":"Predicting Individual Corporate Bond Returns","authors":"Xindi He, Guanhao Feng, Junbo Wang, Chunchi Wu","doi":"10.2139/ssrn.3870306","DOIUrl":"https://doi.org/10.2139/ssrn.3870306","url":null,"abstract":"This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a Fama-Macbeth-type predictive performance evaluation. In addition to robust predictability evidence, there are four main findings. First of all, we find the lagged corporate bond market return as the most important predictor, suggesting a short-term market reversal story. Second, this paper concludes that equity information is conditionally redundant for similar public and private company bond performance. Third, a model-forecast-implied long-short strategy delivers 1.48% monthly returns and 1.4% alpha during the last two decades, which substantially drops if we do not consider private company bonds. Finally, the return predictability is mainly due to the cash flow component instead of the discount rate component.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74655773","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 study quantifies firm-specific operating exposure to cumulative unexpected weather variations and examines how it affects earnings predictability and analysts’ forecasts. Two competing hypotheses are tested. The reduction in earnings seasonality hypothesis posits that operating weather exposure reduces earnings seasonality, thereby increasing forecast dispersion and reducing forecast accuracy. The increase in short-term earnings persistence hypothesis posits that operating weather exposure makes short-term earnings more persistent, leading to lower forecast dispersion and higher accuracy. The results provide strong evidence that firms with higher operating weather exposure display lower earnings seasonality but higher short-term earnings persistence. The net effect is that analysts’ forecasts become significantly noisier with more dispersion and lower accuracy. These results are stronger for industries with higher seasonality and for regions experiencing extreme weather conditions. Further analysis shows that firms’ profit margin and asset turnover exposures to abnormal precipitation and temperature variations contribute to the overall weather effects.
{"title":"Operating Exposure to Weather, Earnings Predictability, and Analyst Forecast","authors":"Lei Zhang","doi":"10.2139/ssrn.3865166","DOIUrl":"https://doi.org/10.2139/ssrn.3865166","url":null,"abstract":"This study quantifies firm-specific operating exposure to cumulative unexpected weather variations and examines how it affects earnings predictability and analysts’ forecasts. Two competing hypotheses are tested. The reduction in earnings seasonality hypothesis posits that operating weather exposure reduces earnings seasonality, thereby increasing forecast dispersion and reducing forecast accuracy. The increase in short-term earnings persistence hypothesis posits that operating weather exposure makes short-term earnings more persistent, leading to lower forecast dispersion and higher accuracy. The results provide strong evidence that firms with higher operating weather exposure display lower earnings seasonality but higher short-term earnings persistence. The net effect is that analysts’ forecasts become significantly noisier with more dispersion and lower accuracy. These results are stronger for industries with higher seasonality and for regions experiencing extreme weather conditions. Further analysis shows that firms’ profit margin and asset turnover exposures to abnormal precipitation and temperature variations contribute to the overall weather effects.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82510471","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 proposes a new method for extracting the market’s expected return of a stock from options prices while also calculating option-specific risk discounts (calls) and premiums (puts). However, first, I revisit the variable μ (expected return of a stock) as it relates to stock prices in the Black-Scholes formula derivation. I postulate that μ is itself a function of time and therefore the partial derivative equation Black, Scholes, and Merton solved was incomplete. Importantly, this undermines the conclusion Black, Scholes, and Merton came to, that an option’s price is not a function of the expected return of the underlying stock. To extract the expected return from options prices, I begin by proposing formulas for call and put prices introducing variables for strike price specific discounts and premiums. Known qualities of options, required to satisfy the no arbitrage assumption, are then used to solve for these discounts and premiums as a function of the implied expected price of a stock and σ. Finally, implied expected price and σ are solved for using numerical analysis.
{"title":"Extracting Implied Stock Returns from Options Prices","authors":"Nikhil Jaisinghani","doi":"10.2139/ssrn.3855124","DOIUrl":"https://doi.org/10.2139/ssrn.3855124","url":null,"abstract":"This paper proposes a new method for extracting the market’s expected return of a stock from options prices while also calculating option-specific risk discounts (calls) and premiums (puts). However, first, I revisit the variable μ (expected return of a stock) as it relates to stock prices in the Black-Scholes formula derivation. I postulate that μ is itself a function of time and therefore the partial derivative equation Black, Scholes, and Merton solved was incomplete. Importantly, this undermines the conclusion Black, Scholes, and Merton came to, that an option’s price is not a function of the expected return of the underlying stock. To extract the expected return from options prices, I begin by proposing formulas for call and put prices introducing variables for strike price specific discounts and premiums. Known qualities of options, required to satisfy the no arbitrage assumption, are then used to solve for these discounts and premiums as a function of the implied expected price of a stock and σ. Finally, implied expected price and σ are solved for using numerical analysis.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"49 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78141995","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}
Abstract Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account We propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings where the number of features is large compared to the number of observations and the underlying states differ only with respect to a subset of the features. We develop and implement a coordinate descent algorithm that alternates between selecting the features and estimating the model parameters and state sequence, which scales to large data sets with large numbers of (noisy) features. We demonstrate the usefulness of the proposed framework by comparing it with a number of other methods on both simulated and real data in the form of financial returns, protein sequences, and text. By leveraging information embedded in the ordering of the data, the resulting sparse jump model outperforms all other methods considered and is remarkably robust to noise.
{"title":"Feature Selection in Jump Models","authors":"P. Nystrup, Petter N. Kolm, Erik Lindström","doi":"10.2139/ssrn.3805831","DOIUrl":"https://doi.org/10.2139/ssrn.3805831","url":null,"abstract":"Abstract Jump models switch infrequently between states to fit a sequence of data while taking the ordering of the data into account We propose a new framework for joint feature selection, parameter and state-sequence estimation in jump models. Feature selection is necessary in high-dimensional settings where the number of features is large compared to the number of observations and the underlying states differ only with respect to a subset of the features. We develop and implement a coordinate descent algorithm that alternates between selecting the features and estimating the model parameters and state sequence, which scales to large data sets with large numbers of (noisy) features. We demonstrate the usefulness of the proposed framework by comparing it with a number of other methods on both simulated and real data in the form of financial returns, protein sequences, and text. By leveraging information embedded in the ordering of the data, the resulting sparse jump model outperforms all other methods considered and is remarkably robust to noise.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76250517","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 Case-Shiller is the reference repeat-sales index for the U.S. residential real estate market, yet it is released with a two-month delay. We find that incorporating recent information from 71 financial and macro predictors improves backcasts, now-casts, and short-term forecasts of the index returns. Combining individual forecasts with recently-proposed weighting schemes delivers large improvements in forecast accuracy at all horizons. Additional gains obtain with mixed-data sampling methods that exploit the daily frequency of financial variables, reducing the mean square forecast error by as much as 13% compared to a simple autoregressive benchmark. The forecast improvements are largest during economic turmoils, throughout the 2020 COVID-19 pandemic period, and in more populous metropolitan areas.
{"title":"Backcasting, Nowcasting, and Forecasting Residential Repeat-Sales Returns: Big Data meets Mixed Frequency","authors":"Matteo Garzoli, Alberto Plazzi, Rossen Valkanov","doi":"10.2139/ssrn.3798356","DOIUrl":"https://doi.org/10.2139/ssrn.3798356","url":null,"abstract":"The Case-Shiller is the reference repeat-sales index for the U.S. residential real estate market, yet it is released with a two-month delay. We find that incorporating recent information from 71 financial and macro predictors improves backcasts, now-casts, and short-term forecasts of the index returns. Combining individual forecasts with recently-proposed weighting schemes delivers large improvements in forecast accuracy at all horizons. Additional gains obtain with mixed-data sampling methods that exploit the daily frequency of financial variables, reducing the mean square forecast error by as much as 13% compared to a simple autoregressive benchmark. The forecast improvements are largest during economic turmoils, throughout the 2020 COVID-19 pandemic period, and in more populous metropolitan areas.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83744102","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}
Network analysis has demonstrated that interconnectedness among market participants results in spillovers, amplifies or absorbs shocks, and creates other nonlinear effects that ultimately affect market health. In this paper, we propose a new directed network construct, the liquidity network, to capture the urgency to trade by connecting the initiating party in a trade to the passive party. Alongside the conventional trading network connecting sellers to buyers, we show both network types complement each other: Liquidity networks reveal valuable information, particularly when information asymmetry in the market is high, and provide a more comprehensive characterization of interconnectivity in the overnight-lending market.
{"title":"Liquidity Networks, Interconnectedness, and Interbank Information Asymmetry","authors":"Celso Brunetti, J. Harris, Shawn Mankad","doi":"10.2139/ssrn.3576512","DOIUrl":"https://doi.org/10.2139/ssrn.3576512","url":null,"abstract":"Network analysis has demonstrated that interconnectedness among market participants results in spillovers, amplifies or absorbs shocks, and creates other nonlinear effects that ultimately affect market health. In this paper, we propose a new directed network construct, the liquidity network, to capture the urgency to trade by connecting the initiating party in a trade to the passive party. Alongside the conventional trading network connecting sellers to buyers, we show both network types complement each other: Liquidity networks reveal valuable information, particularly when information asymmetry in the market is high, and provide a more comprehensive characterization of interconnectivity in the overnight-lending market.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89017717","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 study examines the ability of online ticker searches to serve as a valid proxy for investor sentiment and forecast stock returns and trading volumes in the Indian financial market. In contrast to the common findings, we observe that ticker search volumes do not exhibit any predictive value for future excess stock returns. However, we find a weak but significant positive effect of ticker search volumes on trading volume with a two-week lag. A battery of robustness checks supports our findings. Our work warns the investors from possible misleading insights arising from search volume and stock returns related studies.
{"title":"Predicting Power of Ticker Search Volume in Indian Stock Market","authors":"Ishani Chaudhuri, P. Kayal","doi":"10.2139/ssrn.3775341","DOIUrl":"https://doi.org/10.2139/ssrn.3775341","url":null,"abstract":"This study examines the ability of online ticker searches to serve as a valid proxy for investor sentiment and forecast stock returns and trading volumes in the Indian financial market. In contrast to the common findings, we observe that ticker search volumes do not exhibit any predictive value for future excess stock returns. However, we find a weak but significant positive effect of ticker search volumes on trading volume with a two-week lag. A battery of robustness checks supports our findings. Our work warns the investors from possible misleading insights arising from search volume and stock returns related studies.","PeriodicalId":11495,"journal":{"name":"Econometric Modeling: Capital Markets - Forecasting eJournal","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87717794","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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