Pub Date : 2016-06-30DOI: 10.3905/jot.2016.11.3.061
Ying Zhang, Hongfei Tang, Wikrom Prombutr, S. V. Le
This article investigates pre-event trading behaviors and investment returns surrounding Value Line’s weekly Timeliness rank-change announcements. The findings indicate that pre-event trading is accompanied by abnormal returns and volumes that are subject to rank changes. However, pre-event trading is not detected for stocks given Value Line Initial Reviews. Performance tests show that abnormal returns for pre-event trader portfolios are unexplained by a conventional four-factor asset-pricing model. Additional tests attest that pre-event traders generate superior performance, robust to adjustments for earnings shocks, transactions costs, size effect, and market conditions. With simultaneous upgrade and downgrade information, pre-event hedging strategies are further shown to be feasible and profitable. The authors contend that Value Line’s weekly Timeliness rank-change announcements generate abnormal returns for pre-event traders, exploiting an information asymmetry.
{"title":"Pre-Event Trading Based on Value Line’s Weekly Rank-Change Announcements","authors":"Ying Zhang, Hongfei Tang, Wikrom Prombutr, S. V. Le","doi":"10.3905/jot.2016.11.3.061","DOIUrl":"https://doi.org/10.3905/jot.2016.11.3.061","url":null,"abstract":"This article investigates pre-event trading behaviors and investment returns surrounding Value Line’s weekly Timeliness rank-change announcements. The findings indicate that pre-event trading is accompanied by abnormal returns and volumes that are subject to rank changes. However, pre-event trading is not detected for stocks given Value Line Initial Reviews. Performance tests show that abnormal returns for pre-event trader portfolios are unexplained by a conventional four-factor asset-pricing model. Additional tests attest that pre-event traders generate superior performance, robust to adjustments for earnings shocks, transactions costs, size effect, and market conditions. With simultaneous upgrade and downgrade information, pre-event hedging strategies are further shown to be feasible and profitable. The authors contend that Value Line’s weekly Timeliness rank-change announcements generate abnormal returns for pre-event traders, exploiting an information asymmetry.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126363166","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 : 2016-06-30DOI: 10.3905/jot.2016.11.3.032
Ananth Madhavan, Stephen Laipply, A. Sobczyk
The over-the-counter global corporate bond market, characterized by opacity and illiquidity, is undergoing a rapid transformation driven by new regulations and technology. Bond exchange-traded funds (ETFs) offer one vision of the possible future of the market, trading on organized exchanges with typically narrow spreads and high liquidity. The success of bond ETFs relies critically on the efficient functioning of arbitrage. In recent years, improved real-time technology combined with greater post-trade transparency (e.g., through TRACE) has made it possible to generate intraday estimates for a fixed-income portfolio based on individual bond data and macro-market parameters. In this article, the authors describe one possible approach to developing and implementing such an intraday estimate. From a practical perspective, they illustrate how investors and traders can use these estimates as a complement to existing data (such as end-of-day NAV) to better understand the underlying bond portfolio value during the trading day and for transaction cost analysis. More generally, the article illustrates the potential for new analytics to increase transparency and further accelerate the ongoing evolution of fixed-income markets.
{"title":"Toward Greater Transparency and Efficiency inTrading Fixed-Income ETF Portfolios","authors":"Ananth Madhavan, Stephen Laipply, A. Sobczyk","doi":"10.3905/jot.2016.11.3.032","DOIUrl":"https://doi.org/10.3905/jot.2016.11.3.032","url":null,"abstract":"The over-the-counter global corporate bond market, characterized by opacity and illiquidity, is undergoing a rapid transformation driven by new regulations and technology. Bond exchange-traded funds (ETFs) offer one vision of the possible future of the market, trading on organized exchanges with typically narrow spreads and high liquidity. The success of bond ETFs relies critically on the efficient functioning of arbitrage. In recent years, improved real-time technology combined with greater post-trade transparency (e.g., through TRACE) has made it possible to generate intraday estimates for a fixed-income portfolio based on individual bond data and macro-market parameters. In this article, the authors describe one possible approach to developing and implementing such an intraday estimate. From a practical perspective, they illustrate how investors and traders can use these estimates as a complement to existing data (such as end-of-day NAV) to better understand the underlying bond portfolio value during the trading day and for transaction cost analysis. More generally, the article illustrates the potential for new analytics to increase transparency and further accelerate the ongoing evolution of fixed-income markets.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133437465","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 : 2016-05-16DOI: 10.3905/jot.2018.13.4.062
Ananth Madhavan, Stephen Laipply, A. Sobczyk
The over-the-counter global corporate bond market, characterized by opacity and illiquidity, is undergoing a rapid transformation driven by new regulations and technology. Bond exchange-traded funds (ETFs) offer one vision of the possible future of the market, trading on organized exchanges with typically narrow spreads and high liquidity. The success of bond ETFs relies critically on the efficient functioning of arbitrage. In recent years, improved real-time technology combined with greater post-trade transparency (e.g., through TRACE) has made it possible to generate intraday estimates for a fixed-income portfolio based on individual bond data and macro-market parameters. In this article, the authors describe one possible approach to developing and implementing such an intraday estimate. From a practical perspective, they illustrate how investors and traders can use these estimates as a complement to existing data (such as end-of-day NAV) to better understand the underlying bond portfolio value during the trading day and for transaction cost analysis. More generally, the article illustrates the potential for new analytics to increase transparency and further accelerate the ongoing evolution of fixed-income markets.
{"title":"Toward Greater Transparency and Efficiency in Trading Fixed-Income ETF Portfolios","authors":"Ananth Madhavan, Stephen Laipply, A. Sobczyk","doi":"10.3905/jot.2018.13.4.062","DOIUrl":"https://doi.org/10.3905/jot.2018.13.4.062","url":null,"abstract":"The over-the-counter global corporate bond market, characterized by opacity and illiquidity, is undergoing a rapid transformation driven by new regulations and technology. Bond exchange-traded funds (ETFs) offer one vision of the possible future of the market, trading on organized exchanges with typically narrow spreads and high liquidity. The success of bond ETFs relies critically on the efficient functioning of arbitrage. In recent years, improved real-time technology combined with greater post-trade transparency (e.g., through TRACE) has made it possible to generate intraday estimates for a fixed-income portfolio based on individual bond data and macro-market parameters. In this article, the authors describe one possible approach to developing and implementing such an intraday estimate. From a practical perspective, they illustrate how investors and traders can use these estimates as a complement to existing data (such as end-of-day NAV) to better understand the underlying bond portfolio value during the trading day and for transaction cost analysis. More generally, the article illustrates the potential for new analytics to increase transparency and further accelerate the ongoing evolution of fixed-income markets.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129739916","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 : 2016-05-06DOI: 10.3905/jot.2016.11.3.006
J. Blocher, Ricky Cooper, J. Seddon, Ben Van Vliet
This article examines every NASDAQ ITCH feed message for S&P 500 Index stocks for 2012 and identifies clusters of extremely high and extremely low limit-order cancellation activity. The authors find results consistent with the idea that cancel clusters are the result of high-frequency traders jockeying for queue position and reacting to information to establish a new price level. Furthermore, few trades seem to be executed during cancel clusters or even immediately after them. Low cancellation activity seems to be markedly different, with many level changes all caused by executions. The results are consistent with high-frequency trading firms behaving as agents who bring efficiency to the market without the need to have executions at intermediate prices. The authors also discuss the misconception that investors and low-frequency traders are synonymous and its implications for policy given these results.
{"title":"Phantom Liquidity and High-Frequency Quoting","authors":"J. Blocher, Ricky Cooper, J. Seddon, Ben Van Vliet","doi":"10.3905/jot.2016.11.3.006","DOIUrl":"https://doi.org/10.3905/jot.2016.11.3.006","url":null,"abstract":"This article examines every NASDAQ ITCH feed message for S&P 500 Index stocks for 2012 and identifies clusters of extremely high and extremely low limit-order cancellation activity. The authors find results consistent with the idea that cancel clusters are the result of high-frequency traders jockeying for queue position and reacting to information to establish a new price level. Furthermore, few trades seem to be executed during cancel clusters or even immediately after them. Low cancellation activity seems to be markedly different, with many level changes all caused by executions. The results are consistent with high-frequency trading firms behaving as agents who bring efficiency to the market without the need to have executions at intermediate prices. The authors also discuss the misconception that investors and low-frequency traders are synonymous and its implications for policy given these results.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114168356","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 : 2016-04-19DOI: 10.3905/jot.2016.11.3.016
Jiasun Li
Almost all U.S. firms now announce earnings outside of regular trading hours. This article studies how stock prices incorporate information in after-hours trading. The author finds slow price adjustment accompanied by significant trading volume. During the 2002–2012 period, 5,881 rule-based trading opportunities generated an average return of 1.53% within four hours. After costs (assessed by a trading experiment), an investor who properly exploited the slow adjustment beat the market by 11.5% a year. The slow price adjustment persists under various levels of investor inattention, limited arbitrage capital, and short-sale constraints.
{"title":"Slow Price Adjustment to Public News in After-Hours Trading","authors":"Jiasun Li","doi":"10.3905/jot.2016.11.3.016","DOIUrl":"https://doi.org/10.3905/jot.2016.11.3.016","url":null,"abstract":"Almost all U.S. firms now announce earnings outside of regular trading hours. This article studies how stock prices incorporate information in after-hours trading. The author finds slow price adjustment accompanied by significant trading volume. During the 2002–2012 period, 5,881 rule-based trading opportunities generated an average return of 1.53% within four hours. After costs (assessed by a trading experiment), an investor who properly exploited the slow adjustment beat the market by 11.5% a year. The slow price adjustment persists under various levels of investor inattention, limited arbitrage capital, and short-sale constraints.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"346 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133791386","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 : 2016-03-31DOI: 10.3905/jot.2016.11.2.001
Brian R. Bruce
DaviD anTin CEO Dave BliDe Publisher We open the Spring issue with Mozes and Steffens, who introduce a model for forecasting future volatility using fundamental factors. These fundamental factors include the extent to which the market’s valuation deviates from its predicted value, the losses reported by companies with negative earnings, projected earnings growth rates, and Treasury bill rates. Chung and Kissell then propose a transaction cost analysis portfolio optimization procedure that incorporates transaction costs directly into the problem of the objective function of portfolio optimization. Their results show that a manager can start with a seemingly suboptimal or inefficient ex ante portfolio in traditional mean variance space and earn higher ex post net returns after accounting for transaction costs. Sommer and Pasquali discuss the lack of a universally agreed upon and adopted measure or model that adequately captures cost and time to liquidation in bond (OTC) markets. After a review of 40 years of research, they propose a more adequate measure and further suggest that machine learning methods are a natural candidate to overcome the main obstacles. Next, Polidore, Jiang, and Li study methods of altering the standard approach to volume weighted average price such that it respects stock-specific volume volatility. They also argue that traders should not choose algorithms based on stock characteristics; instead, algorithm choice should focus on the tradeoff between cost and timing risk. Ceccon, Thukral, and Eleuterio evaluate momentum strategies. They look at four popular languages used by quantitative researchers and traders to program their models from a performance point of view while considering how easy it is to obtain programs that run in an acceptable amount of time. In our special section on market structure and trading related activities, Virgilio presents the results of an agent-based model simulation under two different cases: a quiet situation and a market following a trend. Results suggests that the interaction between high-frequency and low-frequency traders, rather than the mere participation of high-frequency traders, may be the main cause of higher-than-normal volatility. Lewis and McPartland describe the CHX SNAP, the proposed intraday, on-demand auction service of the Chicago Stock Exchange, which represents the first significant attempt to incorporate batch auctions into U.S. equity markets. If commercially successful, the CHX SNAP auction would allow institutional traders to leave hidden resting equity orders at the CHX out of the vision of digital traders that might otherwise attempt to profit from such knowledge. We conclude the issue with Kumiega, Sterijevski, and Van Vliet, who present an overview of the complexity of the automated market network and describe how market participants interact through the exchange mechanism. They define new terms and a new framework for understanding the risk of extreme market moves fr
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jot.2016.11.2.001","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.001","url":null,"abstract":"DaviD anTin CEO Dave BliDe Publisher We open the Spring issue with Mozes and Steffens, who introduce a model for forecasting future volatility using fundamental factors. These fundamental factors include the extent to which the market’s valuation deviates from its predicted value, the losses reported by companies with negative earnings, projected earnings growth rates, and Treasury bill rates. Chung and Kissell then propose a transaction cost analysis portfolio optimization procedure that incorporates transaction costs directly into the problem of the objective function of portfolio optimization. Their results show that a manager can start with a seemingly suboptimal or inefficient ex ante portfolio in traditional mean variance space and earn higher ex post net returns after accounting for transaction costs. Sommer and Pasquali discuss the lack of a universally agreed upon and adopted measure or model that adequately captures cost and time to liquidation in bond (OTC) markets. After a review of 40 years of research, they propose a more adequate measure and further suggest that machine learning methods are a natural candidate to overcome the main obstacles. Next, Polidore, Jiang, and Li study methods of altering the standard approach to volume weighted average price such that it respects stock-specific volume volatility. They also argue that traders should not choose algorithms based on stock characteristics; instead, algorithm choice should focus on the tradeoff between cost and timing risk. Ceccon, Thukral, and Eleuterio evaluate momentum strategies. They look at four popular languages used by quantitative researchers and traders to program their models from a performance point of view while considering how easy it is to obtain programs that run in an acceptable amount of time. In our special section on market structure and trading related activities, Virgilio presents the results of an agent-based model simulation under two different cases: a quiet situation and a market following a trend. Results suggests that the interaction between high-frequency and low-frequency traders, rather than the mere participation of high-frequency traders, may be the main cause of higher-than-normal volatility. Lewis and McPartland describe the CHX SNAP, the proposed intraday, on-demand auction service of the Chicago Stock Exchange, which represents the first significant attempt to incorporate batch auctions into U.S. equity markets. If commercially successful, the CHX SNAP auction would allow institutional traders to leave hidden resting equity orders at the CHX out of the vision of digital traders that might otherwise attempt to profit from such knowledge. We conclude the issue with Kumiega, Sterijevski, and Van Vliet, who present an overview of the complexity of the automated market network and describe how market participants interact through the exchange mechanism. They define new terms and a new framework for understanding the risk of extreme market moves fr","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126766660","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 : 2016-03-31DOI: 10.3905/jot.2016.11.2.065
Rebecca Lewis, J. McPartland
The Financial Markets Group of the Federal Reserve Bank of Chicago has a keen interest in frequent batch auctions as a potential tool to diminish the utility of raw speed in executing trades on electronic financial markets. The Chicago Stock Exchange has received approval from the Securities and Exchange Commission to launch an innovative variant of batch auctions, the CHX SNAP auction. This article describes the CHX SNAP auction concept in great detail. If commercially successful, the CHX SNAP auction would allow institutional traders to leave hidden resting equity orders at the CHX out of the vision of digital traders who might otherwise attempt to profit from such knowledge.
芝加哥联邦储备银行(Federal Reserve Bank of Chicago)的金融市场小组(Financial Markets Group)对频繁的批量拍卖非常感兴趣，认为这是一种潜在的工具，可以降低电子金融市场执行交易时的原始速度。芝加哥证券交易所已获得美国证券交易委员会的批准，将推出一种创新的批量拍卖，即CHX SNAP拍卖。本文详细介绍了CHX SNAP拍卖的概念。如果在商业上取得成功，CHX SNAP拍卖将允许机构交易员在CHX留下隐藏的静息股票订单，否则数字交易员可能会试图从中获利。
{"title":"A New Approach to Stock Market Execution","authors":"Rebecca Lewis, J. McPartland","doi":"10.3905/jot.2016.11.2.065","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.065","url":null,"abstract":"The Financial Markets Group of the Federal Reserve Bank of Chicago has a keen interest in frequent batch auctions as a potential tool to diminish the utility of raw speed in executing trades on electronic financial markets. The Chicago Stock Exchange has received approval from the Securities and Exchange Commission to launch an innovative variant of batch auctions, the CHX SNAP auction. This article describes the CHX SNAP auction concept in great detail. If commercially successful, the CHX SNAP auction would allow institutional traders to leave hidden resting equity orders at the CHX out of the vision of digital traders who might otherwise attempt to profit from such knowledge.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131507296","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 : 2016-03-31DOI: 10.3905/jot.2016.11.2.041
Ben Polidore, lin jiang, Yichu Li
The goal of this research was to study methods of altering the standard approach to volume weighted average price such that it respects stock-specific volume volatility. The early returns are promising, and we think this concept can be applied to other algorithms where inappropriately tight constraints create excess cost. In this article, we review the state of the art for volume forecasting and how these efforts are rewarded. We show the results of a random trial of orders that use a static tolerance around the target schedule versus orders that use a tolerance set by the volume volatility of the stock. The results show less aggressive trading. We also argue that traders should not choose algorithms based on stock characteristics. Instead, algorithm choice should focus on the tradeoff between cost and timing risk.
{"title":"A Better Way to Trade Small Caps: The Power of Volume Volatility in Algorithm Design","authors":"Ben Polidore, lin jiang, Yichu Li","doi":"10.3905/jot.2016.11.2.041","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.041","url":null,"abstract":"The goal of this research was to study methods of altering the standard approach to volume weighted average price such that it respects stock-specific volume volatility. The early returns are promising, and we think this concept can be applied to other algorithms where inappropriately tight constraints create excess cost. In this article, we review the state of the art for volume forecasting and how these efforts are rewarded. We show the results of a random trial of orders that use a static tolerance around the target schedule versus orders that use a tolerance set by the volume volatility of the stock. The results show less aggressive trading. We also argue that traders should not choose algorithms based on stock characteristics. Instead, algorithm choice should focus on the tradeoff between cost and timing risk.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121065323","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 : 2016-03-31DOI: 10.3905/jot.2016.11.2.055
G. Virgilio
This article presents the results of an agent-based model simulation under two different cases: a quiet situation and a market following a trend. Although the quiet situation does not identify any abnormal behavior, participation of high-frequency (HF) traders leads to a statistically significant increase in volatility when the market is under stress. This result can be explained by the delay suffered by market orders posted by low-frequency traders during a trend. These trades are often executed at a price that, because of its rapid movements, is worse than was intended when it was posted a few milliseconds earlier, thus increasing volatility. As the number of HF traders increases, volatility starts to diminish again. This can be explained by the more homogeneous situation that occurs when most trading is executed by players experiencing similar latencies.
{"title":"The Impact of High-Frequency Trading on Market Volatility","authors":"G. Virgilio","doi":"10.3905/jot.2016.11.2.055","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.055","url":null,"abstract":"This article presents the results of an agent-based model simulation under two different cases: a quiet situation and a market following a trend. Although the quiet situation does not identify any abnormal behavior, participation of high-frequency (HF) traders leads to a statistically significant increase in volatility when the market is under stress. This result can be explained by the delay suffered by market orders posted by low-frequency traders during a trend. These trades are often executed at a price that, because of its rapid movements, is worse than was intended when it was posted a few milliseconds earlier, thus increasing volatility. As the number of HF traders increases, volatility starts to diminish again. This can be explained by the more homogeneous situation that occurs when most trading is executed by players experiencing similar latencies.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125808595","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 : 2016-03-31DOI: 10.3905/jot.2016.11.2.005
Haim A. Mozes, John Launny Steffens
This article introduces a model for forecasting future volatility using fundamental factors, including the extent to which the market’s valuation deviates from its predicted value, the losses reported by companies with negative earnings, projected earnings growth rates, and Treasury bill rates. The main result is that fundamental factors provide significant incremental explanatory power for predicting volatility relative to that provided by past volatility realizations alone. The explanatory power of fundamental factors is greatest when the VIX Index is at moderate rather than extreme levels so there is no expectation of long-term mean reversion for volatility. In addition, the explanatory power of fundamental factors is greatest when the model forecasts an increase in VIX. The overall conclusion of this study is that forecasts of future volatility should incorporate fundamental factors.
{"title":"Using Fundamental Earnings Factors to Forecast Equity Market Volatility","authors":"Haim A. Mozes, John Launny Steffens","doi":"10.3905/jot.2016.11.2.005","DOIUrl":"https://doi.org/10.3905/jot.2016.11.2.005","url":null,"abstract":"This article introduces a model for forecasting future volatility using fundamental factors, including the extent to which the market’s valuation deviates from its predicted value, the losses reported by companies with negative earnings, projected earnings growth rates, and Treasury bill rates. The main result is that fundamental factors provide significant incremental explanatory power for predicting volatility relative to that provided by past volatility realizations alone. The explanatory power of fundamental factors is greatest when the VIX Index is at moderate rather than extreme levels so there is no expectation of long-term mean reversion for volatility. In addition, the explanatory power of fundamental factors is greatest when the model forecasts an increase in VIX. The overall conclusion of this study is that forecasts of future volatility should incorporate fundamental factors.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114351093","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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