Eero J. Pätäri, Sheraz Ahmed, Tuomas Lankinen, J. Yeomans
This paper contributes to the existing stock market anomaly literature by being the first to analyze the benefits of combining two distinct anomalies; specifically, the low-volatility and mean-reversion anomalies. Our results show that on a long-only basis, these two time-varying anomalies could be combined into a double-sort investment strategy that includes some desirable characteristics from each of them, thereby making the portfolio return accumulation more stable over time. As the added-value of low-volatility investing stems mostly from the risk-reduction side, while contrarian stocks are generally highly volatile with remarkable upside potential, the use of the double-sort portfolio-formation in which the contrarian stocks are picked from the sub-set of below-median volatility stocks can shorten the below-market performance periods that have occasionally materialized for plain low-volatility or plain contrarian investors.
{"title":"Combining low-volatility and mean-reversion anomalies: Better together?","authors":"Eero J. Pätäri, Sheraz Ahmed, Tuomas Lankinen, J. Yeomans","doi":"10.3233/af-220441","DOIUrl":"https://doi.org/10.3233/af-220441","url":null,"abstract":"This paper contributes to the existing stock market anomaly literature by being the first to analyze the benefits of combining two distinct anomalies; specifically, the low-volatility and mean-reversion anomalies. Our results show that on a long-only basis, these two time-varying anomalies could be combined into a double-sort investment strategy that includes some desirable characteristics from each of them, thereby making the portfolio return accumulation more stable over time. As the added-value of low-volatility investing stems mostly from the risk-reduction side, while contrarian stocks are generally highly volatile with remarkable upside potential, the use of the double-sort portfolio-formation in which the contrarian stocks are picked from the sub-set of below-median volatility stocks can shorten the below-market performance periods that have occasionally materialized for plain low-volatility or plain contrarian investors.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"12 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139230490","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, a shorter and more publication focused version of our recent article “A Bottom-Up Approach to the financial Markets” (Mahdavi-Damghani, & Roberts, S. 2019.) is presented. More specifically we propose a new approach to studying the financial markets using the Bottom-Up approach instead of the traditional Top-Down. We achieve this shift in perspective, by re-introducing the High Frequency Trading Ecosystem (HFTE) model Mahdavi-Damghani, B. 2017. More specifically we specify an approach in which agents in Neural Network format designed to address the complexity demands of most common financial strategies interact through an Order-Book. We introduce in that context concepts such as the Path of Interaction in order to study our Ecosystem of strategies through time. We show how a Particle Filter methodology can then be used in order to track the market ecosystem through time. Finally, we take this opportunity to explore how to build a realistic market simulator which objective would be to test real market impact without incurring any research costs.
本文是我们最近一篇文章《金融市场的自下而上方法》(Mahdavi-Damghani， &罗伯茨，S. 2019)。更具体地说，我们提出了一种新的方法来研究金融市场，使用自下而上的方法，而不是传统的自上而下的方法。我们通过重新引入高频交易生态系统(HFTE)模型Mahdavi-Damghani, B. 2017，实现了这一观点的转变。更具体地说，我们指定了一种方法，其中神经网络格式的代理旨在解决大多数常见金融策略的复杂性需求，通过订单簿进行交互。在此背景下，我们引入了互动路径等概念，以便研究我们的战略生态系统。我们展示了如何使用粒子过滤器方法来跟踪市场生态系统。最后，我们借此机会探讨如何建立一个现实的市场模拟器，其目的是在不产生任何研究成本的情况下测试真实的市场影响。
{"title":"Guidelines for building a realistic algorithmic trading market simulator for backtesting while incorporating market impact","authors":"Babak Mahdavi-Damghani, Stephen Roberts","doi":"10.3233/af-220356","DOIUrl":"https://doi.org/10.3233/af-220356","url":null,"abstract":"In this paper, a shorter and more publication focused version of our recent article “A Bottom-Up Approach to the financial Markets” (Mahdavi-Damghani, & Roberts, S. 2019.) is presented. More specifically we propose a new approach to studying the financial markets using the Bottom-Up approach instead of the traditional Top-Down. We achieve this shift in perspective, by re-introducing the High Frequency Trading Ecosystem (HFTE) model Mahdavi-Damghani, B. 2017. More specifically we specify an approach in which agents in Neural Network format designed to address the complexity demands of most common financial strategies interact through an Order-Book. We introduce in that context concepts such as the Path of Interaction in order to study our Ecosystem of strategies through time. We show how a Particle Filter methodology can then be used in order to track the market ecosystem through time. Finally, we take this opportunity to explore how to build a realistic market simulator which objective would be to test real market impact without incurring any research costs.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135482973","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}
Andy M. Yip, W. Ng, Ka-Wai Siu, Albert C. Cheung, Michael K. Ng
We present an algorithmic trading strategy based upon a graph version of the dynamic mode decomposition (DMD) model. Unlike the traditional DMD model which tries to characterize a stock’s dynamics based on all other stocks in a universe, the proposed model characterizes a stock’s dynamics based only on stocks that are deemed relevant to the stock in question. The relevance between each pair of stocks in a universe is represented as a directed graph and is updated dynamically. The incorporation of a graph model into DMD effects a model reduction that avoids overfitting of data and improves the quality of the trend predictions. We show that, in a practical setting, the precision and recall rate of the proposed model are significantly better than the traditional DMD and the benchmarks. The proposed model yields portfolios that have more stable returns in most of the universes we backtested.
{"title":"Graph embedded dynamic mode decomposition for stock price prediction","authors":"Andy M. Yip, W. Ng, Ka-Wai Siu, Albert C. Cheung, Michael K. Ng","doi":"10.3233/af-220432","DOIUrl":"https://doi.org/10.3233/af-220432","url":null,"abstract":"We present an algorithmic trading strategy based upon a graph version of the dynamic mode decomposition (DMD) model. Unlike the traditional DMD model which tries to characterize a stock’s dynamics based on all other stocks in a universe, the proposed model characterizes a stock’s dynamics based only on stocks that are deemed relevant to the stock in question. The relevance between each pair of stocks in a universe is represented as a directed graph and is updated dynamically. The incorporation of a graph model into DMD effects a model reduction that avoids overfitting of data and improves the quality of the trend predictions. We show that, in a practical setting, the precision and recall rate of the proposed model are significantly better than the traditional DMD and the benchmarks. The proposed model yields portfolios that have more stable returns in most of the universes we backtested.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"10 1","pages":"39-51"},"PeriodicalIF":0.5,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42760677","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 obtain the bond price formula for the fractional Cox-Ingersoll-Ross model. Then we obtain option price formula for the bond. Finally we apply it to derive option price formula in fractional Heston model.
{"title":"Interest rate derivatives for the fractional Cox-Ingersoll-Ross model","authors":"J. Bishwal","doi":"10.3233/af-220467","DOIUrl":"https://doi.org/10.3233/af-220467","url":null,"abstract":"We obtain the bond price formula for the fractional Cox-Ingersoll-Ross model. Then we obtain option price formula for the bond. Finally we apply it to derive option price formula in fractional Heston model.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"10 1","pages":"53-66"},"PeriodicalIF":0.5,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49536303","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}
Smart Beta Investing has revolutionized investment management field with the ability to offer higher returns with lower costs. The momentum factor in the Smart Beta universe often outperforms other popular factors, besides being well documented in the literature, it is found to be pervasive across different geographies and asset classes. In this paper, we implement a long-only momentum based investment strategy for the Indian equity markets that delivers superior risk-adjusted performance, derived upon comparing multiple strategies across time frames. Based on these tests, we find that the lagged 6-months’ compounded returns indicator with quarterly rebalancing can be used to generate the highest risk-adjusted performance.The paper also tests a related phenomenon called the Accelerated Effect of momentum as documented by Ardila et. al. (2021) for the Indian equity market, and finds that the accelerated momentum effect underperforms the traditional momentum both on an absolute and risk-adjusted basis.
{"title":"How smart is a momentum strategy? An empirical study of Indian equities","authors":"Apurv Nigam, P. Pandey","doi":"10.3233/af-220399","DOIUrl":"https://doi.org/10.3233/af-220399","url":null,"abstract":"Smart Beta Investing has revolutionized investment management field with the ability to offer higher returns with lower costs. The momentum factor in the Smart Beta universe often outperforms other popular factors, besides being well documented in the literature, it is found to be pervasive across different geographies and asset classes. In this paper, we implement a long-only momentum based investment strategy for the Indian equity markets that delivers superior risk-adjusted performance, derived upon comparing multiple strategies across time frames. Based on these tests, we find that the lagged 6-months’ compounded returns indicator with quarterly rebalancing can be used to generate the highest risk-adjusted performance.The paper also tests a related phenomenon called the Accelerated Effect of momentum as documented by Ardila et. al. (2021) for the Indian equity market, and finds that the accelerated momentum effect underperforms the traditional momentum both on an absolute and risk-adjusted basis.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"10 1","pages":"21-37"},"PeriodicalIF":0.5,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49244786","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 article uses Principal Component Analysis to compute and extract the main factors for the financial risk of a portfolio, to determine the most dominating stock for each risk factor and for each portfolio and finally to compute the total risk of the portfolio. Firstly, each dataset is standardized and yields a new datasets. For each obtained dataset a covariance matrix is constructed from which the eigenvalues and eigenvectors are computed. The eigenvectors are linearly independent one to another and span a real vector space where the dimension is equal to the number of the original variables. They are also orthogonal and yield the principal risk components (pcs) also called principal risk axis, principal risk directions or main risk factors for the risk of the portfolios. They capture the maximum variance (risk) of the original dataset. Their number may even be reduced with minimum (negligible) loss of information and they constitute the new system of coordinates. Every principal component is a linear combination of the original variables (stock rate of returns). For each dataset, each financial transaction can be written as a linear combination of the eigenvectors. Since they are mutually orthogonal and linearly independent and that they capture the maximum variance of the original data, the risk of the portfolio is calculated by using the principal components, then they have been used to calculate the total risk of the portfolio which is a weighted sum of the variance explained by the principal components.
{"title":"Computation of financial risk using principal component analysis","authors":"Mavungu Masiala","doi":"10.3233/af-220339","DOIUrl":"https://doi.org/10.3233/af-220339","url":null,"abstract":"This article uses Principal Component Analysis to compute and extract the main factors for the financial risk of a portfolio, to determine the most dominating stock for each risk factor and for each portfolio and finally to compute the total risk of the portfolio. Firstly, each dataset is standardized and yields a new datasets. For each obtained dataset a covariance matrix is constructed from which the eigenvalues and eigenvectors are computed. The eigenvectors are linearly independent one to another and span a real vector space where the dimension is equal to the number of the original variables. They are also orthogonal and yield the principal risk components (pcs) also called principal risk axis, principal risk directions or main risk factors for the risk of the portfolios. They capture the maximum variance (risk) of the original dataset. Their number may even be reduced with minimum (negligible) loss of information and they constitute the new system of coordinates. Every principal component is a linear combination of the original variables (stock rate of returns). For each dataset, each financial transaction can be written as a linear combination of the eigenvectors. Since they are mutually orthogonal and linearly independent and that they capture the maximum variance of the original data, the risk of the portfolio is calculated by using the principal components, then they have been used to calculate the total risk of the portfolio which is a weighted sum of the variance explained by the principal components.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"10 1","pages":"1-20"},"PeriodicalIF":0.5,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43062957","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 pattern of dependence between liquidity, durations (orders and trades) and bid-ask spreads in a limit order market are examined in high resolution invoking copulas and graph theory. Using intraday data from a sample of NASDAQ 100 stocks and an experimental design, we study the information pathways in markets in the presence of algorithmic traders. Our results confirm that multivariate analysis is more appropriate to investigate these information pathways. We observe that the strength and nature of the dependence between variables vary through the trading day. We confirm the existence of stylised aspects of algorithmic trading, such as tail dependence in trade durations, a balance between buy and sell side in order durations, liquidity and bid-ask spreads, and the bid-ask spread and liquidity trade-off in the dependence structure.
{"title":"Investigating Intertrade Durations using Copulas: An Experiment with NASDAQ Data","authors":"Ranjan R. Chakravarty, Sudhanshu Sekhar Pani","doi":"10.3233/af-200362","DOIUrl":"https://doi.org/10.3233/af-200362","url":null,"abstract":"The pattern of dependence between liquidity, durations (orders and trades) and bid-ask spreads in a limit order market are examined in high resolution invoking copulas and graph theory. Using intraday data from a sample of NASDAQ 100 stocks and an experimental design, we study the information pathways in markets in the presence of algorithmic traders. Our results confirm that multivariate analysis is more appropriate to investigate these information pathways. We observe that the strength and nature of the dependence between variables vary through the trading day. We confirm the existence of stylised aspects of algorithmic trading, such as tail dependence in trade durations, a balance between buy and sell side in order durations, liquidity and bid-ask spreads, and the bid-ask spread and liquidity trade-off in the dependence structure.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"81-102"},"PeriodicalIF":0.5,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44966684","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}
Catalina Hurwitz, Suchi Mishra, R. Daigler, Ihsan Badshah
We examine the effect of VIX futures’ new trading hours on price discovery as these causal relations have not been investigated before and are consequential for regulators and practitioners involved in the VIX futures market. Our data include VIX futures and VIX ETPs for four different periods in which trading hours were changed. Employing three different measures of information share, we find that VXX ETN leads VIX futures in 2009 and 2010, while in 2011 and 2013, the ETPs’ leadership varies depending on the exchange-traded product under consideration. Furthermore, in 2013 before the change of trading hours, the VIX futures contribute more to price discovery than they do after trading hours expansion. Less of the price discovery occurs from the exchange-traded products in the latter half of the trading period in 2010. OLS regression results of the determinants of price discovery as well as panel regression results show that the effect of volume and spread, which are the main determinants of price discovery in the prior literature, change significantly before and after futures trading hour expansions, for both VIX futures and ETPs.
{"title":"Dynamics of information leadership in the volatility complex with trading time changes: Evidence from VIX futures and VIX ETPs","authors":"Catalina Hurwitz, Suchi Mishra, R. Daigler, Ihsan Badshah","doi":"10.3233/af-200342","DOIUrl":"https://doi.org/10.3233/af-200342","url":null,"abstract":"We examine the effect of VIX futures’ new trading hours on price discovery as these causal relations have not been investigated before and are consequential for regulators and practitioners involved in the VIX futures market. Our data include VIX futures and VIX ETPs for four different periods in which trading hours were changed. Employing three different measures of information share, we find that VXX ETN leads VIX futures in 2009 and 2010, while in 2011 and 2013, the ETPs’ leadership varies depending on the exchange-traded product under consideration. Furthermore, in 2013 before the change of trading hours, the VIX futures contribute more to price discovery than they do after trading hours expansion. Less of the price discovery occurs from the exchange-traded products in the latter half of the trading period in 2010. OLS regression results of the determinants of price discovery as well as panel regression results show that the effect of volume and spread, which are the main determinants of price discovery in the prior literature, change significantly before and after futures trading hour expansions, for both VIX futures and ETPs.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"63-79"},"PeriodicalIF":0.5,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45116707","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}
Using an index fund is a popular strategy that is designed to simulate the behavior of a market index and obtain the excess return that is more stable than other mutual funds. In setting up an index fund, investors must first choose a small number of stocks and then assign a weight to each selected stock. However, with traditional methods, investors hardly determine how well the designed index fund can mimic the market index. The main objective of this paper is to demonstrate the improvement of index fund performance by using a multi-objective optimization algorithm that can assign weights automatically.
{"title":"Portfolio optimization with tri-objective for index fund management","authors":"Yao-Tsung Chen, Y. Sheng","doi":"10.3233/af-200378","DOIUrl":"https://doi.org/10.3233/af-200378","url":null,"abstract":"Using an index fund is a popular strategy that is designed to simulate the behavior of a market index and obtain the excess return that is more stable than other mutual funds. In setting up an index fund, investors must first choose a small number of stocks and then assign a weight to each selected stock. However, with traditional methods, investors hardly determine how well the designed index fund can mimic the market index. The main objective of this paper is to demonstrate the improvement of index fund performance by using a multi-objective optimization algorithm that can assign weights automatically.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"121-127"},"PeriodicalIF":0.5,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48385324","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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