{"title":"Algorithmic Finance - A Companion to Data Science","authors":"C. Ting","doi":"10.1142/12315","DOIUrl":"https://doi.org/10.1142/12315","url":null,"abstract":"","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"12 1","pages":"1-408"},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83577679","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}
{"title":"Point-to-point stochastic control of a self-financing portfolio","authors":"M. Masiala","doi":"10.3233/AF-200397","DOIUrl":"https://doi.org/10.3233/AF-200397","url":null,"abstract":"","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"129-143"},"PeriodicalIF":0.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69725154","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}
A central counterparty (CCP) interposes itself between buyers and sellers of financial contracts to extinguish their bilateral exposures. Therefore, central clearing and settlement through a CCP should affect how financial institutions engage in financial markets. Though, financial institutions’ interactions are difficult to observe and analyze. Based on a unique transaction dataset corresponding to the Colombian peso non-delivery forward market, this article compares—for the first time—networks of transactions agreed to be cleared and settled by the CCP with those to be cleared and settled bilaterally. Networks to be centrally cleared and settled show significantly higher connectivity and lower distances among financial institutions. This suggests that agreeing on central clearing and settlement reduces liquidity risk. After CCP interposition, exposure networks show significantly lower connectivity and higher distances, consistent with a reduction of counterparty risk. Consequently, evidence shows CCPs induce a change of behavior in financial institutions that emerges as two distinctive economic structures for the same market, which corresponds to CCP’s intended reduction of liquidity and counterparty risks.
{"title":"Do central counterparties reduce counterparty and liquidity risk? Empirical results","authors":"Carlos León, R. Mariño, Carlos Cadena","doi":"10.3233/AF-200341","DOIUrl":"https://doi.org/10.3233/AF-200341","url":null,"abstract":"A central counterparty (CCP) interposes itself between buyers and sellers of financial contracts to extinguish their bilateral exposures. Therefore, central clearing and settlement through a CCP should affect how financial institutions engage in financial markets. Though, financial institutions’ interactions are difficult to observe and analyze. Based on a unique transaction dataset corresponding to the Colombian peso non-delivery forward market, this article compares—for the first time—networks of transactions agreed to be cleared and settled by the CCP with those to be cleared and settled bilaterally. Networks to be centrally cleared and settled show significantly higher connectivity and lower distances among financial institutions. This suggests that agreeing on central clearing and settlement reduces liquidity risk. After CCP interposition, exposure networks show significantly lower connectivity and higher distances, consistent with a reduction of counterparty risk. Consequently, evidence shows CCPs induce a change of behavior in financial institutions that emerges as two distinctive economic structures for the same market, which corresponds to CCP’s intended reduction of liquidity and counterparty risks.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"25-34"},"PeriodicalIF":0.5,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-200341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44888882","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}
Index tracking is one of the most popular passive strategy in portfolio management. However, due to some practical constrains, a full replication is difficult to obtain. Many mathematical models have failed to generate good results for partial replicated portfolios, but in the last years a data driven approach began to take shape. This paper proposes three heuristic methods for both selection and allocation of the most informative stocks in an index tracking problem, respectively XGBoost, Random Forest and LASSO with stability selection. Among those, latest deep autoencoders have also been tested. All selected algorithms have outperformed the benchmarks in terms of tracking error. The empirical study has been conducted on one of the biggest financial indices in terms of number of components in three different countries, respectively Russell 1000 for the USA, FTSE 350 for the UK, and Nikkei 225 for Japan.
{"title":"Heuristic methods for stock selection and allocation in an index tracking problem","authors":"Codrut-Florin Ivascu","doi":"10.3233/af-200367","DOIUrl":"https://doi.org/10.3233/af-200367","url":null,"abstract":"Index tracking is one of the most popular passive strategy in portfolio management. However, due to some practical constrains, a full replication is difficult to obtain. Many mathematical models have failed to generate good results for partial replicated portfolios, but in the last years a data driven approach began to take shape. This paper proposes three heuristic methods for both selection and allocation of the most informative stocks in an index tracking problem, respectively XGBoost, Random Forest and LASSO with stability selection. Among those, latest deep autoencoders have also been tested. All selected algorithms have outperformed the benchmarks in terms of tracking error. The empirical study has been conducted on one of the biggest financial indices in terms of number of components in three different countries, respectively Russell 1000 for the USA, FTSE 350 for the UK, and Nikkei 225 for Japan.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"103-119"},"PeriodicalIF":0.5,"publicationDate":"2021-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44607495","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 paper studies the effects of new product rumors about the iPhone on the stock price of the Apple company. We scrape iPhone rumors from Macrumors.com, and obtain a dataset covering 1,264 articles containing 180 words on average between January 2002 and December 2015. Moreover, we construct a market-decided lexicon to transform qualitative information into quantitative data, and analyze what type of words and what information embedded in the rumors are apt to impact on Apple’s stock price. Unlike previous studies, we do not rely on the widely-adopted Harvard-IV-4 dictionary, as the coefficients of the words from the dictionary are neither significant nor consistent with their polarities, compared with our results. The paper obtains three main findings. First, the spread of rumors has a significant impact on the stock price. Second, positive words, rather than negative words, play an important role in affecting the stock price. Third, the stock price is highly sensitive to the words related to the appearance of the iPhone.
{"title":"Market Reaction to iPhone Rumors","authors":"Zhang Wu, T. Chong, Yuchen Liu","doi":"10.3233/AF200302","DOIUrl":"https://doi.org/10.3233/AF200302","url":null,"abstract":" The paper studies the effects of new product rumors about the iPhone on the stock price of the Apple company. We scrape iPhone rumors from Macrumors.com, and obtain a dataset covering 1,264 articles containing 180 words on average between January 2002 and December 2015. Moreover, we construct a market-decided lexicon to transform qualitative information into quantitative data, and analyze what type of words and what information embedded in the rumors are apt to impact on Apple’s stock price. Unlike previous studies, we do not rely on the widely-adopted Harvard-IV-4 dictionary, as the coefficients of the words from the dictionary are neither significant nor consistent with their polarities, compared with our results. The paper obtains three main findings. First, the spread of rumors has a significant impact on the stock price. Second, positive words, rather than negative words, play an important role in affecting the stock price. Third, the stock price is highly sensitive to the words related to the appearance of the iPhone.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"1-23"},"PeriodicalIF":0.5,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF200302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42750751","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 task of portfolio selection as a time series prediction problem. At each time-step we obtain prices of a universe of assets and are required to allocate our wealth across them with the goal of maximizing it, based on the historic price returns. We assume these returns are realizations of a general non-stationary stochastic process, and only assume they do not change significantly over short time scales. We follow a statistical learning approach, in which we bound the generalization error of a non-stationary stochastic process, using analogues of uniform laws of large numbers for non-i.i.d. random variables. We use the learning bounds to formulate an optimization algorithm for portfolio selection, and present favorable numerical results with financial data.
{"title":"Portfolio selection in non-stationary markets","authors":"E. Kenig","doi":"10.3233/AF-200349","DOIUrl":"https://doi.org/10.3233/AF-200349","url":null,"abstract":"We consider the task of portfolio selection as a time series prediction problem. At each time-step we obtain prices of a universe of assets and are required to allocate our wealth across them with the goal of maximizing it, based on the historic price returns. We assume these returns are realizations of a general non-stationary stochastic process, and only assume they do not change significantly over short time scales. We follow a statistical learning approach, in which we bound the generalization error of a non-stationary stochastic process, using analogues of uniform laws of large numbers for non-i.i.d. random variables. We use the learning bounds to formulate an optimization algorithm for portfolio selection, and present favorable numerical results with financial data.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"35-47"},"PeriodicalIF":0.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-200349","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69724750","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 concept of clearing or netting, as defined in the glossaries of European Central Bank, has a great impact on the economy of a country influencing the exchanges and the interactions between companies. On short, netting refers to an alternative to the usual way in which the companies make the payments to each other: it is an agreement in which each party sets off amounts it owes against amounts owed to it. Based on the amounts two or more parties owe between them, the payment is substituted by a direct settlement. In this paper we introduce a set of graph algorithms which provide optimal netting solutions for the scale of a country economy. The set of algorithms computes results in an efficient time and is tested on invoice data provided by the Romanian Ministry of Economy. Our results show that classical graph algorithms are still capable of solving very important modern problems.
{"title":"A novel algorithm for clearing financial obligations between companies - an application within the Romanian Ministry of Economy","authors":"Lucian-Ionut Gavrila, Alexandru Popa","doi":"10.3233/AF-200359","DOIUrl":"https://doi.org/10.3233/AF-200359","url":null,"abstract":"The concept of clearing or netting, as defined in the glossaries of European Central Bank, has a great impact on the economy of a country influencing the exchanges and the interactions between companies. On short, netting refers to an alternative to the usual way in which the companies make the payments to each other: it is an agreement in which each party sets off amounts it owes against amounts owed to it. Based on the amounts two or more parties owe between them, the payment is substituted by a direct settlement. In this paper we introduce a set of graph algorithms which provide optimal netting solutions for the scale of a country economy. The set of algorithms computes results in an efficient time and is tested on invoice data provided by the Romanian Ministry of Economy. Our results show that classical graph algorithms are still capable of solving very important modern problems.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"9 1","pages":"49-60"},"PeriodicalIF":0.5,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-200359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47020769","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 the present study, a deterministic model is introduced to explain the stylized facts of financial data. The adaptation introduced by the labyrinth chaos model can reproduce phenomena such as heavy tails observed in financial returns, volatility clustering and jumps. The model is based on the assumption that many unstable stationary states arise from the interaction or feedback between financial prices. Model tests are performed, and the results show that the model generates series that reject a normal distribution of the returns and which can be represented by the GARCH model. An analysis applying symbolic dynamics shows similar behaviors in a system with three stock indices, three currency relations and three prices generated by the introduced model. We observe sequences that have not been produced by any of the three systems, suggesting that in a three-dimensional space, the paths traveled by the real series and those of the model may not be completely random.
{"title":"Modeling the financial market with labyrinth chaos","authors":"W. Risso","doi":"10.3233/AF-190245","DOIUrl":"https://doi.org/10.3233/AF-190245","url":null,"abstract":"In the present study, a deterministic model is introduced to explain the stylized facts of financial data. The adaptation introduced by the labyrinth chaos model can reproduce phenomena such as heavy tails observed in financial returns, volatility clustering and jumps. The model is based on the assumption that many unstable stationary states arise from the interaction or feedback between financial prices. Model tests are performed, and the results show that the model generates series that reject a normal distribution of the returns and which can be represented by the GARCH model. An analysis applying symbolic dynamics shows similar behaviors in a system with three stock indices, three currency relations and three prices generated by the introduced model. We observe sequences that have not been produced by any of the three systems, suggesting that in a three-dimensional space, the paths traveled by the real series and those of the model may not be completely random.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"8 1","pages":"57-75"},"PeriodicalIF":0.5,"publicationDate":"2019-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-190245","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44604529","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}
Previous studies have found that one of the main challenges in the area of time-series analysis is the lack of ability to reveal the hidden profiles of observed dynamic systems. Therefore, this study applies an adaptive clustering method named the Localized Trend Model to extract and group dynamic recurring trends from trajectories of multiple time-series data to expose their underlying profiles of movement. Consequently, in this research localized dynamic profiles of movement between sectoral indexes from the Indonesia stock exchange market in the year of 2016 are extracted, analyzed and utilized to predict their future values as a case study. Results of conducted experiments confirmed that the employed method is capable to perform movement profiling for the Indonesia sectoral indexes and be of help to better understand their imperative basic behavior. Furthermore, the study has also verified the proposition that the ability to better understand profiles of movement in a collection of time-series data would benefit to increase prediction accuracy.
{"title":"Localized trend model for stock market sectoral indexes movement profiling","authors":"H. Widiputra","doi":"10.3233/AF-180235","DOIUrl":"https://doi.org/10.3233/AF-180235","url":null,"abstract":"Previous studies have found that one of the main challenges in the area of time-series analysis is the lack of ability to reveal the hidden profiles of observed dynamic systems. Therefore, this study applies an adaptive clustering method named the Localized Trend Model to extract and group dynamic recurring trends from trajectories of multiple time-series data to expose their underlying profiles of movement. Consequently, in this research localized dynamic profiles of movement between sectoral indexes from the Indonesia stock exchange market in the year of 2016 are extracted, analyzed and utilized to predict their future values as a case study. Results of conducted experiments confirmed that the employed method is capable to perform movement profiling for the Indonesia sectoral indexes and be of help to better understand their imperative basic behavior. Furthermore, the study has also verified the proposition that the ability to better understand profiles of movement in a collection of time-series data would benefit to increase prediction accuracy.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"8 1","pages":"27-46"},"PeriodicalIF":0.5,"publicationDate":"2019-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-180235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45076575","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 propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a synthetic scenario inspired by real data and then apply it to two real-world databases, a publicly available dataset about the position of investors in Spanish stock market and proprietary data from BNP Paribas Corporate and Institutional Banking.1,2
{"title":"Deep Prediction of Investor Interest: a Supervised Clustering Approach","authors":"Baptiste Barreau, Laurent Carlier, D. Challet","doi":"10.3233/AF-200296","DOIUrl":"https://doi.org/10.3233/AF-200296","url":null,"abstract":"We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a synthetic scenario inspired by real data and then apply it to two real-world databases, a publicly available dataset about the position of investors in Spanish stock market and proprietary data from BNP Paribas Corporate and Institutional Banking.1,2","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"8 1","pages":"77-89"},"PeriodicalIF":0.5,"publicationDate":"2019-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-200296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43853882","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：