Pub Date : 2020-08-20DOI: 10.1108/978-1-78973-893-320201015
S. Chakravarty, Palash Sarkar
HREE innovations in electronic trading of stocks and options have been in the headlines recently: high-frequency trading, flash trades, and dark pools. Technical improvements such as these are usually assumed to raise efficiency, but these innovations challenge such assumptions and may pose some public interest concerns because of their effect on stability. Studying market microstructures illuminates the processes through which prices are determined. Markets often appear to be magic black boxes. Supply and demand go into the box and an invisible hand pulls out the price—much like a magician producing a rabbit from a hat. But important things happen inside those boxes. In the case of electronic trading of securities and derivatives, the microstructure inside the box includes the mechanisms for submitting buy and sell orders (that is, bid and offer quotes) into a market, viewing of those quotes by market participants, and executing trades by matching orders to buy and sell. If this is done in an immediate and transparent manner that enables all market participants to see and trade at the same prices, then reality approaches the ideal of the efficient-market hypothesis. When markets become segmented and informational advantages are built into market mechanisms, efficiency is impaired and fairness undermined. This article explores these financial policy issues to explain how they impact pricing efficiency at the market microstructure level and to discuss how corrective regulation can improve efficiency. High-frequency trading, flash trading, and dark pools all have their origin in two key marketplace innovations—electronic trading and the closely related alternative trading systems (ATS). Electronic trading has quickly come to dominate traditional trading, both on exchanges and in over-the-counter markets. Computer systems automatically match buy and sell orders that were themselves submitted through computers. Floor trading at stock and derivatives exchanges has been eliminated in all but the largest and most prominent markets, such as the New York Stock Exchange (NYSE), and even in those markets floor trading coexists with electronic trading. ATS are computer-automated order-matching systems that offer exchange-like trading opportunities at lower costs but are often subject to lower disclosure requirements and different trading rules. High-frequency trading (HFT), also called black box trading, uses high-speed computers governed by algorithms (or instructions to the computer) to analyze data, identify investment opportunities, and manage order flow to the markets. An HFT firm can submit a thousand orders a minute to an exchange and just as quickly cancel them and …
{"title":"High-frequency Trading","authors":"S. Chakravarty, Palash Sarkar","doi":"10.1108/978-1-78973-893-320201015","DOIUrl":"https://doi.org/10.1108/978-1-78973-893-320201015","url":null,"abstract":"HREE innovations in electronic trading of stocks and options have been in the headlines recently: high-frequency trading, flash trades, and dark pools. Technical improvements such as these are usually assumed to raise efficiency, but these innovations challenge such assumptions and may pose some public interest concerns because of their effect on stability. Studying market microstructures illuminates the processes through which prices are determined. Markets often appear to be magic black boxes. Supply and demand go into the box and an invisible hand pulls out the price—much like a magician producing a rabbit from a hat. But important things happen inside those boxes. In the case of electronic trading of securities and derivatives, the microstructure inside the box includes the mechanisms for submitting buy and sell orders (that is, bid and offer quotes) into a market, viewing of those quotes by market participants, and executing trades by matching orders to buy and sell. If this is done in an immediate and transparent manner that enables all market participants to see and trade at the same prices, then reality approaches the ideal of the efficient-market hypothesis. When markets become segmented and informational advantages are built into market mechanisms, efficiency is impaired and fairness undermined. This article explores these financial policy issues to explain how they impact pricing efficiency at the market microstructure level and to discuss how corrective regulation can improve efficiency. High-frequency trading, flash trading, and dark pools all have their origin in two key marketplace innovations—electronic trading and the closely related alternative trading systems (ATS). Electronic trading has quickly come to dominate traditional trading, both on exchanges and in over-the-counter markets. Computer systems automatically match buy and sell orders that were themselves submitted through computers. Floor trading at stock and derivatives exchanges has been eliminated in all but the largest and most prominent markets, such as the New York Stock Exchange (NYSE), and even in those markets floor trading coexists with electronic trading. ATS are computer-automated order-matching systems that offer exchange-like trading opportunities at lower costs but are often subject to lower disclosure requirements and different trading rules. High-frequency trading (HFT), also called black box trading, uses high-speed computers governed by algorithms (or instructions to the computer) to analyze data, identify investment opportunities, and manage order flow to the markets. An HFT firm can submit a thousand orders a minute to an exchange and just as quickly cancel them and …","PeriodicalId":329471,"journal":{"name":"An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125053839","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 : 2020-08-20DOI: 10.1108/978-1-78973-893-320201018
S. Chakravarty, P. Sarkar
{"title":"Cryptocurrency: Basics","authors":"S. Chakravarty, P. Sarkar","doi":"10.1108/978-1-78973-893-320201018","DOIUrl":"https://doi.org/10.1108/978-1-78973-893-320201018","url":null,"abstract":"","PeriodicalId":329471,"journal":{"name":"An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain","volume":"265 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130836506","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 : 2020-08-20DOI: 10.1108/978-1-78973-893-320201014
S. Chakravarty, P. Sarkar
{"title":"Measures of Risk","authors":"S. Chakravarty, P. Sarkar","doi":"10.1108/978-1-78973-893-320201014","DOIUrl":"https://doi.org/10.1108/978-1-78973-893-320201014","url":null,"abstract":"","PeriodicalId":329471,"journal":{"name":"An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125362276","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 : 2020-08-20DOI: 10.1108/978-1-78973-893-320201021
S. Chakravarty, P. Sarkar
{"title":"Applications of Blockchain","authors":"S. Chakravarty, P. Sarkar","doi":"10.1108/978-1-78973-893-320201021","DOIUrl":"https://doi.org/10.1108/978-1-78973-893-320201021","url":null,"abstract":"","PeriodicalId":329471,"journal":{"name":"An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126902502","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 : 2020-08-20DOI: 10.1108/978-1-78973-893-320201017
S. Chakravarty, P. Sarkar
{"title":"Introduction to Blockchain","authors":"S. Chakravarty, P. Sarkar","doi":"10.1108/978-1-78973-893-320201017","DOIUrl":"https://doi.org/10.1108/978-1-78973-893-320201017","url":null,"abstract":"","PeriodicalId":329471,"journal":{"name":"An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134310068","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 : 2020-08-20DOI: 10.1108/978-1-78973-893-320201001
S. Chakravarty, P. Sarkar
{"title":"Background and Preliminaries","authors":"S. Chakravarty, P. Sarkar","doi":"10.1108/978-1-78973-893-320201001","DOIUrl":"https://doi.org/10.1108/978-1-78973-893-320201001","url":null,"abstract":"","PeriodicalId":329471,"journal":{"name":"An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131457830","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 : 2020-08-20DOI: 10.1108/978-1-78973-893-320201013
S. Chakravarty, Palash Sarkar
In this talk we review portfolio optimisation, with a focus on financial applications. Here the problem is to decide the assets (a portfolio) to hold that have desired characteristics. Markowitz mean-variance portfolio optimisation is relatively well known, but has been extended in recent years to encompass cardinality constraints. Less considered in the scientific literature are problems such as: • index tracking – where the objective is to match the return achieved on a benchmark index such as the S&P500 • enhanced indexation-where the objective is to exceed the return achieved on a benchmark index; here we may have a desired specified excess return, or we simply wish to do better than the benchmark • absolute return – where the objective is to achieve a desired return (irrespective of how the market, as represented by the benchmark index, performs) We will outline the mathematical optimisation models that can be adopted for portfolio problems such as these and review the results achieved to date. Markowitz mean-variance portfolio optimisation To proceed with Markowitz mean-variance portfolio optimisation we need some notation, let: N be the number of assets (e.g. stocks) available µ i be the expected (average, mean) return of asset i ρ ij be the correlation between the returns for assets i and j (-1≤ρ ij ≤+1) s i be the standard deviation in return for asset i R be the desired expected return from the portfolio chosen Then the decision variables are: w i the proportion of the total investment associated with (invested in) asset i (0≤w i ≤1) Using the standard Markowitz mean-variance approach we have that the unconstrained portfolio optimisation problem is:
{"title":"Portfolio Optimisation","authors":"S. Chakravarty, Palash Sarkar","doi":"10.1108/978-1-78973-893-320201013","DOIUrl":"https://doi.org/10.1108/978-1-78973-893-320201013","url":null,"abstract":"In this talk we review portfolio optimisation, with a focus on financial applications. Here the problem is to decide the assets (a portfolio) to hold that have desired characteristics. Markowitz mean-variance portfolio optimisation is relatively well known, but has been extended in recent years to encompass cardinality constraints. Less considered in the scientific literature are problems such as: • index tracking – where the objective is to match the return achieved on a benchmark index such as the S&P500 • enhanced indexation-where the objective is to exceed the return achieved on a benchmark index; here we may have a desired specified excess return, or we simply wish to do better than the benchmark • absolute return – where the objective is to achieve a desired return (irrespective of how the market, as represented by the benchmark index, performs) We will outline the mathematical optimisation models that can be adopted for portfolio problems such as these and review the results achieved to date. Markowitz mean-variance portfolio optimisation To proceed with Markowitz mean-variance portfolio optimisation we need some notation, let: N be the number of assets (e.g. stocks) available µ i be the expected (average, mean) return of asset i ρ ij be the correlation between the returns for assets i and j (-1≤ρ ij ≤+1) s i be the standard deviation in return for asset i R be the desired expected return from the portfolio chosen Then the decision variables are: w i the proportion of the total investment associated with (invested in) asset i (0≤w i ≤1) Using the standard Markowitz mean-variance approach we have that the unconstrained portfolio optimisation problem is:","PeriodicalId":329471,"journal":{"name":"An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124379867","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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