This research focuses on developing a bankruptcy prediction model in the UK construction industry. It fills the gap of previous researches which did not focus on the construction industry and in the UK. The aim of this research is to establish accurate model which successfully classify firms into their respective financial status of failed and non-failed. This research analyses financial variable extracted from FAME database by Bureau Van Dijk for the development of bankruptcy prediction model. Ranging from SIC trade codes of 41100 (Development of building projects), 41 (Construction of buildings), 42(Civil Engineering), 43 (Specialised construction activities), 68 (Real estate activities), and 71(Architectural and engineering activities and related technical consultancy). Then the model is tested against the collected sample and compared with two other Z-score model. These models have an average accuracy of 80%. The model with the strongest predictive power (SIC 42) correctly predicts 93.2% (non-bankrupt) and 90.9% (bankrupt) while the weakest model (SIC 68) classify 51.2% (non-bankrupt) and 70% (bankrupt) correctly into their respective group. The developed model is far superior in classifying firms into their financial status when compared to the other two prediction models. Furthermore, the established model is robust enough that it has an average classification accuracy over the years of 70%. A practical case study is conducted utilising the developed Z-score model and ratio analysis. Macroeconomic variable is then analysed where long term interest rate, inflation, 3 months’ treasury bills has a negative impact on the firms Z-score. However, construction output and gilt repo interest rate has a positive relationship to the Z-score of companies. The estimate of covariance is also seen to build up from the year 2006 which acts as a warning sign that a financial crisis is emerging. This result point towards the fact that both macroeconomics and microeconomics contributes significantly to business failure. The developed bankruptcy prediction model serves as a warning sign for management to pay attention to. As firms enter the “grey zone”, management should act to save firms from defaulting. Ultimately, this model is applicable widely for other industry of world economies.
本研究的重点是建立一个破产预测模型在英国建筑业。它填补了以前的研究没有集中在建筑业和英国的空白。本研究的目的是建立准确的模型，成功地将企业分为失败和非失败的财务状况。本研究分析了Bureau Van Dijk从FAME数据库中提取的金融变量，以开发破产预测模型。包括SIC行业代码41100(建筑项目发展)、41(建筑物建造)、42(土木工程)、43(专门建筑活动)、68(房地产活动)和71(建筑及工程活动及相关技术顾问)。然后对所收集的样本进行检验，并与另外两种Z-score模型进行比较。这些模型的平均准确率为80%。预测能力最强的模型(SIC 42)正确预测了93.2%(非破产)和90.9%(破产)，而最弱的模型(SIC 68)将51.2%(非破产)和70%(破产)正确分类到各自的组中。与其他两种预测模型相比，该模型在对企业财务状况进行分类方面具有明显的优越性。此外，所建立的模型具有足够的鲁棒性，其多年来的平均分类准确率为70%。利用开发的Z-score模型和比率分析进行了实际案例研究。然后分析宏观经济变量，其中长期利率，通货膨胀，3个月国库券对公司z得分产生负面影响。而建设产出和金边债券回购利率与企业z得分呈正相关。协方差的估计也从2006年开始增加，这是金融危机正在出现的警告信号。这一结果表明，宏观经济学和微观经济学对企业失败都有重要影响。建立的破产预测模型为企业管理层敲响了警钟。随着公司进入“灰色地带”，管理层应该采取行动，避免公司违约。最终，该模型可广泛适用于世界经济的其他行业。
{"title":"Forecasting Corporate Business Failure with an Innovative Model: An Application on UK Construction Companies","authors":"Yenn Shern Leow, Xuxin Mao","doi":"10.2139/ssrn.3022168","DOIUrl":"https://doi.org/10.2139/ssrn.3022168","url":null,"abstract":"This research focuses on developing a bankruptcy prediction model in the UK construction industry. It fills the gap of previous researches which did not focus on the construction industry and in the UK. The aim of this research is to establish accurate model which successfully classify firms into their respective financial status of failed and non-failed. This research analyses financial variable extracted from FAME database by Bureau Van Dijk for the development of bankruptcy prediction model. Ranging from SIC trade codes of 41100 (Development of building projects), 41 (Construction of buildings), 42(Civil Engineering), 43 (Specialised construction activities), 68 (Real estate activities), and 71(Architectural and engineering activities and related technical consultancy). Then the model is tested against the collected sample and compared with two other Z-score model. These models have an average accuracy of 80%. The model with the strongest predictive power (SIC 42) correctly predicts 93.2% (non-bankrupt) and 90.9% (bankrupt) while the weakest model (SIC 68) classify 51.2% (non-bankrupt) and 70% (bankrupt) correctly into their respective group. The developed model is far superior in classifying firms into their financial status when compared to the other two prediction models. Furthermore, the established model is robust enough that it has an average classification accuracy over the years of 70%. A practical case study is conducted utilising the developed Z-score model and ratio analysis. Macroeconomic variable is then analysed where long term interest rate, inflation, 3 months’ treasury bills has a negative impact on the firms Z-score. However, construction output and gilt repo interest rate has a positive relationship to the Z-score of companies. The estimate of covariance is also seen to build up from the year 2006 which acts as a warning sign that a financial crisis is emerging. This result point towards the fact that both macroeconomics and microeconomics contributes significantly to business failure. The developed bankruptcy prediction model serves as a warning sign for management to pay attention to. As firms enter the “grey zone”, management should act to save firms from defaulting. Ultimately, this model is applicable widely for other industry of world economies.","PeriodicalId":11044,"journal":{"name":"delete","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84082872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper straightforwardly discloses how an article written by Taiwanese scholars and published in Applied Economics is ridden with suspicious omissions. This misconduct leads to misinformation being printed in prominent journals as well as mistakenly listing eight medical doctors and three psychologists among the 16 productive “financial risk” scholars. Hence, we demonstrate that a lack of academic integrity indicates a disregard for the pursuit truth, which in turns will result in the circulation of erroneous knowledge.
{"title":"Disclosing the Misconduct in Chiang and Yang's 'A Bibliometric Study of Financial Risk Literature: A Historic Approach'","authors":"Jen-Chang Liu, Mark Yeats","doi":"10.2139/ssrn.3008772","DOIUrl":"https://doi.org/10.2139/ssrn.3008772","url":null,"abstract":"This paper straightforwardly discloses how an article written by Taiwanese scholars and published in Applied Economics is ridden with suspicious omissions. This misconduct leads to misinformation being printed in prominent journals as well as mistakenly listing eight medical doctors and three psychologists among the 16 productive “financial risk” scholars. Hence, we demonstrate that a lack of academic integrity indicates a disregard for the pursuit truth, which in turns will result in the circulation of erroneous knowledge.","PeriodicalId":11044,"journal":{"name":"delete","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75034515","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}
Markov chain approximation is a general method for option pricing and hedging in Markovian models with continuous-state spaces. A key issue for its efficiency is how to design the grid for the Mark...
{"title":"Analysis of Markov Chain Approximation for Option Pricing and Hedging: Grid Design and Convergence Behavior","authors":"Gongqiu Zhang, Lingfei Li","doi":"10.2139/ssrn.3007318","DOIUrl":"https://doi.org/10.2139/ssrn.3007318","url":null,"abstract":"Markov chain approximation is a general method for option pricing and hedging in Markovian models with continuous-state spaces. A key issue for its efficiency is how to design the grid for the Mark...","PeriodicalId":11044,"journal":{"name":"delete","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87936485","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}
Much of financial engineering is based on so-called “complete markets” and on the use of the Black-Scholes formula. The formula relies on the assumption that asset prices follow a log-normal distribution, or in other words, the daily fluctuations in prices viewed as percentage changes follow a Gaussian distribution. On the contrary, studies of actual asset prices show that they do not follow a log-normal distribution. In this paper, we investigate several widely-used heavy-tailed distributions. Our results indicate that the Skewed t distribution has the best empirical performance in fitting the Canadian stock market returns. We claim the results are valuable for market participants and the financial industry.
{"title":"Comparing Heavy-Tailed Distributions in Fitting the Canadian Stock Market Returns","authors":"D. Eden, Paul Huffman, John Holman","doi":"10.2139/ssrn.3013860","DOIUrl":"https://doi.org/10.2139/ssrn.3013860","url":null,"abstract":"Much of financial engineering is based on so-called “complete markets” and on the use of the Black-Scholes formula. The formula relies on the assumption that asset prices follow a log-normal distribution, or in other words, the daily fluctuations in prices viewed as percentage changes follow a Gaussian distribution. On the contrary, studies of actual asset prices show that they do not follow a log-normal distribution. In this paper, we investigate several widely-used heavy-tailed distributions. Our results indicate that the Skewed t distribution has the best empirical performance in fitting the Canadian stock market returns. We claim the results are valuable for market participants and the financial industry.","PeriodicalId":11044,"journal":{"name":"delete","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88441944","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}
There is no doubt that more people are taking web surveys using their mobile devices. In non-English speaking countries, in particular, more respondents complete online surveys using mobile devices than using desktop or laptop computers. This phenomenon has a huge implication on survey research as different devices are associated with different survey measurement and nonresponse errors, which in turn could affect the survey estimates themselves. Also, data quality, as measured by survey satisficing theory, could also be affected by the survey-taking device. An increasing amount of research has been devoted to examining and comparing survey data collected from mobile and non-mobile devices. This study will expand the existing literature by examining mobile web survey data from six countries, up to three samples within each country, and across three waves of data collection. Specifically, the study was conducted in the U.S., U.K. Australia, China, Brazil, and India. Within each country, data were collected through up to three online panel providers, including SurveyMonkey Audience, Cint, and TapResearch. In addition, for each country/sample combination, three waves of data collection were performed, with each wave one month apart. The samples were independent across the three waves. Respondents could choose the device (as opposed to been assigned to) for taking the survey. The same questionnaire (except minor changes to the demographic questions) was used in the study. In the presentation, we will present whether the mobile web usage different by country and sample. Also, we will show the demographic and data quality differences, if any, between mobile and non-mobile respondents by country and sample. This is the first study of this kind that examines the mobile web in such large scale and cross-cultural perspective.
{"title":"Mobile Web Survey in the International Setting (Presentation Slides)","authors":"Mingnan Liu, Laura Wronski, Nick Inchausti","doi":"10.2139/ssrn.3000554","DOIUrl":"https://doi.org/10.2139/ssrn.3000554","url":null,"abstract":"There is no doubt that more people are taking web surveys using their mobile devices. In non-English speaking countries, in particular, more respondents complete online surveys using mobile devices than using desktop or laptop computers. This phenomenon has a huge implication on survey research as different devices are associated with different survey measurement and nonresponse errors, which in turn could affect the survey estimates themselves. Also, data quality, as measured by survey satisficing theory, could also be affected by the survey-taking device. An increasing amount of research has been devoted to examining and comparing survey data collected from mobile and non-mobile devices. This study will expand the existing literature by examining mobile web survey data from six countries, up to three samples within each country, and across three waves of data collection. Specifically, the study was conducted in the U.S., U.K. Australia, China, Brazil, and India. Within each country, data were collected through up to three online panel providers, including SurveyMonkey Audience, Cint, and TapResearch. In addition, for each country/sample combination, three waves of data collection were performed, with each wave one month apart. The samples were independent across the three waves. Respondents could choose the device (as opposed to been assigned to) for taking the survey. The same questionnaire (except minor changes to the demographic questions) was used in the study. In the presentation, we will present whether the mobile web usage different by country and sample. Also, we will show the demographic and data quality differences, if any, between mobile and non-mobile respondents by country and sample. This is the first study of this kind that examines the mobile web in such large scale and cross-cultural perspective.","PeriodicalId":11044,"journal":{"name":"delete","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76221902","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}
Specializing in local, organic meat and produce, Retail Relay developed a new business model for online grocery shopping and delivery. Having succeeded in the small market of Charlottesville, Virginia, it was considering expanding to other, larger markets. This case focuses on customer lifetime value analysis and how this analysis, when paired with previous experience with consumer promotions, can guide thinking in developing the best strategies and tactics for entering new markets. Excerpt UVA-M-0784 Rev. Jun. 3, 2014 Retail Relay (a) The last-mile delivery cost kills most home-delivery businesses. I knew we could find a better way. —Zach Buckner, CEO of Retail Relay During the summer of 2007, Zach Buckner, the 31-year-old founder and CEO of Retail Relay, was again confronted with an ongoing frustration of daily suburban life. After his third trip to a local hardware store to get supplies for the same home improvement project, Buckner realized that a one-day project had now effectively become an all-weekend affair. He had spent more time shopping than installing new wiring in his 1930s-era house. Buckner had studied electrical and systems engineering and completed many consulting assignments for companies looking to improve their business operations. He drew on that knowledge and experience to come up with the concept of Retail Relay (Figure 1). And a new paradigm for online shopping was born. . . .
{"title":"Retail Relay (a)","authors":"Kelly Brandow","doi":"10.2139/ssrn.2974674","DOIUrl":"https://doi.org/10.2139/ssrn.2974674","url":null,"abstract":"Specializing in local, organic meat and produce, Retail Relay developed a new business model for online grocery shopping and delivery. Having succeeded in the small market of Charlottesville, Virginia, it was considering expanding to other, larger markets. This case focuses on customer lifetime value analysis and how this analysis, when paired with previous experience with consumer promotions, can guide thinking in developing the best strategies and tactics for entering new markets. Excerpt UVA-M-0784 Rev. Jun. 3, 2014 Retail Relay (a) The last-mile delivery cost kills most home-delivery businesses. I knew we could find a better way. —Zach Buckner, CEO of Retail Relay During the summer of 2007, Zach Buckner, the 31-year-old founder and CEO of Retail Relay, was again confronted with an ongoing frustration of daily suburban life. After his third trip to a local hardware store to get supplies for the same home improvement project, Buckner realized that a one-day project had now effectively become an all-weekend affair. He had spent more time shopping than installing new wiring in his 1930s-era house. Buckner had studied electrical and systems engineering and completed many consulting assignments for companies looking to improve their business operations. He drew on that knowledge and experience to come up with the concept of Retail Relay (Figure 1). And a new paradigm for online shopping was born. . . .","PeriodicalId":11044,"journal":{"name":"delete","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83690866","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}
Social media have been extensively praised for increasing democratic discussion on social issues related to policy and politics. However, what happens when this powerful communication tools are exploited to manipulate online discussion, to change the public perception of political entities, or even to try affecting the outcome of political elections? In this study we investigated how the presence of social media bots, algorithmically driven entities that on the surface appear as legitimate users, affect political discussion around the 2016 U.S. Presidential election. By leveraging state-of-the-art social bot detection algorithms, we uncovered a large fraction of user population that may not be human, accounting for a significant portion of generated content (about one-fifth of the entire conversation). We inferred political partisanships from hashtag adoption, for both humans and bots, and studied spatio-temporal communication, political support dynamics, and influence mechanisms by discovering the level of network embeddedness of the bots. Our findings suggest that the presence of social media bots can indeed negatively affect democratic political discussion rather than improving it, which in turn can potentially alter public opinion and endanger the integrity of the Presidential election.
{"title":"Social Bots Distort the 2016 US Presidential Election Online Discussion","authors":"Alessandro Bessi, Emilio Ferrara","doi":"10.5210/FM.V21I11.7090","DOIUrl":"https://doi.org/10.5210/FM.V21I11.7090","url":null,"abstract":"Social media have been extensively praised for increasing democratic discussion on social issues related to policy and politics. However, what happens when this powerful communication tools are exploited to manipulate online discussion, to change the public perception of political entities, or even to try affecting the outcome of political elections? In this study we investigated how the presence of social media bots, algorithmically driven entities that on the surface appear as legitimate users, affect political discussion around the 2016 U.S. Presidential election. By leveraging state-of-the-art social bot detection algorithms, we uncovered a large fraction of user population that may not be human, accounting for a significant portion of generated content (about one-fifth of the entire conversation). We inferred political partisanships from hashtag adoption, for both humans and bots, and studied spatio-temporal communication, political support dynamics, and influence mechanisms by discovering the level of network embeddedness of the bots. Our findings suggest that the presence of social media bots can indeed negatively affect democratic political discussion rather than improving it, which in turn can potentially alter public opinion and endanger the integrity of the Presidential election.","PeriodicalId":11044,"journal":{"name":"delete","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76289332","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 : 2016-09-10DOI: 10.1142/S2010495217500038
Joerg Osterrieder, Julian Lorenz
We provide an extreme value analysis of the returns of Bitcoin. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate extreme value analysis. Those properties will be compared to the traditional exchange rates of the G10 currencies versus the US dollar. For investors, especially institutional ones, an understanding of the risk characteristics is of utmost importance. So for Bitcoin to become a mainstream investable asset class, studying these properties is necessary. Our findings show that the bitcoin return distribution not only exhibits higher volatility than traditional G10 currencies, but also stronger non-normal characteristics and heavier tails. This has implications for risk management, financial engineering (such as bitcoin derivatives) — both from an investor's as well as from a regulator's point of view. To our knowledge, this is the first detailed study looking at the extreme value behavior of the cryptocurrency Bitcoin.
{"title":"A Statistical Risk Assessment of Bitcoin and Its Extreme Tail Behaviour","authors":"Joerg Osterrieder, Julian Lorenz","doi":"10.1142/S2010495217500038","DOIUrl":"https://doi.org/10.1142/S2010495217500038","url":null,"abstract":"We provide an extreme value analysis of the returns of Bitcoin. A particular focus is on the tail risk characteristics and we will provide an in-depth univariate extreme value analysis. Those properties will be compared to the traditional exchange rates of the G10 currencies versus the US dollar. For investors, especially institutional ones, an understanding of the risk characteristics is of utmost importance. So for Bitcoin to become a mainstream investable asset class, studying these properties is necessary. Our findings show that the bitcoin return distribution not only exhibits higher volatility than traditional G10 currencies, but also stronger non-normal characteristics and heavier tails. This has implications for risk management, financial engineering (such as bitcoin derivatives) — both from an investor's as well as from a regulator's point of view. To our knowledge, this is the first detailed study looking at the extreme value behavior of the cryptocurrency Bitcoin.","PeriodicalId":11044,"journal":{"name":"delete","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85568471","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 first attempt at obtaining market--directional information from a non--stationary solution of the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented. We demonstrate that the concept of price impact is poorly applicable to market dynamics. Instead, we consider the execution flow $I=dV/dt$ operator with the "impact from the future" term providing information about not--yet--executed trades. The "impact from the future" on $I$ can be directly estimated from the already--executed trades, the directional information on price is then obtained from the experimentally observed fact that the $I$ and $p$ operators have the same eigenfunctions (the exact result in the dynamic impact approximation $p=p(I)$). The condition for "no information about the future" is found and directional prediction quality is discussed. This work makes a substantial contribution toward solving the ultimate market dynamics problem: find evidence of existence (or proof of non--existence) of an automated trading machine which consistently makes positive P&L on a free market as an autonomous agent (aka the existence of the market dynamics equation). The software with a reference implementation of the theory is provided.
{"title":"Market Dynamics. On a Muse of Cash Flow and Liquidity Deficit","authors":"V. Malyshkin","doi":"10.2139/ssrn.3040580","DOIUrl":"https://doi.org/10.2139/ssrn.3040580","url":null,"abstract":"A first attempt at obtaining market--directional information from a non--stationary solution of the dynamic equation \"future price tends to the value that maximizes the number of shares traded per unit time\" [1] is presented. We demonstrate that the concept of price impact is poorly applicable to market dynamics. Instead, we consider the execution flow $I=dV/dt$ operator with the \"impact from the future\" term providing information about not--yet--executed trades. The \"impact from the future\" on $I$ can be directly estimated from the already--executed trades, the directional information on price is then obtained from the experimentally observed fact that the $I$ and $p$ operators have the same eigenfunctions (the exact result in the dynamic impact approximation $p=p(I)$). The condition for \"no information about the future\" is found and directional prediction quality is discussed. This work makes a substantial contribution toward solving the ultimate market dynamics problem: find evidence of existence (or proof of non--existence) of an automated trading machine which consistently makes positive P&L on a free market as an autonomous agent (aka the existence of the market dynamics equation). The software with a reference implementation of the theory is provided.","PeriodicalId":11044,"journal":{"name":"delete","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85964081","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 : 2016-07-13DOI: 10.6084/m9.figshare.3437765.v3
Wenfa Ng
Perceptions of economic trends and growth rates around the world over the past two decades, especially in advanced economies, suggests that the world is in a period of low growth without external stimulus delivered via speculative activities in the financial markets or fiscal stimulus. But is the intuition backed up by real data? If real, is the concept specific to the contemporary environment where there is increased (and increasing) integration of the world’s financial markets coupled with a global savings glut and a shortfall in aggregate demand? More precisely, does the observed phenomenon describes periodic gyration in economic activities, local or global; or is it just a name to annotate a period of economic history unlikely to repeat itself? In addition, what are the factors that have potentiated (observed) surreal economic readout: rapid growth followed by sharp drop in economic activities in many emerging economies over the last decade? Is herd behavior, bundled structured products or bonds (of questionable risk profile), high speed trading and dark pools, alone or in combination, pushing trading activities on various financial markets to artificially high levels? Do the above mask deeper problems such as low rates of wage growth (relative to inflation), which manifest, at the population level, as reasonable GDP growth rates, but increased income inequality?
{"title":"Are Advanced Economies Increasingly Reliant on Speculative Activities in Financial Markets and Fiscal Stimulus to Nudge Anemic Growth Upwards?","authors":"Wenfa Ng","doi":"10.6084/m9.figshare.3437765.v3","DOIUrl":"https://doi.org/10.6084/m9.figshare.3437765.v3","url":null,"abstract":"Perceptions of economic trends and growth rates around the world over the past two decades, especially in advanced economies, suggests that the world is in a period of low growth without external stimulus delivered via speculative activities in the financial markets or fiscal stimulus. But is the intuition backed up by real data? If real, is the concept specific to the contemporary environment where there is increased (and increasing) integration of the world’s financial markets coupled with a global savings glut and a shortfall in aggregate demand? More precisely, does the observed phenomenon describes periodic gyration in economic activities, local or global; or is it just a name to annotate a period of economic history unlikely to repeat itself? In addition, what are the factors that have potentiated (observed) surreal economic readout: rapid growth followed by sharp drop in economic activities in many emerging economies over the last decade? Is herd behavior, bundled structured products or bonds (of questionable risk profile), high speed trading and dark pools, alone or in combination, pushing trading activities on various financial markets to artificially high levels? Do the above mask deeper problems such as low rates of wage growth (relative to inflation), which manifest, at the population level, as reasonable GDP growth rates, but increased income inequality?","PeriodicalId":11044,"journal":{"name":"delete","volume":"210 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75141055","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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