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The Journal of Prediction Markets最新文献

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COVID-19 and Two different Stock Markets: An Event Study Analysis COVID-19和两个不同的股票市场:事件研究分析
The Journal of Prediction Markets Pub Date : 2021-08-27 DOI: 10.5750/jpm.v15i2.1850
Fakhrul Hasan, Ahmed Shahbaz
{"title":"COVID-19 and Two different Stock Markets: An Event Study Analysis","authors":"Fakhrul Hasan, Ahmed Shahbaz","doi":"10.5750/jpm.v15i2.1850","DOIUrl":"https://doi.org/10.5750/jpm.v15i2.1850","url":null,"abstract":"In this study, we examine two different stock markets’ response to the COVID-19 pandemic using event study methodology and a novel linear regression model. We use LSE (UK) as a proxy for the developed countries stock market and DSE (Bangladesh) as a proxy for the developing countries stock market. Using the daily COVID-19 confirmed cases and deaths and stock market returns data from these two countries (UK and Bangladesh) over the period November 01, 2020 to August 07, 2020. Our main research question was, which stock market suffered more during the COVID-19 pandemic, whether developed countries stock market or developing countries stock market. We find that developed countries stock markets (LSE as proxy) responded negatively to the growth in COVID-19 confirmed cases and deaths in COVID-19. We further find that developing countries stock markets (DSE as proxy) did not responded to the growth in COVID-19 confirmed cases and deaths in COVID-19.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127393029","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}
引用次数: 5
Is There a Method to Madness? Predicting Success of Bollywood Movies 疯狂有方法吗?预测宝莱坞电影的成功
The Journal of Prediction Markets Pub Date : 2021-08-27 DOI: 10.5750/jpm.v15i2.1890
Sumod S D, P. Premkumar, Krishnan Jeesha, S. Chowdhury
{"title":"Is There a Method to Madness? Predicting Success of Bollywood Movies","authors":"Sumod S D, P. Premkumar, Krishnan Jeesha, S. Chowdhury","doi":"10.5750/jpm.v15i2.1890","DOIUrl":"https://doi.org/10.5750/jpm.v15i2.1890","url":null,"abstract":"The objective of the study is to develop a parsimonious model to predict the box office success of a Bollywood movie before its release. A movie is considered successful if the revenue generated is greater than its budget, in other words, a Revenue to Budget Ratio (RBR) greater than 1. An original data set of 1698 Hindi movies released across a period of 13 years is used to identify the success factors of a movie in the Indian context. Predictive models are developed using traditional methodologies like multiple regression and logistic regression, as well as, contemporary approaches like regression trees and classification trees. The results highlight a unique mix of elements that a producer should consider to ensure the success of a movie in the highly competitive Indian movie market.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123142929","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}
引用次数: 2
Crowd-sourced Manipulation and Fraud Detection 众包操纵和欺诈检测
The Journal of Prediction Markets Pub Date : 2021-07-13 DOI: 10.5750/JPM.V15I1.1846
Simon Kloker
{"title":"Crowd-sourced Manipulation and Fraud Detection","authors":"Simon Kloker","doi":"10.5750/JPM.V15I1.1846","DOIUrl":"https://doi.org/10.5750/JPM.V15I1.1846","url":null,"abstract":"Prediction markets are a common tool of companies for idea management and evaluation during the innovation process, which enables them to include expectations and opinions of stakeholders across organizational boundaries. However, prediction markets are also known for their susceptibility to manipulation in theory and practice. The irregular and multifaceted occurrence of these phenomena, with sometimes very creative strategies, makes it difficult to detect manipulation and fraud based on algorithms. To ensure robust and reliable forecasts, which are of utmost importance for a focused and successful digital innovation process, there is a need for a monitoring approach capable of dealing with these specific problems. In an Action Design Research project, we address this problem by developing a crowd-sourced manipulation and fraud detection tool. The artifact enables the crowd to successfully decompose the large set of trading data and successfully find even creative strategies without guidance. The artifact is implemented and evaluated in the field in the prediction market [blinded for review]. We conclude, that a crowd-sourced approach can be suggested to monitor ambiguous and rare events with a varying character in our context and presumably other contexts as well.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132621368","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}
引用次数: 0
Predicting Intraday cryptocurrency returns – A Sparse Signals approach 预测日内加密货币回报-稀疏信号方法
The Journal of Prediction Markets Pub Date : 2021-07-13 DOI: 10.5750/jpm.v15i1.1840
Vaibhav Lalwani, V. Meshram
{"title":"Predicting Intraday cryptocurrency returns – A Sparse Signals approach","authors":"Vaibhav Lalwani, V. Meshram","doi":"10.5750/jpm.v15i1.1840","DOIUrl":"https://doi.org/10.5750/jpm.v15i1.1840","url":null,"abstract":"We test for the existence of sparse and short-lived signals in minute-by-minute cryptocurrency returns. Using a large set of linear as well as non linear predictors and a machine learning technique called the LASSO, we generate 1-minute ahead out of sample return forecasts for ten major cryptocurrencies. The forecasts obtained from the LASSO are statistically superior to those generated by the benchmark models. The LASSO based estimation selects predictors that are sparse and quite short lived.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":" 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132159690","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}
引用次数: 0
Dividend Changes as Predictors of Future Profitability 股息变动作为未来盈利能力的预测指标
The Journal of Prediction Markets Pub Date : 2021-07-13 DOI: 10.5750/JPM.V15I1.1849
Fakhrul Hasan
{"title":"Dividend Changes as Predictors of Future Profitability","authors":"Fakhrul Hasan","doi":"10.5750/JPM.V15I1.1849","DOIUrl":"https://doi.org/10.5750/JPM.V15I1.1849","url":null,"abstract":"This study investigates “the information content of dividends hypothesis” using data on UK firms from 1990-2015. Dividends act as an important conveyor of information. Dividend changes may trigger changes in stock prices because they may convey new information about the firm’s future earnings and profitability. Why do companies pay dividends (or analogously why are stockholders interested in receiving dividends), given that it is well known that dividends are often taxed heavily? This question is of special interest in the UK, where the dividend tax is higher than the capital gain tax. Previous research has used a number of dividend policy theories to explain the dividend policy puzzle. We carry out several estimations and find out that contrary to some other studies, there is no evidence that dividend increases (decreases) provide information about the future profitability or earnings of UK firms.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131284818","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}
引用次数: 7
Tracking Errors and Their Determinants 跟踪错误及其决定因素
The Journal of Prediction Markets Pub Date : 2021-07-13 DOI: 10.5750/JPM.V15I1.1851
Patrick Kuok-Kun Chu, D. Xu
{"title":"Tracking Errors and Their Determinants","authors":"Patrick Kuok-Kun Chu, D. Xu","doi":"10.5750/JPM.V15I1.1851","DOIUrl":"https://doi.org/10.5750/JPM.V15I1.1851","url":null,"abstract":"The purposes of this study are to compare the tracking error between 53 sampled physical and 15 over-the-counter (OTC) swap-type exchange-traded funds (ETFs) on the Tokyo Stock Exchange, and to contribute to a better understanding of the impact of selected determinants on the daily tracking error. The sample synthetic ETFs are found having higher tracking error than the sampled physical ETFs. The synthetic-type ETF managers may be difficult in using derivatives to replicate the benchmark performance. A panel regression model with cross-section fixed effects indicates the tracking error of the sampled physical ETFs is negatively related to size but positively related to expense ratio, dividend yield, trading volumes, market risk, and number of constituents in the target indexes. The results conform with the hypotheses that the expense, delay in receiving dividends, the trading cost and the market risk may erode the tracking ability; on the other hand, the economies of scale will improve the tracking ability. This study may help to raise a broader discussion of potential tracking error determinants and to provide some new insights.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"553 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133876157","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}
引用次数: 1
Can Machine Learning Predict Quantity and Duration of Migration to the USA 机器学习可以预测移民到美国的数量和持续时间吗
The Journal of Prediction Markets Pub Date : 2021-07-13 DOI: 10.5750/JPM.V15I1.1859
Seyedsoroosh Azizi, Chigozie Andy Ngwaba, U. Ekhator-Mobayode
{"title":"Can Machine Learning Predict Quantity and Duration of Migration to the USA","authors":"Seyedsoroosh Azizi, Chigozie Andy Ngwaba, U. Ekhator-Mobayode","doi":"10.5750/JPM.V15I1.1859","DOIUrl":"https://doi.org/10.5750/JPM.V15I1.1859","url":null,"abstract":"The number of Mexican migrants in the USA has become tripled between 1990 and 2017. This surge in Mexican migrants has attracted a lot of attention not only from policymakers but also from economists. We use a set of pre-immigration variables for more than 25,000 individuals from Mexico to predict (i): whether individuals from Mexico migrate to the USA (ii) if they do so, how long they stay in the USA. We use 8 machine learning techniques and we conclude that we can predict correctly 72% of Mexicans who migrate to the USA and 93% of Mexicans who do not migrate to the USA. However, by using only pre-immigration variables our model does not perform well in predicting how long Mexican migrants will stay in the USA. We can only predict 35% of the variation in the number of months that Mexican migrants will stay in the USA.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115266692","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}
引用次数: 0
Comparing trading behaviour and profit composition in prediction markets 比较预测市场中的交易行为和利润构成
The Journal of Prediction Markets Pub Date : 2020-12-21 DOI: 10.5750/jpm.v14i2.1561
Martin Waitz, A. Mild
{"title":"Comparing trading behaviour and profit composition in prediction markets","authors":"Martin Waitz, A. Mild","doi":"10.5750/jpm.v14i2.1561","DOIUrl":"https://doi.org/10.5750/jpm.v14i2.1561","url":null,"abstract":"Prediction markets have established itself as forecasting technique, especially within the IT industry. While the majority of existing studies focuses either on the output of such markets or its design settings, the traders who actually produce the forecasts got only little attention yet. Within this work, we develop a classification scheme for traders of a prediction market that is grounded on both, financial and prediction market literature. Over a period of three years, 127 prediction markets have been observed and its 4.329 traders are separated into seven subgroups (beginners, noise traders, average traders, experts, donkey traders, market makers and superior traders), based on their knowledge, experience and selectivity. We find empirical evidence for the existence of these subgroups and thus for the heterogeneity among the traders. For each of these subgroups, we analyze the trading behaviour and the profit composition.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132787557","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}
引用次数: 0
The Comparison of GARCH and ANN Model for Forecasting Volatility: Evidence based on Indian Stock Markets GARCH和ANN模型预测波动率的比较:基于印度股市的证据
The Journal of Prediction Markets Pub Date : 2020-12-21 DOI: 10.5750/jpm.v14i2.1843
Muneer Shaik, Aditya Sejpal
{"title":"The Comparison of GARCH and ANN Model for Forecasting Volatility: Evidence based on Indian Stock Markets","authors":"Muneer Shaik, Aditya Sejpal","doi":"10.5750/jpm.v14i2.1843","DOIUrl":"https://doi.org/10.5750/jpm.v14i2.1843","url":null,"abstract":"In this paper, we study the performance of the Artificial Neural Networks (ANNs) and GARCH modelsto predict the volatility of the Indian stock market indices namely, NIFTY 50, NIFTY Bank and NIFTYFMCG. We have used the GARCH (1,1) and Recurrent Neural Network, a type of neural network whichis widely used for predicting time series data. The purpose of the study is to investigate if the ArtificialNeural Networks perform better than the traditional GARCH (1,1) model. An out of sample testingmethodology is applied to the most recent 20 percent of the observations for all the three indices. Wehave used Root Means Squared Error (RMSE) and Mean Absolute Error (MAE) as metrics to evaluatethe volatility predicting performances of the models. The results show no clear evidence of ANN modelperforming better than GARCH model for any of the three indices. ANNs may prove to be betterindicators in periods with low volatility while its performance deteriorated in periods with highvolatility.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127502196","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}
引用次数: 2
Can Webometrics Predict the Academic Rankings of Institutes? 网络计量学能否预测学院的学术排名?
The Journal of Prediction Markets Pub Date : 2020-12-21 DOI: 10.5750/jpm.v14i2.1816
Saurabh Kumar
{"title":"Can Webometrics Predict the Academic Rankings of Institutes?","authors":"Saurabh Kumar","doi":"10.5750/jpm.v14i2.1816","DOIUrl":"https://doi.org/10.5750/jpm.v14i2.1816","url":null,"abstract":"Webometrics can be used for understanding the quantitative aspects of web resources. The present study investigates the role of webometrics in determining the academic ranking of the institute. The extensive research was conducted on a sample of 59 reputed academic institutes based out of India. The data was analysed using two techniques viz. linear regression and classification and regression tree. From the results of the study, it was found that among all the webometrics parameters, Alexa rank and Semrush rank of the website was found to be the most crucial factor for determining the academic ranking of the institute. The study has insights for policymakers of the institute as the results of the study can be used for devising various ways to improve the webometrics parameters in order to enhance the academic ranking of the institute.","PeriodicalId":352536,"journal":{"name":"The Journal of Prediction Markets","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116658929","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}
引用次数: 0
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