Muneer Shaik, Abhishek Sahjwani, Kesava Sai Krishna Kondepudi
This research investigates the effectiveness of various machine learning models, including Random Forest, Neural Networks, Adaboost, Discriminant Analysis, Logit Model, Support Vectors, and Kernel Factory. The study aims to forecast fluctuations in the ASEAN-5 stock index prices within an eleven-year period. The study provides useful information about how well machine learning techniques can predict changes in the stock market, with potential implications for both academic researchers and market participants. The findings imply that Adaboost consistently outperforms all others in predicting price changes accurately. This shows that machine learning algorithms are capable of accurately forecasting the movement of the ASEAN-5 stock index values. This study contributes to the growing body of research on the use of machine learning techniques in finance and provides investors with information to make informed decisions about investments in the ASEAN-5 region, ultimately leading to increased returns and improved portfolio performance.
{"title":"Forecasting ASEAN-5 Stock Index Price Movement Using Machine Learning Techniques","authors":"Muneer Shaik, Abhishek Sahjwani, Kesava Sai Krishna Kondepudi","doi":"10.5750/jpm.v18i1.2119","DOIUrl":"https://doi.org/10.5750/jpm.v18i1.2119","url":null,"abstract":"This research investigates the effectiveness of various machine learning models, including Random Forest, Neural Networks, Adaboost, Discriminant Analysis, Logit Model, Support Vectors, and Kernel Factory. The study aims to forecast fluctuations in the ASEAN-5 stock index prices within an eleven-year period. The study provides useful information about how well machine learning techniques can predict changes in the stock market, with potential implications for both academic researchers and market participants. The findings imply that Adaboost consistently outperforms all others in predicting price changes accurately. This shows that machine learning algorithms are capable of accurately forecasting the movement of the ASEAN-5 stock index values. This study contributes to the growing body of research on the use of machine learning techniques in finance and provides investors with information to make informed decisions about investments in the ASEAN-5 region, ultimately leading to increased returns and improved portfolio performance.","PeriodicalId":477301,"journal":{"name":"The journal of prediction markets","volume":"1979 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141707400","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}
Russia’s invasion of Ukraine on February 24, 2022, emerged as Europe’s most significant military conflict post second world war, with global economic and geopolitical consequences. Using a broad (95-country) sample, the study examines the impact of the Russia–Ukraine war on global stock markets surrounding the war announcement. It applied the event study method and used short and long event windows to examine the war’s immediate and intermediate impacts. Global stock markets delivered negative 1.90% abnormal returns on the day of the war announcement, and Russia saw the biggest fall. However, after the initial adverse reaction, stock markets reacted asymmetrically. Stock markets of the countries in geographic proximity and high trade intensity with Russia and Ukraine, and net importers of energy and food grains negatively reacted more than the rest. The regional results show that Asia Pacific and Europe reported negative returns across event windows. In contrast, the Americas, Africa, and the Middle East did not react negatively, even in the shortest event window. Adverse war reactions moderated over time. Equity investors and portfolio managers who aim to protect their investments should buy stocks in countries that are net exporters of commodities made in war-torn countries and switch to stock markets geographically far from the war zone.
{"title":"Asymmetric Impact of Russia–Ukraine War on Global Stock Markets","authors":"M. Joshipura, Ashu Lamba","doi":"10.5750/jpm.v18i1.2115","DOIUrl":"https://doi.org/10.5750/jpm.v18i1.2115","url":null,"abstract":"Russia’s invasion of Ukraine on February 24, 2022, emerged as Europe’s most significant military conflict post second world war, with global economic and geopolitical consequences. Using a broad (95-country) sample, the study examines the impact of the Russia–Ukraine war on global stock markets surrounding the war announcement. It applied the event study method and used short and long event windows to examine the war’s immediate and intermediate impacts. Global stock markets delivered negative 1.90% abnormal returns on the day of the war announcement, and Russia saw the biggest fall. However, after the initial adverse reaction, stock markets reacted asymmetrically. Stock markets of the countries in geographic proximity and high trade intensity with Russia and Ukraine, and net importers of energy and food grains negatively reacted more than the rest. The regional results show that Asia Pacific and Europe reported negative returns across event windows. In contrast, the Americas, Africa, and the Middle East did not react negatively, even in the shortest event window. Adverse war reactions moderated over time. Equity investors and portfolio managers who aim to protect their investments should buy stocks in countries that are net exporters of commodities made in war-torn countries and switch to stock markets geographically far from the war zone.","PeriodicalId":477301,"journal":{"name":"The journal of prediction markets","volume":"2018 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706864","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 present a supervised machine learning approach to predict the winner of a Twenty20 (T20) international match. The prediction dynamically changes as the match progresses. We also use explainable machine learning techniques (SHAP scores) to understand the importance of various features in making the decision at various stages of the T20 match. We present results on a dataset of 808 men's T20 international matches. The dynamic accuracy increases from about 55% in the initial stages of the T20 match to a maximum of about 85% in the final stages of the match (with an overall accuracy of about 63% in innings 1 and 74% in innings 2). SHAP scores reveal that team strength is an important feature in making the prediction in initial stages of the match; however, in the final stages, match situation plays the dominant role in the decision making process. Our work may help team coaches and captains to assess their chances of winning and/or chart a course towards winning in the ongoing T20 match, as well as be useful for sports analytics and gambling websites and apps.
{"title":"Predicting the Winner of a Twenty20 International Cricket Match: Classification and Explainable Machine Learning Approach","authors":"Yash Agrawal, Kundan Kandhway","doi":"10.5750/jpm.v18i1.2109","DOIUrl":"https://doi.org/10.5750/jpm.v18i1.2109","url":null,"abstract":"We present a supervised machine learning approach to predict the winner of a Twenty20 (T20) international match. The prediction dynamically changes as the match progresses. We also use explainable machine learning techniques (SHAP scores) to understand the importance of various features in making the decision at various stages of the T20 match. We present results on a dataset of 808 men's T20 international matches. The dynamic accuracy increases from about 55% in the initial stages of the T20 match to a maximum of about 85% in the final stages of the match (with an overall accuracy of about 63% in innings 1 and 74% in innings 2). SHAP scores reveal that team strength is an important feature in making the prediction in initial stages of the match; however, in the final stages, match situation plays the dominant role in the decision making process. Our work may help team coaches and captains to assess their chances of winning and/or chart a course towards winning in the ongoing T20 match, as well as be useful for sports analytics and gambling websites and apps.","PeriodicalId":477301,"journal":{"name":"The journal of prediction markets","volume":"20 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141715605","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}
Road accidents are one of the leading causes of death worldwide. The present study analysed the pattern of road accident deaths in India from the year 2014 till the year 2022. The data was taken from the government website, and we have split it into training and testing datasets. The training dataset was from the year 2014 to 2020, and the forecasting was done for the years 2021 and 2022. We have used the SARIMA model to forecast the number of road accidents in India for the years 2021 and 2022. The accuracy of the SARIMA model in forecasting the number of road accidents in India is also established in the present study. The study has insights for policymakers and administrators. Some of the policies that can be enforced to decrease the number of road accidents in India are better road infrastructure for vehicles across India, enforcement of safety regulations, easy access to trauma care centres, strictly following the speed limits on the road and so on.
{"title":"Forecasting Road Accident Deaths in India Using SARIMA","authors":"Saurabh Kumar","doi":"10.5750/jpm.v18i1.2116","DOIUrl":"https://doi.org/10.5750/jpm.v18i1.2116","url":null,"abstract":"Road accidents are one of the leading causes of death worldwide. The present study analysed the pattern of road accident deaths in India from the year 2014 till the year 2022. The data was taken from the government website, and we have split it into training and testing datasets. The training dataset was from the year 2014 to 2020, and the forecasting was done for the years 2021 and 2022. We have used the SARIMA model to forecast the number of road accidents in India for the years 2021 and 2022. The accuracy of the SARIMA model in forecasting the number of road accidents in India is also established in the present study. The study has insights for policymakers and administrators. Some of the policies that can be enforced to decrease the number of road accidents in India are better road infrastructure for vehicles across India, enforcement of safety regulations, easy access to trauma care centres, strictly following the speed limits on the road and so on.","PeriodicalId":477301,"journal":{"name":"The journal of prediction markets","volume":"110 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713349","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 note presents the impact of pandemic on bank subordinated bonds. Using weekly data for the period 10/1/2020-12/3/2021 of 14 US, UK, Spanish, Italian, German, and Canadian banks this note provides empirical evidence that the health risk due to the COVID-19 increases the bank yields, and the stimulus packages achieved the main objective which was to reduce the risk of the bond markets and the yields. The impact of pandemic could be measured by the searches of COVID-19 related terms on Google trends. Moreover, the empirical section shows that subordinated bond yields are influenced negatively by the performance of the stock price and positively by the government yields.
{"title":"Health risk, stimulus packages, and subordinated bank yields: evidence from the COVID-19 outbreak.","authors":"Evangelos Vasileiou","doi":"10.5750/jpm.v16i3.1957","DOIUrl":"https://doi.org/10.5750/jpm.v16i3.1957","url":null,"abstract":"This note presents the impact of pandemic on bank subordinated bonds. Using weekly data for the period 10/1/2020-12/3/2021 of 14 US, UK, Spanish, Italian, German, and Canadian banks this note provides empirical evidence that the health risk due to the COVID-19 increases the bank yields, and the stimulus packages achieved the main objective which was to reduce the risk of the bond markets and the yields. The impact of pandemic could be measured by the searches of COVID-19 related terms on Google trends. Moreover, the empirical section shows that subordinated bond yields are influenced negatively by the performance of the stock price and positively by the government yields.","PeriodicalId":477301,"journal":{"name":"The journal of prediction markets","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385038","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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