Social media sentiment application for financial market trading strategies
This study tests the hypothesis of financial market stock price movement prediction possibility with an application of quantified human sentiment indices gathered from Twitter social network. Using Python text analysis and machine learning algorithms.
| Рубрика | Экономика и экономическая теория |
| Вид | дипломная работа |
| Язык | английский |
| Дата добавления | 07.12.2019 |
| Размер файла | 2,5 M |
Отправить свою хорошую работу в базу знаний просто. Используйте форму, расположенную ниже
Студенты, аспиранты, молодые ученые, использующие базу знаний в своей учебе и работе, будут вам очень благодарны.
Based on this, this study hypothesis that using the illustrated technique it is possible to predict stock price movements of Apple, Tesla and Walmart via sentiment signals gathered using the illustrated procedure from Twitter and possibility to construct on this basis profitable risk-adjusted trading strategy were disproved.
It is clear that suggested goal requires more in-depth machine learning techniques, far more careful work with key words, superior computational resources and extensive experiments with the research setup.
Further steps
Results of my research failed to discover the existence of correlation of stock movements of Apple Inc., Tesla Inc., and Walmart Inc. with their quantified expressions of human sentiments gathered from the Twitter social network. Based on the obtained results, one cannot state that market for the considered stocks is efficient, because in this study multiple limitations were present that need to be further considered.
In particular:
1. Increase in precision
2. Precision of the NLP models developed under this study does not reach the levels of the progressive models of Anshul Mittal and Arpit Goel in their work in 2010 «Stock prediction using Twitter sentiment analysis» and J. Bollen, H. Mao and Xiao-Jun Zeng «Twitter mood predicts the stock market» (2010) which secured 75-85% prediction accuracy. Further work is required at the stage of text normalization, as relatively more picky rules in wordings filtering failed to process on existing for this study personal and borrowed computational resources.
3. The same reason led to consideration of daily stock movements instead of more frequent ticker values. Post and tweets at the beginning of the day frequently posses no relevance for the stock price at the end of the trading session, which further decreases the accuracy of the study.
4. Finally, there exist other models that fit the goal of stock movement prediction such as developed by Yandex CatBoost model which is being effectively used in recent works on classification and regression. This promising technique may increase accuracy of the endpoint estimation.
5. Extension of sample size
6. This study applies stock prices of several US companies that surely cannot represent a well diversified portfolio of assets appropriate for investing and for drawing global conclusions on market efficiency or EMH. The choice of major market players intended to secure enough public attention for sentiment evaluation. However, as mentioned earlier, computational resources proved to be the bottle neck in this study, not the sufficiency of data. As one of the major next steps, simultaneous consideration of the companies in the global indices is planned such as S&P 500 or DJIA accompanied with the extension of the time horizon under the consideration and frequency of signals under evaluation.
7. Automation
8. The end-point of the study from my personal perspective remains the creation of high-frequency trader bot that will allow to implement in an online mode developed trading strategy on practice. With this paper I took one more step towards this goal but there is a lot of work ahead. I still lack knowledge on trading bot creation using Python algorithms. Further literature review and self-education is required to complete this goal. But one thing is certain - the road will be mastered by the one who moves forward.
List of references
1. «The Behavior of Stock-Market Prices» by Eugene Fama, 1965
2. «Efficient Capital Markets: A Review of Theory and Empirical Work» by Eugene Fama, 1970
3. «The Behavior of Stock Prices on Fridays and Mondays» by Frank Cross, 1973
4. «Stock returns and the weekend effect» by Kenneth R.French, 1980
5. «Mean reversion in stock prices: evidence and implications» by Poterba and Summers, 1988
6. «The Efficient Market Hypothesis and Its Critics» by Burton G. Malkiel, 2003
7. «Judgment Under Uncertainty: Heuristics and Biases» by D. Kahneman and A.Tversky 1982
8. Technical Analysis of the Financial Markets, by J.J.Murphy, 1999
9. Investor Sentiment as a Contrarian Indicator. Wayne A. Thorp, CFA. 2004
10. Trading Strategies to Exploit Blog and News Sentiment. Wenbin Zhang, Steven Skiena, 2010
11. Stock Movement Prediction from Tweets and Historical Prices. Yumo Xu, Shay Cohen, 2018
12. A sentiment-based model for the bitcoin: theory, estimation and option pricing. Alessandra Cretarola, Gianna Figа-talamanca, and Marco Patacca, 2017
13. Stock prediction using Twitter sentiment analysis by Anshul Mittal and Arpit Goel, 2010
14. «Twitter mood predicts the stock market» by J. Bollen, H. Mao and Xiao-Jun Zeng, 2010
15. Sentiment Analysis of Twitter Data for Predicting Stock Market Movements by V.S.Pagolu, K.N.R.Challa and G.Panda, 2016
16. Cryptocurrency market efficiency analysis based on social media sentiment by Oleg Kheyfets, 2018
17. Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model. Tianyu Ray Li, Anup S. Chamrajnagar, Xander R. Fong, Nicholas R. Rizik, Feng Fu. 2018
18. Social signals and algorithmic trading of Bitcoin. Royal Society open science. David Garcia, Frank Schweitzer. 2015
19. Speech and Language Processing. Daniel Jurafsky, James H. Martin. 2016
20. CS838-1 Advanced NLP: Text Categorization with Logistic Regression. Xiaojin Zhu. 2007
21. Logistic regression and artificial neural network classification models: a methodology review. Stephan Dreiseitla, Lucila Ohno-Machadob. 2002
22. Understanding the Random Forest with an intuitive example. William Koehrsen. 2017
23. CatBoost: unbiased boosting with categorical features. Prokhorenkova, Gusev. 2017
24. Random Forests. Matthew N. Bernstein. 2016
25. Introduction to information retrieval by Christopher D. Manning, Prabhakar Raghavan, 2008
26. DataCamp online coding platform https://www.datacamp.com/
27. Codecademy online coding platform https://www.codecademy.com/
Appendix
Chart #1: Simplified scheme of a Random Forest procedure
Размещено на Allbest.ru
...Подобные документы
The stock market and economic growth: theoretical and analytical questions. Analysis of the mechanism of the financial market on the efficient allocation of resources in the economy and to define the specific role of stock market prices in the process.
дипломная работа [5,3 M], добавлен 07.07.2013Financial bubble - a phenomenon on the financial market, when the assessments of people exceed the fair price. The description of key figures of financial bubble. Methods of predicting the emergence of financial bubbles, their use in different situations.
реферат [90,0 K], добавлен 14.02.2016Economic entity, the conditions of formation and functioning of the labor market as a system of social relations, the hiring and use of workers in the field of social production. Study of employment and unemployment in the labor market in Ukraine.
реферат [20,3 K], добавлен 09.05.2011Law of demand and law of Supply. Elasticity of supply and demand. Models of market and its impact on productivity. Kinds of market competition, methods of regulation of market. Indirect method of market regulation, tax, the governmental price control.
реферат [8,7 K], добавлен 25.11.2009Natural gas market overview: volume, value, segmentation. Supply and demand Factors of natural gas. Internal rivalry & competitors' overview. Outlook of the EU's energy demand from 2007 to 2030. Drivers of supplier power in the EU natural gas market.
курсовая работа [2,0 M], добавлен 10.11.2013The necessity of using innovative social technologies and exploring the concept of social entrepreneurship. Analyzes current level of development of social entrepreneurship in Ukraine, the existing problems of creating favorable organizational.
статья [54,5 K], добавлен 19.09.2017General characteristic of the LLC DTEK Zuevskaya TPP and its main function. The history of appearance and development of the company. Characteristics of the organizational management structure. Analysis of financial and economic performance indicators.
отчет по практике [4,2 M], добавлен 22.05.2015Directions of activity of enterprise. The organizational structure of the management. Valuation of fixed and current assets. Analysis of the structure of costs and business income. Proposals to improve the financial and economic situation of the company.
курсовая работа [1,3 M], добавлен 29.10.2014Basic rules of social protection in USA. Maintenance of legal basis, development and regular updating of general(common) methodological principles of state guarantees and methodical development in sphere of work. Features of payment of work by worker.
курсовая работа [29,4 K], добавлен 12.04.2012A theoretic analysis of market’s main rules. Simple Supply and Demand curves. Demand curve shifts, supply curve shifts. The problem of the ratio between supply and demand. Subsidy as a way to solve it. Effects of being away from the Equilibrium Point.
курсовая работа [56,3 K], добавлен 31.07.2013The definition of Corporate Social Responsibility and main approaches. Stakeholder VS Shareholders. The principles of CSR: features and problems. Sanofi Group Company and its Social Responsibility program. Results and Perspectives, the global need.
курсовая работа [43,2 K], добавлен 09.03.2015The global financial and economic crisis. Monetary and financial policy, undertaken UK during a crisis. Combination of aggressive expansionist monetary policy and decretive financial stimulus. Bank repeated capitalization. Support of domestic consumption.
реферат [108,9 K], добавлен 29.06.2011Analysis of the status and role of small business in the economy of China in the global financial crisis. The definition of the legal regulations on its establishment. Description of the policy of the state to reduce their reliance on the banking sector.
реферат [17,5 K], добавлен 17.05.2016Transition of the Chinese labor market. Breaking the Iron Rice Bowl. Consequences for a Labor Force in transition. Labor market reform. Post-Wage Grid Wage determination, government control. Marketization Process. Evaluating China’s industrial relations.
курсовая работа [567,5 K], добавлен 24.12.2012Assessment of the rate of unemployment in capitalist (the USA, Germany, England, France, Japan) and backward countries (Russia, Turkey, Pakistan, Afghanistan). Influence of corruption, merges of business and bureaucracy on progress of market economy.
реферат [15,5 K], добавлен 12.04.2012Mergers and acquisitions: definitions, history and types of the deals. Previous studies of post-merger performance and announcement returns and Russian M&A market. Analysis of factors driving abnormal announcement returns and the effect of 2014 events.
дипломная работа [7,0 M], добавлен 02.11.2015The influence of corruption on Ukrainian economy. Negative effects of corruption. The common trends and consequences of increasing corruption. Crimes of organized groups and criminal organizations. Statistical data of crime in some regions of Ukraine.
статья [26,7 K], добавлен 04.01.2014Prospects for reformation of economic and legal mechanisms of subsoil use in Ukraine. Application of cyclically oriented forecasting: modern approaches to business management. Preconditions and perspectives of Ukrainian energy market development.
статья [770,0 K], добавлен 26.05.2015Gas pipeline construction: calculating the pipe diameter, the pressure required for the transportation of natural gas compressors. The definition of capital costs for construction and operation of the pipeline. Financial management of the project.
статья [774,7 K], добавлен 05.12.2012The first stage of market reforms in Kazakhstan is from 1992 to 1997. The second phase is in 1998 after the adoption of the Strategy "Kazakhstan-2030". The agricultural, education sectors. The material and technical foundation of the medical institutions.
презентация [455,3 K], добавлен 15.05.2012
