Big Data Algorithmic Trading Systems Based on Investors’ Mood

Martínez, Raúl Gómez; Román, Miguel Prado; Casado, Paola Plaza

doi:10.1080/15427560.2018.1506786

Cited by 12 publications

(5 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this, algorithmic trading is done. Researchers found that this algorithmic trading based on investor's moods can predict the IBEX 35 with an accuracy of more than 50% while the returns from trading are higher than market returns (Gómez Martínez, Prado Román, & Plaza, 2019).…”

Section: The Application Sectors Of Ai In Finance and Financial Marketsmentioning

confidence: 99%

Artificial intelligence techniques in finance and financial markets: A survey of the literature

Milana

Ashta

2021

Strategic Change

View full text Add to dashboard Cite

Both academic and nonacademic literature is evolving following the oscillating development of artificial intelligence (AI) and computing power's evolution in their application to finance and financial markets. The limits to economic growth encountered after the financial crisis and, successively, the recent pandemic bursts have posed new challenges to AI-related technologies. The surveyed publications have given hope in harvesting improved efficiency, new data, information, advisory and management services, risk mitigation, and some unanswered questions regarding negative impacts on sustainable growth and increasing economic welfare.

show abstract

Section: The Application Sectors Of Ai In Finance and Financial Marketsmentioning

confidence: 99%

Artificial intelligence techniques in finance and financial markets: A survey of the literature

Milana

Ashta

2021

Strategic Change

View full text Add to dashboard Cite

show abstract

“…Gomez, Prado, and Plaza [14] generated an algorithm based on arti cial intelligence that uses investors' sentiment to open long or short positions in the future Ibex 35. To measure investors' sentiment, the authors used Semantic Analysis Algorithms that quali ed as good, bad, or neutral any communication related to the Ibex 35 made on Twitter or in the media [28].…”

Section: Sentiment Analysis and Financementioning

confidence: 99%

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Valle-Cruz

Fernandez-Cortez

López-Chau

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract Backgroud:Investors are always playing with the fears and desires of buyers and sellers. Stock exchange markets are not the exception. Financial sentiment analysis allows us to understand the effect of reactions and emotions on social media in the stock market. In this research, we analyze Twitter data and financial indices to answer the question: How do polarity generated by the posts on Twitter influence financial indices behavior in pandemic seasons? Methods:The study is based on the sentiment analysis of influential Twitter accounts in this field and its relationship with the behavior of important financial indices. To achieve this, we tested four lexicons to detect polarity on Twitter. Results:Our findings shows that the period in which the markets reacted was 6 to 13 days after the information was shared and disseminated on Twitter in the COVID-19 season, and 1 to 2 day for H1N1 season. Furthermore, in our analysis, we found that the lexicons that got the best results for sentiment analysis on Twitter were S140 and Affin.Conclusions:Financial sentiment analysis is an important technique to forecasting stock market and polarity is the most widely used technique in the financial area. There is a relationship between the polarity in Twitter and the financial indexes behavior. The most influential Twitter accounts during the pandemic season were The New York Times, Bloomberg, CNN News, and Investing, presenting a very high relation between sentiments on Twitter and the stock market behavior.

show abstract

“…Its application in producing predictive analytics has been widely employed in exchange rate forecasts (Zheng et al 2019 ), in stock markets (Srivastava et al 2021 ; Yin et al 2021 ), while Sevim et al, ( 2014 ) have used it in macroeconomic predictions and forecasts. Further, ML-based algorithmic trading models monitor and analyse real-time data to detect patterns, thereby giving traders a distinct advantage over the market average (Gómez Martínez et al 2018 ). It is noted that the increasing transactional events and customer participation in the financial environment makes it vulnerable to security threats.…”

Section: Introductionmentioning

confidence: 99%

Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis

2023

View full text Add to dashboard Cite

This paper surveys the extant literature on machine learning, artificial intelligence, and deep learning mechanisms within the financial sphere using bibliometric methods. We considered the conceptual and social structure of publications in ML, AI, and DL in finance to better understand the research’s status, development, and growth. The study finds an upsurge in publication trends within this research arena, with a bit of concentration around the financial domain. The institutional contributions from USA and China constitute much of the literature on applying ML and AI in finance. Our analysis identifies emerging research themes, with the most futuristic being ESG scoring using ML and AI. However, we find there is a lack of empirical academic research with a critical appraisal of these algorithmic-based advanced automated financial technologies. There are severe pitfalls in the prediction process using ML and AI due to algorithmic biases, mostly in the areas of insurance, credit scoring and mortgages. Thus, this study indicates the next evolution of ML and DL archetypes in the economic sphere and the need for a strategic turnaround in academics regarding these forces of disruption and innovation that are shaping the future of finance.

show abstract

Big Data Algorithmic Trading Systems Based on Investors’ Mood

Cited by 12 publications

References 33 publications

Artificial intelligence techniques in finance and financial markets: A survey of the literature

Artificial intelligence techniques in finance and financial markets: A survey of the literature

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis

Contact Info

Product

Resources

About