Leveraging Technical Analysis & Artificial Intelligence - Optimization of Global Portfolio Management through World Indices

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Shalini Aggarwal ◽  
Vishal Kamra ◽  
Nitin Kulshrestha
Keyword(s):  
Artificial Intelligence ◽  
Portfolio Management ◽  
Technical Analysis

scholarly journals Early Adopters to Early Majority - What's Driving the Artificial Intelligence and Machine Learning Powered Transformation in Financial Services?

2021 ◽  
Vol 12 (4) ◽  
pp. 43
Author(s):  
Srikrishna Chintalapati
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Portfolio Management ◽  
Value Chain ◽  
Financial Services ◽  
Retail Banking ◽  
Early Adopters ◽  
Customer Life Cycle ◽  
Digital Disruption ◽  
The Impact

From retail banking to corporate banking, from property and casualty to personal lines, and from portfolio management to trade processing, the next wave of digital disruption in financial services has been unleashed by the concepts and applications of Artificial Intelligence (AI) and Machine Learning (ML). Together, AI and ML are undoubtedly creating one of the largest technological transformations the world has ever witnessed. Within the advanced streams of research in AI and ML, human intelligence blended with the cognitive reasoning of machines is finally out of the labs and into real-time applications. The Financial Services sector is one of the early adopters of this revolution and arguably much ahead of its leverage compared to other sectors. Built on the conceptual foundations of Innovation diffusion, and a contemporary perspective of enterprise customer life-cycle journey across the AI-value chain defined by McKinsey Global Institute (2017), the current study attempts to highlight the features and use-cases of early-adopters of this transformation. With the theoretical underpinning of technology adoption lifecycle, this paper is an earnest attempt to comment on how AI and ML have been significantly transforming the Financial Services market space from the lens of a domain practitioner. The findings of this study would be of particular relevance to the subject matter experts, Industry analysts, academicians, and researchers focussed on studying the impact of AI and ML in the financial services industry.

Artificial Intelligence in Portfolio Management

2002 ◽  
pp. 403-409 ◽  
Author(s):  
Man-Chung Chan ◽  
Chi-Cheong Wong ◽  
W. F. Tse ◽  
Bernard K.-S. Cheung ◽  
Gordon Y.-N. Tang
Keyword(s):  
Artificial Intelligence ◽  
Portfolio Management

Portfolio Management Using Artificial Intelligence

2020 ◽  
pp. 207-215
Author(s):  
Rakshit Gupta ◽  
Yogesh Mahajan ◽  
Punit Mukesh Ahuja ◽  
Jyoti Ramteke
Keyword(s):  
Artificial Intelligence ◽  
Portfolio Management

scholarly journals ВИКОРИСТАННЯ СИСТЕМ ШТУЧНОГО ІНТЕЛЕКТУ У ЗАДАЧАХ ПРОГНОЗУВАННЯ ФІНАНСОВИХ ІНДЕКСІВ: ОГЛЯД НАУКОВИХ ДЖЕРЕЛ

2020 ◽  
pp. 108-117
Author(s):  
Николай Ярославович Кушнир ◽  
Катерина Токарева
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Large Scale ◽  
Scientific Literature ◽  
Financial Time Series ◽  
Technical Analysis ◽  
Soft Data ◽  
Scientific Publications ◽  
Financial Time ◽  
Stock Indices

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. The usage of well-known methods of artificial intelligence in forecasting and analysis of time series is investigated. Financial time series are inherently highly dispersed, complex, dynamic, nonlinear, nonparametric, and chaotic nature, so large-scale and soft data mining techniques should be used to predict future values. As the scientific literature superficially describes the numerous artificial intelligence algorithms to be used in forecasting financial time series, a detailed analysis of the relevant scientific literature was conducted in scientometric databases Scopus, Science Direct, Google Scholar, IEEExplore, and Springer. It is revealed that the existing scientific publications do not contain a comprehensive analysis of literature sources devoted to the use of artificial intelligence methods in forecasting stock indices. Besides, the analyzed works, which are related in detail to the object of our study, have a limited scope because they focus on only one family of artificial intelligence algorithms, namely artificial neural networks. It was found that the analysis of the use of artificial intelligence systems should be based on two well-known approaches to predicting the behavior of financial markets: fundamental and technical analysis. The first approach is based on the study of economic factors that have a possible impact on market dynamics and more common in long-term planning. Representatives of technical analysis, on the other hand, argue that the price already contains all the fundamental factors that affect it. In this regard, technical analysis involves forecasting the dynamics of price changes based on the analysis of their change in the past, ie time series. Although today there are many developed models for forecasting stock indices using artificial intelligence algorithms, in the scientific literature there is no established methodology that defines the main elements and stages of the algorithm for forecasting financial time series. Therefore, this study has improved the methodology for forecasting financial time series.

scholarly journals The Effectiveness of the Transaction Systems on the Dax Index

2020 ◽  
Vol 4 (28) ◽  
pp. 149-174
Author(s):  
Marek Trembiński ◽  
Joanna Stawska
Keyword(s):  
Portfolio Management ◽  
Stock Exchange ◽  
Technical Analysis ◽  
Quality Measures ◽  
Daily Basis ◽  
Significant Loss ◽  
Investment Strategies ◽  
Trading Systems ◽  
Management Quality ◽  
Analysis Tools

The purpose of the article/hypothesis: The aim of this article is to examine the effectiveness of trading systems built on the basis of technical analysis tools in 2015–2020 on the DAX stock exchange index. Efficiency is understood as generating positive rates of return, taking into account the risk incurred by the investor, as well as achieving better results than passive strategies. Presenting empirical evidence implying the value of technical analysis is a difficult task not only because of a huge number of instruments used on a daily basis, but also due to their almost unlimited possibility to modify parameters and often subjective evaluation.Methodology: The effectiveness of technical analysis tools was tested using selected investment strategies based on oscillators and indicators following the trend. All transactions were carried out on the Meta Trader 4 platform. The analyzed strategies were comprehensively assessed using the portfolio management quality measures, such as the Sharpe measure or the MAR ratio (Managed Account Ratio).Results of the research: The test results confirmed that the application of described investment strategies contributes to the achievement of effective results and, above all, protects the portfolio against a significant loss in the period of strong turmoil on the stock exchange. During the research period, only two strategies (Ichimoku and ETF- Exchange traded fund) would produce negative returns at the worst possible end of the investment. At the best moment, however, the „passive” investment achieved the lowest result. Looking at the final balance at the end of 2019, as many as four systems based on technical analysis were more effective than the „buy and hold” strategy, and at the end of the first quarter of 2020 – all of them. When analyzing the management quality measures, it turned out that taking into account the 21 quarters, the passive strategy had the lowest MAR index. The Sharpe’s measure is also relatively weak compared to the four leading strategies.

scholarly journals The Use of Technical Analysis in the US, European and Asian Stock Markets

2020 ◽  
Vol 8 ◽  
pp. 302-318
Author(s):  
Deimante Teresiene ◽  
Margarita Aleksynaite
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Stock Markets ◽  
Investment Decisions ◽  
Technical Analysis ◽  
Big Data Analysis ◽  
The Us

Technical analysis is a widely used tool in making investment decisions. Nowadays it becomes very popular in the context of big data analysis and artificial intelligence framework. Although the analysis of the results of indicators in certain markets often becomes the axis of technical analysis research, it is difficult to find articles aimed at applying and comparing this analysis in different markets. This paper attempts to answer the question of whether technical analysis indicators work in the same or different ways in the US, European, and Asian stock markets. For this purpose, 8 indicators are calculated, and their results are compared in three selected markets. The correlation between the indicators themselves in individual markets is also determined. It has been observed that the performance of technical analysis is similar in different markets so this type of analysis can be used in artificial intelligence framework.

Artificial Intelligence chip and its Technical Analysis

2021 ◽  
Author(s):  
Benna Cui ◽  
Xiaosong Wu ◽  
Yijing Li ◽  
Jiaojiao Li ◽  
Zijian Gu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Technical Analysis
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