2
Group Research Project: Part 2
(‘TEAM ‘2’)
Members:
Sahil Kuranjekar
Han Jia
Guo Jia
Karmjeet
Yorkville University
New Westminster Campus
BUSI_2093 ’B’
Prof. Audrey Lowrie
November 22, 2020
Group Research Project: Part 2
The use of machine learning and artificial intelligence in investment management
The capital management industry is experiencing a revolutionary era. Artificial intelligence, cloud computing, big data, blockchain, etc. will bring disruptive changes to the industry. Therefore, in this research, people will mainly learn about the use of machine learning and artificial intelligence in investment management. In investment management, artificial intelligence and machine learning can help people collect data. Therefore, people will mainly understand the positive effects of machine learning and artificial intelligence on investment management and how to use them. In addition, people also hope to understand to transform asset management. Standardization in machine learning improves efficiency because the speed of machine processing is usually higher than the speed of the normal work of the human brain. On the other hand, artificial intelligence can reduce errors that are easy to occur in this link, and also improve accuracy and efficiency (Viriato, 2019).
Some of the key issues or risks associated with artificial intelligence in investment management.
Operational risk: On operating, there is always a risk in operational level, this includes occurrence of errors in the phase of investment or error in transaction that was intended to investment. There is always a risk in which the Artificial Intelligence can face a bug or malfunction is due to not implementing the software properly.
Model risk: Investment model consist of several quantitative models, and in order to manage documentation, monitoring and testing of artificial intelligence directing the investment. Having a risk management that handles different model of investments very essential but handling multiple investment portfolio is risky.
Third party risk: There are some third-party integrations from third party companies to the artificial intelligence, this gives potential risk to the asset management and diverging crucial information to other parties. Confidentiality becomes a risk as privacy terms are not clear.
Technological risk: Artificial intelligence or machine learning operates on various technological factors, problems like power outage, viruses or software bugs can negatively impact the system and the whole invest management.
Current state analysis
Analysis of current state of Use of Machine Learning and Artificial Intelligence in Investment Management
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Future directions.
Artificial intelligence is considered to end up gradually playing a role that is great in the critical assessment of the human judgements within investment frameworks fir decision-making, in moderating or countering behavioral biases that are shirt term or even the making of judgements at the absence of human input. This clearly indicates an ability that is streamlined into predicting what will take place as well as the most suitable course of action for the financial forecasting. This tends to be perceived as an enhancement on the present manual processes that suffer from human biases that are inherent. Artificial intelligences are not capable of displacing the investment managers. This is because they still need the presence of humans. Organizations will require investing within experts for the AI monitoring and ensuring it operates and adjusts whenever necessary. This clearly shows that the investment decisions to a great extent will be dependent on personal judgement in addition to popular sequences of biases, which is similar to the previous 20 years (Davenport and Ronanki, 2018).
Machine learning is capable of helping with many of the tasks related to the portfolio construction such as generation of ideas, allocations of assets, optimization of weight, alpha factor design, testing of the strategies in addition to position sizing. ML is specifically considered to be adaptable to the securities investing mainly due to the fact that the insights it gets to collect, are capable of being acted on rapidly as well as efficiently. It also makes it possible for algorithms that are powerful thing analyze huge sets of data so as the make predictions against the goals that are defined. These algorithms self-adjust via a trial-and-error process for production of accurate data (Raisch and Krakowski, 2021).
References
Artificial intelligence (n.d.).| The next frontier for investment management firms. Deloitte. Retrieved from: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Financial-Services/fsi-artificial-intelligence-investment-mgmt.pdf
Basilico, E., & Johnsen, T. (2019). Big Data and Artificial Intelligence: A Revolution in Investment Management. In Smart (er) Investing (pp. 113-126). Palgrave Macmillan, Cham.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192- 210.
Viriato, J. C. (2019). AI and Machine Learning in Real Estate Investment. The Journal of Portfolio Management, 45(7), 43–54. https://doi.org/10.3905/jpm.2019.45.7.043
This has the potential to be an interesting paper. However, your paper is brief, and the bulk of it is based on paraphrasing and summarizing the small numbers of articles referenced. There is a lot of good work here but unless you cite your sources correctly, I have no alternative but to grade you at 0. You must cite your work. If it is not your own idea but something you have read, then you must cite it properly and include the citation in text plus in the references at the end.
This paper should show clear evidence of your own analysis and evaluation of the subject and not just be a summary of your reading.
In terms of your content, one of the requirements of the project is to relate your answer to the concepts we covered, and in this paper I see little evidence of that. For your final submission, try to ensure that you incorporate concepts covered in class. These could include TVM, corporate winners and lisers, risk and return – as examples.
As a guide for the final submission: if I think it is not properly cited, if sources are not correctly acknowledged then I will grade the entire project at 0.
Activity/Competencies Demonstrated
% of Final Grade
a. Paper is logically organized into sections
15/20
b. Paper integrates course topics
10/40
c. Content is current and relevant
20/30
d. Grammar, spelling, and APA
0/10
Total
45/100
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