Maf702-Role Of Artificial Intelligence In Assessment Answer

Findings and conclusion section: This will cover finding discovered from an analysis conducted in the literature review section.   

Literature review

Role of artificial intelligence in wealth management

AI is the foundation for various quantitative investment models. AI has many features such as it is nonlinear and high dimensional learning procedure that naturally search for replacing the reasoning of the human. One analysis of this model can be assumed of as learning (and learning to apply) “Graham and Dodd” style systematic rules. This feature differentiates the AI from the other less ambitious machine learning, which looks for the machine that can work without clear programming (Russell, & Norvig, 2016). Algorithmic trading with the application of financial models has rich multi-decade history, and artificial intelligence (AI) is the new emerging tendency in the asset administration landscape. Conventional trading algorithms were constructed to achieve specific opportunities on the other hand new cohort of algorithms utilizes the power of AI to truly act as sovereign mediators participating in market activities, working day and night in such a manner that humans simply can’t match.

As per the opinion of the Trippi (2002), AI is one of the new technologies of the Wall Streets. AI is implemented for taking the investment decision, as AI can manage the more information, respond very fast, and take very reliable decision as compare to the group of human expert (Brynjolfsson, Rock and Syverson, 2017). Moreover, in this book, the author systematically manages more information, react very fast and make the reliable decision than the group of human expert.  Along with this the authors also explain in this book that how the return of the investment can be enhanced by implementing the AI. This book provides the all real life and practical example, which are required by financial professionals for understanding and observing the AI. This book is very important for the investors who want to get the knowledge about the latest technology. The best part of Trippi and Lee’s (2002) comprehensive guide is consist of the overview of artificial intelligence in investment management, components of the artificial intelligence system, portfolio selection system issues, managing investment uncertainties, practice exercises with K-Folio, a typical artificial intelligence system.

Methods and technologies used for investment decisions in mutual funds

In case of AI, the portfolio executive is not pursuing in accordance with a trade gesture and composing a decision in accordance with the same. Moreover, the AI model is stipulating the activities that are necessary to be taken which comprises both the scenarios, whether it is a buyer or a vendor (Cohen and Feigenbaum, 2014). On the basis of the experience of theories, it must be superior since it has the capability to crunch a larger universe of daily information and risk-weigh as well as implement it to a portfolio much sooner and effectively in comparison to a human. Moreover, the computer learns from its past activities and the outcomes of the same as well as gets smarter over time (Russell, Dewey and Tegmark, 2015). It also eliminates any human discrimination since it is a machine, at the same time it is still new.  Even the probability of mistakes is reduced in case of application of Artificial Intelligence.

Sam Masucci, who is the founder and CEO if ETF Managers Group, currently conversed discussion related to firm’s investment in AI Fund. Further, the analysis was also made to the reason behind providing a better return on investment in comparison to human variety. The answers have been edited for clearness and length so that the core of the same can be understood with ease. In case it is believed that algorithmic trading or robo-advisers are instruments which are utilized by human portfolio executives in order to compose decisions on what to invest in (Lu, Li, Chen, Kim, and Serikawa, 2018). AI is extremely diverse. Though there are humans that apparently are providing data to the computer on a continuous basis, AI environment is, i.e. extracting that information and formulating the critical portfolio selection.

Considering either the algorithmic trading or robot-advisers, these both are assessed as tools which are employed by human portfolio managers to decide effectively regarding investments. However, the artificial intelligence is said to highly diverse, while there is the presence of individuals that are constantly feeding data into the computer (Petrovi?, 2018). The Artificial intelligence environment that conducts information distilling and converting the overall selection of portfolio.  

Mutual fund management supported by AI use technology such as responsiveness, actual time optimization of sales and marketing connections and services to the client, analytical market modelling on the basis of instant processing of teraflops of data, self-creating exchange with complete price transparency (Copeland,2015). Implementation of the AI in the financial prediction is also the challenging area. The major challenge with related to finance is that how to get the useful information from the market with the formless data and how to predict correct asset price. AI techniques such as ANN, ANFIS, and decision trees assist in solving the above problem effectively.  They are capable of finding out the useful information from the data and implement them in a forecasting model, their improvement in choosing the best training window leads to achieve the better result. Binoy B. Nair (2015) determines that many financial forecasting models needed artificial intelligence for predicting the exchange rates, decision support system, evaluating the manner of much financial time series, stock index and stock price prediction.  In this area various techniques, the author concluded the available method  

• ANN  such as Enns for the stock price prediction, RBFNNs for exchange rate prediction, TDNNs for index prediction,
• Support Vector Machines (SVMs), this applies for separating the data into two classes; this method can capture the nonlinear features and forecast the index and price movement.
• Fuzzy logic and adaptive-network-based fuzzy inference system (ANFIS), this is the best method for forecasting the price, than any other method. However, making an effective fuzzy system is not an easy task.
• Evolutionary algorithms (EAs), such as GP (genetic program) for exchange rate prediction, PSO (particle swarm optimization) for selecting the characteristics. 

Mutual funds managed by artificial intelligence

Artificial Intelligence refers to a part of computer science which concentrates on the production of intellectual machines which works and react similarly to human beings.  Further, Artificial Intelligence ETFs significantly stand to take advantage form to enhance the acceptance and application of artificial intelligence (Renzi, Leali, Cavazzuti and Andrisano, 2014). It comprises those who are engaged with industrial and non-industrial mechanisms, mechanization, 3D printing, natural language processing, social media and autonomous means of transport.  Contrasting the time spent by organizations discussing AI in their income calls exposes an interesting justification of AI’s potential as judged by the market (Russell, Dewey and Tegmark, 2015). Organizations which spend more time on discussing AI in their investor incomes meeting calls have started to do better than those who spent less time on it, not considering the segment.  Moreover, Artificial Intelligence ETFs refers to funds which congregate at least one of the three criterion’s that are given below:

• The funds which are particularly invested in organisations engaged with the creation of new goods and services, technological enhancements into scientific research for Artificial Intelligence.
• The funds that consist of at least 25% of portfolio exposure to organisations that invest large funds on AI Research and Development (R&D) expenditures. For instance, Amazon, Tesla Motors, Apple and Alphabet.
• Funds that utilizes AI methods in order to choose individual securities for enclosure in the fund.

Further, AI comprising a capability to scrutinize data and even learn from it is believed helpful in implementing some investing models, for example, elevated frequency trading as well as in helping fund executives with tasks that rely on meetings and understanding reams of information (Gil, Greaves, Hendler and Hirsh,  2014). Contrasting the time spent by organizations discussing AI in their income calls exposes an interesting justification of AI’s potential as judged by the market. Organizations which spend more time on discussing AI in their investor incomes meeting calls have started to do better than those who spent less time on it, not considering the segment.

Benefits of the introduction of artificial intelligence in wealth management through investment in mutual funds

Financial institutions are pursuing innovative ways to use a large amount of data from all over the world for taking the significant investment decision. Since the current business intelligence is generally constructed on organized historical and present data, which are existed within the firm, but now the enterprise seeks to use the rising unorganized data also. By implementing the predictive analytics, artificial intelligence and machine learning enterprise can find out patterns which are concealed in the organized and unorganized data which assist in improving the correctness of key investment decision (Russell, Dewey and Tegmark, 2015).

The innovative AI technology provides a solution to handle various real-world investment portfolio administration issues, for example, vagueness and financial time-series prediction, which was until now fundamentally controlled by experienced investors (Scherer, 2015). The major results of this shift must be lower administration fees since the AI-based executives have a very scalable offering as well as elevated returns since algorithms never snooze, so they should make less error in comparison to the human beings (Mikhaylov, Esteve and Campion, 2018). The adaptability of the AI architecture is such that it effectively controls universally diversified investment portfolios which might comprise not merely equities and bonds but also substitute investment like gold, real property, natural resources, forex, cryptocurrencies and other investment alternatives at a quite cheaper cost.

It is said by the Furman (2016), that EquBot’s Artificial Intelligence imitators the investment procedure related to an army of equity research psychoanalyst working around the clock. Apart from this, it has its data on 6000 organizations, one million articles and fillings a day along with data on market outlook (Dunis and et al., 2016).  Further, after categorizing the above-specified information and utilizing its programming in order to make sure that it has what it considers the best portfolio the fund usually makes at least one trade each day it frequently fine-tunes its stock-picking techniques (Scherer, 2015). AI has the edge over the natural kind since due to the intrinsic emotional and psychological limitations that hinder human reasoning.

Methodology

Research design

It is considered as the set structure that can be formulated to look out for possible solutions for research problems and questions (King and Mackey, 2016). The present study is conducted by making use of qualitative research design as it aims to analyse the role of artificial intelligence in the making investment decision in mutual funds. 

Data collection

Primary there are two sources of data collection, i.e. primary sources and secondary sources. For the present study, data has been collected through secondary sources and viewpoint of previous researches will be analyzed through thematic analysis.  

Data analysis

Data analysis has the role of studying the methods and concepts by which the data is analyzed in carrying out the research (Johnston, 2017). Data analysis is a procedure of examining, moderating and cleaning the data with the aim of exploring beneficial information which supports decision making and conclusions. For the present study thematic analysis has been used by considering the qualitative nature of the study.

Analysis

Changing technologies for management of mutual funds

Financial institutions are significantly changing such as changing demographics and perspective of the customers, strict rules and regulations, vital digital technologies, and increasing the competition from fintech companies. For solving the all above matter, banks can work together with the fintech companies that give an innovative solution in the environment of AI (Cohen, & Feigenbaum, (2014). Individual firms require modifying the solution as per their investment strategy and procedure requirement. At the time of selecting the solution, firms have to consider their data, administered learning requirement and accuracy of the model. Banks can with the help of the AI and machine learning, can expand their growth of the business as this technology assist to banks for better decision making.

Mutual funds of Australia managed through artificial intelligence:

Sprott BUZZ Social Media Insights ETF

It is a new exchanged based traded fund that is developed to serve exposure to the BUZZ Social Media Insights Index; it tends to determine the US stock that is ranked at the highest position in context with affirmative insight, inclusive of brand value perceptions and discussion breadth from social media mention and etc. It brings a strong integration of big data analytics with social media. It forms and manages strategies of the proprietary quantitative portfolio on the basis of Big Data analytics models. It is a relatively new fund, which was launched on April 18, 2016, and helps in tracking the buzz NextGen AI the US Sentiment Leaders Index followed by the 76 well-known holdings via news articles and other social media sources (BusinessWire, 2016).

The Global X Robotics & Artificial Intelligence ETF (BOTZ)

This fund seeks to make an investment in the firms that possibility compete to merit from higher adoption and use of AI and robotics inclusive of the one engaged with non-industrial robots, industrial robotics, autonomous vehicles and automation (Jocelyn Aspa, 2018). It offers exposure of cross-sector to corporate operating to develop and produce AI and robotics by a market cap opt and weighted index. It came into effect on September 12, 2016, and provided exposure to the organizations engaged in the international automation and industrial robotics. A huge investment has been made by the fund in the market like hardware and software and managerial system of automated products and services.

ARK Industrial Innovation ETF 

This fund was introduced on September 30, 2014, and its foremost focus is on advances in the energy storage technology, robotics, 3-dimensional printing and autonomous vehicles that makes improvisation in productivity and reduction in costs (ARK Invest, 2018). It actively manages ETF and requires long-term capital growth by making the investment as per the normal situations majorly in domestic basis and US companies.

Robo Global Robotics and Automation Index 

This fund tracts corporate engaged in automation as well as robotics, and lately tracts approx 88 companies, with the top holdings inclusive of Keyence and Mazor Robotics. This investment tries to offer investment outcomes that prior to fees usually communicate to the performance and price of ROBO Global Robotics and Automation Index. Further, this index is developed to gauge the performance of companies related to robotics and automation (RoboGlobal, 2018).

Significance and limitations of Artificial intelligence in mutual funds management

AI is capable of carrying the best of passive and active investing worlds into one portfolio.  The capability to manage the risk with the return will transform the asset management, as it facilitates the institutional l investors and fund managers to take a compound position in the market despite the fact of keeping the high-level ratio of stable and growth investments.  In the AI, which handle the asset management, conditions for the consistent and stable returns and high risk for high reward can be programmed into the AI engine, which can be accustomed by investors over the time according to their risk-taking capacity which generally changes over a long period of time (Makridakis, 2017).

At the same time, it is very early to say that whether AI Powered Equity ETF would be a trendsetter or the profits which are expected from the same is only a curiosity.  As AI keeps on being more and more complicated and difficult but so do the market trends. However, the fact cannot be denied that a track record of below a year on a single fund isn’t nearly sufficient to measure the advantages of the AI approach. In addition to this, like any fund, one should consider potential gains along with the fund’s fees which is being charged. 

Due to some reason, the debate between technology and investment authorities relating to the role of Artificial Intelligence will be continued.   Lu, Wu, D Mao, M., Wang, W. and Zhang,  (2015) asserts that AI ETF will merely ever be a tool, helpful but subordinate to its flesh-and-blood masters, on the other hand, some say that it is taking control and composing decisions for various funds.  In addition to this, it is said by Art Amador, the founder of EquBot, organisation which produced the funds that, Artificial Intelligence ETF is different and not same as other equity investment. The reason behind same is that it utilizes algorithms to go to the last mile and choose its possessions, generally 30-70 stocks. He had the support of an IBM program which advances the technology start-ups, as well as ETF, runs many of its computations on IBM’s Watson supercomputer.  

Findings and conclusion 

Financial institutions are using ethical methods of using voluminous data which has been attained from all over the world in order to make an appropriate investment decision. However, existing business intelligence is developed on the basis of past and present data which is available within forms. Even the unstructured data is used for taking an important decision.  Thus, artificial intelligence assists in detecting a hidden pattern of unstructured and structured data in order to enhance the accuracy of key investment decision.

According to the study of Booth (2018), a record of lower than a singular fund is not closely sufficient to measure the advantages of the AI approach. In addition, same as funds, one is required to consider the possible profits in opposition to the fees of funds. On a theoretical basis, it is required to be better, as it has the capability to the much larger amount of information and risk measurement and using the same to the portfolio more effectively and rapidly than humans (Einav & Levin, 2017). Additionally, the computer machine derives learning from the historical actions and the consequence of such actions it makes an improvement over time and removing biases simultaneously.

As per the study of Mikhaylov, Esteve and Campion (2018), AI comprises the ability to assess the data as well as learns from the data. It is mainly considered useful in presenting specific investment models, which comprises high-frequency trading and assist in fund managers with operations which rely on collecting and interpreting reams of information. In the AI case, the managers who supervise portfolio is not considering trade signal while making decisions. The model of AI is describing the measures that are required to be taken, if or if not it is a buy or sell (Wi?niewski and Ophir, 2017).

Some human thinks that is risky to implement the AI, by decentralizing the human actions it takes away the control from the human being. Sometimes AI may not take the decision as the humans can because human considers every situation at the time of taking the decision (Bond and Gasser, 2014).

Comparing the time spent by organizations discussing AI in their incomes calls exposes an interesting justification of AI’s potential judged by the market. Further, it is concluded that organizations that spent much time discussing AI in their investor earnings meeting calls have begun to outperform those who spent less time on it, despite segment. The financial market is treated as the human market, in which the opportunities for earning the profit exist as the time changes, further it depends on the changing strategies of the rivals. Thus the important thing is that the input may change over the time. The AI system should be implemented by managing the data, its selection, asset valuation as well as portfolio management component. The future goal of the AI system is to combine all the three techniques such as knowledge-based system, machine learning, and natural languages processing into the one system by which the data collection, asset management, and portfolio management can be handled at the same time. Such a system makes the connection with the human by interacting with them. Thus a human can specify their priorities and make the difficult decision, however in some areas such as program trading; these complex system could compete with each other.   

References

ARK Invest, (2018).  ARK Industrial Innovation ETF. Retrieved from https://ark-funds.com/arkq

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Booth, M. (2018). ETFs: Look closer. Professional Planner, 105, 18.

Brynjolfsson, E., Rock, D. & Syverson, C., (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In Economics of Artificial Intelligence. University of Chicago Press.

BusinessWire, (2016).  Investing Gets “Social” with Sprott BUZZ Social Media Insights ETF Retrieved from  https://www.businesswire.com/news/home/20160419005303/en/Investing-%E2%80%9CSocial%E2%80%9D-Sprott-BUZZ-Social-Media-Insights

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Copeland, J. (2015). Artificial intelligence: A philosophical introduction. John Wiley & Sons.

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