Ai Investing : The Future of Finance

Ai Investing

Investors have always valued safety in their investment decisions and have placed definite expectations on their returns. The COVID-19 pandemic has shifted the safety value in investment management.

On March 11, 2020, the Dow Jones Industrial Average (DJIA) fell by 20.3% from a record high point of 29,551.42.

The striking plunge launched a bear market for the first time in 11 years (Amadeo 2020) and, as a result, many investors experienced severe losses they did not anticipate (Krantz 2020).

The huge unpredictability of the previous year has intensified the value of investor safety.

Having experienced the shocks of the pandemic, an investor will prioritize efficient risk management and higher certainty on their investments more than ever before.

The pandemic’s intensified values of safety and efficiency highlights the intersection of Artificial Intelligence (Ai) and human capital.

Ai investing is the means for an investor to avoid future recessions and to ensure a safe and efficient investment management without any major losses. 

With the recent developments of Ai and machine learning technologies, a number of hedge funds and investment management firms have adopted Ai as a means to provide an alternative, technology-driven investment experience to their clients.

Ai is a technology capable of recognizing patterns in the provided data and predicting the outcomes of current events (DTTL 2019).

For instance, a number of Ai companies were able to predict the COVID-19 pandemic outbreak in late December 2019 based on the news articles and the historical knowledge about infectious outbreaks (Heaven 2020).

When applied to investing, Ai provides suggestions to the investors on where and when to invest and/or sell (Idzelis 2020).

At a glance, it seems like the Ai investing works the same way regular investing does: an investment assistant – either human or artificial – analyzes the provided historical data and investors’ expectations to give suggestions on the best possible stocks to invest in.

In fact, Ai is able to make more accurate and safe investment decisions than human analysts can, garnering returns that frequently outperform the traditional means of investing. 

The underlying difference between human and Ai lies within the efficiency of information processing powers. Ai is able to process and analyze significantly more information than a human mind is.

According to Deloitte’s report on Artificial intelligence in investment management, Ai machines are 2,000 times faster at processing and analyzing data than human analysts are (DTTL 2019).

Digesting the same volume of information machine data analytics does each year would require the resources of 8,774 data analysts working 8-hour shifts 5 days a week for 52 weeks per year (Pace 2017).

Overall calling for an inefficiently huge amount of monetary resources from both companies and clients. Capable of analyzing a much larger volume of data than human analysts are, Ai is able to accurately discern even the minor patterns in the provided information. 

The ability of meticulous analysis gives Ai the power to fit its existing knowledge to an investor’s expectations and come up with individually-tailored investment plans and decisions.

The investors would get decisions based on the analysis of a larger amount of historical financial or non-financial data. This would result in substantially more accurate investment choices.

Likewise, looking to garner the same or more output in client satisfaction with as little input as possible, the companies are more likely to opt for Artificial Intelligence rather than hire more than 8,000 analysts to process the same information.

In times of financial crisis, Ai is able to protect the investors from unexpected and large losses by detecting the tendencies of the stock market (Yijie Xu 2019). Ai avoids investing in assets that it finds risky and evaluates the real necessity to sell stocks. Thus, Ai reflects the pandemic’s intensified values of safety and

efficiency. The significantly more accurate decisions put forward by the Ai model is more likely to result in safer and more productive investment decisions with fewer losses for both investors and investment companies. 

The lack of human involvement in the Ai powered investment process eliminates the confirmation and emotional bias of the investment managers.

Analysts are confined within the narrow perspectives of their own knowledge and experience, so the tendency to interpret events in a way that aligns with one’s views and beliefs is very common.

The emotional state and mental health of an analyst can bar them from making analyses and decisions in a clear head.

Such barriers might lead an investment manager to misinterpret the current events and data and make investment commitments that bear grave consequences for their clients. 

Although susceptible to bias, human analysts are able to adapt to the changing environment and expand their knowledge. They learn from their mistakes and adapt their methods and techniques according to the client’s needs.

The ability to evolve and innovate is one of the cherished characteristics that Ai also shares.

In fact, once the Ai framework is developed, there is no real need for any further human intervention to update or change the model (Rebellion Research 2020).

The technology is able to continually update itself to the changing environment to produce up-to-date and accurate investment decisions.

Once again reflecting the value of safety, Ai is able to manipulate the raw data and information without any human bias and produce results that are safer and up-to-date with as few external influential factors as possible. 

While the automation of the investment process has its advantages, the lack of human contact might be a drawback for many investors who are used to having face-to-face relationships with their portfolio managers.

However, the development of Ai promises user-friendly platforms that offer a virtual experience as sophisticated as the one in-person. If the Ai is able to establish trust by explaining why certain decisions are made, the transition to the virtual environment would be effortless for many investors.

Moreover, with the younger generation being more comfortable with technology, the establishment of Ai investing would be natural for them (Alexander and PWC 2017).

In fact, with the boost from the pandemic, robo-advisor platforms, such as Wealthfront and TD Ameritrade have experienced a significant surge in account sign-ups (Casperson 2020). With most of the new accounts belonging to young investors, the increasing trust in Ai investing is evident. 

Being prepared for future crises and shocks is more important than ever for investors. Ai, with the capabilities of powerful information processing and the lack of bias, can provide efficient risk management and safety to stockholders.

Had the technology been implemented widely, Ai would have predicted the stock market crash of 2020 by picking up the patterns early on (Heaven 2020). With the help of Ai, investors would have been able to rearrange their assets in a way that could prevent substantial losses.

Unfortunately, the shocks of the pandemic cannot be reversed now. It is in the best interest of all to learn from the past mistakes and avoid the future crises. So, the safety and efficiency values proposed by Ai will make the technology the future of investment management.

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Written by Lika Mikhelashvili

Edited by Calvin Ma, Alexander Fleiss & Gihyen Eom

References 

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https://www.pwc.com/us/en/industries/financial-services/library/artificial-intelligence-inv esting.html. 

Amadeo, Kimberly. 2020. “How Does the 2020 Stock Market Crash Compare With Others?” The Balance. 

https://www.thebalance.com/fundamentals-of-the-2020-market-crash-4799950. Casperson, Nicole. 2020. “Robo-adviser accounts surge during pandemic: Report.” InvestmentNews. 

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Goodwin, Jazmin, and David Goldman. 2020. “US Stocks Surge, Erasing 2020 Losses.” CNN Business. 

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Idzelis, Christine. 2020. “AI-Powered Hedge Funds Vastly Outperformed, Research Shows.” Institutional Investor. 

https://www.institutionalinvestor.com/article/b1mssrswn1mpr0/AI-Powered-Hedge-Fund s-Vastly-Outperformed-Research-Shows. 

Jackson, Anna-Louise. 2020. “December 2020 Stock Market Outlook.” Forbes. https://www.forbes.com/advisor/investing/december-stock-market-outlook/. Krantz, Matt. 2020. “Investors Lose $609 Billion On 8 Giant Stocks This Year.” Investor’s Business Daily.

https://www.investors.com/etfs-and-funds/sectors/sp500-giant-stocks-cost-investors-609- billion-year/. 

Pace, Chris. 2017. “Man vs. Machine: Speed and Scale in Threat Intelligence.” Recorded Future. https://www.recordedfuture.com/machine-learning-results/. 

Rebellion Research. 2020. “Why RebellionResearch.com?” Rebellion Research. https://www.rebellionresearch.com/blog/why-rebellionresearch-com. 

Seiler, Daniel. 2020. “Artificial Intellect in Investing.” Vescore by Vontobel Asset Management.” 

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Vardi, Nathan. 2016. “The Quants Are Taking Over Wall Street.” Forbes. https://www.forbes.com/sites/nathanvardi/2016/08/17/the-quants-are-taking-over-wall-str eet/?sh=7c4c92c4666c. 

Yijie Xu, Adrian. 2019. “Foreseeing Armageddon: Could AI have Predicted the Financial Crisis? A Scenario Study using Recurrent Neural Networks.” Medium. 

https://medium.com/gradientcrescent/foreseeing-armageddon-could-ai-have-predicted-th e-financial-crisis-1a44ca62b4f5.

Ai Investing