AI Automated Investing: Friend Or Foe?

Advancements in artificial intelligence (AI) have seen us place a lot of trust in machine automation to make important decisions on our behalf. Allowing computers to make decisions for us makes it possible to replace human intervention with quantitative data and predictive algorithms.

Using artificial intelligence as part of our decision-making process enables us to have a more broad-based predictive outcome, enabling us to make more informed and data-driven choices.

This approach has seen growing acceptance among financial professionals, including a cohort of novice investors using mobile trading platforms. Trust in AI-powered automated trading tools has surged in recent years, with around 49 percent of Americans using robo-advisors, or likely to use one compared to a traditional financial advisor.

However, in financial markets, trust is earned, and for inexperienced investors using computerized algorithms to make important investment decisions, how will automated investing influence their long-term financial planning and ensure they achieve their investment objectives more efficiently compared to using traditional financial advisors?

Automated investing: from robo-advisors to quantitative analysis

Despite the recent interest in automated investing, the practice has been around for quite some time, witnessing surging popularity during the height of the pandemic. During the last few years, there has been a growing acceptance of automated investing, especially among novice investors seeking to leverage democratized financial markets.

Automated investing replaced traditional financial decision-making activities with robo-advising, which in turn uses algorithms and computer programs to manage individual portfolios and make investment decisions on a person’s behalf.

Robo-advising has helped break the barriers to entry for younger, less experienced individuals seeking to tap into financial markets. Not only this, but this technology has helped older investors make more data-driven decisions, which in turn could provide better upside returns and portfolio performance.

Acceptance of automated investing, especially robo-advisors, is highest among older generations, including Boomers and Generation X. Roughly 62 percent of investors from these groups are satisfied with using artificial intelligence tools to make investment decisions compared to 38 percent of Millennials and Gen Z investors.

Making AI-driven financial tools more accessible means that more people, or at least one in three investors, would be willing to allow a trading bot or automated investment algorithm to make all their decisions for them.

Beyond the ability to make decisions on our behalf, automated investment tools enable more first-time users to access advisor services, removing another barrier that could often place strain on investors’ portfolios.

The tools that ordinary investors can leverage make it possible for them to make more relevant investment decisions that are not purely based on market sentiment but are instead designed by digital analysis of market behavior and historic performance and help to provide portfolio optimization and technical analysis of stock movements.

An artificial shift for investment strategies

With investors not bound by traditional means of investing, artificial intelligence has helped shape a new approach to developing more technical investment strategies that can simultaneously account for various moving parts, while executing trades and identifying patterns.

Stock picking

Stock picking is perhaps one of the most widely used AI-powered tools among novice investors. Though the technology behind these tools continues to develop and change, current systems can sort through vast amounts of data and information, and identify a handful of stocks that align with investors’ forward-looking objectives.

Not only do these tools act as stock screeners, which can assist investors in uncovering any potential risks they might encounter with a specific stock, but help to provide a more predictive analysis of current and future price movements.

Forecasting

Knowing in which direction a specific stock or investment vehicle will move requires a deep understanding of market developments, including social, political, and economic behavior. In this case, it’s possible that artificial intelligence can develop a more accurate forecasting model for investors and traders.

By accounting for the various factors that can influence stock performance, investors can use consolidated tech stacks for IR (investor relations) to equip themself with a forward-looking model of potential stock developments and whether holding for a long or shorter term would provide them with the best portfolio benefits.

Risk assessment

Effectively navigating market risks and downturns can be one of the most challenging things for any seasoned investor. In a traditional environment, experienced investors and traders will leverage experience, knowledge, and expertise to understand critical pain points within the market and their portfolio.

However, combining this expertise with the technical capabilities of machine learning can help to build an enterprise risk management framework that would allow investors and traders to make more logical decisions that will minimize and potential negative impact on their portfolios.

In short, this would remove any influencing factors and instead focus on building a framework that assists with operational risk management to provide investors with a more autonomous approach to achieving their investment objectives.

Trading analysis

Another useful case of machine learning is in the process of absorbing countless amounts of data and helping investors make sense of various investment obstacles that they may encounter.

By automatically clearing up data sets, investors will have deeper insights, ensuring they can make more informed decisions, and accurately track performance across multiple asset classes, markets, or portfolios.

Automated reporting

Routine data updates will help to provide more accurate reporting, which in turn will generate recurring portfolio reports for investors tracking market movements. This is especially useful for financial professionals who have to track a large number of portfolios and various investment vehicles.

On the other hand, automated reporting can help ordinary investors have more up-to-date information regarding their investments, which could help them adjust their forward-looking strategy, and allow them the opportunity to make regular decisions that are supported by accurate data.

How automated investing can deliver failures?

It’s important to draw attention to the disadvantages of automated investing. Though the technology supporting automated investing remains a tremendous breakthrough in digital engineering, there are currently limitations that could provide negative, and seemingly damaging outcomes for investors that are unaware of these risks.

Lack of personalization

Some AI tools do allow individuals to create more personalized experiences. For instance, e-commerce platforms allow you to choose your desired shopping preferences, similar to social media content.

These developments are already present with automated investing, however, this technology is considered to be a “one-size-fits-all” scenario, which uses a digital framework that’s often replicated across multiple portfolios.

This is not only risky for investors but also removes the importance of having financial and investment goals. This can put investors at risk of making ill-advised decisions and influencing the return on specific investment vehicles.

Limited automation features

Not every investment platform is equipped with automation features, and for less experienced investors this might create barriers to entry. More than this, automation features are often not included with beginning investment subscriptions, meaning that sooner or later novice investors will be required to pay for certain features and services.

While paying for these features might still be more affordable compared to using a traditional financial planner or investment manager, many might experience limited access to the necessary tools or resources that can be used to equip themselves with the necessary skills to become more confident in their trading.

Limited asset choice

Automation features are not spread equally across all asset classes. In some instances, investment brokers will only allow automated investments for specific asset classes to mitigate potential risks.

While this may help investors navigate potential risks more effectively, this could drive further problems for novice traders who are not equipped with the experience or skills to successfully execute trades.

More than this, investors might have fewer assets to choose from compared to traditional investors, which in turn might limit their potential and portfolio opportunities.

No guarantee of a return

Investing comes with exposure to various market risks including volatility. Automated investing can be influenced by these volatile conditions, and can impact portfolio performance and near-term asset return.

Although automated investment tools can help provide more predictive analysis, these might not always be accurate, or in some cases create further complexities for investors who are not comfortable with taking on increased risk exposure.

Reduced human capital

Another potential drawback to consider is that many automated investment tools will remove human capital from the investment process, which can potentially create more problems for novice traders who may need guidance.

Human experts can help deliver a more tailored experience, understanding individual needs, but taking into consideration a person’s level of risk and portfolio objectives. Without having these assets, investing can seem less personal, and instead become a synthetic process that only focuses on algorithms instead of personal needs.

The Verdict

Investment automation will only continue to grow in the coming years, seeing more widespread adoption among various brokerage platforms and investment service providers. While this might remove some barriers to entry for novice traders, it’s important to consider the near-term drawbacks that too much investment automation can have, and instead overcomplicate the investment process for less experienced individuals.

The reality would be to apply a balance of both automation capabilities and human capital that seeks to deliver quantitative analysis that can help to make informed investment decisions and mitigate market risks. Furthermore, there is a need to create automation tools that can create a more personalized experience for investors, allowing them to have better flexibility over their investments, and provide them the opportunity to navigate market challenges more effectively.