On the premise that average humans are bad investors, as they can make irrational decisions and let emotions come in the way of their returns, technology-based investment advisers, simply called robo-advisers, try to equip investors with necessary information for them to make better decisions that don’t require a deep financial background.
“Investors are going to machine-based investing to overcome their limitations in terms of behaviour. Humans react to emotions, machines don’t, which means they don’t care about what’s happening in the markets as long as the data is showing that something has to be sold; they will go ahead and sell,” said Prashanth Krishna, founder, Portfolio Yoga, a firm that advises investors.
According to a Deloitte report, the global robo-advisory market is expected to rise to over $16 trillion (about ₹1,193 trillion) assets under management (AUM) by 2025. In India, AUM in the robo-advisers segment is projected to reach $13 billion in 2021, and this figure is projected to reach $53.9 billion by 2025, according to a report by market and consumer data provider Statista.
“The core proposition of machine investing is that it can figure out what the market is doing today, as it can crunch large amount of data easily, unlike humans,” said Atanuu Agarrwal, co-founder of Upside AI, a tech-based investment manager.
Started in 2018, Upside AI is a Sebi-registered portfolio management service (PMS). It is one of the first PMSes in India to combine machine learning and fundamental-based investing. Over the past one year, the startup has grown over 10 times on a small base, and is today managing ₹70-80 crore from CXOs, high net-worth individuals and family offices. The startup has delivered 35% annualized returns since July 2019, when their products went live.
“These products are essentially algorithms, and are not tethered to a value, growth or momentum investor. The algorithms (algos) figure out what are good businesses and good stocks, and pick companies that are at the intersection of these two factors,” said Agarrwal.
Ideally, to select a portfolio of 10-20 stocks, an individual investor has to go through hundreds of company documents, and gather in-depth knowledge on macro and geopolitical factors that may affect a particular sector. This kind of preparedness takes a lot of effort and resources.
This is why big fund managers and investment bankers have teams of quant research analysts who constantly analyze market movements and try to make predictions.
“Now, imagine if the team of analysts could be put in a machine. It bridges this information gap, using artificial intelligence to provide the same high-quality information to a regular investor. Think of it like an investor’s Iron Man suit. It enables a normal investor to do things they could not do earlier,” said Akshaya Bhargava, founder and executive chairman, Bridgeweave, which recently launched an AI-powered personal investment analyst for retail investors called InvestorAi. InvestorAi claims to cover more than 4,500 global stocks and 1,500 exchange-traded funds (ETFs) in 15 markets, and the algos perform over 800 million calculations every day.
This is something impossible for a regular retail investor to do.
“A value guy will only buy cheap stocks, a momentum guy will only buy stocks that are performing well right now. Such investors can get fixated to their principles and perform well in certain periods, and then revert to mean. The core theme is that technology is the fund manager and the decision-maker as we believe that on average humans are bad investors,” said Agarrwal.
The key question that arises then is: How can algorithm-based investing strategies plan or prepare for black swan events such as covid? According to Prateek Mehta, co-founder and chief business officer of Scripbox, a digital personal wealth management platform, algos have stood the test of time. Scripbox has an AUM of about ₹4,000 crore, with 90,000 clients.
“When we talk of investing, people often talk about the equity side. But even on the debt side, our algos never had any of the Franklin Templeton funds or the credit risk funds even at the top of their game. The risk management part is often ignored, especially in today’s time when a lot of investors are managing their own portfolios,” he said. Mehta, however, is of the opinion that India’s markets are not so deep that investing can completely rely on machine learning. “There’s 95% AI and 5% human factor. So, it is kind of bionic,” he said.
Machine-based investing in India is not entirely limited to investing or wealth management. Trading segment is also trying to utilize this space.
Upstox founder Raghu Kumar and hedge fund manager Harsh Agarwal’s fintech firm Rain Technologies recently launched Rain Trader, a marketplace of fully automated trading and investing algorithmic models.
Kumar says algos will not take away the thrill of trading in the market. “Thrill is there even with automated trading, because you are able to see what’s happening to your stocks every day. With automated models, you eliminate the fear and negative emotions, anxieties and stress,” said Kumar.
According to experts, the industry dialogue is shifting from ‘advice’ to ‘self-directed’ investors. Self-directed investors want high-quality, reliable investment ideas that they can use to make investment decisions. “With the click of a button, users can see how any algorithm is performing by way of accuracy, returns and productivity, and can make their own decisions. We believe that this is a much more transparent relationship between humans and machines than one tends to see in a relationship between an investor and an adviser,” said Bhargava.
However, experts warn that there is a big risk in machine-based investing with regard to the responsibility for making the wrong decisions. “Historical data may not predict the future. For example, we never had a March 2020 kind of crisis before. So, when an unprecedented crisis hits, how will machines react? The key question is what kind of risk management has been put in place by the algo firm,” said Krishna.
Before taking the plunge into machine-based investing, individuals should check if an adviser has adequate controls around their automated systems, has required regulatory approvals, has advanced and resilient technological setup, and can invest based on your risk profile.
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