Why even in age of AI investing, human judgment reigns supreme

In investing, one could employ multiple strategies to achieve success. The usual parameters like profitability, revenues, competitive advantage, earnings growth, price-to-earnings ratio and price-to-book ratio, etc. could define and distinguish a particular stock with another. This is how mostly a bottom-up strategy works. Then there’s macro-based investment where the approach is on the factors like economic prospects, political stability, policy decisions, industry or sector growth, etc. which define the investor to zero-in on the investment decisions.

While success is achieved and was achieved in either of the ways, it’s the investor’s decisions or indecisions based on these factors. So, it’s not whether someone has employed the fundamental analysis (where the company fundamentals are considered) or technical analysis (where the stock past price movement is used to forecast the future movement), the human element, who and when opts to take a decision is what makes or breaks an investment. And with each passing day, the growing field of behavioural finance throws interesting insights on the various biases and inhibitions we carry and how it impacts our investment decisions.

To counter and eliminate most part of these biases, quantitative investment strategies came to play. Quantitative or in short quant strategies make investment decisions based on data, advanced mathematical models and quantitative analysis. They rely on algorithmic or systematically programmed investment strategies, according to Investopedia. So, by avoiding the experience, judgements or opinions of humans to make decisions, they bring rational methodology to investing.

Humongous data on company’s available information, observable news or correlated information that impacts the stock price performance are analyzed by the computer programs designed around the algorithms. They provide as a screener to filter out and bring in the desired list of companies or stocks that meet the criteria set by the algorithm. These stocks are again analyzed or back-tested at various scenarios (of the past) and sometimes even the factors are back-tested for various past events to check their reaction and performance. Accordingly, the remaining chunk of stocks are considered for the portfolio along with the allocation limits.

These funds are programmed to generate algorithms that factor in various data points like economic data, global trends, real-time company news, policy decisions and even demand (for the stock or trading volumes), etc. to come up with various strategies. These include sophisticated models using proprietary software around momentum, value, quality and growth, etc.

These were first introduced in the 1970’s in the US but took more than three decades to reach Indian shores. They were initially not taken serious by the either the industry or investors due to the lack of depth of Indian markets and partly due to the lack of understanding. But, over the years as the markets began to mature and the computing prowess increased, they began to make a mark on both the investors’ minds and the industry. Now, there’re multiple offerings by the Portfolio Management Services (PMS) players and Mutual Fund (MF) houses.

As the markets transitioned more transparent, greater access to data and the various solutions to process the big data became a potent force for the proliferation of this style of investing. Developments in automation, speed of computing and communication along with the financial technologies (in ease of operation) has allowed them to extend their research over a broad area and timelines.

Now with the advent of Artificial Intelligence (AI) and Machine Learning (ML) tools being developed, these funds could make a potent contender for investor’s share in the coming years. However, as they say, no one strategy trumps all kinds of markets, these funds offer no panacea for investors woes. While true that these funds limit the human intervention, one must understand that the factors identified or the algorithm written is by humans. Some might counter that with the arrival of AI, this could be minimized but multiple people employ the same strategy to gain, the opportunity to profit could shrink. Having an allocation to these funds could help add a flavour to the portfolio but one must check what methodology the fund is using before making an investment.

(The author is a co-founder of “Wealocity”, a wealth management firm and could be reached at [email protected])