Stock Trading Changes Course: What's in it for Retail Investors?

The new Stock Trading is Machine driven

The share of algorithmic trading in the total volume of trades in the National Stock Exchange (NSE) of India has gone past 50% at the beginning of FY 25-26. As for derivative trading, the transition to majority trading being in machine mode happened way back in 2015.

What does this mean for the large number of individual retail traders who are engaged in trading by themselves with a touch of thrill, anxiety and intelligent guessing?

Gone are the days when trading was an art and risk management was more of an individual weigh-in of macro and company-level trends in costs, efficiency and proactiveness. Trading is fast turning into a science and little art is left in it.

With data analytics driving predictions and conjectures, and the speed and volumes at play in the market being massive, there is little margin for errors in judgement and guestimates.  

Today, scale is critical to surviving and recovering in trading. Hence smaller players run the risk of being caught up in vicious undercurrents and ruthless market manipulations by the sharks of trading.

The thrill of being a loner, one who steers the boat through tempestuous seas, is no longer a wise option for retail traders.  Retail operators must willy-nilly follow the trends and swings of the market. Machine mode trading is more of a swing trade ecosystem where market momentum driven by seasoned shrewd operators determines the tiny headroom that smaller individual players in the market have.

Like many of the legacies of the past which got disrupted and obliterated with the onset of newer methods and applications, individual, stand-alone stock trading too is likely on the way to becoming history. Trading operations are going to be increasingly externally driven and based on chip-based intelligence.

We are quite likely at the end of the road for stand-alone and traditional stock trading practices. Attempts to trade sans the benefit of machine-prompted analytics and predictions may prove too risky and could bleed the investors’ finances disastrously.  AI-driven, ultra-fast, super-volume algorithmic trading model with pooled funding and dispersed deployments is the way forward.