Impact of Algorithmic Trading on Market Efficiency and Liquidity

In today's stock market, prices that fluctuate every second are driven not just by human decisions but by high-speed algorithmic trading. This technology is profoundly affecting market liquidity, spreads, and price movements. SEBI is also implementing new regulations in 2025 to make trading more transparent and secure. In this blog, we will explore what algorithmic trading efficiency is and its impact on market quality, and how this system is making the market better or more complex.

What Is Algorithmic Trading?

Algorithmic trading, or “Algo Trading,” is a trading technique in which computer programs automatically buy and sell based on set rules and market data. These systems make decisions many times faster than humans in milliseconds or microseconds. Today, large institutions, brokers, and even some advanced retail traders use it in the Indian markets. The rapid spread changes you see daily, the suddenly deepening order book, or the instantaneous fluctuations in liquidity, all of this is due to the large volume of algorithmic orders.

How Algo Trading Redefined the Trading Environment

A Practical Picture of Changes in Market Structure : 

Prices in financial markets were previously determined primarily by human judgment, broker intervention, and low-technology order processes. However, the situation has changed significantly today. Order books, price movements, spreads, and liquidity are now increasingly driven by automated mechanisms. Such changes have made the market more competitive, faster, and data-driven than ever before.

Technological Upgrades: Changes at Exchanges : 

Major exchanges in the Indian market, such as the National Stock Exchange of India (NSE) and the Bombay Stock Exchange (BSE), have significantly improved their infrastructure to handle the speed and volume of algorithmic trading. For example, the NSE has added a large number of co-location racks over the past year to provide traders and algo firms with reduced latency. Furthermore, the order-matching engine and data distribution network have also been upgraded to modern server structures, allowing for significantly more orders to be processed per second.

The New Environment of Liquidity, Order Flow, and Information Latency : 

As algorithms trade at scale, questions like "when is liquidity available," "when does an order disappear from the screen," and "when do prices change quickly" have become even more important. For example, the "two-sided" liquidity seen in the order book is no longer constant; it can suddenly disappear due to large orders or algorithmic cancellations. This behavior has changed the way markets previously operated.

Becoming an Algorithm-Friendly Market: A Unique Approach : 

An important point is that the market itself has adapted to the speed and demands of algorithms. This means that instead of the speed of human decision-making, the speed of machine-determined order flow is what matters. This has transformed the market model; the structure that once relied on human feedback is now influenced by orders that operate in milliseconds and microseconds. This change is not just technical, but also strategic; traders, investors, and regulators all must now act wisely in this new environment.

Positive Impact of Algorithmic Trading on Market Efficiency

Fast and Accurate Price Discovery : 

Algorithmic trading has made the market price discovery process faster than ever before. When major news arrives, such as the RBI's interest rate announcement, a company's quarterly results, or a sudden change in global markets, algorithms read these signals in a matter of milliseconds and adjust prices to the new information. The direct benefit of this rapid price discovery is that the market reflects real information immediately, reducing the chance of mispricing.

Lowering Bid-Ask Spreads : 

Algo trading has significantly tightened bid-ask spreads in the market. Because algorithms continuously update buy and sell quotes, the gap between buyers and sellers narrows.

This reduces transaction costs for traders and maintains stable liquidity in the market. Importantly, tightening spreads allows even small and medium-sized investors to trade at better prices, something previously only possible for large institutions.

Reduction of Human Errors and Emotional Mistakes : 

The biggest problem in trading often arises from human emotions and wrong decisions, such as panic selling, overconfidence, or fear-based entries. Algorithmic trading significantly reduces human errors by making the entire process rule-based. Since algos are not influenced by emotions, they operate based solely on set conditions and data. This makes execution more stable, fast, and professional.

Improved Execution Quality for Institutional Investors : 

Large investors such as mutual funds, FPIs, and banks often need to purchase thousands or millions of shares. If they place orders all at once, market prices can fluctuate rapidly. Execution algorithms solve this problem. Strategies such as VWAP, TWAP, and POV break down large orders into smaller ones and execute them gradually, thus reducing market pressure and providing investors with better-than-average prices.

How Algorithmic Trading Enhances Market Liquidity

Consistent Two-Way Quotes and Stable Depth : 

Algorithmic trading has made it easier to provide consistent quotes on both the buy and sell sides of the market. Previously, only a few active traders maintained depth in the market, but now algorithms update their quotes every second, ensuring consistent liquidity. This stable depth helps investors, from small to large, achieve smoother execution.

Increased Liquidity from Modern Market-Making : 

Algorithmic systems handle a large portion of market-making today. These systems maintain appropriate spreads in the rapidly changing market. This ensures that the availability of buy and sell options in the market is never disrupted, and the quality of liquidity remains better than before. This is particularly evident in equity derivatives.

Improved Price Alignment from Arbitrage Algorithms : 

Algo-based arbitrage has significantly reduced price differences between the NSE, BSE, and global markets. As soon as a divergence between two markets increases, algorithms immediately balance the price. This not only makes the market more efficient but also has a positive impact on the overall impact of algorithmic trading on markets, as prices align quickly across markets.

High-Frequency Trading: Increased Trade Volume and Faster Liquidity Cycle : 

High-Frequency Trading (HFT) firms now strengthen liquidity by quickly placing and withdrawing orders. Although HFT is controversial, research and recent market data show that HFT has significantly increased daily turnover and market participation. HFT speeds up the market cycle, making liquidity readily available and keeping spreads low.

Facilitating Institutional Orders : 

Liquidity used to be a major challenge for large investors buying and selling millions of shares. Algo-driven execution systems solve this problem. These systems smartly trim orders based on market depth, preventing sudden market pressure. This provides better execution to institutional investors and also keeps the overall liquidity stable.

Challenges and Risks: When Algorithmic Trading Reduces Market Quality

Liquidity Illusion :

The biggest risk of algorithmic trading is the "liquidity illusion" that is, market depth appears on screen, but in reality, it isn't sustainable. Many algorithmic systems constantly place and immediately cancel orders, preventing consistent depth. This isn't a problem under normal circumstances, but as soon as news, a rise, or a fall in the market, liquidity suddenly disappears. This can cause prices to rise or fall sharply, leading to losses for investors.

Flash Crashes and Increased Intraday Volatility : 

Flash crashes are situations where prices fall or rise abnormally within a few seconds. This often occurs when multiple algorithms act simultaneously, and their collective behavior destabilizes the market. Such incidents have been observed in markets across various countries, and sudden intraday spikes in India are also indicative of this. Retail traders immediately feel the impact of these spikes: stop-losses are hit, false entries are created, and unwanted whipsaw movements are the result.

Algo Herding: The Crowding Effect of Identical Strategies : 

Many algorithms are based on similar data points, news signals, or technical patterns. When a large number of algos trade in the same direction, it is called "algo herding." In this situation, prices begin to move in one direction at an unnatural pace, increasing volatility and market fragility. Herding behavior is especially pronounced in event-driven sessions, where large price swings occur within seconds.

Challenges for Traditional and Manual Traders : 

Algo trading has made the competition significantly tougher for manual traders. Many breakouts seen on charts are now not real but are caused by algo-driven liquidity pulls. This has led to problems such as false breakouts, increased slippage, and sudden reversals. Small traders need to be more careful than ever when reading price action, as the natural flow of the market has been replaced by rapid orders from algorithms.

High Cancellation Ratio and Increased Market Noise : 

Many algorithms send and cancel thousands of orders every second. This behavior makes the order book “noisy” and reduces the reliability of depth. SEBI is also tightening the order-to-trade ratio (OTR) in 2024-2025 for this reason, to reduce problems like market manipulation. A high cancellation ratio makes it difficult for traders to distinguish between true liquidity and temporary liquidity.

Balanced View: Does Algorithmic Trading Truly Improve Market Quality?

Market Efficiency : 

Algorithmic trading in India has made price discovery faster and more accurate. Whenever a company's results, RBI decisions, or global news are released, algo systems react immediately. This helps prices reach their true value faster. This change strengthens overall efficiency in Indian markets.

Market Liquidity : 

Algo trading has improved liquidity on both the NSE and BSE, especially in derivatives and large-cap stocks. Market makers and arbitrage algorithms maintain depth by providing consistent quotes. However, when volatility increases, liquidity can suddenly decrease, a phenomenon known as a "liquidity drop."

Market Stability : 

Algorithms keep the market running smoothly during normal times, but during sharp declines or sudden news, these systems can sometimes react haphazardly. This can increase short-term volatility. To address this risk, SEBI is strengthening order-to-trade ratios and real-time monitoring to limit volatility.

Fairness : 

In India, SEBI has taken several steps to bridge the gap between retail investors and algo traders such as stricter regulations on co-location, audit trails, and crackdowns on unauthorized algos. These steps are making the market more transparent and fair.

Balanced Conclusion : 

Overall, algorithmic trading in Indian markets improves efficiency and liquidity, but extreme volatility can pose stability challenges. Therefore, the impact of algorithmic trading is positive provided strong regulations, risk controls, and continuous monitoring are in place.

Regulatory View: How SEBI and Global Regulators Are Responding

Securities and Exchange Board of India (SEBI) Guidelines : 

• On February 4, 2025, SEBI issued a circular titled "Safer Participation of Retail Investors in Algorithmic Trading," which provides comprehensive guidelines for retail investors to safely participate in algorithmic trading.

• Mandating prior approval of algorithmic strategies by brokers/exchanges for retail investors.

• Ensuring a unique order identifier and audit trail so that every order can be tracked.

• Defining the responsibilities of brokers and exchanges on API-based algorithmic platforms.

• Deadline Extension: The rules were originally scheduled to come into effect on August 1, 2025, but SEBI has extended the deadline to October 1, 2025, to allow brokers and platforms to complete these preparations.

• These regulations aim to enhance market quality, transparency, and investor protection through algorithmic trading, particularly considering algorithmic trading efficiency and its impact on market quality.

Global Framework: 

• The Financial Conduct Authority (FCA) published a Multi-Firm Review in the UK on August 21, 2025, analyzing the state of algorithmic trading control systems, regulatory compliance, and risk management.

• The review focused on governance (administration and accountability), testing and deployment protocols, kill-switches (emergency order cancellation systems), and monitoring latency and order cancellations.

• In the European Union, specific technical standards for algorithmic trading apply under the Markets in Financial Instruments Directive II (MiFID II), such as Direct Electronic Access (DEA) controls, order management systems, and risk control parameters.

• It is clear that regulators globally have become proactive, and while the risks associated with algorithmic trading are increasing, rulemaking and oversight processes are also being strengthened.

Conclusion

Algorithmic trading has made the Indian market faster, more efficient, and more liquid than ever before, but it has also introduced new risks such as sudden volatility and liquidity loss. Overall, market quality appears to be improving, but stronger regulations, transparency, and accountability are essential to maintain balance. In the future, only those investors will succeed who understand technology and learn to work with it, not against it.

FAQs

Q1. What is algorithmic trading?

Algorithmic trading is the process of rapid buying and selling using computer programs.

Q2. Does algorithmic trading improve market efficiency?

Yes, it helps determine prices quickly and reduces spreads.

Q3. Is algorithmic trading safe for retail investors?

With proper regulations and a reliable platform, it can be considered safe.

Q4. Can algorithmic trading increase market volatility?

Yes, during sudden news, multiple algos activate simultaneously, increasing volatility.

Q5. Does algorithmic trading affect market liquidity?

Yes, it increases liquidity, and can sometimes reduce it immediately during sharp declines.