Relevance of Lewis’ Flash Boys in Indian Bull Market

In India, of the total daily equity volume of about $38 billion, including both cash and derivatives, algo trading accounts for about 30%, according to the National Stock Exchange.

“The increased complexities of algorithm coding and reduction in latency due to faster communication platforms need focused monitoring as they may pose risks in the form of increased possibilities of error trades and market manipulation,” the RBI said in the Financial Stability Report published 25 June.

Following the release of Michael Lewis’ latest book ‘Flash Boys’, wherein he argues that high frequency traders (HFTs) are ripping the slower execution investors and traders off, there has been renewed clamour by certain sections of the financial industry to curb the activities of the former.

Intelligent Investors Vs Artificial Intelligence

HFT algorithms come in two broad varieties – the arbitrage variety and the market making variety. The former attempts to exploit mis-pricing between related securities. For instance, Tata Motors might be trading at 280 on the National Stock Exchange (NSE) while it might be trading at 285 on Bombay Stock Exchange (BSE). An arbitrage HFT would attempt to lock in the 5 rupee differential by selling on the BSE and buying on the NSE. The latter attempts to deal in financial market securities by expressing a continuous interest in both buy and sell in a portfolio of securities. For instance, a market-making HFT might simultaneously place limit orders to buy Tata Motors at 282 and sell it at 283. In the event that both these orders get executed, the HFT has scalped 1 rupee. The risk that the HFT runs is that only one of the orders gets executed and the price runs away from his position.

Relevance of Lewis’ Flash Boys in Indian Bull MarketClick To Tweet

It should be fairly obvious the benefits both these algorithms bring to any financial market. One, arbitrage algorithms ensure that market participants don’t need to bother about finding the best price across exchanges because the arbitrage algorithms are ensuring that the prices move lock-step. Two, market making algorithms add liquidity to the market by packing up the order book with limit orders thus providing price resilience and limiting the elasticity of the order book in the face of large and rapid one-directional order flow.

Lewis vehemently argues that speed has unfairly tilted financial trading activity in favour of HFTs. But, speed is not a new facet of trading. Before the advent of computer-driven algorithmic trading, the manual execution trader with the fastest fingers would have a significant advantage. Tracing back Lewis’ argument to that period would have amounted to imposing curbs on traders with the fastest fingers!

If an order is placed in the market, it constitutes publicly available information, and market participants are free to react to the placement or withdrawal of large orders. However, Lewis has spun a bizarre connotation on the phrase ‘front running’. According to him an HFT algorithm stepping ahead by placing an order at a better price than the disclosed price of this large order constitutes front running. This is ludicrous! With the provision of Direct Market Access (DMA) to financial institutions, a lot of the traditionally accepted front-running engaged in by unscrupulous brokers has been precluded. With DMA placed orders by institutions, brokers are no longer in the know – in advance – about such client orders, and hence are unable to front run price moving institutional order flow.

In India, interest groups tend to blindly apply Western debates to the domestic situation without an examination on merit. One outlandish proposal in recent times has been for exchanges to maintain two separate queues of incoming order flow – one queue catering to orders generated from co-located servers, and the other queue catering to orders generated from outside the co-location facility. Regulators would do well to be informed that such a system will add a layer of complexity without solving anything. Indian authorities must also note the differences between Indian and Western financial markets. Indian markets are completely order driven. We do not have private exchanges or dark pools. All trades in India are executed in full sight of other participants. The question of any trader or investor getting ripped off in India does not arise, as one can always express an interest to execute in the market in form of a limit order.

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To summarize, HFT profits are a reward for intellectual efforts in pricing securities and investment in technology. Old school manual execution broker-dealers have seen their profits diminish in recent years owing to the proliferation of smart trading algorithms. This has caused a lot of resentment, particularly because they have failed to mould their businesses to the changing landscape of trading activity. Lewis’ frustration with the HFT industry simply echoes this resentment.

With the boom in technological advancements in trading and financial market applications, algorithmic trading and high frequency trading is being welcomed and accepted by exchanges all over the world. Within a decade, it is the most common way of trading in the developed markets and rapidly spreading in the developing economies. Start learning algorithmic trading today!