Drawbacks Of Algorithmic High-Frequency Trading

Drawbacks Of Algorithmic High-Frequency Trading

Capital Markets CIO Outlook | Monday, July 05, 2021

Algorithmic trading is executing trades based on pre-defined criteria and dividing them into smaller blocks to minimize the influence on the stock or asset's price.

FREMONT, CA: Using computer algorithms to trade large volumes of stocks or other capital assets while reducing the financial impact of all such deals is known as algorithmic trading (or "algo" trading). Algorithmic trading is executing trades based on pre-defined criteria and dividing them into smaller blocks to minimize the influence on the stock or asset's price.

The most significant concern of algorithmic HFT is that it can magnify systemic danger. Its propensity for escalating market volatility might spread to other markets, causing investor anxiety. Moreover, repeated episodes of exceptional market volatility may erode many investors' faith in the market's integrity.

Let’s see few disadvantages of Algorithmic High-Frequency Trading

  • Amplification of Systemic Risk

The threat that algorithmic HFT represents to the financial system is one of the most severe concerns. Because of the substantial interconnections across financial markets, such as those in the United States, algorithms running across markets can quickly convey shocks from one market to the next, exacerbating systemic risk, as per a study.

Although most algorithmic high-frequency trading, performed at such a high pace, a single incorrect or flawed algorithm can cause millions of dollars in losses in a short amount of time. As a result, investors were alarmed by the abnormally unpredictable trading action, which came just over a year after the markets had recovered from their worst losses in more than six decades.

  • Intensifying Volatility

First, because there is so much algorithmic HFT activity in today's markets, most techniques can outsmart competitors. Second, algorithms can react to market conditions in real-time. Consequently, during volatile markets, algorithms may expand their bid-ask spreads or momentarily halt trading, reducing availability and exacerbating market volatility.

  • Uncertainty

Exaggerated market volatility is a significant contributor to algorithmic HFT that could raise investor concern in the short term and impair consumer confidence in the long run. When a market unexpectedly crashes, investors are left perplexed as to why such a drastic change occurred. Large traders (including HFT firms) may lower overall trading account to reduce risk, mostly during informational void that mainly occurs at such times, putting extra downward pressure upon its stocks.

  • Huge Investor Losses

Volatility surges intensified by algorithmic HFT can result in significant losses for investors. As a result, many investors put stop-loss procedures on existing company stock at levels that are 5percent below current trading prices regularly. These stop-losses will be activated if the markets gap down for no apparent cause (or even a worthy cause).

To add to the confusion, if markets quickly recover, investors will have unnecessarily lost money on their trades and their investments. During exceptional instances of market instability, such as the Flash Crash and the Knight debacle, some businesses were reversed or canceled, but most trades were not.

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