Also known as algorithmic trading, automated trading refers to the trading done by traders and investors using computer software. The algorithms create the orders to buy and sell on various exchanges or stock markets when certain preprogrammed conditions are met. The software can also adopt specific trading strategies that can be customized, including placing orders based on pre-determined guidelines programmed into the algorithms. These systems still need human oversight though.
These algorithms are much more efficient in creating orders to buy and sell. Because they’re pre-programmed, the system can follow any strategy much more accurately than a human. As automated trading uses advanced mathematical formulas, traders use computer systems capable of processing complex calculations in an instant. These computers can make multiple trade decisions in seconds that would have taken humans much longer to decide. This is also where interfaces like NinjaTrader algorithmic trading software can be helpful.
How automated trading affects stock market trading
Strategy and investment decisions still have to be made by humans though. These actions have to be factored into the algorithms.
However, the algorithms have advanced enough that a company’s financial data, like its profitability, earnings, market sentiment, stock patterns, and other variables can be factored in. With these variables included, a trading algorithm can be enabled to identify and purchase growth stocks at a competitive price.
Changes in these variables don’t immediately affect the market in the old, manual days—it can sometimes take hours for the market to react. But with automated trading, the reaction is immediate. So, stock prices can be considered accurate using the current supply and demand as a basis. Traders using algorithms can speedily determine a stock’s price. The algorithms do this by inferring from past trade patterns and stock performance.
A smart trader does this as a matter of course, but with algo traders, the price discovery is much faster. As a consequence, this type of trading can drive up trading volumes. In turn, higher trading volumes can potentially lower the price of single transactions.
Big data and automated trading
However, a company stock’s fair value isn’t that straightforward for some traders. Basing the stock price on the current supply and demand isn’t enough. There are many more seemingly minor variables to be considered, which involve scouring the Internet for any information about the company. Some of these data points may include a company’s published job postings, an estimate of people entering the business, and others.
For investors, determining a stock’s value is like weaving a patchwork of various data points. No data is too small or insignificant. These data points can eventually form a pattern which the algorithms can interpret. The advent of big data makes that patchwork of various data points more substantial.
Technical analysis, one of the tools used in automated trading, refers to investment and trading opportunities evaluation using price behavior and volume. With big data feeding this tool multiple data points, the analysis is more thorough and more accurate. Also, as the algorithms are fed more data, machine learning (ML) enables the system to learn from experience and get smarter over time.
With all the tools available at their fingertips, automated traders can accurately determine a stock’s value, ensuring that the stock’s price is neither under nor overvalued. This system isn’t perfect, of course. Market dynamics are complex, and nobody—and certainly no system—has it all down pat. Gaps are inevitable. It still depends on the traders’ decisions and how well they use these tools.
So, how does this ability impact the market? Does automated trading reduce stock market volatility?
Automated trading’s effect on stock market volatility
Volatility in the stock market is caused chiefly by uncertainty. This uncertainty is influenced by outside forces like inflation, interest rates, industry adjustments, changes in taxation and monetary policies, as well as global and local events.
During a period of volatility, traders’ ability to react quickly is essential. This ability is something that automated trading can do efficiently, which can dampen volatility. Of course, the risks of quick investor withdrawals are always there. It’s the stock market, after all. Fast withdrawals during times of uncertainty happen, algorithms or not. However, algorithms can be programmed to avoid decisions that can potentially lead to volatility.
As a result, traders and investors find it much easier to navigate the market and make decisions during times of uncertainty. The algorithms help them modify their strategies, which means improving flexibility and making it painless to adjust to the changing dynamics of the market. As the system doesn’t panic and can make trade decisions without emotion, it can be taught to make minor market adjustments until conditions are stabilized.
The system, essentially, can navigate through stomach-churning market conditions and make decisions based on facts, not emotions. This disciplined, pre-defined trading approach means fewer human errors. An algorithm trading platform can also be instructed to make smaller purchases instead of one major purchase. This strategy can avoid inflating the stock prices too much.
During periods of volatility, this approach is especially valuable. Stock market volatilities usually make investors panic and engage in panic selling. This behavior often results in a bad (financially speaking) outcome—often worse than if they had just left their investment alone.
Automated trading is a system of stock trading that uses computer software. An investor can pre-program it to follow pre-determined buy and sell orders when certain conditions are met. This system results in faster and more accurate execution of a trader’s investment strategy and enables them to react quickly to the changing market.
Algorithmic trading, with the help of machine learning, makes impartial decisions that can guide investors in navigating the market during a volatile period. The system can be instructed to make adjustments, which can dampen stock market volatility.
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