There was a time when financial literature was rare, and all the analysts and investors relied on their knowledge and gut feeling to execute trades in the market. Some trades were successful, while some resulted in hefty losses. With an open outcry system, where investors had to physically make every trade and transaction, there was a limitation on how many trades they could execute in a single day, forcing them to lose out on potential profits.
These models help traders to execute thousands of trades at once to increase their profit margins. Furthermore, these models help avoid the possibility of human errors to improve the overall investment outcome. One such technique is Quantitative Trading. However, before you learn about quantitative trading and the advantages and disadvantages of quantitative trading , it is vital to understand the meaning of quantitative analysis.
What is Quantitative Analysis?
Quantitative Analysis is a trading and investing technique that includes statistical and mathematical modelling, research and measurement to understand the current share market and investor behaviour. It aims to present the current reality and the events that are affecting a specific investment instrument as per numerical value. Through quantitative analysis, analysts and investors measure an investment instrument’s performance and valuation while analysing and predicting affecting factors such as GDP. With the results of quantitative analysis, analysts and investors can compare past and present events to predict possible future outcomes.
Now that you have understood the main concept of Quantitative Analysis under the umbrella of advantages and disadvantages of quantitative trading, you can better understand quantitative trading.
What is Quantitative Trading?
Quantitative Trading is a trading and investing concept that uses strategies based on quantitative analysis. Under the strategies of quantitative analysis, the process of quantitative trading relies on statistical models, number crunching and mathematical computations to identify possible profitable trading opportunities.
In essence, quantitative trading is the process of trading using the tools and strategies of quantitative analysis. The transactions involved in quantitative trading are usually immensely high in volume and involve the purchase or sale of securities in hundreds of thousands in one go. Hence, quantitative trading is mostly used by big financial institutions and hedge funds that manage huge quantities of securities.
How does quant trading work?
Quantitative analysts believe that investments made using algorithms and models that collect, analyse, and provide results are more likely to be successful than decisions based on human knowledge. Hence, they take advantage of modern statistical and mathematical models included in quantitative analysis to implement quantitative trading.
The process of quantitative trading collects and analyses a large amount of current and historical data to provide outcomes that traders use to make informed investment decisions. The traders pick a quantitative analysis algorithm or model with predefined rules for buying and selling signals. The model scans the market for data at preset intervals and analyses the data along with the prevailing market factors to provide possible future outcomes.
Advantages and Disadvantages of Quantitative Trading
As with any statistical and numerical model, it comes with some advantages and disadvantages. The same is the case with quantitative trading. Here are the advantages and disadvantages of quantitative trading:
Advantages of quant trading
- Eliminates Human Errors: Quantitative trading allows analysts and traders to exclude the possibility of human error as almost all the decisions are taken by an algorithm or chosen model. The chosen strategies check the process multiple times before giving the final results, reducing the chances of human errors drastically.
- Faster transactions: Quantitative trading can allow analysts and investors to trade at an unprecedented rate. A quantitative analysis algorithm can analyse over 100 strategies in seconds, allowing for high volume trading in a fraction of time.
- Back testing: Quantitative trading lets traders backtest on historical data without any room for interpretation. It means that the model tests the present data with historical data, allowing for better judgement and decision making.
- Data Analysis: It is humanly impossible for a trader to analyse the immense share market data to ensure the decisions follow successful implementations. However, quantitative trading analyses an immensely high volume of data effectively to cut the associated risk.
- Fewer Resources: Time is one of the most important resources for traders or investors. The time they spend on analysing market data can be used through quantitative trading to trade in the market. As quantitative trading implements a high volume of traders, the chances of profits increase by a hefty margin.
Disadvantages of quant trading
- Curve Fitting: The financial market comes with numerous variables and randomness. The process of quantitative trading is vulnerable to curve fitting and optimisation due to the heavy reliance on historical data. It can lead to irrational outcomes as the model can crumble because of market randomness.
- Extensive skills: As quantitative trading includes the use of statistical and mathematical models, implementation is a tough task. It demands that the person have the knowledge of quant and know how to code and program to use the models effectively. If you are not well-versed with quant and models, the chances of misinterpretation rise, which can cause hefty losses.
- Technical errors: Although quantitative trading reduces the chances of human intervention and errors, it has its technical flaws. Quantitative trading sees a lot of errors in code or the whole model stopping to work mid-way. Furthermore, a small mistake in adding the parenthesis can alter the whole model, forcing you to realise big losses.
- Loss of control: When an investor trades using quantitative trading, control is lost on important aspects as the model ensures that there is no human intervention. This can result in a situation where there is enormous volatility in the market without the model letting you shut down or adjust a trade accordingly.
- Continuous adjustments: The financial market is dynamic and is affected by various market factors, which are changing at a regular interval. Hence, quant trading requires continuous development and adjustment of new and existing strategies, which is a tedious task to perform. In the long run, most quantitative strategies become obsolete and have to be changed entirely.
What steps are required for quant trading?
Here are the steps required for developing and executing a quant strategy:
Quantitative trading is what modern trading looks like, where advanced technology helps execute trades faster without any human interference. The market is filled with immense historical and current data regarding trend, volume, price, investor sentiment, external factors etc. Quantitative trading executes strategies for effective analysation of such data and ensures that the results allow investors to make informed investment decisions.
Investors who want to rely on mathematical and statistical models can use quantitative trading for better profits. However, as quantitative trading can include strategies and models that may over-rely on data and ignore real-time market randomness, investors should ensure that the strategies they are developing are adjusted according to the current market events.
- First, you need to create a plan or a hypothesis on the factor you want to utilise while trading. The strategy can include momentum, trend-following, mean-reversion etc.
- Next, you create the design of the model to implement your idea. However, make sure that the creation is done to pinpoint accuracy.
- After you have created an automated strategy, you can include the new strategy into your existing quantitative trading strategies. It will add value and diversity to the quantitative trading model.
- Afterwards, you should run the diversification criteria and the out-of-sample tests on the created model. Once the model passes both the tests, you can add it to your software and portfolio for further implementation.
- When all of the above tasks are done, you can finally implement them by running the model in the software without any external interference.
