The Signal-to-Noise (SNR) ratio is essentially the ratio between the power of a signal or the information desired and the power of the background noise or the undesired information. SNR is a measurement guideline as well that is used in the fields of engineering and science. It is used to compare the desired signal level to that of the background noise level. In simpler terms, it is the ratio used between the signal power and the noise power.
A more straightforward comparison that can help understand SNR other than the technical definitions is to imagine yourself having a conversation with another person in a large room. Still, other people in the room are having conversations as well. Moreover, some of the other people in the room have a voice pattern similar to yours and the person involved in your discussion. With this, you can easily see that it can be tough to understand who is saying what.
Following up on the earlier comparison, you can better understand what an unwanted noise or signal means. This also means that it would be almost impossible to understand the other person involved in your discussion. In such circumstances, this would be considered a signal-to-noise problem or the correspondent to a signal-to-noise ratio below the acceptable parameters.
Let’s assume that the desired signal is essential data with a narrow or strict tolerance for errors. There are also other signals that your desired signal is being disrupted by. This makes the task of deciphering the desired signal a lot more challenging for the receiver. To summarise it all, therefore, it is so important to have a high signal-to-noise ratio.
This also plays a vital role in wireless technology. It is essential to differentiate between any background signals or noise from the spectrum from the applied signals that are legitimate information. This also improves and ensures better functionality from the wireless gadgets.
One of the essential prospects of active trading is noise removal. Traders can get a much clearer picture of the overall trend after the employment of noise removal techniques. In trading systems, noise in data is one of the most persistent and long-standing problems, which often drowns some alpha signals. This builds up many problems for trading systems that rely on the signal readings, raising even more significant uncertainties about the trading outcome. Some research has shown that a big chunk of the noise in the market comes from the trading itself.
Not a lot can be done about this situation. Apparently, yes, you can always trade overnight or after hours. However, the benefits of the contamination of lower signals from the noise traders are canceled out due to the poor liquidity disadvantage. Therefore, amplifying the signal is the only way to get most of the analysis in this area. To achieve this, we often use techniques related to engineering studies and borrowed from signal processing.
The signal-to-noise ratio sums up how much predictability is existing within the system. For example, if we take a dog image recognition, most people looking at a thousand images can, almost with a 100% success rate, correctly identify those with a dog on them. What this indicates is that this is a setting that is in a high signal-to-noise environment. The signal being the image of the dog dominates any sources of noises in the pictures, such as background images, blur, and more.
If we were to compare this to financial markets, which are noisy, even the best investment portfolio or stock in the world would most certainly experience violent swings in performance caused by surprising news on any given day, month, or year. Simple economic forces of competition and profit maximization are what constantly reinforce the low signal-to-noise ratio. If a trader had some info that could reliably predict the future rise in prices, meaning a solid signal, they wouldn’t just sit on that information passively. Instead, they would start to trade. The prices are pushed up or down by nothing but the very act of their predictive information exploitation, which then, in turn, sucks out some of the predictability that was in the market. The only thing that keeps the market moving with the predictability being already priced in is the surprising news or the shocks, meaning the noise.
The machine learning challenges posed by the low signal-to-noise ratio are further perplexed due to the dynamic characters of the markets. Suppose a researcher successfully identifies another new signal that can capture a particular form of asset mispricing, which can be used to predict prices. In that case, as soon as the signal becomes more widely known, a lot more traders will act on it, which then corrects the prices a lot quicker.
That information is absorbed by the market eventually, and the process of generating data is also changed. In the same way, innovations in technology can easily alter the economic structure and reshape the way we interact with the market. While the borderlines of machine learning have developed a few tools that might help with adaptive phenomena like this, they also highlight the fact that compared to other fields of machine learning research, finance is a lot more complex. You can quickly tell that the dogs do not start to morph into cats when the algorithm begins to get good at dog recognition.
Amplifying the signal-to-noise ratio by separating the data into numerous subsets representing different market regimes can increase the system’s effectiveness and improve trading performance.