First, there is a distinction between machine learning and artificial intelligence. The idea that a machine might think like a person is known as artificial intelligence (AI). While machine learning, a subset of artificial intelligence, refers to a computer’s capacity to learn from data without explicit programming.
Quantitative trading is a growing field, and AI and machine learning are popular themes used in DCPTG. Yet both have their roots in mathematics, despite being thought of as magic by others. Quants have long employed machine learning techniques, which are driven by statistics.
The availability of massive data, improvements in computer processing speed, and media attention have all generated hype. Based on the Gartner hype curve, some claim that we are currently at the apex of overblown expectations.
The trading life-cycle process’s data processing and modeling, forecasting and signal research, risk management, and execution are some of the areas where machine learning is most successful.
ML facilitated the analysis of data.
Machine learning has benefited modeling and data processing. It has significantly facilitated the gathering and analysis of data. A quant can examine far more data in a shorter time thanks to ML.
Given the amount of data we produce, alternative data will increase over the next ten years. By 2025, the World Economic Forum projects that we will produce 463 Exabytes every day! In 2012, the internet produced just one Exabyte each day. 18 zeros follow one byte to form an Exabyte. According to a survey, 69% of funds currently use alternative data. Using alternative data, machine learning is used to detect new signals or improve old ones.
There are several instances of the usage of alternative data. In one famous instance, a fund utilized aeroplane tracking information to foretell a merger. In certain places, commodities trade uses satellite images to evaluate agricultural yields. Equities utilize both foot traffic and credit card data, even though sentiment analysis is an effective predictor.
Although alternative data is appealing, very big data sets with a lengthy history are necessary for ML to be effective. Any machine learning algorithm is only as good as the data we feed it. Thus high-quality data is required. Big data sets are only a couple of years old and can be incomplete/inaccurate, so they provide little predictive value.
Privacy is the major reason.
As a result, some question the value of the insights, and developing a model is challenging due to the poor signal-to-noise ratio. Credit card information won’t reveal if a sale was taking place, which would have raised expenditure and made it less likely to, for example, result in a rise in profits.
The privacy of the data, how it was collected, and who owns the rights to the data are further issues. With Apple’s most recent update, this motif has become more widespread after years of growth.
Forecasting and the discovery of new patterns have been and will continue to be impacted by machine learning. It could uncover undiscovered elements. The chances of this happening are minimal because they would still need to be based on a fundamental economic principle that is publicly understood. Having said that, machine learning expands the scale at which a quant can operate, such as the volume of data they can evaluate or the scope of their study. For instance, machine learning could be more effective at aggregating several weak predictors or merging non-linear inputs.
Because it offers exceptional prediction potential, Deep learning, a subset of machine learning, significantly impacts forecasting. However, we need help comprehending how this predictive ability is produced, which might be problematic for internal analysis. Understanding how to assess and explain the model is essential for risk analysis, investor confidence, and compliance.
Therefore, at DCPTG, a completely automated ML-based quant approach can handle the entire investing process without any human involvement. We have accomplished it, and it’s probably too good to be true!
Despite this, machine learning is in an exciting phase and will have a major influence on quantitative trading over the next years. Concentrating in particular on specific stages of the investment process, such as forecasting, modeling, or execution.