Huge volumes of data are found in various sectors of the economy in all sorts of formats from a variety of different sources. Such huge volumes of such data, what is termed big data, become easily accessible because of better use of technology, especially enhanced computing capabilities and cloud storage. Companies and governments recognize the great insights that can be derived from tapping into big data but cannot afford neither the time nor the resources needed to sift through its wealth of information. Artificial measures by AI are hence being employed by different industries to gather, process, communicate, and share useful information from data sets. One method of AI that is increasingly being used to process big data includes machine learning.
The algorithm or source code, which is built into the machine or computer, forms a complex creation of data applications of machine learning. This code forms the model the machine or computer identifies, makes predictions around, and builds into the patterns for its decision-making process through parameters inside the algorithm. When introducing new or additional information, the algorithm changes its parameters in a dynamic shift to check if a pattern change exists, among other things. However, the model should not change. Machine learning is used in different industries for various reasons. Trading systems can be calibrated to look out for new potential investments. Marketing and e-commerce platforms can be optimized to provide users with accurate and personalized recommendations based on a user’s internet search history or previous transactions. Lending institutions can utilize machine learning to predict bad loans and build a model for credit risk. Banks can develop fraud detection tools based on machine learning techniques. The applications of machine learning in the digital-savvy world are limitless because business owners and governments begin to realize the opportunities arising from big data. An illustration within the financial world can better elaborate how machine learning works. Traditionally, investment players in the securities market, such as financial researchers, analysts, asset managers, and individual investors, scan through much information provided by various companies across the world to make profitable investment decisions. However, this information may not have been distributed or advertised as much in the media and could be known only to a few who are lucky to be either employees of the company or residents of the country where the information originated. Also, not only can humans gather only so much information and process it within a given timeframe, but they are also bound by several limitations. This is where machine learning comes in. AI has swiftly become the front row in the new world of quantitative investment, providing a large number of advanced techniques that can analyze the financial market. AI algorithms are enabled to process vast amounts of both structured and unstructured data in search of patterns and anomalies and predictive signals in the markets. They learn from changes in conditions, such as the condition of the market, where certain investments might be more efficient and effective. Other chart patterns—head and shoulders, triangles, etc.—are ways of predicting what technical analysis can anticipate a market reaction to.
AI applications extend to more creative data than traditional financial metrics for quantitative investing. AI systems can also assess alternative data sources from social media and news sources to the advantage of an investor. A subset of AI refers to natural language processing: it analyzes textual data, which goes into earnings call transcripts and financial news into generating trading signals. Nevertheless, it is still much debated and researched by many on complications regarding overfitting where AI relies too heavily on history in an altered environment—and data snooping, a form of statistical inference. In addition, computationally intensive and expert-laden requirements to apply AI to the investment strategies are very high.Â
It may, for instance, use machine learning in the investment analysis and research sectors. Suppose the asset manager invests only in mining stocks. The model embedded in the system then browses the web and gathers all news events from different businesses, industries, cities, and countries, and the information gathered forms the data set. The asset managers and researchers of the firm could not have gathered the information in the data set by means of their human powers and intellects. The parameters developed alongside the model bring out only data regarding mining companies, regulatory policies on the exploration sector, and political events in certain countries from the data set. Quantitative investment strategies rely more on mathematical models and algorithms to identify investment opportunities. These strategies must, therefore, be systematic and eradicate much of the emotional element from investing. Among the common approaches to quantitative investment strategies are statistical arbitrage, factor investing, risk parity, machine learning, and artificial intelligence (AI). Machine learning techniques have really transformed how many now think about quantitative investment strategies. It relies on computational algorithms that help break down large data sets in relation to decisions on investments. In essence, the strategy shall comprise supervised, unsupervised, and reinforced learning techniques to identify patterns, anomalies, and predictive signals in the financial markets. Traditionally, the approach could apply to either conventional financial metrics or alternative data or both to predict asset prices, manage risk, and improve portfolios. The application of machine learning in quantitative investing will automate complex decisions and, with it, potentially increase the efficiency and effectiveness of some investment strategies. For example, the same algorithm trained on machine learning can be adapted to analyze sentiment in social media, news articles, and even satellite images for competitive advantages. Its use is not without challenges though—overfitting, data snooping, and what have you, and robust protocols for backtesting must be in place. Much computational resources and expertise in finance and machine learning are also demanded under the strategy.
