In the world of the stock market, the most important factor that investors keep in mind when investing in stocks is which stock will yield the greatest ROI (Return on Investment). In order to do this, investors need to be able to predict which stocks will perform the best and bring them the highest profit margin. For the past century, analysts have had to gather massive amounts of data and manually analyze it to gain an understanding of a company and its stock value. However, with the recent emergence of Quantitative Finance, analysts have been able to harness the power of computer science to produce stock market predictions that surpass that of human accuracy. Now comes the question, what is Quantitative Finance, how has it emerged, and what role does it hold in the future of the stock market?
What is Quantitative Finance?
The field of quantitative finance lies in the intersection of data collection and analysis. Analysts gather years, or even decades of historical data regarding the companies that they are looking into such as stock prices, market share, economic indicators, and more. After accumulating and cleaning the data, the quantitative analysts combine complex mathematical concepts from fields such as linear algebra and differential equations, and combine it with computational simulations to make mathematical models that are able to price securities and measure risk (Investopedia 1). In order to go into quantitative finance, one would need to have an advanced understanding in areas such as finance, computer science, and applied mathematics, and would also need to develop analytical and research skills to do the work needed in this field.
Emergence of Modern Quant Models
The first major use of quant models came in the 1970s, where Wall Street physicist Emanual Derman developed multiple financial models such as the Black-Derman-Toy model, which was a short-rate model used in the pricing of bond options, and Derman-Kani local volatility model, which was used to predict risk on investment. These models were the cornerstone of modern quantitative finance and are still widely used by many analysts (CQF 1). Today, the models used are more complex, but accurate, than ever. An example of this is with the model that J.P. Morgan, a leader in investment banking, uses in their financial trading. According to their global research program, “The J.P. Morgan Macrosynergy Quantamental System (JPMaQS) tracks macroeconomic concepts, like growth, inflation and macroeconomic balance sheets, and transforms them into macroeconomic quantamental indicators, making it easy to use quantitative-fundamental information for algorithmic trading and for the development of discretionary trading tools.” (J.P Morgan 1). The model staggeringly utilizes over 1 billion data points to clean and analyze using their machine learning process that provides thousands of real-time insights on economic developments that correlate with market trends.
Current Limitations
Current limitations with quantitative finance are centered around the type and quality of the data that is fed to the models that make the market predictions. For example, quantitative models cannot understand qualitative factors like brand reputation and consumer approval rates because those are subjective factors that the model cannot be fed in the form of data, but it is still essential in the valuation of a company. Furthermore, these quantitative models heavily rely on historical data of the company, but sometimes this past data does not properly reflect future trends because of the unpredictability and volatility of the stock market. Additionally, quant models cannot properly capture human emotions: this is because quant models rely on numerical values, while human emotions are erratic and constantly changing, meaning quant models cannot account for the random changes in human emotions and behavior.
Path for The Future
In conclusion, the future of quantitative finance holds strong promise and growth. As more fields combine with quantitative finance such as artificial intelligence (AI) and machine learning (ML), the accuracy and complexity of quantitative finance analysis grow even more, making them even more useful and subjective. As more and more companies adopt and create quantitative finance models, the need for quant finance professionals also grows, making the quant finance profession a lucrative career path. From innovative trading algorithms to sophisticated risk-management models, the field of quantitative finance still holds the potential to completely change the landscape of the modern financial industry, especially with the emergence of AI and ML concepts that can be used to solve limitations of quant finance. An example of this is with sentiment analysis for qualitative data. Traditionally, quantitative finance heavily relies on numerical data such as financial statements, and economic indicators. However, it often overlooks qualitative aspects like public perception of a company. Sentiment analysis, a branch of natural language processing (NLP), can analyze text data from news articles, social media, and other sources to understand public sentiment towards a company. This sentiment can be valuable towards machine learning algorithms, which can use sentiment analysis alongside traditional financial data to build more accurate predictive models.
