Making math accessible and fascinating for everyone.
Why are the Chinese buying up so much US Land? Blended market indicators offer a nuanced approach to understanding the growing economic divergence between the United States and China since 2020.
Deep dive into quantitative analysts, or “quants,” who blend mathematics, statistics, computer science, and financial knowledge to analyze large datasets and develop financial models. It covers their history, tools, daily work, required skills, and the growing demand for their expertise across various industries beyond finance.
Researchers have combined EGARCH with advanced AI models like neural networks and genetic algorithms to significantly improve cryptocurrency trading predictions. This integration helps investors, regulators, and developers navigate the volatile crypto market with greater accuracy and confidence.
We look at the use of kernel methods in machine learning and finance, highlighting their ability to transform complex, non-linear problems into solvable linear ones, thus revealing hidden patterns in data. Kernel methods, including Support Vector Machines and Radial Basis Function kernels, are widely applied in fields such as image and speech recognition, natural language processing, and bioinformatics, offering powerful tools for pattern analysis and prediction.
Why I love the Hull Moving Average (HMA), a sophisticated and responsive tool created by Alan Hull, which offers reduced lag and smoother trend indications compared to traditional moving averages like SMA and WMA. The HMA’s versatility extends beyond finance, finding applications in fields such as science, engineering, and meteorology for analyzing complex data.
The Riemann Hypothesis, an unsolved mathematical problem, focuses on the distribution of prime numbers and could revolutionize fields like cryptography, computer science, and medicine if proven. The Clay Mathematics Institute offers a $1 million prize for its proof, highlighting the significance of this elusive hypothesis.
Markov Chains, named after mathematician Andrey Markov, are models used to predict the future state of systems based solely on their present state. These versatile models find applications in various fields including economics, finance, biology, physics, and computer science, notably underpinning Google’s PageRank algorithm and aiding in stock market analysis and speech recognition.
Jim Simons’ Medallion Fund leveraged Game Theory to achieve unparalleled financial success by predicting market movements and exploiting inefficiencies through strategic decision-making and interdisciplinary expertise. By understanding and applying concepts like Nash Equilibrium, the fund’s team could anticipate and capitalize on other market participants’ behaviors, resulting in consistent high returns.
Erika Barker’s journey and methods for screening stocks, including the use of tools like Bloomberg Terminal and VectorVest to perform fundamental and technical analysis. Emphasizing risk management and the development of custom screening criteria, Erika shares her strategies for navigating the stock market and making informed investment decisions.
The Law of Large Numbers (LLN) is a fundamental statistical concept that ensures the average outcome of many trials converges to the expected value, as showcased by magician Derren Brown’s trick with horse race predictions. It highlights LLN’s historical roots, mathematical formulation, and diverse applications in fields such as finance, insurance, quality control, and everyday scenarios.