Blog Posts

The Zillow Zestimate and other Automated Valuation Models (AVMs) like it are designed to provide an estimate of a property’s market value using algorithmic modeling. These models typically rely on a combination of publicly available data, historical transaction data, and various computational techniques. They involve statistical analysis (like regressions) and often basic machine learning algorithms. […]

Non-parametric models in finance are valuable for their flexibility and adaptability to various types of financial data. They’re particularly useful in scenarios where the underlying data doesn’t conform to standard distributional assumptions (e.g., normal distribution). We’ll cover many examples of non-parametric mathematical models in finance.   Key Takeaways – Non-Parametric Models in Finance & Trading […]

There are many options pricing models with complex mathematical foundations and variables that go into determining what an option is worth. But in terms of the big-picture intuitive understanding of an option’s value is, it really boils down to two main factors: the probability that an option will be in the money (ITM) by expiration […]

Banks in the US use a clever method called synthetic risk transfers to handle the challenges of tough banking rules and the effects of higher interest rates. This involves selling specialized financial products to investment funds. This lets the banks protect themselves from some of the risks associated with the loans they give out. It’s […]

Hedge funds rely on data analytics and advanced storage solutions to manage vast amounts of real-time and historical data, ensuring both performance and security. Algorithmic trading systems central to many types of hedge funds (i.e., systematic in nature) require continuous refinement for executing trades and integrating with risk management systems for real-time portfolio monitoring. Robust […]

Here we look at the foundational concepts in financial risk modeling. Financial risk modeling includes various methods and theories to quantify, model, and hedge against financial risks. The concepts listed are integral to risk management and investment analysis. Let’s look into each one: 1. Arbitrage Pricing Theory (APT) APT is an asset pricing model that […]

Risk-Adjusted Return on Capital (RAROC) is a financial metric used by banks, investment firms, and corporations to measure the return on investment adjusted for the risk taken. RAROC is a tool for evaluating the performance of an investment portfolio, a business unit, or an entire firm by considering both the returns it generates and the […]

The Hedging Irrelevance Proposition is a financial theory suggesting that hedging doesn’t have a direct effect on a firm’s value. The proposition assumes perfect markets and posits that investors can replicate any corporate hedging strategy at the same cost. Based on this argument, the company’s decision to hedge is irrelevant to its overall valuation.   […]

Crowding out occurs when government borrowing leads to higher interest rates, making it more expensive for the private sector to borrow and invest, potentially stifling economic growth. Conversely, crowding in happens when government spending catalyzes private investment – particularly when it enhances public infrastructure or stimulates demand that the private sector can meet. Effective public […]

When designing trading algorithms, selecting the right programming language is important. Consider execution speed, ease of development, library support, and community resources.   Key Takeaways – Best Programming Languages for Trading Algorithm Development Python: The Swiss Army knife of coding. Python is an all-rounder with rich libraries ideal for various financial algorithm needs. C++: C++ […]