Quantitative Finance Exposed: The Money Formula Uncovers the Flaws and Risks of Financial Modeling
# The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets ## Introduction - What is the book about and who are the authors - Why is it relevant and important to understand quantitative finance - What are the main themes and arguments of the book ## Chapter 1: Early Models - How mathematics and finance have been intertwined since ancient times - How early models of interest rates, annuities, and probability were developed - How the modern financial system emerged from the Renaissance and the Enlightenment ## Chapter 2: Going Random - How randomness and uncertainty became central to financial modeling - How the normal distribution and the Brownian motion were discovered and applied - How the Black-Scholes formula revolutionized option pricing and risk management ## Chapter 3: Risk Management - How risk is measured and managed in finance - How the value-at-risk (VaR) method became popular and controversial - How the financial crisis of 2007-2008 exposed the flaws and limitations of VaR ## Chapter 4: Market Makers - How market makers provide liquidity and profit from bid-ask spreads - How high-frequency trading (HFT) exploits speed and technology to gain an edge - How HFT affects market efficiency, stability, and fairness ## Chapter 5: Deriving Derivatives - How derivatives are contracts that derive their value from underlying assets - How derivatives can be used for hedging, speculation, or arbitrage - How derivatives can create complex and interconnected risks and opportunities ## Chapter 6: What Quants Do - Who are quants and what skills and tools do they use - What are some of the main types and roles of quants in finance - What are some of the ethical and social challenges that quants face ## Chapter 7: The Rewrite - How the authors propose a new approach to quantitative finance - How complexity science and network theory can offer better insights and models - How agent-based modeling and behavioral finance can capture human behavior and interactions ## Chapter 8: No Laws, Only Toys - How financial models are not laws of nature but toys of human imagination - How models can be useful but also dangerous if misused or misunderstood - How models can be tested, validated, and improved ## Chapter 9: How to Abuse the System - How financial models can be manipulated or exploited for personal gain or fraud - How some examples of financial scandals and crimes involved model abuse - How regulation and oversight can prevent or deter model abuse ## Chapter 10: Systemic Threat - How financial models can create systemic risk and instability in the global economy - How some examples of financial crises and crashes involved model failure or feedback loops - How resilience and diversity can reduce or mitigate systemic risk ## Conclusion - What are the main takeaways and lessons from the book - Why is it important to be aware and critical of quantitative finance - What are some of the future challenges and opportunities for quantitative finance ## FAQs ### Q1: What is quantitative finance? A1: Quantitative finance is the application of mathematics, statistics, computer science, and other disciplines to financial problems and decisions. ### Q2: What is the difference between a derivative and an underlying asset? A2: A derivative is a contract that derives its value from an underlying asset, such as a stock, a bond, a commodity, or a currency. An underlying asset is a tangible or intangible asset that has a market value. ### Q3: What is high-frequency trading (HFT)? A3: High-frequency trading (HFT) is a type of algorithmic trading that uses high-speed computers and networks to execute large numbers of trades in fractions of a second. ### Q4: What is agent-based modeling (ABM)? A4: Agent-based modeling (ABM) is a type of computational modeling that simulates the behavior and interactions of individual agents, such as investors, traders, consumers, firms, etc. ### Q5: What is systemic risk? A5: Systemic risk is the risk that a failure or disruption in one part of the financial system can cause a chain reaction or contagion that affects the whole system.
The Money Formula: Dodgy Finance, Pseudo Science, and How Mathematicians Took Over the Markets book