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The Man Who Solved the Market by Combining Maths, Luck and Common Sense   Jim Simons – How did he do it?   From a young age, Simons knew he enjoyed maths. From calculating simple mental arithmetic to solving complex equations, Simons enjoyed anything that involved working with numbers.   After graduating from MIT in 1958, Simons travelled to Colombia with his friends to start a business. It was uncharacteristic, as his parents thought Simmons would pursue a career in academia. Well, actually he did. After setting up the business in Colombia, he returned to the U.S and taught mathematics at MIT and Harvard.   Most notably, he invented the Chern-Simons quantum theory in the field of physics. At the same time, he was working as a code breaker for the U.S. Secret Service. Simons is the definition of a creative mathematician. He saw clear patterns where most people saw one-time anomalies, a skill that would come to serve him well later in life.   Back in Colombia, his business venture flourished. Simons sold the business and gave the money to his friend to invest. He achieved a 10x return on investment (ROI) within a relatively short time. This was a lightbulb moment for Simons. He knew he could potentially achieve the same if not more.   Aged 40, Simons decided to start his journey in the world of investing by launching Renaissance Technologies. He took a radical approach. Simons decided to apply a scientific & computational approach to investing. He developed computer algorithms that identified persistent anomalies using different data points such as weather, annual reports and past economic events.   Applying science & automation to investing allowed him to:   1. Reduce volatility in the portfolio & still achieve superior returns 2. Eliminate human emotion in the investment process 3. Achieve narrow spreads for bid and ask prices 4. Invest based on hard evidence as opposed to intuition     Simons is regarded by many as the greatest Wall Street trader of all time. He fundamentally changed the hedge fund industry including the type of talent recruited in the industry. Nearly all of the talent at Renaissance Technologies comes from a scientific background. When asked about this approach Simons replied “You can teach a physicist finance, but you can’t teach a finance person physics”   Simons Rules for Success:   1. Do not micromanage – get the best people and leave them to do their thing 2. Be persistent – failure only occurs when you give up 3. Increase your luck curve – play to your strengths and hope to be lucky 4. Surround yourself with good smart people – as a leader you cannot be the smartest person in the room, it makes decision-making difficult 5. Delegate – do what you do best and delegate the rest

#Book, #ideas,... #Ask the #boss for more #NVIDIA GPUs and faster computers. "The more GPUs you buy, the more money you save" - Jensen Huang. The #economics of #money, #banking, and #financial #markets is perfect for custom #GPT s and other #LLM s. #Ai, #cloudcomputing, #technology. "There has never been a more exciting time to #teach #money and #banking."

Compounding interest is an interesting concept for several reasons: 1. Exponential growth Compounding interest leads to exponential growth, which is a fascinating concept in mathematics. The growth rate accelerates over time, leading to a snowball effect that can be mesmerizing to observe. 2. Counterintuitive results Compounding interest often leads to results that defy intuition. For example, a small change in interest rates or time frames can lead to drastically different outcomes, making it a fascinating topic to explore. 3. Real-world implications Compounding interest has significant real-world implications, affecting personal finance, investing, and economic growth. Understanding compounding interest can help individuals make informed decisions about their financial lives. 4. Compound frequency The frequency of compounding interest can have a significant impact on the outcome. Daily compounding can lead to vastly different results than monthly or annually compounding, making it an interesting aspect to explore. 5. The Rule of 72 Compounding interest is closely related to the Rule of 72, which estimates how long it takes for an investment to double in value based on the interest rate. This rule has been widely used and is a fascinating example of the power of compounding interest. 6. Historical significance Compounding interest has played a significant role in shaping the world's economy and financial systems. Understanding its history and evolution can provide valuable insights into the world of finance. 7. Mathematical beauty The mathematics behind compounding interest are elegant and beautiful, involving concepts like exponential functions and geometric progressions. 8. Surprising applications Compounding interest has applications beyond finance, such as population growth, chemical reactions, and even biology, making it a fascinating topic to explore from different angles. Overall, compounding interest is an interesting concept because it combines mathematical elegance, real-world implications, and counterintuitive results, making it a fascinating topic to explore and understand. Hope you have a lovely day.

Unlocking the secret of stock markets! SFI’s LMF Model, proposed by Doyne Farmer, Fabrizio Lillo, and Szabolc Mike, explains the “long memory” behavior in stock markets. A recent study using Japanese stock market data confirms the model’s predictions, showcasing quantifiable laws in #finance. Financial markets, akin to physics, follow precise models. Despite the influence of buy and sell orders on prices, the resulting deviation from market efficiency is relatively small on average. h Understanding the intricacies of long-memory in order arrival opens new avenues for exploring market dynamics.

What is Modern portfolio theory (MPT) ? Modern portfolio theory (MPT) is a mathematical framework for constructing a portfolio of assets that maximizes expected return for a given level of risk. It was developed by Harry Markowitz in the 1950s, and he was awarded the Nobel Memorial Prize in Economic Sciences for his work. MPT is based on the idea that diversification is the key to reducing risk. Diversification means investing in a variety of different assets that are not perfectly correlated with each other. This means that if one asset performs poorly, the others may still perform well, which can help to offset the losses. MPT also takes into account the expected return and risk of each asset in the portfolio. The expected return is the average return that an investor expects to earn on an asset over a certain period of time. The risk of an asset is the volatility of its returns. To construct a portfolio using MPT, an investor would first need to determine their risk tolerance. This is the amount of risk that they are comfortable taking on in order to achieve their investment goals. Once they have determined their risk tolerance, they can then select a portfolio of assets that meets their needs. MPT is a complex topic, but the basic idea is simple: investors can reduce risk and improve their returns by diversifying their portfolios. Here is a simple example of how MPT works 🔻............

I discovered this something to think about Science- Reasoning Technology - Test Reasoning Engineering - Prove the Testing of Reasoning Business- Prove the Testing of Reasoning is viable Economics - Optimizing to Prove the Testing of Reasoning is viable

Political economists, both mainstream and Marxist, find it difficult to reconcile the «real» and «financial» appearances of capital. The conventional view is that «real» capital is an objective productive entity; that «finance» merely reflects this reality; and that, unfortunately, the reflection is often inaccurate, causing the two to «mismatch». This convention, we argue, is baseless if not fraudulent. First, although economists know full well that «real» capital, comprising different capital goods, cannot have a unique objective quantity – they measure this pseudo quantity anyway, arbitrarily. Second, when they realize that their arbitrary measure of «real» capital differs greatly from the corresponding magnitude of finance, they blame the deviation on invisible fluctuations in intangible capital, investor irrationality and market imperfections. And third, they insist that «real» accumulation drives «financial» accumulation, even though their own measures show that the two processes move in opposite directions!

🔳 ◼ ◾ ▪ EconCS Seminar ▪ ◾ ◼ 🔳 As algorithms increasingly interact with rational, self-interested individuals, their design must take participants’ incentives into account. The traditional focus in algorithmic game theory literature has been the design of algorithms that incentivize truthfulness (as seen, for example, in ad auctions). In this talk, we shift focus to algorithms that incentivize effort, aligning with the economic discipline of contract design. The goal of this talk is to address some generalizations of the classic contract design model and demonstrate how algorithmic approaches contribute to this research domain. Our primary focus will be a natural contractual problem involving both aspects of effort and truthfulness incentivization. We will see that the optimal mechanisms can be complex and unintuitive, and do not resemble contracts used in practice. We then shift perspective to simple contracts, such as linear (aka commission-based) contracts and demonstrate that they are close to optimal under certain natural conditions. This provides a justification for the prevalence of simple contracts in practical applications. Tal Alon

1/1: Title: Integrating Motion Physics in Economic Analysis: Leveraging Weigh Bridge Dynamics to Measure Economic Inflation Introduction Economic activities are akin to a vehicle in motion, constantly subject to changes in velocity and direction based on various external and internal forces. This paper explores how principles from motion physics, specifically those used in weigh bridge operations, can be applied to measure and analyze economic inflation. By understanding the dynamics of a constantly moving economy, policymakers and economists can better manage inflationary pressures, much like how weigh bridge systems are used to prevent overloading and ensure safety in vehicular motion. Analysis of Economic Motion Using Physics Principles 1. Economic as a Dynamic System: • Continual Motion: Just as vehicles continually move and interact with varying road conditions, the economy is in a state of perpetual motion, influenced by policy changes, consumer behavior, and external economic conditions. • Economic Weighing: Economic ‘weighing’ can be conceptualized as the process of measuring inflation and other economic pressures, akin to how trucks are weighed to ensure they are not overloaded and are safe for travel. 2. Applying Weigh Bridge Principles to Economic Inflation: • Dynamic Weighing of Economic Loads: Just as dynamic weigh bridges measure the load of a vehicle in motion, economic policies need to dynamically assess inflationary pressures as they occur in real-time, allowing for timely adjustments in monetary policy. • Feedback Systems: Weigh bridges use feedback from load measurements to immediately inform truck drivers and authorities if a vehicle is over its limit. Similarly, real-time economic data can serve as feedback for policymakers to apply corrective measures against inflation. 3. Physics of Stability and Economic Equilibrium: • Critical Velocity in Economics: In motion physics, the critical velocity is essential for maintaining vehicle stability. In economics, ‘critical velocity’ could be seen as the rate of economic growth that must be maintained to balance inflation without leading to overheating or recession. • Load Balancing: Economic load balancing involves distributing the weight of government spending, taxation, and regulatory measures in a manner that does not overburden any single sector of the economy, similar to how cargo is balanced within a truck.