Mzingaye Moyo’s Post

Has anyone found a frequently repeated error in the Banking Financial Economic System of the World? What about the ideal Solution to the Problem of the World? How many times since 1700 every 50 years Crisis where the error is? why didn't they fix it? - Did you count? The same thing, loss of capital Inflation Default market collapse - optimization? Your Boom sawmills of Ii Codes will not last long - but how will you compete with Ii I am a mathematician and a former graduate of 20 universitiesYou and the Machine to CompeteThe machine is never wrong, the human is always alwaysI studied 2 Books constantly in my briefcase for 20 years and not only read the Anvil of Practice and Experience for 20 years1 Handbook of Higher Mathematics M.Ya. Vygodsky, 19792 Sun Tzu - The Art of War 6th century BC10 Years of Book Business Best of the Best - I didn't appoint the MarketAnd 4 years ago I threw them out of the Teacher’s Portfolio and gave the Programmer happiness.I don't need them for too long I studied other rubbishTo throw them out, you need to not only read them, take notes, try them on, improve them - 20 years of practical experienceYou need to get the Result, give them an upgrade - close all the Errors of the Past now and the Future with strategic model tactical ThinkingI wish you creative success -And this one shows All the Holes - your civilization itself for 2 thousand years out of 15⁹ years of the universeHOW MANY Processes - Before Capturing the Target - Lifting into the Air from 1 Click of a Button Press - How Many Cogs Must Start - ENERGY CONSUMPTION EFFICIENCY (Without Useful Energy of Time Resources) - . Phrase - Decomposition of Matter Bodies Compounds into Atoms - Extraction of Atoms of Any Element - Remove Cancer Cells from the Brain - By Detachment of Evil Quality CELLS - decomposition of Minerals Poisons BAS Value of Nature Plant Cells Increase Decreased Growth -1 Implement it

Regression analysis is a statistical measure that we use in investing, finance, sales, marketing, science, mathematics, etc. It tries to determine how strongly related one dependent variable is to a series of other changing variables. We usually refer to them as independent variables. The dependent variable is the one that we focus on. Put simply, we want to know whether it is being affected, and if so, by how much, and by what. Independent variables are the factors that may or may not affect the dependent variable. Dependent receives the impact, while Independent provides (or not) the impact.

A lot of people talk about mathematics. At the same time, it is important to understand that maths is useless if applications of mathematics are not used to full potential. Like, leveraging statistical tools using maths for finance is a great way to calculate risk & estimated expected returns. 📈 There are many complex problems which cannot be just leveraged with normal methods as there are different parameters for consideration and calculations. Picking them accordingly is also important. 💁🏻♂️ In this nuance, implementation of mathematical models from statistics, probability and stochastic calculus in finance is a turnaround in today’s world to perform hedging strategies and find arbitrage opportunities in a mispriced market using certain algorithms. So, developement in mathematical applications in various fields still has a long way to go to create maybe profitable trading algorithms, time series and other strategises as well. #riskmanagement #finance #mathematics #hedging #arbitrage #linkedin #linkedinforcreators

𝗧𝗵𝗲 𝗩𝗶𝗿𝘁𝘂𝗲 𝗼𝗳 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗶𝗻 𝗥𝗲𝘁𝘂𝗿𝗻 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻   📚✨ If you're in the mood for some enlightening nighttime reading, I've got just the thing for you. I recently came across a paper that completely turned my understanding of (predictive) time-series modeling on its head.   🔍 Back in my student days, my econometrics classes drilled into us a cardinal rule: for time-series models to be well-behaved and performant, 𝙩𝙝𝙚 𝙣𝙪𝙢𝙗𝙚𝙧 𝙤𝙛 𝙤𝙗𝙨𝙚𝙧𝙫𝙖𝙩𝙞𝙤𝙣𝙨 𝙢𝙪𝙨𝙩 𝙨𝙞𝙜𝙣𝙞𝙛𝙞𝙘𝙖𝙣𝙩𝙡𝙮 𝙤𝙪𝙩𝙬𝙚𝙞𝙜𝙝 𝙩𝙝𝙚 𝙣𝙪𝙢𝙗𝙚𝙧 𝙤𝙛 𝙥𝙖𝙧𝙖𝙢𝙚𝙩𝙚𝙧𝙨 𝙤𝙧 𝙥𝙧𝙚𝙙𝙞𝙘𝙩𝙤𝙧𝙨 – 𝙩𝙮𝙥𝙞𝙘𝙖𝙡𝙡𝙮 𝙗𝙮 𝙖 𝙛𝙖𝙘𝙩𝙤𝙧 𝙤𝙛 10 𝙩𝙤 15. But this paper throws that old doctrine into question and thus presents a compelling argument in favor of 𝙢𝙖𝙘𝙝𝙞𝙣𝙚 𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙢𝙚𝙩𝙝𝙤𝙙𝙨, which often harness a multitude of parameters.   🌟 Whether you're a seasoned professional or a curious learner, this paper might interest you.   #Econometrics #MachineLearning #DataScience #Innovation #Research https://lnkd.in/eD4N-XvQ

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.

"Game Theory": How can it change our thinking process? So the world... 🤔 Whether in the field of Science, Politics, Economics, or any other field, Game Theory plays a crucial role. Games as the Foundation for Understanding Complex Economic Issues : Game theory emanates from studies of games such as chess or poker. The foundations for using game theory in economics were introduced in a monumental study by John von Neumann and Oskar Morgenstern entitled "Theory of Games and Economic Behavior" (1944). Today game theory has become a dominant tool for analyzing economic issues. "Strategic Interaction" Game theory provides a framework for analyzing strategic interactions among agents, particularly when a few entities dominate a market or need to coordinate on policies. While classical economic analyses often assume that agents can ignore the reactions of others, this simplification is not valid in scenarios involving negotiations in trade, labor, and regulatory decisions. In such cases, understanding the expectations and reactions of other agents becomes crucial for effective decision-making. Game Theory: Whereas mathematical probability theory ensued from the study of pure gambling without strategic interaction, games such as chess, cards, etc. became the basis of game theory. read full article here: https://lnkd.in/gtgZ4WRz

Memo from Dept of Get a Substack, Man! [Note: this post is mainly for our RAs at the New York Fed and those in the Fed System; it’s in the weeds] One way to summarize the craft of high frequency macroeconomics might be: macro policy is a thing we always want to do better, it is both fiscal and monetary, and we want to know two things. First, we want to know how *systematic* policy works, and how genuinely exogenous shocks to things like TFP and external environs matter, and the role of systematic policy in altering these responses, ideally in a way that limits the damage of those shocks to the population’s well-being. Second, we want to know how *shocks* to the policies themselves matter. Of course, we’d ideally not like policy itself to be a source of noise, but if we consider that policy —especially fiscal and regulatory—emerges from political processes, outcomes are often not fully predictable. In both cases, analysis often boils down to producing and then interpreting impulse responses (IRFs). Maybe not always literal IRFs but certainly in spirit. And here, it’s clear to me that modern DSGE models are among the most complicated constructions around in economics. The models allow for a wide array of forces (income, wealth, substitution effects, heterogeneously-sized to boot, and dynamic effects telescoped into the present because rational agents are, well, rational: They take the future ruthlessly into account.) The subtlety and often opposing forces that are necessarily unleashed when policy, and policy shocks, hit fully rational agents, can be hard to keep track of indeed. For me, staying coherent and applying “common sense” (my prior knowledge, especially of the quantitative strength of parameters/forces) when extracting “the plausible” from the purely-model-implied path of outcomes in *business-cycle* settings, is the hardest part of my job as a policy advisor. A lucky break for me and people here is that we have colleagues like Marco Del Negro who work daily with these constructions. Hard or not, it’s the part of the profession perhaps most fully engaged with answering questions about the economy that all of us, lay people or otherwise, ask all the time. If you find this area of interest, many great books have been written, but to me Cooley (95) stands out. And as for “how to solve it”, it’s not Polya here, but rather McCandless (2008). It’s great—Full of examples, long on details, and uses what old heads like me were taught decades ago: the method of undetermined coefficients. Luckily, a brilliant—and I mean a tour de force of exposition—are the notes of Uhlig (95). And Harald’s notes are free here! https://lnkd.in/e3zrtdcM Have fun!

Stochastic calculus is a branch of mathematics that deals with processes involving random noise. It extends calculus to functions of stochastic processes, such as Brownian motion, which models the random fluctuating paths of particles such as stock prices or physical phenomena. The calculus is built on two key concepts: differential equations driven by stochastic processes and integration with respect to these processes. This mathematical framework is crucial in finance for modeling and predicting the behavior of markets and financial instruments under uncertainty.

🔍 The Power of Derivatives: How Calculus Transforms Diverse Fields 📊✨ Derivatives, a fundamental concept in calculus, are more than just mathematical abstractions, they are powerful tools that drive innovation and problem-solving across a multitude of disciplines. Let’s explore how this mathematical concept is applied to transform various fields: Finance 💹: In finance, derivatives are used to model and predict changes in asset prices. Financial instruments such as options and futures are grounded in the principles of derivatives, helping investors manage risk and optimize portfolios. Engineering 🛠️: Engineers use derivatives to design and analyze systems, from optimizing structural designs to controlling dynamic systems. For instance, derivatives help in determining the most efficient shapes and materials for construction projects. Physics 🚀: In physics, derivatives describe the rate of change of physical quantities. Whether it's understanding motion, electricity, or thermodynamics, derivatives provide essential insights into how things change over time and space. Economics 📈: Economists apply derivatives to analyze how changes in one economic variable affect another. For example, marginal cost and marginal revenue are concepts derived from calculus that help businesses make informed production and pricing decisions. Data Science 📊: Data scientists use derivatives in machine learning algorithms to optimize models. Techniques such as gradient descent rely on derivatives to find the best-fit parameters and improve predictive accuracy. Medicine 🩺: In medicine, derivatives assist in modeling the spread of diseases and the impact of treatments. They are used to analyze patient data, optimize drug dosages, and understand the dynamics of biological systems. Environmental Science 🌍: Derivatives help scientists model environmental changes, such as the effects of pollutants or climate change. They are crucial for developing strategies to mitigate environmental impact and manage natural resources. The application of derivatives is a testament to the elegance and versatility of calculus. They turn complex, dynamic systems into comprehensible and actionable insights, driving advancements and solutions across a broad spectrum of fields. #Calculus #Derivatives #Finance #Engineering #Physics #Economics #DataScience #Medicine #EnvironmentalScience #Innovation #STEM #Maths

A lot of buzz around how a certain genAI product gave hilariously inaccurate results, but really what lies behind all of this is a math algorithm. I would highly recommend reading this book (even if growing up, math was not your favorite subject 😊) Like most things in life, as you sow, so shall you reap. In the case of any LLM, it is the quantity, quality, specificity, accessibility and diversity of training data with considerations for the training goals and related ESG adjustments. #trainingdata #genai #trainingbias #trustandsafety