#quantitative finance

[quantcoaching]

Quantitative Finance

Quantitative Finance uses mathematical models, statistics, and computational techniques to analyze financial markets, manage risk, and optimize investments. It combines finance, data science, and programming, driving decisions in trading, portfolio management, and risk assessment.

[lorxus-is-a-fox]

busy busy fox

these past couple of days I've been working my way through (thus far) the first 3 problem sets for MIT OCW 6.0001 and the first part of the Lean game, getting my github squared away to have a repo+folder system for a coding portfolio

kind of a long shot, I know, but if any of you reading this (yes! you! even though you don't think it'll make a difference!) want to pass me on as a reference for SWE/datasci/quant finance jobs... please please do? I really need to get hired somewhere??? relocation is not an issue and if anything it's ideal! hopefully I end up in Chicago or SF or Seattle but I am not actually that picky.

or if you have some advice or words of kindness please DM! I really really do not want to have to give up and grind myself into a paste in the Precalc Adjunct Crusher!

frankly I'm kinda frustrated with how much of the wall out of academia I've been trying to climb for the last couple of years has been industry... not wanting people without prior work experience, unless they're interns, and they don't offer internships to postdocs regardless of the whole "2020-2022 was just plain fucked for internships" thing. which is a shame because that is when I graduated!

so please: help me get a tech job of some kind so that I can start functioning at my fullest again!

[blackbooklog]

For building up skills in both machine learning and quantitative finance, here are some valuable resources that can help get to an intermediate level:

1. Machine Learning:

Courses: Coursera’s Machine Learning by Andrew Ng is foundational, while Deep Learning Specialization dives deeper if you’re interested in neural networks.

Books: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron is a practical guide for applied ML. Pattern Recognition and Machine Learning by Christopher Bishop is more theoretical.

Practice Platforms: Kaggle offers datasets and competitions, which are a great way to apply what you’re learning in real scenarios.

2. Quantitative Finance:

Courses: EdX’s Foundations of Data Analysis for Finance by MIT is a great start. Quantitative Finance on Coursera offers a good overview as well.

Books: Quantitative Finance for Dummies gives a solid foundation if you’re just starting. Options, Futures, and Other Derivatives by John Hull is an industry standard for diving deeper.

Coding Libraries: Libraries like QuantLib (for finance-specific computations), as well as Pandas and NumPy for data manipulation, are crucial.

3. Combining ML and Quant Finance:

Projects: Try projects like developing a simple stock prediction model, or creating a risk management algorithm. Start with accessible datasets like Yahoo Finance’s data on stock prices.

Specialized Books: Advances in Financial Machine Learning by Marcos López de Prado merges both fields, focusing on ML applications in trading and finance.

[notayesmanseconomics]

The ECB will be ignoring Euro area money supply growth if it continues its interest-rate cuts

Tomorrow the ECB will announce its latest interest-rate decision and in a way the mood music has already been set this morning by the Riksbank of Sweden. Inflationary pressures are deemed consistent with inflation of around two per cent. At the same time, economic activity is weak, although there are signs of a rebound. The Executive Board has decided to cut the policy rate from 2.5 to 2.25 per…

[maal-wave]

Top-Paying Jobs in Finance

[stockmarketanalysis]

Mastering Quantitative Analysis: Navigating the World of Data-Driven Decision Making

In the ever-evolving landscape of business, economics, and research, the term "quantitative analysis" has become increasingly prominent. This analytical methodology focuses on the objective measurement and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques. The essence of quantitative analysis lies in its ability to turn complex phenomena into simple, quantifiable units, which can be systematically measured and analyzed for patterns, trends, and predictions.

The Genesis and Evolution of Quantitative Analysis

Quantitative analysis has its roots in the early methods of statistics and mathematics. However, its real development started with the advent of computers and advanced statistical software, allowing for more complex data analysis than ever before. Today, it encompasses a wide range of statistical and mathematical techniques, from basic models like linear regression to sophisticated algorithms used in machine learning and artificial intelligence.

Applications in Diverse Fields

The utility of quantitative analysis spans across various sectors. In finance, it is used to assess risk, evaluate the performance of stocks, and optimize investment portfolios. In marketing, quantitative techniques help in understanding consumer behavior, market segmentation, and product positioning.

In public health, it assists in analyzing epidemiological data, improving patient outcomes, and policy planning. The field of economics uses quantitative analysis for modeling economic data, forecasting market trends, and informing policy decisions.

Tools and Techniques

Quantitative analysis relies on a plethora of tools and techniques. Statistical software like SPSS, SAS, R, and Python are commonly used for data analysis. These tools offer capabilities for data manipulation, statistical modeling, and visualization, making them indispensable for quantitative analysts. Techniques like regression analysis, hypothesis testing, factor analysis, and time series analysis are some of the fundamental methods used to explore and make inferences from data.

The Importance of Data Quality

The validity of quantitative analysis heavily depends on the quality of data. Data accuracy, completeness, and consistency are critical. Poor data can lead to incorrect conclusions, making data verification and validation an essential step in the quantitative analysis process.

Challenges and Considerations

Quantitative analysis, while powerful, is not without challenges. The interpretation of data can be complex, and the results are often sensitive to the choice of model and assumptions made during the analysis. Additionally, the reliance on numerical data means that qualitative aspects like context, emotion, and subjective experiences are often overlooked.

Ethical and Privacy Concerns

With the rise in data availability, ethical and privacy concerns are paramount. Analysts must ensure data confidentiality, consent, and comply with data protection laws. The misuse of data, especially in sensitive areas like healthcare and finance, can have significant consequences.

The Future of Quantitative Analysis

The future of quantitative analysis is intertwined with advancements in technology. Big data, artificial intelligence, and machine learning are pushing the boundaries of what can be quantified and analyzed. These technologies enable the analysis of unstructured data, like text and images, opening new avenues for quantitative research.

Quantitative Analysis in Education

In education, quantitative analysis is gaining importance. It helps in assessing student performance, evaluating educational policies, and understanding learning behaviors. This data-driven approach can lead to more effective educational strategies and policies.

Quantitative vs. Qualitative Analysis

While quantitative analysis provides a numerical insight, qualitative analysis offers depth and context. A combined approach, known as mixed methods research, leverages the strengths of both, providing a more holistic understanding of the research subject.

Conclusion

Quantitative analysis is a critical tool in modern decision-making. Its ability to provide clear, objective, and data-driven insights makes it invaluable across various fields. However, it is essential to use this tool judiciously, considering the quality of data, the appropriateness of methods, and the ethical implications.

As we move forward, the integration of new technologies and methodologies will undoubtedly expand the scope and impact of quantitative analysis, making it an even more potent instrument in understanding and shaping the world around us.

[pissanddie]

Goldin+Senneby, Quantitative Melencolia. Commissioned by the Whitworth, The University of Manchester. Courtesy of the artist and Nome, Berlin. Photo: Michael Pollard. More info

[soon-palestine]

We write this call from our student movement in the Gaza Strip, from the heart of occupied Palestine, from under the brutal Zionist bombing, explosions, and the clutches of the monstrous nightmare of death that lurks around us in every corner, house, and street.

We raise it from prison cells, from beneath the destruction, and from inside the rubble, to send it to our fellow students, our comrades, brothers and sisters, in all the universities, schools and institutes of the world everywhere, & we address the global student movement… that was launched in order to stop the genocidal war that is being engineered and financed by the governments of the United States, Britain, Germany, the Netherlands, Canada, Australia and others… this courageous student movement that was born in the universities as an integral part of our struggle, that expresses the conscience of students and peoples who yearn for justice and freedom.

We in the Gaza Strip look at you with pride and honour, as you are a revolutionary fighting vanguard, and a natural and integral part of our Palestinian liberation movement. You have come in a resounding, honest and clear response against the Israeli massacres and those who finance them, confronting the companies of the Zionist war of genocide and ethnic cleansing that have claimed the lives of thousands of Palestinian students of all ages… including hundreds of struggling Palestinian student cadres, wounded and imprisoned, in addition to our great loss in the martyrdom of our professors and teachers, and the destruction of our universities, institutes and schools.

Today, we call on you, from the midst of massacres and siege, to a new revolutionary phase of comprehensive escalation. We call on you to raise the pace and ceiling of your struggle and your honorable stances, quantitatively and qualitatively, against the institutions, corporations, and governments that participate in the slaughter of our children, our students, and our people.. In Rafah, Jabalia, Khan Younis, and the entire Gaza Strip, and against the settler gangs, armies of Zionist killers, that commit their crimes in camps, cities and villages in the occupied West Bank and Jerusalem. We call on you to besiege the White House in Washington, and to surround Western colonial governments and Zionist embassies, and the corporations that finance the Zionist entity and arm its criminal army with all kinds of bombs and means of death and destruction. These criminal colonial symbols represent the forces that support “Israel” to kill us – with your tax money and the money spent at complicit corporations, to destroy our homes, our society, and our future.

Therefore, we call on you to blockade them until the American Zionist aggression against our people in the Gaza Strip stops. At the same time, we renew our call to the teaching, academic, and union bodies in universities, as well as cultural, academic, and scientific figures, to advocate for and support student movements until they achieve their goals. Today we turn to high school students all over the world to participate widely in the struggles and activities of the university student movement, organizing demonstrations, and organizing educational days about the Palestinian struggle for liberation and return.

Secondary schools constitute a strong fortress and a great support for university students everywhere. Once again, we send special greetings to our brothers and sisters, the students of Palestine in the diaspora.

We greet our comrades and colleagues in Students for Justice in Palestine, the Samidoun Palestinian Prisoner Solidarity Network, Palestine Action, and the academic boycott and divestment campaigns, and we salute everyone who participated and participates in student encampments. The duty and responsibility of Palestinian students in the Gaza Strip and all of occupied Palestine is steadfastness, commitment, resistance, unity, and alignment with the resistance and the people… …until the U.S. – Zionist aggression stops and the occupation is defeated and removed from our land — all our land, from the river to the sea.

Long live the struggle of Palestine’s students for return and liberation.

Long live international solidarity. And together we will be victorious!

Secretariat of Palestinian Student Frameworks – Gaza Strip

(available in AR original, EN, ES, FR, NL, DE)

https://samidoun.net/2024/05/a-call-from-the-palestinian-student-movement-in-gaza-time-for-revolutionary-escalation-of-the-global-intifada/

[ceausescue]

some tshirt concepts ive been mulling over

- acronym expansion of lgbtq except it's liberty, guns, beer, transgender, the "quantitative turn". could also do quantitative finance, which is funny for the picture it paints but is perhaps less true of me

- im not gay but for 20 dollars i won't be homophobic

- hetero boy

- bad code/html deliberately aping the style of a shirt a non technical relative who calls you sheldon might get you because you "know all that computer wizard stuff"

- a qr code leading to this blog (high risk but potentially funny for the sheer pointlessness of that risk)

- a qr code leading to someone else's blog

[genius-mathematics-consultants]

Volatility smoothing algorithms to remove arbitrage from volatility surfaces

Implied volatility surfaces are usually constructed from the market prices of a number of options of different strikes and expiries. Because of poor data quality, a trader valuing options from such a surface will typically generate arbitrage opportunities. In fact, even if the pillar observations are free of arbitrage, cubic spline interpolation can introduce arbitrage.

Volatility smoothing is the process of finding the cubic spline surface that fits the input data as closely as possible, under the restriction that it be arbitrage free. This turns out to be a quadratic optimization problem, as discussed in the very useful paper "Arbitrate Free Smoothing" of M.R Fengler.

We've implemented Fengler's algorithm in python, and in this short article we illustrate how it can be used to dramatically improve the quality of volatility data. The algorithm is fast enough to be run across millions of rows of volatility data.

Read the full article

[cognitivejustice]

Free online courses on Nature -Based Infrastructure

"Two training courses on making a case for and valuing Nature-Based Infrastructure. This training is free of charge.

Participants will learn how to:

Identify nature-based infrastructure (NBI) and its opportunities for climate adaptation and sustainable development.

Make the case for NBI by explaining its potential economic, environmental, and social benefits.

Understand the risk profile and the climate resilience benefits of NBI compared to grey infrastructure.

Explain the basics of systems thinking, quantitative models, spatial analysis, climate data and financial modelling applied to NBI.

Appreciate the results of integrated cost-benefit analyses for NBI.

Use case studies of NBI projects from across the world as context for their work.

This course was developed by the NBI Global Resource Centre to help policy-makers, infrastructure planners, researchers and investors understand, assess, and value nature-based infrastructure. The course familiarizes participants with several tools and modelling approaches for NBI, including Excel-based models, system dynamics, spatial analysis and financial modelling. In addition, the training presents a variety of NBI case studies from across the world.

Why do this course?

This course will help you gain valuable skills and insights which will enable you to:

Gain knowledge and tools for informed infrastructure decision-making, with a focus on advancing nature-based solutions for climate adaptation at a systems level.

Understand and measure the benefits, risks, and trade-offs of nature-based infrastructure.

Understand the importance of systemic thinking for infrastructure planning, implementation, and financing strategies.

Communicate persuasively and effectively with stakeholders to advocate for nature-based infrastructure.

Collaborate with peers around the world and become part of the NBI Global Resource Centre alumni.

[phlebasphoenician]

This is absolutely true when it comes to quants in the investment field.

The problem is that quant finance is a multidisciplinary field - quants need to devise strategies based on mathematical models that predict the market, code programs that will chew through decades of past data (data that must be paid for) to prove the theoretical validity of their models, and then sell that model to investors.

Investors who will all to often have no idea what the innards of the model are and may not care.

So a quant cannot stand there and describe awesome math - they must be salespeople.

(Or at least work with some.)

On top of all that, they must have the iron nerve to hold their course and trust their model when their portfolios are diving like rocks and investors are screaming at them to Do Something Damnit.

The four skill sets are therefore:

Math

Programming

Sales

Finance

Bear in mind that the market is also flooded with people trying to become quants - business majors taking masters of financial engineering and sweating the math, math majors complaining about needing to talk to business people - and the people doing the hiring often have no idea if their candidates are good or not. They can be tricked by fast talk and buzzwords, but that can backfire if the interviewer is an Actual Quant who will peg a fake or sub-par candidate a mile away.

Plus the boards of more traditional investment firms often know that quant is a black box so they’re wary of it.

So.

To succeed as a quant, you can go for a regular investment fund and try to get a job - knowing all the while that what you do is the equivalent of inexplicable magic and the bosses will be thrilled with you as long as your portfolio is up but won’t understand or be sympathetic when the market is down. So you had better be smooth talking and very convincing.

Or you can try and get hired by one of the very small number of exclusively quant funds - but face stiff competition from the best of the best.

Or you could take the secret third option.

Which is to go find a trusted well known quant at a traditional fund and beg them to put you on their team. You won’t get paid as much as going the straight quant fund route, you will still have to talk business and sell, but less so and at least your boss will be a fellow quant who will hopefully be understanding of your troubles.

But note that all of this - all of this - requires networking.

Good luck!

Hey Steven, maybe you can help me with something I'm trying to articulate, but I'm not even sure is accurate, just seems like something I've observed, but I could be wrong.

So I've often heard about how the STEM fields are becoming more and more prioritized over the humanities in colleges, and that seems broadly true to me (though there's also other stuff that gets promoted over the humanities like business and law, it feels, though they might qualify as humanities, I don't know), and yet, in my opinion, it seems too broad, cause it seems less STEM and more TE, with the S and M put next to the humanities as fields you shouldn't bother with if you want a profitable career.

Technology and Engineering, it seems to me, are boosted over all else. I don't exactly see people saying get a job as an astronomer or a physicist or a biologist or a mathematician unless it's specifically to "contribute towards society in a profitable way (like, I hear geologists can get employed to help find new sources of fossil fuels, for example)," but meanwhile I hear people say, "just learn to code!" or get a degree in some engineering field or something like that. Basically, a focus on fields that contribute directly to someone making a profit instead of enriching society through the arts or through new discoveries. I don't know if what I said made any sense but I wonder your thoughts on all of it.

(Business I grant you, although a lot of that is due to employers subsidizing MBAs for their white collar workers. However, while Law used to be quite profitable for both parties, it's been in a bit of a demographic crisis for a few years now due to the fact that the number of legal jobs that pay well enough to afford law school tuition have declined massively and the number of people applying to law schools started to nosedive as well. Paul Campos, my colleague at Lawyers, Guns, and Money, has been on that beat for years.)

With regards to STEM, I think it is true that these things are pushed only in so far as they can be harnessed to the generation of profit. Technology and Engineering we agree on; these workers are highly prized by existing industries, they lend themselves well to both start-ups and spin-offs, and their work can be patented in ways that generate profit for both corporations and the university.

However, when it comes to Science, you need to remember that the "S" includes both applied and theoretical sides - and applied sciences look a lot like Technology and Engineering when it comes to industry demand for skilled workers, the potential for start-ups and spin-offs, and the profitability of patents. Think bio-medical, think bio-chemical, think Pharma, think materials and nano-tech and on and on. However, you are quite right when it comes to the theoretical sciences; you do that for the love of the game.

It is true that Mathematics is the most abstract, the most academic, and the hardest to monetize in the ways described above. However, as I learned from my union colleague who was in the Math department (who ironically went on to a career as a union organizer rather than attempt a career as a mathematician), there is one avenue for money-making with a Mathematics degree:

Finance.

I don't know whether this is still as true as when I was in grad school, but it used to be that Wall Street would throw very handsome salaries indeed at anyone with quant skills from any branch of STEM. (In fact, I remember complaints from some Engineering professors that industries that actually make stuff couldn't get enough engineers because they could make more money working for a hedge fund than actually engineering things.)

[sataniccapitalist]

“Years of quantitative easing has fed an asset price bubble of monumental proportions… Since the banking system went belly up in the financial crisis of 2008-10, non-bank forms of finance have ballooned, and today account for around half of all UK and global financial sector assets.”

[xyymath]

Real-Life Uses of Calculus

Calculus isn’t just an abstract, ivory tower concept relegated to textbooks—it’s a powerful tool woven deeply into the fabric of our daily lives, from the precision of medical dosage to the unpredictability of the stock market.

1. Medicine: Optimizing Drug Dosage

Calculus plays a key role in pharmacokinetics, the branch of science that deals with the absorption, distribution, metabolism, and excretion of drugs in the body. When doctors prescribe medication, they need to ensure that drug levels remain within therapeutic bounds, not too high to cause toxicity and not too low to be ineffective. This is where differential equations, a core part of calculus, come into play. The rate of change of drug concentration over time is modeled with calculus to determine optimal dosage and scheduling for sustained, effective drug levels.

Take antibiotics, for example: they must be administered at specific intervals to maintain an effective concentration in the bloodstream while preventing bacterial resistance. Calculus allows for the continuous monitoring of drug levels and the adjustment of dosages based on individual metabolism rates, ensuring maximum therapeutic benefit.

2. Physics and Engineering: Motion and Forces

In classical mechanics, calculus is used to describe motion. Newton's laws of motion and universal gravitation are based on derivatives and integrals, the foundational elements of calculus. The change in velocity (acceleration) is the derivative of position with respect to time, while the area under the velocity-time graph gives us the distance traveled.

For instance, when designing cars, engineers use calculus to model the forces acting on the vehicle, such as friction, air resistance, and engine power. Calculus helps optimize everything from fuel efficiency to safety features, ensuring that a car can handle various conditions without exceeding performance thresholds.

3. Economics and Finance: Predicting Stock Market Trends

In economics, calculus is used to understand and predict market behavior. The concept of marginal analysis—examining the effects of small changes in variables—relies heavily on calculus. For example, marginal cost is the derivative of total cost with respect to quantity, and marginal revenue is the derivative of total revenue with respect to the quantity of goods sold.

In the stock market, calculus is utilized in quantitative finance to model stock prices using stochastic differential equations. Techniques like Black-Scholes for options pricing rely on calculus to determine the fair price of financial derivatives by analyzing how small fluctuations in stock prices impact their expected value. The concept of risk management—how much risk is worth taking for a given return—also uses derivatives to evaluate the rate of change of potential outcomes over time.

4. Environmental Science: Climate Modeling

Climate change models are inherently tied to calculus. Calculus is used to model the flow of energy through the Earth's atmosphere, oceans, and land, and how this energy affects global temperatures. The change in temperature over time is governed by differential equations, accounting for factors like greenhouse gas emissions, solar radiation, and ocean currents. As a result, climate scientists use calculus to predict future climate scenarios under various emission levels, helping inform policy decisions on global warming and sustainability.

5. Computer Science and Machine Learning: Optimization Algorithms

In machine learning, algorithms are designed to optimize a given function—whether it's minimizing the error in predictions or maximizing efficiency in a task. These algorithms often rely on derivatives to find the minimum or maximum of a function. For example, gradient descent, a popular optimization algorithm, uses the derivative of a function to iteratively adjust parameters and reach the optimal solution.

In computer graphics, calculus is essential for creating smooth curves and realistic animations. The mathematical process of curvature, which is the rate of change of direction along a curve, is vital for rendering images in 3D modeling and augmented reality.

6. Astronomy and Space Exploration: Orbital Mechanics

In space travel, calculus is crucial in calculating orbits, trajectories, and spaceship velocity. The path a spacecraft takes through space is influenced by gravitational forces, which can be modeled using calculus. For example, NASA’s mission to Mars relied on calculus to calculate the optimal launch window by accounting for the positions and motions of both Earth and Mars, ensuring the spacecraft would reach its destination efficiently.

[dailyanarchistposts]

When Elon Musk took over Twitter and the platform began to tank, the stock value plummeted, and people were leaving in droves, many of us thought he was just an arrogant doofus, a parasitic man-child who became a billionaire by throwing around free money, more recently billions in government subsidies but originally, as a kid, his massive inheritance from South African diamond mines. And he is all those things, but there is also something more going on here.

The Twitter takeover, in fact, possesses an opaque but important similarity with—of all things—the Chinese government’s COVID policy. If we assume that Musk’s many fumbles with one of the world’s largest social media platforms is nothing but a blunder, nothing but stupidity, then we miss out on an illuminating question. Which, it turns out, is the same question we miss when we assume the Chinese government’s zero tolerance COVID policy is a mere example of totalitarian inclinations or a different public health culture (both of which are explanations infused with racist stereotypes).

So what on Earth connects Elon Musk to China’s COVID policy? For one thing, one of Musk’s other companies, Tesla, became the first foreign company to wholly own a car factory in China when they opened an assembly plant in Shanghai in 2019. The Shanghai Gigafactory is one of Tesla’s largest, though it ran into problems when the government temporarily closed it down in 2020, and again in March 2022, to enforce a COVID quarantine. As the threat of new quarantines pops up, Musk might consider sending new investments to countries with weaker regulations like India. Apple, for example, is increasingly relying on India over China for iPhone production, meaning China’s COVID policy is costing them foreign direct investment.

There’s the similarity. A government policy causing a loss in revenue. A new corporate policy causing a plummet in stock value. Are we to judge both of these policies failures, or at the least, ineffective, because they lost money?

And that gets us to our central question: do companies and governments in this capitalist world system exist to make money? Is money, capital accumulation, the fundamental driving force of our world?

If it is, then both the turbulence Elon Musk has caused at Twitter and the stagnation the Chinese government has inflicted on its own economy due to its zero tolerance COVID policies have to be viewed as blunders, as they have unarguably caused a loss of economic value. However, in both cases, we might at least entertain the possibility that such an argument is reductionist if it hides other factors and outcomes that cannot be so easily quantified.

And quantification is an angle we need to explore to be able to answer this question. Even though the vagaries of international finance make it an obscure field, economic loss is easy to measure relative to qualitative forms of evaluation. Did Twitter lose value? Did the growth rate of the Chinese economy contract? Since both of these questions can be reduced to a number and real numbers are arranged along a single dimension, meaning we can always say whether one number is more or less than another number, then yes, Twitter lost value, and yes, the Chinese economy began to grow at a slower rate. So if it’s all about money, both of these policies were mistakes.

Before considering the case closed, should we be thinking about any kinds of qualitative as opposed to quantitative analysis that might illuminate the topic? After all, the knowledge systems of all the dominant institutions of our society are heavily biased in favor of quantitative and objective frameworks of thought; in fact this epistemology is central to the rationalism of the modern state and of capitalism itself, given that they allow for reproducibility and thus industrialism as both an economic and a political or war-making mode, and they allow ethical and spiritual frameworks to be subsumed into the construction of society itself, therefore making them invisible and immune to being questioned. If you want me to explain this idea more, let me know and I’ll devote some time to it in the future, but for now, let’s get back to Twitter.

What did Musk accomplish at Twitter, aside from losing unimaginably vast sums of money and showing the entire world that he’s not as intelligent as he thinks he is? He has taken a huge step to create a more right-wing media environment in what might become the biggest change to the landscape since the emergence of Fox News. True, Twitter’s algorithms always favored the specific content and also the controversy-seeking, baiting tactics of the Right. It is also true that conversation on Twitter was more often than not superficial and demeaning. However, we should not deny that anarchists and other anticapitalists saw Twitter as an important space for organizing and outreach. I had never been on social media my entire life, until finally around the end of 2019, when other anarchists convinced me that it did not make sense for me to spend so much time writing if I was going to avoid the platforms where writing and political analysis were actually being distributed in the current day.

And there are other corners of Twitter where emotional supportiveness, care, and mutual aid are actually the norm, spaces important in many people’s lives for building safety and opportunities for healing and connection, in rejection of the ableist, trans- and homophobic, racist culture that predominates in public space.

So yes, Twitter is a hellsite, but if we so quickly forget about some of the things that brought us there, we risk missing the relevance of this moment. Musk’s takeover of Twitter has enabled a fierce campaign of censorship against anarchist and other anticapitalist accounts, frequently executed by Musk himself, to such an extent that we should seriously consider that this was one of his primary motivations, more than making money. We already know that restoring Trump’s account was a motivator for him.

Meanwhile, the centrist media has given massive coverage to the Right’s “free speech” anti-censorship alibi. They continue to portray Musk as an anti-censorship figure, restoring far-Right accounts that had been banned, and they refuse to mention the accounts that Musk has been banning.

What about the Chinese government’s zero-tolerance COVID policy? Obviously, shutting everything down in a neighborhood, a city, or an entire region as soon as a rise in COVID cases is detected is going to be disruptive to the economy, as when when authorities closed down Tesla’s Shanghai Gigafactory and so many other thousands of factories. For a while now, Chinese planners and economists internationally have figures detailing how the zero-tolerance and other regulatory policies are slowing the economy and causing unemployment to skyrocket.

It’s important to mention that GDP growth is not just a metric imposed by Western observers. The Chinese Communist Party under Xi Jinping has made GDP growth targets a central part of their ruling strategy and their conceptualization of development. And yet, midway through the year, when it became clear they would not even meet their already reduced target of 5.5% growth, they chose to prioritize their restrictive no COVID policies.

Most countries in the world chose to allow a massive number of deaths in exchange for better economic growth. In the US, that’s over 1 million deaths, a figure we don’t see the media mention very often. However, the Chinese government cannot accurately be accused of humanitarianism, given that their solutions have included locking workers into their factories. In fact, their zero-tolerance COVID policy bears a striking similarity to Mao’s Four Pests Campaign, which sought to drive animals like flies and sparrows to extinction as a part of the government’s ambitious agricultural program. The purpose is less to save lives and more to eliminate external, natural forces capable of disrupting a rational, quantitative planning process.

A couple notes here, for accuracy. Mao is frequently lambasted for trying to eliminate sparrows, and the disastrous ecological consequences that policy had. At the same time (late ‘50s) and for significantly longer, the US government was trying to exterminate the wolves. Also, Western hacks and mainstream media frequently refer to socialist states as “planned economies” and NATO states as “free market economies.” Though there are significant differences in the strategies of state intervention in the economy, these labels are bogus since all modern states exist on the same continuum. The US government, from the beginning but even more so since FDR, engages in substantive economic planning, deciding which sectors will get the most capital, deciding interest rates, setting targets for inflation; and the Chinese government allows and encourages a massive private sector that is more responsive to market forces.

The reason all states engage in planning, and a more accurate framework for understanding the nature of that planning, is social control.

What is social control? The Marxist I like the most told me it is a fetishistic, meaningless category. Actually, it’s a necessary concept for explaining some glaring holes in Marxism itself and in any framework that sees capital accumulation as the be-all and end-all for understanding our society.

Musk’s actions make sense, even though they lost him $9 billion dollars, because like any capitalist he is worried about fundamental questions of social control that allow him to be a capitalist in the first place. The Chinese government’s actions make sense because developing techniques that allow a state to neutralize and surpass epidemics would greatly increase that state’s planning powers, and even if they fail they are testing and amplifying their arsenal of social control techniques, and social control is the fundamental concern of any state and thus the fundamental concern of capitalism, being an economic system entirely dependent on state power.

In this context it is worth noting that the Chinese government decided to relax their COVID policy not in early July, when they were forced to choose that policy over their economic growth targets, but at the end of November, when mass protests bordering on insurrection against the policy broke out. The policy got in the way of economic accumulation: they stuck to it. The policy got in the way of social control: they abandoned it.

Academically trained Marxists are going to be biased in favor of a quantitative analysis, like seeing capital accumulation as the fundamental force in our society, for the same reasons that all our dominant institutions are biased in favor of quantitative analysis. A qualitative analysis is not reproducible, and the modern state needs access to reproducible sciences.

This seems like a contradiction to claim that the state is fundamentally motivated by a qualitative science, like social control, and yet constantly in need of a quantitative science like capital accumulation. In fact, this contradiction traces a tense balance, a relation, that has come to shape the entire planet in these last centuries. The fundamental truth of the State is social control, an existential war waged by centralized power against all life. And the most effective motor the State has ever developed to fuel its war is not a winning religion, it’s not a more streamlined process for the transfer of power, it’s economic accumulation. Before capitalism, states were exponentially weaker, frequently overthrown by the societies they tried to dominate, even when state and society shared the hierarchical culture produced by patriarchy and organized religion.

Capitalism, which requires the enclosure of the commons and the alienation of all life, cannot exist without the planning and war-making powers of the State. And once capitalism emerged, created in a continuum by the Italian city-states, the Castillian-Aragonese state, and finally in its modern form by the Dutch state, it bestowed the states that adopted it with such power that henceforth it became the duty of every government on the planet to embrace capitalism, lest they be overwhelmed by those that already had. This sheds light on one of the reasons that colonialism spread in such a rapid wave, especially where there were already states that could be instrumentalized in the conquered territories. And it helps explain why socialism, by not rejecting the state, was fully absorbed by capitalism in the early 20th century, and why all Marxist-inspired states are fully capitalist, fully colonial, and every bit as imperialist as their geopolitical circumstances allow them to be.

Capital accumulation is a necessary motor for the state; it is also a favored metric for a quantitative science of power. Given that accumulation is a result of oppressive, exploitative processes and it cannot happen without the domination of society and nature, high rates of accumulation are generally a good indicator that state power is firmly ensconced, that the State is winning its war against life. Still, the fundamental question is that of social control. Many capitalists, as specialists, will lose sight of this as they become obsessed with their numbers game, but in the end it’s just a game, a highly useful game, and when push comes to shove, questions of social war will always be more important for the institutions of power. The trick for them is to make sure that seeking capital accumulation and seeking social control always go hand in hand, rather than entering into contradiction.

As for anticapitalist movements, we lose sight of the social war at our own risk. The reasons for this are multiple. Marxism’s predictive power regarding the development of the revolution is nil, displaying a profound lack of understanding of what revolution actually means. Attempts to combine materialist with geopolitical analysis, as with Giovanni Arrighi’s development of world systems theory (on the whole an illuminating theoretical framework) also demonstrate their inaccuracy and disconnection from living history wherever they focus too heavily on quantitative questions of capital accumulation, a weakness explored in Alex Gorrion’s “Anarchy in World Systems.” These are not just obscure questions relating to debates from past centuries, given how academic, materialist-oriented journals and discussion groups continue to falsify the history of revolutionary struggle as we live it, claiming, for example, that the major uprisings of the past two decades have occurred as a result of the crisis of accumulation, when in fact the uprisings preceded the manifestation of that crisis and have occurred in countries experiencing polar opposite moments in the kinds of crises capitalism constantly produces.

(I shouldn’t have to provide this rebuttal, but alas, experience tells me I do: it is intellectually dishonest and a waste of everyone’s time to start off by claiming that rebellion is “produced” by a specific quantitative crisis in accumulation, to then be shown that in fact rebellions are occurring in completely different economic circumstances—the crises associated with growth, the crises associated with recession, the crises associated with inflation—and then to double back around and claim that one’s original argument was that crisis produces rebellion. Given that capitalism is a constant string of crises, this is a meaningless statement with nothing predictive or scientific about it, and it sets up the dishonest strawman that non-materialists believe that rebellions come out of thin air, in no way a response to their surroundings.)

Time and again, the first sign of crisis that materialists notice is the rebellion itself, meaning they are rarely on the front lines. Those who are more present tend to be those who decide to fight back even if objective conditions are supposedly unfavorable.

For our survival, we need to understand the ways the State is designing a constant war against us, and always has been, and always will be. For our liberation, we need to understand unquantifiable life, abundance without capital, and we need to develop an intelligence for a kind of struggle that also subverts the logic of warfare. A collective sight that can perceive the battlefield but destroy the opposing army by moving sideways, by burrowing, by climbing into the trees, by turning the battlefield back into a field, a forest, a community.

[girliestwomaninstem]

introductory post (⁠◍⁠•⁠ᴗ⁠•⁠◍⁠)⁠✧⁠*

Hellooo <3 I've been on Tumblr for a few years now, however, lately I have become a bit of a slacker in terms of my student and professional life and a lot of y'all have inspired me to get a studyblr + accountability blog to help keep myself in check, focused and driven

🌻 about me ♡

name: tofu

age: 23

pronouns: she/her

zodiac: ⊙ aries, ☽ scorpio, ↑ scorpio

languages I speak: english, hindi, japanese (beginner)

🌻 my favourite subjects ♡

- academic: chemistry, cybersecurity, creative writing, biology, personal finance, physics, discrete math, intro to programming (the easiest part about a cs degree yet daunting)

- non-academic: cosmetic science, psychology, literature, ancient/modern history, physics, astronomy, linguistics

I'm trying to once again pick up hobbies that I used to have as a child, such as reading, singing, gardening, cooking/baking, scrapbooking

In my free time, I love watching asian soap operas, Studio Ghibli, and sitcoms that I'd like to call my comfort shows and video essays related to all my non-academic subject interests

I'm an undergrad student currently enrolled in a computer science/fintech double major and I'm preparing either to enter the workforce or pursue a masters in either quantitative finance or bioinformatics engineering or data science (wow, the existential crisis that came with typing up that sentence). I could also talk more about my interests in the above-mentioned subject areas, or new ones as they come up. My goal is to create a routine for myself that I can actually stick to, and spend each day having learnt at least something, no matter how small. I feel like the only way to achieve that is by comparing myself to my peers (I know that is v toxic but hey it helps). Additionally I really want to learn how to drive this year, learn to crochet and keep up with new technologies, do some art journalling to take my mind off stress.

I'm so excited to meet new people on here and keep myself busy and productive! ❤️