#quantitative finance

[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.

[genius-mathematics-consultants]

The Longstaff-Schwartz method for early exercise derivative pricing using machine learning and optimization techniques

If you're interested in pricing derivatives with early exercise using Monte-Carlo, you may have heard of the Longstaff-Schwartz method.

When pricing a derivative using Monte-Carlo, you generate a large number of potential price paths of the underlying, and determine the value of the derivative payoff on each path. The fair price of the derivative is then simply the average across all of the potential paths. When the derivative is callable / has an early exercise feature, an additional complexity is that to value the derivative on each path you need to work out at which time step the holder would exercise the derivative. This is the first time step at which the payoff from exercising is higher than the expected payoff from continuing.

Longstaff and Schwartz devised a clever method for determining the expected value of continuing. It involves fitting a straight line to a scatterplot, and then applying an optimization routine to adjust the line into the position that exercises/continues optimally.

I wondered, what if instead of a straight line we used a more complex non-parametric curve? Could we get better results?

What I found was interesting. The straight line approach is sufficient as long as the function representing the value of exercising and the function representing the value of continuing do not intersect at more than two points. For a derivative with a complex payoff with more than two intersection points, the straight line method would fail and a non-parametric curve fitting would succeed.

I also found that the essence of the Longstaff-Schwartz method is not really curve fitting, but something more akin to machine learning, and classification methods like a support vector machine.

Read the full article

[maal-wave]

Top-Paying Jobs in Finance

[notayesmanseconomics]

The ECB is trying to reduce what is already weak money supply growth

These are awkward times for the ECB and its President Christine Lagarde. There are obvious issues in her home country of France which is at the heart of the Euro project. But my main issue is her policy of abandoning Forward Guidance on interest-rates and then promising an interest-rate cut in June. This came with the implication that we were moving into a sequence of cuts with another next…

[dayofbanks]

Monetary Policy and the Evolution of Wealth Disparity - An Assessment Using US Survey of Consumer Finance Data.

This session examines the distributional effects of recent - monetary policies on income and wealth. Using the Federal Reserve Board's Survey of Consumer Finances, the research tracks key subpopulations as monetary policy shifted from conventional interest rates to Quantitative Easing. Employing advanced modeling techniques, the study analyzes volatility and bifurcation in capital gains and incomes among U.S.

Watch the Monetary Policy and the Evolution of Wealth Disparity - An Assessment Using US Survey of Consumer Finance Data!

[marketxcel]

What Is Trend Analysis in Research? Types, Methods, and Examples

Explore the essence of trend analysis in research, encompassing its diverse types, methodologies, and real-world examples. Unravel the significance of tracking trends to glean insights and make informed decisions in various fields.

[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

[thecreativecorner10]

THE BEST CHAT GPT STOCKS TO BUY NOW

Investing in stocks, especially chat gpt stocks, is a great way to diversify your portfolio and potentially increase profits in the long–term. Chat gpt stocks are stocks in companies that develop and/or market software and services that enable communication over the internet, such as instant messaging, video conferencing, and voice over internet protocol (VoIP). These stocks have been in the…

[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

[symbiosisint]

Quantitative Finance Courses in India- SIBM Bengaluru

Pursue Quantitative Finance Courses in India with one of the top college SIBM Bengaluru. Quantitative finance is a branch of finance that uses mathematical modelling techniques to study financial markets. The course offers a platform for students to develop their quantitative expertise and fundamental financial theories. https://www.sibmbengaluru.edu.in/mba-in-quantitative-finance/

[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/

[cauchesque]

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

[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.”

[dysfunctional-programming]

if the {ethical,work-life balance,achievability,} problems were solved i think i would really enjoy quantitative finance work. an environment where correctness and (execution) speed are the main goals is very appealing to me, and they usually get to play with more toys than e.g. react.js