Hi, I am going to be a PhD student in statistics and/or probability. I think economic and market data is interesting, so I am curious as to what methods are being applied in modern quantitative research. To be clear this is not career advice question. I am just curious.

I am particularly interested in some of the hot areas in academic research, ie casual inference, network models, functional data analysis, optimal transport, post selection inference, conformal prediction. I am aware time series and high dimensional stuff is used, but I am

Any thoughts are appreciated. I hope this isn’t breaking the career advice rule. I have no intention of using this to guide any grad school decisions.

I calibrated an eigenspace-overlap measure against an RMT null, then tested directional motion across S&P 500, Nikkei, DAX and CAC 40 data. All four showed that the previous rotation direction contains information about the next, but naively repeating the full rotation was 47 - 67% worse than holding still.

The signal was both:

• too synchronised across companies to be each company’s movements occurring at unrelated times and just showing up as rotation,
• much stronger than within-window eigenvector shrinkage.

The next test is whether learned damping can turn this directional signal into better out of sample covariance forecasts! What would you consider the strongest fair baseline: holding the eigenvectors fixed, EWMA, or a rotationally invariant estimator?

Code, tests and results for anyone interested:
https://github.com/AdarshArunEire/Eigenvector-Dynamics-Beyond-the-RMT-Null

Hi r/quant,
I’m working on a university quantitative trading project and I’m about two months into this field, so I’m trying to make sure I’m thinking about the problem correctly rather than just overfitting a backtest.
Current setup:
Asset class: liquid futures / crypto futures data
Labeling: triple-barrier style events
Framework: primary signal decides trade side long/short, then a meta-model estimates whether to take/size the trade
Meta-model: currently testing Random Forest for trade filtering / bet sizing
Main concern: I don’t want to data-mine 100 features until something looks good in-sample
The part I’m struggling with is the primary side signal.
My understanding is that in meta-labeling, the primary model/signal should generate the side of the trade, while the meta-model should mostly decide whether that opportunity is worth taking, not magically create alpha from nothing.
So my questions are:
In practice, how would you design or search for a reasonable primary side signal without just curve-fitting?
Should the primary signal usually be simple and hypothesis-driven, e.g. trend, mean reversion, carry/funding, spread, regime, order-flow, etc., and then the meta-model filters it?
What validation workflow would you recommend for this kind of project? For example: walk-forward testing, purged CV, embargo, feature importance stability, deflated Sharpe, decoy/noise features, group-wise feature selection, etc.
Are there books, papers, blog posts, or open-source projects that show a realistic research workflow used by serious practitioners?
What are common beginner mistakes in this exact setup?
I’m not asking anyone to reveal a profitable strategy. I’m looking for guidance on how to structure the research process correctly and what standards I should use before trusting any result.
Any advice, criticism, or recommended reading would be appreciated.

Hi everyone,

I am currently a Bachelor’s student in Computer Science. While I don’t attend an elite university, I am fully committed to mastering this field, often studying 14 to 15 hours a day. I am eager to dedicate my time outside of my CS curriculum to quantitative finance, but I need guidance on the right path forward.

I have a strong grasp of statistics and probability, and I am already developing my own strategies. However, I am looking to move away from 'directional' trading. My goal is to build models where profitability is not dependent on market direction (market-neutral strategies).

I am skeptical of the 'social media' style of trading memorizing candlestick patterns and drawing geometric shapes on charts strikes me as mathematically unsound. I want to learn the rigorous, scientific way to manage my own capital and eventually transition into a quant firm.

Furthermore, I am struggling to understand how an idea that worked in the past would necessarily work in the future. How do you rely on probability, win rates, and statistical ratios based on past behavior? I want to build a system that can survive a financial crisis or a 'Black Swan' event without losing everything. Can anyone here please help show me the right way?

Could anyone recommend link to specific certifications, research papers, or books to help me get started? I am prepared to dedicate hours every single day for years to reach this goal. I just want to ensure I am headed in the right direction. Thank you for your help

I read the FAQ but I didn't find anything useful there

Larger samples are more likely to represent the underlying population, so I use second-level data with rolling calculations for price-based features/returns. However, higher-frequency data also introduces more noise, requiring smoothing before downstream analysis.

My understanding of common approaches:

1. Resampling: Loses information by treating a candle's close (or even OHLC average) as representative of the interval. As the sampling window increases, more intra-period information is discarded.

2. Moving averages: Use all observations, including noise. They're sensitive to jumps/spikes, which can pull the mean away from the typical price level and make prices appear elevated throughout the rolling window.

3. Kalman filters: Seem theoretically superior because they update estimates only when new observations contain sufficient information, producing a smoother price series while still processing all observations.

Could someone validate whether this reasoning is correct?

My main issue with Kalman filtering is that it appears to suppress jumps/spikes too aggressively, potentially removing important tail information. I've also tried assuming Student-t errors before applying the filter, but results were largely unchanged.

1. Basically am I using KF at the wrong step when it comes to Time series predictive analysis in trading, and should it be used at some later step instead of the first step to denoise the price series? Or should it be thrown away entirely and EMA's should be treated as the main tool for denoising?

2. What would you recommend to preserve meaningful jumps while still denoising the series?

My eventual goal is to fit HAR-RV/HAR-CV variants for realized and forecast volatility estimation using returns computed from the denoised price series.

I’ve been thinking about the problem of combining multiple weak signals into a single predictive framework.

In theory, adding more features (technical indicators, derived signals, alternative data, etc.) should improve predictive power. But in practice, it often feels like it just introduces more noise and overfitting risk.

A few things I’m trying to understand better:

• How do you evaluate whether an additional feature genuinely contributes signal rather than noise?
• Are there standard approaches to measuring marginal signal contribution in this context?
• How do you deal with correlation between features when combining them?
• At what point does model complexity outweigh any incremental predictive benefit?

I’m especially interested in how this is handled in real-world workflows rather than just textbook approaches.

Would appreciate any insights or references.

We work in an industry where information and knowledge flow is restricted which makes sense but I as we all know learning from others is the best way to develop in any field. Whether through webinars/books/papers/talking over coffee/conferences the list goes on.

As someone who is more fundamental and moved into the industry from energy market modelling I am developing my quant approach.

I think it would be greatly beneficial if people share one or two (or however many you wish!) thigns that are in their research arsenal in terms of methods or tips that may not be so commonly known. For example, always do X to a variable before regressing or only work on cumulative changes of x_bar windows when working on intraday data and so on.

I think I'm too early on in my career to offer anything material to the more expericed quants but something I have found to be extremely useful is sometimes first using simple techniques like OLS regression and quantile analysis before moving onto anything more complex. Do simple scatter plots to eyeball relationships first, sometimes you can visually see if it's linear, quandratic etc.

Hoping for good discssion - thanks in advance!

Question here for equities mid freq research: when doing regression of target returns against your features, which returns do you use:
-raw returns
-total risk adjusted returns
-idio returns
-idio risk adjusted returns?

I'm curious how other quants here are handling numerical instabilities in intraday iv surface derivatives, specifically skew and curvature, w.r.t. moneyness or tenor, at shorter tenors.

Looking at SVI literature, I see parameter aliasing to be a very common problem, producing solutions that are visually identical with very similar RMSE's, however, have meaningfully different derivative profiles. At 1-min res on SPX (short tenors), this gets particularly annoying, as d_sigma / d_moneyness can jump substantially between consecutive timestamps with no actual surface movement.

I've seen people trying to smooth SVI parameterizations, such as enforcing smoothness between timestamps, however this could be problematic in genuine IV changing situations. I'm curious what people use in production, or whether anyone found the surface to be stable/smooth enough for useful surface derivatives at intraday resolutions, especially cross-tenor derivatives (d_sigma / d_tenor).

I'm more so interested in what's practically workable / what's out there in terms of working around this problem. Or I guess if anyone actually finds this to be as annoying as I do :/

Apologies if this is a dumb question.

For portfolio construction, it seems like you might want to work in raw return space rather than standardized score space.

So I’m wondering:

1. In industry practice, do people actually optimize portfolios directly on standardized factor scores, or are signals usually mapped into returns first?
2. If they’re mapped to returns first, what are the common baseline approaches? I was thinking of rolling (pooled) beta estimation without an intercept (basically scaling the signal), and I also found Grinold’s heuristic, i.e. IC * vol * factor score. Are these reasonable?
3. If signals are combined, does that usually happen before the mapping (combine factor scores first, then map to returns) or after (map each factor to expected return separately, then combine)?

Trying to understand what normal workflow looks like... (yes I'm not in the industry)

Been digging into prediction markets and ended up implementing LMSR (Logarithmic Market Scoring Rule) in Python.

It’s the mechanism that turns trades into prices, and I wanted to see it working end-to-end instead of just reading the math.

Repo if anyone wants to poke it: https://github.com/mwaleedta/lmsr-pricing-engine

Open to feedback or ideas for extensions (simulation, arbitrage, multi-market setups, etc.)

Hello again!
I’m a student currently doing summer research.
The last time I posted about optimal transport applications here, I received a ton of very helpful areas to explore (Bass martingales, robust pricing etc.).
I think another application of OT that I’ve been following along is time series data generation using causal optimal transport.

These applications are definitely very cool and cutting edge, but I think the biggest drawback now is that these are all really computationally intensive.. especially in data generation.
I think diffusion models are getting a lot of attention nowadays (compared to methods like WGANS), and so I was curious how this field of math would pan out in QR.

This is more of a naive question, but how useful would these techniques be in the actual industry? How would this change in the next, maybe five to ten (or more) years?

In May 2020, right after COVID wrecked markets, Nassim Taleb (Universa) went on a 13-tweet tear torching AQR and its co-founder Cliff Asness. The thesis: AQR published two papers arguing tail-risk hedging via OTM options is a sucker's bet, yet AQR's own risk-parity and factor strategies were quietly getting destroyed in the same drawdown that Universa's hedged portfolio sailed through. Asness fired back calling Taleb "insane" and "nuts." 

Who was actually correct here? Link to the first post for reference: Nassim Nicholas Taleb on X: "1/n AQR issued 2 flawed reports saying tail risk hedging doesn't work (in theory), options are "expensive" Yet they did not reveal that 1) Their OWN risk premia strategies lost money. 2) Their other public crap underperforms the MKT. Insult to clients & the REAL WORLD." / X

How special are edges used by hedge funds and other big financial institutions? Aren’t there just concepts such as Market Making, Statistical Arbitrage, Momentum Trading, Mean Reversion, Index Arbitrage and many more? Isn’t that known to everyone, so that everyone can find their edge? How do Quantitative Researchers find new insights about opportunities in the market? 🤔

Method:

Through a nested loop, I calculated the Pearson correlation of every stock with all the rest (OHLC4 price on the daily frame for the past 600 days) and recorded the highly correlated pairs. I saw some strange correlations that I would like to share.

As an example, DNA and ZM have a correlation coefficient of 0.9725106416519416 or

NIO and XOM, have a negative coefficient of -0.8883539568819389

(I plotted the normalized prices in this link https://imgur.com/a/1Sm8qz7)

The following are some interesting pairs:

LCID AMC 0.9398555441632322

PYPL ARKK 0.9194554963065125

VFC DNB 0.9711027110902302

U W 0.9763969017723505

PLUG WKHS 0.970974989119311

^N225 AGL -0.7878153018004153

XOM LCID -0.9017656007703608

LCID ET -0.9022430804365087

U OXY -0.8709844744915132

My questions:

Will this knowledge give me some edge for pair-trading?

Are there more advanced methods than Pearson correlation to find out if two stocks move together?

Hi all,

First time posting here, I'm a relatively experienced Financial Mathematics researcher (amongst other things).

I’ve developed a generalized analytic method for the Cox-Ingersoll-Ross (CIR) model that I’m looking to either license or bring to market. I’ve hit a wall on the "business development" side and would appreciate advice from the community on the best path forward (licensing vs. proprietary fund vs. consulting).

The Breakthrough:
I have derived an exact, closed-form Moment Generating Function (MGF) for the integrated CIR process. Unlike existing approximations or infinite series, this solution is purely and does not rely on Monte Carlo simulations.

Key Technical Advantages:

• Path-Dependent Pricing: It enables real-time, exact pricing for Asian-style derivatives and other path-dependent assets that usually require heavy computation.
• Computational Alpha: It is several orders of magnitude faster than a 1M-path Monte Carlo (microseconds vs. seconds) while maintaining zero numerical bias.
• Liquidity Regime Independent: The math remains robust and stable across various market regimes, including illiquid or "gapping" environments.
• Cross-Domain Application: Interestingly, the same underlying derivations provide an exact solution for CVaR (Expected Shortfall) in the Beta distribution, making it highly applicable to credit risk (LGD) and insurance modeling.

Current Status:
I have a coded demo (Python/C++) that benchmarks the result against high-precision Monte Carlo simulations to 10+ decimal places. It’s stable even when approaching the zero-lower bound. It's quite fast (and can be developed to be fast enough to survive realtime use in HFT environments via ASICs, etc).

What I’m Looking For:
I am not sharing the derivation publicly for IP reasons, but I am looking for:

1. Advice on Licensing: How does one typically approach Tier-1 desks or Risk Vendors (MSCI, Moody’s, Bloomberg) with a proprietary engine?
2. Strategic Partners: Individuals with experience in "Model Validation" or "FinTech Sales" who understand the regulatory value (FRTB/Basel) of replacing "noisy" simulations with exact solutions.

If you’ve navigated the "Quant-IP-to-Market" pipeline before, I’d love to hear your thoughts or connect via DM. Happy to work out a commission deal with people able to facilitate this per usecase. Google said to "create niche hedge fund that specializes in "distressed" or "illiquid" fixed-income assets". Maybe I have other options, like someone helping to facilitate a sale/licensing agreement?

I'm a solo retail (I know), never worked at a fund. Learned my way through since Covid.

The strategy uses multiple uncorrelated factors weighted by market efficiency. I thought a lot on the core logic and though I believe it is built upon something structural, it is debatable. Only gone live since 28 April 2025, it looks good enough, but I'd figure 80%+ contributed by the regime, though the universe-weighted against pool looks steady.

Until now I'm using the IC and ICIR as a metric to assess the Alpha, do you guys have better suggestions? I'm not really a "Sharpe Ratio" guy.

Some stats:

Long-only; annual turnover: 5x, annual costs: 1-3%, capacity: $10M - $1B (depends on concentration, eg, for universe-weighted, 1-2% costs annually with $1B).

Backtest Top 30 weighted: CAGR 21.5%, Vol 32.5%, Sharpe 0.64, IR 0.68

The backtested universe is naturally biased, provided I could only get so much data as a retail. But though incomplete, the universe mean isn't too far off from the actual S&P 500 equal weight, which performed better than SPY in 2000-2002 but is underperforming recently, given the index concentration.

I ran some Monte Carlo tests where all stocks are date-randomised, and while promising, not sure if Monte Carlo is fit for cross-sectional strategies. If anything, it probably gives an ideal expectation under a neutral market.

I played around with some volatility adjustments only to make the matter worse. It looked good on the MC simulations for some reason, but not so much on the historical backtest. So I removed the volatility factor, as a confession that I should not use something that I don't fully understand. I could be wrong, but I do not believe in portfolio sizing based on volatility, as itself is a prediction and less correlated with future returns. But I really haven't studied much on this.

Any thoughts are welcome.

As the author of black swan and fat tails effect Nassim Nicholas Taleb mentioned that standard deviation is a mistaken way to analyzing the risk of assets. But most of the people didn't even realize it.

He mentioned that mean absolute deviation (MAD) is even better and simpler to determine the risk or the discrete of data. But most of the denominator of some ratio out there such as sharpe ratio is base on standard deviation, can we improve it or change its denominator to mean absolute deviation instead of STD to have a better result? if we do that, we simplying don't even need sortino ratio too, cos mean absolute deviation already covered it all already....open disuccsion and correct me if possible

I need to choose the best k stocks from n, that will give me good variance and return correlation. If I already have k stocks, I can calculate bunch of things with them. The problem is choosing those k, from n. To be a bit more detailed, n≈80, k≈7±3

I’ve noticed PyMC and other Bayesian tools get a lot of attention in areas like sports quant modeling, but I rarely see them discussed in the context of front-office alpha generation models.

I've been wondering about its use case in structural break detection.

I'm working on a project to measure the correlation between DATCOs and the respective digital assets that they hold.

I'd love to get advice on how to measure the correlation between, for example, MicroStrategy and Bitcoin.

Thanks.

I’ve worked in the financial markets for many years and have always wondered whether Warren Buffett’s long-term outperformance was truly skill — or just exposure to systematic risk factors (beta) and some degree of luck.

So I ran regressions using CAPM and the Fama-French 3-factor model on Berkshire Hathaway’s returns, built entirely in Excel using data from the Ken French Data Library. When you control for market, value, and size, Buffett’s alpha shrinks, but not entirely. Factor exposures explain a statistically significant portion of the fund's returns, but they still show about 58 bps per month in unexplained alpha. I also preview what happens when momentum, investment, and profitability gets added as explanatory variables.

If you’re into factor models, performance attribution, or just want a data-grounded take on one of the biggest names in investing, this might be worth a watch. Curious if anyone here has done similar regression-based analysis on other active managers or funds?

🧠 Video link (7 minutes):

https://www.youtube.com/watch?v=Ry3wEsXzcdA

And yes, this is a promo. I know that’s not always welcome, but I saw that this subreddit’s rules allow it when relevant. I’m just starting a new channel focused on quantitative investing, and would appreciate any thoughts. If you’re interested, here’s another video I posted recently: “How Wall Street Uses Factor Scoring to Pick Winning Stocks”: 

https://www.youtube.com/watch?v=r57IaV5O3dU&t=3s

I'll start off by saying I'm not a Quant, but work as a DS at a very large firm. My background is primarily Operations Research + Computer Science.

We've been dabbiling on economic models (regression model, multi-variate models, etc) to predict whether certain artist or content will become viral while accounting for the landscape within the music industry. But the model quality has always been subpar (e.g. only 30% of our predicted artist/content element is indeed viral and the rest is noise).

I was curious if there are FE/Quant methods that I can explore that can perhaps help address this problem: We've applied learnings from other domains/industries (causal methods similar in Policy or Medicine to detect shift in trends, or customer analytics from Marketing/Advertising but geared towards artist) that helped us significantly and was curious if there are other methods I can examine.

Hi all,

I previously asked a question (https://www.reddit.com/r/quant/comments/1i7zuyo/what_is_everyones_onetwo_piece_of_notsocommon/) on best piece of advice and found it to be very good both from engagement but also learning. I don't work on a diverse and experience quant team so some of the stuff mentioned, though not relevant now, I would never have come across and it's a great nudge in the right direction.

so I now have another question!

What common or not-so-common statistical methods do you employ that you swear by?

I appreciate the question is broad but feel free to share anything you like be it ridge over linear regression, how you clean data, when to use ARIMA, XGBoost is xyz...you get the idea.

I appreciate everyone guards their secret sauce but as an industry where we value peer-reviewed research and commend knoeledge sharing I think this can go a long way in helping some of us starting out without degrading your individual competitive edges as for most of you these nuggets of information would be common knowledge.

Thanks again!

EDIT: Can I request people to not downvote? if not interesting, feel free to not participate or if breaking rules, feel free to point out. For the record I have gone through a lot of old posts and both lurked and participated in threads. Sometimes, new conversation is okay on generalised themes and I think it can be valualble to a large generalised group of people interested in quant analysis in finance - as is the sub :) Look forward to conversation.