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Questions tagged [machine-learning]

Algorithms that allow computers to evolve behaviors based on empirical data. Approaches include genetic programming, artificial neural networks, decision trees, support vector machines, and cluster analysis.

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How do I predict future earnings dates if I have a database of all prior earnings dates?

So I have a database of all earnings announcements for all US stocks down to the millisecond for the past 10 years, and I want to make reasonable predictions on when exactly next earnings will be ...
viking's user avatar
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2 votes
1 answer
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Weak Stationarity for Neural Network Input?

I am taking a course that detailed that input data into neural networks should be at least weakly predictive and weakly stationary (stable mean). Does this principle apply to other ML models like tree-...
Dylan McClish's user avatar
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1 answer
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Proper Use of CPCV for Hyperparameter Tuning and Backtesting in a Trading Strategy

I'm working on a binary classification model for a month-end trading strategy with 6 months of data. Initially, I split the data by using the last month for evaluation and backtesting, but this left ...
June's user avatar
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3 answers
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Does AI-based trading assume efficient market hypothesis?

When we use AI (machine learning/deep learning) in trading does that assume efficient market hypothesis? I know quantitative finance assumes price moves are random (efficient market hypothesis). Does ...
quanity's user avatar
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Corporate Credit Risk Modeling Books

Can anybody refer me to a good corporate credit risk modeling book? I'm looking for something more advanced than what's in Hull's very good risk management book. There seems to be many excellent ...
Charles0349's user avatar
2 votes
1 answer
188 views

In which context do hedge funds use the Gauss Markov Theorem?

Hedge Funds really like asking questions about linear regression during interviews. Especially about the properties of the OLS. But I don't understand in which context this is used. For example the ...
confucius_is_confused's user avatar
2 votes
0 answers
88 views

Stambaugh inference for Investment Analysis when History Lengths Differ

This pertains to Stambaugh in the JFE (vol. 45, 1997 pp 285-331), and I have a question about Proposition 1 results (page 292). (link) To set the background, let's take the smallest relevant ...
Woodpecker's user avatar
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70 views

Boosting models for algo trading

I’m currently working on a xgboost model to predict the price change above or below a given percentage between a candle’s open price and the next candle’s close price. I use a wide range of features, ...
daniel dvali's user avatar
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1 answer
130 views

Market data and machine learning

I have the following general question regarding the use of ML in quantitative finance: Lets say I want to train a model (for simplicity lets consider a neural network), so that I feed some market data ...
KT8's user avatar
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2 votes
1 answer
122 views

Deep vs "shallow" calibration of option pricing models

I am currently investigating the application of deep learning in calibrating option pricing models, specifically, models of rough volatility, such as rBergomi. While there is a lot of research on ...
dasfobia's user avatar
0 votes
1 answer
76 views

How should I create a Risk measurement Variable?

I have clients who take loans (Advances) weekly. The way that they repay the advance is after 3 weeks when their goods are sold, using the sales proceeds of the goods. But if the goods don't sell for ...
user70803's user avatar
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104 views

Potential problems with trying to apply reinforcement learning to algorithmic trading

I have been attempting to develop an algorithmic trading agent for a single asset pair and upon researching, it seems as if, in theory, reinforcement learning would be a natural way to approach this ...
QMath's user avatar
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Is the impact of "small" orders on market dynamics more than is commonly assumed?

When modeling the dynamics of a market, a common assumption is that the impact of a "small" (e.g. very low percentage of daily traded volume) order on current and future observations of the ...
QMath's user avatar
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46 views

Predict a company business classification

I am trying to predict whether companies belong to a universe considered by an index provider for a particular thematic index using natural language processing techniques. In this particular example, ...
wissam124's user avatar
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0 answers
73 views

Machine learning techniques for small datasets

I am dealing with financial data which is available on a Monthly basis. I am planning to apply machine learning techniques like LSTM but issue here is that overall I have very limited training dataset ...
Add's user avatar
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1 vote
1 answer
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Sampling dollar bars for ML model of multiple tickers

I have a Neural Network model that provides predictions for the future returns of a portfolio comprising stocks and cryptocurrencies. The original model operates on standard time bars and generates ...
apt45's user avatar
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0 answers
114 views

Lopez de Prado Advances in Financial Machine Learning- entropy for adverse selection

In chapter 18: Entropy Features, Lopez de Prado discusses how entropy can be used to estimate adverse selection. He suggests a method where order imbalance is mapped to quantiles and entropy is ...
Cameron's user avatar
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1 vote
1 answer
141 views

Forecasting Realized Volatility with Machine Learning [closed]

How is the daily realized variance calculated for an intraday one minute data. How can realized volatility be forecasted using machine learning techniques such as neural network and LSTM. Any detailed ...
Samuel Gyamerah's user avatar
3 votes
0 answers
135 views

Understanding the Intersection of "Advances in Financial Machine Learning" and "Asset Pricing in Stock Market Prediction"

I have been reading "Advances in Financial Machine Learning" by Marcos Lopez de Prado and "Machine Learning in Asset Pricing" by Stefan Nagel, and I noticed that there seems to be ...
RRR's user avatar
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0 answers
73 views

Stock clustering for Statistical Arbitrage Trading

Has ML based stock-clustering been practically adapted by the industry in arbitrage trading strategies like pairs trading for forming pairs instead of other traditional techniques like cointegration?
Rohan Kuntoji's user avatar
1 vote
0 answers
225 views

Technical Analysis Indicators as input of a LSTM Neural Network ? Need advices

I'm trying to make a trading strategy by training a LSTM neural network with input features being typical technical analysis metrics: RSI, MACD, ema ratio (EMA 50 divided by EMA 200, so that the NN ...
Jerem Lachkar's user avatar
2 votes
1 answer
572 views

Advances in financial machine learning (Marcos López de Prado): explanation of snippet 3.1

I have been reading AFML ( Marcos López de Prado ) and I am having trouble understanding snippet 3.1 which provides the following code: ...
md0101's user avatar
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2 votes
1 answer
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Derivatives without analytic expressions? [closed]

I was wondering if there exist options or other derivatives that do not have a known closed-form analytic expression (i.e., some sort of Black-Scholes PDE) and are usually priced using Monte Carlo ...
Physics Penguin's user avatar
1 vote
0 answers
42 views

Having a hard time getting sentiment analysis bot to calibrate bet probabilities well

Training a language model for taking bets in the stock market based on news. For each news release I have three categories for each stock: long bet, short bet, or neither. The NN outputs probabilities ...
Alex Amadori's user avatar
1 vote
0 answers
71 views

Should we split data into several periods before calculating class weight? (Advances in Financial Machine Learning)

In the book, section 4.8 class weights, Marcos suggests applying class weight, which I agree because sometimes you have more bullish price action than bearish price action e.g. 52% of the time is ...
offchan's user avatar
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2 votes
0 answers
314 views

Interpretation of Chu-Stinchcombe-White CUSUM Test results

Context: I am new to quant finance. I am doing some structural break analysis on a future price time series. I applied the Chu-Stinchcombe-White CUSUM Test from Chap 17 (Advances in Financial Machine ...
dragondragon's user avatar
5 votes
2 answers
614 views

ML/AI in fixed income vs equity

From my perception of learning different ML/AI applications to finance I found there are lots of them in equity and not as many in fixed income. I wonder if the markets are different in some ways and ...
Medan's user avatar
  • 493
0 votes
0 answers
23 views

Machine learning on NASDAQ [duplicate]

I want to perform an ML analysis on NASDAQ, based on historical fundamentals. Suppose I want to take a temporal window of 10 year and quarterly data. I have a question: I suppose I should know the ...
piravi's user avatar
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1 vote
1 answer
143 views

Using regression for binomial prediction of tomorrow's return

I completed a challenge which asks the user to predict tomorrow's market return. The data available is prices data and the model must be logistic regression. They call it "machine learning" ...
s5s's user avatar
  • 442
1 vote
1 answer
274 views

Lopez de Prado's Triple Barrier Method - do you reset the barriers on every timestep, or only when you reach a hi/lo/no barrier hit?

I've used de Prado's trend scanning labels but now I want to try the triple barrier method idea. I understand the rough set up, and I understand the logic behind it. However, what I don't understand ...
Vladimir Belik's user avatar
1 vote
0 answers
57 views

ML/DS in fixed income asset management

I am new to the topic but I would like to read papers/books/anything interesting to learn more how ML and data science is used in buy side Fixed income Asset management firms. Factor investing/signals/...
Medan's user avatar
  • 493
1 vote
1 answer
543 views

Suggestions for backtesting machine learning 'model'/strategy in Python

I have coded a machine learning algo (sklearn) in Python, that uses different 'look back periods' for training a model, which is then used to predict future prices of a stock. It has a 52% accuracy in ...
Cairan Van Rooyen's user avatar
0 votes
1 answer
693 views

What is industry best practice to combine alphas?

Say I have 100 different alphas that all have statistically significant returns in-sample. Is the best practice to use historical covariance matrix plus Markowitz portfolio theory to create an optimal ...
lara_toff's user avatar
  • 113
7 votes
3 answers
906 views

What are some interesting recent machine learning related developments in the QF domain?

In 2020 I wrote a MSc thesis on the hedging of exotic options using recurrent neural networks (loosely based on the paper Deep Hedging (2018)by Buehler et al.). Since then I have been interested in ...
QuantNero's user avatar
  • 233
0 votes
1 answer
120 views

Differences vs ratios

High, I am working on an exercise which involves performing a regression analysis to predict market direction (e.g. up or down). I am using daily OHLCV data. I've created various factors from the ...
s5s's user avatar
  • 442
0 votes
1 answer
370 views

AFML (by Lopez De Prado) Vs ESL by Trevor Hastie

The books "The Elements of Statistical Learning" by Trevor Hastie, and "Advances in Financial Machine Learning" by Lopez De Prado are highly recommended books for ML. They both ...
TryingHardToBecomeAGoodPrSlvr's user avatar
-1 votes
1 answer
537 views

Yield curve PCA: levels or daily moves?

I have tried using both yield curve levels as well as daily moves (absolute change) while doing PCA. Using both types of input/dataset gives me roughly the same shape in terms of principal components ...
abckk's user avatar
  • 1
0 votes
0 answers
214 views

Bet sizing to actual orders

At chapter 10.2 in Advances in Financial Machine Learning it says: Suppose that one strategy produced a sequence of bet sizes $[m_{1,1}, m_{1,2}, m_{1,3}] = [.5, 1, 0]$, as the market price followed ...
amilkov's user avatar
1 vote
1 answer
526 views

Calculate core deposits in commercial bank

Given 10 years history of past balances of deposit accounts in a commercial bank, I need to calculate what part of those deposits were core, month by month. This is my thinking: for each account/month ...
ps0604's user avatar
  • 50
0 votes
1 answer
301 views

Sampling dollar bars for a machine learning model

I'm trying to understand the rationale behind using information drive bars over traditional time bars and specifically when it comes to practically feeding those in to a machine learning model to run ...
PlatinumMaths's user avatar
3 votes
1 answer
278 views

How to merge ML-based $\alpha$-signal with stochastic control approach?

I'm having a hypothetical situation where I have a set of ML-based alpha signals $\{\alpha_i\}_{i=1}^{N}$ that describe a different states of order book - imbalances, order flow, spread properties etc....
Accelerate to the Infinity's user avatar
1 vote
0 answers
219 views

Is there a Pytorch counterpart of the book Machine Learning for Algorithmic Trading?

As a beginner in fintech, I am reading the book Machine Learning for Algorithmic Trading by Stefan Jansen. I think it is a really helpful book. But most of the codes are written in tensorflow 2. I ...
user900476's user avatar
0 votes
0 answers
79 views

Disecting a log diff transformation for time series analysis and prediction

I have been working in a predictive ML model that uses financial time-series as predictor variables. In one of the academic papers I used as reference, and to do feature engineering for building the ...
SkyWalker's user avatar
  • 101
0 votes
0 answers
39 views

Suggestion on the models to estimate public indeces future returns

I would like to to estimate the future returns of some public indeces. I have several of them so it is a multivariate problem. The series are quarterly and the estimation should be of at least 15-20 ...
Dark2018's user avatar
  • 101
2 votes
1 answer
199 views

R-squared to be computed on training sample or test sample?

I am currently going through the book Machine Learning For Factor Investing whose online version can be read here: http://www.mlfactor.com In the section on model validation, one can read the ...
wissam124's user avatar
0 votes
0 answers
118 views

Good (non-random walk) financial time series to perform forecasting on

I would like to start with a brief caveat, namely that I am by no means a domain expert in financial markets. Therefore the question I am asking may sound silly to a practitioner but I am asking it ...
Mark Fisher's user avatar
0 votes
0 answers
382 views

Hidden Markov Model Stock Prediction Next Level

I was able to fit HMM Model in Python on stocks data. I have completed the training and testing part. The overall fit looks good. However, I have a question, I am not able to predict the next "t+...
Add's user avatar
  • 1,397
0 votes
0 answers
98 views

transforming a model to long short instead of long-only

I am currently trying to adapt a model to a long short portfolio strategy. The model is stated here: A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem by Jiang, Xu,...
user101893's user avatar
0 votes
2 answers
316 views

Stress testing by Banks

AFAIK typically banks stress test it trading portfolio by assuming stressed value of risk factors or by considering times series corresponding to some historical ...
Brian Smith's user avatar
-4 votes
1 answer
198 views

How to identify between Analytical, Numerical and ML Model based option pricing? [closed]

I am new to Quantitiative Finance. Coming from Computer Science domain, I wanted to clear the key distinguishing factor between analytical, numerical and ML based models for option pricing. As far as ...
nainometer's user avatar

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