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.
257 questions
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Stock clustering as a part of portfolio diversification
I have a research hypothesis and now I'am trying to look at it from different angles.Now I am a bit puzzled.Maybe someone is also interested in machine learning application(especially clustering) in ...
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Associative rule mining in quant finance
Has anyone seen/used associative rule mining in quant finance? I'm particularly curious in seeing if it has applications in differentiating between manager skill vs. luck but other applications are ...
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Metric for volatility time series similarity - European swaptions
I'm trying to estimate the volatility surface of illiquid swaptions (say CHF) given hourly data (atm vol, skew, for different strikes) of other liquid swaptions (EUR, USD, etc.). Having the underlying ...
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How to represent dividend schedule to ML Model for barrier Options
I am looking at creating an ML model to price an exotic equity option which has a barrier where the buyer is paid out if the barrier is crossed, and multiple observation dates where the price is ...
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Signal update frequency and predicting directional return one step ahead
I am trying to get some insights on this specific sort of problem from experienced people, as I do not have much experience in this field.
I have a family of features that for simplicity I will just ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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" ...
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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/...
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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 ...
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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 ...
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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 ...
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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 ...
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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....
