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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1answer
159 views

Risk-return ratio using ML default probability

I have access to a very large bond database (>20m rows) where 50% of the set are matured bonds for which a dummy variable identifies whether the bond defaulted or not. The remaining 50% are 'live' ...
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2answers
138 views

Any research on label/target variable design for ML training?

is there any discussion or paper about how to define/design the labels for the ML training? Intuitively I can think of: Net return of the next future day Net return using the max candle-high value of ...
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1answer
185 views

Real time stationarity test

I have a trading system based on Machine Learning which is trading 8 symbols intraday. From the results I found out that some weeks of trading are successful for some symbols, then it usually switches ...
3
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1answer
107 views

Forecasting volatility with machine learning: Performance comparison

ARIMA and GARCH are old news for predicting volatility time series of asset returns. How does the performances of machine learning algorithms compare (such as random forest, support vector ...
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2answers
93 views

Does the non-causal nature of quant models limit their applicability?

I understand that to describe financial data, we build stochastic models and calibrate their parameters to past data. When coming up with new algorithms, we rely on rigorous backtesting to convince ...
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1answer
15k views

How do I use machine learning to build a credit scoring model? [closed]

There are currently a lot of ways for credit scoring. The most popular one is the FICO score, and its variants. For my masters thesis, I would like to work on making my own credit scoring system using ...
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2answers
159 views

When to stop training?

I have built a deep reinforcement learning based portfolio optimisation agent. At a high level it is using macro economic data, valuations of the assets and a few technical indicators as the features. ...
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1answer
885 views

CAPM and factor modeling: Machine learning

Excuse my ignorance with this I am still trying to wrap my head around the interpretation of the Fama French 1992 factor paper. I come from a computer science background but I am interested in ...
2
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1answer
2k views

Feature for Maching Learning(SVM) in High Frequecy Order Book?

I am trying to implement machine learning to predict the movement of bid and ask price but is unable to find the proper feature for training set. I am using Support Vector Machine for binary ...
2
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1answer
232 views

How to group mutual funds by volatility?

I want to group Mutual Funds by their volatility. Ideally, I would like to end up with the mutual funds beings attached to different groups: High volatility Medium volatility Low Volatility My ...
2
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1answer
125 views

Building a semi-discretionary system

I've been investing for the last 15 years in a weird Buffett/Soros way. For the last few years I've been toying with the idea of modeling myself. I want to build a 'stock screener' that will be able ...
2
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1answer
67 views

Labeling and excluding specific market conditions

I'm going through "Advances in Financial ML" book and got stuck with something which is not covered there (correct me if I'm wrong). Let's assume I labeled data to 0, 1, 2 according to triple barrier ...
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2answers
149 views

Flexible horizon in Triple Barrier Method

I'm going through "Advances in Financial ML" book and I really like the ideas behind Triple Barrier Method and using a flexible horizontal threshold based on volatility. What bothers me is that an ...
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1answer
95 views

Choosing best expressions from all possible combinations on variables, unary operators and binary operators along with hyper parameters

I have a few financial variables of a stock universe like OHLC prices, volume, and other fundamentals with varying time-frequency. Using this set I'm creating an expression that gives the weights to ...
2
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1answer
141 views

Calculating PD of commercial bank loan

I have two main options to calculate PD of a loan in a commercial bank; with and without machine learning. On one hand, there are traditional methods such as Merton or KVM. On the other hand, I could ...
2
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1answer
239 views

How can I 'quantize' a time-series in 'groups' exhibiting similar patterns? [closed]

In Signal processing, there is a topic of 'Quantization' (the process of mapping input values from a large set to output values in a (countable) smaller set ('states') ). I would like to construct a ...
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1answer
808 views

Analog - Pattern Recognition model using KNN

I'm building a pattern recognition model for my master thesis. The idea is to build a framework with some Macro variables (long/short term rates; rates differential; equity; fx; vix) in order to find ...
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0answers
37 views

What benefits do using log returns for model training provide?

I came across a paper that uses Support Vector Machines to classify a buy/sell/hold decision each hour at the $\pm$0.5% threshold. The paper can bee seen here. The ...
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0answers
74 views

Meta Labeling for trading opportunities

In Advances in Financial Machine Learning, Lopez explains how we should build a primary exogenous model (binary classifier) to identify trading opportunities and a secondary meta model to filter out ...
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0answers
57 views

machine-learning method to predict PCA weights

I have been using certain linear-regression to extract the PCA (top 3) weights relating to a certain data-set. I was wondering, instead of using linear-regression to generate the weights, I can use ...
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0answers
48 views

Conceptual help - Machine Learning on finance data set [closed]

I am working on Anomaly detection model problem for a finance data set - set of gift card activation transactions. My team member suggested an idea that " First train the model with normal instances ...
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0answers
355 views

Information Driven Bars (Advances in Financial Machine Learning)

My team and I are busy coding up a python implementation of the information driven bars (imbalance and run bars) mentioned in Chapter 2 of the text book Advances in Financial Machine Learning. There ...
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0answers
521 views

Getting over bid-ask bounce

One property of High-Frequency data is it's subject to bid-ask bounce. Description : Unlike traditional data based on just closing prices, tick data carry additional supply-and-demand information in ...
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0answers
119 views

What is a good algorithm to predict volatility in metals commodity markets? [closed]

I'm trying to create a script to predict major swings in the price of Aluminium. I am trying to implement a dynamic time warping algorithm for the same. Was wondering if this really is the best ...
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0answers
544 views

stochastic modeling and machine learning [closed]

For a little bit of background, I've been studying stochastic calc and a few of it's applications (currently I'm still at the early stages of learning applications) and have been curious as to whether ...
2
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0answers
377 views

Differential Sortino Ratio

I'm attempting to optimize a reinforcement learning system to maximize risk adjusted returns. I have currently defined the reward as the differential Sharpe ratio at each step: the influence of the ...
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0answers
294 views

Good books on predictive modeling (for alpha signal research)

In terms of books on predictive models, I find ESL (elements of statistical learning) trying to cover too much and serves more like a reference, instead of explaining and developing the theories for ...
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0answers
173 views

What are examples of boosting, bagging, stacking or subspace method in quantitative finance?

The above ensemble methods appear useful in several machine learning competitions, like Netflix prize or KDD. They work by diversifying between several model variants. Are they also useful in ...
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3answers
239 views

What's a good resource of book for Python programming in relation to quantitative finance?

I know some of base Python, but I have only briefly used numpy, pandas, etc... I was wondering what's a good resource to learn Python specifically for quantitative finance. I know of plenty of books/...
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3answers
691 views

Research methodology of systematic strategies

Can someone please share your research methodology of systematic trading strategies? I feel like I am always using the a same data driven procedures over different underlyings and would like to get ...
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4answers
2k views

What Is A Good Success Rate Using Machine Learning For A Beginner?

I know this question will be quickly destroyed and my account summarily banned, but I just have to ask: For a trader using machine-learning algorithms (SVMs, ANNs, GAs, Decision Trees) for ...
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1answer
177 views

Should there be a relation between stocks when used as input data for integrating Technical Analysis with Machine Learning?

I'm integrating Technical Analysis with Deep Learning for the first phase of my research. I wanted to know how should I pick (or group) stocks as input data and whether there should be relation ...
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2answers
113 views

Measuring correlation between random variables when they are not normally distributed?

I want to perform some analysis on portfolio that consists of hedge funds (thus fund of hedge funds) In particular, I want to know the relationship between the funds during the downmarket. The ...
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2answers
518 views

Choosing attributes for SVM classification?

Let's assume I am classifying every trading day as a 1 or a 0. Exactly what I am classifying doesn't matter, but for the sake of this question let's say I am predicting direction of price change. So, ...
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1answer
59 views

LSTM for trend prediction

Been wanting to get my hands dirty with ML for a while now and since I'm interested in finance and trading as well, I figured this would be a good project to get started after reading Deep LSTM with ...
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1answer
54 views

What's the meaning of linearity in classical statistics in Prado's book?

I am reading Prado's new book, Machine Learning for Asset Managers. In the page1 of his book, there is this sentence. To a greater extent than other mathematical disciplines, statistics is a ...
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1answer
94 views

Modeling mortgage loan defaults

I have a machine learning model trained with a list of mortgage features that include macro variables where the field to predict (the label) is "Mortgage Defaulted" = 1 or 0 (Yes or No). Now, I need ...
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2answers
429 views

Use machine learning to find exercise boundary of American put option

I am working on using machine learning to obtain American Put's early exercise boundary. To train the model, I need an output label (known boundaries values). Is there a fast way to obtain the ...
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2answers
99 views

Lower MSE results in less profit when using Machine Learning

When using Machine Learning for predicting stocks, can a lower Mean Squared Error result in less profit after Backtesting or is there a mistake in the experiment?
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1answer
74 views

How can I reproduce the experimental verification of the “False Strategy” theorem plot?

I recently came across the following blog post talking about the importance of back-testing overfitting, and a plot claiming to be an experimental verification of the False Strategy theorem. The ...
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2answers
1k views

Using Technical Indicators for forecasting Financial time series using Machine learning models

Hi I am trying to use financial technical Indicators for forecasting, using machine learning models. The usual approach in time series cross validation is to use a moving window or growing window. ...
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2answers
882 views

Howto Calculate An Error's Partial Derivative in ANN

This is a follow-on question from this post I made, "Multilayer Perceptron (Neural Network) for Time Series Prediction", a few months back. I'm constructing a feed-forward artificial neural network, ...
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1answer
31 views

Dealing with stochastic results of Machine Learning Models

I'm building stock selection models, and pick top 5 and bottom 5 stocks. Given the variability in Stochastic gradient decent results, they keep changing. One way to get consistent results is to use ...
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1answer
24 views

Mutual fund rating predictions

I am working on a dataset with aim to predict the MF ratings. There are cols like, 10 yr, 7 yr, 5 yr etc returns. I also have commencement date of MFs, the question is there are MFs with commencements ...
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1answer
77 views

Random Forests - Trees vs Predictors

This question relates to the use of random forests in finance and the relationship between the number of features, the observations, and the number of trees. Consider the relation between an RF, the ...
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1answer
151 views

How can stationary time series data be used as input in an ML model?

I am halfway through "Advances in Financial Machine Learning" by Marcos Lopez de Prado. I understand that a time series like stock prices can be transformed to make it sufficiently stationary. ...
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1answer
206 views

Which machine learning model rely on the normality assumption?

In the machine learning project, when the target variable is skewed, we need to use box-cox transformation to turn that into a normal distribution. But why do we need to do that? I mean, besides the ...
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1answer
86 views

Combine fundamental and market data into one ML model

What are the best tested ways to preprocess data with very different frequencies such as fundamental and market data into same ML model for quant trading?
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2answers
184 views

Seeking papers that deal with stock market analysis

I am sure there are a lot of papers that are related to stock market analysis.. but I haven't been able to find ones that fit my needs most. I want to read papers, replicate their analysis, and use ...
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2answers
106 views

How to assign n day target variables in machine learning

I am trying to forecast future price using supervised machine learning. My logic is to take open and close price from t, t-1, t-2 and t-3 period to predict future close price in the period t+1,t+3 ...