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
91 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 ...
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1answer
56 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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3answers
189 views

Getting sets of random correlated variables

For the training of a machine learning model I need to add additional features (macro variables), and these features are correlated. I need to run the model N times, and for each time I have to add ...
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1answer
67 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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1answer
76 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
44 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
128 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 ...
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1answer
72 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
67 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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0answers
54 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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4answers
9k views

What types of neural networks are most appropriate for trading?

What types of neural networks are most appropriate for forecasting returns? Can neural networks be the basis for a high-frequency trading strategy? Types of neural networks include: Radial Basis ...
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0answers
204 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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1answer
531 views

What machine learning method is more suitable for prediction of financial time series?

I have time series data for various assets and which I transform to create various features. I have framed the problem as a classification task where I attempt to predict either a positive or negative ...
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2answers
209 views

Defining an objective function for machine learning task of trading

A simplified example. Given: asset's price time series fixed distances to stop and target. A function of these inputs has two possible output values: $1$ if price is likely to hit the target ...
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4answers
3k views

How to normalize technical indicators for machine learning?

I'm using around 130 technical indicators for 100 different companies. Each company's stock price moves in a different range, see FTSE 100. In addition, each technical indicator moves in a different ...
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1answer
937 views

How quants use ML models for stock market prediction

I am learning machine learning to use it for stock market price forecasting. While doing that I got this question. If we take any country with stock exchange they have more than one investment assests ...
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2answers
204 views

Which close price should we use for machine learning?

I am building a machine learning model using historical prices and I am using data from yahoo finance. Currently yahoo finance data have two close prices one normal close price(close) and other ...
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2answers
1k views

Machine learning techniques for quantitative finance?

I am a mathematician who wants to learn about quantitative finance, in particular how machine learning can be applied to it. I assume some machine learning techniques are more applicable than others ...
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2answers
83 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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3answers
157 views

Combining Quantitative data with fundamental data

These day, there is relatively new phenomena of combining quantitative data and fundamental data called 'Quantamentals'. In this regards, I was wondering how to combine Four Essential Types of ...
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1answer
136 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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4answers
4k views

Is it really possible to create a robust algorithmic trading strategy for intraday trading?

I'm an engineer doing academic research for my master thesis in the area of quantitative finance, basically the purpose is to study the possibility to create an intraday-trading algorithm. I've tried ...
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1answer
185 views

Algorithmic Trading: Normalization and Selection of Technical Indicators for Artificial Neural Networks [closed]

I study on algorithmic trading for a while based on technical indicators. I started to learn about neural networks and want to use technical trading indicators in this approach. However, I am not ...
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6answers
9k views

Machine Learning vs Regression and/or Why still use the latter?

I come from a different field (Machine learning/AI/data science), but aim to ask a philosophical question with the utmost respect: Why do quantitative financial analysts (analysts/traders/etc.) prefer ...
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14answers
163k views

How can I go about applying machine learning algorithms to stock markets?

I am not very sure, if this question fits in here. I have recently begun, reading and learning about machine learning. Can someone throw some light onto how to go about it or rather can anyone share ...
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0answers
157 views

The “Universal Model” by Justin Sirignano and Rama Cont

In the nicely written article https://arxiv.org/abs/1803.06917 by Justin Sirignano and Rama Cont, they explained that their model is universal and stationary. I am a bit confused about some questions. ...
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45 views

Historical intraday data [duplicate]

I have been researching on the current APIs that I can request for the most detailed historical end of day intraday quote and trade data available in an easy to use format for research, backtesting ...
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2answers
5k views

Gradient tree boosting — do input attributes need to be scaled?

For other algorithms (like support vector machines), it is recommended that input attributes are scaled in some way (for example put everything on a [0,1] scale). I have googled extensively and can't ...
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0answers
82 views

Machine Learnign for Factor Model python [closed]

I have read several articles about Factor Model using Deep Learning or machine learning, but none of them post the code. Where can I find the python code for anything similar?
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1answer
655 views

Machine Learning usage in Q part of Quant Finance

Machine Learning algorithms is broadly used in trading strategies and in general when it comes to working with financial time series. The webpage Quantopian is a platform to see some of the ...
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2answers
372 views

Hedging with machine learning

I’ve been thinking about an interesting problem lately: Suppose I have a position in an exotic derivative. How can I automate the hedging process? Traditionally, one build a pricing model and ...
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0answers
219 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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1answer
275 views

Why are there so few published research papers that apply Deep Learning to Algorithmic Trading?

The only related papers I can find are: Financial Trading as a Game: A Deep Reinforcement Learning Approach (2018) Deep Neural Networks in High Frequency Trading (2018) MACHINE LEARNING FOR TRADING (...
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2answers
239 views

Predict the financial markets in the fashion of a video game?

DeepMind have demonstrated amazing capabilities of a reinforcement machine learning agent to competently play Atari video games. It is most astounding that that during training nothing more than the ...
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2answers
79 views

Imputation of missing returns

I'm trying to calculate a historical VaR for a portfolio of futures, however there are certain days for which some assets are missing prices. Since the portfolio consists of many spread positions, the ...
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1answer
164 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
753 views

Where I can find historical earnings dates for stocks?

I'm trying to find all of the historical earnings dates (just the dates is good enough) for certain stocks ranging back to their IPOs. I'm plan to use it for my machine learning project. Yahoo and ...
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8answers
12k views

How are cryptography and speech recognition technology applied to forecasting financial markets?

One of the answers to my previous question regarding the strategy of Renaissance Technologies, there was a reference to The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly ...
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2answers
110 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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1answer
616 views

Trading Strategy adapting to my trading frequency

We want to predict the direction towards which the price will change. In this work the term price is used to refer to the mid-price of a stock, which is defined as the mean between the best bid ...
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4answers
317 views

Determine the right order size with market making strategy

In a market market strategy https://web.stanford.edu/class/msande448/2017/Final/Reports/gr4.pdf, how can we determine the right order size? Assuming I use a market making strategy and on a specific ...
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10answers
9k views

Usage of Random forests in Quantitative analysis of stocks

I have a question about Random forests and how they could be utilized in trading? I heard Random forests are used for classification, is that accurate? If so, could someone give an example of what ...
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1answer
269 views

Limit and Market Order for training a ML model

Goal : Using deep learning to build a ML model which would predict the right places where a stock price will increase, decrease or stay stable. For the current question, assume the labels are well ...
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1answer
115 views

Market, Limit and Cancellation orders

From the paper https://web.stanford.edu/class/msande448/2017/Final/Reports/gr4.pdf page 8, I need at least the limit and market order. I can easily find the full depth from dxfeed or algoseek, but I ...
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1answer
102 views

Separate market and limit orders from market depth/tick data

From the website https://www.algoseek.com/equities/, we can get a sample of the full depth market/tick data. From the paper https://arxiv.org/pdf/1710.03870.pdf page 8, I would like to extract the ...
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0answers
28 views

Given historical performance of a financial index, how to categorise different historical periods depending on the market regime at the time?

We are trying to work on a Machine Learning application to attempt to predict market regime changes (bull, bear, stale?). Generally a ML algorithm needs well defined training data for establishing its ...
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2answers
327 views

Can someone please share examples of machine learning in quantitative finance? [closed]

There has been a lot said about the application of AI, ML and Neural Networks in trading for predictive modelling. I was unable to find any relevant examples that prove a credible output based on ...
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0answers
213 views

How Machine Learning model addresses adverse action concerns -credit scorcard?

How to find the variables involved in the decision to report adverse action when the origination scorecard is developed using Machine Learning - XGBOOST with monotonic constraints (80 variables) ...
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2answers
171 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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1answer
67 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 ...