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.

128
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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 ...
37
votes
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 ...
36
votes
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 ...
34
votes
1answer
3k views

Is my trading strategy search methodology sound?

I'm building an algorithmic trading business. I'd be grateful for informed comments and opinions on my trading strategy search methodology. Goal Develop (profitable!) fully automated intra-day ...
30
votes
8answers
5k views

Excellent information source on advanced machine learning / data mining based trading?

I did check the related posts, like this one here. However, given if one already has knowledge in finance, machine learning and statistics, and wants to know something more advanced on machine ...
25
votes
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 ...
19
votes
3answers
3k views

How to incorporate technical indicators into neural networks?

I plan to develop a neural network to trade commodities futures, but while messing around with some code, a question came up. If I understand correctly, people use various technical indicators with ...
18
votes
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 ...
18
votes
5answers
6k views

How to cluster stocks and construct an affinity matrix?

My goal is to find clusters of stocks. The "affinity" matrix will define the "closeness" of points. This article gives a bit more background. The ultimate purpose is to investigate the "cohesion" ...
17
votes
2answers
377 views

Regression model when samples are small and not correlated

I received this question during an onsite interview for a quant job and I'm still scratching my head on how to solve this problem. Any help would be appreciated. Mr Quant thinks that there is a ...
14
votes
4answers
2k views

Statistical learning libraries

Is there a general (or specialised) FREE library to solve learning problems such that found in the book "The Elements of statistical Learning". As it is often time consuming to write all the ...
14
votes
1answer
760 views

Has any research used Bayesian networks to estimate risk factor betas?

Is there any published research on estimating the beta of a security with respect to one or more risk factors via Bayesian networks? I'd like to see if this is a promising angle of research.
14
votes
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 ...
13
votes
1answer
6k views

Multilayer Perceptron (Neural Network) for Time Series Prediction

I have it in mind to build a Multilayer Perceptron for predicting financial time series. I understand the algorithm concepts (linear combiner, activation function, etc). But while trying to build the ...
11
votes
5answers
5k views

What are your opinions on WEKA KnowledgeFlow, Rapidminer, and other rapid development environments for machine learning?

Which is the most extensible? Which is the most efficient in terms of a minimal learning curve while providing a meaningful degree of flexibility and performance? Any of these tools really limited ...
11
votes
1answer
3k views

Which are useful applications of clustering in quantitative finance?

Several machine learning algorithms have been applied in finance/trading. Focusing on clustering (k-means, k-medoids) what are useful and successful applications in quantitative finance? What is used ...
10
votes
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 ...
9
votes
1answer
3k views

Optimal execution and reinforcement learning

Suppose a fairly simple problem: You have to buy (resp sell) a given number of shares V in a fixed time horizon H with the aim to minimize your capital spent (resp maximize your revenue). There are ...
9
votes
3answers
2k views

Determine trends of data (direction detection or turning point detection)

I'm working on a model to determine trends (direction detection or turning point detection). Suppose that we have a stock trend which is illustrated below. Blue line is real trend of stock close ...
9
votes
2answers
1k views

How do I replicate John Hussman's recession forecasting methodology?

John Hussman has a recession forecasting methodology he often posts about on his blog, and I am trying to replicate it using publicly available data. I would like to assess his accuracy in predicting ...
8
votes
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 ...
8
votes
2answers
206 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 ...
8
votes
1answer
648 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 ...
8
votes
2answers
793 views

Is there any research on applying state-space or dynamic linear models to forecasting equity risk premia?

Is there any research on applying state-space or dynamic linear models to forecasting equity risk premia on a security-by-security basis with a medium term horizon (say 3 month to 12 months horizon)? ...
8
votes
1answer
376 views

How to perform Empirical Mode Decomposition?

I am trying to use the EMD applied to EURUSD open price to train a machine learning algo (RVM). I have run only once the EMD on my training set and once on the training+test set. The results on the ...
8
votes
1answer
690 views

How to better understand trading signals?

I am looking to get a better understanding of an output from a trading strategy. Basically I have a daily equity curve lets call it $Y_t$. I have defined a bunch of independent variables $X_{it}$ that ...
6
votes
2answers
1k views

How to calculate optimal standard deviation bands for trading?

I am trading with standard deviation bands (6 bands) on de-trended data. How can I find the most profitable signals with neural network or GA with standard deviation bands? Should I first find the ...
5
votes
1answer
612 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 ...
5
votes
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 ...
5
votes
1answer
529 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 ...
4
votes
2answers
238 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 ...
4
votes
3answers
370 views

selecting test data for neural networks

I have been working on a neural network based on certain technical indicators. As people familiar with neural networks would know after developing a hypothesis, the developer is also supposed to ...
4
votes
2answers
200 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 ...
4
votes
2answers
346 views

Machine learning for non optimal behaviour

I was working on the pricing of complex bermudean swaption when I noticed that the exercise is often (very) subobptimal. It seems that the clients are more sensitive to past growth or drop in rates ...
4
votes
1answer
931 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 ...
4
votes
0answers
154 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. ...
4
votes
0answers
568 views

Algorithms for predicting a couple points in the future

I'm familiar with supervised learning algorithms like regression and neural networks which look at a bunch of input points and learn a function which outputs a value (the value varying depending on ...
3
votes
4answers
303 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 ...
3
votes
2answers
395 views

What is the machine learning language of choice in this industry for unsupervised learning

I was wondering from those with commercial machine learning financial experience, what the machine learning language of choice in this industry in the most general sense. Also, what would be the ...
3
votes
3answers
171 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 ...
3
votes
1answer
232 views

approach on trading algorithm using machine learning [closed]

let's say I am supervising a algorithmic trading project using machine learning. I don't have involvement in the technical side but am involved in the high level planning. the style is likely ...
3
votes
1answer
181 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 ...
3
votes
2answers
716 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 ...
3
votes
1answer
304 views

Scaling (Data prep) & Feature selection for the financial Data for LSTM Models

Overview I'm training an index e.g. FTSE100, where I have 8 years of past data (daily). I also have a list of its constituents. For each stock, I have the following features: ...
3
votes
1answer
264 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 ...
3
votes
1answer
135 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' ...
3
votes
2answers
80 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 ...
3
votes
1answer
163 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 ...
2
votes
1answer
269 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 (...
2
votes
1answer
13k 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 ...