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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2answers
97 views

Machine Learning approach for the probability estimation of certain events

I am planning a research project on estimating the probability of corporate takeovers. I think that different variables could be indicators to predict takeover bids. For example, price increases in ...
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19 views

How to decide which sentiment analyzer is the better model?

Assume one has trained different sentiment analysis models that assigns sentiment scores to the financial news or documents. How would one should approach testing the different models and decide which ...
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24 views

Minimize Composite Dispersion

Let's say that we have a composite of 10 fixed income portfolios, each with the same benchmark, the US Aggregate. Additionally, let's say that each portfolio has a position in Corporation ABC. The ...
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46 views

What is the best approach to modeling local time series house prices?

I have a dataset of house prices over time and have broken it down into neighbourhoods. For each neighbourhood I would like to create a time series model that captures the local price movements. What ...
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1answer
133 views

Backshifting Price Timeseries with Memory Preservation

In Advances in Financial Machine Learning the author makes a case for fractionally differentiated price returns in chapter 5. The reason is to both maintain memory and to generate a stationary time ...
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1answer
67 views

Using candlesticks for Stock price direction prediction

I am working on a college project wherein I want my machine learning model to predict the one-day-ahead direction of a given stock (i.e. whether the closing price of the stock would rise or fall as ...
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1answer
35 views

References on cashflow modelling for private equity

I would like to build a model to predict capital calls and distributions of a private equity fund. The first question is: does any of you can address me towards the state of art for it? also machine ...
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1answer
93 views

How are the values of the ARMA process linked in python

In the code below, you can see that 'ret' is an ARMA process, and I am trying to see how the ret[0], etc... ret3, ret4, etc. are linked to each other, and although I know the formula for the ARMA ...
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1answer
99 views

What data should I use for a machine learning model

I would like to ask you for an advice of any of you could help me with this information it would be really helpful. I am trying to build a reinforcement learning trading bot that based on the current ...
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35 views

What is the best way to impute missing values for financial data?

I've been tasked with imputing missing values for a dataset of ca. 4000 firms and 225 key metrics (e.g. revenue, net income, EPS, PE etc.). Since I haven't found a thread on here which answers my ...
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1answer
65 views

Steps to fit a Machine learning model for prediction of up and down market movement

I have around 5 years of data of an index containing many features on a daily basis. I want to classify whether the index will move up or down the next trading day (up or down movement is determined ...
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9answers
13k views

Why are there no papers about stock prediction with machine learning in leading financial journals?

I'm writing my master's thesis about stock price prediction using machine learning methods. During my literature review, I noticed that a lot of research produced on this topic is of poor quality, ...
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41 views

advances in financial machine learning - problem regarding to insufficient number of financial data to train ML algorithm

After reading 'Advances in Financial Machine Learning' by Marcos Lopez de Prado, I wonder how can we train machine learning algorithm with too few financial data. If we use cumsum filter etc the ...
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1answer
51 views

How to use 'purging' in predicting stock price tomorrow based on information today?

Q1. How to create an 'overlap' when we predict a stock price tomorrow based on information today? According to the book 'Advances in Financial Machine Learning' written by Marcos Lopez de Prado, the ...
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64 views

In Lopez de Prado's Advances in Financial Machine Learning, what is meant by “unnecessary labels”?

In Lopez de Prado's Advances in Financial Machine Learning, Chapter 3, Prof. Lopez de Padro talks about dropping rare labels: ...
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0answers
16 views

Understanding additive profit of risky and risk free asset

I was going through paper "Learning to Trade via Direct Reinforcement" by Moody and Saffell. It explains additive profit as follows Additive profits are appropriate to consider if each ...
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30 views

Computing statistics from historical returns

I'm reading age 35 of "Advances in Machine Learning" by de Prado. Consider an IID multivariate Gaussian process characterized by a vector of means μ, of size Nx1, and a covariance matrix V, ...
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1answer
84 views

Cannot achieve generalization of machine learning model

I'm working on a balanced, binary classification problem in a time-series (financial) dataset. I am using K-fold cross validation that is adapted for time-series (so that I'm never using future data ...
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1answer
66 views

House price inflation modelling

I have a data set of house prices and their corresponding features (rooms, meter squared, etc). An additional feature is the sold date of the house. The aim is to create a model that can estimate the ...
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0answers
50 views

How to use machine learning to generate optimal allocations for an instrument?

What is the idea behind using Machine Learning in finance? Let's assume that we have just one instrument given by its prices. At a given moment of time, we can "compress" the available ...
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67 views

Leakage and bias in XGBoost trading strategy

I apologize for my persistence, i'm on a course of study and doubts increase every day. My goal is "just" to code a profitable forex trading strategy with machine learning. I'm trying to ...
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155 views

Bug found in Optimal Number of Clusters algorithm - from de Prado and Lewis (2018)

I believe I have found a bug in Optimal Number of Clusters (ONC) from the paper "Detection of False Investment Strategies Using Unsupervised Learning Methods". ...
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0answers
75 views

Machine/Deep Learning for Exotic Option Pricing - Reference Request

Exotic options, in general, have very time-consuming valuation models. I believe in recent years there has been some research done on using supervised machine/deep learning to predict the valuation ...
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3answers
416 views

Deep Reinforcement Learning in Quant Finance?

I've been struggling to find engaging papers on the application of deep reinforcement learning in quantitative risk analysis, portfolio management, algorithmic trading and/or options pricing. What are ...
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2answers
259 views

Forecasting non-maturity deposits with machine learning

I need to forecast non-maturity deposits in a bank. My intent is to use Recurrent Neural Networks (aka deep learning) to model time series. The model will learn from past bank data and macroeconomic ...
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1answer
120 views

Fair Value Regression Methods

Recently we had an invited talk at our university (I'm Ph.D. student in ML department, so I'm sorry if my question is stupid, since I do not have quantitative finance background), where one researcher ...
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25 views

How to deal with outliers when normalizing price related input in algorithmic trading

I am training a CNN model for trading using indicator and MA lines to compose a 2D array as input. I want to normalize MA data(ema, sma...) into range between -1 and 1, I have tried several techniques ...
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0answers
60 views

Optimising returns weighted by Sharpe ratio in the context of Supervised Learning

In the Kaggle Jane Street market prediction competition we are put in a Supervised Learning Framework to deal with 'trade opportunities'. That is, we are given instances of previous trade ...
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182 views

Markov Property in Optimal Execution?

After reading papers on reinforcement learning with respect to the problem of optimal execution (Nevmyvaka et al (2006), Ning et al (2018), etc), I was wondering if the Markov property assumed in all ...
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2answers
241 views

Limit order book modeling based on computational statistics

Is someone aware of publications that try to model limit order book (and market mircostructure) in general using CS tools (such as online machine learning, game theory ecc...) and not stochastic ...
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0answers
129 views

Machine Learning model forecasting on real time data in python

I’m building a Forex trading system based on machine learning with Python and brokers API. I get price time series data + fundamental data and then i train the model on that. Model means SVM, RF, ...
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2answers
198 views

Reinforcement learning in finance

In brief, what are some mainstream and recent applications of reinforcement learning in finance that fall outside of the usual scope of agent-based modeling?
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2answers
97 views

Spectral clustering in finance

What are some examples of applying spectral clustering to financial times series data or other areas of finance? Why spectral clustering was used for each application rather than other types of ...
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1answer
92 views

How can deep learning methods measure implied volatility?

Why and how should we utilize deep learning methods to calculate implied vol of options? I've also heard that finding the fair price of the option is not nearly as important as finding a numerical ...
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1answer
134 views

D-Limit and Crumbling Quote Indicator

I've been following the development of the D-Limit order at IEX for some time. In the last couple of days I see the SEC has been sued by Citadel Securities for approving this order type. Can anyone ...
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19 views

Models that can improve FHS (with possible residuals manipulation)

The Filtered Historical Simulation (FHS) is a tough benchmark. By: choosing among the most complicated ARMA-GARCH variants with automatic model and lag selection, manipulating standardized residuals ...
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0answers
73 views

How often to tune the regularisation parameter in LASSO?

I'm trying to implement the following paper: Avellaneda & Lee (2010), Statistical Arbitrage in the US equities market. To build the strategy, the idea is to trade a stock and hedge using a basket ...
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0answers
127 views

Machine learning algorithms that generate trading models (literature)?

Is there any academic literature on machine learning algorithms that are able to generate functioning trading models? Would this even be feasible at all, now or in the future? Could you point me to ...
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0answers
57 views

Methods for feature selection in quant finance dataset

I want to perform features selection on my dataset. I've split my data into train, test and out-of-sample set. The dataset is time-series based, so the split is sequenced in the order that train set ...
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1answer
97 views

Feature engineering for mid-price prediction - quickly changing features

I'm training a fully-connected feed-forward neural network on HFT (limit order book) data to predict the midprice at timepoint $t+\Delta t$ (assuming that $t$ is the current moment, and $\Delta t$ is ...
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31 views

Reliable metric to predict out of sample performance of trading strategy

How can one estimate the performance of a trading strategy on out of sample dataset? Yes, the good old model selection problem. Everyone knows sharpe ratio of your in-sample dataset by itself is a ...
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67 views

Isn't portfolio optimization basically just feature selection?

Statistical learning has a large assortment of tools for conducting feature selection such as PCA analysis, ridge regression, LASSO, SVM and almost every other machine learning algorithm. In portfolio ...
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1answer
91 views

How to combine different strategies in a backtest (and IRL)

I am trying to combine long and short strategies into an L/S strategy in my backtesting program. The way I have my backtester set up is it takes a signals object (...
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115 views

Machine learning - assigning a value to each tradable moment

I've been looking at machine learning trading strategies for some time and realized recently that I've been neglecting a very important part of the equation in terms of training an effective model. In ...
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7answers
1k views

Learning and applying Quantitative Finance successfully as an individual instead of a team

In the past few months, I became really interested in using machine learning techniques in the realm of quantitative finance and trading. I made a few rudimentary models and I immediately realized how ...
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1answer
37 views

In your experience, when trying to predict something that occurs, do you model with a fixed time period?

Let's say you are building a simple model (like the classroom examples) of trying to predict, given past information, if the stock goes up or down in the future. One could, like in classroom examples, ...
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2answers
284 views

NLP related finance projects [closed]

fist of all I do apologize if my question is not fit for this forum, but after much research I didn't find a better place to ask this question. I am a PhD student in mathematics. I do know some ML and ...
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2answers
277 views

What are important statistical concepts used as a quant?

I'm interviewing for some quantitative researcher positions at some hedge funds, and I've been told that there will be one interview session focused on stats, and one focused on ML, among others. This ...
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60 views

Is non-linear correlation problematic in financial time series prediction?

Many traditional finance models assume linear relationships between variables and features. Aren't linear correlations/covariances unable to capture financial processes empirically since they actually ...
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85 views

Proof of variance reduction of bagging

In Lecture 4 of the following course: Advances in Financial Machine Learning: 10 Lectures by Marcos Lopez de Prado link in the proof of variance reduction for a ...