38
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Why are there no papers about stock prediction with machine learning in leading financial journals?
I think you're overlooking a third explanation:
Nobody that found a successful technique to generate alpha has published it. I can think of the following causes:
If you're an academic, why share your ...
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30
votes
Why are there no papers about stock prediction with machine learning in leading financial journals?
In the early 2000s I met the Quant Team at Barclays Global Investors in San Francisco and I tried to convince them to submit some of their research to the journal I was managing at the time, ...
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24
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Why are there no papers about stock prediction with machine learning in leading financial journals?
There are very many finance papers using machine learning
One the of the top finance journals is the Review of Financial Studies (RFS). You find 87 published and peer-reviewed papers if you look up ...
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15
votes
Usage of Random forests in Quantitative analysis of stocks
Recently I attended a presentation by the first author of the following paper who gave us quite a creative and illuminating (kind of meta-)use of random forests in Quant Finance:
All that Glitters Is ...
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14
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Why are there so few published research papers that apply Deep Learning to Algorithmic Trading?
I would say that most ML methods risk overfitting and it depends very much on the asset class. The only area where more sophisticated ML methods such as deep learning appear to make a major difference ...
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12
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Is it really possible to create a robust algorithmic trading strategy for intraday trading?
Here's my favorite example of an intraday strategy on S&P500 futures that at least used to work:
Intraday Share Price Volatility and Leveraged ETF Rebalancing
I pull it out whenever people start ...
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10
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Is it really possible to create a robust algorithmic trading strategy for intraday trading?
Such a complex question...
Geometric Brownian Motion (GBM) will not typically work to aid one finding strategies based on technicals, as the pursuit of the technical trader is to find market ...
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9
votes
How do I use machine learning to build a credit scoring model?
One excellent resource is to try Kaggle and to examine some of the competitions, some of which are specifically on the application of machine learning to credit scoring.
https://www.kaggle.com/c/...
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9
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Forecasting non-maturity deposits with machine learning
This boils down to timeseries forecasting which is particularly challenging for financial data because finance exhibits all the things that makes forecasting hard: non stationary, low signal to noise, ...
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8
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Which are useful applications of clustering in quantitative finance?
One potential use I could imagine would be identifying paradigm shifts / regime change. Just as a quick toy example, maybe you're interested in how gold is often considered a hedge against downturns ...
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8
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Why are there no papers about stock prediction with machine learning in leading financial journals?
Adding to the answer of @BobJansen there are some additional worries with complex machine learning models (eg. Neural Networks of any kind and complex tree-based approaches) that you can encounter, ...
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7
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What’s the derivative of the sharpe ratio for one asset? Trying to optimize on it for a model
I agree that the paper could be much clearer: what it calls the “Sharp ratio derivative” is actually the “differential Sharpe ratio” proposed in a NIPS paper by Moody & Safell.
In Section 2.2 of ...
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7
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Machine Learning usage in Q part of Quant Finance
There is at least one clear area of application of ML in Q quant finance, it is the LSM algorithm invented by Longstaff, Schwartz and Carriere in the late 1990s for the valuation of callable exotics ...
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7
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Defining an objective function for machine learning task of trading
The code below is written in Wolfram Mathematica.
For example, we have some training data. And we are trying to predict: long (1) or short (0).
...
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Why are there no papers about stock prediction with machine learning in leading financial journals?
Was debating if I should even comment on this but then thought tonight I'm gonna have myself a real good time.
JPMorgan Machine Learning in Financial Markets Conference, Paris 2019 offers a ...
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7
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What are some interesting recent machine learning related developments in the QF domain?
Sirignano, J., & Cont, R. (2019) (High-frequency stock forecasting):
The authors apply a large-scale deep learning model (recurrent neural network with Long Short-term Memory units) to high-...
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6
votes
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CAPM and factor modeling: Machine learning
1) In an academic sense could it be enough to use ML to create a new factor portfolio?
The original FF papers (92,93) said something deep because they contradicted the dominant theory of the day. ...
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6
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approach on trading algorithm using machine learning
'Machine learning' describes a very broad spectrum of algorithms. Just briefly here are a few conceptual areas;
Neural networks
Reinforcement learning
Genetic algorithms and genetic programming
...
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6
votes
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Determine the right order size with market making strategy
"I need to get an algo or a formula to determine to right quantity to trade each time I place the pair (limit_buy_order, limit_sell_order)."
Actually, you need a formula for determination of the ...
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6
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Why meta-labeling is is robust?
The following presentations will shed some light:
Class notes from Cornell: Lopez de Prado
Ernie Chan's presentation of Meta-Labelling
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6
votes
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LSTM for trend prediction
The first thing you can do to help a neural network learn more rapidly is to normalize all inputs between 0 and 1. The library sklearn has a preprocess.scale() function that does just that -- make ...
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6
votes
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Learning and applying Quantitative Finance successfully as an individual instead of a team
Welcome to Quant-Stackexchange Sleepy Panda, this is an interesting question and it also seems to be an interesting book.
Regarding your Question:
It depends on your goal and your definition of ...
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6
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What are some interesting recent machine learning related developments in the QF domain?
Empirical Asset Pricing via Machine Learning (2020) by Gu, Kelly and Xiu
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5
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Machine learning techniques for quantitative finance?
I think the bible of machine learning in finance has become: Advances in Financial Machine Learning by Marcos Lopez de Prado 2018.
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5
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What machine learning method is more suitable for prediction of financial time series?
From what I have read, there are 3 popular algorithms for financial time series. Random Forests and SVMs, then followed by Neural Network Architectures.
There are a couple of good papers, to name a ...
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5
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Determine trends of data (direction detection or turning point detection)
The graph you attached suggests that you were trying to find swings between major highs and lows. This can be done by simply finding local extrema in the price series. The concept is:
find local ...
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5
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Trading Strategy adapting to my trading frequency
Machine learning could be integrated into anyone of these strategies.
Order flow prediction strategies would be the "easiest" of these examples, specifically for integrating neural networks and ...
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5
votes
Algorithmic Trading: Normalization and Selection of Technical Indicators for Artificial Neural Networks
This question is broad, and the normalisation strategy is going to depend on the nature of your indicator.
Assuming the technical indicators are a time series, then two simple approaches for ...
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5
votes
Sample uniqueness and sample weight in AFML book
That is a very good question.
If you look at sklearn fit() method parameters, you can find sample_weights parameter which tells the model which samples it should give more attention/weight when the ...
5
votes
Accepted
Backtest overfitting - in-sample vs out-of-sample
It's not out of sample. This is known as the walk-forward backtest and the problem is that you adjust your model based on the PnL curve. You add improvements to reduce drawdowns and increase returns ...
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