10 votes
Accepted

What is the stambaugh bias? Why is it important for predictability regressions?

The bias comes from the paper Stambaugh (1999) and has nothing to do with small sample bias. It has to do with point (1) below. The argument goes as follows: Typical lagged explanatory variables ...
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  • 6,800
8 votes
Accepted

Are returns predictable, Campbell and Shiller (1988)

Let me start with a simple example. Suppose you have a dividend strip that pays an unknown dividend $D_T$. The gross return (something like 1.05 and NOT 5%!) on this security is, by definition, $$R_{t\...
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  • 1,856
7 votes

Predict the behavior of a time series (P&L trading desk)

Without seeing your trading desk's P&L it's impossible to say whether it is predictable or not. But here are a few thoughts - There's no reason to think that it isn't predictable. In general, ...
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  • 5,603
6 votes

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

Sorry, but despite being used as a popular example in machine learning, no one has ever achieved a stock market prediction. It does not work for several reasons (check random walk by Fama and quite a ...
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  • 61
6 votes
Accepted

Tools/R code for predicting Dragon-Kings

My favorite tool is Sornette's own Finanical Crisis Observatory: http://tasmania.ethz.ch/pubfco/fco.html If you are interested, I have developed my own tool in Java and JavaCL which can be found here:...
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5 votes

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 votes
Accepted

GARCH model and prediction

The mean could be the long run variance which is sig2 = fit.Constant/(1-fit.GARCH{1}-fit.ARCH{1}); I hope this explains. If not, note I ran this model through ...
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  • 1,688
5 votes

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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  • 181
5 votes
Accepted

Predict probability of returns: How does changing volatility affect the return pdf?

I have written an entire paper on this approach at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2828744 As to your specifics 1) "Volatility" as defined by variance does not exist, which is ...
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  • 4,074
5 votes
Accepted

how are financial data with sparse and asynchronous features imputed in predictive modeling?

There is large literature on MIDAS (mixed-frequency data sampling) models, the leading scholars being Eric Ghysels and Rossen Valkanov — google their research for references. However, the ...
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4 votes

Any research on how natural language processing can be used to forecast stocks?

Recent research A recent article by Frank Zhao is interesting to get started: Natural Language Processing - Part I: Primer. You will find more papers on this repo (too long to copy all here): ...
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  • 319
4 votes

How quants use ML models for stock market prediction

The correlation matrix is a very important part of modeling stock returns. It is often better to build a model that takes in multiple assets features so that it can use this correlation to its ...
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4 votes

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

I wrote a masters thesis related to machine learning in finance, and during this process I surveyed about 200 of the research papers that were written about the topic since 2018. This is the ...
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  • 141
4 votes
Accepted

What is time-varying risk premium? Forecasting stock returns

Another way of staying "time-varying risk-premium", is saying that the risk-premium is predictable. However, that the fact that the risk-premium is predictable does not means that you can make money ...
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  • 6,800
4 votes
Accepted

Does predictability in a VAR process imply mean reversion or momentum?

The point of confusion may be in thinking that a predictable price process is synonymous with a mean-reverting process while using the definitions in these papers, it's actually the opposite! In the ...
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  • 6,294
4 votes
Accepted

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

There are a few exclusions that I have commonly seen: Excluding thinly traded stocks. The price that shows up in your data feed may not relate to actual tradable prices. Filtering for ADR/Pink ...
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  • 2,403
3 votes
Accepted

Technical Indicators reference

The TA_lib Technical Analysis library here has open source code for numerous indicators.
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3 votes

What is currently predictable in the stock and bond markets and what is not

The renowned CXO Advisory Group has a section "What Works Best?". Here some general information is given and many links to their research articles which e.g. summarize lots of current academic ...
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  • 26.7k
3 votes

time series data modeling for deep learning

I am not sure I perfectly understand your question, the concept of "time series with varying density over time" is not very clear. One thing is for sure, the optimal way to "feed" a neural network is ...
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  • 10.5k
3 votes

Using candlesticks for Stock price direction prediction

The accuracy of a model is only 1 factor in determining usefulness. Aside from the accuracy, it would help to determine how you would implement it in a simulated trading environment and look into the ...
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  • 3,715
3 votes

Paper on returns from perfect market timing?

In the long term, you will outperform buy & hold with a market timing accuracy of > 65%. See these papers for more: Bauer, R.; Dahlquist, J.: „Market Timing and Roulette Wheels“, Financial ...
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2 votes

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

You can try this course on Udactiy https://www.udacity.com/course/machine-learning-for-trading--ud501
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2 votes

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

Blair Hull as an idea: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2609814 He says he sold his automated trading firm to Goldman for 300 million $.
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  • 21
2 votes

Implementing A 50/50 Prediction Model Strategy

A prediction model that is correct $50\%$ of the time can be profitable if the model gains more when it is right than it loses when it is wrong. You could simplify it like this: A trading strategy is ...
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  • 2,874
2 votes

Quant teams predicting the World Cup

An interesting variant from Reuters (you can do your own "simulations"): https://www.breakingviews.com/considered-view/numbers-add-up-to-germany-retaining-world-cup/ Another paper from a renowned ...
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  • 26.7k
2 votes

Technical Indicators reference

The Technical Analysis of Financial markets is considered as a milestone of the matter. I suggest to read that before starting to test your strategy. It explains well the use of each indicator, ...
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  • 2,438
2 votes

Technical Indicators reference

A very good reference can be found here: http://www.asiapacfinance.com/trading-strategies/technicalindicators
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  • 26.7k
2 votes

predict next day's close price using hmm

I cannot seem to find that article for free, so here is a more generalized answer. 1.what are the hidden states and what are the observation states. The hidden states are said to be that of an ...
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2 votes
Accepted

Window length for predictive regressions

Which strategy will work better is an empirical question that depends on the data at hand. That is, you cannot prove theoretically that one approach is better than the other without some extra ...
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2 votes

What is currently predictable in the stock and bond markets and what is not

I think you have a small misunderstanding in terms of what the folks with all of the various incarnations of quantitative degrees are doing. There are always people trying to punt on the direction of ...
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