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 ...
- 8,022
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\...
- 1,886
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, ...
- 5,891
7
votes
Accepted
One-day-ahead prediction of S&P500 with Temporal Convolutional Networks
As @Bob Jansen says above the last price is actually an excellent predictor, but you should do it in the return space.
Goyal and Welch (2007) try to do this with multiple predictors and find that ...
- 8,022
6
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 ...
- 4,359
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 ...
- 191
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 ...
- 924
5
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 ...
- 8,022
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 ...
- 932
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 ...
- 924
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): ...
- 319
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 ...
- 141
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 ...
- 6,924
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 ...
- 2,588
4
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 ...
- 4,738
4
votes
Understanding how markets predict BoC's policy interest rate decisions
I trade interest rate derivatives. I can definitively tell you the best way of analysing what is priced in is to identify the liquid and tradeable instruments that most closely aligns with the central ...
- 9,215
4
votes
One-day-ahead prediction of S&P500 with Temporal Convolutional Networks
Because the last known price is an excellent predictor.
I would try modelling returns instead of prices. Now the model can't converge to last price.
- 8,438
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 ...
- 27.3k
3
votes
What is time-varying risk premium? Forecasting stock returns
Up until the work of Robert Shiller in about 1980, it was thought that the expected excess return on the market $(R_M−R_f)$ is constant and is an equilibrium risk premium. Shiller showed that this is ...
- 9,332
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 ...
- 11.5k
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 ...
- 27.3k
3
votes
Is there any utility to being able to predict an assets current price?
You can provide estimates for missing data. Backfilling / interpolation of market time series is a common problem in the industry.
- 6,425
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
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 $.
- 21
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 ...
- 27.3k
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 ...
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 ...
- 2,726
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 ...
- 2,588
2
votes
R squared statistic in predictions of returns
The goal of regression is to account for the variance in $y$. If you are able to do that, then your predictions of the conditional mean of $y$ (conditioned on the values of your features) will be ...
- 353
2
votes
Accepted
Trouble understanding lookahead bias
The issue is how do you evaluate the success of your trades. If the P&L in your simulation is measured as C(t+1)-C(t) then your simulation is not completely realistic, because in real life by the ...
- 9,332
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