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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 ...
phdstudent's user avatar
  • 8,381
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\...
fni's user avatar
  • 1,896
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, ...
Chris Taylor's user avatar
  • 5,931
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 ...
phdstudent's user avatar
  • 8,381
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 ...
Dave Harris's user avatar
  • 4,299
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 ...
Jacques Joubert's user avatar
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 ...
phdstudent's user avatar
  • 8,381
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 ...
mac13k's user avatar
  • 191
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 ...
Igor Pozdeev's user avatar
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 ...
Oeyvind's user avatar
  • 141
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): ...
Maxime's user avatar
  • 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 ...
Jacques Joubert's user avatar
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 ...
Matthew Gunn's user avatar
  • 6,964
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 ...
JoshK's user avatar
  • 2,623
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 ...
Attack68's user avatar
  • 10.5k
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.
Bob Jansen's user avatar
  • 8,562
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 ...
vonjd's user avatar
  • 27.5k
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 ...
Alex C's user avatar
  • 9,382
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 ...
lehalle's user avatar
  • 12.2k
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 ...
amdopt's user avatar
  • 4,348
3 votes

What kinds of data should be curated for day trading?

Historically systematic trading strategies have used historical price data (and traded volumes) fundamental data (balance sheets of companies) alternative data (satellite images, texts, supply chain, ...
lehalle's user avatar
  • 12.2k
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 ...
vonjd's user avatar
  • 27.5k
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.
Kermittfrog's user avatar
  • 6,683
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
Omorhefere imoloame's user avatar
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 $.
Lars's user avatar
  • 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 ...
vonjd's user avatar
  • 27.5k
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 ...
Richard Hardy's user avatar
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 ...
JoshK's user avatar
  • 2,623
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 ...
Dave's user avatar
  • 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 ...
Alex C's user avatar
  • 9,382

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