Using neural networks with jumps in stock returns
$begingroup$
I am using an LSTM network to analyse stock return patterns. A problem is that, there is usually huge jumps in stock returns but if you are only using the trading data, the jumps would seem pretty random. (For example, the jumps from SEC ruling against or in favor of a company.)
Thus, if the neural network learns too much from the jumps, the results would not generalize well. One might cap the returns or use auto encoders. What are some other methods to regularize such jumps and limit the changes the jumps cause to the network?
machine-learning neural-network
asked Oct 17 '18 at 17:07
togatoga
112
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bumped to the homepage by Community♦ 13 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am using an LSTM network to analyse stock return patterns. A problem is that, there is usually huge jumps in stock returns but if you are only using the trading data, the jumps would seem pretty random. (For example, the jumps from SEC ruling against or in favor of a company.)
Thus, if the neural network learns too much from the jumps, the results would not generalize well. One might cap the returns or use auto encoders. What are some other methods to regularize such jumps and limit the changes the jumps cause to the network?
machine-learning neural-network
asked Oct 17 '18 at 17:07
togatoga
112
$endgroup$
bumped to the homepage by Community♦ 13 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am using an LSTM network to analyse stock return patterns. A problem is that, there is usually huge jumps in stock returns but if you are only using the trading data, the jumps would seem pretty random. (For example, the jumps from SEC ruling against or in favor of a company.)
Thus, if the neural network learns too much from the jumps, the results would not generalize well. One might cap the returns or use auto encoders. What are some other methods to regularize such jumps and limit the changes the jumps cause to the network?
machine-learning neural-network
asked Oct 17 '18 at 17:07
togatoga
112
$endgroup$
I am using an LSTM network to analyse stock return patterns. A problem is that, there is usually huge jumps in stock returns but if you are only using the trading data, the jumps would seem pretty random. (For example, the jumps from SEC ruling against or in favor of a company.)
Thus, if the neural network learns too much from the jumps, the results would not generalize well. One might cap the returns or use auto encoders. What are some other methods to regularize such jumps and limit the changes the jumps cause to the network?
machine-learning neural-network
machine-learning neural-network
asked Oct 17 '18 at 17:07
togatoga
112
asked Oct 17 '18 at 17:07
togatoga
112
edited Oct 17 '18 at 17:12
toga
asked Oct 17 '18 at 17:07
togatoga
112
asked Oct 17 '18 at 17:07
togatoga
112
asked Oct 17 '18 at 17:07
togatoga
112
112
bumped to the homepage by Community♦ 13 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 13 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
In standard models that try to characterise jumps, such as the Jump Diffusion model from Mertons (a short introduction), the model consists of two main parts:
- Brownian motion; a random walk to account for the random path, perhaps with some drift - when the values head upwards or downwards in a consisten manner. And
- An additive Possion process, which with some probability add a jump in a time-step, with a given probability.
Drawing from this approach, you could also consider using two models that work independently (or separately) to model the overall market and trend, along with a model that introduces jumps at certain timesteps and itnervals.
You could try using different input data for the second model, such as signals taken from text, such as news feeds or newpapers that discuss current market dynamics/politics, possible decisions from SEC and the like. The first (stable) part could be modelled by your current neural network.
This is just a high level idea, and I haven't actually seen any research that already tried it, so unfortunately cannot provide any links to literature.
answered Oct 18 '18 at 5:51
n1k31t4n1k31t4
6,5612421
$endgroup$
$begingroup$
Thanks for the reply. The problem about the tradition research is that they can not predict anything. The jumps are random like you said. In a sense tho almost nobody could predict the jumps. Not long ago TSLA went down 20% during the after hours when SEC sued Elon but then immediately went back up the second day when Elon and SEC agreed to settle. The neural network should not learn too much from the jump (a 15% - 20% of daily swing) but in a vanilla neural network it definitely would put a lot of weight on that. Maybe I will use wavelet transform etc.
$endgroup$
– toga
Oct 18 '18 at 9:55
$begingroup$
I understand, and that is exactly why I suggested two models. Obviously the model predicting/filtering jumps will need input from external sources (such as embeddings from news articles and twitter), not just prices from previous days, as the news is not factored into the actual market price before it is too late. The model for the general drift of the price path could take in daily prices along with some smoothed variant to make it a little more robust to spikes. You should really be looking at included realized volatility in such a model. Prices alone (even logs of returns) will not suffice.
$endgroup$
– n1k31t4
Oct 18 '18 at 9:59
add a comment |
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$begingroup$
In standard models that try to characterise jumps, such as the Jump Diffusion model from Mertons (a short introduction), the model consists of two main parts:
- Brownian motion; a random walk to account for the random path, perhaps with some drift - when the values head upwards or downwards in a consisten manner. And
- An additive Possion process, which with some probability add a jump in a time-step, with a given probability.
Drawing from this approach, you could also consider using two models that work independently (or separately) to model the overall market and trend, along with a model that introduces jumps at certain timesteps and itnervals.
You could try using different input data for the second model, such as signals taken from text, such as news feeds or newpapers that discuss current market dynamics/politics, possible decisions from SEC and the like. The first (stable) part could be modelled by your current neural network.
This is just a high level idea, and I haven't actually seen any research that already tried it, so unfortunately cannot provide any links to literature.
answered Oct 18 '18 at 5:51
n1k31t4n1k31t4
6,5612421
$endgroup$
$begingroup$
Thanks for the reply. The problem about the tradition research is that they can not predict anything. The jumps are random like you said. In a sense tho almost nobody could predict the jumps. Not long ago TSLA went down 20% during the after hours when SEC sued Elon but then immediately went back up the second day when Elon and SEC agreed to settle. The neural network should not learn too much from the jump (a 15% - 20% of daily swing) but in a vanilla neural network it definitely would put a lot of weight on that. Maybe I will use wavelet transform etc.
$endgroup$
– toga
Oct 18 '18 at 9:55
$begingroup$
I understand, and that is exactly why I suggested two models. Obviously the model predicting/filtering jumps will need input from external sources (such as embeddings from news articles and twitter), not just prices from previous days, as the news is not factored into the actual market price before it is too late. The model for the general drift of the price path could take in daily prices along with some smoothed variant to make it a little more robust to spikes. You should really be looking at included realized volatility in such a model. Prices alone (even logs of returns) will not suffice.
$endgroup$
– n1k31t4
Oct 18 '18 at 9:59
add a comment |
$begingroup$
In standard models that try to characterise jumps, such as the Jump Diffusion model from Mertons (a short introduction), the model consists of two main parts:
- Brownian motion; a random walk to account for the random path, perhaps with some drift - when the values head upwards or downwards in a consisten manner. And
- An additive Possion process, which with some probability add a jump in a time-step, with a given probability.
Drawing from this approach, you could also consider using two models that work independently (or separately) to model the overall market and trend, along with a model that introduces jumps at certain timesteps and itnervals.
You could try using different input data for the second model, such as signals taken from text, such as news feeds or newpapers that discuss current market dynamics/politics, possible decisions from SEC and the like. The first (stable) part could be modelled by your current neural network.
This is just a high level idea, and I haven't actually seen any research that already tried it, so unfortunately cannot provide any links to literature.
answered Oct 18 '18 at 5:51
n1k31t4n1k31t4
6,5612421
$endgroup$
$begingroup$
Thanks for the reply. The problem about the tradition research is that they can not predict anything. The jumps are random like you said. In a sense tho almost nobody could predict the jumps. Not long ago TSLA went down 20% during the after hours when SEC sued Elon but then immediately went back up the second day when Elon and SEC agreed to settle. The neural network should not learn too much from the jump (a 15% - 20% of daily swing) but in a vanilla neural network it definitely would put a lot of weight on that. Maybe I will use wavelet transform etc.
$endgroup$
– toga
Oct 18 '18 at 9:55
$begingroup$
I understand, and that is exactly why I suggested two models. Obviously the model predicting/filtering jumps will need input from external sources (such as embeddings from news articles and twitter), not just prices from previous days, as the news is not factored into the actual market price before it is too late. The model for the general drift of the price path could take in daily prices along with some smoothed variant to make it a little more robust to spikes. You should really be looking at included realized volatility in such a model. Prices alone (even logs of returns) will not suffice.
$endgroup$
– n1k31t4
Oct 18 '18 at 9:59
add a comment |
$begingroup$
In standard models that try to characterise jumps, such as the Jump Diffusion model from Mertons (a short introduction), the model consists of two main parts:
- Brownian motion; a random walk to account for the random path, perhaps with some drift - when the values head upwards or downwards in a consisten manner. And
- An additive Possion process, which with some probability add a jump in a time-step, with a given probability.
Drawing from this approach, you could also consider using two models that work independently (or separately) to model the overall market and trend, along with a model that introduces jumps at certain timesteps and itnervals.
You could try using different input data for the second model, such as signals taken from text, such as news feeds or newpapers that discuss current market dynamics/politics, possible decisions from SEC and the like. The first (stable) part could be modelled by your current neural network.
This is just a high level idea, and I haven't actually seen any research that already tried it, so unfortunately cannot provide any links to literature.
answered Oct 18 '18 at 5:51
n1k31t4n1k31t4
6,5612421
$endgroup$
In standard models that try to characterise jumps, such as the Jump Diffusion model from Mertons (a short introduction), the model consists of two main parts:
- Brownian motion; a random walk to account for the random path, perhaps with some drift - when the values head upwards or downwards in a consisten manner. And
- An additive Possion process, which with some probability add a jump in a time-step, with a given probability.
Drawing from this approach, you could also consider using two models that work independently (or separately) to model the overall market and trend, along with a model that introduces jumps at certain timesteps and itnervals.
You could try using different input data for the second model, such as signals taken from text, such as news feeds or newpapers that discuss current market dynamics/politics, possible decisions from SEC and the like. The first (stable) part could be modelled by your current neural network.
This is just a high level idea, and I haven't actually seen any research that already tried it, so unfortunately cannot provide any links to literature.
answered Oct 18 '18 at 5:51
n1k31t4n1k31t4
6,5612421
answered Oct 18 '18 at 5:51
n1k31t4n1k31t4
6,5612421
answered Oct 18 '18 at 5:51
n1k31t4n1k31t4
6,5612421
answered Oct 18 '18 at 5:51
n1k31t4n1k31t4
6,5612421
6,5612421
$begingroup$
Thanks for the reply. The problem about the tradition research is that they can not predict anything. The jumps are random like you said. In a sense tho almost nobody could predict the jumps. Not long ago TSLA went down 20% during the after hours when SEC sued Elon but then immediately went back up the second day when Elon and SEC agreed to settle. The neural network should not learn too much from the jump (a 15% - 20% of daily swing) but in a vanilla neural network it definitely would put a lot of weight on that. Maybe I will use wavelet transform etc.
$endgroup$
– toga
Oct 18 '18 at 9:55
$begingroup$
I understand, and that is exactly why I suggested two models. Obviously the model predicting/filtering jumps will need input from external sources (such as embeddings from news articles and twitter), not just prices from previous days, as the news is not factored into the actual market price before it is too late. The model for the general drift of the price path could take in daily prices along with some smoothed variant to make it a little more robust to spikes. You should really be looking at included realized volatility in such a model. Prices alone (even logs of returns) will not suffice.
$endgroup$
– n1k31t4
Oct 18 '18 at 9:59
add a comment |
$begingroup$
Thanks for the reply. The problem about the tradition research is that they can not predict anything. The jumps are random like you said. In a sense tho almost nobody could predict the jumps. Not long ago TSLA went down 20% during the after hours when SEC sued Elon but then immediately went back up the second day when Elon and SEC agreed to settle. The neural network should not learn too much from the jump (a 15% - 20% of daily swing) but in a vanilla neural network it definitely would put a lot of weight on that. Maybe I will use wavelet transform etc.
$endgroup$
– toga
Oct 18 '18 at 9:55
$begingroup$
I understand, and that is exactly why I suggested two models. Obviously the model predicting/filtering jumps will need input from external sources (such as embeddings from news articles and twitter), not just prices from previous days, as the news is not factored into the actual market price before it is too late. The model for the general drift of the price path could take in daily prices along with some smoothed variant to make it a little more robust to spikes. You should really be looking at included realized volatility in such a model. Prices alone (even logs of returns) will not suffice.
$endgroup$
– n1k31t4
Oct 18 '18 at 9:59
$begingroup$
Thanks for the reply. The problem about the tradition research is that they can not predict anything. The jumps are random like you said. In a sense tho almost nobody could predict the jumps. Not long ago TSLA went down 20% during the after hours when SEC sued Elon but then immediately went back up the second day when Elon and SEC agreed to settle. The neural network should not learn too much from the jump (a 15% - 20% of daily swing) but in a vanilla neural network it definitely would put a lot of weight on that. Maybe I will use wavelet transform etc.
$endgroup$
– toga
Oct 18 '18 at 9:55
$begingroup$
Thanks for the reply. The problem about the tradition research is that they can not predict anything. The jumps are random like you said. In a sense tho almost nobody could predict the jumps. Not long ago TSLA went down 20% during the after hours when SEC sued Elon but then immediately went back up the second day when Elon and SEC agreed to settle. The neural network should not learn too much from the jump (a 15% - 20% of daily swing) but in a vanilla neural network it definitely would put a lot of weight on that. Maybe I will use wavelet transform etc.
$endgroup$
– toga
Oct 18 '18 at 9:55
$begingroup$
I understand, and that is exactly why I suggested two models. Obviously the model predicting/filtering jumps will need input from external sources (such as embeddings from news articles and twitter), not just prices from previous days, as the news is not factored into the actual market price before it is too late. The model for the general drift of the price path could take in daily prices along with some smoothed variant to make it a little more robust to spikes. You should really be looking at included realized volatility in such a model. Prices alone (even logs of returns) will not suffice.
$endgroup$
– n1k31t4
Oct 18 '18 at 9:59
$begingroup$
I understand, and that is exactly why I suggested two models. Obviously the model predicting/filtering jumps will need input from external sources (such as embeddings from news articles and twitter), not just prices from previous days, as the news is not factored into the actual market price before it is too late. The model for the general drift of the price path could take in daily prices along with some smoothed variant to make it a little more robust to spikes. You should really be looking at included realized volatility in such a model. Prices alone (even logs of returns) will not suffice.
$endgroup$
– n1k31t4
Oct 18 '18 at 9:59
add a comment |
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