<p>As previously stated by EnergyNumbers, ML is very often used in financial and economics forecasting, just like OLS. The deep learning is less useful in economics academia because you do not know the cause and it will be hard to publish a well-explained paper based on that.</p>

<p><em>What kind of methods/algorithms/models, if any, are frequently used for what kind of purposes among what kind of economists? Where can I see their work (e.g. blog, paper, etc...)</em></p>

<p>You could just use goole scholar and search for "financial time series forecasting machine learning". GARCH, SVM, and ARIMA are often used. You could get started here: </p>

<p>Kim, Kyoung-jae. "Financial time series forecasting using support vector machines." Neurocomputing 55.1 (2003): 307-319.</p>

<p>Hsu, Ming-Wei, et al. "Bridging the divide in financial market forecasting: machine learners vs. financial economists." Expert Systems with Applications 61 (2016): 215-234.</p>

<p>Shynkevich, Yauheniya, et al. "Forecasting stock price directional movements using technical indicators: investigating window size effects on one-step-ahead forecasting." Computational Intelligence for Financial Engineering &amp; Economics (CIFEr), 2104 IEEE Conference on. IEEE, 2014.</p>

<p>Tan, Elliot. "Forecasting Foreign Exchange Rates in Response to Federal Reserve Communication: A Machine Learning Approach." (2016).</p>

CC BY-SA 3.0
2017-08-11T18:39:29.380Z
GARCH and ARIMA hardly are machine learning approaches; rather, they are traditional tools in financial econometrics. (You could of course go as far as to call regression analysis a machine learning techique; but then the distinction between traditional econometric methods and machine learning would be blurry.) SVM, on the other hand, is a "good" example of machine learning.
Richard Hardy
CC BY-SA 3.0
2017-08-13T09:57:44.487Z
Thanks but then why is SVM used while DNN not? Seems to me that SVM is also pretty hard to detect the cause.
Blaszard
CC BY-SA 3.0
2017-08-15T15:21:42.167Z
@Blaszard DNN is used, but less than SVM, because of the number of parameters (over-fitting). Moreover, using SVM you need less experiments and to spend less time play-around. There are also other minor issues such as global optimum, etc. SVM is definitely more interpretable than DNN.
High GPA
CC BY-SA 3.0
2017-08-11T11:28:59.063Z
2017-08-11T11:28:59.063Z
High GPA
<p>I will attempt to answer the first (<em>How prevalent is it?</em>) and last question (<em>Where can I see their work?</em>).</p>

<p>There was a <a href="https://www.economist.com/news/finance-and-economics/21710800-big-data-have-led-latest-craze-economic-research-economists-are-prone" rel="nofollow noreferrer">recent</a> post in <em>The Economist</em> about the emergence of machine learning into standard economic papers. They produced a nice graph, reproduced below:</p>

<p><a href="https://i.sstatic.net/5Q9ua.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/5Q9ua.png" alt="enter image description here"></a></p>

<p>Albeit machine learning is confounded with big data, it shows a rise in their use since 2014. Surely, this is a very narrow selection of the literature (NBER working papers, and only abstracts), but is a start. </p>

<p>Regarding where you can find papers using machine learning, you can use "keyword" search <a href="http://econpapers.repec.org/scripts/search.pf?kw=Machine%20learning" rel="nofollow noreferrer">here</a> (26,665 documents are found), or <a href="https://ideas.repec.org/cgi-bin/htsearch?ul=&amp;q=%22machine+learning%22&amp;cmd=Search%21&amp;wf=0F00&amp;s=R&amp;dt=range&amp;db=&amp;de=&amp;m=all&amp;fmt=long&amp;sy=1&amp;ps=10" rel="nofollow noreferrer">here</a> (337 documents are found). The latter allows you to select dates, which you could use to build your own indicator of machine learning related publications per year.</p>

<p>Finally, related to blogs, I am not aware of a dedicated ML/DL economist's blog. However, <a href="https://econacademics.org/" rel="nofollow noreferrer">here</a> there is a very useful blog aggregator, which has indexed most of the "serious" blogs available. The full list of blog posts indexed is <a href="https://econacademics.org/list.alpha.html" rel="nofollow noreferrer">here</a>. A search for "machine" or "deep" does not yield a result. However, there are some hits for "econometric" and "statistics" which might help you.</p>

CC BY-SA 3.0
2017-08-11T09:09:30.060Z
2017-08-13T11:37:13.710Z
2017-08-13T11:37:13.710Z
luchonacho
<p>I wonder how machine learning, particularly deep neural net which is considered the state-of-the-art among many data scientists, is used among economists, or other experts who is in in the similar field to predict economic situations, such as bank analysts, etc...</p>

<p>I know in econometrics people heavily use statistics. But it is focused on  statistics, not on machine learning, let alone deep learning.</p>

<p>How prevalent is it? What kind of methods/algorithms/models, if any, are frequently used for what kind of purposes among what kind of economists? Where can I see their work (e.g. blog, paper, etc...)</p>

CC BY-SA 3.0
2017-08-11T07:17:23.027Z
"How prevalent" is hard to measure. I guess that is why two people have already voted to close this question. Perhaps you would be better off asking for examples of famous econ papers that use machine learning.
Giskard
CC BY-SA 3.0
2017-08-11T07:24:44.183Z
It's heavily uesd in financial and economic forecasting. But if a method works, it won't be published. If it's published, it doesn't work.
410 gone
CC BY-SA 3.0
2017-08-11T11:14:02.823Z
If it's published, it will still possibly work if the primary author is an assistant professor.
High GPA
CC BY-SA 3.0
2017-08-11T15:22:13.947Z
First,  you need to pull up the economist jokes. In fact, ML is just all about garbage in garbage out. Garbage economic assumption will return garbage output.
mootmoot
CC BY-SA 3.0
2017-08-13T10:01:24.883Z
@denesp Sorry if that is too broad but I don't see the closed votes casted...
Blaszard
CC BY-SA 3.0
2017-08-13T10:18:13.300Z
@Blaszard Not too broad, just hard to measure. You only see the votes if you have a high reputation. Currently there is only a single vote to close, so I guess the question will be fine.
Giskard
<statistics>
How prevalent is the use of machine learning among economists?
Blaszard
# How prevalent is the use of machine learning among economists?

(Asked by: username_0 on 2017-08-11)

I wonder how machine learning, particularly deep neural net which is considered the state-of-the-art among many data scientists, is used among economists, or other experts who is in in the similar field to predict economic situations, such as bank analysts, etc...

I know in econometrics people heavily use statistics. But it is focused on statistics, not on machine learning, let alone deep learning.

How prevalent is it? What kind of methods/algorithms/models, if any, are frequently used for what kind of purposes among what kind of economists? Where can I see their work (e.g. blog, paper, etc...)

**Tags:** statistics
> **Comments:**
> **username_1**: "How prevalent" is hard to measure. I guess that is why two people have already voted to close this question. Perhaps you would be better off asking for examples of famous econ papers that use machine learning.  
> **username_2**: It's heavily uesd in financial and economic forecasting. But if a method works, it won't be published. If it's published, it doesn't work.  
> **username_3**: If it's published, it will still possibly work if the primary author is an assistant professor.  
> **username_4**: First,  you need to pull up the economist jokes. In fact, ML is just all about garbage in garbage out. Garbage economic assumption will return garbage output.  
> **username_0**: @denesp Sorry if that is too broad but I don't see the closed votes casted...  
> **username_1**: @Blaszard Not too broad, just hard to measure. You only see the votes if you have a high reputation. Currently there is only a single vote to close, so I guess the question will be fine.  

---

## 2 answers

### Answer by username_6 on 2017-08-11. Score: 4 | Accepted answer

I will attempt to answer the first (*How prevalent is it?*) and last question (*Where can I see their work?*).

There was a [recent](https://www.economist.com/news/finance-and-economics/21710800-big-data-have-led-latest-craze-economic-research-economists-are-prone) post in *The Economist* about the emergence of machine learning into standard economic papers. They produced a nice graph, reproduced below:

[![enter image description here](https://i.sstatic.net/5Q9ua.png)](https://i.sstatic.net/5Q9ua.png)

Albeit machine learning is confounded with big data, it shows a rise in their use since 2014. Surely, this is a very narrow selection of the literature (NBER working papers, and only abstracts), but is a start.

Regarding where you can find papers using machine learning, you can use "keyword" search [here](http://econpapers.repec.org/scripts/search.pf?kw=Machine%20learning) (26,665 documents are found), or [here](https://ideas.repec.org/cgi-bin/htsearch?ul=&q=%22machine+learning%22&cmd=Search%21&wf=0F00&s=R&dt=range&db=&de=&m=all&fmt=long&sy=1&ps=10) (337 documents are found). The latter allows you to select dates, which you could use to build your own indicator of machine learning related publications per year.

Finally, related to blogs, I am not aware of a dedicated ML/DL economist's blog. However, [here](https://econacademics.org/) there is a very useful blog aggregator, which has indexed most of the "serious" blogs available. The full list of blog posts indexed is [here](https://econacademics.org/list.alpha.html). A search for "machine" or "deep" does not yield a result. However, there are some hits for "econometric" and "statistics" which might help you.


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### Answer by username_3 on 2017-08-11. Score: 2

As previously stated by EnergyNumbers, ML is very often used in financial and economics forecasting, just like OLS. The deep learning is less useful in economics academia because you do not know the cause and it will be hard to publish a well-explained paper based on that.

*What kind of methods/algorithms/models, if any, are frequently used for what kind of purposes among what kind of economists? Where can I see their work (e.g. blog, paper, etc...)*

You could just use goole scholar and search for "financial time series forecasting machine learning". GARCH, SVM, and ARIMA are often used. You could get started here:

Kim, Kyoung-jae. "Financial time series forecasting using support vector machines." Neurocomputing 55.1 (2003): 307-319.

Hsu, Ming-Wei, et al. "Bridging the divide in financial market forecasting: machine learners vs. financial economists." Expert Systems with Applications 61 (2016): 215-234.

Shynkevich, Yauheniya, et al. "Forecasting stock price directional movements using technical indicators: investigating window size effects on one-step-ahead forecasting." Computational Intelligence for Financial Engineering & Economics (CIFEr), 2104 IEEE Conference on. IEEE, 2014.

Tan, Elliot. "Forecasting Foreign Exchange Rates in Response to Federal Reserve Communication: A Machine Learning Approach." (2016).

> **Comments:**
> **username_5**: GARCH and ARIMA hardly are machine learning approaches; rather, they are traditional tools in financial econometrics. (You could of course go as far as to call regression analysis a machine learning techique; but then the distinction between traditional econometric methods and machine learning would be blurry.) SVM, on the other hand, is a "good" example of machine learning.  
> **username_0**: Thanks but then why is SVM used while DNN not? Seems to me that SVM is also pretty hard to detect the cause.  
> **username_3**: @Blaszard DNN is used, but less than SVM, because of the number of parameters (over-fitting). Moreover, using SVM you need less experiments and to spend less time play-around. There are also other minor issues such as global optimum, etc. SVM is definitely more interpretable than DNN.  

---

