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fist of all I do apologize if my question is not fit for this forum, but after much research I didn't find a better place to ask this question. I am a PhD student in mathematics. I do know some ML and specifically recently been reading/programming some minor NLP related projects. I was wondering what would you suggest as some NLP/finance related project that is a cool/impressive idea and can be done in a reasonable amount of time like 1 month.

I have done google search and read some ideas but most of the ideas that I have read sound like generic projects. Things like web-scraping news or tweets and doing sentiment analysis seems a little bit generic to me and not too much differentiating. (I could be wrong though)

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here is a quick list you can apply for quant finance and use as projects:

Risk ( as markets seem quite uncertain )

Predict the risk factors exposure of a stock given its quarterly reports and press releases. If a stock started trading only recently, you have very little information to assess its exposure to risk factors. NLP can help by using the reports of the company to predict its factor exposures.

Equity

Predict the impact of a particular report on the stock price. Predict the market capitalisation based on the latest available quarterly reports, press releases, etc.

Fixed Income

Predict the credit rating (default probability) of a particular issuer given its reports (quarterly, press releases, etc). For example, it could be valuable to predict which bonds in your universe will go from BB to BBB (rising angels prediction) and vice versa.

Sustainability

Predict the ESG scores of companies given their sustainability reports. It is hard to manually follow every company in your investible universe to assess their ESG scores (Environmental, Social, and Governance). Predict the probability for a particular company to join the pension fund .

Predict the correlation/covariance matrix between assets. Useful if you do not have a significant historical period to compute the matrix.

Dataset XpressFeed from S&P Neuralyst: NLP Dataset for the Stock Market. Quandl , quantopian for idea of making and understanding algo's

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This is really a career advice question, which doesn't belong here.

But if it were rephrased to ask for ideas for a cool / impressive NLP school project, I'd suggest:

  • parse a financial derivative term sheet, decide whether it is a "vanilla" trade that we know how to book, or it may have some exotic features that a human needs to look at;

  • parse an exotic financial derivative term sheet, figure out what models can be used to price it, what market data is needed, what risks need to be calculated.

  • parse a term sheet (interest rate swap or loan), figure out the LIBOR-SOFR migration implications (e.g., does it reference LIBOR? if so, does it contain the desired fallback language?).

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  • $\begingroup$ Sorry for my lack of financial knowledge. Where can one find these "financial derivative term sheets"? Where can I learn more about "determining pricing model by reading an exotic financial derivative term sheet"? $\endgroup$ – user127776 Jul 3 '20 at 21:41
  • $\begingroup$ googling... rbccm.com/structuredrates/file-566984.pdf (example CAD IR swap referencing CDOR, with no CORRA fallback language), Example exotic sec.gov/Archives/edgar/data/70858/000119312512021960/… $\endgroup$ – Dimitri Vulis Jul 3 '20 at 22:29
  • $\begingroup$ @DimitriVulis +1 Great! Please ping me when that AI (or is it ML? :)) app is available for sale (preferably pre-validated by regulators). $\endgroup$ – ir7 Jul 3 '20 at 23:18
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    $\begingroup$ @ir7 1 I know several sinstitutions that use NLP on term sheets, some are very happy with the results 2 years ago I and my summer student manually reviewed a few hundred term sheets (that was before NLP tools were easy to find), found booking errors that affcted the P&L by "a lot" (net positive, luckily). $\endgroup$ – Dimitri Vulis Jul 3 '20 at 23:32
  • $\begingroup$ actually, looking again, the CAD link isn't a vanilla IR swap. $\endgroup$ – Dimitri Vulis Jul 4 '20 at 1:28

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