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https://github.com/firmai/financial-machine-learning.git
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{chore: autopublish 2021-04-12T21:15:21Z}
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-------------------------------|:-------------------------------|:------------------------|:------------------------------------|:--------------------|
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| <sub>[NLP](https://github.com/toamitesh/NLPinFinance)</sub> | <sub>This project assembles a lot of NLP operations needed for finance domain.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis)</sub> | <sub>Extracting sentiment from financial statements using neural networks.</sub> | <sub>2018-06-04 20:54:14</sub> | <sub>2018-06-04 20:56:02</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb)</sub> | <sub>Comprehensive NLP techniques for accounting research.</sub> | <sub>2017-10-25 07:10:26</sub> | <sub>2020-06-05 03:28:46</sub> | <sub>73.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[NLP Event](https://github.com/yuriak/DLQuant)</sub> | <sub>Applying Deep Learning and NLP in Quantitative Trading.</sub> | <sub>2018-07-02 23:50:52</sub> | <sub>2019-01-31 14:08:20</sub> | <sub>70.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis)</sub> | <sub>Sentiment, distance and proportion analysis for trading signals.</sub> | <sub>2017-06-23 00:05:49</sub> | <sub>2019-01-26 03:35:55</sub> | <sub>48.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Fund classification](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Fund classification using text mining and NLP.</sub> | <sub>2018-04-16 22:18:55</sub> | <sub>2018-06-07 22:01:32</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Earning call transcripts](https://github.com/lin882/WebAnalyticsProject)</sub> | <sub>Correlation between mutual fund investment decision and earning call transcripts.</sub> | <sub>2017-12-30 08:56:03</sub> | <sub>2018-01-11 02:11:11</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb)</sub> | <sub>Using deep-learning frameworks to identify accounting anomalies.</sub> | <sub>2017-05-24 12:36:38</sub> | <sub>2019-08-07 21:47:08</sub> | <sub>110.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Buzzwords](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds)</sub> | <sub>Return performance and mutual fund selection.</sub> | <sub>2018-02-04 21:51:16</sub> | <sub>2018-02-04 21:57:09</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>repo</sub> | <sub>comment</sub> | <sub>created_at</sub> | <sub>last_commit</sub> | <sub>star_count</sub> | <sub>repo_status</sub> | <sub>rating</sub> |
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|:------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-------------------------|:-------------------------|:------------------------|:------------------------------------|:--------------------|
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| <sub>[NLP](https://github.com/toamitesh/NLPinFinance)</sub> | <sub>This project assembles a lot of NLP operations needed for finance domain.</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>nan</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis)</sub> | <sub>Extracting sentiment from financial statements using neural networks.</sub> | <sub>6/4/18 20:54</sub> | <sub>6/4/18 20:56</sub> | <sub>8.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb)</sub> | <sub>Comprehensive NLP techniques for accounting research.</sub> | <sub>10/25/17 7:10</sub> | <sub>6/5/20 3:28</sub> | <sub>73.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[NLP Event](https://github.com/yuriak/DLQuant)</sub> | <sub>Applying Deep Learning and NLP in Quantitative Trading.</sub> | <sub>7/2/18 23:50</sub> | <sub>1/31/19 14:08</sub> | <sub>70.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis)</sub> | <sub>Sentiment, distance and proportion analysis for trading signals.</sub> | <sub>6/23/17 0:05</sub> | <sub>1/26/19 3:35</sub> | <sub>48.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Fund classification](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb)</sub> | <sub>Fund classification using text mining and NLP.</sub> | <sub>4/16/18 22:18</sub> | <sub>6/7/18 22:01</sub> | <sub>4.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Earning call transcripts](https://github.com/lin882/WebAnalyticsProject)</sub> | <sub>Correlation between mutual fund investment decision and earning call transcripts.</sub> | <sub>12/30/17 8:56</sub> | <sub>1/11/18 2:11</sub> | <sub>3.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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| <sub>[Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb)</sub> | <sub>Using deep-learning frameworks to identify accounting anomalies.</sub> | <sub>5/24/17 12:36</sub> | <sub>8/7/19 21:47</sub> | <sub>110.0</sub> | <sub>:heavy_check_mark:</sub> | <sub></sub> |
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| <sub>[Buzzwords](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds)</sub> | <sub>Return performance and mutual fund selection.</sub> | <sub>2/4/18 21:51</sub> | <sub>2/4/18 21:57</sub> | <sub>1.0</sub> | <sub>:heavy_multiplication_x:</sub> | <sub></sub> |
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