FANG Stocks Prediction and Quantitative Trading using Sentiment Analysis

This project aims to investigate the prediction power of public sentiment on FANG, i.e Facebook, Amazon, Netflix and Google, extracted from Economics Subreddit and New York times by comparing the prediction accuracy of different machine learning methods.

We conclude that simpler models, such as linear regression and random forest better predict (63.16% accuracy) on stock return's growth trend using sentiment, largely due to high correlations between sentiment effects and stock prices.

RESULT:

ASSUMPTION:

DATA:

MODEL:

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Table of Contents

• Abstract
• Data Source
• Exploratory Data Analysis
• Benchmark
• Sentiment Analysis
• Trading Strategy
• Conclusion
• Team

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Data Source

Sorted New York Times Data Stock Price from Nov 26th 2018 to Nov 11th 2019:

Emotional Analysis with NRC emotion lexicon: counting 8 emotions and 2 sentiments

def return_emotions(stock_df):
count = 0
columns = ['anticipation', 'sadness', 'joy', 'negative', 'trust', 'positive', 'surprise', 'disgust', 'anger', 'fear']
emotions = pd.concat([pd.DataFrame(emotion_analyzer(i), columns=columns) 
for i in stock_df['lead_paragraph']], ignore_index=True)
return emotions

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New York Times Headline Daily Sentiment Daily

Exploratory Daily Sentiment Data Analysis

Daily Sentiemnt

Contributors

Danielle (zw2631)	Mary Shao

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License