Trading research is critical to individual and institutional investors, as well as to the entire economy. Through in-depth research, market participants can make more informed, information-based decisions, thereby promoting economic stability and sustainable development. Now we can use machine learning and artificial intelligence to better operate transactions.
What it does
We extract and analyze news events to predict market changes, manage risks and optimize investment strategies, so that we can make as much money as possible.
How we built it
Data collection and cleaning:
Obtain text data: Collect text data related to the stock market including news, social media, company reports, etc.
Data cleaning: Clean and preprocess text data, including removing stop words, punctuation marks, stemming or lemmatization, and other operations.
emotion analysis:
Use sentiment analysis techniques to determine the sentiment polarity of a text, that is, whether the text is positive, negative, or neutral. This can help capture the sentiment of market participants and influence stock price movements.
Adjust investment strategy:
Based on the conclusions obtained from the model, we appropriately adjust our investments to maximize benefits.
Challenges we ran into
Data quality and availability: The quality of stock market-related text data can be affected by factors such as the reliability of news sources, social media noise, and inconsistencies in text data. Ensuring data quality and availability is an important challenge.
Irrational behavior of the market: The stock market is often affected by investor sentiment, herding behavior and other irrational factors, which makes predicting market movements complicated. Sentiment analysis may not fully capture these factors.
Real-time requirements: In a real-time trading environment, models need to be able to process large amounts of data and make quick decisions in a short period of time. This is a challenge for model design and performance.
Accomplishments that we're proud of
Based on the data set, we successfully trained a model that can predict the rise and fall of stocks based on news, and after rigorous calculations, we obtained the best investment portfolio.
What we learned
Have a deeper understanding of market trading models, eg, dual listed company, and natural language processing.
What's next for Optiker_Challenge
The next step can be to further optimize the language processing model so that it can not only predict the rise and fall of stocks, but also predict the specific number of rises and falls. It can also analyze the historical trend of stocks, which will help predict future trends.
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