1596333780
Machine Learning and Trading on Sentiment
What You Will Learn and Skills to Take Away
- How to build multiple classification machine learning models into a composite ensemble model
- Use time-series split and randomized cross-validation for hyperparameter tuning
- Explore important classification metrics, such as F1 score and accuracy, in accessing the quality of the models
- Portfolio performance and construction code that can be applied across multiple strategies factoring in real-world transaction costs
- How to build a feature set using One Hot Encoding
- Discover whether short term price movements can be predicted from basic machine learning models
In my previous article, I explored if it was possible to use a single sentiment indicator and a simple linear model to systematically outperform the US stock market. Unsurprisingly, I found that a simple system did not work by a long shot. But maybe a machine learning model that combines different sentiment signals could create a profitable strategy?
Literature Review
With the advent of social media, analysts have used social media analytics and natural language processing to distill emotional sentiment around specific companies, with firms like Ravenpack finding alpha in long high positive sentiment companies and short negative sentiment firms. They found a 50 bps to 100 bps return on a long/short top decile bottom decile portfolio over a 2–3 day period.
Shlaefer found that there was 1 to 12 months underreaction (stock price goes higher after good news and lower after bad news) revealing that it takes time for investors to process new information, but that over 3–5 years investors overreact and pay too much for a string of positive earnings and ignore the mean-reverting nature of company fundamentals.
Baker and Stein found that a period of high liquidity and small bid-ask spreads result in lower future returns. Since the irrational investors tend to make the market more liquid, measures of liquidity provide an indicator of the relative presence or absence of these investors, and hence of the level of prices relative to fundamentals.
Baker and Wurgler hypothesize that stocks most sensitive to investor sentiment will be those of companies that are younger, smaller, more volatile, unprofitable, non-dividend paying, distressed or with extreme growth potential, or having analogous characteristics. Whereas “bond-like” stocks will be less driven by sentiment. They found that sentiment can be a strong short term contrarian indicator with small-cap, speculative stocks that are young, highly volatile, near financial distress, and unprofitable. Specifically, within this group of companies, their monthly return was on average -.34% when sentiment was one standard deviation below their historical average and 1.18% when their sentiment is above by one standard deviation.
Borovoka and Dijkstra realized a 60% accuracy in their deep neural network model in forecasting the EURO STOXX 50 price movement based on high-frequency news sentiment around the individual index companies.
Tetlock found that media sentiment, as measure by the Wall Street Journal’s _Abreast of the Market _column, was a confirming indicator over a few days with regards to the overall stock market returns, and especially so with small stocks. Also, he found that unusually high or low market pessimism led to high trading volume.
Data and Methodology
None of the research investigated sector-specific effects driven by overall investor sentiment, so I was curious to explore it.
I utilized the 9 SPDR Sector ETFs (XLU, XLK, XLB, XLI, XLV, XLF, XLE, XLP, XLY) that had their inception in December 1998, providing over 21 years of daily OHLC and volume data from Yahoo Finance. Note that free data, such as Yahoo Finance, is not always the cleanest price data but I kept it as is so you can utilize the code with minimum cost in case you would like to add different ETF or stock tickers to test the model.
I also utilized data from Sharadar, the St. Louis Federal Reserve, Duke University, the Chicago Board of Options Exchange, the University of Michigan, Economic Policy Uncertainty, and
Combining multiple classification machine learning models from the scikit-learn python library into an ensemble classification, I hope that a diversified model will perform well out of sample compared to any individual model.
The dataset into a training, validation, and test set:
- Training/Validation (In Sample) — Beginning of data until the end of 2013, with training comprising 80% and validation the remaining 20% of In Sample data
- Test (Out of Sample) — January 2014 — May 2020
Musings and Curiosities
The questions I will try to answer:
- Over what time frame are Sector returns most predictable based on sentiment? Since high OHLC and volume data, I will only be able to buy on the open of the next day and close out any position on n days later.
- What models should be used to combine in a final ensemble model?
- What hyperparameters are optimal for predicting out of sample data?
- After combining the best models, will the machine learning portfolio outperform after transaction costs, since this would be a high turnover strategy?
- Can the model outperform a passive buy and hold strategy?
Building the Feature Set
Throughout the article, I will share parts of the code and not all due to readability, but you can access the data (except Sharadar due to licensing restrictions) and python files on GitHub for your personal use.
The following compose the feature set from which the algorithm will predict the desired value, namely whether the security had a positive or negative return over n days.
- Market Turnover (% of total shares outstanding traded in a day)
- Advance/Decline Ratio (total number of shares increasing/shares decreasing)
- Simple Moving Average/Price iterated over rolling 2 to 22 trading days
- Annualized Standard Deviation iterated over rolling 2 to 22 trading days
- VIX
- Sahm Rule — which has been useful in predicting recessions in real-time
- Investment Grade — High Yield Corporate Bond Spread
- Political Uncertainty Index
- Duke CFO Optimism Index
- Put/Call Ratio
- AAII Investor Sentiment Index
- University of Michigan Consumer Sentiment Index
- Yale/Shiller Investor Sentiment Index
One Hot Encoding
I was curious if the individual sector ETFs performed differently under the same Feature set, and thus I used One Hot Encoding to create columns for each ETF that had a 1 if used and 0 otherwise (see below picture).
The final feature and value pandas dataframe looks like the following that is then converted to a NumPy array.
Credit Spread US Shiller Valuation Index Indiv (Lag) \
date
2003-10-17 3.95 66.24
2003-10-17 3.95 66.24
2003-10-17 3.95 66.24
2003-10-17 3.95 66.24
2003-10-17 3.95 66.24
US Shiller Valuation Index Inst (Lag) ...
date ...
2003-10-17 76.64 ...
2003-10-17 76.64 ...
2003-10-17 76.64 ...
2003-10-17 76.64 ...
2003-10-17 76.64 ...
XLK XLP XLU XLV XLY
date
2003-10-17 0.0 0.0 0.0 0.0 0.0
2003-10-17 0.0 1.0 0.0 0.0 0.0
2003-10-17 1.0 0.0 0.0 0.0 0.0
2003-10-17 0.0 0.0 0.0 0.0 1.0
2003-10-17 0.0 0.0 0.0 0.0 0.0
Creating Train, Validation, and Test Set
You can easily create a train and validation set, note the need to place shuffle=False to prevent a shuffling in the data that would leave to a model overfit since the financial data is not independent. What I like to do is keep my in sample data (train and validation) in one python file and keep the out of sample (test) data in a separate file to prevent any temptation to cheat and look at the future.
#machine-learning #python #investing #trading #sentiment-analysis #deep learning
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Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.
Transportation industry
Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.
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Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.
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Transportation industry
Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.
- ML and AI can offer high security in the transportation industry.
- It offers high reliability of their services or vehicles.
- The adoption of this technology in the transportation industry can increase the efficiency of the service.
- In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.
Healthcare industry
Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.
- Identifying Diseases and Diagnosis
- Drug Discovery and Manufacturing
- Medical Imaging Diagnosis
- Personalized Medicine
- Machine Learning-based Behavioral Modification
- Smart Health Records
- Clinical Trial and Research
- Better Radiotherapy
- Crowdsourced Data Collection
- Outbreak Prediction
**
Finance industry**
In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.
- Fraud prevention
- Risk management
- Investment predictions
- Customer service
- Digital assistants
- Marketing
- Network security
- Loan underwriting
- Algorithmic trading
- Process automation
- Document interpretation
- Content creation
- Trade settlements
- Money-laundering prevention
- Custom machine learning solutions
Education industry
Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.
Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.
- Improved Unsupervised Algorithms
- Increased Adoption of Quantum Computing
- Enhanced Personalization
- Improved Cognitive Services
- Rise of Robots
**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.
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1626077565
Sentiment Analysis in Python using Machine Learning
Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?
We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.
But why do we need sentiment analysis?
Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.
It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.
Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.
Lastly, they are also used for spam filtering and removing unwanted content.
How does sentiment analysis work?
NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.
NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.
Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.
Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are
- Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
- Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
- The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc
Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.
- Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
- This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
- Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.
In this machine learning project, we will use recurrent neural network for sentiment analysis in python.
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