ISE 537 Final Project 2022
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ISE 537
Final Project
2022
1. Dynamic Trading Strategy Design
• Lecture Notes 6-9 and Jupyter Notebooks (“Lecture 8: Regression for Pair Trading.ipynb” are related to this project.
• Design an improved version of the pairs trading strategy (introduced in class) by includ- ing more stocks in your strategy.
• You can consider using the top 100 stocks (https://companiesmarketcap.com/) and their daily close price during the period of 2019 - 2020 [Note: you may not have access to all the stocks from Yahoo Finance during this period; please explain how you handle this issue].
• Explain whether dimension reduction techniques are needed.
• Please show the out-of-sample performance and explain what criterion you use for the out-of-sample test.
• Show evidence that the strategy you develop is profitable.
• Compare the out-of-sample performance between your strategy and a pairs-trading strat- egy. Explain the results.
2. Price Prediction: Comparison between ARIMA Model and LSTM
• Lecture Notes 11-13 and 21-22 and Jupyter Notebooks (“Lec 10-11 Time Series,”“Lec 12-13 Time Series.ipynb,”“Lectures 21-22: Deep Learning,” and “Lecture 22 Stock Price Prediction using LSTM”) are related to this project.
• Choose two stocks as you wish for the prediction. Do predictions separately for each stock.
• Pick a period (explain why) and use daily close price from Yahoo Finance for the pre- diction.
• Show evidence of convergence for the LSTM model and explain how you pick the lags for the ARIMA model.
• Compare the out-of-sample performance between ARIMA Model and LSTM.
• Explain the pros and cons of both methods. Any difference between the performances of the two stocks you pick?
3. Reinforcement Learning for Optimal Execution
• Lecture Notes 14, 17-20 and the Jupyter Notebook “Lecture19-Reinforcement Learning for Optimal Execution” are related to this project.
• Train the Q-learning algorithm with the simulation environment provided in the Jupyter Notebook (“Lecture 21: Reinforcement Learning for Optimal Execution”) on Black- board.
• Show evidence of convergence.
• Use Sharpe Ratio as the criterion and compare the performance of Q-learning to the following two strategies:
(a) executing with a constant trading speed,
(b) executing everything at time 0.
• Design a new simulation environment and repeat the above procedures. Explain your results.
General Instruction for Final Projects:
• The final report is expected to contain the following components:
– Introduction to the dataset you use, the data format and the steps you perform for data pre-processing.
– Brief introduction to the models you use in the project.
– Detailed discussion for the model evaluation: proper choice of evaluation criterion (or loss function), out-of-sample test, comparison to benchmark models.
– Detailed explanation on how model parameters are selected.
– Detailed explanation of your results.
– Discussion on financial interpretation or economic insights of your model.
– A conclusion paragraph to summarize the methodology and your results.
– *Optional* If you explore some other related aspects for the project of your choice, extra credits will be granted based on the quality of the additional materials. If you decide to do some extra work, please clearly indicate what are additional in your report.
• Do not include your codes in the report (nor attach them at the end). Codes should be submitted via a separate submission channel on Blackboard.
• Page limit: minimum of 3 pages and maximum of 10 pages.
• You can use the Python codes on Blackboard or any open-sourced codes you find online. Please acknowledge the source in your report if you use open-sourced codes.
2022-11-28