1. Using Monte Carlo Simulation for Library Science
By Josephine Choi and Lei Jin
Ryerson University Library

2. Background

3. Framing the business question
Wide scope:
How could we better predict the journal/database pricings?
Break-down the key factors
Format
Publisher
Currency exchange rate

4. Volatile Canadian Dollar

5. Narrower scope
Focusing on the key contributing factor in price increase
How could we better predict the USD/CAD exchange rate?
The main idea behind this method is that the results are computed based on repeated random sampling and statistical analysis. The Monte Carlo simulation is in fact random experimentations, in the case that, the results of these experiments are not well known. Monte Carlo simulations are typically characterized by a large number of unknown parameters, many of which are difficult to obtain experimentally
It is widely used in physical science, biology, law, AI, search and rescue, and business and finance
“Monte Carlo simulation is a methodology to build predictive model based on a range of possible outcomes. It acknowledges the difficulties in predicting outcome based on known variables; rather than relying on known facts, it tackles uncertainty by simulating a large number set of scenarios and then adjust the data with probabilities. While it is widely used in research, finance and operation management, it is relatively new in the field of library science. “

6. Building Financial Model

7. Monte Carlo Simulation (MCS)
Got its name as the code word for work that von Neumann and Ulam was doing during World War II
However, earlier example could be found in 1901, when it was used to solve Buffon’s needle problem, where needles were physically thrown randomly onto a gridded field to estimate the value of π
Capable of handling the more complex models (beyond a manual approach in which we reduce uncertainty to point estimates )

8. Simulation
Dice roll simulator

9. In essence
We assume that the currency exchange rate would either go up or down
Instead of making predictions based on factors that may contribute to the changes, we build a model that will simulate the changes that would happen in the future
By generating plenty number of simulations, we hope to capture all possible movements
Through aggregation, we hope to predict the future based on theories of probability

10. Key concepts Drift Stochastic Random Variable Standard deviation
Random Walk
Brownian Movement
Central Limit Theorem

11. Simplest model Without Drift TP = YP + Random stochastic variable
With Drift
TP = YP + Random stochastic variable + drift
TP = Today’s price
YP = Yesterday’s price

12. Difference between the simplest model and the simple model
Modify the stochastic variable by dividing it with 365
Simple Model
Modify the stochastic variable by multiplying the log of standard deviation (based on past data)

13. Using MS Excel for rapid prototyping
Simplest model
Simple model

14. End-use model Built with Jupyter (ipython) in Python
Code shared in Github
Built with code that was found online (assuming that FX movement is similar to stock pricing movement)

15. Steps 1: Loading the data into Python
2: Inspect the data and cleaning the data
3. Exploratory Data Analysis (EDA)
4: Building the financial model
5: Analysis

16. Application/Discussion

17. How good is the model? Predicting current rate based on past data
The start date was Mar 15 , 2018

18. 30-day prediction

19. Exchange rate of Apr 15,2018
April 15, 2018 (Sunday)

20. Thinking Probabilistically
Thinking in range
Accept that aggregating the mean may not be the best way of making a prediction

21. Application
Facilitate budget calculation
To evaluate CRKN FX Project

22. FX Project

23. Predictions by bank

24. Other possibilities of predicting and analysing pricing info
Random Forest (Decision Tree)
Code shared in Github
Applying MCS to other contributing factors
Increase rate
Survival rate

25. Thank you
Josephine Choi
Lei Jin