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

Background

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

Volatile Canadian Dollar

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. “

Building Financial Model

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 )

Simulation Dice roll simulator

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

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

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

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)

Using MS Excel for rapid prototyping Simplest model Simple model

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)

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

Application/Discussion

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

30-day prediction

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

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

Application Facilitate budget calculation To evaluate CRKN FX Project

FX Project

Predictions by bank

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

Thank you Josephine Choi jochoi@ryerson.ca Lei Jin leijin@ryerson.ca