2024 FIN655 Final Exam Preview PDF

Final Exam will be in class during class time from 6 pm to 8 pm on December 16, 2024 (Monday). I have extra office hours from 3:30 pm - 5:30 pm on December 12 (Thursday), 2 pm - 4 pm on December 16 (Monday), in addition to my regular office hours from 4 pm - 5 pm on December 13 (Friday). You can als...

Final Exam will be in class during class time from 6 pm to 8 pm on December 16, 2024 (Monday). I have extra office hours from 3:30 pm - 5:30 pm on December 12 (Thursday), 2 pm - 4 pm on December 16 (Monday), in addition to my regular office hours from 4 pm - 5 pm on December 13 (Friday). You can also email me if you have questions. The examination policy is stated in the course syllabus. Let me repeat it here: There will be one mid-term exam and a final exam. The final exam is comprehensive, but will mainly focus on the materials covered after the mid-term exam. Both the mid-term exam and the final exam will be taken by the students as per the course schedule. The exams will be close book and close notes. No electronic device is allowed in the Exams. However, calculators are allowed. You are also allowed to bring in a piece of formula sheet in the exam. The formula sheet should be on a standard 8.5 x 11 (a standard Xerox paper) paper, either on both sides or on one side two pages. The details of exam arrangements will be shared in class later. There are previews before the midterm and final exam. For the final exam, you are allowed to bring two piece of formula sheets. There will be 60 multiple choice questions in the final exam, each question one point. They include concepts and some computation problems. They are mixed together. The final exam will be converted to 40 points for the total course grade. This exam covers the contents that I have lectured in class, except Chapter 1. There are extra credit opportunities for this class: You can earn 5 extra points if you submit the solutions of multiple choice questions from chapters 7, 8, 9, and 12 by Dec 6, 2024. However, you won’t get them if your original course grade is B+ or above. The University has an online Course Evaluation System (CES) for our FIN 655 class. You will have until Thursday, December 12, 2024 to view and submit your surveys. To do so, please log in to CES at http://www.hawaii.edu/ces using your UH username and password. Thank you for your participation and your support! The final exam grade will be posted on laulima.hawaii.edu one week after the final. You can check your course grade from myuh.hawaii.edu since December 24, 2024. Thank you very much for taking this course with me this semester. I wish you happy holidays! Ch 2: Organizing, Visualizing, and Describing Data Numerical and Categorical Data Numerical (Quantitative) Data: Continuous and Discrete Data Categorical (Qualitative) Data: Nominal and Ordinal Data Cross-sectional, Time-series, and Panel Data Structured and Unstructured Data Frequency Distribution: absolute, relative, and cumulative frequency Contingency Table Histogram Bar Chart: Grouped (clustered) and Stacked bar chart Tree Map, Heat Map Word Cloud Line Chart: bubble line chart Scatter Plot (Matrix) Measures of Central Tendency: the arithmetic mean, the median, the mode, the weighted mean, the geometric mean, and the harmonic mean Other measures of location: quartiles, quintiles, deciles, percentiles Box-Whisker Plot Measures of Dispersion: range, mean absolute deviation (MAD), variance, standard deviation, semivariance and semideviation, target semivariance and semideviation, coefficient of variation (CV) Sample mean, Sample variance, Sample standard deviation Skewness: positively skewed (long tail on the right side): modemean Sample skewness, Sample (excess) kurtosis Kurtosis: leptokurtic, fat tails Normal Distribution Correlation, Sample Correlation Ch 3: Probability Concepts Definition of Probability: mutually exclusive and exhaustive Empirical probability, subjective probability, priori probability Odds for and against Unconditional probability, conditional probability, marginal probability Multiplication Rule for Joint Probability Addition Rule for Probabilities Dependent, Independent Events Total Probability Rule Bayes’ Formula, Posterior (updated) Probability Ch 4: Common Probability Distributions Probability distribution, discrete and continuous random variable Probability (density) function, cumulative distribution function (cdf) Discrete uniform distribution, Binomial Distribution, Bernoulli random variable (mean and variance) Continuous uniform distribution (mean and variance) Lognormal Distribution Monte Carlo Simulation Ch 5: Sampling and Estimation Sampling, Statistic Simple random sample, Stratified random sampling Sampling error, sampling distribution, standard error Central Limit Theorem Distribution of the sample mean Point estimate, Confidence Interval Estimator: unbiased, efficient, consistent Degree of confidence Z and t statistics (for normal/nonnormal distribution, large sample) Data-mining bias, sample selection (survivorship) bias, look-ahead bias, time-period bias Ch 6: Hypothesis Testing Null and alternative hypotheses Two-sided and one-sided hypotheses Type I and II Errors Level of significance, power of a test, p-value Hypothesis tests concerning the mean Hypothesis tests concerning differences between means Paired comparisons test Hypothesis tests concerning variance: χ2 Hypothesis tests concerning the equality of two variances: F Hypothesis tests concerning correlation: t(n-2) Nonparametric inference, rank test Ch 7: Introduction to Linear Regression Linear regression and the six assumptions Standard error of estimate (SEE) Coefficient of determination (R^2) Hypothesis test concerning the regression coefficient: t(n-2) Confidence interval of the regression coefficient Analysis of variance and the F-test Ch 8: Multiple Regression Multiple linear regression and the six assumptions T-test concerning the regression coefficient with degree of freedom n-(k+1) F-test for overall significance Predicting the dependent variable Coefficient of determination (Adjusted R^2) Intercept and slope dummy variables Three regression violations: heteroskedasticity, serial correlation, and multicollinearity Their effects, how to test them (Breusch-Pagan, Durbin-Watson test), and their solutions Model specification and errors in specification: omitted variable bias, nonlinearity, pooling data from different samples Qualitative Dependent Variable: logit model, probit model Ch 9: Time-series Analysis Linear trend and log-linear trend models Three requirements of covariance stationary AR(p) model, T-test whether the autocorrelations of the residual are significantly from 0 Mean reversion, Chain rule of forecasting, Root mean squared error (RMSE) MA(q) model, Comparison of AR and MA models ARMA(p, q) model, Q statistic, Portmanteau test Random walk, Non-stationarity Unit root, First-differencing, Dickey-Full test Seasonality ARCH(p) model, Conditional heteroskedasticity Cointegrated Ch 10: Machine Learning Generalization (find the pattern, apply the pattern) Supervised learning, unsupervised learning, and deep learning/reinforcement learning Supervised learning: Labeled dataset, target, features Supervised learning: regression and classification Unsupervised learning: dimension reduction and clustering Training, validation, and test samples Underfitting and overfitting, tradeoff between bias and variance error LASSO, PCA (Principal Components Analysis) Ch 11: Big Data Projects Big data: volume, variety, velocity, veracity Steps for ML model building: structured and unstructured data Data preparation and wrangling (Data cleansing and preprocessing) Text wrangling: tokens, BOW, document term matrix (DTM) Exploratory data analysis, feature selection, and feature engineering Ch 12: Using Multifactor Models Arbitrage opportunity, APT model and assumptions Carhart four-factor model Macroeconomic, fundamental, statistical factor models Active return (as a sum of two components) Active risk squared (as a sum of two components)