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Для подготовки к этапу интервью мы советуем ознакомиться со списком из 101 вопроса ниже. Собеседования проходят на русском языке.

Quantitative Methods and Econometrics

1. Measures of central tendency: population mean, sample mean, arithmetic mean, geometric mean, harmonic mean
2. Measures of location and dispersion: quantile, mean absolute deviation, sample variance and standard deviation
3. Skewness, kurtosis, and correlation
4. Expected value, variance and covariance
5. Confidence interval
6. Normal distribution. Standard normal distribution. Lognormal distribution
7. Student’s t-distribution
8. Chi-square distribution
9. Monte Carlo simulation
10. Probability sampling methods: systematic sampling, stratified random sampling, cluster sampling
11. Non-probability sampling methods: convenience sampling, judgmental sampling
12. Resampling: bootstrap, jackknife
13. Biases: data snooping, sample selection, survivorship, look-ahead, time-period
14. Central limit theorem
15. Hypothesis tests: null vs. alternative hypothesis, one-tail vs. two-tail hypothesis test
16. Type I and Type II errors
17. Statistical significance and interpretation. P-value
18. t-test, z-test and chi-square test
19. Simple linear regression model: sum of squared errors (SSE), slope coefficient interpretation
20. Homoskedasticity vs. heteroskedasticity
21. Analysis of variance (ANOVA): total sum of squares (SST), regression sum of squares (RSS), sum of squared errors (SSE)
22. Standard error of estimate (SEE), coefficient of determination (R-square), F-statistics
23. Multiple linear regression model. Adjusted R-square, Dummy variables
24. Serial correlation. Durbin-Watson statistic
25. Multicollinearity problem
26. Time-series analysis: linear and log-linear trend models
27. Autocorrelations and autoregressive time-series models (AR)
28. Unit root test of nonstationarity
29. Moving-average time-series models (MA)
30. Seasonality in time-series models
31. Autoregressive moving-average models (ARMA) and autoregressive conditional heteroskedasticity models (ARCH)
32. Cointegrated series. (Engle-Granger) Dickey-Fuller test
33. Panel data series

Calculus

34. Limits and derivatives
35. L’Hospital’s rule
36. Integration
37. Partial derivatives and multiple integrals
38. Taylor’s series
39. Newton’s method
40. Lagrange multipliers


Linear Algebra

41. Basic operations with matrices and vectors: addition, multiplication, transposition
42. Linear dependence, basis and dimension
43. Determinant. Properties and signs. Eigenvalues ​​and vectors
44. Inverse matrix, matrix invertibility criterion, inversion and transposition
45. Moore-Penrose pseudoinverse matrix. Skeleton decomposition. Ordinary Least Squares. Singular value decomposition
46. Metrics and norms of vectors. Hölder norm. Euclidean norm
47. Linear spaces. Euclidean space. Scalar product. Orthogonal systems
48. Matrix norms and induced norms
49. Singularity and spectral radius. Low-rank approximation

Probability Theory

50. Basic probability definitions and set operations: outcome, sample space / probability space, event, mutually exclusive, exhaustive events, random variable
51. Combinatorial analysis: permutations, combination, binomial theorem, Inclusion-Exclusion Principle
52. Unconditional and conditional probability. Joint probability
53. Law of total probability
54. Bayes’ formula
55. Discrete and continuous distributions: common function of random variables, discrete random variables, continuous random variables

Stochastic Process

56. Definition of a (stochastic) random process. Trajectory and finite-dimensional distribution of a random process
57. Markov chain
58. Martingale and random walk
59. Brownian motion
60. Weiner process
61. Properties of random processes: stationarity (in the narrow and broad sense), ergodicity
62. Ito’s lemma

Machine Learning

63. Supervised vs. unsupervised learning
64. Deep learning and reinforcement learning
65. Evaluating ML algorithm performance: generalization and overfitting
66. Penalized regression
67. Support vector machine
68. K-nearest neighbor (KNN)
69. Classification and regression tree (CART)
70. Clustering: K-mean, hierarchical, agglomerative, divisive
71. Neural networks

Python

72. Python as an object-oriented programming language. Functions isinstance( ), type( )
73. Python basic data types: numbers, strings, booleans, tuples, lists, dictionaries, sets. Basic methods and properties of basic data structures: iterable, ordered, mutable, hashable, etc.
74. Loops: for loop and while loop. Why using loops in Python might not be the best idea?
75. List, set, dictionary comprehensions. Iterators and generators
76. Functions in Python. Function as an object. Lambda functions
77. Basic principles of OOP: encapsulation, polymorphism, inheritance. Magical methods.
78. O(n) notation. Search / insert / delete arrays in Python. Hash tables.

Macroeconomics

79. Aggregate demand and supply
80. Business cycles: expansion, peak, recession and trough
81. Examples of economic indicators: leading, coincident, lagging
82. Types of unemployment: frictional, structural, cyclical
83. Types of inflation: hyperinflation, disinflation, deflation
84. Quantity theory of money: types of demand for money (transaction, precautionary, speculative)
85. Fisher effect
86. Monetary policy tools
87. Fiscal policy tools
88. Use of inflation, interest rate, and exchange rate targeting by central banks
89. GDP calculation: income vs. expenditure vs. value-added approach

Finance

90. Time value of money
91. Option pricing
92. Put-call parity
93. American vs. European options
94. Black-Scholes-Merton differential equation and Black-Scholes formula
95. Greeks: delta, gamma, theta, vega, rho
96. Option portfolios: bull spread, straddle, binary options, exchange options
97. Portfolio optimization
98. Value at risk
99. Duration and convexity
100. Forward and futures
101. Interest rate models: Vasicek model

Recommendation: As a starting point of preparation process please refer to
· CFA L1&L2: Study Notes by Kaplan Schweser
· A Practical Guide to Quantitative Finance Interviews by Xinfeng Zhou

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http://www.mergersandinquisitions.com/investment-banking-resume-university-student/

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