Download free Morgan Stanley placement papers 2025 with DSA questions, system design problems, and solutions. Access previous year papers, exam pattern, eligibility, salary, and complete preparation guide.
Morgan Stanley is a leading global financial services firm providing investment banking, wealth management, and trading services. Founded in 1935, Morgan Stanley operates in 40+ countries. Morgan Stanley India has technology divisions working on trading platforms, risk systems, and financial applications, making it a top recruiter for software engineers in India with a focus on financial technology and innovation.
Headquarters: New York, USA
Employees: 80,000+ globally
Industry: Investment Banking, Financial Services
Revenue: $54+ Billion USD (2023)
Academic Requirements
Minimum CGPA Required for Placement in Morgan Stanley:
10th Standard: 70% or 7.0 CGPA (preferred)
12th Standard: 70% or 7.0 CGPA (preferred)
Graduation: 7.0+ CGPA or 70%+ (aggregate) - Top colleges preferred
Degree: B.Tech/B.E./M.Tech in CS, IT, ECE, or related fields
Year of Study: Final year students and recent graduates (within 1 year)
Backlogs: No active backlogs at the time of application
Branch Eligibility
Eligible Branches: CS, IT, ECE, and related engineering streams
Programming Focus: Strong skills in Data Structures, Algorithms, and System Design
Experience: Freshers and up to 2 years experience (for Technology Analyst roles)
Additional Criteria
Coding Skills: Proficiency in at least one language (C++, Java, Python)
Gap Years: Maximum 1 year gap allowed
Course Type: Full-time regular courses only
Nationality: Indian citizens and eligible for work in India
Technology Analyst Role
Primary Role: Technology Analyst (Entry Level)
Salary Package: ₹25-35 LPA for freshers (total compensation)
Selection: Through Morgan Stanley placement process (5 rounds)
Focus: DSA, system design, financial systems, trading platforms
Access free Morgan Stanley placement papers PDF and Morgan Stanley previous year question paper with detailed solutions. Download Morgan Stanley last year question paper and Morgan Stanley question paper PDF from previous years with comprehensive question banks covering DSA problems, system design questions, and coding interview questions.
2024 Placement Papers PDF
Download Morgan Stanley placement papers 2024 PDF with previous year coding questions, DSA problems, solutions, and exam pattern analysis.
2025 Placement Papers PDF
Download latest Morgan Stanley placement papers 2025 PDF with current year coding questions, DSA problems, solutions, and updated exam patterns.
2026 Preparation Guide
Prepare for Morgan Stanley placement 2026 with expected exam pattern, coding questions, system design problems, and comprehensive preparation strategy.
What's Included
Complete Morgan Stanley placement papers (2024-2026) with coding questions, DSA problems, system design questions, detailed solutions, answer keys, exam pattern analysis, and topic-wise organization.
On-Campus Recruitment
Primary Method: Through college placement cell
Process: College placement cell coordinates with Morgan Stanley for campus drives
Off-Campus Application
Method: Apply via Morgan Stanley Careers portal
Process: Submit application online, await OA invitation
Employee Referral
Method: Through current Morgan Stanley employees
Process: Get referred by employee, direct application review
The Morgan Stanley placement process emphasizes strong technical skills, problem-solving abilities, and system design knowledge. Understanding the detailed exam pattern is crucial for effective preparation.
Online Assessment (OA) - 90 minutes
Total Duration: 90 minutes Total Questions: 2-3 coding problems + 10-15 mathematical aptitude questions Format: Online (HackerRank or Codility platform) Negative Marking: No Platform: HackerRank or Codility
Section-wise Breakdown:
| Section | Questions | Time | Difficulty | Focus Areas |
|---|---|---|---|---|
| Coding Problems | 2-3 | 60-90 min | Medium-Hard | Arrays, Trees, Graphs, DP |
| Mathematical Aptitude | 10-15 | 15-20 min | Medium | Probability, Statistics, Quantitative |
Section Details:
Important Notes:
Success Rate: ~20-25% of candidates clear this round
Technical Interview Round 1 - 45 minutes
Format: Virtual (video call with shared coding editor) Total Problems: 2-3 coding problems Language: C++, Java, Python (candidate’s choice) Time: 45 minutes
Problem Types:
Passing Criteria: Solve problems with optimal approach and explain reasoning
Evaluation:
Success Rate: ~40-50% of OA candidates advance
Technical Interview Round 2 - 45 minutes
Format: Virtual (video call with shared whiteboard/document) Focus Areas: System design, technical depth, projects
Topics Covered:
Evaluation:
Success Rate: ~50-60% of Round 1 candidates advance
Managerial Round - 45 minutes
Format: Virtual (video call) Focus Areas: Behavioral fit, technical depth, leadership potential
Topics Covered:
Evaluation:
Success Rate: ~60-70% of Round 2 candidates advance
HR Interview - 30 minutes
Format: Virtual (video call)
Success Rate: ~80-90% of Managerial Round candidates get offers
| Phase | Duration | Key Activities |
|---|---|---|
| Application | 1-2 weeks | Submit application, resume screening |
| Online Assessment | 1 week | OA invitation, completion, result evaluation |
| Technical Interviews | 1-2 weeks | Round 1 and Round 2 scheduling and completion |
| Managerial Round | 3-5 days | Scheduling and completion |
| HR Interview | 2-3 days | Final interview and offer discussion |
| Result Declaration | 1-2 days | Offer letter and joining details |
Practice with 20+ Morgan Stanley placement paper coding questions covering DSA problems, system design questions, and mathematical aptitude. These questions are representative of what you’ll encounter in Morgan Stanley’s Online Assessment and technical interviews.
What’s Included:
Complete Morgan Stanley Coding Questions Guide
Access complete guide to Morgan Stanley placement paper coding questions including:
Problem: Given an array of stock prices, find the maximum profit you can make by buying and selling stocks. You can make at most 2 transactions.
Example:
Input: [3, 3, 5, 0, 0, 3, 1, 4]Output: 6Explanation: Buy at 0, sell at 3 (profit 3), buy at 1, sell at 4 (profit 3). Total = 6Solution:
def maxProfit(prices): if not prices: return 0
# First transaction: buy and sell once profit1 = 0 min_price = prices[0] for price in prices: min_price = min(min_price, price) profit1 = max(profit1, price - min_price)
# Second transaction: buy and sell again profit2 = 0 max_price = prices[-1] for i in range(len(prices)-1, -1, -1): max_price = max(max_price, prices[i]) profit2 = max(profit2, max_price - prices[i])
return profit1 + profit2Explanation: Use dynamic programming approach. First pass finds maximum profit from one transaction. Second pass finds maximum profit from second transaction starting from the end.
Time Complexity: O(n) Space Complexity: O(1)
Problem: Design a trading system that can process 1 million orders per second with latency < 1ms.
Requirements:
Approach:
Problem: Given a portfolio of stocks with their weights and correlation matrix, calculate portfolio risk (standard deviation).
Solution:
def portfolio_risk(weights, covariance_matrix): # Portfolio variance = w^T * Cov * w portfolio_variance = 0 n = len(weights)
for i in range(n): for j in range(n): portfolio_variance += weights[i] * weights[j] * covariance_matrix[i][j]
# Portfolio risk (standard deviation) = sqrt(variance) portfolio_risk = portfolio_variance ** 0.5 return portfolio_riskExplanation: Portfolio risk is calculated using the covariance matrix and portfolio weights. The formula is: σ² = w^T * Σ * w, where w is the weight vector and Σ is the covariance matrix.
Data Structures & Algorithms
Arrays & Strings: Manipulation, searching, sorting, two-pointer technique
Trees: Binary trees, BST, tree traversals, tree construction
Graphs: DFS, BFS, shortest path algorithms, topological sort
Dynamic Programming: Memoization, tabulation, optimization problems
Greedy Algorithms: Activity selection, interval scheduling, optimization
System Design
Low-Latency Systems: In-memory processing, optimized data structures
Distributed Systems: Load balancing, sharding, replication, consistency
Trading Systems: Order matching, order book, risk management
Real-Time Processing: Stream processing, event-driven architecture
Programming & Tools
Languages: C++, Java, Python (proficiency in at least one)
Concepts: OOPs, multithreading, concurrency, memory management
Databases: SQL, NoSQL, database design, query optimization
Tools: Version control (Git), build tools, testing frameworks
Learn from real Morgan Stanley placement interview experiences shared by candidates who successfully cleared the placement process. These authentic stories help you understand what to expect and how to prepare effectively.
Key Insights from Interview Experiences:
Complete Interview Experiences
Read detailed Morgan Stanley placement interview experiences including:
Candidate Background: B.Tech CS from IIT, 8.5 CGPA, 2 internships in fintech
Round 1 (Technical - 45 min):
Round 2 (Technical - 45 min):
Managerial Round:
HR Round:
Tips: Focus on system design for financial systems, practice explaining your approach clearly, and research Morgan Stanley’s technology divisions.
Candidate Background: B.Tech IT from NIT, 8.0 CGPA, 1 year experience in backend development
Online Assessment:
Technical Interview 1:
Technical Interview 2:
Managerial Round:
HR Round:
Tips: Practice system design questions related to trading and finance, be ready to discuss your experience in detail, and prepare behavioral examples.
Common Questions Asked:
Tips:
Prepare for Morgan Stanley placement HR interview with common questions and effective strategies. Morgan Stanley HR interview focuses on cultural fit, behavioral assessment, and career alignment.
Common HR Interview Topics:
Complete HR Interview Guide
Access complete guide to Morgan Stanley HR interview questions including:
Morgan Stanley placement papers are previous year question papers from Morgan Stanley recruitment tests and interview rounds. These papers help students understand the exam pattern and prepare effectively.
Yes, all Morgan Stanley placement papers on our website are completely free to access and download. You can practice unlimited questions without any registration or payment.
We provide Morgan Stanley placement papers from recent years including 2024 and 2025. Our collection is regularly updated with the latest questions and exam patterns.
Yes, you can access Morgan Stanley placement papers online with previous year coding questions, DSA problems, system design questions, and interview questions. Our website provides Morgan Stanley placement papers PDF download, Morgan Stanley previous year questions with solutions, Morgan Stanley coding questions, and Morgan Stanley interview questions. All papers are completely free and require no registration.
Morgan Stanley placement process includes: 1. Application (on-campus via college placement cell, off-campus via Morgan Stanley Careers portal, or through referrals), 2. Online Assessment (OA) - 2-3 coding problems and mathematical aptitude (90 minutes), 3. Technical Interviews (2 rounds) - DSA, system design, projects (45 minutes each), 4. Managerial Round - Technical depth and behavioral fit (45 minutes), 5. HR Interview - Behavioral and offer discussion (30 minutes). Total duration: 3-4 weeks from application to offer.
Morgan Stanley interview process consists of 5 rounds: 1. Online Assessment (OA) - 90 minutes (coding and aptitude), 2. Technical Interview Round 1 (45 min) - DSA and coding problems, 3. Technical Interview Round 2 (45 min) - System design and technical deep dive, 4. Managerial Round (45 min) - Behavioral questions and technical discussion, 5. HR Interview (30 min) - General discussion and offer. Total duration: 3-4 weeks from application to offer.
Morgan Stanley Online Assessment (OA) exam pattern includes: 2-3 coding problems (60-90 minutes), mathematical aptitude questions (15-20 minutes). Platform: HackerRank or Codility. Focus areas: Data structures (arrays, trees, graphs), algorithms (dynamic programming, greedy), problem-solving, quantitative reasoning. Passing criteria: Solve at least 2 coding problems correctly with optimal time complexity. The OA tests both coding skills and mathematical aptitude relevant to financial services.
Morgan Stanley interview questions include: Coding problems (DSA, arrays, trees, graphs, dynamic programming), System design (trading systems, low-latency systems, risk management), Technical concepts (OOPs, DBMS, OS, networking), Mathematical aptitude (probability, statistics, quantitative reasoning), Behavioral questions (STAR format, problem-solving, teamwork), and Project discussion (technologies used, challenges faced, impact). All questions focus on problem-solving skills and system design for financial applications.
Morgan Stanley Online Assessment (OA) typically allows C, C++, Java, and Python for coding sections. Candidates can choose their preferred language. The platform (HackerRank or Codility) supports multiple languages. It’s recommended to be proficient in at least one language (preferably C++ or Java) and understand its standard library well. Python is also commonly used for its simplicity in solving algorithmic problems.
Morgan Stanley eligibility criteria for freshers 2026 include: Minimum 70% or 7.0+ CGPA in 10th, 12th, and graduation. Degree required: B.Tech/B.E./M.Tech in Computer Science, IT, ECE, or related fields. Final year students and recent graduates (within 1 year) are eligible. No active backlogs allowed. Strong programming skills in C++, Java, or Python are preferred. Top colleges (IITs, NITs, IIITs) typically see higher selection rates.
The minimum CGPA required for Morgan Stanley placement is 7.0 CGPA (70%) across all academic levels. However, candidates with higher CGPA (8.0-8.5 or 80-85%) from top colleges (IITs, NITs, IIITs) have better chances. Strong coding skills, DSA knowledge, and system design understanding can compensate for slightly lower CGPA. For technology roles, practical skills often matter more than just academic scores.
Morgan Stanley eligibility criteria for freshers 2025 are similar to 2026: Minimum 70% or 7.0+ CGPA in 10th, 12th, and graduation. Degree required: B.Tech/B.E./M.Tech in CS, IT, ECE, or related fields. Final year students and recent graduates (within 1 year) are eligible. No active backlogs allowed. Strong programming skills preferred.
Morgan Stanley salary for freshers (Technology Analyst role) ranges from ₹25-35 LPA total compensation in India. This includes base salary (₹18-22 LPA), performance bonuses, and comprehensive benefits. Salary varies by location (Mumbai, Bangalore), college tier, and interview performance. Higher packages (₹35-45 LPA) are possible for exceptional candidates from top colleges. Investment banking roles may have different compensation structures.
Morgan Stanley offers Technology Analyst roles for freshers, focusing on software development, system design, and financial technology. Roles include: Software Development (trading platforms, risk systems), System Design (low-latency systems, distributed systems), Data Engineering (analytics, reporting), and Infrastructure (cloud, DevOps). All roles require strong DSA knowledge, system design skills, and interest in financial technology.
To prepare for Morgan Stanley placement: 1. Master DSA fundamentals (arrays, trees, graphs, dynamic programming) - 40% time, 2. Practice 100+ coding problems on LeetCode/HackerRank focusing on medium-hard problems - 30% time, 3. Learn system design basics (low-latency systems, distributed systems, trading platforms) - 15% time, 4. Study quantitative aptitude and mathematical reasoning - 10% time, 5. Prepare for behavioral interviews and research Morgan Stanley culture - 5% time. Focus on problem-solving skills and system design for financial applications.
Focus on: Data Structures (arrays, trees, graphs, hash maps), Algorithms (dynamic programming, greedy, graph algorithms), System Design (low-latency systems, trading platforms, risk management), Programming (C++, Java, Python proficiency), Mathematical Aptitude (probability, statistics, quantitative reasoning), and Core CS subjects (OOPs, DBMS, OS, networking). See the preparation strategy section for detailed topic breakdown and time allocation.
Preparation time for Morgan Stanley placement varies: Intensive 2-3 months plan for candidates with strong DSA background, Extended 4-6 months plan covering all topics from scratch, Practice-focused 1-2 months plan for candidates with good fundamentals. Focus on DSA (40% time), coding practice (30%), system design (15%), quantitative aptitude (10%), and behavioral prep (5%). Practice with Morgan Stanley placement papers for realistic preparation.
Morgan Stanley offers: Excellent compensation (₹25-35 LPA for freshers), Strong technology focus (trading platforms, risk systems, financial applications), Learning opportunities (cutting-edge financial technology, mentorship programs), Career growth (clear progression paths, diverse technology projects), Work-life balance (better than other investment banks), Global exposure (work with international teams), and Innovation culture (emphasis on technology and innovation in finance). Morgan Stanley is ideal for candidates interested in financial technology and system design.
Morgan Stanley’s work culture emphasizes: Client focus (putting clients first), Integrity (ethical behavior and transparency), Excellence (high standards and continuous improvement), Innovation (technology-driven solutions), and Diversity (inclusive and diverse workforce). The technology division focuses on collaboration, learning, and impact. Work-life balance is better compared to other investment banks, with emphasis on sustainable work practices.
Morgan Stanley (₹25-35 LPA): Technology focus, trading platforms, risk systems, strong work-life balance, good learning opportunities.
Goldman Sachs (₹30-40 LPA): Innovation-driven, quantitative finance, high-pressure environment, excellent learning, higher compensation.
JP Morgan (₹25-35 LPA): Large-scale systems, diverse technology projects, stable career growth, good work culture.
All three are excellent investment banks. Choose Morgan Stanley if you prefer technology focus and work-life balance. Choose Goldman Sachs if you want higher compensation and innovation-driven work. Choose JP Morgan if you prefer large-scale systems and stable growth. See our guides: Goldman Sachs Placement Papers | JP Morgan Placement Papers
Key Preparation Principles:
Preparation Approaches:
Complete Preparation Guide
Access comprehensive Morgan Stanley placement paper preparation guide including:
| Level | Experience | Base Salary | Total Package | Typical Background |
|---|---|---|---|---|
| Technology Analyst | Fresher | ₹18-22 LPA | ₹25-35 LPA | B.Tech CS/IT from top colleges |
| Associate | 1-2 years | ₹22-28 LPA | ₹30-40 LPA | Previous experience in tech |
| Vice President | 3-5 years | ₹35-45 LPA | ₹45-60 LPA | Strong technical background |
| Role | Level | Total Package | Requirements |
|---|---|---|---|
| Quantitative Developer | Entry | ₹30-40 LPA | Strong math and programming skills |
| Risk Technology Analyst | Entry | ₹25-35 LPA | DSA, system design, risk knowledge |
Hiring Trends 2025
Increased Technology Hiring: Morgan Stanley is expanding technology teams in India, focusing on trading platforms and risk systems.
Focus on System Design: Strong emphasis on system design skills, especially for financial systems and low-latency applications.
Diversity Initiatives: Increased focus on diversity and inclusion in technology hiring.
Process Changes
Virtual Interviews: All interviews conducted virtually, with focus on coding and system design.
OA Platform: Using HackerRank or Codility for Online Assessments, with emphasis on optimal solutions.
Behavioral Assessment: Increased weightage on behavioral fit and cultural alignment.
New Initiatives
Technology Innovation: Focus on AI/ML in financial services, blockchain, and cloud technologies.
Campus Programs: Enhanced campus recruitment programs with better engagement.
Mentorship Programs: New mentorship initiatives for new hires.
Company Growth
India Expansion: Continued expansion of technology centers in Mumbai and Bangalore.
Technology Investment: Significant investment in financial technology and innovation.
Market Position: Strong position in investment banking technology, competing with Goldman Sachs and JP Morgan.
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Goldman Sachs Placement Papers
Another top investment bank with similar technology focus
JP Morgan Placement Papers
Large-scale systems and diverse technology projects
Ready to start your Morgan Stanley preparation? Practice with our placement papers and focus on strong fundamentals in data structures, algorithms, and system design for financial applications.
Pro Tip: Focus on system design for trading systems and low-latency applications. Morgan Stanley values candidates who can design scalable, high-performance systems for financial services.
Last updated: November 2025