Leture 3 Flashcards
How is the History of Hedge funds?
- in 1949, Alfred Winslow Jones invented hedged fund
- aim: protect from downside market risk, by short-selling stocks
- borrowed capital to increase his positions (leverage)
- his funds increased by 670% between 1955 - 1965
- same year: Richard Donchian established the first commodity trading advisor that traded futures based on moving average trend detection
What are the drivers of hedge fund returns
What are Hedge funds?
What different Styles do exist in Hedgefund? What what are the underlying Strategies?
Relative value: What are Key characteristics?
- Exploit price differences between two similar or related instruments
- Mostly operate within fixed income space
- Rely on a wide variety of fundamental and quantitative models
Relative value: What are key return drivers?
- corporate activity
- manager skill in researching and picking thos events that are most likely to reach completion is the key element
Relative value: What are the key risk drivers?
- Illiquidity - although a source of return, it can make it difficult to unwind position
- the highly leveraged nature can cause serious losses in times of adverse markets or in the case of wrong trading decisions
Relative value: Asset Class overview, what are some examples?
- convertible bond arbitrage
- fixed income arbitrage
- EMN & Stat Arb
- Multi-strategy
Event driven: What are Key characteristics?
- Focus opportunities arising from corporate transaction events
- uncertainty about such events create mis-pricings, which they can exploit
- generally use stocks / derivates as their tools
Event driven: What are the Key return drivers?
- corporate activity
- manager skill in researching and picking those events that are most likely to reach completion is the key element
Event driven: What are the Key risk drivers?
- Transaction risk is the primary risk
- once invested in a deal, should the deal fall apart, the manager risks losing significant capital
- sudden market downturns can lead deals to fall apart
Event driven: What are some examples?
- merger arbitrage
- special situations
- distressed debt
- activist and Multi-Strategy
Tactical Trading: What are the key characteristics?
- opportunistic managers that invest in emerging trends across asset classes.
- make use of the full spectrum of available instruments to exploit htese trends.
- most use relatitvely liquid instruments
- historically generate excellent returns in times of greater market volatility and/or illiquidity
Tactical Trading: What are the key return drivers?
- The univers of macro / trading hedge fund investment opportunities and approaches is very broad
- manager skill is paramount to a successful strategy
Tactical Trading: What are the key risk drivers?
- Illiquidity - although a source of return, it can make it difficult to unwind positions
- The highly leveraged nature can cause serious losses in times of adverse markets or in case of a wrong trading decisions
Tactical Trading: What are some examples?
- Systematic traders
- Discretionary macro
Directional: What are some Key characteristics?
- the oldest hedge fund investment style
- promising equities and/or equity derivatives are purchased
- to hedge the investment, less promising stocks are shorted
Directional: What are some key return drivers?
- stock selection is the primary driver, hence manager skill is paramount
- market movements and company specific factors such as earnings, market sentiment or equity multiples
Directional: What are some key risk drivers?
- manager selection is paramount as stock selection is so important
- stock market sentiment, especially bear sentiment
- Liquidity can be a concern for those trading mid or small caps
Directional: What are some examples?
- long / short
- regional focus
- market sector or factor focused
- long or short bias
What is the difference between systematic and discretionary trading?
What are some quantitative strategies?
Trend following strategies
- Initially, easiest and simplest to apply as they do not make predictions or forecast prices
- Based on occurrence of desirable trend
Trading Range (Mean Reversion)
- Strategy based on the idea that stocks and assets revert to their mean periodically
- Algorithms place traders when the price breaks in and out of its predefined range
Arbitrage Opportunities
- Buying and selling related instruments simultaneously
- Takes advantage of prices differing in two instruments or markets
Execution: Volume Weighted Average Price
- Breaking up large orders, releases smaller chunks into the market based on stock profile
- Relies on the stock’s historical volume profiles and aims to execute close to this VWAP
What is momentum in finance?
- Momentum is the tendency for rising asset prices to rise further and falling prices to keep falling.
- It is a market anomaly difficult to explain using traditional finance theories.
- Often attributed to cognitive biases (behavioral economics) or underreaction to new information.
What is cross-sectional momentum?
- Stocks are ranked by performance during a formation period.
- Top-performing stocks (e.g., top 33%) go into the winner portfolio.
- Worst-perorming stocks (e.g., bottom 33%) go into the loser portfolio.
What is quantitative investing (systematic investing)?
- Uses mathematical modeling, computers, and data analysis to calculate the probability of profitable trades.
- Examples: High-frequency trading, statistical arbitrage, cross-sectional momentum, quality factors, and sentiment trading.
Why has quantitative investing become more widespread?
- Computational power and data storage have become cheaper.
- Tools like machine learning, alternative data, and factor investing are now used by institutional investors for trading and portfolio decisions.
What is Statistical Arbitrage (Stat Arb)?
- Originated in the mid-1980s at Morgan Stanley.
- Aims to identify temporary mispricings in statistically proven short-, mid-, and long-term relationships.
- Often relies on mean reversion principles.
What are examples of Statistical Arbitrage strategies?
1. Pairs Trading
* Exploits deviations in relationships between two securities (e.g., BMW vs. Mercedes).
2. Cross-Market Arbitrage:
* Exploits price discrepancies of the same company across markets (e.g., ADR arbitrage).
3. Cross-Asset Arbitrage:
* Bets on price discrepancies between a financial asset and its underlying (e.g., stock index vs. component stocks, or gold vs. gold miners).
4. ETF Arbitrage:
* Identifies discrepancies between the value of an ETF and its underlying assets.
How does Statistical Arbitrage differ in theory vs. practice?
- Theory: Arbitrage should allow risk-free profits.
- Practice: Focuses on identifying temporary mispricings and exploiting statistical relationships, with no guaranteed risk-free profit.
How does mean reversion relate to Statistical Arbitrage?
- Stat Arb often bets that prices deviating from a historical mean will revert over time.
- Example: Pairs trading identifies when the price ratio between two securities deviates significantly from the mean.
What is factor investing, and how does it relate to hedge fund strategies?
- Factor investing combines market returns (passive investing) and active returns (active management).
- Involves rule-based and transparent implementation to capture systematic factors like value, momentum, and quality.
What are the three types of hedge fund replication strategies?
1. Distribution Replication:
Focuses on statistical properties of returns (not monthly tracking).
2. Rules-Based Replication:
Uses trading rules to mimic hedge fund styles (e.g., mechanical replication from 13F filings).
3. Factor Replication:
Clones return streams of hedge fund sub-styles by regressing traditional market factors on returns.
What are common systematic factors in factor investing?
- Value: Captures excess returns from undervalued stocks.
- Size (Small Cap): Exploits returns from smaller firms.
- Momentum: Focuses on stocks with strong recent performance.
- Low Volatility: Targets stable, low-risk stocks.
- Dividend Yield: Invests in high-dividend stocks.
- Quality: Prioritizes financially strong firms (e.g., low debt, high earnings stability).
What is factor cyclicality, and why is it important in factor investing?
- Factor returns exhibit cyclicality, with periods of strong and weak performance (e.g., Value vs. Growth across years).
- Diversification is key since no factor consistently outperforms.
What are the advantages of replication strategies?
- Reduces risks like illiquidity, lack of transparency, and fraud compared to direct hedge fund investments.
- Provides access to Hedge Fund Beta, avoiding the zero-sum alpha game.
What caused Black Monday in 1987?
- U.S. markets fell over 20% in a single day (October 19, 1987).
- Triggered by portfolio insurance strategies using computer-driven trading models.
- Selling pressure created a pricing imbalance as futures markets opened but stock markets were closed.
What regulatory measures were introduced after Black Monday?
-
Circuit breakers were installed to halt trading during extreme price drops.
-> 7% drop: 15-minute pause.
->20% drop: Market shuts for the day. - Aimed to prevent panic selling and allow markets to stabilize.
What caused the 2007 quant crash?
- Sudden liquidation by a multi-strategy fund or trading desk triggered cascading losses.
- Hedge funds using long/short equity strategies were impacted due to margin calls and stop-loss triggers.
- Highlighted issues of crowded trades, illiquidity, and leverage.
What was the recovery after the 2007 quant crash?
- A significant rebound occurred by August 10, supporting the hypothesis of a temporary liquidity-driven event.
- Exposed risks in quantitative strategies relying on high leverage and speed.
What was the Dodd-Frank rule, and how did it impact markets?
- Banned proprietary trading desks in banks.
- Reduced dealer inventories, impacting market liquidity and depth.
- Shifted market-making to private entities with less regulatory obligation.
What systemic risks emerged after the Global Financial Crisis (GFC)?
- Increased central bank balance sheets and sovereign wealth fund activity.
- Significant growth in outstanding debt with less market liquidity.
- Hedge funds now compete directly, adding risks due to leverage and reduced market making.
What trend occurred with U.S.-listed companies from 1996 to 2016?
- The number of U.S.-listed companies halved during this period.
- Since 2016, it has stabilized at ~4,000.
- Meanwhile, the number of ETFs and derivatives grew significantly.
How has the number of listed companies in China changed since 1995?
The number of China A-Share listed companies grew from 323 in 1995 to over 5,000 today.
What is the level of concentration in the S&P 500 index?
- The top 10 companies now make up >30% of the S&P 500’s market cap.
- Concentration is driven by U.S. tech mega-caps.
How did index concentration affect stock performance during COVID?
- Few stocks (e.g., FB, AMZN, AAPL, MSFT, GOOGL) outperformed significantly, driving the index higher.
- Most S&P 500 companies underperformed.
What impact has algorithmic trading had on market structure?
- Algo-trading dominates daily volume in U.S. and global exchanges.
- Expanded to asset classes like FX and fixed income.
What is Payment for Order Flow (PFOF)?
- Controversial practice where brokers (e.g., Robinhood) route customer trades to specific market makers for a fee.
- Came under scrutiny during the 2020/2021 meme stock craze.
Who dominates market making today?
- Firms like Citadel and Jane Street handle significant U.S. equity volume.
- They now overshadow traditional Wall Street banks.
How much data does the world create daily?
- 90% of the world’s data was created in the last two years.
- Google processes 8.5 billion searches/day.
- Twitter (X) handles 500 million tweets/day.
- “Data is the new oil.”
What is alternative data in finance?
- Data used by investors outside traditional sources (e.g., financial statements).
- Examples: Website scraping, satellite imagery, geolocation, and credit card tracking.
What are some examples of alternative data sources?
- Website Scraping: Consumer trends via reviews, pricing data.
- Satellite Imagery: Crop yields, parking lot activity.
- Geolocation: Foot traffic at stores and restaurants.
- Credit Card Tracking: Real-time consumer spending trends.
Why is machine learning critical in finance?
- Handles the exponential growth of data.
- Captures non-linear relationships that traditional statistics miss.
- Applications: Fraud detection, algorithmic trading, sentiment analysis.
What is a regime-shifting model in machine learning?
- Adjusts strategies to align with market conditions.
- Example: Adapting portfolio weightings in response to changing environments.
Which firms use machine learning in finance?
- The Voleon Group
- Edgestream Partners
- Voloridge Investment Management
What is high-frequency trading (HFT)?
- Uses algorithms for rapid trading, often within milliseconds.
- Key needs: Minimal data latency and full automation.
What are the advantages of HFT?
- Exploits small price inefficiencies.
- Relies on high-speed data analysis and execution.
What caused the collapse of Long-Term Capital Management?
- Over-leveraged convergence trades on mispriced securities.
- Lost $1.9 billion in a month, jeopardizing global markets.
- Required a Federal Reserve-organized bailout.
What were the key lessons learned from LTCM’s failure?
- Risks of excessive leverage and poor risk management.
- Importance of regulating OTC derivatives.
- Highlighted the “too big to fail” doctrine.
Why did Amaranth Advisors fail in 2005?
- Brian Hunter bet on natural gas futures (long on winter, short on summer).
- Extreme market moves led to $6.6 billion in losses.
- Could not meet margin requirements
What was the mechanism of Bernard Madoff’s Ponzi scheme?
- Promised consistent high returns using split-strike conversion strategy.
- Paid old investors with funds from new ones.
- Defrauded investors of $65 billion over decades.
Who exposed Bernard Madoff’s fraud and how?
- Harry Markopolos submitted reports to the SEC years before the scandal.
- Highlighted red flags: Unexplainable returns, poor transparency, no visible trades.
What was John Paulson’s strategy during the 2008 crisis?
- Shorted the U.S. housing market using credit default swaps on subprime mortgages.
- Made $15 billion for his fund in 2007 alone.
What makes Renaissance’s Medallion Fund exceptional?
- Sustained 66% annual returns over 30 years.
- Uses mathematics, algorithms, and data to identify patterns.
- Relies on proprietary models and cheap leverage.
What are the key takeaways from Renaissance’s success?
- Superior talent and unique management approaches.
- Execution infrastructure keeps costs low.
- Emphasis on data cleaning and iterative improvements.
What are key philanthropic contributions from hedge fund leaders?
- George Soros: Billions to charities; supports Tanzanian orphans via Hakuna Matata.
- Chris Hohn: Donated £4 billion to climate and children’s causes; major donor to Extinction Rebellion.
- Paul Tudor Jones: Founded Robin Hood Foundation, donating $2 billion to NYC poverty programs.
How have hedge funds contributed to conservation and wealth pledges?
- Louis Bacon: Spent $400 million conserving 202,000 acres in the U.S.
- Ray Dalio & Seth Klarman: Pledged $20 billion and $2 billion, respectively, via the Giving Pledge.
What are other notable hedge fund donations?
- Ken Griffin: Donated $300 million to Harvard University (2023).
- Foundations by Mandel, Englander, and Renaissance lead in U.S. philanthropy.
