The Math That Explains How Multi-Strategy Hedge Funds Make Money | Odd Lots - YouTube
Multi-strategy hedge funds are still all the rage on Wall Street, but what does it actually mean to be a pod shop and how are they being set up? On this epis...
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Understanding the Variations and Math Behind Multi-Strat Hedge Funds
- There are various opinions and variations of pod shops in terms of design, compensation structures, coordination among different pods, and centralization of ideas and research.
- One big question is why multi-strat hedge funds with different strategies and trades consistently produce impressive returns instead of average returns.
- Another question arises when pod shops become very large, leading to concerns about replicating the market and diminishing alpha opportunities.
- Dan Millo, co-founder of Freestone Grove partners and former Citadel executive, is the perfect guest to discuss these variations and the mathematical foundation behind multi-strat funds.
- Millo has extensive experience in the hedge fund industry, particularly in quantitative finance, and has held various roles in leading firms like BlackRock and Citadel.
- Millo's expertise and insights will shed light on the functioning and success of multi-strat hedge funds.
The Role of Quantitative Analysis in Finance
- Quant in finance refers to the use of quantitative methods and tools to make disciplined and systematic decisions.
- It involves running code, analyzing data, and applying mathematical models for risk assessment, forecasting, evaluation, and attribution.
- The value of quants in a fundamental equities fund lies in their ability to provide insights and understanding of the mechanics of a firm.
- They support fundamental analysts by utilizing advanced technology, analytics, and forecasting techniques to gather and interpret data.
- Quants help identify what is likely to be surprising about a firm's revenue, earnings, margins, etc., and how it may differ from market expectations.
- They also contribute to the analysis of alternative data and behavioral patterns, which can help overcome human biases and provide a more objective perspective.
The Theory and Thesis Behind Freestone Grove
- Freestone Grove believes it can compete at the highest level of the industry.
- They focus on optimizing key business decisions rather than relying on one magic factor.
- They believe in optimal organization, compensation, and mix of quantitative vs fundamental analysis.
- They aim to optimize their business design, which they believe is superior to many other platforms.
- They emphasize systematic analysis and data-driven decision-making.
- The term "Dan's Math" was coined by Bloomberg, referring to the systematic approach to decision-making at Freestone Grove.
- They challenge the notion that more is always better, questioning the effectiveness of scaling up in terms of portfolio managers and assets.
The Impact of Adding More People on Performance and Correlation
- Adding more people does not always result in a significant difference in performance.
- Hiring practices aim to select individuals with a mean performance, measured in sharp ratio.
- The sharp ratio represents the expected return per unit of risk deployed.
- Performance is adjusted based on the specific risk associated with different areas, such as biotech or banking.
- Diversification leads to higher sharp ratios as more people are added, assuming zero correlation.
- With zero correlation, the sharp ratio increases with the square root of the number of people.
- However, correlation among individuals' returns sets a maximum limit to the aggregate sharp ratio.
- In real-world scenarios, it is difficult to achieve zero correlation among traders with different focuses.
- Returns tend to be correlated due to factors like the Federal Reserve's decisions.
- Higher correlations among individuals' returns reduce the effectiveness of diversification.
- The correlation coefficient can be computed by observing daily returns and calculating correlation in tools like Excel.
- Low correlation allows for more benefits from adding more people, while high correlation restricts the potential increase in performance.
- The correlation level significantly influences the maximum sharp ratio achievable.
- For example, a correlation of 20% with a mean person having a sharp ratio of 0.75 would result in a maximum sharp ratio of around 2.4.
Correlation, Performance, and Scale in Hedge Funds
- Correlation has a significant impact on the delivery of returns in hedge funds.
- A high correlation can limit the maximum returns that can be achieved.
- It is possible to achieve close to the maximum returns without a large number of individuals.
- Reducing correlation can be more beneficial than adding more people to the team.
- Scaling a hedge fund for the sake of running more money does not necessarily improve performance.
- Scale brings complexity and potential performance-reducing factors.
- Hedge funds often promise uncorrelated returns to investors.
- Correlation among hedge funds is often driven by common factors.
- Allocators (such as pension funds and endowments) seek returns by taking on risks.
- Most risks that pay returns can be allocated to for a fraction of the cost.
Evaluating the skill of PMs and analysts at Freestone Grove
- Freestone Grove aims to make clean allocations by investing in hedge funds that can deliver uncorrelated returns.
- The key objective is to understand the mechanism through which the PMs deliver their claimed skill.
- Hiring at Freestone Grove involves a systematic process combined with an art of assessment.
- Past returns are not solely relied upon as a basis for hiring decisions.
- For analysts, the common claim is to have the ability to predict surprises in fundamentals.
- The hiring process involves assessing the candidate's modeling capabilities and intuition in understanding how things work.
Factors that attract good portfolio managers to a firm
- Having a well-defined and disciplined process for understanding and evaluating financial estimates and projections.
- Offering autonomy and a culture that encourages portfolio managers to understand and improve their own skills.
- Providing a competitive package that may not match the offerings of giants in the industry, but offers other benefits and opportunities.
- Offering the opportunity to work in a smaller team, where portfolio managers have a better chance of obtaining the necessary resources and support to excel in their roles.
- Providing access to corporate executives and other key figures in the industry, allowing for better insights and opportunities for information gathering.
- Having a strong focus on data science, quantitative analysis, portfolio construction, and risk management, providing portfolio managers with the necessary resources and expertise to make informed investment decisions.
- Creating an environment where portfolio managers can have a truly integrated and partner-like relationship with the resources and support available to them.
- Allowing portfolio managers to have a significant impact and influence on the firm's investment strategies and decisions.
Advantages of working as a portfolio manager at a smaller firm
- Smaller firms often offer higher compensation for portfolio managers compared to larger firms.
- With fewer people, portfolio managers at smaller firms have the opportunity to run larger scale teams.
- Portfolio managers at smaller firms have access to better resources and a more integrated platform, which can reduce technology and corporate access risks.
- Moving from a larger firm to a smaller firm can provide portfolio managers with better opportunities for growth and development.
- Smaller firms often have a preference for developing talent internally rather than relying on external hires, creating potential career advancement opportunities for analysts.
- However, turnover can be a challenge at smaller firms, as there may be a need to replace portfolio managers who leave unexpectedly or for personal reasons. In such cases, external hiring may be necessary.
- Like any industry, hiring talented individuals and developing them into successful portfolio managers can be a mix of internal training and external hires.
Importance of Understanding a Company's Management Team in Short-Term Investing
- Understanding the company's management team helps in identifying potential misunderstandings about the company compared to other market participants.
- The insights gained from knowing the management team can drive investment decisions and identify short-term trading opportunities.
- Short holding periods do not imply a short-term view of the company. It is important to have a long-term view of the company's prospects while identifying short-term trading opportunities.
- Insight into the management team allows for identifying potential deviations from market expectations and making profitable trades based on these deviations.
- Multi-strategy and factor investing have led to concerns about crowding risk in the markets, particularly in popular stocks like Nvidia.
- The impact of these strategies on the market depends on how individual managers navigate the market and their investment decisions.
Managing Crowding and Organizational Structure in Investment Strategies
- Crowding can be managed rather than being a cause for concern.
- Crowding is the mechanical way of getting paid for being early in an idea.
- Managing crowding exposure is crucial, as being early and getting paid slowly is different from chasing an idea.
- More participants in the market can decrease the mean return but skilled individuals may still profit.
- Being a multi-fund is a way of organizing oneself and not an investment strategy.
- Data does not support the notion of increased crowding due to multi-fund organizational structure.
- The biggest crowding event was in 2007, known as the great Quant crowding event.
- Different types of investments can also contribute to crowding.
- Concerns arise regarding increased volatility and tight stops during turning points in the market.
Compensation Structure and Incentives in Trading and Analyst Roles
- Compensation for traders and analysts can be either discretionary or formulaic.
- Discretionary compensation is when the employer decides the amount based on personal preference or performance, while formulaic compensation follows a specific formula or percentage of gross returns.
- Incentives are more aligned when compensation is formulaic, as it encourages individuals to focus on their specific job responsibilities.
- Discretionary compensation may hinder the direct incentive and focus on specific tasks.
- Netting risk compensation is when each risk taker runs a small team, and compensation is based on a fixed percentage of shares.
- Netting risk compensation may increase overall compensation costs for the firm compared to discretionary compensation.
Capital allocation and the psychology of money in investing.
- Allocating capital to different teams or individuals can have a significant impact on investment outcomes.
- Creating teams within a larger group can allow for more focused incentives and better performance.
- Allocators should consider the structure and allocation method to maximize benefits.
- The psychology of managing larger sums of money can affect investment decisions and comfort levels.
- The optimal amount of money that an individual can comfortably manage varies based on personal psychology.
- The psychological impact of larger sums of money can influence decision-making and create anxiety.
- Allocators need to consider not only the fundamental views but also the psychological factors when allocating capital.
Factors to Consider for Investment Decisions
- Cost and implementation questions
- Liquidity questions
- Scale and its impact on psychology and compensation
- Ability to invest in smaller cap names
- Netting issues and considerations
- Capital allocation based on expected returns
- Noise and unpredictability in sharp ratios
- Observing real ey sharp vs. true sharp ratios
- Equal risk allocation as a starting point
- Deviating from equal risk based on learning and understanding of drivers of returns.
Advice for College Students Interested in a Career in a Multi Strategy Hedge Fund
- Have a strong interest and aptitude in data analysis, as these jobs heavily rely on understanding and interpreting data.
- Be willing to embrace the grind of the job, as it involves covering the same companies and analyzing their data consistently.
- Understand that the job may not always be as exciting as it sounds, and that it requires patience and attention to detail.
- Consider pursuing internships in the industry to gain valuable experience and network with professionals.
- Continuously develop your skills in data analysis and stay updated on market trends and investment strategies.
The Role of AI in Investment Analysis
- AI can be a valuable tool for identifying patterns and potential catalysts in investment analysis.
- AI is part of the ongoing evolution towards more sophisticated data and analytics.
- AI is trained on text data from the internet and aims to predict the most likely answer or outcome based on a given prompt.
- AI can provide useful summaries and insights on what others think about a certain topic or theme.
- However, AI may not provide unique or differentiated views on a particular firm, as it is trained on the average consensus.
- AI can be beneficial for data analysis and understanding market trends, but caution should be exercised in assuming it can provide distinct insights.
Key Takeaways from the AOTs Podcast Episode
- Observing shorter and sharper turning points in the market in recent years.
- Importance of understanding why something works in order to be successful in a job.
- The significance of avoiding correlation between managers and the optimal number of pods.
- The role of compensation and identifying comparative advantage in career success.
- Reminder to stop telling Joe what he missed.
- Access to transcripts, blog, newsletter, and Discord chat for more Odd Lot content.
- Positive reviews on favorite podcast platforms are appreciated.
- Bloomberg subscribers can listen to ad-free episodes on Apple podcast.
Variations and Considerations in Pod Shop Design and Performance
- The design, compensation structures, coordination, and centralization vary among pod shops.
- Multi-strat hedge funds consistently produce impressive returns due to their different strategies and trades.
- Large pod shops raise concerns about replicating the market and diminishing alpha opportunities.
- Dan Millo, co-founder of Freestone Grove Partners, is an experienced hedge fund executive with expertise in quantitative finance.
- Quants in finance utilize quantitative methods and tools for risk assessment, forecasting, and evaluation in fundamental equities funds.
- Quants support fundamental analysts by providing insights and understanding of a firm's mechanics and analyzing alternative data.
- Freestone Grove focuses on optimizing business decisions and believes in a mix of quantitative and fundamental analysis.
- "Dan's Math" refers to the systematic approach to decision-making at Freestone Grove.
- Scaling up with more portfolio managers and assets does not always lead to significant performance improvements.
- Hiring at Freestone Grove is based on mean performance measured by the Sharpe ratio, considering the specific risks associated with different areas.
- Diversification can increase Sharpe ratios if there is low correlation among individuals' returns.
- Correlation among traders limits the effectiveness of diversification and the maximum achievable Sharpe ratio.
- Reducing correlation can be more beneficial than adding more people to the team.
- Scaling a hedge fund for the sake of running more money may not improve performance and can introduce complexity and performance-reducing factors.
- Hedge funds aim to deliver uncorrelated returns, and Freestone Grove invests in funds that can provide such returns.
- The hiring process at Freestone Grove combines a systematic process with assessing candidates' modeling capabilities and intuition.
Key Considerations for Portfolio Managers in Smaller Firms.
- Well-defined and disciplined process for evaluating financial estimates and projections.
- Autonomy and culture that encourages portfolio managers to improve their skills.
- Competitive package with benefits and opportunities.
- Smaller team with better access to resources and support.
- Access to corporate executives and key industry figures for insights and information.
- Strong focus on data science, quantitative analysis, portfolio construction, and risk management.
- Integrated and partner-like relationship with available resources and support.
- Significant impact and influence on investment strategies and decisions.
- Higher compensation and opportunity to run larger scale teams.
- Better resources and integrated platform reducing risks.
- Opportunities for growth and development.
- Potential career advancement opportunities for analysts.
- Turnover challenge may require external hiring.
- Combination of internal training and external hires for talent development.
- Importance of understanding the company's management team.
- Insights into management team driving investment decisions.
- Managing crowding risk and navigating the market.
- Different types of compensation - discretionary and formulaic.
- Alignment of incentives with formulaic compensation.
- Netting risk compensation increasing overall costs.
- Allocation of capital impacting investment outcomes.
- Psychology and comfort levels in managing larger sums of money.
- Consideration of psychological factors when allocating capital.
- Considerations for cost, liquidity, scale, and implementation.
- Ability to invest in smaller cap names.
- Observing real ey sharp vs. true sharp ratios.
- Equal risk allocation as a starting point with deviations based on understanding of returns.
Tips for a Career in Data Analysis and the Role of AI in Investment Analysis
- Have a strong interest and aptitude in data analysis.
- Be willing to embrace the grind and be patient.
- Consider pursuing internships in the industry.
- Continuously develop your skills and stay updated on market trends.
- AI can be a valuable tool in investment analysis.
- AI is trained on text data from the internet to predict outcomes.
- AI can provide useful summaries and insights on certain topics.
- Exercise caution in assuming AI can provide unique insights.
- Market has observed shorter and sharper turning points in recent years.