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    Forward Guidance

    The Laws Of Quantitative Investing | Michael Robbins

    Thu Jul 13 2023
    Quantitative InvestingValue InvestingStock SelectionRisk ManagementInterest RatesBiasesLiquidityShorting InvestmentsAI in InvestingFuture of Quantitative Investing

    Description

    This episode covers various aspects of quantitative investing, value investing, stock selection, risk management, interest rates, biases, liquidity, shorting investments, AI in investing, and the future of quantitative investing. It emphasizes the importance of long-term trends, controlling biases, managing different investments, and considering target met or greater return opportunities when selling. The episode also explores the challenges and complexities of value investing, interest rate effects on equities, risk management strategies, and the role of AI in refining thought processes and decision-making. Additionally, it delves into the impact of liquidity on shorting investments, factors influencing asset performance, and the potential benefits and risks of using technology in investing. The episode concludes with insights into the ongoing competition between humans and computers in quantitative investing and the role of exceptional investors in consistently outperforming the market.

    Insights

    Quantitative investing enables managing many different investments and positions, reducing reliance on luck.

    By utilizing quantitative analysis and systematic approaches, investors can effectively manage a diverse portfolio of investments, minimizing the impact of luck or chance.

    Value investing has become more complex and requires a multi-factor model for analysis.

    The traditional approach to value investing has evolved, necessitating the use of sophisticated multi-factor models to analyze investment opportunities and make informed decisions.

    Passive investing and diversification are easier and more efficient strategies.

    Instead of trying to outsmart the market, passive investing and diversification offer simpler and more efficient strategies for long-term investors.

    AI, machine learning, and quantitative analysis can help investors avoid biases and make more informed decisions.

    By leveraging advanced technologies and data-driven approaches, investors can mitigate biases and enhance their decision-making process.

    Timing and strategy specifics are crucial in investing.

    Successful investing requires careful consideration of timing and strategy specifics, as well as an understanding of market dynamics and historical indicators.

    Quantitative measures consider liquidity in tracking risk assets.

    When assessing risk assets, quantitative measures take into account liquidity factors to provide a more comprehensive analysis.

    Factors abstract away from specific assets and focus on mechanisms that drive asset performance.

    Factor investing involves identifying underlying mechanisms that drive asset performance, allowing investors to make informed decisions based on broader factors rather than individual assets.

    Using computers for investing can lead to more bad decisions if not used properly.

    While technology and AI offer powerful tools for investing, their effectiveness depends on proper usage and understanding. Misuse can result in poor decision-making.

    Quantitative investing is a complex cat and mouse game between humans and computers.

    The ongoing competition between humans and computers in quantitative investing creates a dynamic environment where both sides continuously adapt and strive for an edge.

    Exceptional investors can consistently beat the market on average.

    Despite the rise of quantitative investing and AI, exceptional investors with unique insights and strategies can still outperform the market over the long term.

    Chapters

    1. Quantitative Investing
    2. Value Investing and Interest Rates
    3. Stock Selection and Portfolio Design
    4. Structured Products and Passive Investing
    5. Risk Management and AI in Investing
    6. Biases and Quantitative Analysis
    7. Interest Rates and Stock Performance
    8. Investment Strategies and Factors
    9. Liquidity and Shorting Investments
    10. AI Investing and Technology
    11. Quantitative Investing and the Future
    Summary
    Transcript

    Quantitative Investing

    00:05 - 07:42

    • Quantitative investing helps focus on long-term trends and regime shifts, avoiding short-term noise.
    • Systemization of quantitative analysis allows for control of biases and focusing on what's important.
    • Quantitative investing enables managing many different investments and positions, reducing reliance on luck.
    • Non-quantitative investors often have a bias towards buying decisions and neglect selling decisions.
    • Leaving money on the table is common when non-quantitative investors don't focus on when and what to sell.
    • Quantitative investors should have a thesis before selling, considering target met or greater return opportunities.
    • The performance of stocks is influenced by people's perceptions and expectations rather than just market valuations.
    • Stock prices adapt to expected future earnings, requiring outsmarting others' expectations for price increases.

    Value Investing and Interest Rates

    07:15 - 14:47

    • You have to outsmart everybody else in investing by predicting the future and exceeding expectations.
    • Value investing has become more complex and requires a multi-factor model for analysis.
    • The belief that rising interest rates devalue equities has been challenged by recent market rallies.
    • There is uncertainty about how businesses will react to hiking cycles and rising rates.
    • The narrative that rising rates are good for banks doesn't always hold true, as seen with regional banks.
    • In the past, stock market drawdowns were worse when the Fed was less aggressive with rates.
    • Arbitrage trading can be a way to insulate oneself from disconnects between theory and reality in markets.
    • Pair trades, like basis trades in the bond market, can be used to create tight expressions of trades.
    • A model for taxless harvesting can help identify similar stocks to replace ones being sold.

    Stock Selection and Portfolio Design

    14:17 - 21:39

    • Identifying stocks to sell for taxless harvesting and replacing them with similar stocks in the same sector
    • Isolating trades to bet on specific opinions by tying down moving parts and reducing connections
    • Different correlations and tails of uncorrelated assets depending on investment horizon
    • Uncorrelated assets allow for skillful stock picking and diversification benefits
    • Designing strategies based on skill level and portfolio concentration preferences
    • Rebalancing issues with VIX products leading to negative absolute returns

    Structured Products and Passive Investing

    21:10 - 28:27

    • Structured products like the CTF mimic the VIX but lose money due to rebalancing.
    • Investors should pay attention to the details when dealing with structured assets.
    • VIX products are often misused despite being attractive indicators.
    • Stock picking is difficult and most investors fail at it, even professionals.
    • Passive investing and diversification are easier and more efficient strategies.
    • For long-term investing, investing in the S&P 500 or a diversified global equity market is recommended.
    • Individual investors need to consider gambling theory and risk tolerance when making investment decisions.

    Risk Management and AI in Investing

    28:17 - 36:07

    • Index investing is based on the idea that there will be winners and losers in the index, but the winners will get bigger over time.
    • Investing in individual stocks is difficult and not rewarding for most people.
    • Risk management is important to avoid big losses and improve long-term returns.
    • Kelly sizing focuses on maximizing terminal wealth, but there is a risk of losing all your money.
    • Stock market returns are not always distributed evenly, so assumptions about returns should depend on the investor and market.
    • AI, machine learning, and quantitative analysis can help investors avoid biases and make more informed decisions.

    Biases and Quantitative Analysis

    35:38 - 42:54

    • Biases can persist even when we are aware of them.
    • Professional investors often invest based on beliefs rather than evidence.
    • Backtesting and studies can prove investment strategies wrong.
    • Timing and strategy specifics are crucial in investing.
    • Quantitative investing helps refine thought processes and improve decision-making.
    • Growth stocks are more affected by rising interest rates than value stocks due to longer duration of future earnings.
    • Precise thinking, numerical analysis, and AI can enhance investment strategies.

    Interest Rates and Stock Performance

    42:28 - 49:50

    • A rise in the discount rate hurts growth stocks more than value stocks.
    • The narrative that growth stocks are affected by bond yields exploding higher is intuitive but needs rigorous quantitative analysis to determine its truth.
    • The dividend discount model suggests that interest rates affect the discount rate, which can impact stocks with higher duration more.
    • The O-curve slope may be affected by rates, but its effect on the market's attention is uncertain.
    • Having only ten observations of a track record is not enough to make significant conclusions.
    • The yield curve being inverted six out of six times before a recession is not as good as ten out of ten observations.
    • Inverted yield curves were frequent in the past, even during booms, suggesting mechanical reasons behind them.
    • Timing and applying historical indicators correctly are crucial for investment success.
    • Liquidity and robust sales can boost stock values despite higher borrowing costs.
    • Banks' traditional assumption of sticky deposits proved not to be true in some cases, challenging the direct impact of the yield curve on banks' performance.
    • Simple indicators like the yield curve may not directly address the mechanisms that drive price movements and may have lagged or indirect effects.

    Investment Strategies and Factors

    49:24 - 57:20

    • Simple things with good track records are not always the best bets
    • Diversification is a strong signal that works well in investing
    • Value investing has an advantage due to its thoughtful theory and mechanisms
    • There are many investment options beyond large cap stocks
    • Distressed debt and mergers and acquisitions have more defined bets
    • Causal AI seeks to develop mechanisms with causal effects in trading
    • Graphs with directed acyclic arrows can provide a thesis for consistent trades
    • Testing models through interventions helps ensure accuracy
    • Quantitative measures consider liquidity in tracking risk assets

    Liquidity and Shorting Investments

    57:01 - 1:05:19

    • Quantitative efforts to measure and predict liquidity in the market face challenges such as overfitting and auto correlation.
    • Liquidity is highly manipulated, making it difficult to rely solely on quantitative measures.
    • Shorting investments can be affected by liquidity, with the risk of having profitable shorts pulled or facing high borrowing costs.
    • Shorting can also be challenging due to high fees and implied volatility for options.
    • Delisting of companies can pose problems for short positions.
    • Factor investing involves identifying specific factors that can generate profits, even if they are not persistent or universal.
    • Esoteric factors that are less widely known and monitored can provide more opportunities for skilled traders.
    • Factors abstract away from specific assets and focus on mechanisms that drive asset performance.
    • There are numerous factors beyond mainstream ones like momentum and value, including anomalies and dislocations from yield curves or quality sentiment.

    AI Investing and Technology

    1:04:50 - 1:12:51

    • The accessibility of sophisticated algorithms has increased with tools like MATLAB and Python being accessible to everyone.
    • Large language models have become popular and accessible even to non-programmers.
    • Using computers for investing can lead to more bad decisions if not used properly.
    • High-frequency trading transformed the business of trading from being predictive to building an efficient business that requires capital expenditures.
    • Technology in AI investing will be differentiated with various models and ways to tune them.
    • The training of AI models is crucial, as they need correct data and exposure to the right information.
    • Misinformation and manipulation may occur in AI investing, similar to what happens in markets or legal markets like Distress Debt.
    • Outsmarting machines is still possible, but there will come a point where human input won't have much advantage.

    Quantitative Investing and the Future

    1:12:25 - 1:16:36

    • Quantitative investing is a complex cat and mouse game between humans and computers.
    • The market has a beta of one, meaning its return is the market's return.
    • There are investors who outperform the market net of fees, benefiting at the expense of underperforming investors.
    • In a future where most trading is done by computers, this zero-sum game will still exist.
    • Exceptional investors can consistently beat the market on average.
    • Computers can execute trades rapidly, potentially leading to fast time decay on certain strategies.
    • Larson Financial is an investment advisory firm that uses quantitative methods to design efficient portfolios based on risk tolerances and preferences.
    • They also use quantitative methods to define capital markets assumptions and make portfolio allocation decisions.
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