Part 2 Flashcards
Wisdom of Crowds (Chen et al., 2014): 1. What are the two channels through which social media affects investors, as discussed in ‘Wisdom of Crowds’?
Predictability Channel: Social media posts contain value-relevant information that influences stock prices over time.; Clout Channel: Social media moves prices through sentiment-driven trading, even if the information is false or spurious.;
Wisdom of Crowds (Chen et al., 2014): 2. How does negative sentiment in Seeking Alpha (SA) articles and comments impact stock returns?
A 1% increase in negative words in SA articles leads to future stock returns being 0.332% lower.; For SA comments, the effect is 0.194% lower returns.;
Wisdom of Crowds (Chen et al., 2014): 3. What are the incentives for Seeking Alpha contributors to post stock opinions?
Monetary compensation: $10 per 1,000 page views, with high-quality articles earning more.; Recognition: Successful authors gain visibility, with their articles cited in media outlets like Forbes and WSJ.;
Wisdom of Crowds (Chen et al., 2014): 4. What role does the public feedback mechanism on Seeking Alpha play in financial analysis?
It allows readers to challenge weak arguments, improving the accuracy of investment insights.; If authors have inconsistent track records, reader comments become more reliable predictors of stock prices.;
Wisdom of Crowds (Chen et al., 2014): 5. How does social media-based financial analysis compare to traditional analyst reports?
Social media-driven stock opinions often outperform professional analysts in predicting future stock returns, highlighting the value of peer-generated financial intelligence.;
Wisdom of Crowds (Chen et al., 2014): 6. What does the ‘Wisdom of the Crowd’ principle mean in financial markets?
It suggests that collective opinions from a diverse group of investors can be highly accurate, sometimes even more so than individual experts, as long as the crowd is informed and interactions are structured.;
Wisdom of Crowds (Chen et al., 2014): 7. Under what conditions does social media-based investment research provide the most value?
When credible authors with strong track records post insightful analysis.; When readers engage critically and correct misleading information in comments.;
Wisdom of Crowds (Chen et al., 2014): 8. Why might the ‘Clout Channel’ be less influential than the ‘Predictability Channel’ in social media investing?
The lack of return reversals suggests that social media-based price movements are justified and not purely speculative, reducing the influence of naive trading effects.;
Wisdom of Crowds (Chen et al., 2014): 9. How has social media changed the role of investment analysis?
Social media has democratized investment analysis, allowing retail investors to share and consume stock opinions, reducing reliance on professional analysts.;
Wisdom of Crowds (Chen et al., 2014): 10. What percentage of U.S. investors relied on social media for investment decisions by 2008?
By 2008, 25% of U.S. investors relied on social media for investment decisions, highlighting the shift towards peer-based financial advice.;
Wisdom of Crowds (Chen et al., 2014): 11. Do investor opinions on social media predict stock returns?
Yes. Social media opinions, particularly negative ones, predict future stock returns and earnings surprises, even when controlling for traditional financial sources.;
Wisdom of Crowds (Chen et al., 2014): 12. How does negative sentiment in Seeking Alpha (SA) articles affect earnings surprises?
A 1% increase in negative words in SA articles is linked to earnings surprises being 0.232% to 0.266% below market expectations.;
Wisdom of Crowds (Chen et al., 2014): 13. What is the effect of negative comments on Seeking Alpha (SA) earnings surprises?
A 1% increase in negative words in SA comments is linked to earnings surprises being 0.094% to 0.095% lower than expected.;
Wisdom of Crowds (Chen et al., 2014): 14. Why do SA contributors with strong past track records have greater market influence?
SA authors with consistent, accurate past predictions gain credibility, making their analyses more impactful on market movements.;
Wisdom of Crowds (Chen et al., 2014): 15. How does Seeking Alpha�s public feedback mechanism improve information quality?
Readers challenge misleading or weak arguments in comments, refining the accuracy of investment insights, especially when authors have inconsistent records.;
Wisdom of Crowds (Chen et al., 2014): 16. What are the incentives for Seeking Alpha contributors to post stock opinions?
SA authors earn $10 per 1,000 page views, with high-quality articles earning up to $500, alongside gaining industry recognition.;
Wisdom of Crowds (Chen et al., 2014): 17. How do social media-driven stock opinions compare to traditional analyst reports?
Social media opinions outperform financial analysts in predicting stock price movements, showing that peer-based insights add real value.;
Wisdom of Crowds (Chen et al., 2014): 18. What is the key takeaway from the ‘Wisdom of Crowds’ study?
Peer-based advice on Seeking Alpha provides meaningful insights, with negative sentiment consistently predicting lower stock returns and earnings surprises.;
Wisdom of Crowds (Chen et al., 2014): 19. Under what conditions does the ‘Wisdom of the Crowd’ work effectively?
The accuracy of social media predictions depends on author credibility and reader interactions.; When authors are inconsistent, reader comments are more predictive.;
Wisdom of Crowds (Chen et al., 2014): 20. How is investment research shifting due to social media?
Platforms like Seeking Alpha challenge traditional analysts by democratizing financial information, requiring both investors and professionals to adapt.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 1. Why does the CAPM lead to systematic mispricing in M&A transactions?
CAPM misprices risk by underestimating returns for low-beta firms and overestimating them for high-beta firms. As a result, acquirers overpay for low-beta targets and underpay for high-beta ones, leading to predictable market reactions.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 2. What percentage of deal values do CAPM-based misvaluations typically represent?
Valuation errors due to CAPM reliance in M&A deals range from 12% to 33% of total deal values.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 3. How does the empirical Security Market Line (SML) differ from the one predicted by CAPM?
The empirical SML is flatter than the CAPM-implied SML, meaning low-beta stocks generate higher-than-expected returns while high-beta stocks underperform CAPM predictions.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 4. Why do firms continue to use CAPM despite its empirical flaws?
CAPM is widely taught in academia and remains the dominant framework for estimating the cost of capital, even though empirical evidence suggests it systematically misestimates returns.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 5. How does CAPM mispricing affect capital budgeting decisions?
Managers using CAPM for capital budgeting tend to overvalue low-beta projects and undervalue high-beta projects, leading to inefficient investment decisions.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 6. How does CAPM misvaluation manifest in mergers and acquisitions (M&A)?
Acquirers using CAPM tend to overbid for low-beta private firms, resulting in lower cumulative abnormal returns (CARs) for bidders, while high-beta acquisitions receive more favorable market reactions.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 7. What is the estimated effect of CAPM misvaluation on bidder cumulative abnormal returns (CARs)?
A difference in target betas of one interquartile range (0.49) is associated with a difference in bidder CARs of 0.5 to 1.2 percentage points, equating to 6% to 16% of the interquartile range of bidder CARs.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 8. What alternative valuation models may provide better risk-adjusted return estimates than CAPM?
Multifactor models, such as Fama-French, or valuation methods based on option prices, may better capture risk-adjusted return expectations compared to CAPM.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 9. What evidence suggests that managers relying on CAPM are making valuation errors?
Empirical findings show no long-term return reversal, indicating that CAPM mispricing leads to real misallocations of capital rather than temporary market inefficiencies.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 10. How does corporate governance impact the reliance on CAPM for valuation?
Stronger corporate governance reduces reliance on CAPM, as well-governed firms are more likely to adopt alternative valuation models that better reflect risk-adjusted returns.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 11. Why should adjustments be made when using CAPM for valuation?
CAPM remains a useful starting point, but adjustments�such as shrinking beta estimates or incorporating additional factors�are necessary to correct systematic mispricing.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 12. Beyond M&A, how does CAPM-based misvaluation affect broader corporate investment decisions?
CAPM mispricing extends to general capital budgeting decisions, influencing project selection and potentially leading to inefficient capital allocation.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 13. How do market reactions confirm the CAPM-based misvaluation hypothesis?
Market reactions to M&A transactions show that bidders of low-beta targets experience lower cumulative abnormal returns (CARs), while high-beta acquisitions generate more positive reactions.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 14. What empirical strategy did the authors use to confirm CAPM-driven misvaluation in M&A?
The study used M&A data and regression analysis to link target beta levels with bidder CARs, showing a statistically significant relationship between CAPM mispricing and market reactions.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 15. How do firms with high beta targets tend to be priced compared to CAPM predictions?
High-beta targets tend to be undervalued compared to CAPM predictions, leading to relatively lower bid premiums and higher market approval of these acquisitions.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 16. What role does the use of fairness opinions play in CAPM-based misvaluation?
Fairness opinions often rely on CAPM-based discount rates, reinforcing the valuation distortions by applying misestimated costs of capital to acquisition targets.;
CAPM-Based Misvaluation (Dessaint et al., 2021): 17. What are the normative implications of CAPM-based misvaluation?
If the market is efficient and CAPM misestimates risk, firms using CAPM make systematic valuation mistakes, suggesting that corporate decision-makers should incorporate alternative models.;
Two Pillars of Asset Pricing (Fama, 2014): 1. What are the two pillars of asset pricing according to Fama (2014)?
- Market Efficiency: Asset prices fully reflect all available information.; 2. Asset Pricing Models: Theories that explain how expected returns relate to risk.;
Two Pillars of Asset Pricing (Fama, 2014): 2. What is the joint hypothesis problem in market efficiency tests?
The joint hypothesis problem states that when testing market efficiency, one must assume a pricing model. If a test fails, it is unclear whether the market is inefficient or the asset pricing model is incorrect.;
Two Pillars of Asset Pricing (Fama, 2014): 3. How do anomalies challenge the Efficient Market Hypothesis (EMH)?
Anomalies such as size, value, and momentum effects suggest that asset prices do not always reflect all available information, challenging the pure form of EMH.;
Two Pillars of Asset Pricing (Fama, 2014): 4. Why is testing the Efficient Market Hypothesis (EMH) difficult?
Testing EMH requires an assumption about expected returns. If a test rejects EMH, it could be due to market inefficiency or a bad asset pricing model (joint hypothesis problem).;
Two Pillars of Asset Pricing (Fama, 2014): 5. What are some of the major anomalies that challenge EMH?
- Size effect � Small stocks tend to outperform large stocks.; 2. Value effect � High book-to-market stocks have higher returns than growth stocks.; 3. Momentum effect � Stocks with recent positive returns continue to outperform.;
Two Pillars of Asset Pricing (Fama, 2014): 6. How does CAPM fail to explain the cross-section of stock returns?
CAPM only considers market beta as a risk factor but fails to explain empirical return patterns, such as the size and value effects.;
Two Pillars of Asset Pricing (Fama, 2014): 7. How did the Fama-French Three-Factor Model improve on CAPM?
The model includes size (SMB) and value (HML) factors along with market beta, providing a better fit for observed stock returns.;
Two Pillars of Asset Pricing (Fama, 2014): 8. What are the key limitations of the Fama-French Three-Factor Model?
The model explains size and value effects but fails to capture momentum, profitability, and investment factors.;
Two Pillars of Asset Pricing (Fama, 2014): 9. What are the Intertemporal CAPM (ICAPM) and Consumption CAPM (CCAPM), and how do they extend traditional asset pricing models?
ICAPM incorporates changing investment opportunities over time, while CCAPM links expected returns to consumption risks, but both have mixed empirical support.;
Two Pillars of Asset Pricing (Fama, 2014): 10. Why are asset pricing models essential for market efficiency tests?
Market efficiency cannot be tested without an asset pricing model to define expected returns, making it impossible to determine whether inefficiencies exist.;
Two Pillars of Asset Pricing (Fama, 2014): 11. How does finance compare to other economic disciplines in terms of practical application?
Finance is one of the most successful branches of economics, producing theories widely applied in portfolio management, corporate finance, and investment analysis.;
Two Pillars of Asset Pricing (Fama, 2014): 12. What does Fama conclude about the future of asset pricing research?
Fama suggests refining asset pricing models to better capture return patterns while recognizing that no single model can fully explain market dynamics.;
Two Pillars of Asset Pricing (Fama, 2014): 13. What role do event studies play in testing market efficiency?
Event studies examine how quickly stock prices adjust to new information. If markets are efficient, prices should incorporate new information immediately.;
Two Pillars of Asset Pricing (Fama, 2014): 14. How does time-varying expected stock returns challenge traditional asset pricing models?
Traditional models assume constant expected returns, but empirical evidence shows expected returns vary over time due to changing risk preferences and economic conditions.;
Two Pillars of Asset Pricing (Fama, 2014): 15. What is Fama�s critique of the term ‘bubble’ in financial markets?
Fama argues that there is no statistically reliable evidence that price declines are predictable, making the term ‘bubble’ ambiguous and unscientific.;
Two Pillars of Asset Pricing (Fama, 2014): 16. How does behavioral finance challenge market efficiency?
Behavioral finance suggests that cognitive biases lead to systematic mispricing, challenging the rational assumptions of traditional asset pricing models.;
Two Pillars of Asset Pricing (Fama, 2014): 17. What are the implications of the joint hypothesis problem for empirical asset pricing research?
Since efficiency and asset pricing models are inseparable, rejections of efficiency tests could indicate either market inefficiency or a flawed pricing model, complicating conclusions.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 1. Why do Harvey et al. (2016) argue that many asset pricing factors are likely false discoveries?
Many asset pricing factors are statistical flukes due to data mining. The authors propose a stricter t-statistic threshold (above 3.0) to confirm factor validity.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 2. What statistical threshold do Harvey et al. (2016) propose for determining valid asset pricing factors?
A new factor should have a t-statistic greater than 3.0 to be considered statistically valid, as lower thresholds lead to false discoveries.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 3. Why is traditional statistical significance (t-statistic > 2.0) insufficient for asset pricing research?
Given the large number of tests conducted, a threshold of 2.0 leads to many false positives. A stricter threshold (3.0) is needed to correct for multiple testing biases.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 4. Why has the number of asset pricing factors grown so much in financial research?
Hundreds of factors have been introduced due to data mining and repeated testing on the same datasets, increasing the risk of false discoveries.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 5. What is the issue with using the same financial datasets repeatedly to test new asset pricing factors?
Reusing limited datasets increases the likelihood of finding spurious correlations rather than true predictive factors.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 6. How do multiple testing biases affect asset pricing research?
When many factors are tested, some will appear statistically significant just by chance, leading to an overstatement of their predictive power.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 7. How does the multiple testing framework proposed by Harvey et al. help improve asset pricing research?
It corrects for false discoveries by adjusting statistical thresholds, ensuring that only truly predictive factors are accepted.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 8. Why are many asset pricing factors likely redundant?
Many factors are correlated with each other and do not independently explain asset returns, capturing similar risk premia rather than providing new insights.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 9. What is the role of Bayesian statistical methods in asset pricing factor validation?
Bayesian methods could improve factor discovery, but they also introduce challenges, such as dealing with unobservable hidden factors.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 10. Why do Harvey et al. argue that most asset pricing factors in financial economics are likely false?
Historically, conventional statistical thresholds have overstated factor significance, leading to misleading conclusions.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 11. What is the recommended approach to improve the robustness of empirical asset pricing research?
Re-testing factors across different datasets, markets, and time periods before accepting them as valid.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 12. Why should reproducibility and transparency be emphasized in financial research?
To ensure that discovered factors are not just statistical artifacts but hold up across different contexts and samples.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 13. How does publication bias affect asset pricing factor research?
Significant factors are more likely to be published, while insignificant results are ignored, leading to an overestimation of factor validity.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 14. What is the ‘false discovery rate’ in multiple hypothesis testing?
The proportion of reported significant results that are actually false positives.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 15. Why do researchers recommend using out-of-sample validation for asset pricing factors?
It helps confirm whether factors hold predictive power beyond the original dataset, reducing the risk of data mining biases.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 16. What does Harvey et al. suggest as the best way to detect spurious asset pricing factors?
Using multiple testing corrections and stricter statistical thresholds to filter out noise and false discoveries.;
The Cross-Section of Expected Returns (Harvey et al., 2016): 17. What are the implications of multiple testing biases for portfolio management and investment strategies?
Investors relying on factors that are statistical flukes may experience poor performance when those factors fail to hold up in the real world.;
Hedge Fund Flows (Agarwal et al., 2018): 1. Why do hedge fund investors rely heavily on CAPM when evaluating funds?
CAPM alpha explains hedge fund flows better than more sophisticated models, suggesting investors prioritize simplicity over model complexity.;
Hedge Fund Flows (Agarwal et al., 2018): 2. What are the three return components that hedge fund investors consider when allocating capital?
- Alpha (manager skill); 2. Traditional risk premia (size, value, market beta); 3. Exotic risk premia (momentum, options, derivatives strategies).;
Hedge Fund Flows (Agarwal et al., 2018): 3. Why do investors emphasize exotic risk exposures more than traditional ones?
Hedge funds offer unique risk exposures not available in traditional investment vehicles, reinforcing the notion that ‘exotic beta is hedge fund alpha’.;
Hedge Fund Flows (Agarwal et al., 2018): 4. Why do investors continue to chase past hedge fund returns despite little persistence in performance?
Investors overestimate their ability to pick winning funds based on past success, even though past returns from traditional and exotic risks do not reliably predict future performance.;
Hedge Fund Flows (Agarwal et al., 2018): 5. How do hedge fund fees influence investors’ sensitivity to risk exposures?
Investors paying higher performance fees are more sensitive to exotic risk returns, expecting hedge funds to provide access to unique strategies.;
Hedge Fund Flows (Agarwal et al., 2018): 6. How do funds of hedge funds (FoFs) behave compared to institutional investors?
FoFs allocate more capital toward exotic risk exposures, similar to institutional investors, but do not use more sophisticated performance models.;
Hedge Fund Flows (Agarwal et al., 2018): 7. How did hedge fund investors� risk preferences evolve over time?
Before 2005, investors did not differentiate between traditional and exotic risks.; After 2005, with the rise of advanced risk models, they began distinguishing between them.;
Hedge Fund Flows (Agarwal et al., 2018): 8. Why is CAPM alpha still dominant in hedge fund performance evaluation?
Despite more sophisticated models, CAPM remains widely used because of its simplicity and investors’ preference for easy-to-understand performance metrics.;
Hedge Fund Flows (Agarwal et al., 2018): 9. What is the key implication of the study for investors choosing hedge funds?
Investors should focus on understanding risk exposures rather than blindly chasing past returns, as past performance is not a reliable indicator of future success.;
Hedge Fund Flows (Agarwal et al., 2018): 10. How could investors improve their hedge fund selection strategies?
By using more sophisticated risk models that differentiate between types of risks, ensuring better capital allocation between hedge funds and other investment vehicles.;
Hedge Fund Flows (Agarwal et al., 2018): 11. Why does CAPM alpha explain hedge fund flows better than multifactor models?
Hedge fund investors treat traditional and exotic risk exposures as part of alpha, failing to fully differentiate between risk sources.;
Hedge Fund Flows (Agarwal et al., 2018): 12. What does the study suggest about hedge fund investors’ ability to identify manager skill?
Investors often conflate true alpha with risk-based returns, leading to suboptimal capital allocation decisions.;
Hedge Fund Flows (Agarwal et al., 2018): 13. What role do hedge fund incentives play in investors’ preference for exotic risk exposures?
Hedge fund managers are incentivized to take on exotic risks because investors associate these with skill, leading to higher inflows.;
Hedge Fund Flows (Agarwal et al., 2018): 14. How do institutional investors differ from retail investors in their hedge fund allocation?
Institutional investors allocate more capital toward exotic risk exposures and tend to have longer investment horizons.;
Hedge Fund Flows (Agarwal et al., 2018): 15. What does the study imply about hedge fund investors’ use of risk models?
Despite advances in asset pricing, hedge fund investors still rely on basic models, overlooking the benefits of more accurate multifactor frameworks.;
Hedge Fund Flows (Agarwal et al., 2018): 16. Why do hedge fund investors still chase past returns despite clear evidence against return persistence?
Behavioral biases and historical reliance on past performance lead to repeated misallocation of capital.;
Hedge Fund Flows (Agarwal et al., 2018): 17. What does the study suggest about the future of hedge fund performance evaluation?
Future hedge fund evaluation should integrate better risk differentiation and robustness checks to improve capital allocation decisions.;
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 1. What is the Law of One Price (LOOP) in financial markets?
LOOP states that identical financial assets should have the same price in an efficient market.; However, real-world evidence shows persistent LOOP violations.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 2. What are the key barriers that prevent arbitrage from enforcing the Law of One Price?
- Legal and trading restrictions; 2. Low liquidity; 3. Short-selling constraints; 4. Exchange rate fluctuations; 5. Malicious market activity (e.g., pump-and-dump schemes).
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 3. What role do investor misperceptions play in LOOP violations?
Investors sometimes mistakenly believe there are fundamental differences between identical assets, leading to demand and supply imbalances that distort prices.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 4. Why doesn�t arbitrage always eliminate LOOP violations?
Real-world arbitrage faces constraints such as trading restrictions, legal barriers, and behavioral biases that prevent price equalization.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 5. How do closed-end country funds demonstrate LOOP violations?
Legal and institutional barriers prevent direct investment, causing closed-end funds to trade at significant premiums or discounts to their NAVs.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 6. What are some historical examples of LOOP violations in closed-end country funds?
- Taiwan Fund (1987) traded at a 205% premium due to U.S. investment restrictions.; 2. Germany Fund (1989) surged to a 100% premium after the fall of the Berlin Wall.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 7. How do American Depositary Receipts (ADRs) illustrate LOOP violations?
ADRs, which represent foreign stocks trading in the U.S., sometimes trade at significantly different prices from their underlying shares due to legal and institutional constraints.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 8. What is an example of an ADR that violated LOOP?
Infosys (2000) ADRs traded at a 136% premium to shares on the Bombay Stock Exchange due to trading restrictions and a lack of arbitrage mechanisms.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 9. How do corporate spinoffs contribute to LOOP violations?
Investors fail to properly value subsidiary shares distributed in spinoffs, leading to stub value anomalies where the sum of parts is mispriced.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 10. What factors contribute to market inefficiencies that prevent LOOP from holding?
Restrictions on short-selling, legal constraints, and behavioral biases create persistent price discrepancies.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 11. Why is real-world arbitrage not always risk-free?
Arbitrageurs face risks such as noise trader risk, unpredictable price movements, and systemic market failures (e.g., LTCM collapse in 1998).
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 12. How do LOOP violations challenge classical finance theories?
Traditional finance assumes arbitrage is frictionless and markets price assets correctly, but real-world frictions prevent price convergence.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 13. What is noise trader risk, and how does it impact arbitrage?
Noise traders make irrational trades that push prices away from fair value, increasing risk for arbitrageurs and deterring price corrections.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 14. Why did the Long-Term Capital Management (LTCM) hedge fund collapse despite arbitrage strategies?
LTCM relied on arbitrage strategies assuming LOOP would hold, but extreme market conditions caused spreads to widen, leading to massive losses.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 15. How do limits to arbitrage explain the persistence of mispricings?
Arbitrage is limited by capital constraints, market frictions, and the risk that prices may diverge further before converging.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 16. How does investor sentiment contribute to LOOP violations?
Overly optimistic or pessimistic sentiment can drive asset prices away from their fundamental values, sustaining LOOP violations.
The Law of One Price & Anomalies (Lamont & Thaler, 2003): 17. What implications do LOOP violations have for asset pricing models?
LOOP violations suggest that real-world markets are less efficient than assumed in standard pricing models, requiring adjustments for frictions and investor irrationality.
ETFs 101 (Lettau & Madhavan, 2018): 1. What are ETFs, and why are they considered a major financial innovation?
ETFs are Exchange-Traded Funds that offer intraday trading, low fees, and transparency, growing significantly in size since their introduction in 1993.
ETFs 101 (Lettau & Madhavan, 2018): 2. How do ETFs differ from mutual funds?
Unlike mutual funds, which are bought and sold at end-of-day NAV, ETFs trade throughout the day like stocks, providing greater liquidity and flexibility.
ETFs 101 (Lettau & Madhavan, 2018): 3. What advantages do ETFs have over traditional mutual funds?
ETFs typically have lower fees, better liquidity, tax efficiency, and greater transparency, driving their rapid adoption.
ETFs 101 (Lettau & Madhavan, 2018): 4. How has the ETF universe expanded beyond traditional equity index tracking?
ETFs now include fixed income, commodities, currencies, volatility products, multi-asset strategies, and smart beta, blurring lines between active and passive management.
ETFs 101 (Lettau & Madhavan, 2018): 5. How have ETFs reshaped financial markets?
Institutional investors increasingly use ETFs over futures, swaps, and individual bond trading, partly due to post-2008 regulatory changes and balance sheet constraints.
ETFs 101 (Lettau & Madhavan, 2018): 6. What liquidity risks do ETFs face?
ETF liquidity can diverge from the underlying assets, leading to mismatches; during stress, redemptions and price dislocations may amplify market volatility.
ETFs 101 (Lettau & Madhavan, 2018): 7. Are concerns about ETFs exacerbating market volatility overstated?
Some argue that ETF-driven volatility is minor, though flash crashes and liquidity mismatches remain genuine concerns that warrant further scrutiny.
ETFs 101 (Lettau & Madhavan, 2018): 8. How do flash crashes relate to ETF structural risks?
Past flash crashes revealed sharp ETF price distortions due to rapid redemptions and liquidity bottlenecks, exposing weaknesses in ETF trading mechanisms.
ETFs 101 (Lettau & Madhavan, 2018): 9. Why might some investors misunderstand ETFs?
Investors may not fully grasp different ETF structures (e.g., leveraged, inverse, or smart beta), leading to unexpected risks and mismatches with investment goals.
ETFs 101 (Lettau & Madhavan, 2018): 10. How have regulatory changes impacted ETF growth?
Regulatory approval for novel ETF structures and increased acceptance of passive instruments have supported ETF expansion, though some rules aim to limit systemic risks.
ETFs 101 (Lettau & Madhavan, 2018): 11. How do ETF market makers and authorized participants affect ETF pricing?
They create/redeem ETF shares to keep prices close to NAV, though extreme events can break this mechanism and cause premiums or discounts.
ETFs 101 (Lettau & Madhavan, 2018): 12. What is the impact of ETFs on financial stability?
ETFs can increase liquidity and efficiency but may also contribute to volatility and systemic risks, particularly during market stress events.
ETFs 101 (Lettau & Madhavan, 2018): 13. What does the future of ETFs look like?
ETFs will continue evolving, integrating with algorithmic trading, robo-advisors, and institutional strategies while facing regulatory scrutiny.
ETFs 101 (Lettau & Madhavan, 2018): 14. What risks do leveraged and inverse ETFs pose to investors?
These ETFs amplify gains and losses, making them risky for long-term investors due to compounding effects.
ETFs 101 (Lettau & Madhavan, 2018): 15. What were some key lessons from past ETF-related flash crashes?
Sharp price distortions in 2010 and 2015 illustrated liquidity and structural risks, emphasizing the need for better safeguards.
ETFs 101 (Lettau & Madhavan, 2018): 16. How do ETF flows affect underlying stock market liquidity?
ETF trading can boost liquidity for the underlying assets but may also cause price dislocations during stress periods.
ETFs 101 (Lettau & Madhavan, 2018): 17. Why is it important for investors to differentiate between various ETF types?
Understanding differences between passive, smart beta, leveraged, and inverse ETFs helps investors manage risk and avoid unintended exposures.
ETFs 101 (Lettau & Madhavan, 2018): 18. How do ETFs interact with market regulation and systemic risk concerns?
Regulators track ETF growth to mitigate market distortions and systemic risks, focusing on liquidity mismatches and rapid trading behavior.
Fed Markets (Putnins, 2022): 1. How did the Federal Reserve’s balance sheet change during the COVID-19 crisis?
The Fed doubled its balance sheet from $4.17 trillion to $8.33 trillion between March 2020 and August 2021, marking its most aggressive QE program on record.
Fed Markets (Putnins, 2022): 2. What was the estimated contribution of the Fed’s balance sheet expansion to the stock market rebound in 2020?
Between one-third and one-half of the S&P 500�s 31% rebound from March to May 2020 is attributed to the Fed�s actions.
Fed Markets (Putnins, 2022): 3. How does the Fed typically respond to stock market declines?
A 10% drop in stock prices generally prompts a 5.6% expansion in the Fed�s balance sheet over the next 10�15 weeks.
Fed Markets (Putnins, 2022): 4. How does stock market performance react to Fed balance sheet expansions and contractions?
A 10% balance sheet expansion drives a 9.1% stock price increase, but markets react more negatively to contractions than they do positively to expansions.
Fed Markets (Putnins, 2022): 5. Which market segments react more strongly to Fed interventions?
Small-cap stocks and cyclical sectors (consumer durables, tech) are more sensitive, while large-cap and defensive sectors react less.
Fed Markets (Putnins, 2022): 6. What are the two primary channels through which the Fed�s actions impact stock markets?
- Lower bond yields, reducing discount rates; 2. Improved macroeconomic expectations, boosting future corporate earnings.
Fed Markets (Putnins, 2022): 7. How did the Fed�s 2020 intervention differ from past QE programs?
The Fed purchased corporate bond ETFs for the first time, suggesting it might move further into equity markets if needed.
Fed Markets (Putnins, 2022): 8. Why does Fed intervention create a disconnect between stock markets and the real economy?
Asset purchases decouple market valuations from economic fundamentals, giving the illusion of a stronger recovery than actual conditions might warrant.
Fed Markets (Putnins, 2022): 9. What risks does unwinding the Fed�s balance sheet pose to markets?
A rapid or unexpected contraction could spark volatility and financial instability, as markets are highly sensitive to Fed policy signals.
Fed Markets (Putnins, 2022): 10. Why should stock market participants closely monitor Fed actions?
Investors can position portfolios based on the Fed�s balance sheet stance, as expansions and contractions create both risks and opportunities.
Fed Markets (Putnins, 2022): 11. How do investors� expectations of the Fed�s actions impact stock market behavior?
Anticipation of Fed support bolsters markets, but surprise tightening triggers sharper declines.
Fed Markets (Putnins, 2022): 12. What is the �Fed put� and how does it relate to stock market expectations?
The �Fed put� is investors� belief that the Fed will prevent severe market drops, encouraging riskier behavior and potentially inflating valuations.
Fed Markets (Putnins, 2022): 13. Why do cyclical industries respond more strongly to Fed balance sheet changes?
Cyclical industries like consumer durables and technology are more sensitive to macroeconomic expectations, which are directly influenced by the Fed�s actions.;
Fed Markets (Putnins, 2022): 14. What are the implications of Fed interventions for long-term market stability?
While Fed interventions stabilize markets in the short run, they may create long-term distortions, such as inflated asset prices and increased financial fragility.;
Fed Markets (Putnins, 2022): 15. How did the Fed�s balance sheet expansion during COVID-19 compare to previous crises?
The COVID-19 response was unprecedented in both speed and magnitude, surpassing previous QE programs in scale and directly targeting corporate bond markets.;
Fed Markets (Putnins, 2022): 16. What happens when the Fed signals a reduction in its balance sheet unexpectedly?
Markets typically react sharply, as seen in past taper tantrums, where unexpected tightening leads to rapid declines in stock prices and increased volatility.;
Sovereign & Sustainable Bonds (Cheng et al., 2022): 1. What are the key types of green, social, and sustainability (GSS) bonds?
- Green bonds � Finance climate and environmental projects.; 2. Social bonds � Fund social projects like healthcare, housing, and education.; 3. Sustainability bonds � Combine green and social objectives.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 2. Why were sovereign issuers late to enter the GSS bond market?
Sovereign issuers faced legal and institutional constraints, particularly regarding the fungibility of government debt, which conflicts with the earmarking of proceeds required by green bond frameworks.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 3. What is the main challenge for sovereign green bonds?
The fungibility of government funds means that many countries cannot legally restrict proceeds to specific environmental projects, making it difficult to ensure direct green investment.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 4. How has sovereign green bond issuance influenced private markets?
Sovereign green bonds set higher reporting and verification standards, encouraging private issuers to adopt more rigorous sustainability disclosure practices.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 5. What is the key innovation of sustainability-linked bonds (SLBs) compared to green bonds?
SLBs do not require proceeds to be tied to specific projects but impose financial penalties if sustainability targets are not met, providing issuers with more flexibility.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 6. Why did the issuance of social bonds surge during the COVID-19 pandemic?
Governments used social bonds to fund pandemic-related expenditures such as healthcare services, economic support programs, and social welfare initiatives.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 7. Which countries have integrated all three types of GSS bonds into their financing strategies?
Countries like Chile and Mexico have issued green, social, and sustainability bonds as part of their broader national financing strategies.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 8. What was the significance of Chile�s 2022 sovereign sustainability-linked bond (SLB)?
Chile�s SLB was the first issued by a sovereign and signaled a potential shift towards flexible sustainability financing that allows unrestricted fund allocation while enforcing environmental commitments.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 9. Why do sovereign GSS bonds typically have long maturities?
They often exceed 15 years to align with long-term sustainability goals, making them a strategic tool for climate financing.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 10. What role do sovereign issuers play in promoting sustainability standards?
Sovereign issuers act as benchmark issuers, influencing private sector adoption of green finance and improving overall market transparency.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 11. How do sovereign green bonds address fungibility concerns?
Some sovereign frameworks require a minimum percentage of proceeds to be allocated to new projects rather than refinancing old expenditures.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 12. What are the limitations of sustainability-linked bonds (SLBs)?
SLBs offer flexibility but rely on well-designed penalties and clear sustainability targets to be effective. Weak penalties reduce their credibility.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 13. Why is greenwashing a concern in the sovereign GSS bond market?
Without strong external verification mechanisms, governments might misuse proceeds or fail to meet sustainability targets, undermining investor confidence.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 14. What factors will determine the future growth of sovereign GSS bonds?
Regulatory improvements, investor confidence, and integration with broader fiscal policies will shape the market�s development.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 15. What reporting and verification standards do sovereign GSS bonds typically require?
Sovereign green bond frameworks impose higher disclosure standards, requiring impact assessments and third-party verification.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 16. How do SLBs differ from traditional green bonds in terms of accountability?
SLBs impose financial penalties if sustainability targets are missed, whereas green bonds focus on the earmarked use of proceeds without direct consequences for failing to meet environmental goals.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 17. Why do investors remain skeptical about sovereign SLBs?
Penalties for missing sustainability targets are often too weak to create meaningful incentives for compliance.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 18. What is the impact of sovereign GSS bonds on financial markets?
Sovereign issuance increases market credibility; encourages corporate participation; and improves transparency in sustainable finance.
Sovereign & Sustainable Bonds (Cheng et al., 2022): 19. What role do international organizations play in promoting sovereign GSS bonds?
Institutions like the European Union set frameworks for sovereign green bond issuance; influencing global adoption and market standards.
Equity Valuation Using Multiples (Liu et al., 2002): 1. What is the main premise of using multiples for equity valuation?
The basic premise is that companies with similar fundamentals should trade at similar price multiples (e.g., P/E, P/B, EV/EBITDA); by comparing a firm�s multiple to peers, analysts infer whether it�s over- or undervalued.
Equity Valuation Using Multiples (Liu et al., 2002): 2. Why do analysts often prefer multiples over discounted cash flow (DCF) methods?
Multiples are quicker, rely on fewer assumptions about the future, and reflect market sentiment through peer pricing, while DCF requires projections that are prone to error.
Equity Valuation Using Multiples (Liu et al., 2002): 3. What are some commonly used multiples in equity valuation?
Price-to-earnings (P/E), price-to-book (P/B), enterprise value-to-EBITDA (EV/EBITDA), and price-to-sales (P/S) are among the most frequently used.
Equity Valuation Using Multiples (Liu et al., 2002): 4. Why might the P/E ratio be misleading for some companies?
Firms with volatile earnings, negative earnings, or significant one-off items can distort the P/E ratio, making it less reliable.
Equity Valuation Using Multiples (Liu et al., 2002): 5. How do growth prospects affect multiples like P/E and EV/EBITDA?
Higher growth expectations tend to inflate multiples, as investors are willing to pay more for anticipated future earnings or cash flows.
Equity Valuation Using Multiples (Liu et al., 2002): 6. Which multiple tends to work best for high-growth firms, and why?
EV/EBITDA is often more reliable for high-growth firms, as it adjusts for capital structure differences, taxes, and depreciation, capturing operating performance more consistently.
Equity Valuation Using Multiples (Liu et al., 2002): 7. How does industry comparability factor into multiple selection?
Each industry may favor different multiples; for instance, tech companies often rely on EV/EBITDA, while banks use P/B due to asset-driven balance sheets. Though the paper finds that future earnings is the best estimator for valuation, irrelevant of industry
Equity Valuation Using Multiples (Liu et al., 2002): 8. What are the limitations of simple multiple-based valuations?
They assume peer groups are truly comparable; ignore company-specific factors (capital structure, business model); and can be skewed by short-term market sentiment.
Equity Valuation Using Multiples (Liu et al., 2002): 9. Why might price-to-sales (P/S) multiples be useful for unprofitable companies?
Firms with negative earnings still have revenue streams, making P/S one of the few feasible valuation metrics when earnings-based multiples are negative or undefined.
Equity Valuation Using Multiples (Liu et al., 2002): 10. What are forward-looking multiples, and how do they differ from trailing multiples?
Forward multiples use analysts� forecasts of future earnings (e.g., next year�s EPS), while trailing multiples use historical data; The paper shows that forward multiples are the best for valuation
Equity Valuation Using Multiples (Liu et al., 2002): 11. How do analysts typically select peer groups for multiple-based valuation?
They consider factors like industry classification, revenue sources, capital structure, and growth rates to match companies with similar fundamentals.
Equity Valuation Using Multiples (Liu et al., 2002): 12. What is the role of regression-based approaches in multiple valuation?
By regressing multiples against firm fundamentals (e.g., ROE, growth), analysts can derive �fitted� multiples, reducing the noise from naive peer averages.
Equity Valuation Using Multiples (Liu et al., 2002): 13. Why might enterprise value (EV) be preferred over market capitalization for certain multiples?
EV accounts for both debt and equity, providing a more comprehensive view of a firm�s capital structure and overall valuation. Though the paper finds that Equity rather than EV gives more accurate predictions
Equity Valuation Using Multiples (Liu et al., 2002): 14. How can analysts reduce the subjectivity in multiple selection?
Using multiple metrics (P/E, EV/EBITDA, etc.) and conducting sensitivity analyses across various scenarios can mitigate biases.
Equity Valuation Using Multiples (Liu et al., 2002): 15. What are the most common pitfalls in using multiples for equity valuation?
Overreliance on a single multiple; misidentifying peer groups; ignoring unique company circumstances; failing to adjust for nonrecurring items.
Equity Valuation Using Multiples (Liu et al., 2002): 16. What does empirical research suggest about the accuracy of multiples relative to DCF methods?
Studies find that multiples often perform comparably to DCF in predicting actual market prices, though both approaches have limitations. And may fare better even though they are simpler. Half of the sample’s pricing errors are within 15% of actual stock prices when using forward earnings multiples.
Equity Valuation Using Multiples (Liu et al., 2002): 17. Why might multiples be less useful during periods of extreme market volatility?
During turbulent times, peer valuations can be erratic, and fundamentals may shift quickly, undermining the reliability of multiples.
Equity Valuation Using Multiples (Liu et al., 2002): 18. Why do some practitioners advocate combining multiples with discounted cash flow (DCF)?
Blending multiples-based valuation with a DCF framework can balance market-based insights with fundamentals-driven analysis, improving reliability.
Equity Valuation Using Multiples (Liu et al., 2002): 19. How have technology and data advancements impacted multiples-based valuation?
Improved data availability, real-time updates, and machine learning methods enable more dynamic and nuanced peer group analyses.
Equity Valuation Using Multiples (Liu et al., 2002): 20. What is the key takeaway for analysts using multiples to value firms?
Multiples offer simplicity and market grounding but must be applied carefully, considering company-specific factors, peer comparability, and broader market conditions. Forward Earnings Multiples Perform Best Valuation multiples based on analysts’ forecasted earnings (1-year, 2-year, and 3-year out EPS forecasts) are the most accurate. Ranking of Multiples by Performance Forward earnings measures (e.g., next year’s expected earnings) perform best. Historical earnings measures (e.g., last year’s earnings) rank second. Cash flow and book value measures rank third, tied in accuracy. Sales multiples perform the worst.
