Corporate Finance - Lecture 3.pdf

University of St.Gallen (HSG) 7,107 Corporate Finance – Lecture 3: Efficient Markets and Investment Performance Marc Arnold Objectives IMPROVE YOUR INVESTMENT PERFORMANCE THROUGH MARKET EFFICIENCY Understand the implications of the market efficiency and how to leverage them to improve your own i...

University of St.Gallen (HSG) 7,107 Corporate Finance – Lecture 3: Efficient Markets and Investment Performance Marc Arnold Objectives IMPROVE YOUR INVESTMENT PERFORMANCE THROUGH MARKET EFFICIENCY Understand the implications of the market efficiency and how to leverage them to improve your own investment performance. 1 UNDERSTAND HOW MARKET PARTICIPANTS PROCESS INFORMATION AND HOW INFORMATION IS ‘PRICED’ Learn about the mechanisms through which market participants interpret and integrate information into asset prices. 2 UNDERSTAND THE IMPLICATIONS OF THE EFFICIENT MARKET HYPOTHESIS (EMH) Explore the consequences and significance of the Efficient Market Hypothesis in financial theory and practice. 3 KNOW HOW TO TEST FOR THE DIFFERENT FORMS OF MARKET EFFICIENCY Acquire knowledge on the methodologies and techniques used to evaluate various forms of market efficiency. 4 2 Efficient Markets Definitions Illustration of the Efficient Market Hypothesis (EMH) Larger circle Weak form of market efficiency → More information Security prices only reflect information available from past security contained in prices trading (past prices, returns, trading volume, short interest etc.)​ All information Semi-strong form of market efficiency All publicly available Security prices reflect all publicly available information (fundamental information data such as accounting data, patents possessed, CEO skills, analyst forecasts etc.)​ Strong form of market efficiency Information from Security prices reflect all available information (also information that is past trading not publicly available such as future product launches and proprietary R&D development stages etc.) Strong form includes semi-strong form, which again includes weak form​ 3 Why Are Financial Markets Efficient?​ Traders can arbitrage inefficiencies very fast and at low cost 03 02 04 Professional traders obtain information very Competition fast and at the same time​ Drivers Numerous investors in the identical market ​ 01 05 Arbitrage Condition: Active, liquid trading 4 Excursus: Arbitrage Asset’s Asset’s Asset’s Asset’s Value Asset’s Value Asset’s Value Trading Price Trading Price Trading Price USD 10 Investors Investors USD 100 USD 100 USD 100 USD 100 USD 90 USD 90 01 02 03 Undervalued stock Investors buy the underpriced stock The price climbs until it aligns with the asset's (profit from arbitrage) value 5 How to Test the Efficiency of Markets? Problem Direct test challenging because “true” asset value not observable​ INDIRECT TESTS The following observations do not contradict the EMH Two factors often explain apparent market inefficiencies The CAPM hardly explains real returns​ Prices react slowly in certain markets​ 01 Risk Some new securities are mispriced​ Some firms or persons earn huge abnormal trading gains​ 02 Friction Market crashes New information is “when” the market crashes and by how much​ Historical data patterns 6 Risk Factors Example RISK FACTORS The Small-Firm-Effect The Book-to-Market Effect Companies with lower market capitalizations outperform Companies with higher BTM ratios outperform companies with larger market capitalizations companies with smaller BTM ratios​ Average Annual Return for 10 Size-Based Portfolios, 1926 – 2018 Average Return as a Function of Book-to-Market Ratio, 1926 – 2018 20 20 Annual Return (%) Annual Return (%) 15 15 10 10 5 5 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Size Decile (1 = Small; 10 = Large) Book-to-Market Decile (1 = Low; 10 = High) Source: Ken French’s data library (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html) 7 Arguments Against Market Efficiency PROCESSING FRICTIONS PROBABILITY DISTORTIONS NOT-PROFIT ORIENTED Information is not free, and dissemination takes time Probability distributions of stock returns are changing Investors have perceptual Transaction costs prevent distortions Some market participants, investor reaction → One possible reason are such as central banks, are not economic cycles Loss aversion profit-oriented Information is only processed when it is worthwhile doing so Overconfidence Arbitrage is limited Framing Some securities are illiquid = Behavioral finance 8 Tests of the Efficient Market Hypothesis (EMH) Unlagged vs. One-Month Lagged S&P 500 Returns (1950 – 2022) 20% Relationship between monthly returns and previous month returns 10% 𝑅2 is almost zero → The previous month's return has no explanatory power for the return of the following month Unlagged Returns The regression line has a slope of 0.026, which is statistically not 0% significantly different from zero -10% -20% y = 0.0060775 + 0.0258357x 𝑅 2 = 0.0007 -30% -30% -20% -10% 0% 10% 20% One-Month Lagged Returns Weak-Form Semi-Strong-Form Strong-Form 9 Tests of the Efficient Market Hypothesis (EMH) EUR 1.0430 1.0420 CHFEUR 1.0410 1.0400 1.0392 15:13 16:13 17:13 18:13 19:13 20:13 21:13 Weak-Form Semi-Strong-Form Strong-Form Source: TradingView (September 14, 2022) 10 Discussion Discuss reasons that speak for and against the chances of success of a technical (chart) analysis. (6 minutes) Pro: Con: 11 Warren Buffet on Technical Analyses “… I charted them, I read books on technical analysis, […] I read everything, and I thought […] the important thing was to predict what a stock would do and predict the stock market. […] and I realized, I was doing it exactly the wrong way.” Weak-Form Semi-Strong-Form Strong-Form Source: YouTube 12 Tests of the Efficient Market Hypothesis (EMH) Buy the Sells Do analysts know more than 40% the average investor? Stocks with “sell” ratings from 30% analysts often perform better than stocks with “buy” or “hold” ratings 20% Note: 10% Figure is based on stocks in the S&P 500 followed by at least five analysts. Performance 0% measured over one year. 1994-1995 1995-1996 1996-1997 1997-1998 1998-1999 1999-2000 2000-2001 2001-2002 2002-2003 2003-2004 Rebalanced monthly -10% Stock with the most "sell" ratings Stock with "buy" or "hold" ratings only Weak-Form Semi-Strong-Form Strong-Form Source: Zacks Investment Research 13 Tests of the Efficient Market Hypothesis (EMH) Fraction of Equity Funds That Beat The Market It seems that fund managers 100% are not able to beat the market​ 80% 60% 40% 20% 0% Stocks Worldwide 1 Year 3 Years 5 Years 10 Years Weak-Form Semi-Strong-Form Strong-Form Source: SPIVA Europe Scorecard (2015) 14 Tests of the Efficient Market Hypothesis (EMH) Carhart, M. (2012): On Persistence in Mutual Fund Performance. Journal of Finance. Hardly evidence of investment skill Best performing funds fail to outperform in subsequent periods (rather luck than skill) Persistence in very poor fund performance Expenses are key driver of low fund performance “The results do not support the existence of skilled or informed mutual fund portfolio managers.” Source: Carhart (1997) 15 Tests of the Efficient Market Hypothesis (EMH) Average Returns During Growth And Recession Periods (1970 – 1997) Division between growth 40% and recession based on NBER data 30% www.nber.org/cycles.html 20% Share performance during 10% booms is much better than during recessions 0% Problem: We only know Australia Canada USA UK Finland Germany EAFE Belgium Denmark Netherlands New Zealand Hong Kong Singapore Sweden France Ireland Spain Italy Japan AC World Austria Norway Switzerland -10% ex-ante whether we are in boom or in recession -20% -30% -40% -50% Expansion Contraction Weak-Form Semi-Strong-Form Strong-Form Source: SPIVA Europe Scorecard (2015) 16 Active Fund Management July 27, 2022 MANAGER VS MACHINE REPORT Less than a third of actively managed equity funds have PASSIVE FUNDS delivered a market beating Active funds often underperform passive ones despite higher fees. performance in the first half of 2022 Fund companies often delete badly performing funds, so it looks like their (remaining) funds perform great on their home pages. High-profile managers attract attention, but their consistent outperformance is questionable. Robin Powell, founder and editor of the Evidence-Based Investor website, suggests the industry favors active funds for profit. It does not make sense to set up a fund if you discover a market inefficiency as this would quickly delete the pattern. Active funds still make sense for reasons of access to specific topics/investments, sustainability focus, liquidity, capital protection, etc. Weak-Form Semi-Strong-Form Strong-Form Source: Financial Times (https://www.ft.com/content/c2e9abd0-0edd-4805-b1ab-d55b661c24de) 17 Tests of the Efficient Market Hypothesis (EMH) Does Insider Information Leak Into Prices? (1) Cumulative abnormal returns before takeover (1) (2) attempts: target companies Cumulative Abnormal Return (%) Cumulative Abnormal Return (%) 32 0.50 (2) Stock price reaction to CNBC reports. The figure 24 shows the reaction of the 0.00 stock prices to on-air stock 16 reports during the 8 -0.50 “Midday Call” segment on CNBC. The chart plots 0 -1.00 cumulative returns beginning 15 minutes -8 Midday-Positive Midday-Negative before the stock report -1.50 -16 -135 -105 -75 -45 -15 0 15 -15 -10 -5 0 5 10 15 Days Relative to Announcement Date Minutes Relative to Mention Weak-Form Semi-Strong-Form Strong-Form Source: Busse & Green (2002), Keown & Pinkerton (1981) 18 Tests of the Efficient Market Hypothesis (EMH) One way to test for the strong form market efficiency is to estimate the returns of company insiders (managers) They do possess material non-public information​ Studies analyzing illegal insider trading usually find that insider managers earn substantial returns on their trades Unprofitable Under the strong-form EMH, their trades should Profitable be unprofitable, since private, non-public information is also reflected in market prices​ Weak-Form Semi-Strong-Form Strong-Form 19 Tests of the Efficient Market Hypothesis (EMH) Example INSIDER TRADING (1986) Ivan Boesky An American stock trader, gained notoriety for his central role in a mid-1980s insider trading scandal. He pled guilty, paid a record USD IVAN BOESKY 100 million fine, served three years in prison, and cooperated as an Incredible ability to "anticipate" corporate takeovers informant. When the takeover bids actually came true, the companies' prices rose, and Boesky sold his stake at a significant profit In reality, Boesky got inside information from the investment bankers in charge of the acquisitions 3 years in prison and USD 100 million fine → Insider trading works but is mostly illegal Weak-Form Semi-Strong-Form Strong-Form 20 Tests of the Efficient Market Hypothesis (EMH) Do Brokers Share Their Information With Clients? Profitability of Predatory Trades The figure plots the profits of the managers that are best 60 clients of the aware (green solid line with circles) and Return on Capital (bps) unaware (red dashed line with 40 squares) brokers during the fire sale event 20 0 Best clients of aware brokers Best clients of unaware brokers -20 0 5 10 15 20 Event Time Weak-Form Semi-Strong-Form Strong-Form 21 Tests of the Efficient Market Hypothesis (EMH) SENATOR BURR CHAIRMAN OF THE INTELLIGENCE COMMITTEE He regularly receives briefings on threats to the United States, including the coronavirus He is also a member of the Senate Health Committee Mid-February Two Weeks Later Shortly Thereafter Burr sold blocks of shares Speech at the Capital Hill Club The stock market At that point, the impact of the Warned that the virus could soon collapsed COVID-19 pandemic was heavily cause a major disruption in the United downplayed by President Trump States: "It's probably more comparable and the Republicans to the pandemic of 1918." 2020 February Weak-Form Semi-Strong-Form Strong-Form 22 Implications of the Efficient Market Hypothesis LEGAL IMPLICATIONS Adherence to legal guidelines and regulations is crucial Insider trading can result in severe consequences, including fines and imprisonment DECISION-MAKING Don’t waste time and resources to analyze public information to identify undervalued stocks! Can you let a monkey decide where to invest by throwing darts on the stock price page? → No, you still need to set up an optimal portfolio (diversification, risk-return optimization, needs, preferences, risk-profile…) 23 Diversification Visualization: Systematic and Unsystematic Risks Example: SMI Stocks, 6-Month Horizon 35% 𝜎 30% 25% Portfolio Risk 20% 15% 10% 5% Unsystematic Risk 0% + Novartis + UBS + Sika + Geberit Logitech + Alcon + Swisscom + Lonza + Sonova + Swiss Re + KuehneNagel + Givaudan + Holcim + ABB + Richmont + Roche + Nestle + Swiss Life + PartnersGroup + Zurich Insurance Systematic Risk 𝑛 Diversification mitigates unsystematic risk associated with individual securities or sectors. Even if prices are fair and public information is reflected in security prices, diversification is important. Source: SIX (August 11, 2024) 24 Risk and Expected Return Main Intuition of Pricing Models in Finance 03 More risky assets should generate higher expected returns 02 Prices of more risky assets should be lower 01 Investors prefer low-risk assets compared to high-risk assets → INTUITION ONLY APPLIES TO SYSTEMATIC RISK AS UNSYSTEMATIC RISK CAN BE DIVERSIFIED AWAY! 25 Generate Superior Investment Performance FROM DISCOURAGING TO ENCOURAGING: What does work? Instead of: What does not work? Staying ahead: Generating new information first Bearing systematic risks generates expected returns 26 Generate Superior Investment Performance PROFITABLE STRATEGIES Staying ahead: Generating new information first Yermack (2011) The Michelle Markup: The First Lady’s Impact on Stock Prices of Fashion Companies Fashion company stock prices react when Michelle Obama appears with a designer outfit 12 – 35% stock price increase after public appearance of Michelle Obama in respective dress during 2009 trip to Europe Average effect: 1.0% share price increase in the two days after performance 1.7% share price increase in the week after performance Effect depends on the type of appearance: Only about half as large for "smaller" appearances with less media coverage No "reversal" In later appearances, the price increase began two days before the public appearance Rumors of information leak in the White House 27 Discussion What does this example tell you about market efficiency? (5 minutes) 28 Generate Superior Investment Performance PROFITABLE STRATEGIES Staying ahead: Generating new information first The Case of the Chinese Starbucks Speculator collected 11,560 hours of video footage and over 25,000 payment receipts at Luckin Coffee Shops​ Official sales figures massively inflated (up to 88% in 2019 Q4)​ Fewer customers and more discounts than in the annual report​ January 2020: Simple PDF with facts shared on social media​ Extreme stock slump (from USD 50.00 to USD 1.40)​ Speculator went massively "short"​ in Lucking Coffee before publication 29 Generate Superior Investment Performance What do these two examples have in common? 30 Generate Superior Investment Performance PROFITABLE STRATEGIES Bearing systematic risks generates expected returns Systematic Risk Factors Literature describes large number of systematic risk factors (factor zoo) besides traditional “size” and “market- to-book” factors Factor zoo has grown significantly over the last decades: Many systematic risk factors seem to drive returns Utilizing a comprehensive set of 153 U.S. equity factors, study finds that a set of 10 to 20 factors spans the entire factor zoo Examples of important factors: Operating profits-to-book assets, sales growth, debt-to-market… Source: Swade et al. (2023) 31 Generate Superior Investment Performance PROFITABLE STRATEGIES Bearing systematic risks generates expected returns Machine Learning in Empirical Asset Pricing Machine learning (ML) methods can help improve our understanding of asset returns Different methods (regression trees, neural networks) deliver different results Examples of important factors: Size, liquidity, momentum Source: Gu et al. (2020) 32 Optimal Investment: Exploring the Tangency Portfolio Efficient Frontier Visualization The efficient frontier is the set of optimal portfolios that offer the highest expected return for a defined level of risk or the lowest risk for a given level of expected return 𝐸(𝑟) Tangency Portfolio Efficient Frontier A tangency portfolio is a portfolio that lies at the point where the efficient frontier is tangent to the highest possible capital market line in the risk-return space The tangency portfolio consists of risky assets only and is the 𝑟𝑓 portfolio that maximizes the Sharpe ratio over all risky portfolios Formula 𝐶𝑂𝑉 −1 (𝜇𝐴 −𝑟𝑓 1) Tangency portfolio 𝜆𝑃 = 1𝐶𝑂𝑉 −1 (𝜇𝐴 −𝑟𝑓 1) 𝜎 Source: Munk (2013) 33 Implications for Your (Online) Trading Behavior PURSUE MARKET-BEATING STRATEGIES OTHERWISE, CONSIDER ONLY when armed with novel information Restricting Trading Activity Research indicates that frequent traders tend to Novel information is often unrelated to financial expertise underperform by more than 6% annually (be creative) (Barber and Odean, 2000) Generating such novel information is exceedingly challenging, and any advantage gained is typically fleeting Embracing Diversification Mitigate unsystematic risk by spreading investments across various assets Bearing Systematic Risks Employ the top factors relevant to your geographical portfolio exposure to bear systematic risks Minimizing Investment Costs Opt for passive investment strategies as they consistently outshine active trading in terms of performance 34 Investor Attention Does attention influence trading behavior? Barber and Odean (2008): All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors​ Buy-Sell Imbalances by Investor Type for Stocks Sorted on the Current Day’s News Large discount brokerage Large retail brokerage Small discount brokerage Momentum managers Value managers Diversified managers Number Value Number Value Number Value Number Value Number Value Number Value Partition imbalance imbalance imbalance imbalance imbalance imbalance imbalance imbalance imbalance imbalance imbalance imbalance Panel A: All Days News 9.35 0.07 16.17 -2.36 6.76 1.87 13.38 14.00 6.36 -0.24 6.21 2.26 (0.72) (0.86) (1.29) (1.32) (0.48) (0.72) (1.33) (1.71) (1.59) (2.05) (1.11) (1.50) No News 2.70 -5.62 -1.84 -14.59 -0.66 -4.87 12.20 10.43 10.96 3.62 7.26 1.24 (0.43) (0.63) (0.87) (0.87) (0.58) (1.23) (1.11) (1.16) (1.37) (1.49) (0.97) (0.84) Retail investors tend to buy stocks that catch attention. Attention, however, does not affect investors’ selling behavior. → Professional investors do not show this behavior!​ Source: Arnold et al. (2022), Davydov et al. (2019) 35 Investor Attention Study: Arnold, Pelster, Subrahmanyam (2022) Davydov, Khrashchevskyi and Peltomäki (2019) Attention Triggers and Investors’ Risk-Taking Investor Attention Allocation and Portfolio Performance: What Trading data from a large online broker​ Information Does It Pay to Pay Attention To Broker sends push messages to investors​ Three types of information:​ Attention:​ Analytical: Basic infos, statistics, recommendations, etc.​ Increases trading activity​ Technical: Past prices, patterns, volume, etc.​ Stimulates willingness to take​ risk Education: Concepts, descriptions, theories, etc.​ More attention towards education improves performance​ More attention towards analytical and technical information deteriorates performance Source: Arnold et al. (2022), Davydov et al. (2019) 36 Investor Attention Fang and Peress (2009) Peress and Schmidt (2019) Media Coverage and the Cross-Section of Stock Returns Glued to the TV: Distracted Noise Traders and Stock Market Liquidity​ Number of newspaper articles on a certain company Distraction Days: Days when attention is diverted from trading Stocks that do not appear in the media have a higher return Liquidity and volatility decrease on these days than stocks that are discussed in the media The effect is stronger for small firm stocks, with little analyst coverage, fewer institutional investors, and higher idiosyncratic volatility Reason: Unknown companies must offer higher returns Source: Fang & Peress (2009), Press & Schmidt (2019) 37 Investor Attention Da, Engelberg and Gao (2015) Construction of the FEARS Index The Sum of All FEARS: Investor Sentiment and Asset Prices 1. Google publishes search volume by term in “Google Trends” (SVI) Investor sentiment influences share prices 2. Identification of sentiment-relevant terms from encyclopedias in How to measure investor sentiment? finance and text analysis: crisis, gold, inflation, recession, → Internet search behavior on Google Trends bankruptcy, etc. 3. Download top 10 search terms from Google Trends that are searched in connection with these terms 4. Cleanup 5. Creation of the FEARS Index Google Trends example: Search term “Kamala Harris” in the past 90 days 100 75 50 25 May 20 Jun 15 Jul 11 Aug 6 Source: Da et al. (2015), Google Trends (August 20, 2024) 38 FEARS Index POSITIVE CORRELATION between FEARS and volatility on the same day 03 REVERSAL REVERSAL over the following two days, i.e., sentiment seems to cause 02 04 over the following two days temporary mispricing NEGATIVE CORRELATION MONEY FLOWS between FEARS and stock returns on the same day 01 05 from equity funds into bond funds 39 Unveiling Market Mysteries: An Analysis of Anomalies and Their Impact on Returns Cakici, N., Fieberg, C., Metko, D. and Zaremba, A. (2024): Do anomalies really predict market returns? New data and new evidence. Review of Finance. STATUS QUO RESEARCH QUESTION METHODOLOGY RESULTS Literature describes numerous Can market return predictability Machine learning models „Our findings yield a simple, yet equity anomalies that should by equity anomalies be unequivocal conclusion: predict market returns comprehensively tested? Equity anomalies cannot predict market returns.” Source: Cakici et al. (2024) 40 Trading Dynamics: Insights from Institutional Investors Akepanidtaworn, K., Di Mascio, R. , Imas, A. and Schmidt, L. (2023): Selling fast and buying slow: Heuristics and Trading Performance of Institutional Investors. Journal of Finance. So far: Majority of evidence on trading performance comes from unsophisticated traders such as retail 01 investors Problem: “Comparatively little is known about the decision making of market experts. Whether such experts 02 are prone to behavioral biases and, if so, the extent to which these biases affect performance…” This study: “…examines the decisions of sophisticated market participants—experienced institutional 03 portfolio managers…” 04 Results: Evidence of outperformance (pre-cost) Some skill in buying, but selling decisions underperform substantially “Evidence suggests an asymmetric allocation of cognitive resources such as attention can explain the discrepancy: We document a systematic, costly heuristic process for selling but not for buying.” Adaption of sound selling strategy could help to improve performance of institutional investors Source: Akepanidtaworn et al. (2023) 41 Discussion You set up a professional investment fund with the aim of achieving an excess return on the market. Discuss a concrete idea/approach to generate this excess return. (10 minutes) 42 Key Take-Aways FINANCIAL MARKETS ARE VERY EFFICIENT 1 SOME INEFFICIENCIES OCCUR FROM TIME TO TIME BUT THEY QUICKLY DISAPPEAR 2 IN MOST CASES, ONLY NEWLY GENERATED INFORMATION IS RELEVANT 3 INTENSE COMPETITION AND LOW FRICTIONS PREVENT PERSISTENT EXPLOITATION OF INEFFICIENCIES 4 43 Institute of Accounting, Control and Audit University of St.Gallen (ACA-HSG) Prof. Dr. Marc Arnold Tigerbergstrasse 9 CH-9000 St.Gallen [email protected] Tel.: +41 71 224 74 13 www.aca.unisg.ch