Juhani T. Linnainmaa
Associate Professor of Finance
NBER Faculty Research Fellow
5807 South Woodlawn Avenue
Chicago, IL 60637
Email: jlinnain [at] chicagobooth.edu
Tel: +1 (773) 834 3176
Individual investors lose money around earnings announcements, experience poor post-trade returns, exhibit the disposition effect, and make contrarian trades. Using simulations and trading records of all individual investors in Finland, I find that these trading patterns can be explained in large part by investors' use of limit orders. These patterns arise mechanically because limit orders are price-contingent and suffer from adverse selection. Reverse causality from behavioral biases to order choices does not appear to explain my findings. I propose a simple method for measuring a data set's susceptibility to this limit order effect.
Award: Best finance paper 2010 award from the Foundation for the Advancement of Finnish Securities Market.
Media: Featured in the Chicago Booth Capital Ideas (October 2007) and the Economist Intelligence Unit.
Classical approaches to estimation and decisions requiring estimation often are at odds. When values critical to the decision are convex or concave functions of unknown parameters, the statistician’s estimation error adjustments are the opposite of what is appropriate for the decision. We illustrate the conflict by studying multi-period investment problems. The proper application of Jensen’s inequality to the decision turns finance intuition on its head: multi-period investments with negative risk premia can be profitable, risk-averse investors can have infinite demand for risky securities, settings exist in which risk-averse investors should not diversify, and demand for mutual funds with negative alphas may be rational.
When agents can learn about their abilities as active investors, they rationally "trade to learn" even if they expect to lose from active investing. The model used to develop this insight draws conclusions that are consistent with empirical study of household trading behavior: Households' portfolios underperform passive investments; their trading intensity depends on past performance, and they begin by trading small sums of money. Using household data from Finland, the paper estimates a structural model of learning and trading. The estimated model shows that investors trade to learn even if they are pessimistic about their abilities as traders. It also demonstrates that realized returns are significantly downward-biased measures of investors' true abilities.
Award: One of five papers awarded the "best finance papers of 2011" award by the Foundation for the Advancement of Finnish Securities Market.
Stock market participation is monotonically related to IQ, controlling for wealth, income, age, and other demographic and occupational information. The high correlation between IQ, measured early in adult life, and participation, exists even among the affluent. Supplemental data from siblings, studied with an instrumental variables approach and regressions that control for family effects, demonstrate that IQ’s influence on participation extends to females and does not arise from omitted familial and non-familial variables. High-IQ investors are more likely to hold mutual funds and larger numbers of stocks, experience lower risk, and earn higher Sharpe ratios. We discuss implications for policy and finance research.
Media: Featured in Bloomberg Businessweek ("Smart Money Owns More Equities Says IQ Study of Who Buys Stocks," January 19, 2012) and New York Times ("What High-I.Q. Investors Do Differently," February 26, 2012)
Award: One of five papers awarded the "best finance papers of 2011" award by the Foundation for the Advancement of Finnish Securities Market.
We analyze whether IQ influences trading behavior, performance, and transaction costs. The analysis combines equity return, trade, and limit order book data with two decades of scores from an intelligence test administered to nearly every Finnish male of draft age. Controlling for a variety of factors, we find that high-IQ investors are less subject to the disposition effect, more aggressive about tax-loss trading, and more likely to supply liquidity when stocks experience a one-month high. High-IQ investors also exhibit superior market timing, stock-picking skill, and trade execution.
Award: Runner-up for Goldman Sachs International - Best Conference Paper Award at the 2010 European Finance Association Conference.
This paper investigates the information content of signals about the identity of investors and whether they affect price formation. We use a dataset from Finland that combines information about the identity of investors with complete order flow records. While we document that investors use multiple brokers, our study demonstrates that broker identity can nonetheless be used as a powerful signal about the identity of investors who initiate trades. This finding testifies to the existence of frictions in the economic environment that prevent investors from completely eliminating the information content of broker ID using mixed strategies. We show that the broker ID signal is important enough to affect prices: The permanent price impact of orders coming from different brokers fits the information profile of the investors associated with these brokers. Our results suggest that the market correctly processes the signal embedded in broker identity, and liquidity improvements documented in the literature when exchanges adopt a more anonymous market structure could arise because prices adjust less efficiently to order flow information when the degree of anonymity increases.
This paper shows individuals’ product market choices influence their investment decisions. Using microdata from the brokerage and automotive industries, we find a strong positive relation between customer relationship, ownership of a company, and size of the ownership stake. Investors also are more likely to purchase and less likely to sell shares of companies they frequent as customers. These effects are stronger for individuals with longer customer relationships. A merger-based natural experiment supports a causal interpretation of our results. We find weaker causality in the other direction: inheritances and gifts of stocks have only a modest effect on individuals’ patronage decisions. A setup in which customer-investors regard stocks as consumption goods, not just as investments, seems to best explain our results.
Mutual funds often disappear following poor performance. When this poor performance is partly attributable to negative idiosyncratic shocks, funds' estimated alphas understate their true alphas. This paper estimates a structural model to correct for this bias. Although most funds still have negative alphas, they are not nearly as low as those suggested by the fund-by-fund regressions. Approximately 12% of funds have net four-factor model alphas greater than 2% per year. All studies that run fund-by-fund regressions to draw inferences about the prevalence of skill among mutual fund managers are subject to reverse survivorship bias.
Media: Mutual fund research featured in Time magazine ("The Triumph of Index Funds," September 18, 2014) and "The Big Question: Are successful active managers lucky or skilled?" (Capital Ideas, August 2014).
Daniel and Titman (2006) propose that the value premium is due to investors overreacting to in- tangible information. They therefore decompose five-year changes in firms' book-to-market ratios into stock returns and a residual that is a proxy for tangible information based on accounting performance ("book returns"). Consistent with investors overreacting to intangible information, they find that only stock returns orthogonal to book returns reverse. We show that their decomposition creates a book return polluted by past book-to-market ratios, stock returns, net issuances, and dividends. Empirically, two-fifths of the variation in book returns is due to these factors. In addition, the Daniel and Titman (2006) result is sensitive to methodological choices. When we use the change in the book value of equity as a proxy for tangible information, only the tangible component of stock returns reverses. Moreover, current book-to-market subsumes the intangible return's power to predict the cross-section of average returns, which casts doubt on the argument that book-to-market forecasts returns because it is a good proxy for the intangible return.
Gross profit scaled by book value of total assets predicts the cross-section of average returns. Novy-Marx (2013) concludes that it outperforms other measures of profitability such as bottom-line net income, cash flows, and dividends. One potential explanation for the measure's predictive ability is that its numerator—gross profit—is a "cleaner" measure of economic profitability. An alternative explanation lies in the measure's deflator. We find that net income equals gross profit in predictive power when they have consistent deflators. Deflating profit by the book value of total assets results in a variable that is the product of profitability and the ratio of the market value of equity to the book value of total assets, which is priced. We then construct an alternative measure of profitability, operating profitability, which better matches current expenses with current revenue. This measure exhibits a far stronger link with expected returns than either net income or gross profit. It predicts returns as far as ten years ahead, seemingly inconsistent with irrational pricing explanations.
Media: Featured in Forbes ("The Profitability Factor Redux: Super-Duel in Space," June 2, 2014)
A firm's book-to-market ratio is a function of its expected return, expected profitability, and future book-to-market (Cohen, Polk, and Vuolteenaho 2003). We show that the part of book-to-market that correlates with past price movements is less informative about the latter two components—and therefore more informative about expected returns. Abnormal returns are earned by only those value stocks that turn value because their market values fall. However, all types of value stocks comove. Hence, the optimal exposure to value doubles when an investor uses an HML-like factor to hedge the shared risks. Our decomposition of book-to-market also shows that value outperforms growth when investors receive good news about future economic growth, and it explains why the characteristics-versus-covariances test sometimes rejects the risk-based explanation for the value premium.
Award: Second prize in the academic competition at the Chicago Quantitative Alliance (CQA) Fall 2012 Conference.
Using unique data on Canadian households, we show that financial advisors exert substantial influence over their clients' asset allocation, but provide limited customization. Advisor fixed effects explain considerably more variation in portfolio risk and home bias than a broad set of investor attributes that includes risk tolerance, stage in the lifecycle and financial sophistication. Advisor effects retain their importance even when controlling flexibly for unobserved heterogeneity through investor fixed effects. An advisor's own asset allocation strongly predicts the allocations chosen on clients' behalf. This one-size-fits-all advice does not come cheap. Advised portfolios cost 2.6% per year, or 1.6% more than lifecycle funds.
Award: 2015 CFA Society & Hillsdale Canadian Investment Research Award (announcement)
Media: Featured in Wall Street Journal ("Client Portfolios May Match Advisers’ Own Asset Allocation", December 12, 2014), Globe and Mail ("Make portfolio-building a priority to justify investment adviser fees," December 5, 2014; "Putting a number on the value of financial advice: 3%," June 14, 2015), Fiscal Times ("Expensive, one-size-fits-all advice," December 10, 2014), and Chicago Booth Capital Ideas ("Why financial advice isn't worth the fees," February 25, 2015)
A strategy that selects stocks based on their historical same-calendar-month returns earns an average return of 13% per year. We document similar return seasonalities in anomalies, commodities, international stock market indices, and at the daily frequency. The seasonalities overwhelm unconditional differences in expected returns. The correlations between different seasonality strategies are modest, suggesting that they emanate from different common factors. Our results suggest that seasonalities are not a distinct class of anomalies that requires an explanation of its own—rather, they are intertwined with other return anomalies through shared common factors. A theory that is able to explain the risks behind any common factor is thus likely able to explain a part of the seasonalities.
Note: If you have difficulties replicating the result on long-lasting seasonalities in daily returns, you are probably not accounting for market closures due to U.S. holidays. As the lag k grows, the likelihood that the days go out of sync increases; a regression at lag k=200, for example, is very unlikely a Monday-on-Monday (or Tuesday-on-Tuesday, and so forth) regression. You should "pad" the data with missing values so that there is an observation for every stock-day even when the market is closed. The fact that this alignment matters indicates that these regressions indeed pick up cross-sectional day-of-the-week seasonalities in average returns and not, e.g., autocorrelated innovations measurable in trading time.
We put forward a model in which analysts are uncertain about a firm's earnings process. Faced with the possibility of using a misspecified model, analysts issue forecasts that are robust to model misspecification. We estimate that this mechanism explains approximately 60% of the observed autocorrelation in analysts' forecast errors. The remainder of the observed autocorrelation stems from the cross-sectional variation in mean forecast errors and analysts' estimation errors of the persistence of earnings growth shocks. Consistent with our model, we find that analysts learn about some features of the earnings process but not others, and this learning reduces, but does not eliminate, the autocorrelation of forecast errors as firms age. Other potential explanations for the observed autocorrelation of analyst's forecast errors are rejected. Our model of robust forecasting applies not only to analysts' forecasts but to all model-based forecasts.
Accruals are the non-cash component of earnings. They represent adjustments made to cash flows to generate a profit measure largely unaffected by the timing of receipts and payments of cash. Prior research uncovers two anomalies: expected returns increase in profitability and decrease in accruals. We show that a cash-based operating profitability measure (that excludes accruals) outperforms other measures of profitability (that include accruals) and subsumes accruals in predicting the cross section of average returns. An investor can increase a strategy's Sharpe ratio more by adding just a cash-based operating profitability factor to the investment opportunity set than by adding both an accruals factor and a profitability factor that includes accruals.
We estimate that asset managers held $47 trillion in institutional assets and received $177 billion in annual fees from institutional investors in 2012. The funds offered by asset managers to institutional investors earn positive gross (and net) alphas in market models. Thus, the average non-intermediated dollar loses to the market even before fees. We estimate a tactical beta model based on Sharpe (1992) and find that alphas attenuate and tracking errors decrease. Using fund-specific fee data, we show that institutional investors pay higher fees for "good" factors exposures. Overall, our results suggest that asset manager funds provide institutional investors with tactical factor loadings.
This paper examines the impact of trading constraints on market participation when agents learn about their investment opportunities. The possibility of facing binding constraints in the future creates a feedback that can keep agents out of the market even if the risk premium is high. This effect arises with learning because the changes in investment opportunities are correlated with future realized outcomes: an agent will have a poor investment opportunity set precisely in those future states where her marginal utility is high. Non-participation arises also in an equilibrium model where agents resolve uncertainty about the cash flow covariance between tradable and non-tradable assets. These results suggest that learning and short-sale constraints can simultaneously generate limited participation, higher risk premium, and insignificant contemporaneous correlation between the stock return and the income of those who do not participate in the stock market. We conclude that a standard intertemporal hedging motive, generated by (i) learning about the parameters of the economy or by (ii) changes in the labor income dynamics, may account for agents' seemingly puzzling nonparticipation decisions without relying on non-standard preferences.
This paper shows that individual day traders are reluctant to close losing day trades. They even sell other stocks from their portfolios to finance the unintended purchases. This disposition to ride losers has significant long-term welfare consequences. Day traders hurt their portfolios’ performance up to −6% in three months after a holdings change. The changes in individuals’ exposure to market-wide shocks cause this underperformance: individuals systematically migrate towards small technology stocks with low B/M ratios. We find a negative relation between day trading profits and long-term performance: active day traders have the highest day trading profits but they hurt their long-term performance the most. Our results suggest that behavioral biases can push investors towards portfolios they might feel uncomfortable holding under other circumstances.