Juhani Linnainmaa

Juhani T. Linnainmaa

Associate Professor of Finance

NBER Faculty Research Fellow

University of Chicago Booth School of Business

5807 South Woodlawn Avenue

Chicago, IL 60637

 

Email: jlinnain [at] chicagobooth.edu

Tel: +1 (773) 834 3176

Curriculum Vitae

 

Publications

  1. Linnainmaa, Juhani, 2010, Do Limit Orders Alter Inferences about Investor Performance and Behavior? Journal of Finance 65(4), 1473-1506. [PDF] [Internet Appendix] [SSRN link]

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.

  1. Grinblatt, Mark and Juhani Linnainmaa, 2011, Jensen's Inequality, Parameter Uncertainty, and Multi-period Investment, Review of Asset Pricing Studies 1(1), 1-34. [PDF] [SSRN link]

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.

  1. Linnainmaa, Juhani, 2011, Why Do (Some) Households Trade So Much? Review of Financial Studies 24(5), 1630-1666. [PDF] [SSRN link]

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.

  1. Grinblatt, Mark, Matti Keloharju, and Juhani Linnainmaa, 2011, IQ and Stock Market Participation, Journal of Finance 66(6), 2121-2164. [PDF] [Internet Appendix] [SSRN link]

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.

  1. Grinblatt, Mark, Matti Keloharju, and Juhani Linnainmaa, 2012, IQ, Trading Behavior, and Performance, Journal of Financial Economics 104(2), 339-362. [PDF] [SSRN link]

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.

  1. Linnainmaa, Juhani and Gideon Saar, 2012, Lack of Anonymity and the Inference from Order Flow, Review of Financial Studies 25(5), 1414-1456. [PDF] [SSRN link]

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.

  1. Keloharju, Matti, Samuli Knüpfer, and Juhani Linnainmaa, 2012, Do Investors Buy What They Know? Product Market Choices and Investment Decisions, Review of Financial Studies 25(10), 2921-2958 (lead article). [PDF] [SSRN link]

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.

  1. Linnainmaa, Juhani, 2013, Reverse Survivorship Bias, Journal of Finance 68(3), 789-813 (lead article). [PDF] [SSRN link] [Internet Appendix]

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).

  1. Gerakos, Joseph and Juhani Linnainmaa, 2014, Market Reactions to Tangible and Intangible Information Revisited, Critical Finance Review, forthcoming. [SSRN link]

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.

  1. Ball, Ray, Joseph Gerakos, Juhani Linnainmaa, and Valeri Nikolaev, 2014, Deflating Profitability, Journal of Financial Economics, forthcoming. [SSRN link]

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)

 

Working Papers

  1. Retail Financial Advice: Does One Size Fit All? (with Stephen Foerster, Alessandro Previtero, and Brian Melzer, November 2014, revise and resubmit at Journal of Finance)

Using unique data on Canadian households, we assess the impact of financial advisors on their clients' portfolios. We find that advisors induce their clients to take more risk, thereby raising expected returns. On the other hand, we find limited evidence of customization: advisors direct clients into similar portfolios independent of their clients' risk preferences and stage in the life cycle. An advisor's own portfolio is a good predictor of the client's portfolio even after controlling for the client's characteristics. This one-size-fits-all advice does not come cheap. The average client pays more than 2.7% each year in fees and thus gives up all of the equity premium gained through increased risk-taking.

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), 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)

  1. Common Factors in Return Seasonalities (with Matti Keloharju and Peter Nyberg, December 2014, revise and resubmit at Journal of Finance) [Internet Appendix]

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.

Award: AQR Insight Award Finalist 2015 (final in April 2015)

Note: If you have difficulties replicating the result on long-lasting seasonalities in daily returns, you are probably not taking into account market closures due to U.S. holidays. As the lag k grows, the likelihood that the market is closed during the week increases. As a consequence, a regression at lag k=200, for example, is very unlikely a Monday-on-Monday (or Tuesday-on-Tuesday, and so forth) regression. An intuitive way to resolve this issue is to "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 variation in average returns that is linked to calendar time and not trading time.

  1. Asset Manager Funds (with Joseph Gerakos and Adair Morse, December 2014)

Using data on 44,000 asset manager funds representing $35 trillion in AUM, we document that investors pay $296 billion in annual fees to asset managers, comprising the second largest securities investing component of Philippon's (2014) cost of financial intermediation. We find positive gross (and net) alphas in market models. The average non-intermediated dollar thus loses to the market even before fees. The use of strategy-specific benchmarks does not materially erode alphas, and tracking errors remain large. When we estimate a tactical beta model based on Sharpe (1992), alphas go to zero and tracking errors decrease. These results suggest that asset manager funds provide investors with tactic factor loadings ("smart beta" or "tactical beta") and low tracking errors. In fund-specific fee and flow data, we show that institutional investors both pay higher fees to asset managers for providing factor exposures and tilt their flows toward "good" factor exposures.

  1. Dissecting Factors (with Joseph Gerakos, January 2014)

Size and book-to-market split into two components, one correlated with changes in market value and the other with everything else. Only the market value components have positive risk premia. Average returns are flat across portfolios based on the other parts, but their loadings on SMB and HML differ significantly. This mismatch between covariances and average returns generates significant alphas for high-minus-low portfolios. The estimated fraction of skilled fund managers increases from 4% to 18% when we control for the other parts. Also, the other part of value drives the negative correlation between gross profitability and value.

Award: Second prize in the academic competition at the Chicago Quantitative Alliance (CQA) Fall 2012 Conference.

  1. Reading the tea leaves: Model uncertainty, robust forecasts, and the autocorrelation of forecast errors (with Walter Torous and James Yae, January 2015, revise and resubmit at Journal of Financial Economics, a new version coming soon)

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, it is rational for analysts to issue forecasts that are robust to model misspecification. 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. Our results reject many other potential explanations for the observed autocorrelation of analysts' forecast errors. These errors are not positively autocorrelated, for example, because analysts are overconfident about their own signals or because they herd with each other. Our model of robust forecasting applies not only to analysts' forecasts but to all model-based forecasts.

  1. Learning and Stock Market Participation (November 2005)

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.

  1. The Individual Day Trader (November 2005)

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.

 

Other Publications

  1. Linnainmaa, Juhani, 2009, Review of “The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. By Andrew W. Lo and Jasmina Hasanhodzic. New York: Bloomberg Press, 2009.” Journal of Economic Literature 47(4), 1141-1144. [PDF]