The short-termism of corporate managers and its impact on the economy have been the subject of an active debate (Terry 2015, Barton et al. 2017, Summers 2017, Hackbarth et al. 2018). Short-termism is generally viewed as the result of managers focusing on their company’s stock price rather than on long-term cashflows. Since the stock price contains forward-looking information, shareholders can optimally use it to incentivise managers (Holmstrom and Tirole 1993).

Managers’ preoccupation with the stock price can be damaging if the price is distorted away from the stock’s fundamental value (Stein 1989). The form of the distortions determines the extent of short-termism. In a forthcoming paper with Andrea Buffa (Buffa et al. 2022), we show theoretically that constraining asset managers to choose portfolios that lie close to benchmark indices generates severe distortions, which encourage short-termism. We also provide empirical evidence for the underlying mechanisms.

Tracking constraints of asset managers

Benchmarking portfolios to indices has grown dramatically over the past sixty years due to the growth of delegated portfolio management. The vast bulk of financial assets are held by giant pension, sovereign-wealth and endowment funds, with private investors mainly dependent on mutual funds.

The delegation of portfolio management involves two layers. The trustees and professional staff running the giant funds appoint a subordinate set of intermediaries, the asset managers, to undertake the security selection of specialist portfolios. The trustees are, in turn, agents of the workers, savers, and taxpayers whose wealth is held within the giant funds.

Giant fund trustees and their staff handle the uncertainty about the skill of their asset managers by imposing constraints on their investment scope. These are usually in the form of limits on how far the annual returns of the portfolio, or its composition, may diverge from that of the benchmark index for the asset class they manage.

Tracking constraints cause procyclical trading

Tracking constraints force asset managers to buy into stocks or industry sectors that are on the rise but which they had previously rejected as over-valued when they were cheaper. This gives rise to procyclical trading, amplifying the stocks’ or sectors’ price rises.

Suppose that an industry sector has a 10% weight in a benchmark index, and asset managers who view the sector as overvalued give it a 4% weight because the maximum allowed divergence is 6%. If the sector appreciates and reaches 20% weight in the index, then its weight in the dissenting managers’ portfolios reaches (approximately) 8% but must rise further to 14% so that the constraint is met.

The managers’ procyclical trading could, in principle, be offset by other managers who view the sector as undervalued and give it a greater weight than the index. The latter managers would sell the rising sector to keep the divergence from the index in check. Because they are profiting from the sector’s price appreciation, however, they are achieving their objective of adding value and are under no pressure to sell, so there is limited offset.

Procyclical trading is stronger for assets that rise in price than for assets that drop. This is because assets trading at a low price constitutes a small fraction of benchmark indices, so constraints are less tight. Returning to the previous example, suppose that asset managers who view the sector as undervalued give it 16% weight. If the sector depreciates and reaches 5% weight in the index, then its weight in managers’ portfolios reaches (approximately) 8%, so the 6% divergence constraint is still met. Thus, the tracking constraint does not force the underperforming managers to sell.

The overall impact of funds tracking closely to market indices is to amplify price trends in stocks, sectors, and entire markets. Moreover, the pressures are asymmetric and stronger on the upside than the downside. One implication of the asymmetry is persistent bias to over-valuation in the aggregate stock market.

The same chain of reactions can be found even in the absence of formal tracking constraints. The motivation is that asset managers are keen to avoid shortfalls relative to their benchmark index, lest they convey the impression of incompetence. Even private investors like to keep up with their neighbours.

Risk-return inversion

The analysis throws new light on a long-standing puzzle. The capital asset pricing model – a pillar of standard theory – predicts that risk-averse investors will price high-risk assets to deliver returns greater than those produced by low-risk assets. The security market line (CAPM line) will therefore be upward-sloping, which is also the ingrained belief of investors everywhere. Yet empirical studies of equity and some major bond markets have found either a weak or an inverted negative relationship between risk and return (Black 1972, Ang et al. 2006, Baker et al. 2011, Frazzini and Pedersen 2014).

One explanation of the puzzle (Black 1972, Frazzini and Pedersen 2014) is that when investors and funds seek to leverage their exposure to markets, they find borrowing difficult or impracticable, so they choose to do the next best thing, which is buying high-risk stocks. But this can only explain the flattening of the CAPM line and not its inversion.

Tracking constraints offer an alternative explanation. When stocks or sectors are rising, in some cases because of overvaluation, they attract procyclical buying. Procyclical buying causes volatility to be high.

Momentum trading and short-termism

Momentum investing, or trend following, is the widely adopted strategy of buying stocks or sectors on the rise without regard to fundamental value and selling those that are falling. A vast literature in finance, starting with Jegadeesh and Titman (1992), finds that momentum is a profitable strategy.

If momentum is to do well for the early buyers, there needs to be another set of buyers prepared to enter the market in the later stages of the rise. These are the benchmarkers who are obliged to be late-stage buyers of stocks or sectors that have risen strongly against them. Benchmarking provides opportunities for early momentum buyers to thrive. Consistent with the asymmetry mentioned above, Favilukis and Zhang (2021) find that momentum is more profitable within the set of overvalued stocks.

Tight tracking and momentum investing are both used to target the short-term valuation of stocks without regard to their fundamental value. Together, the twin strategies are both aberrations from what should be the overriding goal of the vast majority of funds, which is to target long-term cash flows and to invest based on the hard work of assessing underlying value. They incentivize investors and corporate managers to focus excessively on short-term price movements rather than on long-term cashflows.

Mitigating dysfunctionality

Those giant-running funds are mostly unaware of the damage their contracts with asset managers are inflicting on both market efficiency and their long-term returns. So the first step is one of education, followed by responsibility for corrective action. Many are public funds which puts them under a greater obligation to reform.

Giant funds must gather more and better information on the composition and trading of the portfolios run by their asset managers. This should identify the extent to which managers are engaged in late-stage buying of stocks, sectors and markets on the rise. So much attention is given to ways of making money, and more should be directed to the ways it is lost. Fund staff will no doubt claim that their emphasis is and always has been on the long term, but such assertions should be verified by internal and external audits based on the new information.

Policymakers and regulators should also get involved first because around half of all investing is based on passive replication of market indices that are now shown to be inefficient. Second, funds cannot claim to be acting towards meeting long-term environmental goals if their investment strategies are overtly short-termist. Paradoxically, it will take a set of theories that explain inefficiency to be the catalyst that brings markets closer to the state described by standard theory.


Ang, A, R Hodrick, Y Xing and X Zhang (2006), “The Cross-Section of Volatility and Expected Return”, Journal of Finance 61: 259-299.

Baker, M, B Bradley and J Wurgler (2011), “Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly”, Financial Analysts Journal 67: 40-54.

Barton, D, J Manyika, and S Williamson (2017), “Finally, Evidence that Managing for the Long Term Pays Off”, Harvard Business Review.

Black, F (1972), “Capital Market Equilibrium with Restricted Borrowing”, Journal of Business 45: 444-455.

Buffa, A, D Vayanos and P Woolley (2022), “Asset Management Contracts and Equilibrium Prices”, Journal of Political Economy, forthcoming.

Favilukis, J and T Zhang (2021), “One Anomaly to Explain Them All”, University of British Columbia working paper.

Frazzini, A and L Pedersen (1993), “Betting Against Beta”, Journal of Financial Economics 111: 1-25.

Hackbarth, D, A Rivera and T-Y Wong (2018), “Assessing the Optimality of Corporate Short-Termism”,, 19 December.

Holmstrom, B and J Tirole (1993), “Market Liquidity and Performance Monitoring”, Journal of Political Economy 101: 678-709.

Jegadeesh, N and S Titman (1993), “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency”, Journal of Finance 48: 65-91.

Stein, J (1989), “Efficient Capital Markets, Inefficient Firms: A Model of Myopic Corporate Behavior”, Quarterly Journal of Economics 104: 655-669.

Summers, L (2017), “Is Corporate Short-Termist Really a Problem? They Jury’s Still Out”, Harvard Business Review.

Terry, S (2015), “The Macro Impact of Short-Termism”,, 17 January.

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