Do investors profit from spending more time and effort researching and analyzing their decisions? Does it pay to be an “infovore” who seeks out an unusual amount of information?
It’s pretty obvious, and well-confirmed by the literature, that the least info-seeking investors do poorly. In stock markets, for instance, individual investors (e.g. day traders) get worse returns than institutional investors. Institutional investors have full-time professionals on staff and access to expensive information sources; individual investors typically know far less.
The interesting question is whether being unusually info-seeking correlates with having higher than median returns, not whether being unusually ignorant correlates with having lower than median returns. Do the most information-seeking investors earn the highest returns?
This Venkat Rao thread claims that a major problem with society is that too many “rich people” are “lazy” when they make resource allocation decisions.
The word “invest” has 2 distinct meanings in relation to capital: Invest (1): Spend money to build a capability. Example: a bridge, a factory, a mansion.
Invest (2): Spend money to acquire a formal stake in an existing capability. Example: stock, bond, lien.
2 now dominates 1.
The two are coupled. For example, you invest (2) in my company so I can invest (1) in a new factory. Instead of shareholder value or a dividend, you might claim a share of the output at a good price for example (offtakes, kinda like futures).
It’s a moral hazard to purely invest (2) in money-in-money-out (MIMO) ways. You have no interest in how black box works. All knowledge risk lies with investee. If your MIMO deal is not honored you can’t tell fraud apart from real problems. You can’t judge whether to grant relief.
This is the definition of dumb money. Of course most real investment (2) does informally include some appreciation of the underlying investment (1), but not in any way that is operationally specific. Few investors who play for even controlling stakes actually desire control.
Control in finance has come to mean “control of the board and ability to hire/fire top executives”, which is increasingly just too shallow to address the complexity of modern principal-agent relations. Money _wants _to be stupid because the rich don’t want to do invest (1) work.
Financialization, “shareholder value” as a disease, etc, all have their tools in a single psychological problem: the rich are really lazy. They don’t want to put in the work to make their money smart. They want to either swarm opportunities via imitation, or rely on “analysts.”
There is a lot of bullshit conservative sermonizing about how the poor deserve their fates because they don’t work hard and that’s why the economy suffers etc. This is not only not the problem, it’s not even true. Most economic problems arise from the rich not working hard enough.
If you have 10s of millions of dollars, it matters how hard you, PERSONALLY, think about where to put it. Even a single degree of delegation causes huge principal-agent distortions. To the extent that some wealth creation opportunities need concentrated capital, the rich must think.
Some rich do think and work hard but they think wide and shallow for “highest ROIC” (where the I is investment (2), not investment (1)) opportunities. They rarely think deep about how a particular investment (2) will drive the invest (1). They don’t care about the meaning of the money.
There is something deeply nihilistic, stupid, fearful, and acting-dead about this. If the only difference between 2 investments for you is return rates, and you are not curious about the futures implied by investing in either, you’re kinda dumb and boring.
This whole point is why I’m not a socialist. I don’t think it matters much who “owns” the capital. Whether it is a workers’ co-op or a fat-cat single rich person, the question is how hard they think.
There’s a lot in this thread, a lot of it resonates with me, and I think it’s worth taking seriously enough to actually unpack the claims and investigate whether they’re literally true.
As I see it, Venkat is making two separate claims.
One is about the relationship between “investment (1)” and “investment (2)”, between constructing physical objects that produce value, and the abstracted process of spending money to turn it into more money.
He seems to believe, and I share the same intuition, that the world would be a better place if more people who had the means to invest in big projects did so because they wanted those projects to exist, not just because they wanted the revenue stream.
Elon Musk wants to go to Mars; he’s also trying to structure his companies to be profitable, but I’m pretty sure he would count it a failure if he made a bunch of money but humanity never became a multiplanetary species. That’s the kind of thing Venkat is talking about.
It’s natural, for kids at least, or for people imagining what they’d do with wealth vastly greater than their own, to think “If I ever have money, I’ll use it to do something awesome.” You could make the world a better place, make your mark on history, or even just have a spectacularly good time.
Venkat seems to be claiming that a.) the people who actually have that much money rarely want to spend it on things they personally find awesome, and b.) it would be better for the world if they did.
I share this intuition, but I don’t really know how I’d go about testing it. Since there’s almost no public data about the psychology of rich investors, we have to rely on anecdote; and while a consultant like Venkat has met many rich investors, he’s seen a very selective picture of how they think and spend their time. My own experience with rich investors is even more limited.
As for how the world would be different if investors put money into projects that they valued for reasons other than revenue, I’m not sure how to seriously answer the question without an incredibly broad theory of economics and ethics.
But Venkat is also making a narrower, more tractable claim: “the rich are really lazy.” That is, investors do not “think hard” about where to put their money. In more neutral terms, investors underinvest in information-gathering and analysis.
This is a testable hypothesis, at least if you only think about the financial self-interest of investors. If the investors who are most inclined to investigate/research/seek information are also earning the highest returns, then most investors are irrationally neglecting research by comparison.
If investors tend to “underthink” relative to what would be economically rational, then they are certainly underthinking relative to Venkat’s socially beneficial ideal, which includes not only considering the effect of an investment on your bank account, but also its effect on the rest of the world.
Conclusions From The Literature
Most of the evidence I could find comes from investments on public markets, since of course there’s far more data on the stock performance of publicly traded companies. I am less confident about what this means about private equity or venture capital investment.
Overall, engaging in more research into company fundamentals, and being in some sense “better at formal analysis”, does correlate reliably with higher returns.
- Hedge funds that make more in-person site visits to portfolio companies have higher returns. Site-visitors have annual returns 6.1 percentage points higher than non-visitors.
- Investment firms that download more SEC filings have higher returns. Downloaders (of any files) have annual returns 1.5 percentage points higher than non-downloaders.
- Financial analysts who download SEC filings make more accurate forecasts than non-downloaders, and the longer they spend reading SEC filings, the more accurate their forecasts.
- “Buy-side analysis” refers to the research and forecasting performed in-house by employees of investment firms. Mutual funds whose investment decisions more closely track the recommendations of their analysts have higher returns.
- Mutual fund managers who score higher on the Cognitive Reflection Test (a measure of how good people are at avoiding intuitive-but-wrong answers; strongly correlated with IQ) have funds with higher Sharpe ratios (the same returns, but less risk) than their low-scoring peers. That is, roughly, being less prone to “jump to conclusions” is associated with better investment performance.
- Angel investors who do more due diligence have a significantly higher proportion of their investments become “home runs”, defined as investments where the investor receives returns of at least 100% upon exit of the company.
Strikingly, the biggest downloader of public financial documents is Renaissance Technologies, whose 66% annual returns over the past 30 years dwarf anything else in the financial markets.
It seems to be the case that spending more time and effort on fact-finding that relates to fundamentals (reading financial documents, visiting factory sites, engaging in due diligence) and listening more closely to the employees who specialize in such research, is profitable – and thus that most firms underinvest in it.
By contrast, “imitative information-seeking”, all things equal, seems to anticorrelate with investment returns and forecasting accuracy.
- Investment analysts who change their forecasts the most in response to changes in other analysts’ published forecasts have worse performance.
- Investment firms tend to perform worse if their portfolios closely track changes in published financial analyst recommendations.
- Investment firms that tend to buy stocks that have been in the news recently, have worse performance than investment firms that are less correlated with the news cycle.
Basically, the evidence available to me suggests that Venkat is right. Institutional investors do perform better when they do more investigation into company fundamentals, and worse when they focus more on how other people perceive a company. The median institutional investor would make more money if they were more investigative and less imitative.
Information Seeking by Investment Analysts
Investment analysts who download more publicly available data about firms tend to make more accurate price forecasts. Financial analysts whose recommendations correlate closely with the average of other financial analysts’ recommendations tend to perform worse than the contrarians. This suggests that analysts who are good at research and do more of it make better predictions.
Strangely, financial analysis firm rankings were found to anticorrelate with the use of internal library resources at the firm (in a study published in 1997, when written information resources were mostly in hard copy.)
Financial analysts who download more publicly available data about firms from EDGAR, the SEC database, perform better. Only 24% of analysts made an EDGAR download within a day before issuing a new recommendation update. Those that did, had a 3.7% reduction in forecast error. Spending more time reading reports also corresponds to better accuracy – each 1% increase in time spent reading files corresponded to an 0.8% increase in forecast accuracy.
However, analysts that base their recommendations more on public consensus tend to perform worse. A measure of Reliance on Public Information (RPI) can be calculated for financial analysts based on the sensitivity of their recommendations to public variables like price momentum, earnings surprise, average turnover, change in consensus recommendation, and change in consensus earnings forecast. A portfolio that follows stock recommendations of analysts in quintile 1 of RPI (the least reliant on public information) produces an average monthly return of 1.83%, or 22% a year. Data was collected on 3480 analysts over the period 1993-2006; returns were calculated over the 31 days following a new recommendation. Low-RPI analysts tend to recommend buying larger firms (factor loading -0.19, p<0.05), while high-RPI analysts tend to recommend shorting larger firms (factor loading (-0.21, p<0.05), and more high-priced firms (-0.29, p<0.05).
A 1997 survey of 100 securities analysts from the largest UK and US investment firms found that firm ranking negatively correlated (p<0.001) with the frequency that respondents answered that their staff checks the internal library.
Information Seeking by Investment Funds and Fund Performance
Use of Public Information
Firms that download more publicly available files about their investments generally perform better than firms that do not. Also, “short sellers” who make leveraged bets against firms earn more profit when they trade immediately after public news about a firm, suggesting that they are profiting from making better use of publicly available information than most investors.
On the other hand, firms whose investment choices are more closely correlated with the public advice of analysts generally perform worse.
Hedge funds that access more SEC filings online perform better. The most frequent downloader of all is Renaissance Technologies. Firms that download files have on average 1.5% higher annualized returns than firms that don’t.
Users who download files from EDGAR, the official website where SEC filings are stored, can be traced to IP addresses corresponding either to institutional investors (e.g. Goldman Sachs, Morgan Stanley) or to individual “retail” investors. There is no correlation between a firm’s future stock performance and how much its filings were downloaded by “retail” investors, but there is a positive correlation (p<0.01) between a firm’s future performance and how much its files were downloaded by institutional investors, and also a positive correlation (p<0.01) between a firm’s institutional-investor downloads and its subsequent change in holdings among those investors, particularly when the 10K filings have a positive overall “tone”, suggesting that institutional investors do in fact invest more in firms when the firms’ publicly available data is favorable.
Short selling does not increase immediately before news about a stock is published, but does increase after, suggesting that short sellers are making their decisions on the basis of public news rather than anticipating that news. Moreover, the returns on short sales are more than twice as high on days when there is news about a stock, which also suggests that short sellers are making better use of public information than other investors.
Short-selling in the COVID-19 pandemic was more profitable for investors who had previously held a short position in any healthcare stock before, suggesting that investors with more familiarity with healthcare were more able to understand what was going on with COVID-19 and how it would affect stock prices.
Reliance on Public Information (RPI) is a measure of how sensitive an investor’s holdings are to changes in public information, in this case changes in analysts’ recommendations. RPI is negatively correlated (r = -0.17) with Carhart 4-factor alpha, in a sample of 1696 mutual funds. Better funds use more private as opposed to public information.
Also, if you construct a measure “RPI_σ” to represent the coefficients from regressing the change in a mutual fund’s % holdings on the change in the standard deviation of analysts’ recommendations, and compare the RPI_σ of 1220 mutual funds to their fund performance, the correlation is significantly negative (p<0.05). 
A measure (PTMC, Propensity to Trade with Media Coverage) of a mutual fund’s tendency to trade stocks more when they’re in the news, negatively correlates with fund performance. Over the period 1993-2002, stocks purchased by top-quintile PTMC funds lost 0.17% monthly in CAPM-adjusted alpha, while stocks purchased by bottom-quintile PTMC funds gained 0.46% monthly. The difference is still significant for Fama-French three-factor adjusted alpha and Carhart 4-factor adjusted alpha.
Use of Private Information
Private research by an investment firm – site visits to portfolio companies, interviews with portfolio CEOs, and in-house analysts estimating stock values – _does _correspond to higher returns. Investment firms that engage in more in-house research activities, or weight in-house research more heavily in investment decisions, tend to perform better.
About ⅙ of hedge funds make site visits to the companies they invest in. Among 2469 Chinese hedge funds over the period 2012-2017, and CSMAR data on corporate site visits in China, 15.43% of hedge fund managers visited at least one company site. Funds that made site visits had significantly (p<0.01) higher returns, CAPM alpha, Sharpe ratio, and volatility than funds that didn’t. Mean annual returns were 21.4% for funds that did site visits, vs. 15.3% for funds that did not.
Female fund managers and managers with a graduate degree are more likely to make site visits. 11% of male managers made site visits, while 33% of female managers did. 12% of managers with graduate degrees made site visits, vs. 9% of managers without graduate degrees.
Chinese mutual funds between 2006-2016 were more likely to make trades on stocks if they had recently made a site visit, indicating that site visits did indeed provide them with decision-relevant information. A portfolio of stocks that were bought by a mutual fund after a site visit had above-market returns after adjusting for Fama-French factors, indicating that mutual funds are successfully using site visits to identify good investments.
Fund managers who report (in a questionnaire) rating conversations with company executives as “very important” to their investment decisions perform significantly better (p <0.05) than fund managers who rate conversations with executives as “not important.”
The added value of buy-side research – that is, the work of investment analysts doing proprietary research for investment firms – can be evaluated by measuring the “buy-side alpha” logged in a private database for an investment firm. All investment analysts at this firm use the same baseline algorithm for valuing stocks; then, their individual research prices some stocks higher or lower than the algorithm would predict. This difference is called the “buy-side alpha.” In the month following portfolio formation, it turns out, the “buy-side alpha” does correlate with actual stock performance.
Stocks with top-quintile buy-side alphas are rated significantly lower by sell-side analysts than stocks with bottom-quintile buy-side alphas (p<0.01), and have higher book-to-market ratios (p <0.01). A portfolio based on buying stocks each month that had seen upgrades in buy-side alpha would earn 1.19% monthly, or an expected 15% per year, while a portfolio based on buying stocks whose buy-side alphas had been downgraded would earn 0.61% monthly, or an expected 7.6% yearly. This is a significant difference, and continues to be significant after adjusting for risk, while the advantage of buying stocks recommended by sell-side analysts is no longer significant after risk adjustment.
Moreover, the mutual funds included in this database did buy stocks more when their buy-side analysts rated them higher, and sell stocks more when their buy-side analysts rated them lower. The regression coefficient of firm investment on buy-side analyst ratings is significantly positive, p<0.01, even when controlling for sell-side analysts’ ratings.
Finally, the risk-adjusted quarterly returns of mutual funds correlate positively (p<0.01) with the sensitivity of their investment decisions to buy-side analyst recommendations, but returns do _not _correlate with the sensitivity to sell-side analyst recommendations. In other words, it pays to rely on (private) buy-side research, but not to rely on (public) sell-side analysis.
Fund Manager Cognitive Characteristics and Fund Performance
84 fund managers from 267 mutual funds were given a battery of online cognitive tests, and their test results were compared to their publicly available fund performance. The abnormal returns of funds (over their benchmark) did not correlate with fund managers’ performance on the Cognitive Reflection Test, their score on a Theory of Mind test, or their self-reported competitiveness. Ambiguity tolerance was positively (p<0.005) associated with abnormal returns.
Unsurprisingly, higher risk tolerance among fund managers in an experimental setting correlated positively with higher volatility in their funds (p<0.005), while a perfect score of 5 on the Cognitive Reflection Test (which 30% of the fund managers got) was negatively correlated with volatility (p<0.05).
Together, these two facts imply that risk-adjusted performance (i.e. Sharpe ratio) is higher for fund managers who get perfect scores on the CRT.
Performance on the CRT by fund managers also has a strong positive correlation (0.51, p<0.005) with performance on the Advanced Progressive Matrices test, a measure of fluid intelligence. 
Information Seeking by Venture Capital and Investment Performance
In a survey of 128 angel investors investing in 1038 new ventures, firms that exited were classified as losing money (Failures), producing an internal rate of return of 0-99%, and exiting at 100% gains or greater (Home Runs). ⅔ of investments were failures, and 20% were home runs. The number of home run exits of an investor correlated positively (0.24, p<0.05) with the investor’s propensity to engage in due diligence.
Limitations and Conclusions
The data we have about the benefits of site visits comes entirely from the Chinese markets, which may not be representative of other investment markets. For instance, checking on a factory to make sure its production is as good as it reports may be more important in a lower-trust environment.
We also are short of data on investment performance in private equity in general, and particularly in the individual characteristics and decision processes of private equity investors that are predictive of fund performance.
Despite these limitations, we do seem to see consistent results pointing towards positive correlations between higher information-seeking activities by investors and higher investment returns, across investment categories.
Gibbons, Brian, Peter Iliev, and Jonathan Kalodimos. “Analyst information acquisition via EDGAR.” Management Science (2020).
Ali, Usman, et al. Analysts’ use of public information and the profitability of their recommendation revisions. Working Paper, Yale University, 2008.
Baldwin, Nancy Sadler, and Ronald E. Rice. “Information‐seeking behavior of securities analysts: Individual and institutional influences, information sources and channels, and outcomes.” Journal of the American Society for Information Science 48.8 (1997): 674-693.
Crane, Alan, Kevin Crotty, and Tarik Umar. “Do hedge funds profit from public information.” Rice University (2018).
Engelberg, Joseph E., Adam V. Reed, and Matthew C. Ringgenberg. “How are shorts informed?: Short sellers, news, and information processing.” Journal of Financial Economics 105.2 (2012): 260-278.
Schattmann, Levy, Jan-Oliver Strych, and P. Joakim Westerholm. “Information Processing Skills of Short Sellers: Empirical Evidence from the COVID-19 Pandemic.” Available at SSRN 3763198 (2021).
Kacperczyk, Marcin, and Amit Seru. “Fund manager use of public information: New evidence on managerial skills.” The Journal of Finance 62.2 (2007): 485-528.
Abdesaken, Gerald. “On the precision of public information and mutual fund performance.” Journal of Asset Management 16.2 (2015): 85-100.
Fang, Lily H., Joel Peress, and Lu Zheng. “Does your fund manager trade on the news? media coverage, mutual fund trading and performance.” Media Coverage, Mutual Fund Trading and Performance (March 18, 2009)
Drake, Michael S., et al. “Is there information content in information acquisition?.” The Accounting Review 95.2 (2020): 113-139.
Chen, Honghui, et al. “The Geography of Information Acquisition.” Available at SSRN 3371978 (2019).
Drachter, Kerstin, Alexander Kempf, and Michael Wagner. “Decision processes in German mutual fund companies: evidence from a telephone survey.” International Journal of Managerial Finance (2007).
Rebello, Michael, and Kelsey Wei. “A glimpse behind a closed door: The investment value of buy-side research and its effect on fund trades and performance.” (2011).
 Farago, Adam, et al. Cognitive skills and economic preferences in the fund industry. No. 2019-16. Working Papers in Economics and Statistics, 2019.
 Wiltbank, Robert. “Investment practices and outcomes of informal venture investors.” Venture Capital 7.4 (2005): 343-357.