New webinar! Very excited to share that on November 17th I’ll be hosting a conversation with the honorable Michael Mauboussin and Kai Wu. Our discussion will focus on Michael and Kai’s excellent research on intangible assets, and the myriad of ways this “dark matter of finance” impacts markets. This is a conversation you won’t want to miss, and there will be opportunity for Q&A with Michael / Kai at the end of our chat.
The history of active management can be characterized as a search for competitive advantages (or “edge”). While there are many ways to gain an edge over the competition, most competitive advantages fall into three broad themes: Access, Speed and Analysis.
- Access: Investors obtain access to market information / data that is not widely available.
- Speed: Investors develop methods for receiving data / information faster than their peers.
- Analysis: Investors outperform the competition through superior analysis of widely available information.
Yet, competitive advantages are rarely permanent. Markets adapt and evolve, technology develops, and investors imitate successful strategies until previous competitive advantages are no longer useful. For instance, access to unique datasets is only a competitive advantage as long as other investors cannot easily access that same information.
Historically, opportunities for gaining a competitive advantage in these themes follow a cyclical pattern based on market conditions.
First, in the access phase, simply having access to unique information or datasets provides investors with a competitive advantage.
Over time, however, new technologies and innovations often help democratize access to that previously inaccessible information. Once that information is widely available, speed becomes the next competitive battleground as investors try to obtain information faster than their peers. Eventually, though, technology reaches a point where investors all receive information at roughly the same time.
At this stage, competitive advantages come down to investors’ analysis. Armed with the same information at the same time, successful investors can only outperform their peers through superior analysis. The competitive advantage no longer lies in acquiring information, but by synthesizing that information into actionable investment ideas.
Then, new data sources inevitably emerge and the cycle resets as investors fight for access to that information.
In today’s article – written in partnership with Daloopa – we will look at how technology influences the progression of this cycle using 19th century markets and ticker technology as an example.
FIGHTING FOR ACCESS
In 1837, officials at the NYSE discovered that “curb traders” (non-NYSE members) had drilled a hole in the institution’s brick exterior. Why?
In the 19th century, Wall Street was an unapologetically two-tiered system; NYSE board members traded in top hats from the comfort of their armchairs, while other traders shouted orders amidst chaos outside. Since they traded on the curb outside the exchange, these traders earned the title “curb traders”.
“Trade in securities was in fact carried out by two wholly distinct classes of brokers. One comprised members of the Regular Board, who inherited or paid hefty sums for their seats (they actually traded in tailcoats and tall hats, sitting on their personal chairs, from a fixed place in the room). The other class comprised brokers of the Open Board, who did not inherit any seats, paid much lower membership fees (less than one-tenth of what Regular Board members paid) and traded in the street (standing, of course)…”
This excerpt conveys how little “Regular Board” members cared about transparency and equal access to information. That said, the public could not even listen to trading sessions until 1869!
Thus, a physical hole in the NYSE’s exterior allowed curb traders to eavesdrop on the trades made inside the exchange. This information was a meaningful advantage for stock trading since the “Regular Board” members’ trades heavily influenced markets.
However, if brokers at the exchange were reduced to secretly eavesdropping, one can infer how little information reached investors outside of NYC.
SPEED: OPTICAL TELEGRAPHS
But how did pragmatic investors along the east coast manage this information asymmetry? They built clunky communications systems like the “optical telegraph”—a large pole outfitted with adjustable boards that could communicate messages based on their positioning.
During the 1830s, a chain of optical telegraphs was constructed to transmit information between New York and Philadelphia. Using this system, investors could relay information between the two cities in just 10–30 minutes, which was a significant informational advantage in this period.
But the system had flaws. In 1834, signals from an optical telegraph in France were corrupted after two bankers committed history’s first cyber-attack.
The pair of bankers had bribed a telegraph operator to transmit secret messages about the bond market that only they would understand. The swindle continued for two years until the telegraph operator being bribed fell ill and told his replacement about the “side hustle” in hopes he would continue perpetuating the scam. Luckily for the two bankers, however, they were not charged at trial because France had “no law against misuse of data networks” (Economist).
This episode highlighted the fallibility of optical telegraphs for transmitting sensitive information. Additionally, these telegraphs did not solve the larger issue of information asymmetries. Even when it worked, optical telegraphs only benefitted small groups of wealthy investors. The average investor was still disadvantaged by a lack of information.
At a higher level, the examples of drilling holes in the NYSE or constructing “optical telegraphs” underscored how much time investors’ spent simply acquiring information.
Instead of researching investment opportunities and generating new investment ideas, investors spent much of their time trying to reduce information asymmetries or obtain data faster. That all changed in 1867.
ANALYSIS: MARKET DATA AND THE TICKER REVOLUTION
When reading about historical events, relating to the people of that era and their problems is difficult because they are so unfamiliar to our lives today. For example, it is difficult to appreciate the challenges of investing with outdated – or no – market data because there is so much available today.
Imagine investing without access to company financials or current stock prices. It would be nuts! Yet this was the world that investors operated in before Edward Calahan invented the ticker.
While Samuel Morse’s telegraph (1844) revolutionized the world, Edward Calahan changed markets forever. One could argue that Calahan’s device is the most influential piece of technology in market history.
“Mr. Calahan had noticed the congestion of business around the halls of the Stock Exchange, which was largely caused by the brokers and their clerks struggling to secure the latest quotations… These were recorded on suitable pads and then carried by hand to the various Wall Street offices. Active brokers and their messengers were at that time often called ‘pad shovers,’… It occurred to Mr. Calahan that an instrument might be constructed which would record automatically the names of securities and the figures representing quotations or selling prices.” (Horace L. Hotchkiss 1905)
Prior to Calahan’s invention, speed was the primary competitive advantage for investors. At that time, even the best New York brokerage firms mailed their clients stock prices that were 10-12 days old. To gain a competitive edge, investors developed methods for obtaining that same information faster.
However, the ticker largely eliminated that edge by making market information available to all investors. Ticker machines connected brokers and investors across the country with a constant stream of data flowing directly from Wall Street. By 1905, Horace Hotchkiss estimated that 23,000 offices in the United States were paying for ticker-tape services.
“Many brokers lease wires to connect their Wall Street offices with branches in other parts of the city and country… Another firm paid last winter a large sum for a private wire connecting with a branch at Palm Beach… The transactions and prices of one market are, by its use, now known simultaneously… Distance and time have been annihilated.” (Source)
Remember, just a few decades earlier curb traders had been eavesdropping on trading sessions through a secret hole, and other investors were communicating prices by optical telegraph. The ticker marked a new era for financial markets. This excerpt from a 1905 investing book perfectly encapsulates this paradigm shift:
“At that time Mr. William Heath was an active broker; he was tall, thin, and exceedingly energetic. It was his custom to run from office to office, supplied with the latest quotations obtainable from the floor of the Exchange. He was generally known as the ‘American Deer,’’ and now was surprised to find in Groesbeck’s [brokerage] office a crowd watching the ‘ticker.’ He created much amusement when offering his quotations and was told he was ‘too late-we have them all on the tape.’
It was some months, however, before he thoroughly realized that the machine could outstrip the ‘American Deer’ in the race of quotations, but eventually he had to surrender, and filed his order for one of the company’s instruments.” (Source)
The American deer had provided brokers with a competitive edge by quickly running to and from the exchange with price quotations. However, he could not outrun ticker machines or telegraph cables—two technologies that eliminated competitive advantages based on speed.
Knock-On Effects of Ticker Technology
In addition to standardizing the dissemination of market prices, the ticker had two key consequences for investors:
- One Wall Street contemporary stated tickers provided “a recorded history of the market”, and this greater volume of pricing data allowed investors to conduct increasingly sophisticated analyses (i.e., technical analysis, early quantitative “factor” research, etc.).
- Importantly, the ticker enabled investors to spend their time more productively by focusing on value-add analysis. Previously, just acquiring information had consumed a significant amount of investors’ time.
The ticker machine was like the printing press in that, by making information available to more people at lower cost than ever before, it democratized access to news and other market-moving data. In this environment, investors’ competitive advantages were derived from what they did with that information, and not how/when they received it.
In 1903, Sereno Pratt stated: “Speed with accuracy [and] promptness in all things. – this is the cornerstone of modern finance. Most of the tools of Wall Street are time savers.” As we know, Pratt wrote this at a time when competitive advantages were derived from analysis, meaning “time savers” that allowed investors to spend more time on analysis – instead of gathering data – were increasingly valuable.
Now, 119 years later, Pratt’s statement still rings true. In this current “age of information”, investors are drowning in a sea of data. While there are always new “alternative datasets” (e.g., credit card data, etc.), information like quarterly earnings, CPI readings, etc. are available to every investor simultaneously. Thus, like the post-ticker environment of 100 years ago, competitive advantages today are largely based on superior analysis and idea generation.
That said, many investment tools today are “time savers” that automate time consuming tasks like data entry, enabling investors to devote more time to value-add analysis. One of those tools is Daloopa, who I’ve partnered with for today’s article.
INTERVIEW WITH DALOOPA CO-FOUNDER THOMAS LI
As I mentioned in the introduction, today’s article is written in partnership with Daloopa, a financial services company dedicated to helping investors do what they do best: research and analysis. Today, all investors receive the same quarterly reports and company financials. Therefore, there is little competitive advantage through “access” or “speed”. Consequently, investors must gain a competitive advantage through superior analysis.
In this short interview, Daloopa’s co-founder Thomas Li explains how the company helps investors spend more time on value-add analysis.
What circumstances or frustrations led to you founding Daloopa?
As a buyside analyst, I spent a significant amount of time each quarter doing the manual work of updating models, scrubbing numbers, and hardcoding data into Excel. These time-consuming tasks took up crucial time during earnings season or when ramping into a new company, time that was necessary to analyze and process. My co-founders and I founded Daloopa to automate the drudgery away so that analysts can spend their time thinking about the numbers instead of getting them into their models in the first place. We were lucky to be able to bring on board a team that believes in the value we add to the investment community.
What modern innovations and technologies make Daloopa such a powerful tool?
We have been building and iterating on the latest developments in artificial intelligence across a wide spectrum of the work that we do, from unstructured data extraction across file formats and languages, to data checking and validation methods. This usage of machine learning technology that only became available a few years ago allows us to work on an unprecedented level of scale and accuracy. Just last year, we only had 300 models. Now, our library has over 3,000 models that are updated every quarter and used daily by our clients at hedge funds around the world.
Our clients with the most frequent usage report significant time savings, greater book velocity, and easier idea generation by using our platform in innovative ways that sometimes we don’t even anticipate.
Active management has suffered outflows for years due to underperformance and availability of cheaper passive options. Are tools like Daloopa key to helping active managers generate investment ideas / alpha by reducing wasted time on things like manually updating spreadsheets, etc.? In other words, help analysts and portfolio managers spend more time on their value-add analysis?
I had a conversation with the CIO of a hedge fund with an AUM of over $20bn, where I told him point blank that Daloopa isn’t meant to generate alpha, but rather to get him back some time to think. He disagreed, saying that alpha can be broken down into the following formula:
Number of ideas per year * hit rate of ideas * slugging percentage
According to this CIO, your hit rate and slugging percentage is up to you, however, the number of ideas can be heavily optimized as it is limited only by the number of names an analyst can handle during each earnings season. A tool that reduces the time to update a model from hours to seconds directly adds to the number of ideas that can be sustained by the firm.
From my own experience, the time that is saved by using Daloopa is rarely ever spent on just going home early. Usually, analysts will repurpose this time into doing a deeper analysis of their companies, and thus make more informed investment decisions as well.
What do you think the future holds for companies and tools in this space?
I’m excited to see us and our peers borrow from the best technologies we have seen in so many traditional “Silicon Valley” firms that serve customers outside of the investment community. Despite huge research budgets and a willingness to shell out for any edge, the problems within the investment community are highly nuanced and thus far have been solved by paying for hardworking talent. This has led to a lack of support services that provide infrastructure level products to the industry. As the usage of new technology becomes more prevalent in the investment community, we have no doubt we will see further adoption and improvement as a traditionally old-school industry modernizes for the future.
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