(and a little alliteration)
From VoxEU:
Technological progress may be changing what we learn and how we trade
Technological change is making it possible to process more and more information. This column looks at the implications of this for trading strategies. It finds that growth in the amount of data investors can process is a logical and predictable cause of a shift from fundamentals-based to order flow-based strategies.
Technological change is making it possible to process more and more information. In financial markets, this change has been accompanied by changes in trading strategies. Fundamental, value investing is on the wane, while strategies that make use of order flow data on others’ trades have flourished. Relative to their predecessors, today’s investors favour trading strategies that depend sensitively on how many buyers and sellers are eager to trade at that moment (Hendershott et al. 2011). What is the logical relationship between progress in financial information technology, trading strategies, and market efficiency?
Why might information technology plausibly be related to trading strategies? If investors make optimal portfolio choices and learn about the same asset fundamentals over time, their investment choices should react to good news and bad news in a consistent, stable way. However, if an investor switches what they learn about, their investment choice should react to changes in the new variable. This change in trading pattern is a change in trading strategy. Put differently, if an investor wants to trade in a different way and still have their portfolio be optimal, they should acquire different information to support that new trading strategy. A technological change that affects information acquisition may therefore change trading strategies.
Yet, the relationship between trading strategy and information technology is not obvious. More data processing does not imply that the data must have a different composition. Perhaps when technology changes, everyone learns more about the same variables and strategies become more successful but not different in nature.
In our paper (Veldkamp and Farboodi 2017), we take a standard noisy rational expectations framework and add two ingredients”
First, investors obtain a growing amount of processed data over time.On a related subject see also 2017's post on simultaneous discovery and other stuff:
Second, they can choose how much of that data they would like to be about the fundamental value of a firm, and how much will be about the non-fundamental demands of other investors.
These two new ingredients are essential to explore changes in information technology and changing choices of trading strategies. We find that growth in the amount of data investors can process is a logical and predictable cause of an aggregate shift from fundamentals-based to order flow-based trading strategies.
To understand how the volume of data affects data choices and thus trading strategies, consider how each form of data is used. The use of fundamental data is straightforward – if the data predict that the firm is more valuable than the price indicates, buy; otherwise, sell. This strategy generates some value, no matter what others do.
The use of demand (order flow) data is more subtle. When an investor sees lots of uninformed buy orders coming in and learns that demand is high, she should sell because it is likely that the price is high, relative to fundamental value. This strategy looks like ‘trading against dumb money’. More charitably, standing ready to trade against agents who need to sell for non-fundamental (liquidity) reasons could also be called ‘market making’. Like our order-flow traders, market makers use order flow data to try and distinguish information-driven and non-information-driven trades, and to trade accordingly. An alternative way to describe this strategy is that the investors use their knowledge of price noise to remove that noise from the price signal, and to better extract the information that others know, from prices. The idea that investors can use order flow data to extract others’ information, or ‘follow smart money’, captures some of the flavour of front-running or trend-chasing. While following smart money and trading against dumb money sound very different, in this simple model, they are formally equivalent. What is crucial is that these strategies are only valuable when some investors are informed. There is no point in searching for dumb money, or trying to follow smart money, if everyone is dumb. As data technology improves and investors become better informed, following the informed orders and avoiding trades against informed order flows becomes paramount....MORE
Let Me Be Clear: I Have No Inside Information On Who Will Win The Man-Booker Prize Next Month (hedge funds, AI and simultaneous discovery)
...On Saturday September 23, 6:28 AM PDT we posted "Cracking Open the Black Box of Deep Learning" with this introduction:
One of the spookiest features of black box artificial intelligence is that, when it is working correctly, the AI is making connections and casting probabilities that are difficult-to-impossible for human beings to intuit.Today Bloomberg View's Matt Levine commends to our attention a story about one of the world's biggest hedge funds and prize-putter-upper of what's probably the most prestigious honor in literature, short of the Nobel, the Man Booker Award.
Try explaining that to your outside investors.
You start to sound, to their ears anyway, like a loony who is saying "Etaoin shrdlu, give me your money, gizzlefab, blythfornik, trust me."
See also the famous Gary Larson cartoons on how various animals hear and comprehend:...
On Tuesday September 26, 2017, 11:00 PM CDT Bloomberg posted:
The Massive Hedge Fund Betting on AI
The second paragraph of the story:
...Man Group, which has about $96 billion under management, typically takes its most promising ideas from testing to trading real money within weeks. In the fast-moving world of modern finance, an edge today can be gone tomorrow. The catch here was that, even as the new software produced encouraging returns in simulations, the engineers couldn’t explain why the AI was executing the trades it was making. The creation was such a black box that even its creators didn’t fully understand how it worked. That gave Ellis pause. He’s not an engineer and wasn’t intimately involved in the technology’s creation, but he instinctively knew that one explanation—“I can’t tell you why …”—would never fly with big clients looking for answers when Man inevitably lost some of their money...Now that is just, to reuse the phrase, spooky. Do read both the Bloomberg Markets and the Bloomberg View pieces but I'll note right now it's only with Levine you get:
"I imagine a leather-clad dominatrix standing over the computer, ready to administer punishment as necessary."...
