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Archives of Business Research – Vol. 12, No. 12
Publication Date: December 25, 2024
DOI:10.14738/abr.1212.18017.
Samsa, G. (2024). Assessment of a Counterintuitive Trading Strategy Based on Market Microstructure. Archives of Business
Research, 12(12). 87-98.
Services for Science and Education – United Kingdom
Assessment of a Counterintuitive Trading Strategy Based on
Market Microstructure
Greg Samsa
Department of Biostatistics and Bioinformatics,
Duke University, Durham NC, USA
ABSTRACT
In general, day trading has little support from empirical research and a justified
reputation of being dangerously unsound. It often relies on positive price
momentum, regardless of the underlying value of the stock. Here, we assess
multiple versions of a protocolized day trading strategy that relies on overreaction
to bad corporate news, as reflected in premarket prices. Relevant stocks are
purchased at the open, when there is an imbalance between buy and sell orders,
and held for no longer than a single trading day. The companies in question
constitute a basket of deplorables, and the trading strategies rely on mean
regression rather than momentum, and also benefit from high levels of inter-day
price volatility as the process of price discovery proceeds. Observed returns were
positive for all versions of the strategy. Conceptual support is provided by the
notion that active money managers have individual incentives to immediately rush
toward the door, and those incentives should continue into the future. In
conclusion, we have described an unusual and counterintuitive strategy that relies
on a conceptually plausible, yet rare and fleeting market inefficiency. It is based on
the microstructure of the stock market.
Keywords: behavioral finance, day trading, investor incentives, stock market research.
INTRODUCTION
Colloquially speaking, the efficient market hypothesis (EMH) asserts that stock prices are
always correct, and so the only way to obtain above-average returns is to accept above-average
risk, manifested by above-average volatility [1]. Among many others, the EMR provides a
conceptual basis for the relationship between risk and return.
The EMH was originally derived using the intellectual scaffolding of classical economic theory
[2]. For example, if investors are perfectly knowledgeable and rational, if trading is without
friction due to trading costs and bid-asked spreads, etc., then any temporary inconsistency
between a stock price and its underlying economic value should be quickly removed by
arbitrage, broadly defined. For example, if a stock is temporarily a bargain relative to its
economic value investors will buy it, causing the price to rise and the bargain to disappear. The
underlying mathematics work most simply if the distribution of stock prices is assumed to be a
function that is continuous and smooth.
Although it is impossible to prove a negative, indirect empirical support for the EMH has been
provided by researchers proposing strategies that hope to beat the market, testing them on
historical databases, and discovering that the resulting risk-adjusted returns are unexceptional.
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Archives of Business Research (ABR) Vol. 12, Issue 12, December-2024
Services for Science and Education – United Kingdom
The tests are accomplished by applying an explicit stock selection criterion on January 1 of year
1 in a historical database, selling the resulting portfolio on December 31, calculating the returns,
repeating the process on year 2, etc. This distribution of annual returns is used to estimate the
mean and the standard deviation, and what is desired is a mean value that is high relative to
that standard deviation [3].
Behavioral finance (BF) questions the assumption that investors are entirely rational, and
instead postulates that they, like all humans, have an evolution-based tendency toward biases
in decision making [4]. For example, one of its creation stories holds that human investors
overreact to bad news because their ancestors who assumed that the rustling in the grass was
always a tiger ran away, didn't become a meal, and survived long enough to reproduce. BF
asserts that biases in decision making can lead to stock prices which are inconsistent with
economic value, and thus suggest investment strategies that can be tested as above. The results
of this research provide encouragement to both camps: proponents of BF can point to some
moderately positive results, whereas proponents of the EMH note that the magnitude of excess
returns is neither consistent nor dramatic, and might disappear, at least in part, once
sufficiently sophisticated risk adjustment is applied [e.g., 5-19].
There are strong and weak versions of the efficient market hypothesis: an especially compelling
one was formulated by Malkiel [2]. It acknowledges (among others) that BF might have a point:
for example, market participants can be irrational at times; stocks can be temporarily
mispriced, both individually and in aggregate, most dramatically in booms and busts; and stock
prices can exhibit greater volatility than suggested by economic considerations such as
earnings and dividends. All of this can be directly observed. Nevertheless, Malkiel argues that
these "features" don't really matter unless investors can earn above-average returns without
accepting above-average risks and, moreover, definitive demonstrations of large "exploitable"
anomalies have been lacking. This highlights the importance of research on "market-beating"
investment strategies by embedding the results of such research into the statement of the EMH.
There is even a "plan B": it is asserted that once such an anomaly is identified, economic
incentives will cause market participants to act in a way that removes it going forward (i.e., via
arbitrage).
One risk in testing "market-beating" strategies is that the wrong strategies might be used. In
particular, if these strategies don't represent what actual investors do (e.g., because they are
oversimplifications), and moreover if the direction of this difference is toward poorer observed
returns, then the result will be a bias in favor of the EMH. To illustrate: BF-based arguments
have been proposed to explain the relative unpopularity of "value stocks" -- essentially, that
humans like bright shiny objects and so investors typically prefer companies in exciting new
industries to those that make bagels and toilet paper. Value stocks often pay high dividends --
in part because their dependable cash flows and lack of growth imply that excess cash should
be returned to shareholders, and part because their stock price is low relative to those
dividends. Accordingly, one possible algorithm for a value strategy would be to buy all the
stocks in a historical database with annual dividends exceeding a certain level (e.g., 5%).
However, a naïve application of this algorithm fails to consider that actual investors would first
review the stocks that meet this criterion and eliminate those whose dividend is in danger or
are otherwise poor candidates for purchase. Moreover, although some of this review would use
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Samsa, G. (2024). Assessment of a Counterintuitive Trading Strategy Based on Market Microstructure. Archives of Business Research, 12(12). 87-98.
URL: http://doi.org/10.14738/abr.1212.18017
quantitative criteria such as the dividend coverage ratio, in actual practice it would be based on
multiple factors, some of which could not be easily operationalized as a formal stock selection
algorithm applied to a historical database.
Here, we test an unusual variation of a BF-based investment strategy, and moreover argue that
that the resulting market inefficiency is likely to continue into the future.
METHODS
Context: Day Trading
The strategy to be tested involves protocolized day trading. On first blush, day trading provides
an especially unpromising context, as its average returns are often negative [20-22]. For
example, using extensive trading data from the Taiwan Stock Exchange, Barber et al found that
fewer than 10% of day traders are consistently profitable: indeed, so much so as to question
why the industry persists [20]. Mahani and Bernhardt, though, argued that some day trading
has a conceptual justification: namely, that "liquidity traders" (i.e., those with strong incentives
to buy or sell a particular stock) are insensitive to price and market makers are often willing to
forgo the associated profits associated with exploiting their behavior, thus providing an
opportunity for especially skilled day traders to do so [22].
Another conceptual justification for day trading is the persistence of short-term price
momentum, regardless of its direction [23]. Indeed, most day trading strategies attempt to
exploit such momentum.
Testing Strategies Based on Response to Bad Corporate News
When bad news about a company appears, it is economically reasonable for its stock to drop in
price. Depending on the severity of the news as well as its impact on the company's long-term
economic prospects, this drop could be small or large, and transitory or longer-lived. While the
strongest form of the EMH effectively asserts that the new lower price ought to immediately
reflect all the impacts of this bad news, BF holds that this initial response will likely be an over- reaction. Moreover, this over-reaction will lead to negative momentum in the short term,
creating a bargain, which will eventually be eliminated by arbitrage, resulting in regression
toward the mean (and, thus, superior risk-adjusted returns) in the longer term [18]. The time
periods in question depend on context.
Operationalizing the Construct of Bad Corporate News
Some stocks are more volatile than others: for example, a 10% daily drop might be
unexceptional for a "high-beta" stock yet rare and informative for a stock that is less volatile.
Moreover, a 10% drop in a stock that has recently risen by 50% might simply represent a
"correction" carrying relatively little information. We operationalized the construct of
"significant" bad news using two criteria. First, using a database of American equities [24], the
final premarket price should represent at least a 10% drop from the previous closing price, thus
suggesting that the drop has a "cause" that became apparent between the previous close and
the start of the next trading day. Second, the final premarket price should be below all prices
during the previous month. This second criterion doesn't necessarily imply that the final
premarket price is a bargain relative to the stock's economic value -- that is, the construct of