Abstract:The recent rapid growth of algorithmic high‐frequency trading strategies makes it a very interesting time to revisit the long‐standing debates about the efficiency of stock prices and the best way to model the actions of market participants. To evaluate the evolution of stock price predictability at the millisecond timeframe and to examine whether it is consistent with the newly formed adaptive market hypothesis, we develop three artificial stock markets using a strongly typed genetic programming (STGP) tradin… Show more
“…We suspect that accessing message level market data is the primary obstacle. Those that do make the connection include: Virgilio, which finds that HFT allows a few fast traders to profit from arbitrage and thus falsifies the EMH [ 43 ]; Manahov and Hudson, which uses simulation to demonstrate that a larger market with more heterogenous traders is the key to increased efficiency [ 3 ]; and Manahov, et al ., which concludes that heuristics enable artificial traders to adapt to changing market environments [ 7 ]. Recently, research on the AMH in nascent cryptocurrency markets has become popular.…”
Section: Background and Literature Reviewmentioning
confidence: 99%
“…These papers—Chu, et al . and Manahov, et al .—agree that studying HFT and the AMH properly requires greater granularity in the data, something not always readily available [ 7 , 44 ]. We benefit from having access to message level exchange data, and this enables us to make an empirical contribution that connects HFT to the AMH on a meaningful timescale.…”
Section: Background and Literature Reviewmentioning
confidence: 99%
“…This supports the argument that HFTs seek to profit and reduce risk by removing short-term inefficiencies [ 6 ]. Thus, HFTs appear to adapt quickly to the evolving level of market efficiency as a means of survival, as suggested by some scholars [ 7 , 8 ]. In our review, this paper is the first to document the relationship between the level of support for the EMH and the level of HFT activity.…”
This paper uses NASDAQ order book data for the S&P 500 exchange traded fund (SPY) to examine the relationship between one-minute, informational market efficiency and high frequency trading (HFT). We find that the level of efficiency varies widely over time and appears to cluster. Periods of high efficiency are followed by periods of low efficiency and vice versa. Further, we find that HFT activity is higher during periods of low efficiency. This supports the argument that HFTs seek profits and risk reduction by actively processing information, through limit order additions and cancellations, during periods of lower efficiency and revert to more passive market-making and rebate-generation during periods of higher efficiency. These findings support the argument that the adaptive market hypothesis (AMH) is an appropriate description of how prices evolve to incorporate information.
“…We suspect that accessing message level market data is the primary obstacle. Those that do make the connection include: Virgilio, which finds that HFT allows a few fast traders to profit from arbitrage and thus falsifies the EMH [ 43 ]; Manahov and Hudson, which uses simulation to demonstrate that a larger market with more heterogenous traders is the key to increased efficiency [ 3 ]; and Manahov, et al ., which concludes that heuristics enable artificial traders to adapt to changing market environments [ 7 ]. Recently, research on the AMH in nascent cryptocurrency markets has become popular.…”
Section: Background and Literature Reviewmentioning
confidence: 99%
“…These papers—Chu, et al . and Manahov, et al .—agree that studying HFT and the AMH properly requires greater granularity in the data, something not always readily available [ 7 , 44 ]. We benefit from having access to message level exchange data, and this enables us to make an empirical contribution that connects HFT to the AMH on a meaningful timescale.…”
Section: Background and Literature Reviewmentioning
confidence: 99%
“…This supports the argument that HFTs seek to profit and reduce risk by removing short-term inefficiencies [ 6 ]. Thus, HFTs appear to adapt quickly to the evolving level of market efficiency as a means of survival, as suggested by some scholars [ 7 , 8 ]. In our review, this paper is the first to document the relationship between the level of support for the EMH and the level of HFT activity.…”
This paper uses NASDAQ order book data for the S&P 500 exchange traded fund (SPY) to examine the relationship between one-minute, informational market efficiency and high frequency trading (HFT). We find that the level of efficiency varies widely over time and appears to cluster. Periods of high efficiency are followed by periods of low efficiency and vice versa. Further, we find that HFT activity is higher during periods of low efficiency. This supports the argument that HFTs seek profits and risk reduction by actively processing information, through limit order additions and cancellations, during periods of lower efficiency and revert to more passive market-making and rebate-generation during periods of higher efficiency. These findings support the argument that the adaptive market hypothesis (AMH) is an appropriate description of how prices evolve to incorporate information.
“…The development of the entrepreneurial sector has remained an essential driver of economic growth in the past. However, digitalization has transformed the scope of challenges faced by entrepreneurs, as well as the behaviour of investors (Alexiou, Vogiazas, & Nellis, 2018; Manahov, Hudson, & Urquhart, 2018). It has significantly changed the ways of funding start‐ups and alternative sources of finance available to the entrepreneurs.…”
This study examines the impact of online feedback on the extent of alternative startups' fundraising success or failure through reward‐based online crowdfunding platforms. By drawing on regulatory focus theory, we theorize that online feedback relating to products or services is crucial in determining the success of entrepreneurial projects. While employing a unique dataset of 620 projects from renowned reward‐based Chinese crowdfunding platform “Demohour” (a major pioneer Chinese crowdfunding platform), the findings show a significant effect of online feedback on the extent of success or failure of reward‐based crowdfunding campaigns. Our findings, which are robust to different measures and methodologies imply that nascent entrepreneurs need to pay keen attention to online feedback about their innovative projects if they are to be successful in their fundraising efforts.
“…Gao, Han, Li, & Zhou, 2018; Marshall et al, 2008). Using a strongly typed genetic programming trading algorithm applied in artificial stock markets, Manahov (2016) and Manahov, Hudson, and Urquhart (2019) investigate stock price predictability at a millisecond time frame. The results indicate profit opportunities whose evolutionary pattern support the adaptive market hypothesis as described in Lo (2004).…”
We evaluate the performance of rules using past information to generate daily trading signals. Assuming generic trading reactions to buy and sell signals, we derive an analytic excess return that isolates commissions, interests, the impact of trading timing, and that of the benchmark's choice. The result is useful in dealing with data snooping through leverage and benchmark tweaking. We illustrate the empirical implications by examining trend‐following performance across Dow Jones Industrial Average (1927–2016) and an international sample of major equity indexes and blue‐chip stocks (1980–2016). The results show substantial, fading, non‐persistent and highly methodology‐sensitive excess returns.
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