The Monty Hall Problem and High-Frequency Trading: The Value of Information and Speed Combined Together

  • Gianluca P.M. Virgilio Universidad Católica Sedes Sapientiae
Keywords: Abnormal return, High-Frequency Trading, Monty Hall Problem, Technical Analysis

Abstract

The counter-intuitive probabilistic features hidden within the Monty Hall problem display a strong relationship to the dynamics of stock market. Decisions in either system are driven by information and decision-makers may decide whether sticking to their original choice or switching to an alternative one based on their epistemic state. One aspect of this paper deals with the connections between optimal information management suggested by the Monty Hall problem and its application to financial markets. However, in modern markets, mere possession of information superior to most other participants cannot guarantee abnormal returns if it is not supported by low execution latency. This is the second aspect discussed in the paper. The conclusion of the paper is that information and speed combined together create the conditions needed to achieve above average returns. It comes to no surprise that heavy investments go to increasing trading speed, potentially leading to a technological ‘arms race’ that carries high private benefits and high social costs. The main contribution of this paper is the use of simple mathematics to demonstrate how publicly available information, when used at high speed, may ensure abnormal market returns for the few ultra-fast traders, so disrupting the social purpose of stock markets.

References

[1] Aït-Sahalia Y., Saglam M. (2024). High frequency market making: The role of speed. Journal of Econometrics 239(2) 105421
[2] Alonso-Monsalve S., Suárez-Cetrulo A., Cervantes A., Quintana D. (2020). Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications 149 113250.
[3] Aquilina M., Budish E., O’Neill O. (2022). Quantifying the High-Frequency Trading “Arms Race”. The Quarterly Journal of Economics 137(1):493–564.
[4] Arnoldi J. (2016). Computer Algorithms, Market Manipulation and the Institutionalization of High Frequency Trading. Theory, Culture & Society 33(1):29–52.
[5] Budish E., Cramton P., Shim J. (2015). The High-Frequency Trading Arms Race: Frequent Batch Auctions as Market Design Response. The Quarterly Journal of Economics 130(4):1547–1621
[6] Bulkowski T. (2005). Encyclopedia of Chart Patterns. New York, NY: John Wiley & Sons, Inc.
[7] Edwards R., Magee J., Bassetti W. (2001). TA of stock trends. Boca Raton, FL: St. Lucie Press
[8] Farmer J.D., Skouras S. (2012). ‘Minimum resting times and transaction-to-order ratios: review of Amendment 2.3.f and Question 20’. UK Government Office for Science (EIA2)
[9] Herbranson W., Wang S. (2014). Testing the limits of optimality: the effect of base rates in the Monty Hall dilemma. Learning & Behavior 42(1):69-82.
[10] Hirschey N. (2021). Do High-Frequency Traders Anticipate Buying and Selling Pressure? Management Science 67(6):3321-3345.
[11] Kirilenko A., Kyle A., Samadi M. and Tuzun T. (2017). The flash crash: high-frequency trading in an electronic market. The Journal of Finance 72(3):967-998
[12] Kirkpatrick C., Dahlquist J. (2011). Technical Analysis - The Complete Resource for Financial Market Technicians. Upper Saddle River, NJ: FT Press
[13] Leber C., Geib B., Litz H. (2011). High Frequency Trading Acceleration using FPGAs. 21st International Conference on Field Programmable Logic and Applications. Chania, Greece
[14] Lewis M. (2014). Flash Boys. New York:W. W. Norton & Company
[15] Lo A. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management 30(5):15–29
[16] MacKenzie D., Beunza D., Millo Y. & Pardo-Guerra J.P. (2012): Drilling Through the Allegheny Mountains. Journal of Cultural Economy 5(3):279-296.
[17] Markham J. (2015). High Speed Trading on Stock and Commodity Markets— From Courier Pigeons to Computers. San Diego Law Review 52(3) 555-618
[18] Menkveld A. (2014). Electronic trading and market structure. Financial Review 49(2):333–344.
[19] Pagnotta E., Philippon T. (2018). Competing on Speed. Econometrica 86(3):1067-1115.
[20] Selvin S. (1975a). A Problem in Probability. The American Statistician. 29(1):67
[21] Selvin S. (1975b). On the Monty Hall Problem. The American Statistician. 29(3):134
[22] Seyfert R. (2016). Bugs, predations or manipulations? Incompatible epistemic regimes of high-frequency trading. Economy and Society 45(2):251–277.
[23] Siddiqi H. (2009). Is the lure of choice reflected in market prices? Experimental evidence based on the 4-door Monty Hall problem. Journal of Economic Psychology 30:203–215.
[24] Stiglitz J. (2014). Tapping the Brakes: Are Less Active Markets Safer and Better for the Economy? 2014 Financial Markets Conference. 15/4/2014. Atlanta. Federal Reserve Bank of Atlanta
[25] Teall J. (2023). Financial Trading and Investing. Academic Press:London, UK
[26] Verousis T., Perotti P., Sermpinis G. (2018). One size fits all? High frequency trading, tick size changes and the implications for exchanges: market quality and market structure considerations. Review of Quantitative and Financial Accounting 50:353–392.
Published
2025-06-27
How to Cite
Virgilio, G. P. (2025). The Monty Hall Problem and High-Frequency Trading: The Value of Information and Speed Combined Together. Review of Applied Socio-Economic Research, 29(1), 95 -107. https://doi.org/10.54609/reaser.v29i1.749