Also: Institutional investors seek safety in recession (counter-cyclical) stocks, including real estate, oil, gas, and low-budget furniture and appliance rental companies.
By Irene Aldridge
May 6, 2022
According to Garthwaite et al. (2005), a good model accurately captures the distribution of the investors’ knowledge and beliefs, regardless of how good that knowledge actually is. Thus, if selected institutional investors believe that the markets are about to go up and reflect that belief in their trades, but the markets end up going down, the knowledge and belief elicitation model still works perfectly as it accurately captures the investors’ beliefs.
Of course, institutional investors, by the sheer size of their positions, have a considerable sway over the markets. As a result, much (but not all) of the time institutional activity actually drives the markets in a certain direction, based on the investors’ beliefs, even if only for a short to medium term. AbleMarkets successfully picks up these beliefs from the general pool of trades. The institutional beliefs are predictive exactly because the institutional investors move markets with their trades.
Using advanced market microstructure and Big Data techniques described in Aldridge (2013), Aldridge and Krawciw (2017) and Aldridge and Avellaneda (2021), we are able to collect a vast array of market activity and extract from all observable orders and trades Institutional and Aggressive High-Frequency Trading. Both of these categories have extensive research budgets and are able to deliver more educated and higher-probability assessments of where the markets are going relative to the rest of the markets.
On May 5, 2022, AbleMarkets data shows that the institutional investors sought safety in natural gas, gasoline, real estate trusts and traditional recession (also known as counter-cyclical) companies like Rent-A-Center Inc. (NASDAQ: RCII) that rents out furniture and appliances like TVs. The institutional investors may be wrong, but they are preparing for a potentially deep and hurtful recession.
References:
Aldridge, I. (2013). High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems. Hoboken, NJ: Wiley. Available on Amazon.com: https://www.amazon.com/High-Frequency-Trading-Practical-Algorithmic-Strategies/dp/1118343506
Aldridge, I. and S. Krawciw (2017). Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes. Hoboken, NJ: Wiley. Available on Amazon.com: https://www.amazon.com/Real-Time-Risk-Investors-FinTech-High-Frequency/dp/1119318963
Aldridge, I. and M. Avellaneda (2021). Big Data Science in Finance. Hoboken, NJ: Wiley. Available on Amazon.com: https://www.amazon.com/Data-Science-Finance-Irene-Aldridge/dp/111960298X
Paul H Garthwaite, Joseph B Kadane & Anthony O’Hagan (2005) Statistical Methods for Eliciting Probability Distributions, Journal of the American Statistical Association, 100:470, 680-701.
