GameStop and Retail

Interesting points made with commentary. Agree its mostly about the ability to manipulate stocks in general.  Does not seem to have a broader retail effect.  

What does GameStop’s wild stock ride mean for retail?   by Tom Ryan  in Retailwire

GameStop, like many retailers struggling in recent years due to digital disruption, has become the poster child of a short-squeeze battle pitting amateur retail traders against Wall Street short-sellers.

Day traders on Reddit joined together to drive the video game retailer’s stock price up over the last two weeks in an effort to capitalize on hedge fund short-sellers betting GameStop would follow the path of Blockbuster.

From just under $20 a share on Jan. 12, shares of GameStop skyrocketed as high as $483 last Thursday, Jan. 28. That day, the stock trading app, Robinhood, and other online brokerages clamped down and curbed trades. GameStop crashed to $112.25 but recovered to close at $325.00 on Friday as some restrictions were removed. Institutional investors have lost billions in a matter of days as they were forced to liquidate their positions. ... " 

Exxon Mobil Removed from Dow

More indication that technical continues the climb:

" .... CHANGE: Exxon Mobil dropped from the Dow after nearly a century. “Exxon Mobil, which joined the Dow Jones Industrial Average in 1928, is being removed from the blue-chip stock market index. Its replacement: enterprise software company Salesforce.com.”  ... "

(Via NewsAlert.)

Simulating the Market

Quite a claim, often mentioned in early AI 'tests'?  Can it work?   Is simulation sufficiently complex proxy for the market?

AI stock trading experiment beats market in simulation  by Chinese Association of Automation in TechExplore

Researchers in Italy have melded the emerging science of convolutional neural networks (CNNs) with deep learning—a discipline within artificial intelligence—to achieve a system of market forecasting with the potential for greater gains and fewer losses than previous attempts to use AI methods to manage stock portfolios. The team, led by Prof. Silvio Barra at the University of Cagliari, published their findings on IEEE/CAA Journal of Automatica Sinica.

The University of Cagliari-based team set out to create an AI-managed "buy and hold" (B&H) strategy—a system of deciding whether to take one of three possible actions—a long action (buying a stock and selling it before the market closes), a short action (selling a stock, then buying it back before the market closes), and a hold (deciding not to invest in a stock that day). At the heart of their proposed system is an automated cycle of analyzing layered images generated from current and past market data. Older B&H systems based their decisions on machine learning, a discipline that leans heavily on predictions based on past performance.....  "

More information: Silvio Barra, Salvatore Mario Carta, Andrea Corriga, Alessandro Sebastian Podda and Diego Reforgiato Recupero, "Deep Learning and Time Series-to-Image Encoding for Financial Forecasting," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 683-692, May 2020. www.ieee-jas.org/en/article/do … 109/JAS.2020.1003132