Whales with a lot of money to spend have taken a noticeably bearish stance on Robinhood Markets.Looking at options history for Robinhood Markets (NASDAQ:HOOD) we detected 33 trades.If we consider the specifics of each trade, it is accurate to state that 27% of the investors opened trades with bullish expectations and 54% with bearish.From the overall spotted trades, 16 are puts, for a total amount of $921,264 and 17, calls, for a total amount of $975,755.What's The Price Target?Analyzing the Volume and Open ...

Group 1 - Significant investors have adopted a bearish stance on Robinhood Markets, with 68% of trades being bearish and only 24% bullish [1] - The total amount for put trades is $468,191, while call trades amount to $1,649,044, indicating a preference for bearish positions [1] - The expected price range for Robinhood Markets over the last three months is between $65.0 and $150.0 [2] Group 2 - Analyzing the volume and open interest of options can provide insights into liquidity and investor interest for Robinhood Markets [3] - Recent options activity shows a notable bearish sentiment, with various trades indicating expectations of price declines [8] - The trading volume for Robinhood Markets stands at 3,588,024, with the stock price currently at $119.27, reflecting a decrease of 2.64% [15] Group 3 - Robinhood Markets is focused on creating a modern financial services platform, offering products like cryptocurrency trading and fractional shares through a cloud-based app [10] - Professional analysts have set an average target price of $159.4 for Robinhood Markets, with individual targets ranging from $135 to $180 [12][13]

Active Learning Methodology - Active learning is presented as an efficient method for building supervised models with unlabeled data [1][4] - The process involves iteratively training a model, identifying low-confidence predictions, and labeling them with human input to improve model performance [2][3][4] - The methodology emphasizes the importance of accurate confidence measure generation for effective training [5] Model Building and Refinement - The initial step involves manually labeling a small percentage of the data to create a seed dataset [2] - Probabilistic models are recommended for confidence level determination, using the gap between the top probabilities as a proxy [3] - Cooperative learning, a variant of active learning, utilizes high-confidence data by incorporating the model's predictions as labels [5] Application and Considerations - Active learning can save significant time when building supervised models on unlabeled datasets [4] - The accuracy of confidence measures is critical, as errors can negatively impact subsequent training steps [5]