AUC Score :
Short-Term Revised1 :
Dominant Strategy : Hold
Time series to forecast n:
Methodology : Multi-Task Learning (ML)
Hypothesis Testing : Statistical Hypothesis Testing
Surveillance : Major exchange and OTC
1The accuracy of the model is being monitored on a regular basis.(15-minute period)
2Time series is updated based on short-term trends.
Summary
Heron Therapeutics Inc. Common Stock prediction model is evaluated with Multi-Task Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the HRTX stock is predictable in the short/long term. Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold
Key Points
- What is statistical models in machine learning?
- Game Theory
- What is the use of Markov decision process?
HRTX Target Price Prediction Modeling Methodology
We consider Heron Therapeutics Inc. Common Stock Decision Process with Multi-Task Learning (ML) where A is the set of discrete actions of HRTX stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4
F(Statistical Hypothesis Testing)5,6,7= X R(Multi-Task Learning (ML)) X S(n):→ 3 Month
n:Time series to forecast
p:Price signals of HRTX stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Multi-Task Learning (ML)
Multi-task learning (MTL) is a machine learning (ML) method in which multiple related tasks are learned simultaneously. This can be done by sharing features and weights between the tasks. MTL has been shown to improve the performance of each task, compared to learning each task independently.Statistical Hypothesis Testing
Statistical hypothesis testing is a process used to determine whether there is enough evidence to support a claim about a population based on a sample. The process involves making two hypotheses, a null hypothesis and an alternative hypothesis, and then collecting data and using statistical tests to determine which hypothesis is more likely to be true. The null hypothesis is the statement that there is no difference between the population and the sample. The alternative hypothesis is the statement that there is a difference between the population and the sample. The statistical test is used to calculate a p-value, which is the probability of obtaining the observed data or more extreme data if the null hypothesis is true. A p-value of less than 0.05 is typically considered to be statistically significant, which means that there is less than a 5% chance of obtaining the observed data or more extreme data if the null hypothesis is true.
For further technical information as per how our model work we invite you to visit the article below:
How do AC Investment Research machine learning (predictive) algorithms actually work?
HRTX Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: HRTX Heron Therapeutics Inc. Common Stock
Time series to forecast: 3 Month
According to price forecasts, the dominant strategy among neural network is: Hold
Strategic Interaction Table Legend:
X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)
Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)
Z axis (Grey to Black): *Technical Analysis%
Financial Data Adjustments for Multi-Task Learning (ML) based HRTX Stock Prediction Model
- For purchased or originated credit-impaired financial assets, expected credit losses shall be discounted using the credit-adjusted effective interest rate determined at initial recognition.
- Alternatively, the entity may base the assessment on both types of information, ie qualitative factors that are not captured through the internal ratings process and a specific internal rating category at the reporting date, taking into consideration the credit risk characteristics at initial recognition, if both types of information are relevant.
- For example, an entity may use this condition to designate financial liabilities as at fair value through profit or loss if it meets the principle in paragraph 4.2.2(b) and the entity has financial assets and financial liabilities that share one or more risks and those risks are managed and evaluated on a fair value basis in accordance with a documented policy of asset and liability management. An example could be an entity that has issued 'structured products' containing multiple embedded derivatives and manages the resulting risks on a fair value basis using a mix of derivative and non-derivative financial instruments
- If a collar, in the form of a purchased call and written put, prevents a transferred asset from being derecognised and the entity measures the asset at fair value, it continues to measure the asset at fair value. The associated liability is measured at (i) the sum of the call exercise price and fair value of the put option less the time value of the call option, if the call option is in or at the money, or (ii) the sum of the fair value of the asset and the fair value of the put option less the time value of the call option if the call option is out of the money. The adjustment to the associated liability ensures that the net carrying amount of the asset and the associated liability is the fair value of the options held and written by the entity. For example, assume an entity transfers a financial asset that is measured at fair value while simultaneously purchasing a call with an exercise price of CU120 and writing a put with an exercise price of CU80. Assume also that the fair value of the asset is CU100 at the date of the transfer. The time value of the put and call are CU1 and CU5 respectively. In this case, the entity recognises an asset of CU100 (the fair value of the asset) and a liability of CU96 [(CU100 + CU1) – CU5]. This gives a net asset value of CU4, which is the fair value of the options held and written by the entity.
*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.
HRTX Heron Therapeutics Inc. Common Stock Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba1 | B1 |
Income Statement | Ba1 | Baa2 |
Balance Sheet | Baa2 | Baa2 |
Leverage Ratios | Ba2 | C |
Cash Flow | B3 | B3 |
Rates of Return and Profitability | Baa2 | Ba1 |
*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?
Conclusions
Heron Therapeutics Inc. Common Stock is assigned short-term Ba1 & long-term B1 estimated rating. Heron Therapeutics Inc. Common Stock prediction model is evaluated with Multi-Task Learning (ML) and Statistical Hypothesis Testing1,2,3,4 and it is concluded that the HRTX stock is predictable in the short/long term. According to price forecasts for 3 Month period, the dominant strategy among neural network is: Hold
Prediction Confidence Score
References
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- R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of Advances in Neural Information Processing Systems 12, pages 1057–1063, 2000
- Ashley, R. (1983), "On the usefulness of macroeconomic forecasts as inputs to forecasting models," Journal of Forecasting, 2, 211–223.
- Tibshirani R, Hastie T. 1987. Local likelihood estimation. J. Am. Stat. Assoc. 82:559–67
- A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
- Y. Chow and M. Ghavamzadeh. Algorithms for CVaR optimization in MDPs. In Advances in Neural Infor- mation Processing Systems, pages 3509–3517, 2014.
- Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. Google's Stock Price Set to Soar in the Next 3 Months. AC Investment Research Journal, 220(44).
Frequently Asked Questions
Q: What is the prediction methodology for HRTX stock?A: HRTX stock prediction methodology: We evaluate the prediction models Multi-Task Learning (ML) and Statistical Hypothesis Testing
Q: Is HRTX stock a buy or sell?
A: The dominant strategy among neural network is to Hold HRTX Stock.
Q: Is Heron Therapeutics Inc. Common Stock stock a good investment?
A: The consensus rating for Heron Therapeutics Inc. Common Stock is Hold and is assigned short-term Ba1 & long-term B1 estimated rating.
Q: What is the consensus rating of HRTX stock?
A: The consensus rating for HRTX is Hold.
Q: What is the prediction period for HRTX stock?
A: The prediction period for HRTX is 3 Month
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