AUC Score :
Short-Term Revised1 :
Dominant Strategy : Buy
Time series to forecast n:
Methodology : Modular Neural Network (DNN Layer)
Hypothesis Testing : Independent T-Test
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.
National Storage Affiliates Trust Common Shares of Beneficial Interest prediction model is evaluated with Modular Neural Network (DNN Layer) and Independent T-Test1,2,3,4 and it is concluded that the NSA stock is predictable in the short/long term. In a modular neural network (MNN), a DNN layer is a type of module that is used to learn complex relationships between input and output data. DNN layers are made up of a series of artificial neurons, which are connected to each other by weighted edges. The weights of the edges are adjusted during training to minimize the error between the network's predictions and the desired output. DNN layers are used in a variety of MNN applications, including natural language processing, speech recognition, and machine translation. In natural language processing, DNN layers are used to extract features from text data, such as the sentiment of a sentence or the topic of a conversation. In speech recognition, DNN layers are used to convert audio data into text data. In machine translation, DNN layers are used to translate text from one language to another.5 According to price forecasts for 16 Weeks period, the dominant strategy among neural network is: Buy

Key Points
- Is it better to buy and sell or hold?
- Is it better to buy and sell or hold?
- Market Outlook
NSA Stock Price Forecast
We consider National Storage Affiliates Trust Common Shares of Beneficial Interest Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of NSA 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
Sample Set: Neural Network
Stock/Index: NSA National Storage Affiliates Trust Common Shares of Beneficial Interest
Time series to forecast: 16 Weeks
According to price forecasts, the dominant strategy among neural network is: Buy
n:Time series to forecast
p:Price signals of NSA stock
j:Nash equilibria (Neural Network)
k:Dominated move of NSA stock holders
a:Best response for NSA target price
In a modular neural network (MNN), a DNN layer is a type of module that is used to learn complex relationships between input and output data. DNN layers are made up of a series of artificial neurons, which are connected to each other by weighted edges. The weights of the edges are adjusted during training to minimize the error between the network's predictions and the desired output. DNN layers are used in a variety of MNN applications, including natural language processing, speech recognition, and machine translation. In natural language processing, DNN layers are used to extract features from text data, such as the sentiment of a sentence or the topic of a conversation. In speech recognition, DNN layers are used to convert audio data into text data. In machine translation, DNN layers are used to translate text from one language to another.5 An independent t-test is a statistical test that compares the means of two independent samples. In an independent t-test, the data points in each sample are not related to each other. The independent t-test is a parametric test, which means that it assumes that the data is normally distributed. The independent t-test is also a two-sample test, which means that it compares the means of two independent samples.6,7
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?
NSA Stock Forecast (Buy or Sell) Strategic Interaction Table
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 Modular Neural Network (DNN Layer) based NSA Stock Prediction Model
- For lifetime expected credit losses, an entity shall estimate the risk of a default occurring on the financial instrument during its expected life. 12-month expected credit losses are a portion of the lifetime expected credit losses and represent the lifetime cash shortfalls that will result if a default occurs in the 12 months after the reporting date (or a shorter period if the expected life of a financial instrument is less than 12 months), weighted by the probability of that default occurring. Thus, 12-month expected credit losses are neither the lifetime expected credit losses that an entity will incur on financial instruments that it predicts will default in the next 12 months nor the cash shortfalls that are predicted over the next 12 months.
- The change in the value of the hedged item determined using a hypothetical derivative may also be used for the purpose of assessing whether a hedging relationship meets the hedge effectiveness requirements.
- An entity has not retained control of a transferred asset if the transferee has the practical ability to sell the transferred asset. An entity has retained control of a transferred asset if the transferee does not have the practical ability to sell the transferred asset. A transferee has the practical ability to sell the transferred asset if it is traded in an active market because the transferee could repurchase the transferred asset in the market if it needs to return the asset to the entity. For example, a transferee may have the practical ability to sell a transferred asset if the transferred asset is subject to an option that allows the entity to repurchase it, but the transferee can readily obtain the transferred asset in the market if the option is exercised. A transferee does not have the practical ability to sell the transferred asset if the entity retains such an option and the transferee cannot readily obtain the transferred asset in the market if the entity exercises its option
- For the purposes of applying the requirement in paragraph 5.7.7(a), credit risk is different from asset-specific performance risk. Asset-specific performance risk is not related to the risk that an entity will fail to discharge a particular obligation but instead it is related to the risk that a single asset or a group of assets will perform poorly (or not at all).
*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.
NSA National Storage Affiliates Trust Common Shares of Beneficial Interest Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | Ba3 |
Income Statement | Caa2 | Baa2 |
Balance Sheet | B1 | C |
Leverage Ratios | Ba2 | Baa2 |
Cash Flow | B3 | Ba3 |
Rates of Return and Profitability | Ba3 | 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?
References
- J. Ott. A Markov decision model for a surveillance application and risk-sensitive Markov decision processes. PhD thesis, Karlsruhe Institute of Technology, 2010.
- Van der Vaart AW. 2000. Asymptotic Statistics. Cambridge, UK: Cambridge Univ. Press
- Thomas P, Brunskill E. 2016. Data-efficient off-policy policy evaluation for reinforcement learning. In Pro- ceedings of the International Conference on Machine Learning, pp. 2139–48. La Jolla, CA: Int. Mach. Learn. Soc.
- A. Eck, L. Soh, S. Devlin, and D. Kudenko. Potential-based reward shaping for finite horizon online POMDP planning. Autonomous Agents and Multi-Agent Systems, 30(3):403–445, 2016
- M. Babes, E. M. de Cote, and M. L. Littman. Social reward shaping in the prisoner's dilemma. In 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2008), Estoril, Portugal, May 12-16, 2008, Volume 3, pages 1389–1392, 2008.
- Abadie A, Diamond A, Hainmueller J. 2010. Synthetic control methods for comparative case studies: estimat- ing the effect of California's tobacco control program. J. Am. Stat. Assoc. 105:493–505
- Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), "Modelling optimal strategies for the allocation of wealth in multicurrency investments," International Journal of Forecasting, 12, 483–493.
Frequently Asked Questions
Q: What is the prediction methodology for NSA stock?A: NSA stock prediction methodology: We evaluate the prediction models Modular Neural Network (DNN Layer) and Independent T-Test
Q: Is NSA stock a buy or sell?
A: The dominant strategy among neural network is to Buy NSA Stock.
Q: Is National Storage Affiliates Trust Common Shares of Beneficial Interest stock a good investment?
A: The consensus rating for National Storage Affiliates Trust Common Shares of Beneficial Interest is Buy and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of NSA stock?
A: The consensus rating for NSA is Buy.
Q: What is the prediction period for NSA stock?
A: The prediction period for NSA is 16 Weeks