AUC Score :
Short-Term Revised1 :
Dominant Strategy : Speculative Trend
Time series to forecast n:
Methodology : Modular Neural Network (DNN Layer)
Hypothesis Testing : Ridge Regression
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
JPMORGAN INDIAN INVESTMENT TRUST PLC prediction model is evaluated with Modular Neural Network (DNN Layer) and Ridge Regression1,2,3,4 and it is concluded that the LON:JII 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 6 Month period, the dominant strategy among neural network is: Speculative Trend
Key Points
- Modular Neural Network (DNN Layer) for LON:JII stock price prediction process.
- Ridge Regression
- How do you pick a stock?
- Understanding Buy, Sell, and Hold Ratings
- Is Target price a good indicator?
LON:JII Stock Price Forecast
We consider JPMORGAN INDIAN INVESTMENT TRUST PLC Decision Process with Modular Neural Network (DNN Layer) where A is the set of discrete actions of LON:JII 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: LON:JII JPMORGAN INDIAN INVESTMENT TRUST PLC
Time series to forecast: 6 Month
According to price forecasts, the dominant strategy among neural network is: Speculative Trend
n:Time series to forecast
p:Price signals of LON:JII stock
j:Nash equilibria (Neural Network)
k:Dominated move of LON:JII stock holders
a:Best response for LON:JII 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 Ridge regression is a type of regression analysis that adds a penalty to the least squares objective function in order to reduce the variance of the estimates. This is done by adding a term to the objective function that is proportional to the sum of the squares of the coefficients. The penalty term is called the "ridge" penalty, and it is controlled by a parameter called the "ridge constant". Ridge regression can be used to address the problem of multicollinearity in linear regression. Multicollinearity occurs when two or more independent variables are highly correlated. This can cause the standard errors of the coefficients to be large, and it can also cause the coefficients to be unstable. Ridge regression can help to reduce the standard errors of the coefficients and to make the coefficients more stable.6,7
For further technical information as per how our model work we invite you to visit the article below:
LON:JII 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 LON:JII Stock Prediction Model
- An equity method investment cannot be a hedged item in a fair value hedge. This is because the equity method recognises in profit or loss the investor's share of the investee's profit or loss, instead of changes in the investment's fair value. For a similar reason, an investment in a consolidated subsidiary cannot be a hedged item in a fair value hedge. This is because consolidation recognises in profit or loss the subsidiary's profit or loss, instead of changes in the investment's fair value. A hedge of a net investment in a foreign operation is different because it is a hedge of the foreign currency exposure, not a fair value hedge of the change in the value of the investment.
- The purpose of estimating expected credit losses is neither to estimate a worstcase scenario nor to estimate the best-case scenario. Instead, an estimate of expected credit losses shall always reflect the possibility that a credit loss occurs and the possibility that no credit loss occurs even if the most likely outcome is no credit loss.
- Paragraph 4.1.1(b) requires an entity to classify a financial asset on the basis of its contractual cash flow characteristics if the financial asset is held within a business model whose objective is to hold assets to collect contractual cash flows or within a business model whose objective is achieved by both collecting contractual cash flows and selling financial assets, unless paragraph 4.1.5 applies. To do so, the condition in paragraphs 4.1.2(b) and 4.1.2A(b) requires an entity to determine whether the asset's contractual cash flows are solely payments of principal and interest on the principal amount outstanding.
- 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.
*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.
LON:JII JPMORGAN INDIAN INVESTMENT TRUST PLC Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | Ba2 | Ba3 |
Income Statement | Ba1 | B2 |
Balance Sheet | Baa2 | B1 |
Leverage Ratios | Baa2 | Ba2 |
Cash Flow | B3 | Baa2 |
Rates of Return and Profitability | Caa2 | B3 |
*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
- H. Khalil and J. Grizzle. Nonlinear systems, volume 3. Prentice hall Upper Saddle River, 2002.
- Arora S, Li Y, Liang Y, Ma T. 2016. RAND-WALK: a latent variable model approach to word embeddings. Trans. Assoc. Comput. Linguist. 4:385–99
- J. G. Schneider, W. Wong, A. W. Moore, and M. A. Riedmiller. Distributed value functions. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999, pages 371–378, 1999.
- M. Colby, T. Duchow-Pressley, J. J. Chung, and K. Tumer. Local approximation of difference evaluation functions. In Proceedings of the Fifteenth International Joint Conference on Autonomous Agents and Multiagent Systems, Singapore, May 2016
- Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
- Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70:191–221
- Chernozhukov V, Escanciano JC, Ichimura H, Newey WK. 2016b. Locally robust semiparametric estimation. arXiv:1608.00033 [math.ST]
Frequently Asked Questions
Q: Is LON:JII stock expected to rise?A: LON:JII stock prediction model is evaluated with Modular Neural Network (DNN Layer) and Ridge Regression and it is concluded that dominant strategy for LON:JII stock is Speculative Trend
Q: Is LON:JII stock a buy or sell?
A: The dominant strategy among neural network is to Speculative Trend LON:JII Stock.
Q: Is JPMORGAN INDIAN INVESTMENT TRUST PLC stock a good investment?
A: The consensus rating for JPMORGAN INDIAN INVESTMENT TRUST PLC is Speculative Trend and is assigned short-term Ba2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of LON:JII stock?
A: The consensus rating for LON:JII is Speculative Trend.
Q: What is the forecast for LON:JII stock?
A: LON:JII target price forecast: Speculative Trend
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