AUC Score :
Short-Term Revised1 :
Dominant Strategy : Sell
Time series to forecast n:
Methodology : Modular Neural Network (Speculative Sentiment Analysis)
Hypothesis Testing : Chi-Square
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
LightJump Acquisition Corporation Warrant prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Chi-Square1,2,3,4 and it is concluded that the LJAQW stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for speculative sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell
Key Points
- What is the best way to predict stock prices?
- Investment Risk
- What are the most successful trading algorithms?
LJAQW Target Price Prediction Modeling Methodology
We consider LightJump Acquisition Corporation Warrant Decision Process with Modular Neural Network (Speculative Sentiment Analysis) where A is the set of discrete actions of LJAQW 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(Chi-Square)5,6,7= X R(Modular Neural Network (Speculative Sentiment Analysis)) X S(n):→ 1 Year
n:Time series to forecast
p:Price signals of LJAQW stock
j:Nash equilibria (Neural Network)
k:Dominated move
a:Best response for target price
Modular Neural Network (Speculative Sentiment Analysis)
A modular neural network (MNN) is a type of artificial neural network that can be used for speculative sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of speculative sentiment analysis, MNNs can be used to identify the sentiment of people who are speculating about the future value of an asset, such as a stock or a cryptocurrency. This information can then be used to make investment decisions, to identify trends in the market, and to target investors with relevant advertising.Chi-Square
A chi-squared test is a statistical hypothesis test that assesses whether observed frequencies in a sample differ significantly from expected frequencies. It is one of the most widely used statistical tests in the social sciences and in many areas of observational research. The chi-squared test is a non-parametric test, meaning that it does not assume that the data is normally distributed. This makes it a versatile tool that can be used to analyze a wide variety of data. There are two main types of chi-squared tests: the chi-squared goodness of fit test and the chi-squared test of independence.
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?
LJAQW Stock Forecast (Buy or Sell)
Sample Set: Neural NetworkStock/Index: LJAQW LightJump Acquisition Corporation Warrant
Time series to forecast: 1 Year
According to price forecasts, the dominant strategy among neural network is: Sell
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 (Speculative Sentiment Analysis) based LJAQW Stock Prediction Model
- A similar example of a non-financial item is a specific type of crude oil from a particular oil field that is priced off the relevant benchmark crude oil. If an entity sells that crude oil under a contract using a contractual pricing formula that sets the price per barrel at the benchmark crude oil price minus CU10 with a floor of CU15, the entity can designate as the hedged item the entire cash flow variability under the sales contract that is attributable to the change in the benchmark crude oil price. However, the entity cannot designate a component that is equal to the full change in the benchmark crude oil price. Hence, as long as the forward price (for each delivery) does not fall below CU25, the hedged item has the same cash flow variability as a crude oil sale at the benchmark crude oil price (or with a positive spread). However, if the forward price for any delivery falls below CU25, the hedged item has a lower cash flow variability than a crude oil sale at the benchmark crude oil price (or with a positive spread).
- For the purpose of applying paragraph 6.5.11, at the point when an entity amends the description of a hedged item as required in paragraph 6.9.1(b), the amount accumulated in the cash flow hedge reserve shall be deemed to be based on the alternative benchmark rate on which the hedged future cash flows are determined.
- An entity shall apply the impairment requirements in Section 5.5 retrospectively in accordance with IAS 8 subject to paragraphs 7.2.15 and 7.2.18–7.2.20.
- An alternative benchmark rate designated as a non-contractually specified risk component that is not separately identifiable (see paragraphs 6.3.7(a) and B6.3.8) at the date it is designated shall be deemed to have met that requirement at that date, if, and only if, the entity reasonably expects the alternative benchmark rate will be separately identifiable within 24 months. The 24-month period applies to each alternative benchmark rate separately and starts from the date the entity designates the alternative benchmark rate as a non-contractually specified risk component for the first time (ie the 24- month period applies on a rate-by-rate basis).
*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.
LJAQW LightJump Acquisition Corporation Warrant Financial Analysis*
Rating | Short-Term | Long-Term Senior |
---|---|---|
Outlook* | B2 | Ba3 |
Income Statement | Caa2 | Ba3 |
Balance Sheet | Ba3 | B1 |
Leverage Ratios | Baa2 | B3 |
Cash Flow | Caa2 | Baa2 |
Rates of Return and Profitability | Caa2 | Caa2 |
*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
LightJump Acquisition Corporation Warrant is assigned short-term B2 & long-term Ba3 estimated rating. LightJump Acquisition Corporation Warrant prediction model is evaluated with Modular Neural Network (Speculative Sentiment Analysis) and Chi-Square1,2,3,4 and it is concluded that the LJAQW stock is predictable in the short/long term. According to price forecasts for 1 Year period, the dominant strategy among neural network is: Sell
Prediction Confidence Score
References
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- Bottou L. 1998. Online learning and stochastic approximations. In On-Line Learning in Neural Networks, ed. D Saad, pp. 9–42. New York: ACM
- Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
- Burgess, D. F. (1975), "Duality theory and pitfalls in the specification of technologies," Journal of Econometrics, 3, 105–121.
- Varian HR. 2014. Big data: new tricks for econometrics. J. Econ. Perspect. 28:3–28
- Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W. 2017. Double/debiased/ Neyman machine learning of treatment effects. Am. Econ. Rev. 107:261–65
- Imai K, Ratkovic M. 2013. Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Stat. 7:443–70
Frequently Asked Questions
Q: What is the prediction methodology for LJAQW stock?A: LJAQW stock prediction methodology: We evaluate the prediction models Modular Neural Network (Speculative Sentiment Analysis) and Chi-Square
Q: Is LJAQW stock a buy or sell?
A: The dominant strategy among neural network is to Sell LJAQW Stock.
Q: Is LightJump Acquisition Corporation Warrant stock a good investment?
A: The consensus rating for LightJump Acquisition Corporation Warrant is Sell and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of LJAQW stock?
A: The consensus rating for LJAQW is Sell.
Q: What is the prediction period for LJAQW stock?
A: The prediction period for LJAQW is 1 Year
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