EFOI Stock: Future Looks Bright; Should You Buy the Stock?

Outlook: Energy Focus Inc. Common Stock is assigned short-term B2 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised¹ :
Dominant Strategy : Buy
Time series to forecast n: for Weeks²
Methodology : Transfer Learning (ML)
Hypothesis Testing : Lasso 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

Energy Focus Inc. Common Stock prediction model is evaluated with Transfer Learning (ML) and Lasso Regression^1,2,3,4 and it is concluded that the EFOI stock is predictable in the short/long term. Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task.⁵ According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: Buy

Key Points

1. Transfer Learning (ML) for EFOI stock price prediction process.
2. Lasso Regression
3. Market Signals
4. Is Target price a good indicator?
5. How can neural networks improve predictions?

EFOI Stock Price Forecast

We consider Energy Focus Inc. Common Stock Decision Process with Transfer Learning (ML) where A is the set of discrete actions of EFOI 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: EFOI Energy Focus Inc. Common Stock
Time series to forecast: 8 Weeks

According to price forecasts, the dominant strategy among neural network is: Buy

F(Lasso Regression)^6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Transfer Learning (ML)) X S(n):→ 8 Weeks $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of EFOI stock

j:Nash equilibria (Neural Network)

k:Dominated move of EFOI stock holders

a:Best response for EFOI target price

Transfer learning is a machine learning (ML) method where a model developed for one task is reused as the starting point for a model on a second task. This can be useful when the second task is similar to the first task, or when there is limited data available for the second task.⁵ Lasso regression, also known as L1 regularization, 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 and to induce sparsity in the model. This is done by adding a term to the objective function that is proportional to the sum of the absolute values of the coefficients. The penalty term is called the "lasso" penalty, and it is controlled by a parameter called the "lasso constant". Lasso regression can be used to address the problem of multicollinearity in linear regression, as well as the problem of overfitting. 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. Overfitting occurs when a model is too closely fit to the training data, and as a result, it does not generalize well to new data.^6,7

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

EFOI 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 Transfer Learning (ML) based EFOI Stock Prediction Model

1. An entity's estimate of expected credit losses on loan commitments shall be consistent with its expectations of drawdowns on that loan commitment, ie it shall consider the expected portion of the loan commitment that will be drawn down within 12 months of the reporting date when estimating 12-month expected credit losses, and the expected portion of the loan commitment that will be drawn down over the expected life of the loan commitment when estimating lifetime expected credit losses.
2. Because the hedge accounting model is based on a general notion of offset between gains and losses on the hedging instrument and the hedged item, hedge effectiveness is determined not only by the economic relationship between those items (ie the changes in their underlyings) but also by the effect of credit risk on the value of both the hedging instrument and the hedged item. The effect of credit risk means that even if there is an economic relationship between the hedging instrument and the hedged item, the level of offset might become erratic. This can result from a change in the credit risk of either the hedging instrument or the hedged item that is of such a magnitude that the credit risk dominates the value changes that result from the economic relationship (ie the effect of the changes in the underlyings). A level of magnitude that gives rise to dominance is one that would result in the loss (or gain) from credit risk frustrating the effect of changes in the underlyings on the value of the hedging instrument or the hedged item, even if those changes were significant.
3. IFRS 17, issued in May 2017, amended paragraphs 2.1, B2.1, B2.4, B2.5 and B4.1.30, and added paragraph 3.3.5. Amendments to IFRS 17, issued in June 2020, further amended paragraph 2.1 and added paragraphs 7.2.36‒7.2.42. An entity shall apply those amendments when it applies IFRS 17.
4. When an entity discontinues measuring the financial instrument that gives rise to the credit risk, or a proportion of that financial instrument, at fair value through profit or loss, that financial instrument's fair value at the date of discontinuation becomes its new carrying amount. Subsequently, the same measurement that was used before designating the financial instrument at fair value through profit or loss shall be applied (including amortisation that results from the new carrying amount). For example, a financial asset that had originally been classified as measured at amortised cost would revert to that measurement and its effective interest rate would be recalculated based on its new gross carrying amount on the date of discontinuing measurement at fair value through profit or loss.

*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.

EFOI Energy Focus Inc. Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	B2	Ba3
Income Statement	Ba2	Caa2
Balance Sheet	Caa2	Baa2
Leverage Ratios	Baa2	B2
Cash Flow	Caa2	Caa2
Rates of Return and Profitability	Caa2	Baa2

*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

1. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
2. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.
3. A. Tamar, D. Di Castro, and S. Mannor. Policy gradients with variance related risk criteria. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, pages 387–396, 2012.
4. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55
5. F. A. Oliehoek, M. T. J. Spaan, and N. A. Vlassis. Optimal and approximate q-value functions for decentralized pomdps. J. Artif. Intell. Res. (JAIR), 32:289–353, 2008
6. Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60
7. V. Borkar and R. Jain. Risk-constrained Markov decision processes. IEEE Transaction on Automatic Control, 2014

Frequently Asked Questions

Q: Is EFOI stock expected to rise?
A: EFOI stock prediction model is evaluated with Transfer Learning (ML) and Lasso Regression and it is concluded that dominant strategy for EFOI stock is Buy
Q: Is EFOI stock a buy or sell?
A: The dominant strategy among neural network is to Buy EFOI Stock.
Q: Is Energy Focus Inc. Common Stock stock a good investment?
A: The consensus rating for Energy Focus Inc. Common Stock is Buy and is assigned short-term B2 & long-term Ba3 estimated rating.
Q: What is the consensus rating of EFOI stock?
A: The consensus rating for EFOI is Buy.
Q: What is the forecast for EFOI stock?
A: EFOI target price forecast: Buy