OXLCZ Oxford Lane Capital Corp. 5.00% Notes due 2027

Outlook: Oxford Lane Capital Corp. 5.00% Notes due 2027 assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Buy
Time series to forecast n: 28 Dec 2022 for (n+4 weeks)
Methodology : Deductive Inference (ML)

Abstract

In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions.(Xia, Y., Liu, Y. and Chen, Z., 2013, November. Support Vector Regression for prediction of stock trend. In 2013 6th international conference on information management, innovation management and industrial engineering (Vol. 2, pp. 123-126). IEEE.) We evaluate Oxford Lane Capital Corp. 5.00% Notes due 2027 prediction models with Deductive Inference (ML) and Multiple Regression^1,2,3,4 and conclude that the OXLCZ stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

Key Points

1. Is now good time to invest?
2. Market Risk
3. What is statistical models in machine learning?

OXLCZ Target Price Prediction Modeling Methodology

We consider Oxford Lane Capital Corp. 5.00% Notes due 2027 Decision Process with Deductive Inference (ML) where A is the set of discrete actions of OXLCZ 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(Multiple Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Deductive Inference (ML)) X S(n):→ (n+4 weeks) $\overrightarrow{R}=\left(r_1,r_2,r_3\right)$

n:Time series to forecast

p:Price signals of OXLCZ stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

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?

OXLCZ Stock Forecast (Buy or Sell) for (n+4 weeks)

Sample Set: Neural Network
Stock/Index: OXLCZ Oxford Lane Capital Corp. 5.00% Notes due 2027
Time series to forecast n: 28 Dec 2022 for (n+4 weeks)

According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

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%

IFRS Reconciliation Adjustments for Oxford Lane Capital Corp. 5.00% Notes due 2027

1. An entity may retain the right to a part of the interest payments on transferred assets as compensation for servicing those assets. The part of the interest payments that the entity would give up upon termination or transfer of the servicing contract is allocated to the servicing asset or servicing liability. The part of the interest payments that the entity would not give up is an interest-only strip receivable. For example, if the entity would not give up any interest upon termination or transfer of the servicing contract, the entire interest spread is an interest-only strip receivable. For the purposes of applying paragraph 3.2.13, the fair values of the servicing asset and interest-only strip receivable are used to allocate the carrying amount of the receivable between the part of the asset that is derecognised and the part that continues to be recognised. If there is no servicing fee specified or the fee to be received is not expected to compensate the entity adequately for performing the servicing, a liability for the servicing obligation is recognised at fair value.
2. In the reporting period that includes the date of initial application of these amendments, an entity is not required to present the quantitative information required by paragraph 28(f) of IAS 8.
3. If a put option written by an entity prevents a transferred asset from being derecognised and the entity measures the transferred asset at fair value, the associated liability is measured at the option exercise price plus the time value of the option. The measurement of the asset at fair value is limited to the lower of the fair value and the option exercise price because the entity has no right to increases in the fair value of the transferred asset above the exercise price of the option. This ensures that the net carrying amount of the asset and the associated liability is the fair value of the put option obligation. For example, if the fair value of the underlying asset is CU120, the option exercise price is CU100 and the time value of the option is CU5, the carrying amount of the associated liability is CU105 (CU100 + CU5) and the carrying amount of the asset is CU100 (in this case the option exercise price).
4. If an entity has applied paragraph 7.2.6 then at the date of initial application the entity shall recognise any difference between the fair value of the entire hybrid contract at the date of initial application and the sum of the fair values of the components of the hybrid contract at the date of initial application in the opening retained earnings (or other component of equity, as appropriate) of the reporting period that includes the date of initial application.

*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.

Conclusions

Oxford Lane Capital Corp. 5.00% Notes due 2027 assigned short-term Ba1 & long-term Ba1 estimated rating. We evaluate the prediction models Deductive Inference (ML) with Multiple Regression^1,2,3,4 and conclude that the OXLCZ stock is predictable in the short/long term. According to price forecasts for (n+4 weeks) period, the dominant strategy among neural network is: Buy

OXLCZ Oxford Lane Capital Corp. 5.00% Notes due 2027 Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	B2	C
Balance Sheet	Baa2	B1
Leverage Ratios	Baa2	Baa2
Cash Flow	Ba3	B2
Rates of Return and Profitability	B3	B2

*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?

Prediction Confidence Score

Trust metric by Neural Network: 81 out of 100 with 804 signals.

References

1. E. Altman. Constrained Markov decision processes, volume 7. CRC Press, 1999
2. Semenova V, Goldman M, Chernozhukov V, Taddy M. 2018. Orthogonal ML for demand estimation: high dimensional causal inference in dynamic panels. arXiv:1712.09988 [stat.ML]
3. A. Tamar, Y. Glassner, and S. Mannor. Policy gradients beyond expectations: Conditional value-at-risk. In AAAI, 2015
4. Hastie T, Tibshirani R, Wainwright M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York: CRC Press
5. Swaminathan A, Joachims T. 2015. Batch learning from logged bandit feedback through counterfactual risk minimization. J. Mach. Learn. Res. 16:1731–55
6. Hastie T, Tibshirani R, Tibshirani RJ. 2017. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv:1707.08692 [stat.ME]
7. Athey S, Imbens GW. 2017b. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31:3–32

Frequently Asked Questions

Q: What is the prediction methodology for OXLCZ stock?
A: OXLCZ stock prediction methodology: We evaluate the prediction models Deductive Inference (ML) and Multiple Regression
Q: Is OXLCZ stock a buy or sell?
A: The dominant strategy among neural network is to Buy OXLCZ Stock.
Q: Is Oxford Lane Capital Corp. 5.00% Notes due 2027 stock a good investment?
A: The consensus rating for Oxford Lane Capital Corp. 5.00% Notes due 2027 is Buy and assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of OXLCZ stock?
A: The consensus rating for OXLCZ is Buy.
Q: What is the prediction period for OXLCZ stock?
A: The prediction period for OXLCZ is (n+4 weeks)

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