**Outlook:**Tandy Leather Factory Inc. Common Stock is assigned short-term B2 & long-term B1 estimated rating.

**AUC Score :**

**Short-Term Revised**

^{1}:**Dominant Strategy :**BuySpeculative Trend

**Time series to forecast n:** for

^{2}

**Methodology :**Statistical Inference (ML)

**Hypothesis Testing :**Wilcoxon Sign-Rank Test

**Surveillance :**Major exchange and OTC

^{1}The accuracy of the model is being monitored on a regular basis.(15-minute period)

^{2}Time series is updated based on short-term trends.

## Summary

Tandy Leather Factory Inc. Common Stock prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Sign-Rank Test^{1,2,3,4}and it is concluded that the TLF stock is predictable in the short/long term. Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

^{5}

**According to price forecasts for 4 Weeks period, the dominant strategy among neural network is: BuySpeculative Trend**

## Key Points

- Statistical Inference (ML) for TLF stock price prediction process.
- Wilcoxon Sign-Rank Test
- What is the best way to predict stock prices?
- How accurate is machine learning in stock market?
- Decision Making

## TLF Stock Price Forecast

We consider Tandy Leather Factory Inc. Common Stock Decision Process with Statistical Inference (ML) where A is the set of discrete actions of TLF 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:**TLF Tandy Leather Factory Inc. Common Stock

**Time series to forecast:**4 Weeks

**According to price forecasts, the dominant strategy among neural network is: BuySpeculative Trend**

^{6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Statistical Inference (ML)) X S(n):→ 4 Weeks $\sum _{i=1}^{n}\left({r}_{i}\right)$

n:Time series to forecast

p:Price signals of TLF stock

j:Nash equilibria (Neural Network)

k:Dominated move of TLF stock holders

a:Best response for TLF target price

Statistical inference is a process of drawing conclusions about a population based on data from a sample of that population. In machine learning (ML), statistical inference is used to make predictions about new data based on data that has already been seen.

^{5}The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a non-parametric test that is used to compare the medians of two independent samples. It is a rank-based test, which means that it does not assume that the data is normally distributed. The Wilcoxon rank-sum test is calculated by first ranking the data from both samples, and then finding the sum of the ranks for one of the samples. The Wilcoxon rank-sum test statistic is then calculated by subtracting the sum of the ranks for one sample from the sum of the ranks for the other sample. The p-value for the Wilcoxon rank-sum test is calculated using a table of critical values. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true.

^{6,7}

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

### TLF 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 Statistical Inference (ML) based TLF Stock Prediction Model

- A hedge of a firm commitment (for example, a hedge of the change in fuel price relating to an unrecognised contractual commitment by an electric utility to purchase fuel at a fixed price) is a hedge of an exposure to a change in fair value. Accordingly, such a hedge is a fair value hedge. However, in accordance with paragraph 6.5.4, a hedge of the foreign currency risk of a firm commitment could alternatively be accounted for as a cash flow hedge.
- This Standard does not specify a method for assessing whether a hedging relationship meets the hedge effectiveness requirements. However, an entity shall use a method that captures the relevant characteristics of the hedging relationship including the sources of hedge ineffectiveness. Depending on those factors, the method can be a qualitative or a quantitative assessment.
- When designating a hedging relationship and on an ongoing basis, an entity shall analyse the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its term. This analysis (including any updates in accordance with paragraph B6.5.21 arising from rebalancing a hedging relationship) is the basis for the entity's assessment of meeting the hedge effectiveness requirements.
- For example, Entity A, whose functional currency is its local currency, has a firm commitment to pay FC150,000 for advertising expenses in nine months' time and a firm commitment to sell finished goods for FC150,000 in 15 months' time. Entity A enters into a foreign currency derivative that settles in nine months' time under which it receives FC100 and pays CU70. Entity A has no other exposures to FC. Entity A does not manage foreign currency risk on a net basis. Hence, Entity A cannot apply hedge accounting for a hedging relationship between the foreign currency derivative and a net position of FC100 (consisting of FC150,000 of the firm purchase commitment—ie advertising services—and FC149,900 (of the FC150,000) of the firm sale commitment) for a nine-month period.

*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.

### TLF Tandy Leather Factory Inc. Common Stock Financial Analysis*

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | B1 |

Income Statement | Caa2 | Caa2 |

Balance Sheet | Baa2 | Baa2 |

Leverage Ratios | Caa2 | B3 |

Cash Flow | Caa2 | Baa2 |

Rates of Return and Profitability | B2 | C |

*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

- Dimakopoulou M, Zhou Z, Athey S, Imbens G. 2018. Balanced linear contextual bandits. arXiv:1812.06227 [cs.LG]
- B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
- Kitagawa T, Tetenov A. 2015. Who should be treated? Empirical welfare maximization methods for treatment choice. Tech. Rep., Cent. Microdata Methods Pract., Inst. Fiscal Stud., London
- D. White. Mean, variance, and probabilistic criteria in finite Markov decision processes: A review. Journal of Optimization Theory and Applications, 56(1):1–29, 1988.
- Gentzkow M, Kelly BT, Taddy M. 2017. Text as data. NBER Work. Pap. 23276
- Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin's Press.
- Athey S, Imbens G. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113:7353–60

## Frequently Asked Questions

Q: Is TLF stock expected to rise?A: TLF stock prediction model is evaluated with Statistical Inference (ML) and Wilcoxon Sign-Rank Test and it is concluded that dominant strategy for TLF stock is BuySpeculative Trend

Q: Is TLF stock a buy or sell?

A: The dominant strategy among neural network is to BuySpeculative Trend TLF Stock.

Q: Is Tandy Leather Factory Inc. Common Stock stock a good investment?

A: The consensus rating for Tandy Leather Factory Inc. Common Stock is BuySpeculative Trend and is assigned short-term B2 & long-term B1 estimated rating.

Q: What is the consensus rating of TLF stock?

A: The consensus rating for TLF is BuySpeculative Trend.

Q: What is the forecast for TLF stock?

A: TLF target price forecast: BuySpeculative Trend

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