| LHC Stock | | | 71,400 200.00 0.28% |
This module uses fundamental data of LHC to approximate its Piotroski F score. LHC F Score is determined by combining nine binary scores representing 3 distinct fundamental categories of LHC. These three categories are profitability, efficiency, and funding. Some research analysts and sophisticated value traders use Piotroski F Score to find opportunities outside of the conventional market and financial statement analysis.They believe that some of the new information about LHC financial position does not get reflected in the current market share price suggesting a possibility of arbitrage. Check out
Correlation Analysis to better understand how to build diversified portfolios, which includes a position in LHC. Also, note that the market value of any company could be closely tied with the direction of predictive economic indicators such as
signals in board of governors.
At this time, it appears that LHC's Piotroski F Score is Inapplicable. Although some professional money managers and academia have recently criticized
Piotroski F-Score model, we still consider it an effective method of
predicting the state of the financial strength of any organization that is not predisposed to accounting gimmicks and manipulations. Using this score on the criteria to originate an efficient long-term portfolio can help investors filter out the purely speculative stocks or equities playing fundamental games by manipulating their earnings..
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Piotroski F Score - Inapplicable
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LHC Piotroski F Score Drivers
The critical factor to consider when applying the Piotroski F Score to LHC is to make sure LHC is not a subject of accounting manipulations and runs a healthy internal audit department. So, if LHC's auditors report directly to the board (not management), the managers will be reluctant to manipulate simply due to the fear of punishment. On the other hand, the auditors will be free to investigate the ledgers properly because they know that the board has their back. Below are the main accounts that are used in the Piotroski F Score model. By analyzing the historical trends of the mains drivers, investors can determine if LHC's financial numbers are properly reported.
About LHC Piotroski F Score
F-Score is one of many stock grading techniques developed by Joseph Piotroski, a professor of accounting at the Stanford University Graduate School of Business. It was published in 2002 under the paper titled
Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers. Piotroski F Score is based on binary analysis strategy in which stocks are given one point for passing 9 very simple fundamental tests, and zero point otherwise. According to Mr. Piotroski's analysis, his F-Score binary model can help to predict the performance of low price-to-book stocks.
About LHC Fundamental Analysis
The Macroaxis Fundamental Analysis modules help investors analyze LHC's financials across various querterly and yearly statements, indicators and fundamental ratios. We help investors to determine the real value of LHC using virtually all public information available. We use both quantitative as well as qualitative analysis to arrive at
the intrinsic value of LHC based on its fundamental data. In general, a quantitative approach, as applied to this company, focuses on analyzing
financial statements comparatively, whereas a qaualitative method uses data that is important to a company's growth but cannot be measured and presented in a numerical way.
Please read more on our
fundamental analysis page.
Pair Trading with LHC
One of the main advantages of trading using pair correlations is that every trade hedges away some risk. Because there are two separate transactions required, even if LHC position performs unexpectedly, the other equity can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in LHC will appreciate offsetting losses from the drop in the long position's value.
The ability to find closely correlated positions to LHC could be a great tool in your tax-loss harvesting strategies, allowing investors a quick way to find a similar-enough asset to replace LHC when you sell it. If you don't do this, your portfolio allocation will be skewed against your target asset allocation. So, investors can't just sell and buy back LHC - that would be a violation of the tax code under the "wash sale" rule, and this is why you need to find a similar enough asset and use the proceeds from selling LHC to buy it.
The correlation of LHC is a statistical measure of how it moves in relation to other instruments. This measure is expressed in what is known as the correlation coefficient, which ranges between -1 and +1. A perfect positive correlation (i.e., a correlation coefficient of +1) implies that as LHC moves, either up or down, the other security will move in the same direction. Alternatively, perfect negative correlation means that if LHC moves in either direction, the perfectly negatively correlated security will move in the opposite direction. If the correlation is 0, the equities are not correlated; they are entirely random. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally considered weak.
Correlation analysis and pair trading evaluation for LHC can also be used as hedging techniques within a particular sector or industry or even over random equities to generate a better risk-adjusted return on your portfolios.
Other Information on Investing in LHC Stock
LHC financial ratios help investors to determine whether LHC Stock is cheap or expensive when compared to a particular measure, such as profits or enterprise value. In other words, they help investors to determine the cost of investment in LHC with respect to the benefits of owning LHC security.