Stock Assessment - Science topic

Dear Friends

Can you please help me where to download FAO-ICLARM Stock Assessment Tools II (FiSAT II) for my upcoming research work.

Evaluation of the fisheries resources of a selected project areas

Sample of collection and preparation stock assessment

What do total, natural, fishing mortality and exploitation rate mean in stock assessment? Kindly provide the concept in simple language rather than technical jargon.

Dear ResearchGate community,

I am currently working on a research project involving the stock assessment of shrimps using monthly length frequency data. To ensure the accuracy and reliability of my analysis, I am in the process of data cleaning and handling.

I would like to inquire about the recommended steps for data cleaning and handling specifically for monthly length frequency data in shrimp stock assessment. Additionally, I am curious to know whether it is necessary to remove outliers from the dataset and if doing so would be beneficial for the analysis.

Your valuable insights and expertise on this matter would be greatly appreciated, as they will significantly contribute to the quality of my research.

I am currently conducting research on shrimp stock assessment using the ‘TropFishR’ package to analyze a monthly carapace length frequency dataset. The package allows for the analysis of one year of data, specifically data collected from January to December of a particular year. Sample code for opening the library, working with an Excel file, and opening the dataset from the working directory is provided below:

However, if we have more than one year of data, how can we feed it into the ‘TropFishR’ package?

I am currently conducting research on shrimp stock assessment, and I am utilizing the ‘TropFishR’ Package to analyze a dataset containing monthly carapace length frequency data. The accurate calculation of natural mortality is essential for my analysis of the exploitation rate and other critical factors. In the ‘TropFishR’ package, there are several methods available for calculating shrimp mortality, including Alverson and Carney (1975), Hoenig (1983) - Joint Equation, Hoenig (1983) - Fish Equation, Pauly (1980) - Length Equation, Then (2015) – tmax, Then (2015) – growth, and other techniques.

However, the majority of these methods have been previously utilized for fish total length and standard length, which was not problematic. When I applied Then (2015) – growth and Pauly (1980) - Length Equation, two of the most widely used methods for calculating natural mortality, to the carapace length of shrimps, which is 3 to 6 times shorter than the total length, I observed abnormally high natural mortality rates.

To overcome this issue, I calculated the total length of the shrimp using a regression relationship between total length and carapace length, which allowed me to recalculate the natural mortality. Unfortunately, the calculated values still remained high (>2), with the exception of Alverson and Carney (1975) and Hoenig (1983) - Joint Equation methods, which yielded natural mortality rates of approximately 1.7.

I would greatly appreciate any suggestions or recommended articles that may assist me in addressing this issue.

In recent years, the importance of examining the correlation between stock market valuation of securities (stocks, bonds, etc.) and the economic and financial situation of business entities is growing, because there are more and more anomalies and speculations on capital markets, which may reduce these correlations. The importance of studying this issue is growing particularly when in certain listed markets valuations of certain financial assets and instruments are less and less related to economic fundamentals and the significance of speculation is growing. If such a lack of correlation between the stock market valuation and the economic and financial situation of business entities is growing, then the financial and / or economic crisis may occur. Such a situation of increase in speculative factors on stock exchanges and commodity exchange commodity markets appeared in 2006-2008 and contributed to the global financial crisis in 2008.

Do you agree with me on the above matter?

In the context of the above issues, I am asking you the following question:

Please reply

I invite you to the discussion

Thank you very much

Best wishes

Stock assessment  and management of fishable marine resources are ultimately based on fishing mortality. If the populations of fishable marine resources appear overexploited, the solution generally recommended to policy makers is to reduce fishing effort. Wisely, many policy makers take into account the mortality of the fishermen through measures such as "loss of income" etc .. Unfortunately policymakers, pressed by the fishermen’s lobby, often excessively favour the fishermen in fear of losing support.

What kind of information in the field of financial market psychology is in your opinion the most important, which should be taken into account when conducting technical analyzes of the valuation of securities listed on the stock exchange in order to achieve the best results from investing activities?

Please reply

Best wishes

For my thesis, I'm trying to annualize conditional volatility of past stock returns since it is the dependent variable in my research.

As an example, I ran in eViews the daily stock returns of a firm and calculated the volatility using GARCH (1,1) (as you can see in the attached picture).

My question is: Do I need to annualize the GARCH(-1) 0.738970 by multiplying it with sqrt(252)?

I'm currently doing a research for my dissertation about R&D expenditures and stock return volatility (both firm specific volatility and systematic volatility). So, volatility is the dependent variable in my study.

Most empirical research I’ve read use the annualized standard deviation of weekly returns as a measure of total volatility and annualized standard deviation of weekly errors from CAPM model to measure firm-specific volatility.

I'm planning on using GARCH (1,1) model to measure volatility since this was suggested by my professor. I’m planning on using weekly stock returns. My question is:

The research focuses on the mangrove forest ecosystem. Then it will conduct biomass and carbon stock estimation at the plot scale? How to scale up forest carbon stock assessment from plot scale to the regional scale? Which factors should be considered for scaling up?

Does the global financial crisis of 2008 still have significant importance on capital markets attributed to behavioral psychology of the behavior of investors operating in these markets?

Are the determinants of behavioral investors' factors still strong in recent years on the largest stock exchanges in the world, including the importance of financial market psychology in interpreting changes in stock exchange trends in these markets?

Please reply

I invite you to the discussion

Thank you very much

Best wishes

Do the significant revaluation of stock quotes on stock exchanges occurring every few or a dozen years is an objective specific feature of this type of financial market or rather it is imperfection of these markets resulting from too high a level of liberalization and deregulation of the mechanisms of these markets, including the reduction control functions of financial supervision institutions?

Since the 1970s, the functioning of individual segments of financial markets has been successively liberalized and deregulated, including primarily the issue of investment banking, international markets and exchange rate systems, rating agencies, financial adversity institutions and financial entities and instruments operating on the securities market. During this time, the scale of the re-valuation of valuations of securities, derivatives, commodities and other assets on the capital markets reached ever higher levels, then spectacularly transformed into a strong decline in these valuations leading to a financial and economic crisis. The last financial crisis in 2008 in many respects, including numerous negative aspects, generated the unruly records characterizing the highest level of investment risk and the scale of financial losses generated by many commercial financial institutions and industrial corporations, which then under the active, interventionist, anti-crisis monetary policy of banking were financed indirectly by public finance funds. Due to this cyclical nature of capital markets, characterized by the growing amplitude of economic fluctuations during periods of bull market and bear market at high levels of overvaluation and investment risk levels and deeper global financial and economic crises, large financial institutions, including investment banks, are becoming larger entities and costs neutralizing the negative aspects of crises is paid off by the whole society, especially by the relatively less-earning middle class.

In the light of the above, encouraging discussion, I turn to you with the following question: Has the time finally come to reform the functioning process and the system of financial markets by restoring former control functions of financial supervision institutions that have been abolished, reduced since the 1970s?

Are increasingly deep financial crises derived from the liberalization and deregulation of financial markets?

Please, answer, comments. I invite you to the discussion.

I want to know how the price level of a stock index is affect by the value of the GDP in a country, That is, the daily return of a stock index will be regressed on the change of year data of GDP. However, the frequency of the stock index is daily whereas that of GDP is yearly. How to convert the yearly data of GDP to the daily data in order to be regressed by the daily return of a stock index?

I have non-stationary time-series data for variables such as stock market returns, money supply, interest rates, exchange rate, inflation,etc. and I want to study the impact of these macroeconomic variables on stock returns.(I have taken the natural logarithms for all the variables)

I performed a linear regression which gave me spurious results (r-squared >0.9)

Now after testing these time series for unit roots using Augmented Dickey- Fuller test all of them were found to be non-stationary and hence the spurious regression. However their first differences were found to be stationary (for money supply - second difference was found to be stationary).

My question is how should I proceed now, can I apply linear regression to the first/second differences of these variables, would those results be non-spurious? If not, what is the way forward?

P.S. - I am an undergraduate student with little knowledge about econometrics.

P.P.S- I would like to apologize in advance if this is an inappropriate question for a forum like this one and if so, I would request anyone to guide me to a better forum where I can get my query resolved. 

In tropical condition what sort of fisheries stock assessment models recently developed ?

In forest carbon stock assessment, we usually use allometric equation to get AGB, times 0.5 to get carbon & times -44/12 to get CO2 emission (removal) value.

List the three categories of information that you think are the most important in fundamental analysis for the valuation of capital companies, issuers of securities and for investing in securities listed on the stock exchange?

Please reply

Best wishes

We work on stock assessment and our fishermen start their activity from september to march in next year. Chelon aurata and Chelon saliens were spawn from july to august and Rutius frisii kutum spawning time will start in march to may . Those species spawning time have relate to temperature .

I have started preparing a paper on population parameters and stock assessment of Daysciaena albida from Chilika Lake, India after continuous 5 years of data collection. If any published papers is available, will be strengthen my study.

In a OLS regression model, to test APT, I realize that I need to find the risk premium for macro factors such as GDP, and exchange rates, etc by subtracting the returns on 3-Months Tbills from macro factor returns. But, should I find the risk premiums for the security specific factors such as returns on sales, return on inventory (for particular company) etc. as above mentioned by subtracting the returns on bonds? Because, as I see, APT is often used to predict the return on diversified portfolios, minimizing the micro factors and thus consider only systematic risks. However, I would like to construct APT on single equity and consider macro as well as micro factors. But, I don't realize, is it necessary to, for example, subtract return on 3-Months Tbill from the return on sales for corresponding time period? Because I am not sure does it make any sense, as micro factors are not "alternative" to the investigated equity, while macro factors are some kind of "alternative" to the given equity. Thanks a lot.

Ventity from Ventana systems tells us how to go from Stock and Flow diagram (SFD) to Entity Relationship (ER) diagram. How to do the reverse? i.e. how to start with ER model and reach SFD. I think that is the natural way of progressing as domain knowledge is often given in the form of ER models. 

Dears,

I need some references that have examined the topic of Stock price or stock price crash risk in the German context. I will be really grateful.

if I have data on "α" and i calculate capital stocks using perpetual inventory method, can I calculate TFP using above equation?

if yes, then why literature suggest to use more complicated functions like derivatives and trans-log functions ?

When calculating returns, volatility or liquidity, I have a bunch of individual stocks that were not traded during substantial duration of exchange days in my sample observations.

How should I take care of the missing value of the stock prices?

Do I have to simply ignore them and work on the available observation only? If so, how does the uneven interval period of each individual stock affect the comparability of the results?

Instead, should I do statistical techniques to make imputation and fill in the non-traded stock days artificially? If so, should I limit the missing values that might be imputed, or is there any reasonal justification in making a limitation on how far the "missing" observations should be imputed or otherwise excluded?

I have been a mathematics fan for a long time. However, most of what I have studied is experimental in terms of quantum sciences or the natural phenomenons. It is easier to make variables there, in terms of observing them and then assigning them values. I have been trying to take mathematics and use it in stock exchanges to be able to predict things and make actual investments since I am very keen on doing that actual investments part.

I found a book called "A Mathematician Play the Stock Exchange" and I feel like that would be a good start. Does anyone have any recommendations that could help me move forward in the practical applications of the mathematics in stock change?

I am trying to describe stock returns of some european companies using APT model. I use 10 years of monthly data and I do not know which security should I use as risk-free rate for some Balkan countries and for Greece. Or should I use the same risk-free rate for all companies from European Union?

how can I run dummy variable regression model for January effect? is there any guide on how to use it?

Please , if anyone give the relations between these variables.

Best Regards,

Lo and MacKinlay's (1988) proposed variance ratio tests to test for random walk behaviour of stock returns. What is the rational behind these tests?

Folks,

May someone please share any excel where they have computed various measures of liquidity? e.g Amihud, ILLQ, Turnover Ration etc.

I am not good to decode the mathematical notations in white papers. So, if excel has formula it will be very easy and quick for me to understand.

My data includes closing prices, volume, float-able number of shares etc

Any help please?

Folks,

I am researching on the impact of a Corporate disclosure on future abnormal returns (in short term). 

After reading few whitepapers, i realized one of the accepted way to measure the impact is removing the Normal Return portion from total return, the residual is Abnormal Return.

Theory sounds good to me, but my question is, how can i calculate the Abnormal return for a Stock? ( I am not well versed with understanding mathematical notation in whitepapers)

I have Closing Price, volume, Corporate news and its disclosure date.

Any guidance on that please?

I want to get my head around corporate performance. More specifically how would I calculate the abnormal return of say 100 companies with daily data over a few months. So I can see that autocorrelation might be a problem but also factors such as economic news, industry news, firm specific news etc How would I control for those? I found some material on the Fama French Model and the granger causality test.  Is there any paper which walks though that in a less academic language?

What are the most practical data-limited approaches used to assess or manage invertebrate stocks such as lobster and conch when catch rates and age/structure are poorly known?

Thanks,

Shannon

Futures allocation using Shaking optimization algorithm.

Advice on (preferably non-destructive) ways to detect and monitor changes in fish and epibenthic communities after trawling is banned in an inshore tropical region (M-BACI design). The trouble is, the water is so highly turbid that video (e.g. BRUVS), more often than not, cannot be used.

I am working on the Octopus cyanea fishery in southwest Madagascar, and we are having problems obtaining accurate total production weight at a small scale from the commercial collectors. Would it be ok to run a stock assessment model using regional level total production data supplied by the Ministry of Fisheries, combined with village level CPUE as the effort measure? Any advice would be much appreciated.

I would like to construct a conceptual model for macroalgal standing stock which comprises production and natural losses (herbivory, fragmentation) etc.

Often we assume the results of a stock assessment model (SAM) represent the reality, so this results are used for management purposes. But, if a SAM is just a simple representation of reality, can we validate a SAM considering a more complex simulated reality?Or we just can validate the model ability to reproduce a simulated reality, but not the true reality?