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edsporl · 6 years ago

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CHAPTER 1 The Basic Rules

Whose poker game is it anyway?

One way to look at investing in the markets is to understand this analogy. If you and five of your friends got together once a week or so for a friendly poker game you would find that at the end of a year each of you would have won or lost a small amount of money. None of you would have taken all the money and no one would have consistently lost everything. Over the course of the year, each player would have had his share of good and bad hands and, in the end, no one would have been much luckier than anyone else.

Now, let’s suppose that one of your friends leaves the regular weekly game and a new player replaces him. Only this time the new player is a professional. He makes his living playing poker. What you would find is that at the end of a year of playing all, or most, of the casual players will have lost consistently, but the professional will have won a large amount. This is not because he got substantially better cards than anyone else, since no one is cheating, but that whatever cards he was dealt, he played better. So he won frequently and most of the other players lost.

Let’s take this analogy further and suppose that you, as an amateur, get invited to play in a game with five professionals. In this case, at the end of a year each of the professionals will have won or lost with roughly equal frequency and each would end up with about the same amount of money, but you will have lost consistently. This will happen, not because you got poor cards or because the professionals cheated, but because they out-played you. This is what investing in the markets is like.

Because the markets are mostly efficient, that is, the same information is available to everyone and the price of a security usually reflects everything that is knowable about that security, it’s not availability of the information that separates the winners from the losers. It is the skill with which they play. Winners have developed a set of disciplines and a methodology for investing that amateurs just don’t have. This doesn’t mean that all of the professionals outperform each other or the markets themselves. In fact, most of the time they roughly equal the market and each other. Someone looking at results like these may jump to the conclusion, erroneously, that all the professionals are stupid and are not earning their fee, or that anyone playing the game could do as well or better, but that would be wrong.

As in the poker game, it is hard for the professional players to consistently beat each other, but the amateur will usually do substantially worse than they will if he plays long enough. If an amateur wants to play in a game like this, the best thing he can do is hire a professional to play for him and be happy to pay him a fee. The fee is small in comparison to the losses the amateur will sustain playing in a game with professionals.

In the long run we’re all dead.

This idea is a reminder of an important aspect of investing, namely, that all investment decisions are made in the short run. Consequently, any study that shows, for example, that stocks outperform bonds over twenty or fifty-year time periods may be interesting, but it does not tell you what will happen this year or next, which is the period an investor is usually interested in. Long-term studies are the basis of much of the academic analysis of investing and, as such, provide some insight into how the markets have generally worked in the past. In an overall sense, this information may be of some value, but it should not be used to predict the immediate future. As far as the markets are concerned, that’s the point to remember.

No two time periods are exactly the same.

In the financial markets, it’s often the case that studies of financial conditions are done over particular, arbitrarily-selected time periods. Often long-term studies may not be possible because the data does not exist. For example, a company that has been in business only for five or ten years cannot be the subject of a thirty-year study. However, this rule also applies even if there is plenty of data.

A lot happens in a five-year or a thirty-year period. Wars are won or lost, economies go through booms and busts, regulations change and science marches ahead. To say that two different time periods are comparable stretches credibility. Certainly the person conducting such a study bears the burden of demonstrating that his conclusions can be applied to other time periods. However it is easy for investors to accept the results without questioning the basic premise of whether the time periods are really comparable. This is a dangerous practice.

Don’t confuse precision with accuracy.

In today’s world of easy access to computers one can effortlessly carry out any calculation to numerous decimal places. For example, if someone said that a package weighs 35.7896543 ounces, the natural tendency of anyone looking at this result is to assume that the scale used to weigh it was very accurate. While this could be true for scales, it is certainly not true for statistical measures, which are what investors often deal with.

Consider these examples. One case where an average is of no use in predicting future results is for a system that is not stable. Take a series of numbers that looks like this: 1, 3, 5, 7, 9. The average of this series is 5, or 5.00000, if you want to carry out the result to more decimal places. But the next few numbers in this series are likely to be 11, 13, 15, 17…, not 5. In this simple example it is more important to look at the numbers first and to see if using an average makes sense than it is to become mesmerized by the precision of the result.

The conclusions that an investor can draw from statistics are strongly affected by another factor. Most statistics become more reliable predictors of the future when they are based on many observations. At the same time, most investment-derived statistics are based on a small number of observations. Here’s an example. Say that the price-earnings ratio for a company in the last two years was 15 and 20. The average of these is 17.5, or 17.500000, with more precision. However, whether 17.5 is a useful number in the first place depends on whether it is representative of the company’s usual price-earnings ratio, which may be much higher or lower than it was in those two years. Consequently, this precise number is of little use in making an investment decision. Even in a stable system, a very precise number may not be useful if it is based on only a few observations.

You know from listening to reports of opinion surveys, for example, that pollsters like to sample as many as 1500 people before they can rely on the results to tell them what the population as a whole is thinking. Even with this large a sample they don’t get it right all the time. There is one thing you can count on: no statistic you’ll see in the investment field will be based on 1500 observations. You’ll be lucky if there are 5 or 10. So the predictability of most investment statistics is low to start with. Always examine statistics closely before you try to apply them to your investment decisions. Remember, just because a statistic is carried out to a lot of decimal places, this doesn’t make it any good for predicting anything.

Managing a diversified portfolio is investing; Managing an un-diversified portfolio is gambling.

It’s important to understand this distinction. A diversified portfolio should look a lot like its market. A diversified portfolio of stocks behaves like the stock market; a diversified portfolio of bonds behaves like the bond market. Virtually all of the analytical tools that an investor has at his disposal are designed to work on diversified portfolios. Fundamental analysis, for example, which looks at a company’s balance sheet and earnings as a guide to whether it is a good buy, may or may not work well on an individual stock, but it works extremely well on a portfolio.

How many issues does a portfolio have to contain to be diversified? That depends a little on whether the companies are in similar or different industries, but, as a general rule, a portfolio of about 25 stocks in a variety of industries is fairly well diversified. Some theorists may want to debate this point, but from a practical standpoint, 25 stocks are enough. More would be better. The more stocks you own, the more your portfolio will look like the market and the less the fortunes, or misfortunes, of one company will affect your investment outcome.

This presents the biggest problem for individual investors and explains the biggest mistake that they often make. It takes quite a lot of work to dig up information on 25 different companies. It also takes a lot of work to keep a portfolio of 25 stocks in balance so that a few do not outweigh most of the others. I believe that most portfolios managed by individual investors are not diversified because it is hard to keep track of so many issues.

In the field of investing, risk has two meanings.

The term risk is confusing because in everyday use it means the chances of losing all of your money. This is the best way to think of it from a psychological perspective. If your portfolio has gone down a lot recently, it will appear to be much riskier than it did before the decline. In the academic world, however, risk just means the volatility of your portfolio over long periods of time.

· The Fear of Large Losses – This is the sick feeling you get in your stomach when your portfolio has taken a big hit in the short run.

· Volatility – This is measured by the Standard Deviation of the Returns over a Long Period of time.

Measure return by comparing cash in to cash out.

The only real way to measure your return over a period of time is to compare the value of a portfolio on one day to its value on another day. You’ve no doubt heard someone argue, “It doesn’t matter that my stock, or bond, is down in price. I’m not planning to sell it anyway.” This is a common line of reasoning, but if you are going to measure how you’ve done in the market, you have to use a yardstick. The best one is what your portfolio would be worth if you sold it. That is the best measure of its value at a point in time and the change in it’s value is the best measure of it’s return.

Asset allocation is the most important determinate of both the risk and return on a portfolio.

One should not lose sight of this general rule. If you own all stocks, your portfolio will behave like the stock market. If you own only cash instruments, it will behave like cash. There is no practical way to pick a portfolio of stocks that will not go down when the stock market does. This means that the first decision an investor must make is what does he want his portfolio to look like from a risk and return standpoint. This will determine the asset allocation. There is a picture of your basic asset allocation choices on Figure 1.

Is the earth round or is it flat?

Of the various rules that I’ve given you, this one best describes what day-to-day investing is really like. The image to keep in mind is this: think of an earlier era, a time of exploration when stern men in small ships set out from Europe sailing west on voyages of discovery. So the story goes, most people at the time thought the earth was flat and that if one sailed far enough, eventually he’d fall off. These sailors stayed close to shore. Yet others, who thought that the earth was round, believed that what really lay beyond the horizon was a world of riches and adventure. Whatever their beliefs, however, the one thing that made the difference between those who survived and those who did not was how they handled their ships during storms. Whether the earth was round or flat ultimately mattered little if they did not get this part right. This means that, whatever your theories on investing may be, you have to get it right in the short run to be successful.

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theliberaltony · 6 years ago

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via Politics – FiveThirtyEight

As the Ukraine scandal continues to dominate the headlines, former Vice President Joe Biden remains the most-mentioned candidate on cable news. But even though Biden has been getting so much attention, Sen. Elizabeth Warren has been slowly and steadily rising in popularity (although not at his expense). Last Tuesday, Warren surpassed Biden in the RealClearPolitics average of polls for the first time. It should come as no surprise, then, that Warren is the next most-mentioned candidate on cable news after Biden and that her share of coverage increased last week from the previous week, according to data from the TV News Archive,1 which chops up TV news into 15-second clips. Though this column typically also includes data from Media Cloud, a database of online news stories, that data is temporarily unavailable due to site maintenance.

Warren got more attention on cable news last week

Share of 15-second cable news clips mentioning each candidate

Cable TV clips the week of … Candidate 9/29/19 10/6/19 diff Joe Biden 69.3% 65.7% -3.6 Elizabeth Warren 12.8 20.2 +7.3 Bernie Sanders 13.9 13.9 +0.0 Kamala Harris 4.0 3.8 -0.2 Beto O’Rourke 1.7 2.3 +0.7 Tom Steyer 0.5 1.4 +0.9 Amy Klobuchar 1.1 1.3 +0.2 Pete Buttigieg 2.1 1.3 -0.9 Cory Booker 2.0 1.2 -0.8 Julián Castro 0.3 0.5 +0.3 Andrew Yang 0.9 0.5 -0.5 Tulsi Gabbard 0.3 0.3 +0.0 Michael Bennet 0.1 0.1 -0.1 John Delaney 0.0 0.1 +0.1 Steve Bullock 0.3 0.0 -0.3 Marianne Williamson 0.1 0.0 -0.1 Tim Ryan 0.2 0.0 -0.2 Joe Sestak 0.0 0.0 +0.0

Includes all candidates that qualify as “major” in FiveThirtyEight’s rubric. Each network’s daily news coverage is chopped up into 15-second clips, and each clip that includes a candidate’s name is counted as one mention. Our search queries look for an exact match for each candidate’s name, except for Julián Castro, for whom our search query is “Julian Castro” OR “Julián Castro.” Percentages are calculated as the number of clips mentioning each candidate divided by the number of clips mentioning any of the 2020 Democratic contenders for that week.

Sources: Internet Archive’s Television News Archive via the GDELT Project

But Warren’s rise in coverage isn’t all about how well she’s doing in the polls, and it’s also not evenly distributed across the three networks that we monitor (CNN, Fox News and MSNBC). This week she was mentioned in 327 clips on Fox News, but only 176 on MSNBC and 108 on CNN, marking the second week in a row that she has been mentioned significantly more on Fox News compared to the other networks.

And it’s not just the amount of coverage that differs. Fox News is also focusing on stories that the other two networks are not devoting as much time to. “Hillary Clinton” and “visibly pregnant” are among the top three two-word phrases most particular to Fox News in clips about Elizabeth Warren last week.2 The phrase “Hillary Clinton” — which appeared in 17 clips on Fox that mentioned Warren, but only two on each CNN and MSNBC — appeared often in segments about Clinton’s response to a tweet from President Trump suggesting Clinton should run for president and “steal it away” from Warren. And the phrase “visibly pregnant,” which appeared in 14 Fox News clips last week but wasn’t mentioned at all on CNN or MSNBC, occurred in segments about Warren’s response to allegations that she misrepresented the details of her departure from a job as a special education teacher in the early 1970s.

If Warren continues to rise in the polls, she could get more media attention (and scrutiny) than she has in the past. We’ll be monitoring just how much attention she gets in cable and online news, and whether the amount and content continues to diverge across different media sources. Stay tuned!

Check out the data behind this series and check back each week for an update on which candidates are getting the most coverage on cable and online.

#IFTTT #Politics – FiveThirtyEight #politics #538 #Dhrumil Mehta

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engrflores · 4 years ago

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Machine learning for Data Analysis

Machine learning for Data Analysis - Week 4 ( Running a k-means Cluster Analysis )

This is the last assignment for the Machine learning for data analysis course, fourth from a series of five courses from Data Analysis and Interpretation administered by Wesleyan University. In this assignment, we have to run a K-means Cluster Analysis.

A k-means cluster analysis was conducted to identify underlying subgroups based on their similarity of responses on 7 variables that represent characteristics that could have an impact on Personal Income. Clustering variables included three binary variables Gender (0=Female,1=Male), Major depression (0=No,1=Yes) and Present situation includes Working Full Time (0=No, 1=Yes), as well as other categorical variables Educational attainment, Self-perceived current health, How often drank liquor and Marital status. All clustering variables were standardized to have a mean of 0 and a standard deviation of 1.

Data were randomly split into a training set that included 70% of the observations and a test set that included 30% of the observations. A series of k-means cluster analyses were conducted on the training data specifying k=1-9 clusters, using Euclidean distance. The variance in the clustering variables that was accounted for by the clusters (r-square) was plotted for each of the nine cluster solutions in an elbow curve to provide guidance for choosing the number of clusters to interpret.

Running a k-means Cluster Analysis

The first thing to do is to import the libraries and prepare the data to be used. To run the k-means Cluster Analysis we must standardize the predictors to have mean = 0 and standard deviation = 1. After that, we make 9 analysis with the data, the first one with one cluster increasing a cluster per experiment.

Selecting K with the Elbow Method

In our case, the bend in the elbow appears to be at two clusters and at three clusters.

To help us figure out which of the solutions is best, we are going to use the canonical discriminate analysis.

First, let’s see the results with three clusters:

We can see that these three clusters here are well separated, but the observations are more spread out indicating less correlation among the observations and higher within cluster variance. This suggests that the four cluster solution might be better. So, let’s see the results with four clusters.

The three clusters plot show us that there is little or no overlap between the clusters, they are well separated and the observations are less spread out indicating higher correlation among the observations and less within cluster variance.

After that, we begin the multiple steps to merge cluster assignment with clustering variables to examine cluster variable means by cluster.

On the first cluster, which is cluster 0, these individuals are likely to be male, likelihood of not in a current relationship, more prone to depression, lower educational attainment, poor self-perception of current health status, likelihood of always getting drunk and not working full time.

On the second cluster, which is cluster 1, these individuals are likely to be male, likelihood of not in a current relationship, lowest likelihood on depression, highest likelihood of having high educational attainment, highest likelihood of having positive self-perception of current health status, likelihood of getting drunk and with highest likelihood of working full time.

On the third cluster, which is cluster 2, these individuals are likely to be female, likelihood of being in a current relationship, less prone to depression, likelihood of having high educational attainment, likelihood of having positive self-perception of current health status, lowest likelihood of getting drunk and with likelihood of working full time.

We'll use analysis of variance to test whether there are significant differences between clusters on the categorical Income variable.

Here are the results. The analysis of variance summary table indicates that the clusters differed significantly on Income.

Finally, let's see how the clusters differ on Income.

When we examine the means, we find that not surprisingly, individuals in cluster 1, are the highest income group, and adolescents in cluster 0, are the lowest income group.

The tukey test shows that the clusters 0 and 1 differed significantly in mean INCOME, although 0 and 2 & 1 & 2 don’t differ significantly, their mean difference is small.

My Python program:

from pandas import Series, DataFrame import pandas as pd import numpy as np import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn import preprocessing from sklearn.cluster import KMeans

# bug fix for display formats to avoid run time errors pd.set_option('display.float_format', lambda x:'%.2f'%x)

#Load the dataset data = pd.read_csv("NESARC_Data_Set.csv", low_memory=False)

# DATA MANAGEMENT # convert values to numeric data['MAJORDEPLIFE'] = pd.to_numeric(data['MAJORDEPLIFE'], errors='coerce') data['S1Q10A'] =pd.to_numeric(data['S1Q10A'], errors='coerce') data['GENDER']=pd.to_numeric(data['SEX'],errors='coerce') data['EDUC']=pd.to_numeric(data['S1Q6A'],errors='coerce') data['CHEALTH']=pd.to_numeric(data['S1Q16'],errors='coerce') data['DRANKLIQ']=pd.to_numeric(data['S2AQ7B'],errors='coerce') data['MARITAL']=pd.to_numeric(data['MARITAL'],errors='coerce') data['WORKFULL']=pd.to_numeric(data['S1Q7A1'],errors='coerce')

# subset data to age 18-35 sub1 = data[(data['AGE'] >= 18) & (data['AGE'] <= 65) & (data['S1Q10A'] >= 0) & (data['S1Q10A'] <= 100000)] B1=sub1.copy()

def INCOME (row): if row['S1Q10A']<=23000: return 0 elif row['S1Q10A']<=100000: return 1 B1['INCOME'] = B1.apply (lambda row: INCOME (row),axis=1)

# recode explanatory variables recode2 = {1:1,2:0} B1['GENDER'] = data['SEX'].map(recode2) B1['WORKFULL'] = data['WORKFULL'].map(recode2) recode3 = {1:4,2:3,3:2,4:1,5:0} B1['CHEALTH'] = data['CHEALTH'].map(recode3) recode4 = {10:0,9:1,8:2,7:3,6:4,5:5,4:6,3:7,2:8,1:9} B1['DRANKLIQ'] = data['DRANKLIQ'].map(recode4)

# convert INCOME to numerical B1['INCOME'] =pd.to_numeric(B1['INCOME'], errors='coerce') B1['GENDER'] =pd.to_numeric(B1['GENDER'], errors='coerce') #B1['DYSLIFE'] =pd.to_numeric(data['DYSLIFE'], errors='coerce') B1['EDUC'] =pd.to_numeric(data['EDUC'], errors='coerce') B1['DRANKLIQ'] =pd.to_numeric(B1['DRANKLIQ'], errors='coerce') B1['MARITAL'] =pd.to_numeric(data['MARITAL'], errors='coerce') B1['WORKFULL']=pd.to_numeric(B1['WORKFULL'],errors='coerce')

data_clean = B1.dropna()

# subset clustering variables cluster=data_clean[['MAJORDEPLIFE','GENDER','EDUC','CHEALTH','MARITAL','DRANKLIQ','WORKFULL']]

# standardize clustering variables to have mean=0 and sd=1 clustervar=cluster.copy() from sklearn import preprocessing clustervar['MAJORDEPLIFE']=preprocessing.scale(clustervar['MAJORDEPLIFE'].astype('float64')) clustervar['GENDER']=preprocessing.scale(clustervar['GENDER'].astype('float64')) clustervar['EDUC']=preprocessing.scale(clustervar['EDUC'].astype('float64')) clustervar['CHEALTH']=preprocessing.scale(clustervar['CHEALTH'].astype('float64')) clustervar['DRANKLIQ']=preprocessing.scale(clustervar['DRANKLIQ'].astype('float64')) clustervar['MARITAL']=preprocessing.scale(clustervar['MARITAL'].astype('float64')) clustervar['WORKFULL']=preprocessing.scale(clustervar['WORKFULL'].astype('float64'))

# split data into train and test sets clus_train, clus_test = train_test_split(clustervar, test_size=.3, random_state=123)

# k-means cluster analysis for 1-9 clusters from scipy.spatial.distance import cdist clusters=range(1,10) meandist=[]

for k in clusters: model=KMeans(n_clusters=k) model.fit(clus_train) clusassign=model.predict(clus_train) meandist.append(sum(np.min(cdist(clus_train, model.cluster_centers_, 'euclidean'), axis=1)) / clus_train.shape[0])

""" Plot average distance from observations from the cluster centroid to use the Elbow Method to identify number of clusters to choose """

plt.plot(clusters, meandist) plt.xlabel('Number of clusters') plt.ylabel('Average distance') plt.title('Selecting k with the Elbow Method') plt.show()

# Interpret 3 cluster solution model3=KMeans(n_clusters=3) model3.fit(clus_train) clusassign=model3.predict(clus_train) # plot clusters

from sklearn.decomposition import PCA pca_2 = PCA(2) plot_columns = pca_2.fit_transform(clus_train) plt.scatter(x=plot_columns[:,0], y=plot_columns[:,1], c=model3.labels_,) plt.xlabel('Canonical variable 1') plt.ylabel('Canonical variable 2') plt.title('Scatterplot of Canonical Variables for 3 Clusters') plt.show()

""" BEGIN multiple steps to merge cluster assignment with clustering variables to examine cluster variable means by cluster """ # create a unique identifier variable from the index for the # cluster training data to merge with the cluster assignment variable clus_train.reset_index(level=0, inplace=True) # create a list that has the new index variable cluslist=list(clus_train['index']) # create a list of cluster assignments labels=list(model3.labels_) # combine index variable list with cluster assignment list into a dictionary newlist=dict(zip(cluslist, labels)) newlist # convert newlist dictionary to a dataframe newclus=DataFrame.from_dict(newlist, orient='index') newclus # rename the cluster assignment column newclus.columns = ['cluster']

# now do the same for the cluster assignment variable # create a unique identifier variable from the index for the # cluster assignment dataframe # to merge with cluster training data newclus.reset_index(level=0, inplace=True) # merge the cluster assignment dataframe with the cluster training variable dataframe # by the index variable merged_train=pd.merge(clus_train, newclus, on='index') merged_train.head(n=100) # cluster frequencies merged_train.cluster.value_counts()

""" END multiple steps to merge cluster assignment with clustering variables to examine cluster variable means by cluster """

# FINALLY calculate clustering variable means by cluster clustergrp = merged_train.groupby('cluster').mean() print ("Clustering variable means by cluster") print(clustergrp)

# validate clusters in training data by examining cluster differences in GPA using ANOVA # first have to merge GPA with clustering variables and cluster assignment data gpa_data=data_clean['INCOME'] # split GPA data into train and test sets gpa_train, gpa_test = train_test_split(gpa_data, test_size=.3, random_state=123) gpa_train1=pd.DataFrame(gpa_train) gpa_train1.reset_index(level=0, inplace=True) merged_train_all=pd.merge(gpa_train1, merged_train, on='index') sub1 = merged_train_all[['INCOME', 'cluster']].dropna()

import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi

gpamod = smf.ols(formula='INCOME ~ C(cluster)', data=sub1).fit() print (gpamod.summary())

print ('means for INCOME by cluster') m1= sub1.groupby('cluster').mean() print (m1)

print ('standard deviations for INCOME by cluster') m2= sub1.groupby('cluster').std() print (m2)

mc1 = multi.MultiComparison(sub1['INCOME'], sub1['cluster']) res1 = mc1.tukeyhsd() print(res1.summary())

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endivesoftware · 5 years ago

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1. Introduction

Introduction: Why Economics?, Meaning and Definitions of Economics, Economic and Non-economic Activities, Economic Groups, Introduction: Statistics, Meaning and Definition of Statistics, Importance of Statistics in Economics, Limitation of Statistics, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Higher Order Thinking Skills [HOTS], Value Based Questions, Multiple Choice Questions (MCQs).

2. Collection of Data

Introduction, Meaning of Collection of Data, Types of Data (Sources of Data), Methods of Collecting Primary Data, Questionnaire and Schedule, Census and Sample Investigation Techniques, Sampling and Non-Sampling Errors, Sources of Secondary Data, Census of India And NSSO, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Higher Order Thinking Skills (HOTS), Value Based Questions, Multiple Choice Questions (MCQs).

3. Organisation of Data

Introduction, Meaning and Definition of Classification, Objectives of Classification, Methods of Classification, Variable, Statistical Series, Individual Series, Discrete Series (Ungrouped Frequency Distribution or Frequency Array), Continuous Series or Grouped Frequency Distribution, Types of Continuous Series, Bivariate Frequency distribution, General Rules for Constructing a Frequency Distribution, or How to prepare a frequency distribution?, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Numerical Questions, Higher Order Thinking Skills (HOTs), Value Base Questions, Multiple Choice Questions (MCQs).

4. Presentation of Data : Tabular Presentation

Introduction, Textual Presentation of Data (Descriptive Presentation), Tabular Presentation of Data, Objectives of Tabulation, Essential Parts of Table, Type of Table, Solved Examples, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Numerical Questions, Higher Order Thinking Skills (HOTS), Value Based Questions, Multiple Choice Questions (MCQs).

5. Presentation of Data : Diagrammatic Presentation

Introduction, Utility or Advantages of Diagrammatic Presentation, Limitations of Diagrams, General Principles/Rules for Diagrammatic Presentation, Types of Diagrams, Bar Diagram, Pie Diagram, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Question, Long Answer Type Question, Numerical Questions, Higher Order Thinking Skills (HOTS), Multiple Choice Questions (MCQs).

6. Presentation of Data: Graphic Presentation

Introduction, Advantages of Graphic Presentation, Construction of a Graph, False Base Line (Kinked Line), Types of Graphs, Limitation of Graphic Presentations, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Numerical Questions, Higher Order Thinking Skills (HOTS), Multiple Choice Question (MCQs).

7. Measures of Central Tendency Arithmetic Mean

Introduction, Meaning and Definition, Objectives and Significance or uses of Average, Requisites or Essentials of an Ideal Average, Types or Kinds of Statistical Averages, Arithmetic Mean (X), Calculation of Arithmetic Mean in Different Frequency Distribution (Additional Cases), Calculation of Missing Value, Corrected Mean, Combined Arithmetic Mean, Mathematical Properties of Arithmetic Mean, Merits and Demerits of Arithmetic Mean, Weighted Arithmetic Mean, List of Formulae, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Numerical Questions, Miscellaneous Questions, Multiples Choice Questions (MCQs).

8. Measures of Central Tendency Median and Mode

Median, Determination of Median, Calculation of Median in Different Frequency Distribution (Additional Cases), Partition Values (Measures of Position or Positional Values based on The principle of Median), Mode (Z), Computation of Mode, Calculation of Mode in Different Frequency Distribution (Additional Cases), Relationship between Mean, Median and Mode, Comparison between Mean, Median and Mode, Choice of a Suitable Average, Typical Illustration, List of Formulae, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Numerical Questions, Miscellaneous Questions, Higher Order Thinking Skills (HOTS), Value Based Questions, Multiple Choice Questions (MCQs).

9. Measure of Dispersion

Introduction, Meaning and Definition, Objectives and Significance, Characteristics of Good Measure of Dispersion, Methods of Measurement of Dispersion, Types of Dispersion, Range, Inter-Quartile Range and Quaritle Deviation, Mean Deviation (Average Deviation), Standard Deviation (s), Relationship between Different Measures of Dispersion, Lorenz Curve, Choice of A Suitable Measure of Dispersion, Typical Illustrations, List of Formulae, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Numerical Questions, Higher Order Thinking Skills (HOTS), Multiple Choice Questions (MCQs).

10. Correlation

Introduction, Meaning and Definitions, Types of Correlation, Coefficient of Correlation (r), Degree of Correlation, Techniques or Methods for Measuring Correlation, Scatter Diagram (Dotogram Method), Karl Pearson’s Coefficient of Correlation, Spearman’s Rank Correlation, Typical Illustrations, List of Formulae, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Numerical Questions, Miscellaneous Questions, Higher Order Thinking Skills (HOTS), Value Based Questions, Multiple Choice Questions.

11. Index Numbers

Introduction, Characteristics or Features, Precautions in Constructions of Index Numbers (Problems), Types or Kinds of Index Numbers, Methods of Constructing Price Index Numbers, Simple Index Number (Unweighted), Weighted Index Numbers, Some Important Index Numbers, Significance or Uses of Index Numbers, Limitations of Index Numbers, List of Formulae, Key Points, Question Bank, Very Short Answer Type Questions, Short Answer Type Questions, Long Answer Type Questions, Numerical Questions, Higher Order Thinking Skills (HOTS), Value Based Questions, Multiple Choice Questions (MCQs).

12. Mathematical Tools Used in Economics

Slope of a Line, Slope of a Curve, Equation of a line.

13. Developing Projects in Economics

Introduction, Steps Towards Making A Project, Suggested List of Projects.

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georgecmatthews · 5 years ago

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Near-term pullback, long-term uptrend

On March 13, 2020, we began talking1 and writing2 about a series of tactical market bottom indicators3 that showed signs of extreme risk-off positioning, which were positive from a contrarian perspective. One of those indicators was the Chicago Board Options Exchange (CBOE) equity put/call ratio. Little did we know it at the time, but ten days later, the S&P 500 Index would put in what now appears to be a major low for the cycle.

What is the CBOE equity put/call ratio and why does it matter?

The put/call ratio is a measure of seller (put) relative to buyer (call) positioning derived from the options market, where a ratio greater than 1 signals more sellers than buyers (or extremely negative investor sentiment) and usually aligns with big market bottoms. On March 12, 2020, the put/call ratio hit 1.28, its highest level since 20084—in the depths of the Great Recession and Global Financial Crisis—meaning risk-off positioning was lopsided and investor pessimism was overwhelming.

The put/call ratio served us well three months ago, but what is it telling us now?

Unfortunately, there may be at least a temporary wrinkle in the bull case for stocks. The recent trough-to-peak surge of 44% on the S&P 500 Index compressed the put/call ratio to 0.37 on June 8, 2020, its lowest level since 2010—just before the peak of the European sovereign debt crisis. In other words, risk-on positioning has gotten stretched, and the bulls are running rampant, raising the likelihood of a near-term pullback in stocks akin to what we saw in 2018, 2015 and 2011.

Figure 1. Too many buyers, not enough sellers

Source: Bloomberg L.P., Invesco, 06/11/20. Notes: CBOE = Chicago Board Options Exchange. The CBOE Equity Put/Call Ratio is a measure of seller (put) relative to buyer (call) positioning derived from the options market, where a ratio less than 1 signals extremely positive investor sentiment. An investment cannot be made in an index. Past performance does not guarantee future results.

Beyond investor positioning, is there a broader bearish case to be made for stocks?

In our view, the bearish case includes 5 points of pessimism, namely:

1) Fears of a potentially deadly second wave of the coronavirus as the economy reopens and we approach cooler fall weather; 2) the risk of a negative feedback loop between stocks and 2Q20 gross domestic production (GDP) and earnings per share (EPS); 3) heightened US-China tensions; 4) overvaluation; and 5) tactical overbought conditions, as discussed. All valid points, any one of which could prove to be the catalyst for a near-term pullback in stocks.

Figure 2. The optimists always win in the end

Source: Invesco, 06/11/20. Notes: GDP = Gross domestic production. EPS = Earnings per share.

How does the bullish case stack up?

That said, we remain compelled by the breadth and scope of the bullish case, which includes the following 10 points of optimism:

1) Massive, unprecedented and coordinated monetary policy support; 2) similarly impressive fiscal policy support; 3) cautious investor positioning in the form of high cash balances and net short positions in stocks; 4) negative investor sentiment as expressed by persistent outflows from stocks and more bears than bulls in the individual investor survey; 5) a structural oversold condition as seen in the rolling 20-year total returns on stocks; 6) plateauing or flattening coronavirus daily cases overall; 7) a potential treatment for the disease that is being researched and developed by a host of companies around the world, and that has already moved to human trials; 8) a potential vaccine for the virus that is evolving similarly; 9) the high-frequency economic data like weekly initial claims for unemployment insurance are improving; and 10) the economy is reopening and activity is moving in the right direction, as evidenced by the daily mobility data.

Is this a cyclical bear market in a secular bull market, or is this a cyclical bull market in a secular bear market?

While it may seem like a daunting task, we believe it is possible to differentiate between secular bull and bear markets. It is said that the trend is your friend except at the end where it bends. Historically, secular bull markets have not ended until stocks have produced 20-year annualized total returns of 11% to 15%. In the current environment, however, 20-year returns remain muted, so much so in fact that they seem more consistent with a secular bull market in its earlier stages than one in its later stages.

Figure 3. The trend is your friend except at the end where it bends

Source: Bloomberg L.P., Invesco, 03/31/20. Notes: Rolling 20-year annualized returns on the S&P 500 Total Return Index, calculated using quarterly data. SE = Standard error. Dark gray areas denote secular bear markets in US stocks. Light gray areas denote secular bull markets in US stocks. An investment cannot be made directly in an index. Past performance does not guarantee future results.

Amidst the biggest economic event in modern history, we must admit to being surprised by the v-shaped stock market recovery and the fact that equities haven’t yet re-tested the March low. Based on some of the tactical breadth and positioning indicators that we monitor, however, there is a case to be made for a near-term pullback in stocks. Short-term volatility aside, we believe the long-term bull case (10 points of optimism) outweighs the bear case (5 points of pessimism). As such, we think investors may benefit from maintaining structural exposure to stocks in the years ahead.

Footnote

1 Source: BNN Bloomberg, Why this strategist sees long-term investment opportunities post-Covid 19 chaos, 03/13/20.

2 Source: Invesco, Chaos can create opportunities, 03/19/20.

3 Source: Invesco, Looking for signs of a bottom in stocks, 04/01/20.

4 Source: Bloomberg L.P. as of 5/31/2020

Important Information

Blog Header Image: Schira Kosmin Rudi / EyeEm/ Getty

Contrarian investing is an investment style in which investors purposefully go against prevailing market trends by selling when others are buying, and buying when most investors are selling.

A put option is an instrument that gives the holder the right to sell an asset, at a specified price, by a specified date to the writer of the put.

A call option is an instrument that gives the holder the right to buy an asset, at a specified price, by a specified date from the writer of the call.

GDP is the total value of all finished goods and services produced within the United States’ borders in a specific time period.

EPS are the total value of US corporate profits divided by the same companies’ common stock outstanding.

The SE is a statistical term that measures the accuracy with which a sample distribution represents a population by using standard deviation.

All investing involves risk, including risk of loss.

A decision as to whether, when and how to use options involves the exercise of skill and judgment and even a well conceived option transaction may be unsuccessful because of market behavior or unexpected events. The prices of options can be highly volatile and the use of options can lower total returns.

In general, stock values fluctuate, sometimes widely, in response to activities specific to the company as well as general market, economic and political conditions.

The opinions referenced above are those of the authors as of June 12, 2020. These comments should not be construed as recommendations, but as an illustration of broader themes. Forward-looking statements are not guarantees of future results. They involve risks, uncertainties and assumptions; there can be no assurance that actual results will not differ materially from expectations. This does not constitute a recommendation of any investment strategy or product for a particular investor. The opinions expressed are those of the authors, are based on current market conditions and are subject to change without notice. These opinions may differ from those of other Invesco investment professionals.

from Expert Investment Views: Invesco Blog https://www.blog.invesco.us.com/near-term-pullback-long-term-uptrend/?utm_source=rss&utm_medium=rss&utm_campaign=near-term-pullback-long-term-uptrend

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spaceexp · 8 years ago

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CERN know-how helps weigh the proton

CERN - European Organization for Nuclear Research logo. 2 Aug 2017 A team in Heidelberg, Germany has made the most precise measurement to date of the mass of a single proton, the particle that – together with the neutron and the electron – makes up all the ordinary matter in the universe, and therefore also us. They found that the proton is about 30 billionths of a percent lighter than previously thought. The result improves by a factor of three on the precision of the accepted value of the Committee on Data for Science and Technology (CODATA) – which regularly collects and publishes the recommended values of fundamental physical constants – and it also disagrees with its central value at a level of 3.3 standard deviations, which means that the new value is significantly different from the previous result. Proton mass is a fundamental parameter in atomic and particle physics, influencing atomic spectra and allowing tests of ultra-precise calculations within Quantum Electrodynamics (QED), the theory that describes how light and matter interact. In particular, a detailed comparison between the masses of the proton and the antiproton offers a stringent test of the fundamental symmetry of the Standard Model, the so-called charge, parity and time (CPT) invariance. This proton lightness could also potentially shed light on other mysteries, such as the well-known discrepancies in the measured mass of the heaviest hydrogen isotope, tritium. The team at the Max Planck Institute for Nuclear Physics (MPIK) in Heidelberg and their collaborators from RIKEN in Japan used a device known as Penning trap, in which a combination of strong electric and magnetic fields, cooled to 4 degrees Kelvin (- 269.15 °C) is able to store individual protons and highly charged carbon ions. In this trap, the magnetic field forces the particles to move in circle and by measuring the characteristic frequencies of the trapped particles when they spin around, the mass of the proton follows directly.

Image above: The MPKI Penning-trap setup for precision mass measurements of single particles. A combination of strong electric and magnetic fields is able to store individual protons and highly charged carbon ions. (Image: Max Planck Institute for Nuclear Physics). The sensitive single-particle detectors were partly developed by the RIKEN group, drawing on experience gained with similar traps for antimatter research at CERN’s Antiproton Decelerator (AD). “The group around Sven Sturm and Klaus Blaum from MPIK Heidelberg that did the measurement has great expertise with carbon, whereas the BASE group contributed proton expertise based on 12 years dealing with protons and antiprotons,” explains RIKEN group leader and spokesperson of the AD’s BASE experiment, Stefan Ulmer. “We shared knowledge such as know-how on ultra-sensitive proton detectors and the ‘fast shuttling’ method developed by BASE to perform the proton/antiproton charge-to-mass ratio measurement.” Although carefully conducted cross-check measurements confirmed a series of published values of the proton mass and showed that no unexpected systematic effects were imposed by the new method, such a striking departure from the accepted value will likely challenge other teams to revisit the proton mass. The discrepancy has already inspired the MPIK-RIKEN team to further improve the precision of their measurement, for instance by storing a third ion in the trap and measuring it simultaneously to eliminate uncertainties originating from magnetic field fluctuations, which are the main source of systematic errors when using the new technique. “It is also planned to tune the magnetic field to even higher homogeneity, which will reduce additional sources of systematic error,” explains BASE member Andreas Mooser. “The methods that will be pioneered in the next step of this experiment will have immediate positive feedback to future BASE measurements, for example in improving the precision in the antiproton-to-proton charge-to-mass ratio.” The research was published on 18 July 2017 in Physical Review Letters: http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.033001 Note: CERN, the European Organization for Nuclear Research, is one of the world’s largest and most respected centres for scientific research. Its business is fundamental physics, finding out what the Universe is made of and how it works. At CERN, the world’s largest and most complex scientific instruments are used to study the basic constituents of matter — the fundamental particles. By studying what happens when these particles collide, physicists learn about the laws of Nature. The instruments used at CERN are particle accelerators and detectors. Accelerators boost beams of particles to high energies before they are made to collide with each other or with stationary targets. Detectors observe and record the results of these collisions. Founded in 1954, the CERN Laboratory sits astride the Franco–Swiss border near Geneva. It was one of Europe’s first joint ventures and now has 22 Member States. Related links: Antiproton Decelerator (AD): http://home.cern/about/accelerators/antiproton-decelerator BASE: http://home.cern/about/experiments/base For more information about European Organization for Nuclear Research (CERN), Visit: http://home.cern/ Image (mentioned), Text, Credits: CERN/Matthew Chalmers. Best regards, Orbiter.ch Full article

#space #science

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bet-sinclair-blog · 6 years ago

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Closing line to test your skill in betting

People often question how much success in betting is down to luck and how much is skill. Want to find out if you're a skilled bettor? I’ve previously published an article how a bettor can test for the evidence of forecasting skill. Such odds are said to be efficient. Certainly, there are profitable bettors failing to beat the closing line who therefore argue against this hypothesis. For the purposes of what follows, let’s take him at his word. There is convincing evidence that the margin by which you beat the closing line (or odds) is a reliable predictor of your profitability. Beat the closing line by 10% and you should expect to make a profit over turnover of 10% over the long run, implying that the closing line accurately reflects ‘true’ chances of sporting outcomes. Instead in this article I want to look at how we might theoretically use the closing line to test for bettor skill, given that the efficient closing line hypothesis is true. and will regress to the mean. Unfortunately, the spread of possibilities due to chance is wide, and it takes a long time for the law of large numbers to exert its influence. Fortunately there is an alternative approach, and it’s one that I’ve touched on previously when I looked at what the closing line can tell us about profit expectation. For these there must then exist two possibilities: either they are wrong, lucky. After all, Marco Blume, has said that the closing line is on average very, very accurate, that the sharps are beating it, and his traders are trying to achieve the most efficient line with the information they have available. This, perhaps, is for another time. By comparing what profits a bettor has actually achieved with what could have happened by chance, we can begin to form a judgement as to whether the record is too unlikely to have happened randomly. The drawback of this approach is the time (or rather the number of bets) it can take before we can form more concrete opinions. A bettor typically betting prices of around 5.0, for example, might take 2,500 bets before they could be confident that such a performance probably wasn’t just lucky - if they were to make five bets per day that would take more than a year. Alternatively, the efficient closing line hypothesis is not quite right, and there are lines, systematically identified by such bettors, that have failed to reach the ‘true’ prices. In this article I don’t intend to address the potential weakness of this hypothesis, suffice to say that I have previously discussed a possible way closing odds could systematically (that is to say non randomly) deviate from full efficiency. Analysing a real betting record The following chart shows the level stakes profit history of real bettor, consisting of 1,214 bets over an 11-week period at the start of 2019, with average betting odds of 2.065 and a profit over turnover of 5.73%.

The blue line shows the actual performance, the red line the expected performance. Clearly, the actual record has overperformed relative to expectation. How did I calculate the expected profit? It closed at 1.85. We need to remove it from the closing price to calculate an estimate of the ‘fair’ ‘true’ price, assuming full price efficiency at market closure. In removing the margin I have also taken into account the favourite-longshot bias, which sees longshots attract a greater weight of the bookmaker’s margin than favourites. After the bookmaker’s margin is removed, the ‘true’ closing price was 1.89. Consequently, the expected advantage the bettor held was 2.13/1.89 = 12.8%. As mentioned earlier, the ratio of these two prices offers us a reliable estimate of the bettors expected advantage. The first bet in the series, for example, was placed at 2.13. This is to say for every 100 such 1-unit bets that might be placed, a profit of 12.8 units could be expected to be made. The average advantage held was 2.19%, implying an expected profit over turnover of 2.19%. The average ‘fair’ closing price was 2.024. Of course, both prices contain the bookmaker’s margin. In their betting history, the bettor has diligently recorded all prices they placed and all closing prices for those bets. Can beating the closing line happen by chance? How likely might it be to randomly pick a sample that showed an average ratio of 1.000, implying a break-even expectation? We can answer this question if we know the standard deviation in opening/’fair’ closing price ratios. Readers of my article on modelling your possible betting returns may recall that the standard deviation in a betting metric average, like yield or in this case opening to closing price ratio, is inversely proportional to the square root of the number of bets. Hence, the standard deviation of average price ratio here can be calculated by dividing 0.114 by the square root of 1,214. Given that 1.000 is over nine standard deviations away from 0.969 this result will hardly come as a surprise to anyone familiar with the statistics of the normal distribution. To investigate how and why a bettor can be beating the closing price like this, we should start by estimating the likelihood of it happening by chance. The answer is effectively 0% (in fact about 1 in 100 million trillion to be more precise). In this sample it was 0.114 (or 11.4%), meaning about two-thirds of individual odds ratios lay between 0.855 and 1.083, as defined by the normal distribution. With this information we can estimate what the standard deviation in the average price ratio of a sample of 1,214 would be. From this sample, 35.7% of home and away opening betting odds (with average and median values of 3.443 and 2.75 respectively) theoretically held a profitable advantage over their ‘fair’ closing prices. To do this I’ve drawn again on a population of 162,672 soccer match betting opening and closing odds from Bet-Sinclair which I analysed in one of my previous articles. This is to say, if we had a large number of 1,214-bet samples with odds like the ones in my population here, we want to know the standard deviation in the average price ratio across those samples. The average ratio of opening to ‘fair’ closing price for this sample was 0.969%, implying an expected level stakes loss over turnover of -3.1% If we randomly picked 1,214 bets from this sample, we should expect the average ratio to be 0.969. Of course, we wouldn’t always get 0.969, just as we don’t always get 10 heads and 10 tails when we toss a coin 20 times. The answer is 0.0033. In other words, for samples of 1,214 bets with odds like my population here, about two-thirds will lie between 0.966 and 0.972. With this figure we can now calculate the probability that an average opening price to ‘fair’ price ratio of 1.000 in a sample of 1,214 bets would happen by chance, given an expected value of 0.969. Evidence of skilled bettor Again, that’s not a surprising observation. The implication of this analysis is clear. Our bettor beat the ‘fair’ closing price 73.5% of the time (beating the published closing price 84.2% of the time). The chances of doing that when the expectation is -2.0%? About one in a quattuorvigintillion (1 with 75 zeros) or about 18.5 standard deviations. In fact, the standard deviation in opening to closing price ratio is proportional to the logarithm of the odds. If a bettor were to show an average bet price to ‘fair’ closing price ratio of 1.000 when the expectation is 0.969 in a sample of 1,214 bets, this categorically cannot have happened because of luck. This bettor was moving lines, and that is because the bookmaker recognised them as someone with better knowledge than the rest of the market at the point they bet the published odds. It’s worth briefly reminding readers that I have also previously attempted to model how often a bettor would theoretically need to beat the ‘fair’ closing price to have any profitable expected value at all. Odds of 1.25 show an average ratio of about 0.99, whilst odds of 5.0 show a figure of about 0.95. The bettor’s average odds of 2.06 would have a standard deviation of about 0.079 and around an average of 0.98. Firstly, we should recognise that their average odds, 2.065, are significantly different to the average odds in my analysis population, 3.443. The figure I came up with was about 70%. Dividing this standard deviation by the square root of 1,214 gives us a figure of 0.0022, so again a ratio of 1.000 is about nine standard deviations away from the expectation of 0.98. Finally, we should remember the bettor here didn’t just match the ‘fair’ closing price on average. They beat it by 2.19%. If that is not the explanation, we still need another causal one; to reiterate, it can’t be good luck. Let’s return to our real bettor and their record. How does this change the calculations? The longer the odds, the more they are likely to move. Instead, the explanation must be causal; the most obvious is bettor skill and the bookmaker reacting to it by shortening their odds. Odds of 1.25 typically saw a standard deviation of about 0.043, whilst odds of 5.0 had a value of about 0.14. Similarly, the average opening to ‘fair’ closing price ratio changes with average odds, falling roughly linearly as the odds increase. If we move a 80%/20% proposition by 5% to 75%/25%, the favourite will move from 1.25 to 1.333 (a ratio of 0.9375) whilst the underdog will move from 5.0 to 4.0 (a ratio of 1.25). Closing line value versus profits and losses Let’s look back at the bettor’s actual profits/losses (P/L) history above. On that evidence it’s probable that something more than luck is at work, but if we had a sample of 200 bettors and this was the best record, we couldn’t really rule out the possibility that there was no skill at all. Compare this to the closing line value (CLV) methodology I’ve described above. Using my traditional significance testing approach, such a performance could arguably happen by chance about once in 200 bettors. The y-axis is logarithmic, measuring the chances that an expected/actual profit over turnover of 2% will happen by chance for the CLV and P/L methods respectively. One in 200 versus one in a quattuorvigintillion. Which is giving us more reliable information about the likelihood of the bettor’s skill? The chart below compares the two approaches for a bettor betting odds of 2.00, illustrating how much faster the CLV methodology provides meaningful information to the bettor about their chances of being a long-term winner with an expected advantage of 2%.

Should such a bettor be showing an expected yield of 2% based on how they beat the fair closing prices of bets they make, just 50 of them would provide the confidence of knowing that this would happen once in about 10,000 times. Statisticians testing hypotheses wouldn’t even blink. Arguably, beating the ‘fair’ closing line tells us much more about the long-term expectation for this bettor. In comparison to the CLV method, using actual profits and losses barely even registers. Possibly two-thirds of our bettor’s actual profitability over 1,214 bets presented in this article was down to luck. Over long term they can be expected to regress back towards 2.19%. By this measure, our bettor did very well with close to a 6% yield. Even after 1,000 bets there’s only a one in 10 probability that luck could have delivered a 2% profit where the expectation is a 2% loss. The key point here, however, is that it takes much longer to separate randomness from causation with profits and losses than it does for closing line value. Provided the closing line value hypothesis is correct (it might not be completely so, but that is for another day), it provides a much more reliable indicator of bettor skill than a mere betting history. Of course, you might justifiably point out that profits and losses are real and closing lines just give us an idea of what to expect. Conclusion The obvious answer is they’re skilled and the bookmaker knows it. Consequently, if one profitable bettor can move lines, why not another? Unless such a bettor can offer reasonable and testable explanations for why they don’t, the likelihood must remain that they’ve simply been lucky. In the words of our bettor here, “I can use CLV as a cliff detector, that way I can slow down, or I can change my direction, so I don’t fall off.” Until bookmakers show us the data on profit/losses versus closing line value we’ll never know for sure how reliable an indicator CLV really is. I know there will still be bettors with profitable records who fail to beat the closing line who will say: “doesn’t my record simply prove that the efficient closing line value hypothesis is invalid for the purposes of estimating expected profitability?” Yes, it might, but there are two points that must be taken into consideration. Firstly, if a bettor is beating the closing line in the way our bettor in this article has done, we have to explain it. Since even small deviations from expectation are highly unlikely over samples as few as 50 bets, the bettor will be able to very swiftly identify if the market thinks they’ve lost their edge. Much of my data analysis into it published in Bet-Sinclair Betting Resources over the years suggests that if it’s not perfect, it’s surely pretty good. After all, Marco Blume has told us that sharps move lines. That’s something completely impossible with profit/loss analysis alone. Remember, you might have a 1-in-a-million betting record, but if you’re the best in a million trackable bettors, what does that really mean? Secondly, given this information, the closing line acts as a gauge, allowing the bettor to very quickly measure their expected performance. Chance alone cannot do that as the numbers have proved. Who am I to argue with that? So, if you are mindful enough to keep a record of your betting odds, keep the closing prices too, and you can determine very quickly if your bookmaker and the market think you’re a winner. Read the full article

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xiaoquebanxiaoqueban · 5 years ago

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High-tech zone whole house custom which is cost-effective

Leipai Furniture was established in 2007 and is located in Chengdu, the beautiful country of Tianfu. Since its establishment, its products include: titanium alloy rotating cabinets, display cabinet display cabinets, and various display racks. The company is located in Chengdu, a beautiful country of Tianfu. , Production and sales. The main products include solid wood wardrobes, solid wood wine cabinets, solid wood cabinets, bookcases, cloakrooms and other custom furniture. Adhering to the business philosophy of 'quality, comfort and health', all products of Chengdu Leipai Furniture Co., Ltd. are used in accordance with the environmental assessment standards. Consumers can buy at ease and comfortably.

Everyone has different pursuits of quality of life, so in the private market, many people will choose private custom furniture. If you choose to customize, then the question of concern should be how much does it cost to customize one square of private furniture? The customized cost of private furniture needs to be determined according to the actual needs of users. However, there are two main methods that can be used to calculate custom costs. One is calculated based on the extended area. The other is calculated based on the projected area. For example, according to the expansion area, it is necessary to calculate the number and area of ??the boards in the wardrobe and then multiply them by the price, which can calculate the cost of the customized wardrobe.

To install the sliding door, you must first fix the track. The top border and lower seal of the sliding door need to be fixed with fixing screws. Next, snap the door frame of the sliding door into the upper seal, fix it with screws, and install the pulley. In the later period, the sliding door at home is not sliding smoothly because of the failure of the track debugging after installation, or the use of plastic pulleys. The next step is to debug and install the edge banding, which can reduce the sliding door sliding noise and increase the sealing effect. Check whether the pulley and track are damaged or deformed; whether the pulley is tightened and whether the left and right sides of the door slide parallel; whether there is something stuck in the movable door wheel and the glass. If the above phenomenon is found, replace it in time. If none of the above is true, check whether the upper and lower guide rails are installed in place, and check whether there is any deviation between the upper and lower holes of the glass. Lubricating oil can also be added to make the door smoother.

High-tech zone whole house custom which is cost-effective

Chengdu Leipai professional solid wood custom furniture is a custom home furnishing enterprise integrating R \u0026 D, design, sales, production and channels of customized solid wood wardrobes, solid wood wine cabinets, solid wood cabinets, bookcases and cloakrooms. Chengdu Leipai Furniture Whole House Customization specializes in the production, sales, and innovation of Chengdu furniture customization, Chengdu customization furniture, and Chengdu wardrobe customization. It is a honest enterprise with a deep cultural precipitation that focuses on the art of home manufacturing. Leipai Furniture is a company that focuses on the customization and installation of green and environmentally friendly furniture. Its business philosophy is: 'fashionable home improvement, green customization'. According to the company's business philosophy, it focuses on the customization of green and environmentally friendly furniture, covering wardrobes, wine cabinets, shoe cabinets, bookcases , Kitchen cabinets, beds, sofas and other types of household products to customize your exclusive fashion for you.

Leipai Furniture is a furniture customization company dedicated to wardrobe customization, sliding door customization, tatami customization and partition cabinet customization. The company's service aim is to provide customers with a one-stop furniture solution for different design styles and materials Requirements, supporting methods, project scale and other factors make detailed and comprehensive planning, and use sophisticated processes to meet the continuous pursuit of refined life by our customers. Since its establishment, the company has become a leading enterprise in the decoration industry with advanced equipment and superb craftsmanship.

High-tech zone whole house custom which is cost-effective

Which brand is good for custom kitchen cabinets in Jinjiang District

To choose furniture, we must pay attention to the environmental protection and characteristics of the board. Which plate is suitable for customized furniture? Blockboard is also known as large core board, ecological board, etc. It has a long history and huge market space in China. Blockboard uses less glue, and its structure is close to solid wood, so it is considered good by many people. However, due to the structure and performance of blockboard, its main uses are cabinets and other packaging boards, etc., and it is not suitable for the production of wardrobes. MDF is divided into high density, medium density, low density and so on.

In today's increasingly fierce competition in the solid wood furniture market, Leipai Furniture will stick to the 'quality' route, constantly improve the process, and provide users with a more stable product series; quality is important, service is more important, Leipai Furniture is being created A complete technical team provides professional technical services for users. 'Wood fragrance overflows the house; Leipai furniture.', All Leipai furniture people unite together, and strive to build 'Leipai furniture' into a pure solid wood furniture custom leading jun brand! At this moment, we are already on the road in the future days, Lei Pai Furniture hopes to sincerely join hands with you to build a better life!

Which brand is good for custom kitchen cabinets in Jinjiang District

In recent years, China ’s economic growth structure has undergone historic changes, and consumption has become an important force in the national economy. However, with changes in people's consumption levels and changes in consumption patterns, people's lifestyles and daily habits have undergone great changes. From these changes, we can investigate and feel the consumption promotion in our country, and can also experience the changes from finished furniture to furniture customization to a certain extent. In recent years, major changes have taken place in the home. Wardrobes, bookcases, doors, windows, cabinets, etc. have changed from the traditional model to the overall furniture customization, and production is based on individualized order requirements.

Which brand is good for custom kitchen cabinets in Jinjiang District

What are the top ten brands for customizing the whole house wardrobe in Tianfu New District?

What exactly is custom furniture, do ordinary families need it? Another kind of 'pseudo-demand' is under the strong advocacy of custom furniture manufacturers, claiming that it can be 'customized for the whole house', and even ordinary families have begun to consider custom furniture. From cabinets to sofas, dining tables and bar counters, it can meet the individual needs of consumers. Under the hot market situation of customization, many companies have also joined the big effort to customize and drive sales with customization. Since then, more and more ordinary families have lost their real needs in the full of customized advertisements. Many apartment problems that could have been solved through space planning and design were handed over to expensive custom furniture. The above introduces you to what is custom-made furniture. Ordinary families need all the content of custom-made furniture.

Chengdu Leipai Furniture focuses on tailor-made overall wardrobes and matching furniture that meet the room type characteristics and individual needs for customers, including custom wardrobes, cloakrooms, bookcases, cabinets, wine cabinets, computer desks, TV cabinets and other furniture. , While constantly expanding its market share while not forgetting to pursue excellence and lead the fashion. Leipai furniture wardrobe products include: cloakroom, wardrobe, TV cabinet, bookcase, wine cabinet, partition cabinet, household cabinet, closet, wardrobe door and other products, quality assurance, worry-free sales.

For many people, the concept of custom-made wardrobes is to do whatever they want, just make what they want. In fact, otherwise, the characteristics of custom wardrobes are that they can be made according to the needs of customers, but there are also great differences in custom wardrobes, and the difference is reflected between 'standard' and 'nonstandard'. Standard customization refers to the customization of the wardrobe under the specifications and dimensions specified by the factory. The standard custom wardrobe advocates functional customization, the purpose is to make the function of the wardrobe conform to our daily living habits, rather than simple appearance customization.

Leipai Furniture Co., Ltd. is based in Chengdu. We are a design-driven office furniture team in Chengdu. We create modern and office space furniture design with customers. Specializing in providing high-quality office furniture customization and supporting solutions, office furniture of the ultimate design, creating a comfortable office lifestyle for customers. With our exquisite service, exceed your expectations. Leipai furniture may not have become the recognized name in the Chengdu office furniture industry overnight. The production base is located in Dayi County, Chengdu. For many years, as an office furniture enterprise integrating design, research and development, production, processing, sales and service, it has rich production experience and boldly wants to successfully make our products help like Companies like you get more job victories. And Leipai Furniture is carrying this mission, providing more and more excellent design, high-quality products and services for more enterprises.

What are the top ten brands for customizing the whole house wardrobe in Tianfu New District?

Lei Paijia has many years of experience in the design and production of medium-g showcases such as cosmetics showcases and watch showcases. A professional design team can tailor-made the showcase that belongs to your brand image according to your needs. The main business of Leipai Furniture involves office environment decoration, office furniture, system customized furniture, communication system customized furniture, office curtains, office carpets, decorations, office environment health management and service products. Provide customers with a systematic and integrated office environment for one-stop service to enterprises, saving enterprise time costs and resource costs.

Leipai furniture mainly produces: wardrobes, cupboards, bookcases, computer desks, shoe cabinets, tatami, cloakrooms, sofas, beds, protective walls and other household products, coffee tables, TV cabinets, sofas, dining tables, wine cabinets. Since its establishment, we have been abiding by The principle of 'being a person first, then doing things' adheres to the concept of 'survive by quality, seek development by word of mouth'. Leipai furniture mainly produces: wardrobes, cupboards, bookcases, computer desks, shoe cabinets, tatami, cloakrooms, sofas, beds, retaining walls, coffee tables, TV cabinets, sofas, dining tables, wine cabinets, etc., covering paint, blister, louver, Glass and other series, the company has specialized in custom-made furniture for more than ten years, welcome new and old customers to inquire and visit the factory.

The core logic of investment in the customized home furnishing industry: the industry is a good track, the industry space is vast, and the furniture industry is a large industry. According to public data, in 2016, China ’s enterprises above the designated size achieved main business income of 856 billion yuan and a total profit of 537.5 100 million yuan. According to estimates, in 2017, the market capacity of custom wardrobes and cabinets exceeded 210 billion yuan, and the market capacity of other types of custom furniture still exceeded 20 billion yuan. The customized furniture market segment is still increasing rapidly at a rate of 15% to 20% every year, much faster than the furniture industry's annual growth rate of 89%.

What are the top ten brands for customizing the whole house wardrobe in Tianfu New District?

What should be paid attention to in Chongzhou whole house customization

Taking the customer as the center, we strive for a perfect fit between home visual design and interactive design. And our diligent and dedicated attitude provides customers with a full range of whole house home design services to drive the continuous growth of corporate value with design! Chengdu Leipai Furniture Co., Ltd. was established in 2007. Leipai Furniture has professional production equipment, CNC cutting, punching and slotting machine, CNC edge banding machine, CNC row drilling; each position is a professional master and technology Personnel; All the furniture of Lei Pai Furniture is trial installed and packed at the Chengdu factory, repaired and repaired before being delivered to the customer's home for installation. At the same time, we also pay attention to health, environmental protection, Lei Pai furniture custom furniture throughout the house, are paint-free panels, truly healthy, environmentally friendly, and pollution-free. To create comfort and health is our unremitting pursuit. Welcome your inquiry! We will serve you wholeheartedly! Main business: custom-made wardrobes, custom-made cabinets, custom-made furniture manufacturers, etc. The company has 11 years of experience in the production of customized furniture, a production workshop of tens of thousands of square meters, a production team of hundreds of people and advanced production equipment Professional design team, tailor-made furniture for consumers.

For those solid wood doors, this is the first choice. Sixth, waxing solid wood doors is also a good way. It is bright and protective. It can be said that it serves two purposes. Seventh, when wiping the solid wood door, it was found that the stain was too heavy, and there was no ready-made curing agent on hand. Toothpaste can be used to temporarily remove it. Eighth, when opening and closing the solid wood door, we must pay attention to the unusable force, strong vibration is the fatal damage of the solid wood door. Ninth, do not use ordinary household cleaning agents (such as washing powder, dishwashing liquid, etc.) to wipe the wooden doors. This kind of chemical products is equivalent to ordinary wood doors.

What should be paid attention to in Chongzhou whole house customization

Leipai Furniture is a furniture customization company dedicated to wardrobe customization, sliding door customization, tatami customization and partition cabinet customization. The company's service aim is to provide customers with a one-stop furniture solution for different design styles and materials Requirements, supporting methods, project scale and other factors make detailed and comprehensive planning, and use sophisticated processes to <a href="https://www.gz-shisheng.com/filing-cabinet.html">zipper slider pressing parts</a> meet the continuous pursuit of refined life by our customers. Since its establishment, the company has become a leading enterprise in the decoration industry with advanced equipment and superb craftsmanship.

What should be paid attention to in Chongzhou whole house customization

Chenghua District custom wardrobe factory direct sale

Leipai Furniture was established in 2007 and is located in Chengdu, the <a href="https://www.gz-shisheng.com/filing-cabinet.html">visit here</a> beautiful country of Tianfu. Since its establishment, its products include: titanium alloy rotating cabinets, display cabinet display cabinets, and various display racks. The company is located in Chengdu, a beautiful country of Tianfu. , Production and sales. The main products include solid wood wardrobes, solid wood wine cabinets, solid wood cabinets, bookcases, cloakrooms and other custom furniture. Adhering to the business philosophy of 'quality, comfort and health', all products of Chengdu Leipai Furniture Co., Ltd. are used in accordance with the environmental assessment standards. Consumers can buy at ease and comfortably.

In fact, since the birth of e-commerce, such an invisible and intangible virtual market has been flooded with low-priced products, which is inseparable from consumers' perception of this channel and the <a href="https://www.gz-shisheng.com/">Contact Us</a> differentiated positioning of e-commerce Open, long-term past has formed the thinking inertia and fait accompli of the low price of e-commerce. Misunderstanding 2: E0 and E1 grades promoted by businesses are polluted. Entering some home furnishing markets, Eurasia will find some businesses bragging about E0 and E1 grades, which are produced using adhesives containing a large amount of formaldehyde, which can easily lead to indoor formaldehyde exceeding the standard.

The thickness of the PVC film used for plastic door panels is generally 0.15mm0.45mm. The price imported from Germany is more expensive, followed by South Korea, Japan and joint venture products, and the domestic price is lower. The good quality blister door base material is not easy to deform, and has good environmental performance, delicate appearance, wear resistance, scratch resistance, high temperature resistance, and flame retardant properties. Poor quality plastic door panel, the substrate is easily deformed, the appearance is rough, and it is afraid of scratching. How to identify the quality problems of plastic door panels? The quality of the membrane directly determines the grade and performance of the finished plastic door panel.

Chenghua District custom wardrobe factory direct sale

personalise. Have you tried the sofa or bookcase wardrobe that you bought, which is not in line with the decoration style in the whole room, a little messy feeling? Customization can not only avoid such things, but make your whole house furniture style seamless, but also according to individual Preferences and requirements, the design adds more personalized elements, truly dominate your house, health and environmental protection, custom overall wardrobe cabinets and other materials generally use MDF, the industry's more influential brand companies The requirements of environmental protection standards are high, the formaldehyde emission meets the E1 standard, the products are automatically edge-sealed, and the process level requirements are relatively high. The products can meet the needs of users ’environmental protection and healthy living.

Chenghua District custom wardrobe factory direct sale

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bettydgunter90 · 5 years ago

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Realty Matters: Risk Management

When talking to prospective clients for my property management business, I always say, “rental property management is really about Risk Management.” I often tell the story of a client, who had previously decided to self-manage. Although he always tried to do the right thing, a small, innocent error led to a spiraling sequence of unfortunate events. The story was an example of how small errors may go undetected, unknown, and remain unrealized, until something goes wrong. Consequences in total were severe, not only financial misfortune, but it also left a lasting and profound psychological impact. But, if nothing goes wrong, is there any risk?

Risk is measured using statistics and derived from both quantitative components and qualitative factors. Qualitative factors are a challenge, particularly for individual or small unit-count property investments. Included are interpersonal relationship dynamics, such as behavior, which is possible to recognize but a challenge to quantify.

Qualitative risk management planning effectively reduces or eliminates consequences — and is the focus of this column — with most benefits achievable without statistics. An annual assessment exercise allows most real estate investors to improve business practices, whether doing rehabs, long-term holds, lending, or some combination. It is not difficult with the right tools and understanding and will elevate your business to the next level resulting in larger gains and smaller losses. Critical thinking and arithmetic are the only things necessary for implementation.

Statistics Deconstructed

The path to eliminate problems before they occur

Calculating risk is an attempt to determine probability of an outcome. A conceptual understanding of how those numbers are derived is critical for effective implementation of risk management practices. A grasp of risk theory only requires a bell curve illustration and a sprinkling of a few statistical terms when combined with practical real estate examples.

The bell curve figure has a symmetrical area under the curve that (eventually) totals 1.00 (one). Where:

μ = [mu, (pronounced “mew”)] is the data mean, the average, (expected outcome or return)

𝝈 = [sigma] the standard deviation, shows six equidistant intervals, known as the range (99.7% of 1.00)

Any given point in the graph is a probability of an event or an accumulated series of events. Events are plotted in relation to the frequency they occur (height) and distance from average (left or right of centerline). Closely plotted points indicate the deviation from average is low; loosely plotted points mean less predictability (the deviation from average is high).

Grasping the concept of risk theory is important for Risk Management planning

Roughly speaking, events that occur with high frequency are prone to have more variation than events that occur less frequently while less frequent events may mean less variation. Symmetrically speaking, consider that high frequency events having low consequence may have an aggregate severity equal to low frequency events having high consequence. Reducing variation of outcome through tighter controls reduces total severity of consequence, which is how you will apply risk management to your business.

Example of Risk Management indicators in real estate

Imagine two Renters, A & B. Renter A paid rent consistently late, on the fourth day of the month with rarely any variation. Plotted on the curve, the average would show a high frequency of late rent as averaging –four. Renter B is different. The average is one, which would indicate rent is paid on average a day early. Except many of the plots are largely spread out into the negative teens and twenties, with a few plots at 30, 40, and 60. Which has higher variation? Which has more risk? Which can you be more confident of getting paid rent on a given day?

As a separate event, the number of opportunities that a basement will flood may be far less than opportunities for rent to be paid on time, yet, the financial consequence may be quite severe. A sump pump with battery backup is installed, so all should be okay, right? Now evaluate the same two renter households. Consider Renter A vs. Renter B. Would you consider the likelihood of tampering with the sump pump between Renter A and Renter B as the same? Why or why not?

Statistics attempt to bring together all events of different types, each having probabilities and frequencies of their own, and normalize them into a single measure (expected outcome) that you determine is important. Managing the causal factors that impact the outcome is up to you.

Application of Risk Management Principles without Statistics

The key is to identify possible causes that prevent achieving the expected outcome.

It has been established now, with the iterative cycle of real estate operations, even though an event never gets tested or realized (a failure does not occur), that risk remains inherit in the system. A step most overlooked is a critical thinking exercise that accounts for possibilities of what can go wrong. Possibilities considered are possibilities managed. Proactive vs. Reactive.

Identification of 95 percent of causes are either obvious or routine enough to identify with a smidge of critical thinking. Reducing variation and consequences of those causes is a qualitative and subjective exercise. Tighter controls and mitigation plans are how risk management can be applied to your business to reduce losses and improve gains. The sum of all the individual multi-factorial events and iterative processes (e.g., getting paid rent on time, having contractors show up on time, detecting water leaks or pests before they become a problem, etc.), over time add up to the expected outcome.

Risk Management Methodology

There is a simple methodology that does not require the use of any statistics to assist with managing mistake-prone qualitative risk factors:

Consider what can go wrong (this is often the hardest part)

Determine how bad the outcome would be (consequences)

Determine how likely it is to happen (likelihood of occurrence)

Determine the risk level of the event with application of a risk score

Manage the event; eliminate or reduce the likelihood; mitigate the consequence.

How it works:

Below are some examples to subjectively score criteria. Consequence criteria may be how severely could it cause financial or legal harm and likelihood indicates likeliness or propensity for realization.

With each event, determine the consequence and likelihood of occurrence to apply a risk score and determine if it requires some form of mitigation management. With Renter B, for example, perhaps a remote monitoring alarm should be installed that would warn you of a high-water level. Consider how to mitigate, and then re-assess:

Eliminate the possibility for the event from happening (risk of ceiling fan breaking, replace with a dome light instead if the market isn’t sensitive to having ceiling fans).

Substitute with an alternative option (unreliable and poor-quality maintenance contractor, utilize a more competent and professional contractor that gets it done right the first time).

Utilize automation and controls (put in place as a proxy assistant to make operations easier and results in less hassle for both you and the tenant — could be software and hardware).

Administrative controls with clear policies and procedures (move-in and move-out procedures, and security deposit management are a big source of dispute, or have your own Terms and Conditions, or quality standards that you expect contractors to follow).

Develop policies, procedures, practices and guidelines, for renters and contractors to mitigate against possible symptoms of dispute. Provide training, instruction, and supervision.

The matrix shown above is an interactive spreadsheet that contains a series of multiple worksheets with internal linking as a roadmap to assist with Risk Management planning. It is available for download through the links provided at RealtyMatters.Online/Column/June-2020. It contains an exhaustive list of 60+ categorized risk components that is applicable to most investment strategies. Go to the links identified to get your free copy.

Please Tell Me What You Think

For those statisticians reading, please tell me how I did.

A simplified Risk Management Approach in Real Estate online course or module for the individual small unit-count investor category is lacking. Would you find value with something like that? Write to me and let me know. Go to RealtyMatters.Online

For Column Notes, Resources and Language Translation for this Column, go to:

RealtyMatters.Online/Column/June-2020

The post Realty Matters: Risk Management appeared first on Think Realty | A Real Estate of Mind.

from Real Estate Tips https://thinkrealty.com/realty-matters-risk-management/

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batleena-blog · 6 years ago

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The Magic of Math in Stock Market Trading

Utilization of mathematics in trading can extend from very essential to very complex. There are quants funds that utilize PhDs who endeavour to discover predict future development depends on the complex arrangement between two markets or two stocks. Quants attempt to see designs which are not noticeable to naked eyes or are not depicted in chart patterns. Algorithmic trading now represents 70 per cent of trading volume in many trades.

For an individual trader, this learning is of little use as they donʹt have the wherewithal or the money to send in the series of yield that is tossed out by such algorithms.

Basic knowledge of mathematics can give an edge to a trader when compared with the amateur. Many charting examples and trading strategies are based on basic mathematical concepts. We will take a look at a couple of concepts.

Read More: Stock Market Terms Every Investor Should Know

Prior to looking at these concepts, for a trader, it is most critical to have decent learning in math. One should be able to perform essential calculations orally as the market is moving. Another imperative point for a trader, particularly novice traders is to think in term of percentages rather than absolutes.

As far as ideas, a trader needs to comprehend probabilities and begin thinking on those lines; this is particularly valid for derivative traders. Trading, such as betting is a round of probabilities, you donʹt need to be correct every time, you simply need to pursue the guidelines and exchange when the edge is to support you.

Another idea that should be comprehended is standard deviation, or all the more vitally the ʹBell Curveʹ. This also is vital for the options traders. ʹBell curveʹ also called as a normal distribution of occasions in a progression of data delineates in a graphical way all conceivable events around the most probable event.

Standard deviations are determined around the ʹmean’ level. Numerous systems that brokers use depend on inversion to the mean. Hence a stock experiencing the oversold level is purchased with the expectation that it will return to the mean.

Read More: The Golden Rules of Investing

One concept that is found liberally in nature and what is called a ʹFibonacci ratioʹ is generally utilized by traders. Fibonacci retracements are made by taking highs and lows of the sharp move and dividing the vertical distance by the ratio 23.6%, 38.2%, half, 61.8% and 100%. Horizontal lines around these dimensions go about as essential to help or resistance levels. These levels are closely trailed by numerous traders and have worked admirably in the past. Traders again just need to know the levels and interpretation rather than the basic math behind it.

Ultimately trading, in the long run, relies upon keeping risk-reward proportion to support you and normal successes to average misfortunes proportion to be as high as possible. For a trader, these two ratios will choose his prosperity or failure and are without a doubt the most imperative scientific number in his trading life.

Profitable trading isnʹt about opinions, not about a prediction, or even great stock tips or picks. Profitable trading is about math, getting more money on profitable trades than you lose on unprofitable trades. How you do this is the details. Trading without understanding the math makes randomness and luck in your process. Understanding to make the math work in your procedure can make a huge difference as you move far from personality and feelings and toward measurement of a system that makes the potential for benefits.

Traders must probably answer the math behind their trading on the off chance that they need to work as an expert and accomplish consistency first and eventual benefit over the long term.

Read More: Stock Tips that should be followed by New Investors

The Top 10 Mathematics of Trading:

Risk/Reward Ratios: What number would you risk for a chance to make every amount?

The Position Estimating: How much capital will you put in one position?

Backtesting: How did the signs you are utilizing perform generally?

Drawdowns: What is the most measure of capital you would need to lose from your equity peak?

Returns: What is the average yearly return zone you are attempting to accomplish?

Win %: What are your desires for the number of winning trades as opposed to losing trades?

Losing Streak Probabilities: What are the chances of your worst case scenario losing streak?

The Danger of Ruin: With your open risk and position sizing what are the chances you, in the long run, explode your account?

Stop Losses: How much will you lose on a trade if you are incorrect?

Benefit Targets: What is your most extreme potential reward in the event that you are right about a trade?

Itʹs all in the math whether over the long haul you make money or lose money.

The earlier you start investing; there is a greater chance of the final results to be the best. Getting the right math and much precise calculations stock and share trading can prove to be a profitable journey. Join Aryaa Money today and start with your Share Market Training, learn technical analysis course to get huge success in trading early in Life.

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hello-rajesh-stuff-blog · 6 years ago

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The Magic of Math in Stock Market Trading

https://www.aryaamoney.com/Utilization of mathematics in trading can extend from very essential to very complex. There are quants funds that utilize PhDs who endeavour to discover predict future development depends on the complex arrangement between two markets or two stocks. Quants attempt to see designs which are not noticeable to naked eyes or are not depicted in chart patterns. Algorithmic trading now represents 70 per cent of trading volume in many trades.