Of Medici & Intersections

MEDICI EFFECT

We all know in order to do a proper analysis & derive conclusion from the captured data we need to think innovatively. This demands creativity. One of the most interesting book I came across about idea creation is “The Medici Effect: What Elephants & Epidemics can teach us about innovation” by Frans Johansson. The idea behind this book is simple: When you step into an intersection of fields, disciplines, or cultures, you can combine existing concepts into a large number of extraordinary new ideas.

For those of you who don’t have time to go through the entire book, here is the brief of the whole book.

Inside the Medici Effect: The book is divided into three parts with a total of fifteen chapters.

Part One: The Intersection

1.The Intersection – your best chance to innovate

There are two kinds of innovation: directional and intersectional. Directional innovations take a particular direction as results of combining ideas within a field. Intersectional innovations, on the other hand, leap to new directions as results of combining ideas from different fields.

Intersectional innovations happen in the Intersection. That’s why the Intersection is the best place for us to innovate.

2. The Rise of Intersections

There were a lot of Intersections in the Renaissance era that produced people like Leonardo da Vinci. But the world then changed and people became more and more specialized. Recently though, the world changed again and Intersections rise. There are three forces behind this rise:

• The movement of people
  Globalization makes more and more people move between nations.
• The convergence of science
  The previously separated fields of science converges and creates combinations like bioengineering.
• The leap of computation
  The increasing power of computation frees people to be more creative and increases communication between them

Part Two : Creating the Medici Effect

3. Break Down the Barriers Between Fields

Whenever we think about an idea, we usually associate it with other ideas. A knife, for instance, is normally associated with cutting. But most people only see the obvious associations. It’s difficult for them to associate knife with, say, music. This difficulty is called associative barrier and it inhibits creativity. If you want to be creative, you should break the barriers between fields.

4. How to Make the Barriers Fall

How can you break down associative barriers? The key is diversity. You should expose yourself to different cultures, learn differently, and see from multiple perspectives. The more you have diversity, the more likely it is for you to associate different ideas.

5. Randomly Combine Concepts

A creative idea has two important characteristics. First, it’s a combination of different concepts. Second, it’s random which is why it’s difficult to trace the origin of an insight. Like it or not, luck is an important factor of innovation.

6. How to Find the Combinations

Luck is essential for innovation. But is there anything you can do about it? Fortunately, yes. While you can’t completely control random factors, you can increase the chance of succeeding.

There are three ways to do it: by diversifying occupations, by interacting with diverse groups of people, and by introducing randomness into your thinking pattern.

7. Ignite an Explosion of Ideas

Here is a defining characteristic of successful innovators: they produce and realize a huge amount of ideas. Though it may seem counterintuitive, the strongest correlation for quality of ideas is quantity of ideas. Linus Pauling said, “The best way to get a good idea is to have a lot of ideas.” It has been proven that scientists, artists, and writers with the best ideas are those who produce the most ideas.

8. How to Capture the Explosion

There are three things you should do to capture creative ideas at the Intersection. First, you must have deep enough understanding of the fields involved. Find the balance between depth and breadth. Second, you must generate many ideas before evaluating them. One way to do this is through brainstorming. Third, you must have enough time for evaluating the ideas. Research shows that, contrary to common belief, being under time pressure actually inhibits creativity.

Part Three: Making Intersectional Ideas Happen

9. Execute Past Your Failures

Getting creative ideas is one thing, but realizing them is another thing. This is the difficult part for many people. Since innovative people pursue more ideas, they also fail more. The key here is to execute past your failures.

10. How to Succeed in the Face of Failure

Since failure is part of innovation, you must plan for it. You must be ready to change your execution plan and don’t think that you will get it right on the first try. To be able to do that, you should reserve resources for trial and error and have intrinsic motivation to remain motivated.

11. Break Out of Your Network

To execute intersectional ideas, often you need to break out of your existing network. Why? Because the network - which consists of your current colleagues, mentors, and customers - often inhibits you from executing intersectional idea. They want you to stay within the field and execute directional ideas that are more predictable.

12. How to Leave the Network Behind

Leaving your network doesn’t mean alienating them. You should keep your relationships with them. Leaving the network means stop relying on them. But, in case you face opposition from them, you should also be prepared to fight.

13. Take Risks and Overcome Fear

Executing intersectional ideas involves taking risk. A logical way to overcome it is by acquiring more resources (time, money) to minimize risk. Unfortunately, this is the wrong way to take. Acquiring more resources doesn’t reduce risk. The reason is because someone’s behavior becomes riskier when the environment is safer.

Therefore, don’t try to minimize risk. Once you have enough resources to execute your idea, do it without waiting for more resources.

14. How to Adopt a Balanced View of Risk

To find the courage to execute your ideas, you should avoid behavioral traps relating to risk. One such behavioral traps is the tendency to stay within a field when things are going well. The reason is because we fear losing. Being aware of the traps is an effective way to overcome them. Another effective way to overcome fear is by acknowledging your fear.

15. Step into the Intersection.

This chapter reminds you of the main idea of the book. The future lies at the Intersection. It’s where breakthrough ideas are. If you want to help create the future, find your way to the Intersection.

I hope this will help you all people.

Group: Finance 2

Author: Vijeta Bhardwaj (13112)

k-Means Clustering - Views of a Marketer!

Of k-Means Clustering

Today we will talk about k-Means clustering …. What it is and where did the term k-means come from.

As a student of marketing we will also try to understand a little about how can k-Means clustering be used in marketing.  

The term "k-means" was first used by James MacQueen in 1967, though the idea goes back to Hugo Steinhaus in 1957. The standard algorithm was first proposed by Stuart Lloyd in 1957 as a technique for pulse-code modulation, though it wasn't published until 1982.

In statistics and data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. The main advantages of this is that it is simple and the most popular method of partitioning data.

To start with today was my second class in Business Analytics on SPSS….. not to mention how I dreaded this class even before it started due to my weakness with numbers. I did never realize that SPSS could so simple a tool to handle even though it is loaded with various resources to make analysis and one of them is what we I am going to talk about.

The benefits of k-Means clustering to a marketing student can be in various fields:

Retail: Can be used to cluster similar Merchandise.

Market Research: Can be used to cluster similar data like demography, etc

For Academic Purpose:

The k-Means Clustering algorithm can be used for prediction of students academic performance.

At the end I would like to say that k-means is a very convenient way to cluster large number of data and also helpful to a marketer.

Author: Archit Tamakhuwala

Group Name: Marketing 2

Dendrograms: Limitations

So here I am writing a blog on dendrogram! Sounds different, sounds interesting! Where do I start from? Do i write about the concepts learnt in class about dendrogram or should I just go a step further and write something new? I guess I’ll choose the latter.

So we all know what exactly a dendrogram is and how it is nothing but a pictorial representation which helps us in clustering. It is basically used to visualize how a cluster is formed.

We know all the good things associated with a dendrogram. However, what about its limitations? Is it always good and what are the shortcomings, if any? We need to address this question as well when we are trying to understand any concept.

Dendrogram can essentially be used only for a small number of observations. As the number of observations increases, it becomes difficult to distinguish the individual leaves. Another valid point is that the vertical axis represents the level of criterion at which any two clusters can be joined. Hence successive joining of clusters implies a hierarchical structure, meaning that these dendrograms are suitable only for hierarchical cluster analysis.

For large numbers of observations, these hierarchical cluster algorithms are proving to be time consuming. The computational complexity of the three popular linkage methods is of order O(n square), whereas the most popular non-hierarchical cluster algorithm, k-means ([R] cluster kmeans, is only of the order O(kn) where k is the number ofclusters and n the number of observations (Hand et al., 2001). Therefore k-means, a non-hierarchical method, is emerging as a popular choice in the data mining community.

Hence there is another popular graph being used now as the number of clusters increases. It is being referred to as the “Clustergram.” s. This graph is useful in exploratory analysis for non-hierarchical clustering algorithms like k-means and for hierarchical cluster algorithms when the number of observations is large enough to make dendrograms impractical.

The Clustergram is understood to be a type of parallel coordinates plot where each observation is given a vector. The vector contains the observation’s location according to how many clusters the dataset was split into. The scale of the vector is the scale of the first principal component of the data.

I guess this amount of knowledge regarding the dendrogram and Clustergram is sufficient for the day. One can always get a number of Pdfs, research papers and dig deeper into the subject and unravel a lot more interesting facts and observations regarding the same.

Harshala D (Roll No 13174)

Finance: Group 5

K-Means Analysis and its Application in the area of Finance:

K-means clustering algorithm: K-means method is widely used due to rapid processing ability of large data. K-means clustering proceeds in the following order. Firstly, K number of observations is randomly selected among all N number of observations according to the number of clusters. They become centers of initial clusters. Secondly, for each of remaining N–K observations, find the nearest cluster in terms of the Euclidean distance. After each observation is assigned the nearest cluster, recompute the center of the cluster. Lastly, after the allocation of all observation, calculate the Euclidean distance between each observation and cluster’s center point and confirm whether it is allocated to the nearest cluster or not.

When to use Hierarchical Clustering (Agglomerative) and K Means?

We can have as many clusters as we do cases, so our last step is to determine how many clusters we need to represent data. We do this by looking at how similar clusters are when we create additional clusters or collapse existing ones. In k-means clustering, we select the number of clusters we want. The algorithm iteratively estimates the cluster means and assigns each case to the cluster for which its distance to the cluster mean is the smallest. In two-step clustering, to make large problems tractable, in the first step, cases are assigned to “preclusters.” In the second step, the preclusters are clustered using the hierarchical clustering algorithm.

So, the suggested approach more likely is;

1. First perform a hierarchical method to define the number of clusters

2. Then use the k-means procedure to actually form the clusters

Source: http://www.mvsolution.com/wp-content/uploads/SPSS-Tutorial-Cluster-Analysis.pdf

http://www.norusis.com/pdf/SPC_v13.pdf

Applications:

1. One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. This paper investigates stock market investment issues on Taiwan stock market using a two-stage data mining approach. The K-means algorithm is a methodology of cluster analysis implemented to explore the stock cluster in order to mine stock category clusters for investment information. By doing so, this paper proposes several possible Taiwan stock market portfolio alternatives under different circumstances.

The above approach can also be used in designing the virtual stock market application.

Source: http://dl.acm.org/citation.cfm?id=1379588

2. Segmentation of stock trading customers according to potential value:

I went through an article while researching the various applications of the K means analysis in the financial sector (specifically, Stock markets). Here the Korean stock market was discussed from 1990s when it was actually expanding. In this article, they use three clustering methods (K-means, self-organizing map, and fuzzy K-means) to find properly graded stock market brokerage commission rates based on the 3-month long total trades of two different transaction modes (representative assisted and online trading system). Results of the empirical analysis indicate that fuzzy K-means cluster analysis is the most robust approach for segmentation of customers of both transaction modes.

3. Post-IPO corporate life cycle and takeovers:
The paper analysed here was an attempt to examine the impact of corporate life cycle on acquisition likelihood. Basically it discusses how the corporate life cycle determines the takeover strategies. This analysis have used corporate life cycle theories to investigate the motives and wealth effects of takeovers by classifying firms into three post-IPO stages using cluster analysis. Some of the findings by this research are:

· Firms at the young stage are more likely to be acquired when they have higher liquidity, less leverage, lower free cash flow and are undervalued.

· Young firms’ acquisition likelihood is negatively related to the existence of golden parachutes and blank check provisions.

· Firms in the mature cluster are more likely to be acquired when they are undervalued, have less free cash flow, golden parachutes, and super majority amendments in place.

· However, the presence of a classified board reduces the likelihood of acquisition in mature firms.

· Finally, old acquired firms have higher free cash flow and more tangible assets than other targets and are less likely to have a supermajority amendment as a takeover defence.

For following the paper, go to http://69.175.2.130/~finman/Orlando/Papers/OwenYawsonFMA.pdf

Sources:

http://www.youtube.com/watch?v=RrsX_yJC--s (A video for K means Clustering example)

http://www.mvsolution.com/wp-content/uploads/SPSS-Tutorial-Cluster-Analysis.pdf

http://www.norusis.com/pdf/SPC_v13.pdf

http://evlm.stuba.sk/~partner2/STUDENTBOOK/English/SPSS_CA_2_EN.pdf

Author: Aditya Mandloi (13122)

Finance_Group 5

Data Clusters

It was my first class today, so to start off with, it wasn’t easy especially hearing the words clusters and hierarchical data clustering and k means for the first time. But after a while, it seemed a bit ok.

Now what on earth is hierarchical data clustering? In simple words, it refers to grouping of data in various clusters in a hierarchical manner such that they amount to the same meaning. The clusters are progressively clustered such that no single element is left out at the end of it. This provides a means to analyse the data through a dendogram, which is a tree graph for displaying the results. The agglomeration schedule gives information on the objects or cases being combined at each stage of a hierarchical clustering process.

K means clustering. In this the algorithm assigns a single point to a cluster whose center is the nearest. And this is taken as the average of all points in the cluster. Usually simple and fast, it is predominantly used for large databases

There are various applications towards clustering. They can be used in medicine, and market research and even to decide demographics for election purposes and towards determining the ideal cd mix for a party. It takes into consideration various points such that the ones which share the greatest correlation are placed closest to each other.

So it really is quite a useful technique. Developed in the late 1960’s, it has found great acceptance in almost each and every scientific field. It provides an invaluable aid in analysing long convoluted surveys which never seem to make much of sense. Data analysis is way more important than the data being gathered, with the result that if one does not apply the right techniques and ideas, the answers may not be so forthcoming.

FINANCE - GROUP 1

AUTHOR - MAC LOBO

Think Innovatively, not Wildly !!

Every subject or study has its applications. This subject which I thought will be more about using the software is in reality not. Its applications are very important as this helps us to apply our minds and make strategic decisions. As Sir has rightly said thinking is important and using it in the right direction is the key. I was very confused as on which topic I should write but then I thought let’s write on Pearson’s Co-efficient.

What exactly is this??? In a layman terms it’s a relationship between two variables. For example: Twins can second guess each other..!!!!

It is a type of correlation coefficient that represents the relationship between two variables that are measured on the same interval or ratio scale. The value of the correlation (i.e., correlation coefficient) does not depend on the specific measurement units used; for example, the correlation between height and weight will be identical regardless of whether inches and pounds, or centimetres and kilograms are used as measurement units.

Numerically, the Pearson coefficient is represented the same way as a correlation coefficient that is used in linear regression; ranging from -1 to +1. A value of +1 is the result of a perfect positive relationship between two or more variables. Conversely, a value of -1 represents a perfect negative relationship. It has been shown that the Pearson coefficient can be deceptively small when it is used with a non-linear equation.

For example, in the stock market, if we want to measure how two commodities are related to each other, Pearson r correlation is used to measure the degree of relationship between the two commodities.

Bottom Line:

Pearson's Correlation Coefficient:

Tells us if there is linear relationship between two variables

Tells us how good the relationship is by seeing if r is close to 1 or -1 or r² is close to 1.0.

Tells us if the relationship is positive or negative by whether r is positive or negative.

It gives an equation for a straight line so that we can predict one score from another.

Group Name: Finance 2

Author: Rishika Agarwal