เอกสารการประชุมวิชาการ ในงาน มหกรรมวิชาการ สกว.
วันพฤหัสบดีที่ 21 มิถุนายน 2555
ณ ห้องประชุม Pheonix 1-4 ฮอลล์ 7-8 อิมแพ็ค เมืองทองธานี
จัดโดย สานักงานกองทุนสนับสนุนการวิจัย (สกว.) 
Monday, June 25, 2012
Tuesday, June 12, 2012
Predicting the Next Big Financial Threat
June 11, 2012 – You weathered the credit crisis. You got through the Flash Crash. You are watching Europe (and the U.S.) struggle to make sure its debt doesn't implode from the global economic downturn. So you're ready for the next big threat to financial markets, right?
Maybe not. It's harder than it sounds, say risk experts, who believe many stock funds still lack tools to prepare for and weather fluctuations.
"There is too much lip service being paid here without any real follow-through," says Nathan Lee, lead portfolio manager at the quantitative hedge and mutual fund firm Hagin Investment Management. "Almost four years since (the disappearance of) Lehman Brothers, headlines still suggest there are risk managers at systemically important firms that are merely figureheads or lack any power to manage risk before the trade."
Any risk management strategy worth its salt forces managers to think about the subject at every stage of their investment process, from brainstorming to asset allocation, say experts. That means not just calculating how much of a portfolio is at risk of being wiped away in the next 30 days, the end of the week or even the day. Now, risk management means spending on technology such as real-time risk modeling, among other tools.
"Managers need to be able to monitor and manage positions and exposures across multiple markets, real-time," says Phil Lynch, president and chief executive of the data management firm Asset Control. "The industry is nowhere near where it needs to be on this. Everyone is behind."
And, oh yeah, there's no such thing as too much research. Looking behind risk numbers and economic statistics is essential.
"Building a team of individuals who see a share of stock as a fractional ownership of a company has a big impact on risk management," says Lee. "A firm that is oblivious to the quality of a company behind a stock is doing a bad job of managing risk, although it may have the biggest software budget."
How to increase risk awareness, whenever and wherever possible? Consider the strategies of Stadion Money Management, a privately owned $5.4 billion asset manager near Atlanta. The Stadion process is governed by a technical model that produces a measurement it calls the "Weight of the Evidence.'' It includes nine statistical indicators, called components, which get scored, weighted and added to the overall measure.
These range from factors such as stock price trends and comparisons between advancing and declining stock issues to help managers pinpoint market movement, says chief investment officer Brad Thompson.
"Our model helps us understand when the sun is shining and the investing probabilities are in our favor or when the clouds are forming overhead and we need to be prepared to seek shelter," he says.
For example, one important component is called "trend capturing," which is composed of three calculations: Stock price movements, up and down; trading volume; and the number of advancing versus declining issues. The first calculation, when positive, indicates an upward price trend. The second and third calculations confirm the trend and determine if it's solid.
Another component is called "relative strength." This is designed to measure sentiment: i.e., are investors actively taking investing risk or have they turned bearish? It's also derived from three calculations related to investing involving stocks with small market capitalizations versus large ones; growth-oriented stocks versus value-oriented issues; and measurements of breadth of interest and prices.
The core of that component: When small caps outperform large caps, investors generally have higher risk appetites.
Each component is assigned a weight, from five to 25 points, and then totaled. Total score ranges have separate color coding, like red for scores between 0 and 30, and green for 80 to 100. Each color spurs a particular set of rules for buying and selling, etc.
At Snow Capital Management, managing director David Jack says his firm's most potent risk tool is its fundamental investment process, geared at finding attractively mispriced stocks. He says his firm constructs diversified portfolios of good, financially strong companies where the stock price is depressed because the company has experienced temporary difficulties of some sort."
This philosophy relies on independent research to determine the nature of the company's problem, assess the likelihood of a solution, and then determine whether the company can survive the difficulty.
Jack argues the downside is protected because the stock price is: already depressed; the company is sound; Wall Street's opinion is already negative, and investor expectations are low.
"Our investment process keeps us from investing in a bad business (at any price) and from paying too much for a good business,'' Jack says.
Franklin Templeton Investments is another company that relies on fundamental drilldowns into the health of companies to gauge risk- but it also tries to harness it.
"Risk and return are like two sides of the same coin, and you can't have one without the other," says Wylie Tollette, Director of Performance Analysis and Investment Risk.
Tollette says that Franklin's risk philosophy is not to avoid or eliminate risk, but rather to seek risks that are: recognized, i.e. well understood, measured and communicated; rational and appropriately sized in relation to the fund's investment mandate, and of course, rewarded.
To illustrate these principles, Tollette gave the example of owning shares in an Australian mining company. One might intuit the performance of those shares would be influenced by the company's operating fundamentals, as well as the performance of the Australian market and economy. Top down analysis might show that many mines are influenced by emerging market growth. The goal, he said, "isn't to avoid that specific emerging market exposure, but to ensure it is recognized, sized rationally in the portfolio and that the additional volatility it could add is compensated over the long term through return."
Everyone is behind...
Friday, June 8, 2012
Organizational Behaviour Theory
Organisation Theory 2011, Advanced Course, 7,5 ECTS
As organisations in their different forms play a central role in modern society, organisational theory and related areas are studied for many purposes and from many different perspectives. This course will focus on modern classical organisational theory. Concepts and paradigms of classical organisational theory will be studied as well as HRM, culture, strategy, power, institutions, gender and structural organisational theory.One central aspect of organisations is that they consist of individuals, or actors, who together create not only organisations, but primarily different perceptions of them. Furthermore, society in our part of the world consists of different types of organisations, in which we live major parts of our lives as family members, employees, students, etc. The qualities and intentions of the actor, as well as the structures which exist in society at large affect what happens in a certain organisation.
Welcome to this year's course!
We are looking forward to meeting you at the introduction on Monday, the 22nd of August 2011.
Alf Crossman, Course director and Examiner
via iei.liu.se
Organizational Behaviour Theory, used in today's parliament debate.
Thursday, June 7, 2012
6 Trends Guiding Financial Customer Data
How do you know a person well enough to understand their motives, their actions, their aspirations and needs? It can be difficult even among friends and relatives one has known for years, let alone for a financial institution with hundreds of thousands of customers. Some people say it's impossible for a large bank like Citi or Bank of America to really know its customers.
Yet the amount of publicly available data about each and every one of us grows all the time, to the point where an organization with the will, the patience and the right software can analyze in detail our financial transactions, our habits, our political leanings, our preferences and our geographic location, among other things.
Banks have been analyzing their customer data for decades, most thoroughly in the credit card business, searching for signs of fraud, willingness to upgrade to a new product or propensity to leave for a competitor. Today, many banks have projects under way to pull together customer data from all channels - branch, ATM, online banking, mobile banking, call center, social media sites - in one place, to mine that data in real time and use it to cross-sell, up-sell, detect fraud and keep customers in the right products. There are six trends guiding such projects.
1. The Big Data myth. The trendy phrase "Big Data" refers to data sets that have grown so large and complex that they become awkward to work with using standard database management tools.
Data volumes undoubtedly increase all the time. IBM estimates 2.5 quintillion bytes of data are created every day from a variety of sources including sensors, social media, and mobile devices around the world. IDC estimates the market for "big data" technology and services will grow at an annual rate of nearly 40 percent to reach $16.9 billion by 2015.
One bank customer recently described banks' data challenge to Boxley Llewellyn, global retail banking director at IBM, as "being in a big room full of data that's a little dark, so sometimes data gets trapped in a corner and sometimes it can't be found quickly enough. A wind of streaming data, social data and unstructured data is knocking at the door, and we're starting to let it in. It's a scary place at the moment."
But the idea that businesses need to store, mine and analyze every scrap of the customer data they collect is not practical.
"A lot of times when analytics and engineering people ask the business people what data they want, they get this answer back: collect everything and we'll sort it out on the back end," says Joseph Stanhope, senior analyst at Forrester Research. "That's not a data management strategy. There is too much data from too many sources coming at us too quickly for us to just save everything forever. You do need to be discerning about what data the business uses, which data goes to a KPI that shows us if we're moving the business forward. If people can't articulate what they need up front, they're not going to pick it up on the back end."
Gaming companies, for instance, don't mindlessly store all the data they collect on gamers, he observes. They curate the data to understand what is useful and what isn't, and create data hierarchies, schemas and categories to manage, condense, add and change information. "To understand this is more than technology, it's about people in the organization and the culture," Stanhope says. "If you can't evolve and change what you curate, then you do have to store and collect everything and business passes you by."
Edgar Enciso, executive vice president and director of customer intelligence at BBVA Compass, concurs. "We have a lot of noise around Big Data," Enciso observes. "The first challenge is to clean that information and define what data and analytic makes sense. We have information for everything and for everyone. However, when you try to be hands-on with the data, we have to clean it up and put it in a meaningful way so we can make the right decisions."
2. The use of predictive models to make better offers to customers." Once banks get that full picture of customers, they interrogate all the data they have and build predictive models," says David Wallace, global industry marketing manager, financial services at SAS. "They match the predicted behavior with campaigns for new or enhanced products, cross sell and up sell. They identify customers at risk of attrition and put programs in place to try and save the customer relationship because it's less expensive to keep customers than to get more. Predictive modeling is at the heart of all those activities."
BBVA is a case in point. It has three main goals for its customer analytics efforts, much of which are carried out in SAS Enterprise Miner analytics software, according to Enciso.
First, the bank is trying to make the right decisions to target the right offers to the right customers, through customer segmentation. The bank segments customers into the categories of wealth management, commercial banking, retail consumer and small business. It also performs lifecycle segmentation, grouping customers according to life stages, such as singles, independent professionals, young families and retirees.
The second goal is to understand customer profitability. "On the customer side and on our side, we want to have the right rates and pricing," Enciso says. "When we find customers who are not profitable, we try to find a way to serve them better, to keep them but put them in the right products."
The third objective is to analyze customers' life events and predict their future needs. "We're trying to see what are the customers likely to buy, what's their next problem?" Enciso says. The bank analyzes patterns in transactions and balance levels. "When we see that our customers are lowering their business with us, we're trying to find a way to keep the business," he says. If, on the other hand, a customer is increasing his balances, the bank tries to move that person to a higher segment with a better service level.
To understanding this is more than technology, it's about people in the organization and the culture.
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