“Smart Beta” Investing

“Smart beta” strategies are a class of investment strategies based on company fundamentals.  In the FTS "Smart Beta Investing"project, students:

• Learn what these strategies are
• Construct and manage their own “smart beta” portfolio
• Compare its performance to a market index.

For background information, we refer you to this general article by the originators of these strategies, Research Affiliates, and to this academic paper describing the methodology.

The project uses

• Valuation Tutor and/or the Financial Statement Analysis Module to select companies based on fundamentals and to construct portfolio weights.
• The FTS Real Time Client for managing the portfolio.  The built in Equity Portfolio Rebalancer allows the automatic implementation of a large portfolio, as well as the rebalancing of the portfolio.

Building Practical and Analytical Skills

Analytical skills are critical in today’s world, specially in areas like finance and accounting.  Trading simulations can be very effective in building these skills for a large base of students.  They have the inherent motivational advantage of being competitive, and the simulations can show students how analysis gives you an advantage and also what trading means in today's markets. 

 

Both our trading systems accomplish this goal:

• Interactive trading simulations with price discovery
• In this trading simulation, students trade securities with the each other; they experience market impact, liquidity, real-time reactions to the strategies of others, and so on.  But they can also prepare strategies to help them, as summarized in the following PDF file: Student Case Preparation Manual. 
• The manual shows students how to model securities,value them in Excel, and use them to guide trading decisions.  They can use Excel formulas as well as VBA macros.  They can even develop automated trading algorithms and see how they algorithm competes against others.
• Beyond that, they can build automated trading strategies in Excel and see how their strategy performs against others. 
• Virtual trading simulations of real world securities
• Here, students trade “against the tape,” i.e. they trade securities at prices that come from real world exchanges, but these are paper trades so they have no market impact.  Our approach is summarized in these projects.
• The projects take them from simple calculations (e.g. how to calculate performance measures) to intermediate calculations (how to use Solver in Excel to create a diversified portfolio) to this comprehensive value investing project that combines stock selection strategies with financial statement analysis, and valuation.
• With derivatives, the analytics come into their own.  I particularly like this option hedging project, where you have to hedge the risk of a broad equity portfolio using only index options and futures (since you want to hedge the portfolio, not individual securities).  The technique they use (described in the project) is to use beta-weighted delta’s to manage risk. 
• This is one place where FTS shines.  Without the real time analytics, managing portfolio risk becomes a very difficult task.  Imagine calculating all the implied volatilities, the option hedge parameters, and the basis risk manually in Excel every few seconds!
• The algorithmic trading capability here is also very powerful.  It lets students develop real-time trading algorithms.  This lets them implement strategies that are close to impossible otherwise, such as optimal trade execution, basket or portfolio trades, and any type of dynamic trading exercise that requires continuous monitoring of markets.
•  This is where FTS differs from other simulations.  Some offer more stocks that can be traded, but without tools that let you develop trading strategies, you are left with what you could do in the 1990's. 
• Our instructor support and student feedback is second to none
• Instructors have complete access to the entire history and performance of every student at any time
• Student reports include detailed profit and loss reports on every trade as well as the usual measures of portfolio performance

Students can use tools such as our financial statement analysis module to build analytical depth,   One feature of this module is that it lets students work from first principles: they learn to be a true analyst, not just a consumer of other peoples research, by working with raw company filings and learning how to analyze and compare companies.  The software makes it easy to study several companies.  One really useful feature is that you can compare your calculations from those of financial data providers; this teaches you what assumptions they make, how they aggregate fields, and what they miss. 

WWW.OSFTS.COM

Please  visit our new website, which contains information on all of the FTS products

WWW.OSFTS.COM

TURNING UP THE HEAT

The FTS Heat Maps are a powerful way to visualize the performance of stocks based on different criteria and over different time periods. They are accessed through the Heat Maps tab and are currently available for the US Equity cases (and will be extended to other countries soon):

This picture shows the performance of stocks, over the past month, sorted by beta. So TRIP has the highest beta among the stocks in the case, SHLD has the second, and so on. If you move your mouse over a box, you will see the full name of the company and its beta. So by scrolling, you can quickly see how high beta stocks are performing relative to low beta stocks.

You can also view this by deciles. In decile analysis, we divide the data into ten parts: the top 10%, the next 10%, and so on. So Decile 1 in the picture below shows you the average performance of the 10% of stocks that have the highest beta within the set of stocks in the case. You can see that over the last 3 months, higher beta stocks have performed better than lower beta stocks:

You can change the timer period using the drop down; during the day, when markets are open, you can see today’s performance as well.

The tree on the left shows you the different sorting capabilities. For example, you could sort by sector. Then, you would see how stocks in sector are performing; they would be shown within that sector:

You can see that AES belongs to the “Basic Industries” sector. You can also see that the next sector (“Capital Goods”) starts with the company whose ticker is A; holding your mouse over the box tells you the sector name.

You can also see how sectors have performed by selecting Sector averages:

We have provided specific dimensions along which you sort or categorize stocks, and these are based both on practice and on what the academic research literature has identified as being potentially important ways to understand stock returns, for example the book value. The sector industry maps, over time, help with sector rotation strategies. Sorting by your position can show you how your long positions are performing relative to your short positions if you are following a long-short strategy.

Creating Real Time Simulations With Your Data Feed

As you know, FTS runs a variety of real time cases based around our teaching guide.  You may not know that if you have a data feed, you can run your own trading simulations using our real time system.  I realized that we had not written much about this capability just the other day, when we were helping a school with Reuter’s data feeds implement their own simulation.

There are quite a few trading rooms that subscribe to real time data from a variety of exchanges.  Even if the data vendor provides a mock trading platform, they are not set up for instructors.  For example, it can be hard to monitor student portfolios and calculate relative performance for a class, and these tools typically don’t provide a structured series of exercise to guide student learning.

The FTS RT Server solves this problem.  All the cases we run use this server.  You have to create the simulation, and this is done in an Excel workbook.  You specify the trading names and passwords, what can be traded, who can trade what, and so on.  We have also greatly simplified the connection to the data feed: all you have to do is bring the real time data into the Excel workbook.  This serves as a generic interface, and can handle quite a large amount of data; every data feed we have encountered in a trading room has a utility that downloads quotes into Excel. 

Students use the FTS Real Time Client to connect to the server, and then it works just as with the simulations we run.  Instructors have the support required, described, for example, here. 

If you are interested in this solution, please contact us; we will be happy to show you and your trading room staff how to create real time simulations with your own real time data feed.

Online Courses, Virtual Trading Rooms

I just returned from a conference where there was a panel discussion on university trading rooms. One point of discussion concerned online and distance education: how can students who are not on campus benefit from a trading room? The question becomes even more important given the large increase in online courses; according to the 2010 Sloan Survey of Online Education, online enrollment increased by 21% while overall enrollment increased by only 2%.

 

A physical trading room is expensive to build and maintain. For example, a 2001 paper by Alexander, Heck, and McElreath (http://www2.stetson.edu/fsr/abstracts/vol_10_num1_p209.pdf) estimated the initial cost to be about $100k. A recent paper by Norton and Kim (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2048506) puts a lower bound of $100k, going up to $500k.  More recent estimates by Rise Vision puts the construction cost of a 30-seat trading room at around $500k and an annual budget of about $75k, and an additional $100k if you have a dedicated lab manager.

The answer to the question is not easy. It obviously depends on why you built the room in the first place and how it is used in the curriculum. 

 

1. If the trading room is like a library, just a repository of information and data, then the answer is simple: find a way for online students to have access, i.e. build a virtual trading room. Ironically, virtual access is the most difficult with some of the most costly resources in trading rooms, like Bloomberg terminals.

2. If it is integrated into a wide range of courses, then you have no choice but to create a virtual environment. By “integrated” I mean that professors regularly use the resources in their courses, for example to conduct trading simulations, assign projects utilizing the data resources, and so on. In this case, you also have to provide a way for students to learn to use the resources without necessarily having access to a person for help.

 

In our case, we have been working on creating usable technology for virtual trading rooms for almost two decades. When I was at CMU, we built a trading room in the early 1990’s, called the FAST Lab. It had live data feeds, data servers, etc. We had about 25 seats. We learned several things very quickly.

First, having the data, such as real time quotes, is not useful unless you developed educational tools to do something with it. This led to what is now the FTS Real Time System. Our goal here was, and remains, providing a set of tools so students can learn concepts, their practical application, and their usefulness. It is not “teach trading” or “stock picking.” Our teaching guide therefore contains a series of projects that students can conduct that help them understand risk and return, portfolio diversification, duration and bond immunization, how to use the option Greeks to hedge, how to apply valuation models, and so on. It provides a tight integration of theory and practice.

 

Second, we learned a lot about complexity. Putting a student into a trading room with all the “latest whatever” is like putting a drivers ed student into a formula 1 racing car and asking them to drive it at 200 mph. You have to step them up to that. Hence our effort on online textbooks, application guides, and so on. Our Financial Statement Analysis module is the latest in this series, and provides a structured, step-by-step way for students to learn financial statement analysis and conduct fundamental analysis. You can combine it with a trading exercise, but that is not necessary. As part of the module, we also provide quick and easy access to financial reports, so students don’t have to spend hours downloading data and copying and pasting; Excel skills are still needed, but for developing higher level skills, not the mechanics of data collection.

 

Third, we learned about access. Students tend not to work around a 9-5 schedule, and their needs depend critically on when something is due. So a room with 25 seats is totally inadequate if 300 students in core investments courses have a project that is due, and they all want to work on it at 1 am. So you have to provide distributed access. You also can’t do many other things with a limited number of seats. For example, the FTS Interactive Market is a trading simulation where students trade with each other. While the trading is organized around cases that reinforce conceptual understanding, students experience many things, such as market impact and liquidity, that are difficult to teach in a textbook and that cannot be experienced by simulations where they trade against the tape. If you want to use this in a class of 45 students, a 25 set room is limiting. So again, our application is accessible anywhere there is an Internet connection.

 

Anyway, we had a physical trading room for a few years, and then moved entirely to a virtual trading room. This also meant that when we expanded distance education and wanted to use these resources (for example, in the computational finance classroom in New York City), it was easy to expand. 

 

When writing this, I recalled an article published by the AACSB in 2003 that discussed may of the issues that we continue to discuss today. You can find a copy at http://gtf.mcmaster.ca/reports/p22-27.pdf    Many of the issues remain the same as what we discussed almost a decade ago.

Announcing the Financial Statement Analysis Module

We are excited to announce our new module: the FTS Financial Statement Analysis Module.    It is a unique hands-on and interactive way to teach/learn ratio analysis.  In this post, I’ll discuss why we developed this module, discuss some of the advantages of the approach we have taken, and how it complements Valuation Tutor.

In my mind, the main difficulty students have with financial statement analysis, and that instructors have with teaching it, is that there is a big gap between theory and practice.  Using a simplified “textbook” example abstracts away from all of the details you encounter with real-world financial statements.  Using the actual statements of only one or two companies is helpful, but the knowledge may not be easily transferable to other companies.  And the more you focus on actual statements, the more you focus on details and perhaps lose sight of the bigger picture.  And this can be boring.

So the challenge was to come up with something that keeps the focus on the conceptual framework and lets you see how the details you encounter in real world financial statements fit into this framework in an exciting way.  At the same time, we wanted a manageable way for students to gain experience and develop judgment by working with a large set of companies if they wanted to.

The FSA module achieves these goals.

First, there is emphasis on practice.  It uses only real world financial statements.  You can use the filings of any of the over 4000 companies we currently cover.  These are (mainly US) companies that file using the SEC’s interactive data format.   The filings are updated every night. Filings from other countries are not as easily accessible; we have a data project underway where we are manually converting statements of Australian, European, and Canadian companies into an accessible format.

Second, there is a conceptual framework through which you analyze companies. The topics here are fairly standard (profitability analysis, risk analysis, operations, earnings quality, and price ratios).  What is unique is that you literally drag the information from the statements and drop it into the conceptual framework.  This way, you see first hand what is needed for what analysis, how companies present information, what items you have to aggregate or disaggregate, and so on.

To help with this, we also provide a Self-Assessment data set.  In this data set, we have gone through the analysis for several companies.  The software then guides you step-by-step though the process of analyzing any company in the data set.  You can ask for detailed help or less detailed help, even no help.  You can practice this repeatedly.  For example, you can start with detailed help, and repeat the process with less help.  Or you can do the opposite: start with no help, and then turn it on if you get stuck.  The ability to practice really helps in bridging the gap between theory and practice.

For individuals, the FSA module provides an intuitive and interactive way to learn the theory and the practice.  The self-assessment data set guides you through the steps so you are not left guessing whether you are doing something correctly.  You can work at your own pace, repeating an exercise as many times as you like.  By saving your work, you can add to your knowledge over time without having to restart every time.

For instructors, we have additional tools, described under the “Instructors” section of this site.  You can assign you own practice data for the students for a company you choose.  You can also “grade” assignments by checking how student solutions compare to yours: you assign a problem and a company; the students work through the exercise and send you their answers; the software quickly compares your solution to those of all the students.

Two features we would like to highlight are:
--- the self-assessment mode, that lets students work though examples at their own pace
--- the instructor grading support, that provides an immediate evaluation of student submissions to your solution

We provide examples for students to work through, but these can easily be constructed and assigned by an instructor.  We also have a test bank dataset that you can use.

The motivation for creating this module came from Valuation Tutor (VT).  We received a lot of very positive feedback on Valuation Tutor and its ability to graphically compare companies along different dimensions.  But  VT assumes that students know how to construct ratios and other performance measures from financial statements, and so focuses on interpretation.

The feedback pointed out the need for a step-by-step way for students to learn how to construct ratios, essentially a pre-requisite for VT.  The FSA module fills exactly this gap.   Please look at the descriptions and videos at www.fsamodule.com and don’t hesitate to contact us for more information

Teaching Bond Valuation

I am struck by how different instructors use various parts of our system to teach essentially the same thing.   In fact, when someone asks how to teach something using our system, the answer mostly depends on your style of teaching.  This is hard to explain in the abstract, so let me give you an example. on teaching Bond Valuation.  A previous post shows you alternative ways to teach portfolio diversification.


The basics of teaching bond valuation include understanding discounting and the price-yield relationship, the term structure of interest rates, spot and forward rates, and perhaps managing interest rate risk using duration and convexity.   These topics are taught in at least 3 different ways:

Method 1: Bond Tutor - This is an interactive textbook; calculators are embedded into the online text, and students can experiment with concepts  as they learn about them.  For example, students can change the yield curve or forward curve, and observe the effect on the other curve and also on bond values:
 

Method 2: The FTS Interactive Markets-  Here, students trade bonds and learning takes place through price discovery and by building an analytical support system to help their trading.   In the simplest case (B01), there is a flat yield curve, so you learn how to discount given a yield.  Students who do not discount correctly lose money in the trading exercise to others.   Cases B02 and B03 introduce spot rates and forward rates.  Case B04 requires them to hedge a bond portfolio using duration.  Subsequent cases build on this, and the most advanced have trading of swaps as well as caps and floors to manage interest rate risk.  This is a synchronous “in class” exercise; students trade with each other, they react to each other.  Learning goes beyond a textbook; you need to understand the concepts, but you also learn how to use the concepts in making a trading decision.  The biggest way in which they lear is by building a decision support system in Excel.  The Student Case Preparation Manual shows you what students have to do to prepare for the case and how the learning takes place.  The following screen shows a real example of the FTS Interactive Trader with trading case B02:

Method 3: The Real Time System- The textbook and interactive markets are great teaching tools, but they do not let you experience some of the complexities of managing a real world bond portfolio.  The FTS Real Time system, with its built in analytics, provides some of this experience.  Here is a sample screen (of the Windows) of our Duration and Interest Rate Risk project

You can see my position in two bonds, and importantly, the portfolio analytics at the bottom right, and these analytics are used by students to make trading decisions.  

Summary These methods are not exclusive, and my description of what is available is not exhaustive (see below).   Bond Tutor represents the smallest deviation from a traditional textbook.  The FTS Interactive Markets are short trading exercises and it is easy to introduce the trading exercise in class and conduct a discussion that relates the concept to the trading exercise and also to markets and price discovery.  The FTS Real Time exercise abstracts away from price discovery but exposes students to application of the concepts to real-world interest rate movements.   

Just as an aside, we have several other modules for teaching fixed income; these include:

--The Treasury Calculator (the relationship between quoted Treasury prices and yields; yield curve approximations and interpolation; understand duration and convexity)

-- The Bond Immunization Lesson (a self contained interactive lesson for understanding how immunization works)

-- The Interest Rate Risk Module (Plot historical yields, spot rates, and forward rates, Animate curves to get a visual feel for long terms trends and mean reversion,  Calculate volatilities and correlations, Conduct principal component analysis, Backtest to understand the efficacy of different immunization strategies)

--The Principal Components Lesson (Understand the essence of principal component analysis with a visual calculator)

--The BDT (Black-Derman-Toy) Module (calibrate a BDT model to your own yield curve data using either yield volatilities or local vitalities, transfer the calculated lattice to Excel to price interest rate derivatives)

Experimental Research with FTS

Experimental research is a big part of our history.  In fact, the first version of the FTS Interactive Markets was developed in 1989 and 1990 for conducting experiments on information aggregation; the resulting paper came out in the Journal of Finance in 1991.  I even have a picture of the old text-based interface, on an original PS2 computer:

 

Since then, the FTS Interactive Markets have been used in many other experiments by different researchers.  Even today, we are helping several people with their new experiments; this includes setting up the trading parameters as well as software modifications to accommodate their needs. 

 

Beyond market experiments, though, we also have a “generic” experimental platform for conducting all sorts of experiments easily; these include behavioral experiments, auctions, and so on.   Examples of how to easily design eperiments are described in this document.

You can also use it to add non-market dimensions to a market; one example is adding a cheap-talk phase before a trading phase in a market, as in http://server1.tepper.cmu.edu/Seminars/docs/JobMarketPaper_0107_HongQu_CMU.pdf 

 

The experimental system is fairly straightforward. I’ll use an auction as an example.  You set up the experiment in an Excel workbook. One worksheet contains common information, such as instructions, sent to all subjects. Another worksheet contains information to be sent to individually, such as a private value or signal for the object being sold in an auction. This area also specifies what input is required from the subjects, such as a bid for the object being auctioned.

 

• You run the experimental server on your computer and connect it to your workbook.

• The subjects all connect to your computer using the client program.

• You send the common information

• You send the private information

• The subjects enter their responses (e.g. bids) during some period of time that you choose.

• When the time is up, you “grab” all their responses, calculate the outcomes in your

workbook (e.g. the price paid and who is allocated the object) and send this information

back to everyone.

 

The information flow described above can also be made continuous; in fact, you can mix and match

information that is updated constantly and that is updated manually. For example, if you are running a sealed bid auction, you only need to retrieve bids at the end of each round. If you are running an English auction, you may want to display the highest bid (or all bids) continuously.

 

The experimenter’s screen looks like this:

In the spreadsheet, you can set the colors of each cell, the text alignment, and you can see all the features in the screen shot.  We have a standard template that you can modify to create a variety of different types of experiments quickly and without a lot of effort.  If you have questions, please contact us, we love to talk about experimental research.

Portfolio Fundamentals

An exciting new capability is the integration of the Real Time System with Valuation Tutor (VT in what follows).  What it lets you do is conduct a fundamental analysis of your portfolio and also helps with stock selection.  I will describe the latter capability in a more general posting about using VT for stock selection.  Here, I will focus on the portfolio analysis.

Recall that in VT, you can analyze and compare companies along many dimensions.  These include common size analysis, cost-volume profit analysis, efficiency ratios, as well as price ratios.  Now, you can do the same for your stock portfolio.  This lets you answer questions such as: what is the ROE of my portfolio?  How does it compare to the stocks in my trading case?  How does it compare to specific industries or sectors?  And ROE is just one metric; you can compare your portfolio to any subset of stocks (in the VT dataset), sector and industry averages, and see exactly what are the fundamentals of your portfolio along any dimension covered by VT.

To use the capability, launch the FTS Real Time client and log in to one of our US Equity cases.  I will use the simplest case, the 30 stock case to illustrate the connection.  After you log in, click on the (new) Valuation Tutor tab:

 When you transfer the data, VT creates an (artificial) stock called “RT Portfolio”

Valuation Tutor

We have had some exciting developments with Valuation Tutor. First, we have added a lot of visualization and comparison capabilities.

 

You can read about these on the Valuation Tutor blog:
Financial Statement Analysis- The Power of Visualization
Financial Statement Analysis- The Power to Compare 
The capabilities are also described in the video at the top of this blog. Second, we have integrated Valuation Tutor with the FTS Real Time Client. I will soon post a description of this, together with an example of using Valuation Tutor for stock selection, so stay tuned!

Report to the Instructor

As we near the end of the semester, I thought I would describe some of the capability available to instructors for evaluating student activity and performance in our real time system.  As I have written elsewhere in this blog, our emphasis is not just on performance, it has much more to do with the practical implementation of principles of investments.  So the market value or the Sharpe ratio may only be a small part of what is needed for a full evaluation.

An instructor can obtain the complete history of every student at any time by selecting “Instructor Reports” from the Reports menu item:

 

 The resulting window gives you everything you could conceivably want (I have hidden the actual numbers in the next picture since these are real accounts):

 The “normal” reports include daily market values compared to the benchmark, as well as a variety of performance measures.  We also show the rank of each student along various dimensions as you can see.  Note the “Export to Excel” button: you can transfer any report into an Excel spreadsheet by clicking this button.

The “Download Trade Activity” button lets you see who traded on what date.  This is critical when you need a project to start on a given date and end on a given date; you can monitor whether students are implementing their positions or not.

Beyond that, you can download the entire trading history of any student (buttons on the right hand part of the image).  This allows you to check their project reports against their actual trades.  You can also generate reports for a specific student once you have downloaded their history:

A students history is, of course, also available to the student; the instructor can additionally access request this for every student.  You can see every trade, a detailed replay of the the history of any security, and, perhaps most useful, the P&L reports.  These come in two forms: a summary report and a detailed report per security.  The following pictures give you an idea:

The detailed report shows you exactly what happened and when by security.

Of course, students are always interested in overall performance.   Here is an example of that report:

Ease of Access: SEC XBRL Filings

The world of financial statement analysis has undergone a dramatic change with the SEC’s Interactive Data requirement.   Effective June 2011, essentially all publicly traded companies “will provide their financial statements to the Commission and on their corporate Web sites in interactive data format using the eXtensible Business Reporting Language (XBRL)."  The SEC's goal in adopting this requirement is "to provide financial statement information in a form that is intended to improve its usefulness to investors. In this format, financial statement information could be downloaded directly into spreadsheets, analyzed in a variety of ways using commercial off-the-shelf software, and used within investment models in other software formats." (Source: sec.gov, Final Rule: Interactive Data to Improve Financial Reporting.)

At FTS, we have been busy bringing the power of the interactive data to our users.  Valuation Tutor now gives you immediate access, with easy search capability, to any interactive filing for companies traded on the major exchanges.  As of the time of this writing, over 9000 10-K's, 10-Q's, and 20-F's were accessible, and we expect to add roughly 5000 filings every quarter.  We are also exploring ways to provide access to filings in other countries, since our system is used in over 20 countries.

Market Microstructure

There are several different methods by which orders are submitted, displayed, and matched on real world exchanges.  There are usually three phases in a trading day: the time period before the market opens, the trading day during which the market is open, and then the time after the market closes.  Sometimes, different methods are used in each of the phases.  For example, many exchanges have a pre-opening call auction but continuous trading when the market opens.   There are variations even within each type.  For example, some exchanges display orders in the pre-opening call auction, some don’t.  Some display the “virtual price” (the volume maximizing price given current orders) and some don’t.  Some even have a random closing time.

The FTS Interactive Markets handle a wide range of mechanisms.  For example, you can run a pre-opening call auction followed by a continuous double auction.  You can run order-driven and quote-driven markets.  You can have competing dealers, who can or cannot trade for their own account.  You can even have traders who have to trade out of a position before they can trade for their own account.  You can hide or display the limit order book.  You can run a pure specialist market.  You can also have an “upstairs market.”

All the variations are listed at the link Microstructure treatments (the second part shows how you modify our trading cases to run a variation).  We also have pre-set cases that run through the variations.  Over the years, we have developed many cases for specific instructors; let us know if you want to modify a case.

Fundamental Value

It has been a busy summer so far.  We have been working on taking Valuation Tutor (www.valuationtutor.com) to the next level.  We have now made it very easy to directly access all company filings that are in the SEC’s interactive data format.  Starting from now, most filings will be in this format, and Valuation Tutor now gives you a simple way to access all the filings (by ticker or company name).  It has a simple data collection utility that lets you extract the information you want; over time, we will be working toward making this even simpler, so stay tuned for further developments.  The data can then be used for financial statement analysis as well as (intrinsic) valuation.  The online textbook now has

Insider Trading Rings

Recent news stories about insider trading and the conviction of a hedge fund manager reminded me of a simulation we used to run that is just as relevant today.  This simulation is different from the ethics simulation I wrote about elsewhere in the blog. The ethics simulation focuses on the individual decision: when faced with the situation, will you make the ethical decision?  The insider trading simulation focuses on groupssharing insider information for mutual profit.

In the simulation, traders receive insider information about the prospects of a company.  Prior to trading, they are allowed to exchange messages; they can choose whom to communicate with, and if they receive a communication, they can choose to see it or not. They know who is sending them the communication.  After the communication, the stocks are traded in the FTS Interactive Markets; in these markets, the trader buy and sell the stocks from each other, and in the particular simulation, the trading mechanism was a double auction where everyone is a dealer, so they trade for their own account.  There were cash prizes for the best performers, and all the information they received from the system was correct (and so unambiguously useful). 

Meet Me On The Web

As the semester draws to a close at US universities, we receive many inquiries that require “interactive” meetings where we can either see someone else’s computer screen or where we can show them how to achieve some educational goal using our system.  We use GotoMeeting to conduct these meetings.  They tend to be of four types.  The first, and most common, is people who are thinking about adopting FTS in the future, and these meetings usually consist of an overview of the system and its application in different courses.  The second is from people using our system for experimental research.  The third, usually before the start of the semester, is from instructors asking how to integrate specific parts of the system in their upcoming course.  The fourth type is from existing users of the system asking “how can I do “ something related to the end of the semester, whether it is students asking how to create reports or instructors asking how to get particular information on student performance.

 

It struck me today, when a flurry of requests for meeting came in, that we actually do a lot of these and on a regular basis, specially at the beginning and end of each semester.  For example, I did a series of meetings in December and January for an instructor who wanted to design a specific type of market microstructure course; the course was taught this spring.  Other example is of meetings on experimental research

Greek Alphabet

No doubt about it, one area where a real-time simulation adds tremendous value is in the teaching of options.  Perhaps more than any other application of textbook finance, you really need analytics to make decisions and to appreciate the usefulness of the models.  

While there are many different exercises you can conduct, one of my favorites is one where students hedge the risk of a stock portfolio using index options, as described in our Hedging with Options real time project. We like using index options because we want to stress hedging on a portfolio-wide basis, not hedging individual securities by themselves (which would over-hedge the portfolio).

This introduces many concepts beyond basic option pricing and hedging: you have basis risk since you don’t have options on your portfolio, you need to understand correlations between the stocks (and whether these are stable), and of course you have to decide how to rebalance.  In this exercise, the equity positions don’t matter; the question is after you take the position, can you effectively hedge the price (and/or volatility) risk?  Variations of the project have options on smaller and larger equity indexes, and you really only need positions in one or two stocks to create an interesting problem.  As an extension, you could add index futures as well.  These types of projects also have the added advantage that to draw realistic conclusions on hedge performance, you only need a short period of time, say a week or two, so they fit in well into course timetables. 

 

In such a project, each student can use their own estimates of parameters (such as risk free rates and volatilities), though we provide default values.  All the analytics can also be calculated using implied volatilities.   In real time, you see both the individual and portfolio-level Greek parameters; aggregation is calculated using beta weightings, as done in practice and as explained in the project writeup.  There are three main analytic screens.  The first shows the Greeks at the portfolio level:

 

  

Caught Short

Short sales can be confusing for students in several ways.  First, they are unintuitive, perhaps because most of their experience in life consists of buying things or selling something they already have.   Second, the concept of a portfolio weight is not as clear as for long positions, and there are at least two ways to define them.  Third, the margin requirements of short sales can be counterintuitive: you are selling something but don’t get the money; in fact, you have to put up additional money.  If they don’t understand this third point, they can run out of money when trying to implement a strategy.

All this comes together in our Portfolio Diversification project where we explain the issues involved.  The project forces them to work through and understand the issues.  The real-time analytics they use to monitor their positions reinforce this, for example:

Report to the Instructor

As we near the end of the semester, I thought I would describe some of the capability available to instructors for evaluating student activity and performance in our real time system.  As I have written elsewhere in this blog, our emphasis is not just on performance, it has much more to do with the practical implementation of principles of investments.  So the market value or the Sharpe ratio may only be a small part of what is needed for a full evaluation.

An instructor can obtain the complete history of every student at any time by selecting “Instructor Reports” from the Reports menu item:

 

 The resulting window gives you everything you could conceivably want (I have hidden the actual numbers in the next picture since these are real accounts):