Monthly Archives: August 2013

The AUS job-Part 2

Debt Negotiations
The first thing to do was re-negotiate debt terms individually with each supplier. The AUS owed money for things such as office supplies, venue rentals and student reimbursements for AUS related expenditure, among many other things.

The amount totaled around $25,000 with the majority of it being owed to 3-4 suppliers. I had meetings separately with these suppliers and promised them that they would be paid back fully as soon as we received our student fees from McGill.

Debt Restructuring
I provided them a timeline, with several scenarios on when we could expect to pay them back. The important thing here was to maintain a good future relationship with the suppliers while obtaining the concessions I felt were necessary to avoid bankruptcy. In hindsight, I was able to do a much better job than I though I would, as at the moment, I was powering through everything in ’emergency mode’.

Or maybe people were more understanding and sympathetic than I thought they would be. After continuous rounds of emails and meetings over two stress-filled weeks, I was able to delay our accounts  payable sufficiently to be able to get us a shot at actually paying back our suppliers.

It was more a case of structuring and eroding away our debt in exactly the right way and making commitments to match with our projected cash inflows, then doing everything possible to ensure that the projected cash inflows turned into actual cash inflows.

Where the debt came from…
The second, and more complicated, part of the equation was getting the inflows of cash started again. The debt restructuring took place during the summer of 2012, when the only cash inflow we could expect was from the student fees held back by McGill for the previous year. The debt had built up because the AUS had continued to function as normal in the prior year, despite not having received the full student fees from McGill for that year.

The AUS had continued to function as normal because the previous AUS executive, in my opinion, were wary of projecting the image that AUS finances were in trouble. My perception of the preceding AUS exec’s reasoning is that they thought they were going through a very politically turbulent year because of the Quebec student strikes, and did not want to add anything else to the mix.

The Theft and its strategic implications
AUS finances that year were already dealing with a bad reputation, due to the theft of  $12,000 from the office earlier in the year, and they did not want to publicly reveal any other negative information on AUS finances. Ideally, however, the AUS would have significantly scaled back its operations to deal with the shortage of incoming student fees.

Before I get into what needed to be done to get our frozen student fees from McGill released , I will need to recap a bit of AUS history. It’s important to understand why exactly McGill froze these funds in the first place.

And so on to the next post!

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The AUS job- Part 1

I have focused this blog on the more quantitative side of finance, but I also want to talk about the softer, less quant-y side. I was elected as the Vice-President Finance for the Arts Undergraduate Society for 2012-2013, out of a student body of 7,600 students. I had based my campaign on minor things like faster response times to funding applications, more open communication from the VP Finance and the creation of a new fund for charitable initiatives. Unknown to me
at that time, the AUS was dealing with several problems which would have resulted in bankruptcy a few months down the line. The most immediate of these problems was an acute cash shortfall and a liquidity crisis.

The cash shortfall was a result of several hundred thousand dollars of frozen funds, frozen by McGill University and by Revenu Quebec (the Quebec Tax agency). The AUS had been facing this problem of frozen funds intermittently since 2009, but had been able to get enough funding from McGill to function, in exchange for promises on shaping up its finances. These promises were subsequently not upheld, despite the best efforts of those in charge of the AUS. Therefore, when I took over the finances of the AUS, because of these string of broken promises, there was almost no room to negotiate with McGill to get enough funding to avoid bankruptcy. So a few weeks into my position, I was in charge of an organization with around $25,000 dollars of debt and less than $800 in the checking and savings accounts, spiraling down the path to insolvency .

In the next post, I will cover the exciting things that happened next!

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Strategy-3: ARMA and GARCH based forecasting- Part-1

One trading strategy we can adopt is to first model the market, forecast returns for the next trading day and then go long or short based on whether the forecasted returns are positive or negative. Model the market, you say? What foolery! Who can possibly hope to capture the millions of individual (human and machine) decisions, which themselves occur in response to millions of external stimuli, which all interact with each other in mysterious and magical ways to lead to market outcomes.

But that is not what a model does- we build a model to capture the essentials of what drives whichever process we are interested in. We abstract away from the details which we think are unnecessary or overly complicate our life. These details can turn out to be contentious as someone else may consider them to be critical and your model useless if it doesn’t capture them. I personally don’t put much faith in forecasting returns but thanks to modern technology, we can test how successful our forecasts are. And besides, if macro-economists can attempt to model whole economies, why pick on an undergrad who tries to model the stock market?

I’ll be using the ARMA (Auto Regressive Moving Average) and the GARCH (Generalized Auto Regressive Conditional Heteroskedasticity) models in combination to compute forecasts. This has been done previously by Quintuitive who runs an extremely informative and helpful blog. I have learned much from his writings and owe a lot of my learning to him. He has also generously hosted the code up for testing these models. I used his code with some very slight modifications, which I’ll detail further on.


Bring on the models

Before that though, a brief introduction to ARMA and GARCH is in order. We are using the ARMA model (with to-be-optimized parameters p and q) to specify the conditional mean of the stock market returns and the GARCH(1,1) model to specify the conditional variance of these returns. Conditional on what, you ask? Conditional on the information set we provide the model with. So for example lets say we have stock data from 2001-01-01 to 2005-01-01. If today is 2004-12-01, the forecast for tomorrow will be based on the returns today, and on a predetermined window of stock returns, for example the last 500 days. Why do we create a window for the information set, instead of using all the data available to us? This is to allow us to be more flexible in determining the process we are trying to model- to allow it to evolve instead of just update with each new data point. It allows for the model to be more responsive subsequent to structural change in the Data Generating Process (DGP). This paper provide more information on how to select the information set.


What is ARMA?

The ARMA(p,q) model consists of an auto-regressive part and a moving average part, with p auto-regressive terms and q moving average terms. The values of p and q are also referred to as the order of the model. The ARMA model is good for modelling situations in which a system is vulnerable to exogenous, unexplained shocks (a stock market!) but also shows mean-reverting behaviour (did you say stock market?). The moving-average part helps model the exogenous shocks to market return and the auto-regressive part allows us to make it mean-revert. The mean-reversion bit is also why it’s better to have a rolling window for our information set.


What is GARCH?

The GARCH(1,1) model is useful for modelling time series when the variance today is a function of some prior variance. This helps model the ‘volatility clustering’ we see in the stock market, where periods of high volatility are clustered around each other and take some time to decay. There are many variations of the ARCH model (GARCH is just one of them), some of which may be better suited to pairing with ARMA, but I’m not familiar with them so I didn’t use them. Here is an easy to read paper on ARCH models. 

A good textbook I found for understanding and implementing these models is Time Series Analysis and its Applications: With R Examples. 

In the next post I will be going over the code posted by Quintuitive to test the forecasts made by this model.

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