Lifetimes Part 2: Gamma Spend Model and Financial Valuation

See here for the full iPython Notebook code.  Some of the descriptions are outdated but the code is almost the same. After getting to know what lifetimes can provide, I started applying it from a financial perspective.  I wanted to answer the most important question for Zakka Canada:  Leveraging our customer analytic models, how can I estimate the firm value that Zakka Canada is worth as of today? The rest of this post is divided into two parts: 1) modelling the monetary value of our customer base and 2) estimating the price of Zakka Canada through a simple present value cash flow valuation […]

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Volatility Models and Backtests on Quantopian

In this blog post, I will present some backtest results on volatility models.  The list I present here are not exhaustive and there are still a gargantuan set of papers focusing on this issue (a good place to start is on vlab).  In the next section, I present some simple notations to define financial volatility and then define each model and show general backtest results with risk attributes.  The premise of the backtest is as follows: financial volatility of an investment portfolio is able to be minimized globally through allocating the correct amount of dollar toward each asset within the portfolio. […]

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Autoregressive Exponential Moving Average Forecasting

I've recently been looking into an automated strategy to implement to my forex trading. School has been real busy and I haven't gotten much time to do technical analysis so I think it'd be better to explore robot trading. In this post I'll be discussing my strategy, it's properties and an example with a simple out-of-sample forecasting benchmarking against an ARMA(2,2) and Random-Walk naive prediction. In the next article, I'll write about implementing it in MetaTrader (MQL4).   An exponential moving average is an extension of a simple moving average where more of the weight is being placed upon the […]

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Cointegration and Statistical arbitrage

Recently, I was introduced to the concept of Cointegration analysis in time-series.  I first read this in a HFT blog at Alphaticks and then the concept came up again when I was looking into Spurious Regressions and why they occur.  Lot's of Quants have blogged about this idea and how it can be applied to the premise of Statistical Arbitrage.  I will do the same and apply this to the not-so-recent Google stock split, however, I will also try to add some math into the mix, briefly touch on Error-correction mechanism and spurious regression.  Finally, I will also give a few criticisms against […]

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U.S Unemployment Time-Series Modelling (Part 1)

One of the many benefits of improving economic forecasts is being able to trade releases with better information through forex and stocks.  Certain sites such as Forexfactory provide a forecast parameter and I was able to play around and figure out some just use standard ARIMA models.  In Part 1, I will show how to estimate unemployment rate log changes and Part 2, I will implement this through a modified BP neural network (if i can get it to work...).  I will be benchmarking my residuals with a standard ARIMA model along with an exogenous regressor (initial claims).  The data was obtained […]

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