Quantitative trading, finance, machine learning, and functional programming.
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Just like how there's more than one way to skin a cat, there's more than one way to construct a portfolio. The first systematic method of construction was Modern Portfolio Theory, put forth by Harry Markowitz. MPT's approach is simple: choose weights for each asset that maximize the amount of return received for the amount of risk or volatility taken. However, trying to maximize the risk-adjusted…
Risk parity is a popular method of investing that aims to outperform the traditional 60/40 equity/bond portfolio by equalizing the risk of the bond portion with that of the equity part of the portfolio and then leveraging up the portfolio to hit the desired risk target. Using the covariance, and volatility of the components, we can generate a portfolio and leverage ratio for a given level of…
The beta exposure of a stock is one of the first and most important statistics any investor looks at. Though perhaps simplistic, it distills a myriad of various properties of a stock into a single, easily digestible number. Using beta, it becomes easy to roughly forecast how an investor's portfolio would perform under different market conditions. Maybe out portfolio has a beta of 1.3 and we expect…
A retail investor would be forgiven for assuming that there are really only two assets classes one should consider for personal investments: equities (stocks) and fixed income (bonds). Wherever you look, these two asset classes dominate the financial landscape. All robo-advisors and most personal advisors implement a mix of bonds and equities for the portfolios of their clients. The idea behind…
Perhaps more than any other development, finance was ushered into the modern era with the development of the Capital Asset Pricing Model (CAPM) by William Sharpe in the early 60s. Though commonly criticized as too simple and reductionist, the model is still used today as an easy way to determine a stock's exposure to the market:
This post is going to be a little different from usual; instead of markets, we're going to look at a video game, namely, Counter-Strike: Global Offensive (CS:GO). CS:GO, like most great games, is easy to learn but deceptively hard to master. For those at are unfamiliar with the game, we'll give a quick overview below.
In the final installment of this three part series, we are going to use our results from the previous two posts to construct a fully automated variable leverage ETF. In part one, we derived the optimal leverage ratio for maximizing returns and in part two we applied the ARMA and GARCH models to forecast returns and volatility, respectively.
In part one, we looked into the relationship between volatility, returns, and leverage and derived an equation for the optimal leverage ratio that maximizes the expected return of a portfolio. This leverage ratio is dependent on two principle components, expected variance and expected returns:
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