Introduction to Monte Carlo Analysis Part 1

By Bonolo Molopyane

The Monte Carlo, filled with a lot of mystery is defined by Anderson et al (1999) as the art of approximating an expectation by the sample mean of a function of simulated variables.  Used as a code word between Stan Ulam and John von Neumann for the stochastic simulations they applied to building better atomic bombs (Anderson, 1999), the term Monte Carlo evolved into a method used in a variety discipline including physic, finance, mechanics and even in areas such as town planning and demographic studies.

Monte Carlo methods are very different from deterministic methods (McLeish, 2004). In the case of a deterministic model the value of the dependent variable, given the explanatory variables, can only be unique value as given by a mathematical formula. This type of model contains no random components (Rotelli, 2015). In contrast, Monte Carlo does not solve an explicit equation, but rather obtains answers by simulating individual particles and recording some aspects (tallies) of their average behavior (Briesmeister, 2000). Given the broad applications and matters involving Monte Carlo Methods we will split this article into three parts to allow for a clear understanding. Read more

Momentum Strategies

By Rutendo Kadzikano

Pinto, Henry, Robinson and Stowe (2010) define momentum indicators as valuation indicators that are based on the relationship between price or another fundamental, earnings for example, to a time series of its historical performance or to the fundamental’s expected future performance values. When the strategy uses earnings then it is an earnings momentum strategy and in the case of using price then it is a momentum strategy. Momentum strategies can either be relative or absolute. Relative strategies compare the momentum of different assets to each other and absolute momentum is not concerned with the performance of other assets as it only focuses on that stocks past return in predicting future returns. Read more

Momentum Crashes

By Fortune Chiwewe

Seminal work by Jegadeesh and Titman (1993) found that past winners outperform past losers over a horizon of 3-12 months. Investors thus take a long position on winner stocks and a short position on loser stocks in order to realise anomalous profits. This strategy is widely adopted and appears to be timeless in terms periodically not functioning but never completely disappearing. This paper sets to investigate what happens when the strategy does not work, i.e. when momentum crashes. Read more

Optimal Stock Quantity, Selection and Weights for Momentum Investing

By Rujeko Musarurwa

To try and maximise return the correct recipe of ingredients must be brought together. Not only do we have to look at the quality of stock selection, but the weights and quantity of stocks required for maximising returns and minimising risk. Momentum investing looks to invest in top performing stocks and combining this technique with good diversification skills and portfolio optimisation should result top performing portfolios. An added benefit is that transaction costs will be minimised if the correct stocks are picked right from the beginning as less buying and selling will have to be done in the future.

This article will aim to answer the following three questions:

  1. How to pick the right amount of stocks to optimize a portfolio?
  2. How to pick the best stocks to include in the portfolio?
  3. How to get the weight for each stock in the portfolio right?

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Market Timing Models For A Momentum Strategy.

By Sandra Makumbirofa

Everyone has an opinion about what the state of the market will be in the short term or long term, never mind that stock prices follow a random walk or the possible clash between that comes between the invisible hand of the market and the regulatory rules made by policy makers.

Returns in the market are limited based on the performance among the wide range of asset classes over a period of time. In the face of such limitations, not every investor in the market can make a high return and in most cases the average investor will manage to earn an average return before the transaction costs are factored in. An efficient market timing model is thus believed to be an means to an end of this hurdle for the investor.

In this article I will make a suggestion of a suitable quantitative model of market timing that will enable us to determine the level of market exposure our momentum strategy should have. Section 2 gives evidence of the some of the market timing models that have worked empirically over the years. Section 3 is an introduction to regime based market timing models that have been chosen for our hedge fund. Section 3.1 introduces and briefly discusses the Hidden Markov Models and Section 4 will give a conclusion to the article. Read more

The Origins of Momentum

By Fortune Chiwewe

Momentum is a market anomaly which many people have tried to explain but have not succeeded to a satisfactory extent. As to the source of momentum profits, others have tried to rationalize their origins whereas an opposing school of thought has searched for their origins in behavioural finance. In this paper I will explore the possible origins of momentum profits through highlighting the important aspects of both the rational and the behavioural field. Read more

Behavioural theories behind momentum


By Rutendo Hazel Kadzikano

There isn’t a general consensus on what really causes momentum. On the one hand others argue using behavioural theories that state that momentum is a result of “naïve investors with biased expectations” Hvidkjaer (2006) and on the other hand, others argue that momentum is a product of the “rational response that individuals have to real market constraints” (Scowcroft & Sefton, 2005). Read more

Introduction to Market timing models

By Sandra Makumbirofa

The study of stock market trading is synonymous for its random walk properties, yet there is still a strong controversial school of thought that supports the idea of ‘market timing’. Detemple and Rindisbacher (2013:2492) describe market timing as the ability to extract information about future market returns of an investment. These market timing models aim to provide a lower-risk strategy for growing portfolios by shifting assets during periods of high risk and low risk.

Since trading on the stock market involves constant buying and selling of stock to increase one’s profitability, traders have long been looking for the key to effectively anticipate the market ahead of other traders. The disadvantage of discovering this key is that a truly successful market timing method is not sustainable in the long-run because as more and more traders discover it, it ceases to be effective (Dickson & Knudsen, 2012:1). Read more

Momentum investing, portfolio optimisation, and diversification

By Rujeko Musarurwa

It is nearly impossible to get the right mix of stocks to achieve maximum returns. A lot of time and effort has been spent trying to renovate and build upon old theories in order for investors to gain maximum returns. Wouldn’t it be ideal to be able to invest in stocks that are increasing in price and will continue to increase into the future? How much more would it benefit investors to know the right moment in which to shift stock weights i.e. buy or sell a stock in order to gain higher returns? These are just some of the questions that I will aim to answer.

This will be the first article in a 4 part series of articles. I will attempt to come up with a portfolio that will provide the best returns for momentum investing whilst actively managing the portfolio. In this first article I will be discussing the combination of momentum investing, portfolio optimisation and diversification and despite some researchers disagreeing with these two theories working together, most researchers agree that combination of strategies work well together and I will discuss how and why these specific strategies can work jointly. The second article will discuss the diversification method and give you an insight on how to pick the correct stocks and correct number of stocks followed by a third article that will discuss the portfolio optimisation area i.e. the risk-return tradeoff that should give the best stock selection by using normal distribution formula and other methods. The fourth article will combine all the topics previously discussed in order to formulate the ultimate model and discuss why the model should work. Read more

Creating an Open Source Hedge Fund Strategy

The Open Source Hedge Fund Project is a community driven project which aims to create the best quant trading systems and operational structures to run a fully functional hedge fund. With the twist that all the IP is open to the public. Contributors will receive full access to this collective and ever evolving “Hedge Fund in a Box”

The Hedge Fund in a Box is all the marketing, legal, business templates, daily operations, fund strategies, and due diligence documents needed to start a hedge fund. In essence it is a hedge fund template where contributors can fill in their names, company details, and then launch a hedge fund.

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