I am passionate about economics, finance and data science, with a strong interest in financial markets, investment strategies, and economic estimation & analysis.
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| Mobile | Academic Email | Personal Email | Linkedin Profile |
|---|---|---|---|
| +44 (0) 7496228178 | d.w.halm-alvarez@lse.ac.uk | dwh.alvarez@gmail.com | Daniel Halm Alvarez |
This project aims to use rental market data to estimate the sensitivity of demand and supply to their respective shifters, both in terms of quantities as a function of price and shifters, as well as prices (inverse supply and demand) as a function of quantity and shifters.
I aim to find evidence that transit-oriented-development (TOD), though a force for raising the level of demand in a city by reducing commute times and hence making housing more attractive at all prices, may, under certain conditions (positive supply shocks), lower overall prices if the level of supply at all prices increases by a greater degree. This is because such development makes it possible (despite not being sufficient on its own) for there to be more housing in time proximity to the places where households wish to live and work.
This project aims to backtest a variety of volatility control strategies on an underlying semiconductor index ETF.
Given the growing demand and high internal rate of return in the semiconductor sector due to high margins and turnover, this thematic strategy has some of the highest annualised returns over 10 years (most semiconductor ETFs see 15% to 21% annualised returns).
However, the cyclical nature of the semiconductor sector and heavy weighting towards high-profile companies such as Nvidia leads to high volatility. Nonetheless, the cyclical nature of this volatility may mean we may be able to predict downturns in the market and reduce our market participation accordingly.
So far, I have created an interactive streamlit website here that lets one compare the backtested performance of investing in the underlying ETF itself to a simple strategy based on technical indicators, whereby if the MACD crosses below its signal line, the portfolio shifts to cash only,
and invests back into the ETF only once the MACD crosses back above the signal line.
A set of economics and finance related pet projects.
Modelling the effect of a universal basic income transfer on income distribution throughout time.
To augment the analysis, I also evaluate how this impact changes depending on how progressive the tax system is, as well as under the appreciation of wealth/capital accumulation, which becomes a source of investment income.
Predicting the returns of equities throughout time and attributing them to income and capital appreciation components.
Predicting the returns of equities based on historic earnings growth, using a geometric series to predict the multiple the market will price in, and hence project price returns. Use this to create a rules-based 'select' index/rules based portfolio.
In future: Aim to use past data to make prediction model stochastic: fit the distribution of the P/E multiple and in turn find the probability distribution of the (dependent) price returns.
In future: Turn into a script that continuously queries the Bloomberg API and rebalances the portfolio. To do this monthly, likely need a cloud tool of some kind.
In this project, I import existing Google MyMaps transit fantasy maps (as KML Files) and take them even further. In other words, planning out the lines, stations and service types I would like to see in an idealised version of certain cities. Purely for fun.
Workings and practice questions for data structures and coding efficiency using leetcode.