Trading securities, hedging and portfolio design, practical

1 Fundamental considerations

• Does It Pay To Do More Research?

2 Behavioural considerations

• Understanding the disposition effect

3 Portfolio design

Diversity and risk hedging.

Bookmarked: Amazing science communication by Jonas Degrave, But why is diversification a free lunch?.

The R package introduction Fast Design of Risk Parity Portfolios by Zé Vinícius and Daniel Palomar is an interesting dummy’s guide to “Modern portfolio” and “risk-parity portfolio” theory.

In 1952, Markowitz proposed in his seminal paper [1] to find a trade-off between the portfolio expected return and its risk measured by the variance:

$$\begin{array}{ll}\underset{\mathbf{w}}{\mathsf{\text{maximize}}}& {\mathbf{w}}^T\mathit{\mu }-\lambda {\mathbf{w}}^T\mathbf{\Sigma }\mathbf{w}\\ \mathsf{\text{subject to}}& \mathbf{w}\ge \mathbf{0},{\mathbf{1}}^T\mathbf{w}=1,\end{array}$$

where $\lambda$ is a parameter that controls how risk-averse the investor is.

Markowitz’s portfolio has been heavily criticised for over half a century and has never been fully embraced by practitioners, among many reasons because:

• it only considers the risk of the portfolio as a whole and ignores the risk diversification (i.e., concentrates too much risk in few assets, which was observed in the 2008 financial crisis)
• it is highly sensitive to the estimation errors in the parameters (i.e., small estimation errors in the parameters may change the designed portfolio drastically).

Although portfolio management did not change much during the 40 years after the seminal works of Markowitz and Sharpe, the development of risk budgeting techniques marked an important milestone in deepening the relationship between risk and asset management.…

The alternative risk parity portfolio design has been receiving significant attention from both the theoretical and practical sides because it

1. diversifies the risk, instead of the capital, among the assets and
2. is less sensitive to parameter estimation errors.

…Risk parity is an approach to portfolio management that focuses on allocation of risk rather than allocation of capital. … While the minimum variance portfolio tries to minimise the variance (with the disadvantage that a few assets may be the ones contributing most to the risk), the risk parity portfolio tries to constrain each asset (or asset class, such as bonds, stocks, real estate, etc.) to contribute equally to the portfolio overall volatility.

4 Algorithmic trading

Financial hacker is pragmatic. I can’t tell if it is fun because I can’t even tell if they are joking but numerai’s introduction to secrecy and information in financial markets is a …singular perspective.

Robert Carver has a simple and excellent blog and a couple of books (Carver 2019, 2015) on what I would call “indolent trading”.

5 Betting

See betting.

6 Statistical considerations

How do you learn the parameters of the model? What do estimation errors do to your return?

• Jonathan Kinlay, on market timing strategies via Machine Learning.
• Zoirro project reading list is interesting.
• Machine learning for portfolio diversification comments on Conlon, Cotter, and Kynigakis (2021).
• Hedged Cryptocurrency Strategies
• Variance risk premia for patient investors

7 Technical considerations

I currently use Interactive Brokers come highly recommended for algo trading (referral link). I use them myself, but I mostly buy and hold rather than doing mad algo trading. They are also good for that.

8 Incoming

• The Greeks

In finance, the “Greeks” refer to the partial derivatives of an option pricing model with respect to its inputs. They are important for understanding how an option’s price may change. I discuss the Black—Scholes Greeks in detail.

• Rohit, People always put their money in futures they predict

  What have we seen so far? People didn’t use to have much disposable income to invest a century ago. When they did, or rather those who did, invested their savings mostly in land or (if they were rich enough) businesses, or commodities.

  Where should I invest my money is a relatively old question, but until recently it wasn’t a very interesting question. This is because until recently the answers were understood, but not that actionable. The futures would get better, things would get built, and you could ride optimism as a thesis if you could find a way how. The avenues available were extremely limited, and the optionality you had was minimal.

• The Analytical Edge

• Weekly Research — Verdad

• MarS: A unified financial market simulation engine in the era of generative foundation models - Microsoft Research

9 References