ParaTopia Glossary

I. Financial factors, metrics, models, or databases. 

I.I. Parametric Portfolio Policy

Using my own words, I would say that the parametric portfolio policy works kind of like an optimizing portfolio sorting strategy. That is, we can sort stocks in a portfolio given certain stock-specific characteristics and the parametric portfolio policy tells us how to sort these given an optimizing objective. Some key formulas and code tied to the parametric portfolio policy are also displayed in A few simple rows. However, this policy is clearly best explained by the original authors and founders [1]. 

I.II Generalized Autoregressive Score / GAS 

Generalized autoregressive score (GAS) models can be used for modelling dynamic variables via functions of lagged and predetermined variables, which framework, therefore, allows for the possibility of predicting future dynamic variables, using a set of available information [2]. In ParaTopia, I take use of GAS in combination with the parametric portfolio policy for modelling dynamic values of theta. For more details, pleases see Let’s turn the GAS on.    

I.III. Past Trading Volume / Dolvol

Past trading volume, or dolvol, is defined as the natural logarithm of the stock price times its trading volume during the last month. Dolvol is supposedly associated with liquidity and can thereby be used as a technical indicator for stock returns [3]. I use dolvol as an asset factor in my trading strategy, displayed in A few simple rows.

I.IV. Market Beta / Beta 

Market beta measures how stock returns move together with the overall market. Stocks with high betas are supposedly associated with higher risk as well as higher returns [4]. I have previously used beta as an asset factor in my trading strategy, displayed in A few simple rows, but I have excluded it in later days according to Why Dolvol and Beta?

I.V. Momentum 

Momentum is calculated as the compounded stock return over some arbitrary period (like the one-year momentum over t-13 to t-1). Momentum can be used as an asset factor for predicting firms with high returns to continue with high future returns [5]. Personally, I really like the momentum factor as it's very flexible,  minimalistic, and simple to obtain and I hope that I will have the opportunity to use it in my investment strategy soon. For more info, see Why Dolvol and Beta?

I.VI.  Fama and French five-factor model

The Fama and French five-factor model can be used to estimate dependencies of stock returns to certain asset factors, including (i) the market excess return, (ii) the differences in returns between a small and big stock portfolios, (iii) the difference in returns between a value and growth stock portfolios, (iv) the difference in returns between a robust and weak profitability stock portfolios, and (v) the difference in returns between a conservative and aggressive investment stock portfolios [6]. 

II. Market indices, stocks, and other securities

II.I. OMX Stockholm 30 Index 

The OMX Stockholm 30 is a stock market index for the Stockholm Stock Exchange. It is a capitalization-weighted index of the 30 most-traded stocks on the Nasdaq Stockholm stock exchange [7]. 

III. Python modules, packages, and resources. 

III.I. yfinance

yfinance offers a threaded and Pythonic way to download market data from Yahoo! finance [8].

References

[1] Brandt, M.W., P. Santa-Clara, and R. Valkanov, Parametric Portfolio Policies: Exploiting Characteristics in the Cross-Section of Equity Returns. The Review of financial studies, 2009. 22(9): p. 3411-3447.

[2] Creal, D., Koopman, S. J., and Lucas, A, Generalized autoregressive score models with Applications. Journal of Applied Econometrics, 2013. 28(5): p. 777-795.

[3] Brennan, M.J., T. Chordia, and A. Subrahmanyam, Alternative factor specifications, security characteristics, and the cross-section of expected stock returns. Journal of financial economics, 1998. 49(3): p. 345-373.

[4] Fama, E.F. and J.D. MacBeth, Risk, Return, and Equilibrium: Empirical Tests. The Journal of political economy, 1973. 81(3): p. 607-636.

[5] Jegadeesh, N., & Titman, S, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of finance, 1993. 48(1): p. 65-91. 

[6] Fama, E.F. and K.R. French, A five-factor asset pricing model. Journal of financial economics, 2015. 116(1): p. 1-22.

[7] Wikipedia. OMX Stockholm 30; [cited 2022 Aug 24]. Available from: https://en.wikipedia.org/wiki/OMX_Stockholm_30

[8] Aroussi R. Download market data from Yahoo! Finance's API; [cited 2022 Oct 16]. Available from: https://github.com/ranaroussi/yfinance