1) We adopt the Hybrid (Type 3) approach in the alpha research process. The hybridity is reflected in our team’s dynamic, with practitioners working on the theoretical models, and traders providing input on the heuristical discovery of alpha.
2) The result is a coherent product in the form an “alpha report”, that premium subscribers get access to weekly.
📌 Sample Article
3) Disclaimer: On our weekly (premium & free) article, our reports are more succinct. However, premium subscribers get to suggest sections they wish to see in future reports. A sample premium article for discussed alpha:
4) The alpha we chose as a case study is not particularly outstanding, but well exhibits alpha research processes behind our publishing. These smaller alpha units, when combined as well as managed carefully under multi-strat, can have remarkable results.
5) Free subscribers get access to promotional content, but for “weaker signals”. Additionally, they are not subject to the same quantitative tests of robusticity and statistical soundness as our premium alphas.
6) The alpha research process is both a challenging and intensive process. Unfortunately, most “alpha leak” services (YouTube, Discord groups) are aimed at luring retail traders with overpromises of ludicrous profit potential.
7) We find that many of such services i) do not give statistical evidence for their edge, ii) overfit data to achieve unobtainable profit conditions, iii) cherry-pick conditions where the trade works to justify their methods.
8) The objective of the HangukQuant newsletter is to provide HONEST and PRACTICAL alpha signals that have been derived from proprietary models based on statistical and mathematical modelling techniques, without such tomfoolery.
9) Although our results might not as shiny as the fake guru’s, but we qualify this that these are candid results of supplementary trading edge. Our alphas are used in production as part of a bigger, multi-strategy quantitative portfolio.
1\13 One of the biggest challenges in quant and alpha research is obtaining clean, error-free data. Models need be built, using assumptions to reduce “dimensions” of reality for tractability.
A THREAD ▼ ▼ ▼
Machine Learning, Sparse Datasets and Error-Free Simulations
2\ Mathematical models, by definition are built to simulate and capture some phenomena, practical or abstract. Often, they are built on data, which themselves are derived from some unknown, statistical distribution.
3\ For example, an alpha model, is backtested upon data where prices/returns are drawn from some distribution. Widely in quantitative finance, they are assumed to be drawn from a log-normal distribution.
Been a fulfilling ~1 month since our launch. For hitting 1k followers, we have a special thread for you, including a premium alpha report and a case study. 🔥
MEGA Thread (N = 60+) : Robust Alpha Research Processes; HangukQuant
1) Type 1: To keep my knowledge of finance, I both subscribe to financial literature, academic or otherwise. That means reading books/papers on finance, trading, economics, podcasts for general knowledge, and a working knowledge “Mathematical Finance”.
2)
Type 2: My knowledge on Computer Science and Statistical methods comes from my background in academia. I also keep up to date on new state-of-the-art research by reading academic literature.
1) Type 1: Understanding market structure and market incentives, and the corresponding flows
Type 2: Finding statistical anomalies within price/non-price data
Type 3: Hybrid approach.
Let’s seek to understand this further.
2) Type 1: There are many reasons for actionable price flows, such as price insensitive liquidation, factor premia et cetera. For an example, a previous thread
1) First, what is alpha research? It is the undertaking of research operations towards finding excess returns on the market. It may come in many forms, from both fundamental and quantitative perspectives.
2) In the quant sphere, it often takes on the form of parametric formulae/rules designed to express underlying trading edge. They also come in many forms, such as stat arb, risk arb, factor models (mom, rev), idiosyncratic premia, predictive modelling et cetera.